International Journal of Computational and Experimental Science and Engineering
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Geometric Optimization of Permanent Magnet Synchronous Machines Using Grey Wolf and Teaching–Learning-Based Algorithms
Permanent Magnet Synchronous Machines (PMSMs) are widely recognized for their high efficiency, low acoustic noise, and extended operational lifetime. This study presents an optimization framework based on two metaheuristic algorithms, namely Grey Wolf Optimizer (GWO) and Teaching–Learning-Based Optimization (TLBO), to determine the optimal geometric parameters of the machine. The proposed methodology combines finite element analysis with numerical optimization by coupling COMSOL Multiphysics and MATLAB in order to model and evaluate the machine performance. The effectiveness of the developed approach is assessed through comparisons with recently published reference studies. The obtained results demonstrate good accuracy and confirm the reliability, robustness, and efficiency of the proposed optimization strategy.
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AI-Driven Capacity Planning for Next-Generation Data Centers
Contemporary data centers face significant resource management challenges due to workload uncertainties and dynamic demand fluctuations. Traditional capacity planning models relying on averages and predictive functions cannot address cloud-native ecosystem complexity. These linear models fail to match modern infrastructure supporting AI applications, big data analytics, and distributed computing, which exhibit nonlinear resource requirements. This mismatch causes persistent over-allocation or performance degradation during peaks. The proposed capacity planning framework utilizing machine learning algorithms provides proactive, non-reactive predictive capabilities. The architecture incorporates automated policy enforcement and scenario simulation for evaluating provisioning strategies. Multi-dimensional demand projection captures compute, storage, and network resource interdependencies ignored by single-resource models. Governance mechanisms ensure decision explainability and audit trail preservation for regulated environments. Human-AI collaboration architectures position intelligent systems as infrastructure assistance tools rather than replacements. The framework provides essential capabilities for managing next-generation data center infrastructure at scale with unprecedented resilience.
Eco-Friendly Medicinal Soap with High Antimicrobial Efficacy: A Natural Formulation from Virgin Olive Oil and Eucalyptus globulus Essential Oil
This study aimed to formulate and characterize an eco-friendly medicinal soap by combining the saponifiable and emollient properties of virgin olive oil with the antimicrobial and antioxidant potential of Eucalyptus globulus essential oil. The essential oil was extracted by hydrodistillation and characterized in accordance with ISO standards, while virgin olive oil was evaluated to ensure its suitability for saponification. Soap formulations were prepared using the hot saponification method and enriched with varying concentrations of essential oil (5%, 8%, and 12%) and 5% extra virgin olive oil. The resulting products were assessed for their organoleptic and physicochemical properties, as well as their antimicrobial and antioxidant activities. Antimicrobial efficacy was determined against selected ATCC bacterial and fungal strains using the agar diffusion method, while antioxidant activity was evaluated using the DPPH assay. The extracted essential oil exhibited a yield of 0.8% and met standard physicochemical criteria. The formulated soaps demonstrated moderate yet significant antioxidant activity, reaching 47.53% at 12% essential oil concentration, and strong antimicrobial effects, particularly against Klebsiella pneumoniae (50 mm) and Escherichia coli (44.5 mm). The inclusion of extra virgin olive oil further improved the overall performance, indicating a synergistic interaction between olive oil polyphenols and eucalyptus bioactive compounds. These findings suggest a sustainable and effective approach for developing plant-based therapeutic soaps with promising applications in natural dermocosmetics and personal hygiene.
Sizing and optimization of a hybrid photovoltaic-grid system: application to the port of ghazaouet (algeria)
This study presents the sizing and techno-economic optimization of a hybrid photovoltaic (PV)-grid system designed for the port of Ghazaouet located in the Tlemcen province Algeria. The main objective is to assess the technical energy and environmental feasibility of integrating photovoltaic energy to reduce dependence on the conventional electrical grid and fossil fuels. The methodological approach involves analyzing local solar potential detailed assessment of the site's energy needs and systematic sizing of system components including the photovoltaic array battery storage system charge controller and inverter. A techno-economic simulation and optimization were subsequently performed using HOMER Pro software enabling comparison of several hybrid configurations and identification of the most cost-effective solution based on life-cycle cost. The results indicate a total annual production of approximately 64207 kWh of which 98.9% is provided by photovoltaic energy confirming the dominant role of solar energy. This study demonstrates that hybrid PV-grid systems significantly reduce CO₂ emissions while ensuring a reliable and continuous power supply confirming their relevance as a sustainable solution for industrial zones in Algeria.
In Silico Evaluation of Molecular Interactions for Parkinson’s Disease Drug Discovery
Monoamine oxidase B (MAO-B) and Catechol-O-methyltransferase (COMT) inhibitors are crucial for the treatment of Parkinson’s disease (PD). Inhibition of these enzymes involves both natural and synthetic compounds. Passiflora incarnata serves as a significant source of natural bioactive molecules, primarily flavonoids (including Apigenin, Luteolin, Myricetin, Orientin, Quercetin, Kaempferol, Vitexin, Isovitexin, and Isoorientin) and alkaloids (such as Harman, Harmine, Harmol, Harmaline, Harmalol, 8-hydroxy Harmine, and Harmine N-oxide). Synthetic options include inhibitors such as Rasagiline, Selegiline, and Safinamide (targeting MAO-B), as well as Entacapone, Tolcapone, and Opicapone (targeting COMT).In this study, molecular docking was employed to identify ligands with optimal interaction energies for each enzyme and to characterize binding affinities, thereby aiding the potential design of new inhibitors. The docking analysis of natural compounds revealed that flavonoids exhibited the strongest affinities and consistent interaction patterns; notably, Myricetin demonstrated the most effective binding to both MAO-B and COMT. Among the synthetic compounds, Safinamide displayed the highest affinity for MAO-B, while Opicapone showed the highest affinity for COMT. These findings highlight the potential of both natural and synthetic inhibitors in reducing enzyme activity associated with PD, providing valuable insights for the development of more effective therapeutic agents.
Molecular Docking Investigation of Natural Compounds from Inula viscosa and Synthetic Derivatives against Breast Cancer Targets
Novel compounds with significant medicinal properties have attracted considerable interest in therapeutic approaches to treating breast cancer. Different synthetic and natural inhibitors were collected from the literature and docked against (Aromatase, mTOR) receptors. The compounds' binding affinities were calculated after minimising interactions within the binding pockets of the macromolecular receptors (Aromatase, mTOR). In this work, we use molecular docking methods to identify the ligand that has the best interaction energy with the fourth macromolecule among synthetic and natural products extracted from Inula Viscosa, and also describe binding affinity in order to design new inhibitor ligands. The result shows that ligands (Nepetin, OSI-027) and (3-O-Methylquercetin, Formestane) best inhibited Aromatase and mTOR, respectively. The latter reaches the same conclusion as the study on experimental inhibition.
Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale
A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. This paper presents a practitioner-informed technical review of a production-grade predictive analytics framework designed to reorganize clinical data around family units, link records across multiple electronic medical record systems using a FHIR-based interoperability layer, and apply machine learning models to identify family-based disease risk at the population scale. The framework integrates graph-structured family relationship models, temporal feature engineering encoding health trajectories across generations, and real-time clinical decision support workflows delivering family risk scores to care coordinators. Drawing on direct implementation experience in large-scale population health programs, we describe the data architecture, identity resolution pipeline, ML model design, and deployment infrastructure required to operationalize multi-generational analytics in production. We evaluate methodological requirements for family-aware model validation, discuss governance challenges specific to family-level health data, including relational consent and genetic discrimination risk, and identify the equity implications of deploying genomically-informed family risk models in ancestrally diverse populations. We further discuss federated learning and social determinant integration as the most consequential near-term extensions of the framework. The paper is intended to bridge the gap between theoretical proposals for multi-generational health analytics and the architectural and governance realities of deploying such systems at the scale of millions of members.
Operational Excellence through Cloud-Native Distributed Systems
Operational excellence has become a critical objective for enterprises operating in complex, digitally driven, and highly distributed environments. Traditional centralized and monolithic system architectures increasingly struggle to meet demands for scalability, resilience, efficiency, and continuous service delivery. This study examines how cloud-native distributed systems enable operational excellence by analyzing the combined effects of architectural modularity, scalability elasticity, automation maturity, and observability. Using a quantitative explanatory research design, operational and system-level data from cloud-native enterprise environments were analyzed through descriptive statistics, correlation analysis, and multivariate regression modeling. The results reveal that cloud-native capabilities significantly improve operational performance, with automation maturity and scalability elasticity emerging as the strongest predictors of operational excellence. Additionally, the findings demonstrate that higher cloud-native maturity reduces operational variability and enhances performance predictability. The study contributes empirical evidence linking cloud-native distributed system design to multidimensional operational excellence outcomes and provides strategic insights for enterprises seeking to build resilient, efficient, and scalable operational models through cloud-native adoption.
Secure Data Services on Container Platforms: Protecting Distributed Query Engines, Messaging Platforms, and Search Systems
As organizations rapidly migrate their data services and other workloads to containers, the responsibility for securing them cannot fall into the hands of the application teams alone. Distributed query engines, messaging systems, search engines, and data stores amass huge troves of organizational data and provide rich query interfaces, making them a target for external and insider adversaries alike. The article describes a reference architecture that serves as an implementation guide for securing data services in cloud-native environments. Based on documentation from the most popular open-source projects in the cloud-native ecosystem and guidelines from the National Institute of Standards and Technology‚ the reference architecture consists of recommendations across four security domains: control plane identity and access management‚ data plane cryptography and network segmentation‚ secrets and key lifecycle management‚ and operational auditability․ We present an anonymized enterprise implementation pattern that assembles such controls into a defensible and auditable posture across portfolios of heterogeneous services.
Filter Regularization Method for an Inverse Problem of Over-damped Harmonic Oscillator Equation
In many fields of applied mathematics and physics, inverse problems involving the harmonic oscillator are common. These problems are often ill-posed in the sense of Jacques Hadamard, where minor changes in the data can result in significant deviations in the solution. In this work, we study an ill-posed inverse problem related to the harmonic oscillator equation with the aim of reconstructing unknown parameters from noisy and indirect observations.We suggest a technique of regularization to address the instability present in this class of problems. Convergence results are obtained under appropriate assumptions on the regularization parameter and noise level, and the regularized problem’s well posed-ness is established. Furthermore, error estimates are obtained to measure how stable the suggested method is. The efficiency and resilience of the approach in recovering stable approximations of the intended solution are illustrated through numerical simulations. The obtained results verify that the suggested regularization technique provides a dependable and effective method for resolving harmonic oscillator inverse problems.
Performance and Exergy Analyses of GT-MHR Nuclear Cycles
In this study, the performance and exergy analyses of the GT-MHR type nuclear power plants with high thermal energy were analysed. The GT-MHR (Gas Turbine-Modular Helium Reactor), produces electricity by using helium gas in the Brayton cycle, and utilizes the waste heat of the system with the Rankine cycle. The Brayton cycle allows helium gas to be heated to high temperatures in the reactor core, then converted into mechanical energy by using a gas turbine. This hybrid structures in the designs of GT-MHRs allow the combined use of nuclear and renewable energy sources.It was found that exergy efficiency reached its maximum level between approximately 14,000 - 16,000 kPa. At this point, the exergy losses of this system approach its lowest level and the components reach the most ideal operating conditions. If the system is operated below or above of this value, the total exergy efficiency will decrease. The exergy efficiency, which is initially approximately 74.5% at 500 kPa pump pressure, increases to 75.1% at 1000 kPa pressure level. While the pump pressure at 500 kPa produces a work of approximately 532,000 kW, at 1000 kPa it increases to 560,000 kW. Thus, GT-MHR pioneers the energy technologies of the future by combining both high efficiency and environmental sustainability.
Development of Apparatus for Increasing Efficiency and Savings in Radiation and Convection Heat Transfer in Stovetops
A new apparatus is designed to increase the efficiency and save fuel in electric and gas stoves used in kitchens. Through analyses and experiments, improvements are made to this apparatus to utilize a larger amount of heat lost through convection and radiation, thus saving electricity and gas fuel used in stoves. Initial and final heat values ​​and cooking energies is compared. A test unit is prepared for this purpose, using an electric and gas stove, a pot with a diameter of approximately 17 cm, and a specific amount of water. Equipment used will include a thermometer, a stopwatch, and a thermal camera. Different diameter and thickness designs of the apparatus is created. These are incorporated heat insulation material to facilitate heat transfer to the outside environment: radiant heat transfer via a bright inner surface, conductive heat transfer via the insulation material, and convective heat transfer by lowering the outer surface temperature with the insulation material. The apparatus is designed with lower openings for air intake to improve gas combustion and upper openings for exhaust outlet. In the experiments, the time it takes for water in a steel pot to reach 80°C is determined for various designs of the appliance on a natural gas stovetop, and the efficiency increases and results are shown graphically. Lots of experiments done and the figures show for electric stove that uninsulated round without sheet metal plates the best performance with 9 minutes obtained for electric stove. For gas stove, with the device developed from 0.5 mm steel the temperature rises from 20°C to 80°C in 9 minutes, which is the best performance among all of the other alternatives.
Territorial Diagnosis and Sectoral Valorisation for Sustainable Development in Tamanrasset, Algerian Sahara
Sustainable development policies require territorially differentiated approaches, particularly in arid and peripheral regions where socio-economic dynamics and access to services are highly uneven. In Algeria, the sharp contrast between the urbanised northern regions and the sparsely populated Sahara raises critical questions regarding the territorial anchoring of development strategies. This article develops an integrated territorial diagnosis of the wilaya of Tamanrasset, located in the Algerian Sahara, by combining municipal-level socio-economic, environmental, and infrastructure indicators with a SWOT-based analytical framework and stakeholder-informed assessment. The findings reveal strong spatial polarisation centred on the city of Tamanrasset, accompanied by pronounced intra-regional disparities in access to services, employment opportunities, and connectivity infrastructure. At the same time, the diagnosis identifies strategic pathways for sectoral valorisation through sustainable tourism, traditional handicrafts, and renewable energy, provided that development interventions recognise functional complementarities and spatial continuity across Saharan systems. A comparative reading with other Saharan wilayas (Adrar, Illizi, and Ouargla) further supports the argument that territorial diagnosis constitutes a decisive tool for place-based policy design and for overcoming the limitations of fragmented governance in low-density arid regions. The paper concludes by positioning Tamanrasset as a Saharan laboratory for sustainable development and by highlighting transferable lessons for peripheral territories across the Global South.
Comparative Antibacterial Study of Raw and C16TMA-Modified Kanemite for Environmental Water Disinfection
Access to microbiologically safe water remains a critical public health challenge. Layered silicates like kanemite offer a platform for water disinfection, but their native form has limited antibacterial efficacy. This study comparatively evaluates raw kanemite and its organically modified form, cetyltrimethylammonium-kanemite (C16TMA-kanemite), to assess the impact of intercalation on antibacterial performance. Both materials were synthesized via a hydrothermal method and characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Antibacterial kinetics and minimum inhibitory concentration (MIC) against Staphylococcus aureus were monitored using UV-Vis spectrophotometry. Structural analysis revealed that while raw kanemite maintained a compact framework (d-spacing = 1.02 nm), the modified hybrid showed significant interlayer expansion to approximately 2.3–2.7 nm. Raw kanemite exhibited only partial inhibition at tested dosages, whereas C16TMA-kanemite achieved complete bacterial eradication (100%) within 15 minutes at 0.05 g/5 mL. These results demonstrate that organic modification is the decisive factor in transforming an inert silicate into a fast-acting biocide through membrane disruption, offering a potent solution for contaminated water disinfection.
A Practitioner’s Runbook for Building Scalable Recommendation Systems in Large-Scale E-Commerce Platforms
Personalized recommendation systems sit at the heart of large-scale e-commerce platforms, directly shaping how users discover products, engage with content, and return over time. Delivering high-quality recommendations is not a secondary concern; it is a primary driver of retention, satisfaction, and sustained platform growth. Building these systems at scale demands careful coordination of several engineering components, from capturing recent user behavior and determining interest categories to constructing features for AI/ML inference pipelines and closing the loop through user interaction feedback integration, all while operating under strict latency and accuracy requirements. The strength of a recommendation system depends as much on how its components are architecturally organized as on the sophistication of the models running inside it. This article covers the end-to-end design of recommendation pipelines across four areas. First, context building draws on user interactions and trending signals to assemble the inputs the algorithm needs. Second, the online inferencing algorithm handles low-latency candidate retrieval, AI/ML model ranking, and post-processing feedback that feeds offline training. Third, scaling and reusability strategies allow generic pipelines to serve multiple pages, surfaces, and category-specific experiences through caching, unified interfaces, and deduplication. Fourth, broader implications are examined, including how well-designed systems build user trust, reduce operational overhead, and support healthy ecosystem diversity. Recommendation systems function as strategic infrastructure rather than isolated technical components. The architectural decisions made during their design shape day-to-day performance as well as the long-term resilience and competitive position of the platform.
Surface water quality of the oued-charef sub-basin: threats to the foum El Khenga dam, Algeria
Insufficient irrigation water supply created an alarming situation for the authorities. A policy of constructing large dams was initiated and implemented. Faced with excessive water pollution and mineralization, the study of these phenomena, their origins, their assessment, and the recommended solutions became crucial, hence this study originated. This study investigates the contamination and pollution of surface waters in the Oued Charef upstream sub-watershed (northeastern Algeria), located in a semi-arid environment. Runoff from carbonate formations and from Triassic, Mio-Pliocene, and Quaternary evaporitic deposits drains toward the Foum El Khenga Dam reservoir.Monthly water sampling campaigns from the dam reservoir, the Sedrata wastewater treatment plant (WWTP) and some tributaries (representative) were carried out on a hydrological cycle for physical (T, EC, TDS), and chemical (major elements and pollution indicators) analyses. In a qualitative context, the origin and mineralization of the waters were determined using diagrams (binary and chemical facies distinction), ionic ratios, and the spatiotemporal evolution of physicochemical parameters. This was supported by a multivariate descriptive statistical analysis, including principal component analysis (PCA), hierarchical ascending classification (HAC), and box plots. All the methods used revealed practically similar results.The compared data revealed three origins or groups of waters: - evaporitic (G2), - carbonate (G1) and - mixture (G3). For a more precise determination of the mixing proportions between fresh and salt water, future research studies will focus on the use of stable isotopes (δ¹⁸O and δ²H). The Sedrata WWTP effectively reduces polluting organic loads by decreasing BOD₅ and COD by more than 95%. Modernizing the existing treatment plant to include a tertiary dephosphorization stage to prevent long-term eutrophication is a crucial task that cannot be overlooked.
The use of multivariate statistics and hydrogeochemical analyses for the characterization of groundwater mineralization. A case study of El Hadaiek aquifer, Nord-East of Algeria
A comprehensive understanding of the physicochemical characteristics of water is essential for evaluating its availability and significance. The study area, located in northeastern Algeria, is characterized by intensive groundwater exploitation, particularly for agricultural activities. Therefore, assessing the quality and characteristics of this resource is crucial. This study aims to evaluate groundwater quality in the El Hadaiek aquifer using multivariate statistical methods and hydrogeochemical analyses, with a focus on identifying the contribution of major ions to groundwater mineralization.A systematic investigation of physicochemical parameters was conducted on collected water samples. The results were analyzed using hydrochemical methods, including the Piper diagram, to identify water facies, alongside multivariate statistical techniques. Principal Component Analysis (PCA), combined with varimax rotation, was applied to determine the main processes controlling water mineralization and to identify relationships among water samples. Additionally, the Saturation Index (SI) was calculated to identify the key geochemical reactions governing the system.The results indicate that groundwater in the study area is slightly acidic and highly mineralized. Most measured parameters remain within the limits established by the World Health Organization (WHO). The dominant hydrochemical facies is characterized by sulfate and chloride with calcium and magnesium (Ca–Mg–SO₄–Cl type). Two principal processes control groundwater mineralization: recharge through soil infiltration and water–rock interaction (the primary mechanism for major ion generation), with a lesser contribution from anthropogenic activities.
Persistent Population Disparities in Algeria (1977–2019): A Lorenz Curve and Gini Coefficient Analysis of Territorial Development
The geographical distribution of the population is a key indicator for understanding the nature of spatial organization and the degree of balance among regions within a country. In this context, this study aims to analyze the evolution of population distribution in Algeria during the period from 1977 to 2019, using a quantitative approach based on the Lorenz curve and the Gini coefficient to measure the degree of spatial inequality. The study relied on official statistical data at the provincial level, where cumulative percentages of population and area were used as the basis for constructing analytical indicators and representing them graphically.The methodology involves processing the data in ascending order of geographic units, allowing for the identification of patterns of concentration and disparity in population distribution over time and linking them to the characteristics of the national territory. The study also seeks to highlight the multiple dimensions of spatial imbalance by analyzing its demographic, economic, social, and environmental implications within a framework that integrates quantitative measurement with spatial interpretation.This study aims to: measure the degree of spatial imbalance in population distribution using standardized quantitative indicators, analyze the implications of this distribution for spatial development trajectories, and explore the potential for developing a strategic approach to restoring regional balance within the framework of sustainable development. This study derives its significance from its contribution to enhancing the quantitative understanding of spatial imbalances and supporting public policy orientations aimed at achieving greater spatial equity and economic efficiency
Exergy Analysis and Optimization of Combined Brayton and Rankine Cycles
There are many energy sources on the World that can be classified as renewable and non-renewable energies. The non-renewable energies which are exhaustible and often harmful to the environment. The usage of all these renewable and non-renewable energies efficiently is very important in technology and industry. The concept of exergy and exergy analysis is very important methods to show the inefficiencies and the irreversibility’s in a system and its devices. In this study, exergy analysis methods are applied to efficient combine cycle and cogeneration systems. The destruction of exergy has been examined for combined cycles and cogeneration system to find the optimum working conditions. It is found that as the pump pressure of the Rankine cycle increases from 400 kPa to 7600 kPa, the exergy efficiency increases from 76% to 85%, and as the compression pressure of the Brayton cycle increases from 400 kPa to 2200 kPa, the exergy efficiency increases from 72% to 79%. When these operating conditions, where the two cycles will operate at their optimum, are applied together, the exergy efficiency of the all cycle exceeds 86%. When the operating conditions, where the Brayton and Rankine cycles are at their maximum efficiency, are applied together, the optimum operating conditions of the system are obtained.
Performance and Exergy Analysis of Cryogenic Cycles
This study examines and demonstrates the concepts of thermodynamics, exergy, and exergy analysis, and shows how these principles are applied to cryogenic cycles using equations. Furthermore, an energy, exergy, and performance analyses of the two-stage vapor compression cascade cryogenic cycle for natural gas liquefaction is conducted, and optimum operating conditions are presented in tables. Based on the data obtained from these equations, exergy analysis of cryogenic cycles was performed, and performance analysis and curves were plotted. When the compressor outlet pressures of the first sub cycle is increased from 3300 kPa to 4590 kPa, the exergy loss of the devices decreases, the exergy efficiencies of the increases, and the COP values ​​increase. When the cooling amount in heat exchangers 1 and 2 is increased from 9 °C to 17 °C, i.e., when cooling is increased, it leads to a slight improvement in the performance of the system. The optimum total compressor consumption according to inlet/outlet temperature difference in heat exchangers 1 and 2 are obtained at about 14-15 °C. When these two improvement methods are applied together, optimum performance values ​​of the system are obtained. The COP value increases from 0.078 to 0.1164. The system is affected by the compressor outlet pressure of the first sub cycle. Increasing the cooling level from 9°C to 17°C in heat exchangers 1 and 2 has a minor impact on system performance.
Online Ontology Refinement for Analytical Agents via Regret-Bounded Feedback Aggregation in Experimental Serving Environments
Large Language Model-based analytical agents deployed in enterprise environments depend on structured semantic layers comprising metric definitions, dimension hierarchies, and entity relationships to ground code generation in domain-specific knowledge and suppress hallucination. These semantic layers are currently maintained as static artifacts by small expert teams, producing two systemic failures: a representation gap, in which the ontology reflects the analytical patterns of a minority of power users while underserving the majority of the user population, and a staleness gap, in which real-world business logic evolves faster than manual ontology updates can accommodate. This article proposes a closed-loop architecture that transforms implicit user feedback signals into automated ontology refinement, golden dataset expansion, and prompt recalibration, with formal convergence guarantees grounded in online convex optimization theory. The framework models the semantic ontology as a structured parameter space and applies regret-bounded gradient updates to both continuous and discrete subspaces, achieving sublinear cumulative regret against the best fixed ontology in hindsight. Evaluation on a simulated multi-tenant analytics platform demonstrates a thirty-one percent reduction in agent error rate, a nine-fold increase in improvement signal capture, and substantially improved representation equity across user segments, with convergence achieved within twenty-one days for the median metric definition.
Deep Learning-Based Melanoma Classification Using Hybrid DCNN-LSTM and DCNN-BiLSTM Architectures
Recent advancements in dermatology have improved the diagnosis and treatment of skin cancer. Early detection is particularly important, especially for melanoma, which is highly aggressive and can metastasize rapidly. However, diagnosing melanoma early is challenging due to its resemblance to atypical moles. While dermatologists assess lesions visually, microscopic examination is required for uncertain cases. The ABCD criteria are commonly used but may miss some melanomas. Dermoscopy offers greater accuracy and is preferred for reliable detection. AI and deep learning are revolutionizing dermatology, particularly in malignant melanoma diagnosis. This paper presents an AI system designed for accurate melanoma detection by incorporating a Deep Conventional Neural Network with transfer learning, data augmentation, and hybrid DCNN-LSTM and CNN-BiLSTM models. These techniques improve the performance of melanoma classification models. Experimental results demonstrate that the proposed methods surpass CNN approaches in accuracy, reaching 97.43%, specificity 99.6%, and, most importantly, sensitivity. This last metric, which represents the number of correctly identified malignant images, reaches 97.4% with the MobileNetV2-BiLSTM model.
Systems Integration in Healthcare: Driving Social Impact Through Centralized Waitlist Systems for Safety-Net Providers
Healthcare delivery in the United States continues to face persistent barriers to equitable access, particularly for underinsured and Medicaid populations. Safety-net providers operate within fragmented administrative and technical environments. Scheduling, referral management, and care coordination are often fragmented, resulting in inefficiencies, duplication of effort, and loss of referral continuity. The lack of coordination in this process creates disparities among these populations. This article examines systems integration using cloud-based, event-driven microservices. Centralized waitlist systems are presented as an example of integration technology that prioritizes patients based on clinical urgency rather than administrative order. The broader implication is that system integration extends beyond efficiency to generate measurable social value. Integrating technology with equity-focused design reduces wait times, improves continuity of access, and strengthens patient trust. Integrated data systems support policymakers in optimizing resource allocation and improving equity across healthcare stakeholders. This framework demonstrates how integration-driven architectures can be operationalized within safety net healthcare systems to improve both operational efficiency and equity outcomes.
Exact Multiplicity of Positive Solutions for a Quasilinear Boundary Value Problem with Density-Dependent Diffusion and Bistable Nonlinearity
We investigate the exact number of positive solutions for a quasilinear Dirichlet problem with a density-dependent diffusion coefficient of the form and a bistable nonlinearity , where , , , , . Using the quadrature (time-map) method, we determine the exact multiplicity of positive solutions for all . For , we identify two critical values such that the problem admits zero, one, or exactly two solutions depending on . The paper also includes a physical interpretation of the parameters and a numerical illustration of the theoretical results for .
Cognitive Load Optimization Models for Enterprise Analytics Dashboards
Enterprise analytics dashboards have become central instruments of organizational decision-making, yet their effectiveness is frequently undermined by cognitive overload. This paper presents a three-layer model for optimizing cognitive load in dashboards, breaking the problem down across three dimensions: user context, data semantics, and interaction design. Drawing on cognitive load theory, graphical perception research, working memory constraints, and recent empirical studies of dashboard usability, the model maps validated reduction strategies to three types of cognitive load (intrinsic, extraneous, and germane) and differentiates their application across four enterprise dashboard archetypes: operational monitoring, executive reporting, investigative analytics, and compliance and audit. The paper further examines measurement methodologies for evaluating cognitive load in dashboard contexts, including physiological, behavioral, and subjective approaches. The framework that comes out of this work equips dashboard architects and design system engineers with a structured approach for making cognitive load a deliberate part of the architecture, rather than leaving it as an afterthought in usability reviews.
Unified AI Security Posture Management Framework for Multi-Cloud Large Language Model Deployments
The rapid proliferation of large language models across multi-cloud environments has introduced new challenges for enterprise security posture management. Existing cloud security posture management tools operate individually, leading to blind spots and delayed threat response for distributed AI workloads. This survey presents emerging Unified AI Security Posture Management (UAI-SPM) frameworks for hosting LLM applications in multi-cloud environments. This article aggregates recent developments in industry, regulatory frameworks, and technological trends, while also addressing needs in security tooling, essential framework components, and novel architectural patterns. The article describes the tradeoffs between autonomy and security in AI systems, provides a literature review for applying zero trust to generative AI systems, and makes evidence-based recommendations for enterprises deploying LLMs at scale. Key findings indicate that attacks against AI services are pervasive and that the majority of risk exposure stems from identity and access misconfigurations. In this context, integrated posture management frameworks that enable continuous discovery, behavioral insights, and adaptive compliance will be foundational to security resiliency in an AI-driven world. Organizations will also have to deal with accelerating regulatory scrutiny around continuous monitoring, transparent governance, and verifiable security controls throughout the AI system lifecycle.
AI-Assisted Integration: Schema Matching, API Mapping, and Workflow Optimization
Enterprise integration across heterogeneous platforms exposes three persistent failure modes: semantic misalignment between schemas and APIs, unsafe contract evolution under version drift, and operationally brittle orchestration tuned by heuristic rather than signal. This article proposes a unified, policy-aware framework that addresses all three simultaneously. A hybrid schema-matching ensemble pairs conservative symbolic matchers with deterministic, grounded LLM scoring to achieve measurable F1 gains over symbolic baselines. API contract normalization, validated through consumer-driven contracts (CDC) and staged rollout, reduces promotion regressions attributable to silent payload changes. An observability-driven control loop (ODCL) treats telemetry as a first-class control input, adaptively tuning retry parameters, concurrency ceilings, and routing decisions against SLO error budgets. Results on open-specification corpora demonstrate hybrid matching F1 improvement of approximately 8 to 15 points, P95 latency reduction of 18% at high concurrency, and a 36% reduction in technical error volume. Governance is formalized through evidence packs, which are immutable bundles of prompts, model identifiers, decisions, and test artifacts. This positions the method for deployment in regulated environments and subjects it to normative scrutiny regarding the appropriate epistemic role of LLM inference in consequential integration pipelines.
Validation-First Bulk Knowledge Content Operations: Spreadsheet-Driven Ingestion Pipelines for Accurate, Auditable, and Scalable Publishing
Large knowledge repositories require continuous bulk updates to keep support content accurate, discoverable, and aligned with fast-moving product and policy changes. Many organizations perform bulk article creation, copying, and retirement through manual steps spread across multiple tools, leading to slow publishing cycles, inconsistent data quality, and avoidable defects. This article presents a practical architectural pattern for bulk knowledge content operations built around a validation-first ingestion pipeline that accepts structured spreadsheet uploads, performs layered validation and transformation, and executes content changes asynchronously with end-to-end traceability. The approach emphasizes data integrity, safe retries, failure recovery, and audit logging so that business teams can scale content operations without compromising correctness. Key implementation techniques include schema contracts for spreadsheet templates, taxonomy-aware referential validation, idempotent execution with operation identifiers, controlled batching to protect downstream systems, and observability instrumentation that connects each uploaded row to its execution outcome. Deployment evidence indicates substantial reductions in manual effort, fewer post-publication defects, and accelerated publishing cycles, demonstrating that disciplined ingestion design can transform content operations into an engineering-grade platform capability.
The method of Quasilinearization for scalar Caputo fractional differential equation
Non-linear problems occurring naturally in many branches of science, engineering, etc. are differential equations with integer order derivative. Fractional differential equations (FDEs) may model more effectively certain problems, than ordinary differential equations. Various methods for solving FDEs are being developed and these solutions have great importance in related fields. The method of replacing the original non-linear Caputo fractional differential equation by differential inequalities, whose associated equations can be solved, is known as ‘quasilinearization’. In this paper we establish the method of upper and lower solutions coupled with the method of quasilinearization that converge to the solution of the Caputo fractional differential equation.
Performance Governance and Reliability Engineering Framework for Mission-Critical Data Platforms
Mission-critical data platforms require predictable performance and availability because service degradation can lead to unacceptable operational, financial, or regulatory consequences. Traditional approaches treat performance tuning and reliability engineering as post-deployment operational activities. However, modern distributed data platforms require these properties to be embedded into system architecture. This paper proposes a Performance Governance and Reliability Engineering (PGRE) framework that integrates workload classification, workload isolation, performance baselining, failure containment, and scalability governance into a unified architectural discipline. The framework introduces governance mechanisms that monitor workload behavior, detect deviations from baseline performance models, and prevent cascading failures through architectural isolation boundaries. Experimental evaluation using mixed transactional and analytical workloads demonstrates that governance-driven approaches provide more stable performance and faster recovery than reactive operational management strategies. The framework provides practical architectural guidance for designing reliable and predictable mission-critical data platforms.
Application of chitosan beads as an effective biosorbent for orange G dye: characterization, optimization, and Equilibrium Studies
Chitosan beads (CTS) were prepared and studied in a batch mode operation for the adsorption of Orange G (OG) dye from aqueous solution. Characterization of the surface of CTS was achieved by using point of zero charge (pHpzc) method, Fourier transform infrared spectroscopy, and scanning electron microscopy. The effect of parameters such as adsorbent dosage (A: 0.02-0.06 g), pH (B: 4-10), time of contact (C: 5-45 min), and temperature (D: 30-60°C) on the color removal of OG was investigated using response surface methodology (RSM) based on Box–Behnken surface statistical design at an initial OG concentration, Co = 100 mg/L as a fixed input parameter. Results revealed that the highest removal (92.14 %) of OG dye was achieved by CTS at adsorbent dosage of 0.04g, solution of pH 4, temperature of 60 °C, and time of 25 min. The adsorption process followed the pseudo-second order (PSO) kinetic, and Freundlich isotherm models. The maximum adsorption capacity of CTS composite for OG dye was recorded to be 416.67 mg/g at 60 °C. This work introduces CTS as an ideal composite adsorbent for removal of textile dyes from the aqueous environment.
A Policy-Driven Adaptive Resilience Framework for Spring Boot Microservices and Angular Frontends
Microservice architectures and rich single-page application (SPA) frontends have become the standard case for enterprise software, yet resilience on both the microservices backend and frontend remains an unresolved engineering problem. The article presents a policy-driven adaptive resilience framework based on runtime contract enforcement and dynamic policy management for Spring Boot microservices and Angular frontends. The architecture consists of three components: first, a central policy engine for all resilience contracts in different versions. Second, a Runtime Contract Enforcer, which transparently intercepts service calls at the back end and the front end. Third, a Monitoring and Feedback Loop component for condition- and knowledge-based policy adaptation. Providing hot-reloadable policy propagation, contract-driven fault tolerance, and full-stack coverage without redeploying services sets apart this article with respect to prior work in static circuit-breaking, Resilience4j, and Istio/Envoy. Businesses evaluate the framework under various common failure scenarios and show improvements to mean time to recover, fault rate under failure, and latency to policy updates. The article concludes that the framework is a scalable and operationally agile solution for dynamic enterprise deployments.
Cloud Workflow Automation: Technical Implementation and Industry Applications
Cloud workflow automation is an innovative technological platform that will allow organizations to simplify the complex operational workflows in several corporate industries by smartly combining cloud computing, standardized APIs, and event-driven architecture patterns. The automation of transaction processing, clinical processes, inventory management, logistics and energy distribution systems have demonstrated to bring large changes in operational efficiency, regulatory compliance, and decision making quality within the organizations. The financial institutions use federated governance structures and machine learning algorithms to have uniform data governance across heterogeneous platforms without violating regulatory standards. Standardized clinical workflows are implemented by healthcare providers so that the care of the patient is automatically coordinated across departments and geographic locations, thereby decreasing delays and administrative load. Retail networks: event-based architecture allows synchronizing stock in real-time across the networks of different selling platforms, which subsequently improves the customer experience by providing the correct information about the product availability. The IoT enables the use of systems by logistics organizations that eliminate paper-based operations with real-time monitoring and automated exception management. The energy providers install modern distribution automation systems that allow self-healing and integration of renewable energy into the system by constant monitoring and intelligent control mechanisms. Cloud technologies in combination with artificial intelligence and real-time data processing can be converged to form whole ecosystems in which the practice of compliance, fraud detection, and data accuracy is performed automatically. The ability of workflow automation is strategically adopted by organizations to achieve sustainable competitive advantage by developing organizational resilience to market disruption through high quality service delivery, fast responsiveness to the market, and high-quality operational performance.
Accessibility Under Distributed State: Engineering Account Access Experiences That Work for Everyone
Account access experiences such as sign-in and authentication, account recovery, and device access enable users to manage their digital identity and to participate in civic, economic, and social aspects of life. Account access architecture is increasingly decentralized, with multiple changes from different identity providers, risk-scoring systems, and messaging services at nondeterministic times. The problems of accessibility are no longer about what is in the markup but about behavioral properties. These include a focus reset on re-render, communicating wait states only through animations, or status updates that never get exposed to assistive technology APIs. To eliminate these failure modes, this paper argues that accessibility must be treated as a first-class reliability property: specifiable, measurable, and stable under change. Accessibility contracts at the component level encode semantic, interaction, and announcement requirements for each component as testable defaults. The other two aspects are state modeling of engineering invariants in the transient, interrupted, and error states, and layered verification of both automated checkers and assistive technology-based manual checks, all along critical asynchronous paths. Release governance frameworks must connect these verification layers to deployment outcomes, ideally through explicit gates and incident-driven feedback loops, while conformance-driven measurement should be supplemented with user experience measures that automated tooling cannot capture. Account access flows that exclude people with disabilities deny them independent entry to the digital infrastructure through which banking, employment, healthcare, and civic participation are now primarily delivered.
Leveraging Integrated Master Data Management for Enhanced Healthcare Decision-Making
Healthcare organizations are experiencing a fundamental shift in data management through the implementation of Master Data Management (MDM) solutions. The integration of advanced technologies, including artificial intelligence and machine learning, has revolutionized how healthcare providers handle patient information, clinical protocols, and treatment strategies. From streamlining clinical trial processes to enhancing patient identification accuracy, MDM systems serve as the cornerstone for modern healthcare delivery. The adoption of standardized protocols and integration frameworks has significantly improved data exchange capabilities while maintaining security and compliance. Through the implementation of sophisticated matching algorithms and validation processes, healthcare organizations have achieved remarkable improvements in patient record management, care coordination, and treatment outcomes. The emergence of AI-driven platforms has particularly transformed oncology care, enabling precise mutation detection and personalized treatment approaches.
Enterprise AI Transformation Using Real-Time Analytics and Scalable Infrastructure Platforms
Enterprises are increasingly adopting artificial intelligence (AI) to enhance decision-making, operational efficiency, and responsiveness in data-intensive environments. This study investigates enterprise AI transformation through the integrated use of real-time analytics and scalable infrastructure platforms. A comprehensive methodological framework is developed that combines streaming data pipelines, AI model deployment, and elastic infrastructure scaling to evaluate system performance and decision impact under varying workload conditions. The results demonstrate that real-time analytics pipelines can sustain low-latency processing across moderate to high data velocities, while optimized AI models deliver accurate predictions without compromising responsiveness. Scalable infrastructure platforms are shown to play a critical role in maintaining system stability and minimizing decision latency as data volume and velocity increase. Furthermore, empirical findings indicate substantial improvements in decision speed, automation coverage, and organizational responsiveness following AI integration. The study highlights the strong interdependency between analytics, AI, and infrastructure layers and emphasizes the need for coordinated architectural design and adaptive scaling strategies. Overall, the research provides actionable insights for enterprises seeking to operationalize AI at scale and achieve sustained transformation through real-time, intelligence-driven systems.
Component-Method Analysis of Composite Beam-to-Column Joints with a Non-Extended End Plate
Composite beam–to-column joints significantly influence the global stiffness, strength, and force redistribution capacity of steel–concrete composite frames. Although the Eurocode component method provides a rational basis for characterization joint, its manual application remains time-consuming and error-prone, which limits its systematic use in design and parametric assessment. This study applies the Eurocode 3/Eurocode 4 component method to composite beam–column joints with a non-extended end plate, and develops a dedicated calculation program to evaluate the joint design moment resistance ( ) and initial rotational stiffness ( ) and to support parametric studies. The parametric results show that increasing the slab longitudinal reinforcement ratio can raise ( ) by up to 27.53% (limited end-plate configuration) and increase ( ) by up to 122.416% (non-extended end plate configuration). Increasing the beam section height enhances ( ) by up to 83.068%, while the stiffness advantage of the flush end plate over the limited end plate can reach 110.944%. The end-plate thickness exhibits a saturation effect: beyond a threshold thickness close to flange thickness of profile tfc, further increases provide negligible gains in ( ) and ( ). These findings provide practical guidance for optimizing joint detailing and avoiding unnecessary over-dimensioning, while the developed software enables rapid Eurocode-consistent evaluation of composite joint performance.
Management of an Energy System based on Hybrid Photovoltaic-Thermal Collectors
Hybrid photovoltaic-thermal (PVT) collectors represent a promising technology for improving the overall energy efficiency of solar conversion systems. This work presents a comprehensive approach to modeling and managing an energy system based on hybrid PVT collectors. The developed model integrates the electrical and thermal aspects of the system, enabling a detailed performance analysis under various operating conditions. Results demonstrate that optimized management can improve the overall system efficiency by 15 to 25% compared to conventional photovoltaic systems, with combined electricity and heat production. In this paper, a development of a unified electro-thermal modeling approach is coupled with a coordinated energy management algorithm that optimizes both electrical power extraction and thermal regulation in PVT collectors. This integrated methodology provides a deeper understanding of the coupled behavior of hybrid solar systems and offers an effective tool for the design and optimization of high-efficiency renewable energy installations. The proposed management algorithm uses an advanced Maximum Power Point Tracking (MPPT) controller coupled with intelligent thermal regulation to maximize energy extraction while maintaining optimal operating temperatures.
Geoheritage as a Tool for Environmental Education: Towards the Development of Sustainable Geotourism in Algeria – The Case of Tassili N’Ajjer
The spectacular Tassili N'Ajjer plateau, along with many other geological features in Algeria, has significant unexploited geo-tourism and environmental learning values when they are scientifically incorporated into the school curriculum and packaged for travel. This study investigates (a) views of the geo-heritage of the country among Earth science students and academics in two universities and (b) how knowledge can lead to more sustainable tourism. In 2025, a structured questionnaire was distributed to a total of 500 (questionnaire with Google Forms) students at Houari Boumediene University of Science and Technology in the City of Algiers and Ziane Achour University of Djelfa, as well as 80 faculty members. Instruments such as descriptive statistics and comparative frequency tables yielded discriminations of degrees of awareness, attitudes, and constraints about the mainstreaming of geo-heritage in curricula and the tourism industry. Interest among students was high, but formal instruction in geo-heritage received low formal attention. The faculty were broadly enthusiastic to integrate relevant teaching but felt that institutions failed to prioritize and support it. They all considered the magnificent Tassili N'Ajjer as the perfect "location" for a research visit and an educational tour, given its colossal geological and cultural importance. Educational investment in its geological heritage could further optimize geo-tourism potential in Algeria, being consistent with sustainable development, and could feed environmental education activities. There are plenty of wondrous geosites in the country that deserve enhanced visibility, which would also result in more informed and responsible tourism.
Geography of the Digital Paradox: A Development Economics Perspective on Algeria’s Structural Transformation (1994–2022)
In the discourse of Development Economics, the transition to a "Smart Society" is often viewed as a catalyst for bypassing traditional industrial stages. However, the spatial and structural reality in emerging markets like Algeria presents a more complex narrative. This study investigates the impact of the digital economy on economic growth in Algeria from a geographical and developmental perspective (1994–2022). Utilizing a multiple linear regression model via EViews 10, we examine the relationship between GDP per capita and key spatial/structural determinants: internet penetration (NET), physical capital (CAP), and trade openness (TR). The results confirm a Digital Productivity Paradox: while physical investment and trade show positive but statistically insignificant effects, internet penetration exhibits a highly significant negative coefficient (-0.0849, p = 0.0003). From a development economics standpoint, this suggests that Algeria is facing a "Leapfrogging Failure," where digital infrastructure exists but lacks productive integration into the national value chain. From a geographical perspective, the findings imply that the digital dividend is likely hampered by a spatial divide between urban centers and rural peripheries, leading to "consumptive" rather than "industrial" connectivity. The study concludes that to achieve the "Digital Algeria 2030" goals, policy must shift from national infrastructure coverage to localized digital industrialization and the closing of the spatial productivity gap.
Retrieval-Augmented Enterprise Analytics with Privacy-Aware Cloud Data Pipelines
The rapid adoption of cloud-native data platforms has intensified the need for enterprise analytics systems that are both contextually intelligent and compliant with strict privacy and governance requirements. Traditional analytics pipelines often struggle to deliver meaningful insights from heterogeneous enterprise data while simultaneously safeguarding sensitive information. This study investigates a retrieval-augmented enterprise analytics architecture integrated with privacy-aware cloud data pipelines to address these challenges. A design-science–oriented methodology is employed to evaluate analytical quality, system efficiency, and governance effectiveness across multiple pipeline configurations. The results demonstrate that retrieval augmentation significantly improves contextual accuracy, relevance, and explainability of analytics outputs, while privacy-aware mechanisms reduce policy violations and unauthorized data exposure without severely impacting system performance. Sensitivity and interaction analyses further reveal that balanced tuning of retrieval depth and privacy thresholds is critical for maximizing overall system effectiveness. The findings highlight that embedding retrieval intelligence and privacy controls as first-class architectural components enables scalable, trustworthy, and regulation-ready enterprise analytics in modern cloud environments.
Scenario-Based Evaluation of Sustainable High-Rise Housing for Refugee Communities under Resource Scarcity Using FAHP–TOPSIS: Evidence from Gaza
The housing problem in conflict-affected areas, especially in Gaza City, is aggravated by a rapid urbanization process, land scarcity, and long periods of humanitarian crisis. Another possible spatial solution is high-rise housing, but its sustainability under extreme resource constraints has not been adequately assessed. This paper proposes a multi-criteria decision-making framework with a scenario-based approach to evaluate sustainable high-rise housing options for refugee communities in contexts with limited energy, water, and material resources. Four architectural situations were developed in accordance with site conditions, community needs, and resource availability. A systematic analysis of the criteria was conducted to assign weights and rank the scenarios using an integrated FAHP to assess environmental performance, resource optimization, indoor environmental quality, and community-centric criteria, and the Fuzzy TOPSIS to rank the scenarios. The outputs of the simulation were added to the decision framework to ensure quantitative rigor, namely, energy demand, CO2 emissions, thermal comfort (PMV), and natural ventilation performance. Findings indicate that the hybrid scenario achieves optimal performance, with substantial changes in operational energy requirements and carbon emissions, under realistic technological conditions and high social functionality.
Vibrational Properties at the Intersection of Embedded Nanostructure and Surface Boundary
We propose an analytical approach to investigate the vibrational scattering and localization at the intersection of an embedded nanostructure and boundaries of crystalline surfaces . The propagating and the evanescent modes are calculated in the bulk material and the energies of localized modes in the neighbourhood of the defect are determined. We show respectively the existence of optical localized modes and acoustical modes induced by the inhomogeneity. Also the number and features of these curves depend strongly of the parameters of a system model. The state densities are calculated by the mean of the Green functions associated to the matching method, for atomic sites that constitute a minimum representation set in the neighbourhood of the defect. The interaction between the propagating bulk modes and the localized phonons induced by the integrated nanostructure give rise to resonances in the subband of the bulk material
Microbial Diversity in Algerian Hypersaline Environments: Insights from Hadjer El-Melh and Zahrez-Gharbi (Djelfa, Algeria)
Hypersaline environments harbor highly specialized and diverse microbial communities shaped by extreme physicochemical conditions. This study investigates the microbial diversity of two hypersaline water samples from Algeria: Hajr El Meleh (Salt Rock) (S1) and Zahrez-Gharbi (S2). Physicochemical analyses revealed extremely high salinities (26.5% and 23.0% NaCl, respectively), near-neutral pH, and elevated concentrations of Na⁺, Cl⁻, Mg²⁺, and SO₄²⁻. Using 16S rRNA gene amplicon sequencing via the Illumina MiSeq platform, we characterized bacterial and archaeal communities at multiple taxonomic levels. Alpha diversity indices (Chao1, Shannon, ACE) indicated higher species richness and evenness in S1. Taxonomic profiling showed dominance of Firmicutes, Proteobacteria, and Bacteroidota in both sites, with sample-specific phyla such as Halobacterota (S2) and Euryarchaeota (S1). At the family, genus, and species levels, significant variations were observed: S1 was enriched in Salinibacter, Methanobrevibacter smithii, and Finegoldia magna, while S2 was characterized by Bacillus, Veillonella, and Campylobacter. The presence of numerous unclassified taxa across both samples highlights the potential for novel microbial lineages in these extreme habitats. Our findings highlight the patterns of microbial community structure and diversity in relation to salinity, ion composition, and geological context, contributing to the understanding of extremophile adaptation in saline ecosystems.
Federated Data Mesh with AI Governance: A Framework for Distributed Banking Analytics with Intelligent Policy Enforcement
Traditional centralized data platforms face fundamental scalability constraints as banking institutions expand their analytical workloads, domain boundaries multiply, and regulatory obligations intensify—creating systemic bottlenecks in data access, innovation throughput, and the application of domain expertise to business-critical decisions. Data mesh architectures decentralize data ownership to domain teams, positioning data as managed products with defined quality, accessibility, and discoverability contracts; yet this decentralization introduces a new class of governance challenges, encompassing how to assure consistent quality across autonomous domains, how to enforce regulatory compliance without imposing centralized control, and how to prevent fragmentation while enabling genuine domain autonomy. The federated data mesh framework presented here addresses these tensions through an integrated suite of machine learning capabilities—combining federated learning for privacy-preserving collaborative model development, graph neural networks for automated lineage and impact discovery across distributed products, reinforcement learning for intelligent resource allocation and query routing, natural language processing for automated metadata enrichment, and distributed policy enforcement enhanced with machine learning-based anomaly detection. Deployed across banking platforms spanning retail banking, wealth management, and commercial lending domains, the framework delivers substantial improvements in data product velocity, data quality, regulatory compliance, and analyst time-to-insight, establishing a validated architecture for data mesh governance at production scale in regulated financial services environments. The seven-stage asynchronous orchestration model, closed governance feedback loops, and domain-level resilience mechanisms introduced here collectively represent a reproducible blueprint for decentralized data governance at enterprise scale.
Data Contracts in Cloud-Native Analytics: Governing Schema and Semantics to Prevent Pipeline Breakage and Accelerate Safe Change
Cloud-native analytics systems face persistent reliability challenges stemming from the unregulated evolution of interfaces between data producers and consumers. Conventional schema validation provides structural guarantees but fails to capture semantic, quality, and operational expectations governing dataset behavior. This paper presents ContractGuard, an architectural framework for data contracts that encodes schema definitions, semantic interpretations, statistical quality constraints, and operational delivery specifications into machine-enforceable interface agreements. The framework comprises four integrated components: contract definition patterns capturing business semantics and measurement units beyond structural validation; automated enforcement architectures instrumenting validation gates at strategic pipeline positions; versioned evolution protocols enabling backward-compatible extensions and systematic breaking change management; and lineage-driven impact prediction mechanisms for assessing change propagation before production deployment. Contract specifications extend beyond field names and data types to encompass temporal semantics, categorical stability guarantees, completeness thresholds, and freshness requirements. Multi-stage validation gates implement shift-left quality assurance, detecting violations at ingestion rather than through downstream consumer failures. Semantic versioning adapted for dataset evolution distinguishes compatible enhancements proceeding through expedited workflows from breaking changes requiring coordinated migration. Lineage integration enables blast radius prediction by identifying affected datasets, transformations, and consumers before changes propagate through pipeline graphs. The framework architecture draws upon established performance characteristics from foundational systems implementing similar validation, lineage capture, and impact prediction components. ContractGuard shifts organizational posture from reactive incident response to proactive contract validation, providing an architectural foundation for faster feature delivery while maintaining platform stability through explicit compatibility specifications and automated compliance mechanisms.
Context-Aware Video Recommendation via Transcript Embeddings and LLM-Based Hashtag Generation
Video recommendation systems usually rely on user behavior patterns and collaborative filtering methods, so they are subject to popularity bias and create filter bubbles that result in content homogeneity. This article presents a complete system implementation using video transcript embeddings and large language model-generated hashtags to enable semantic recommendations. The system uses OpenAI Whisper to convert speech into text, Sentence-BERT to create detailed text representations, GPT-4 with special prompts to extract hashtags, and FAISS with IndexIVFPQ for fast similarity searches. Comprehensive prompt engineering experiments demonstrate that domain-adaptive prompts achieve superior precision and recall in computer science content, substantially outperforming baseline prompts and platform-generated tags in the F1 score. The complete system processes videos at real-time speeds on NVIDIA A100 GPUs, constructs indexes efficiently for large video collections, and delivers top-ranked recommendations with low latency. Reproducible experiments on educational videos across computer science, mathematics, physics, and biology demonstrate significant relevance improvement over description-based search in cold-start scenarios and substantial improvement in long-tail content exposure. All implementation details, prompt templates, evaluation datasets, and performance benchmarks are provided to enable replication and extension.
AI as a Force Multiplier in Enterprise Web Development: A Systematic Review and Collaboration Framework
Enterprise web development encompasses distributed architectures, cloud-native infrastructures, and multi-channel user interfaces, generating substantial cognitive demands on engineering teams. This article investigates artificial intelligence as a productivity force multiplier within enterprise development contexts through systematic review of primary empirical studies spanning knowledge management, code engineering, quality assurance, and onboarding workflows. Analysis reveals that retrieval-augmented generation achieves superior factuality ratings compared to parametric-only baselines in knowledge retrieval tasks. Developer interaction studies demonstrate that exploration mode dominates over acceleration mode when engaging with AI code generation tools, with professional developers allocating greater proportions to acceleration mode as expertise increases. Cross-project prediction research establishes that transfer models rarely achieve acceptable precision, recall, and accuracy thresholds simultaneously, underscoring the necessity for context-specific AI augmentation. Whole test suite generation approaches achieve superior branch coverage compared to single-target methods across benchmark evaluations. The contribution comprises a structured human-AI collaboration framework distinguishing augmentation from substitution, incorporating governance mechanisms addressing code security vulnerabilities, documentation inaccuracies, and skill erosion risks. The framework synthesizes empirical findings into actionable design principles for enterprise AI adoption, positioning artificial intelligence as an embedded acceleration layer within human-directed workflows.
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering https://creativecommons.org/licenses/by/4.0
Cellular Architecture for Financial Fault-Tolerance: Engineering Highly-Isolated, High-Availability Tax Calculation Cells
Horizontally scaled distributed systems carry an inherent vulnerability: when stateful dependencies are shared across services, a single bottleneck propagates failures outward until the entire execution path degrades. Financial environments operating at transaction scale cannot absorb this propagation pattern, as database saturation and regional disruptions translate directly into revenue loss and exposure to regulatory penalties. A cellular architecture addresses this vulnerability by partitioning global workloads into autonomous units that each maintain their own state boundaries, severing the chain reaction before it begins. Removing the database from the critical execution path eliminates the synchronous dependency that historically made checkout pipelines fragile under load. Pre-computed state injection combined with a state-machine-driven orchestration layer keeps business logic insulated from the volatility of persistent storage systems entirely. Controlled simulation data confirms that this architectural shift produces substantial reductions in failure blast radius alongside the operational conditions necessary for near-continuous global availability. Latency improvements follow directly from eliminating round-trip database calls, accelerating the order finalization sequence in ways that database-coupled designs structurally cannot achieve. Multi-billion dollar financial infrastructures operating under complex and shifting regulatory obligations gain a scalable foundation that absorbs compliance transitions without compromising availability.
Comparative agronomic and phytosanitary analysis of parental tomato lines and their F1 hybrids (Lycopersicon esculentum L.) under Mediterranean open-field conditions.
For several years, tomato breeding has focused on developing F1 hybrids adapted to agronomic and pedoclimatic conditions, while identifying the best cultivar in order to produce quality seed at lower cost. Favorable climatic conditions and human expertise in Algeria provide an opportunity to produce vegetable seeds, especially tomatoes. In this research project on producing F1 hybrid seeds, we evaluated the characteristics, agronomic performance, and sanitation of certain tomato varieties by comparing them with their F1 hybrids grown in an open field under Mediterranean conditions. The trial was conducted at the Technical Institute of Vegetable and Industrial Crops in Staouéli, Algeria, during the 2004 season. It used a randomized complete block design with three replications. The parameters measured were phenological (date of appearance of flower clusters, flowering, and maturity), production (number of fruits per plant, fruit weight, and yield per plant), and incidence and severity of late blight.The results reveal an early onset of flower buds and maturity for the H*M hybrid, at 20 and 82 days, respectively. The H*B hybrid shows an earlier flowering time of 35 days. Regarding the abortion rate, the results indicate high to very high rates for all cultivars. Production parameters show different results among cultivars. The hybrid S*H has the highest number of fruits per plant (11), and the hybrid I*M has the best fruit weight (135 g) and yield (1.4 kg). Very low tolerance was observed for all genotypes studied.The present study revealed significant diversity among different parameters of F1 tomato hybrids compared to their parents. Therefore, the study recommends using local, resistant genotypes.
Spatio-seasonal variation in Hyalomma aegyptium infestation of Testudo graeca in the Sénalba Chergui forest of Djelfa (Algeria): main and interactive effects of body size, sex, and microhabitat
This study aims to analyze the biological and environmental factors associated with infestation by the tick Hyalomma aegyptium in the tortoise Testudo graeca in the Sénalba Chergui forest (Djelfa, Algeria). A total of 150 tortoises were examined, and 1128 ticks were collected. Taxonomic identification demonstrated that all tick specimens belonged exclusively to the species Hyalomma aegyptium. The overall prevalence rate was determined to be 97.33%, while mean intensity and mean abundance were estimated at 7.73 and 7.52 ticks per individual, respectively. Infestation of Testudo graeca by Hyalomma aegyptium in the Sénalba Chergui forest is nearly generalized, intense, and strongly structured by ecological and biological conditions. Analysis of variance (ANOVA) revealed significant effects of season, sex, and their interaction on prevalence, mean intensity, and mean abundance. Analysis based on a generalized linear model with a Poisson distribution demonstrated that parasitic infestation increased considerably with increasing body size and varied according to microhabitat. The highest values were observed in microhabitats characterized by litter, shelters, and shrub cover. Furthermore, the chi-square test revealed a significant variation in parasite load according to study sites and seasons. However, the interaction between body size and microhabitat did not show an overall significant effect. The uniqueness of this study resides in its focus on interaction effects, especially between season and sex, underscoring that parasite dynamics are shaped not only by individual biological and environmental factors but also by their interactions. These results suggest that infestation dynamics are based on a close interaction between parasite phenology, host ecology, and environmental heterogeneity. By emphasizing the determining role of habitat microconditions in the maintenance and intensification of parasitism.
LLM-Guided Cross-Platform Optimization of Cloud Analytics Workloads
Large-scale data analytics in the cloud inevitably involves trade-offs among latency, throughput, scalability, elasticity, and cost. Today’s platforms model these trade-offs in very different ways-Amazon EMR builds on managed Hadoop ecosystems, Spark on Kubernetes container-native distributed execution, and Snowflake offers a fully managed data warehousing model. Although prior benchmarks-often based on TPC-DS, TPC-H, or microbenchmarks-have studied these systems, they are typically evaluated in isolation and rely on static configurations, manual tuning, or simplified cost assumptions. As a result, it remains unclear how these platforms compare under realistic, evolving cloud workloads, or how their performance and cost can be jointly optimized in dynamic environments. To bridge this gap, we introduce LLM-TradeOpt, a Large Language Model (LLM)–guided optimization framework that adaptively reasons about workload characteristics, system configurations, and execution traces across heterogeneous analytics platforms. Using CloudSuite v4.0 analytics workloads, our evaluation shows that LLM-TradeOpt consistently improves performance and efficiency, achieving up to 18.7% lower latency, 22.4% higher throughput, and 15.3% cost savings compared to strong baselines on Amazon EMR, Apache Spark on Kubernetes, and Snowflake.
Cloud-Based Artificial Intelligence for Modern Financial Risk Management
Cloud computing and artificial intelligence are changing the way in which it works in financial risk management providing unprecedented powers of fraud detection, credit assessment and regulatory compliance. The financial institutions benefit from cloud-based AI architectures that can help them to improve operations, detect risk and manage costs, and process large amounts of data in real time. They may also be able to help researchers identify sophisticated fraud patterns with unprecedented accuracy, see possible credit default months earlier than most of the traditional tools and provide financial services to previously uninsured citizens with better insights from data. Containerization, micro services and orchestration provide scale with which cloud-native AI systems can be configured to accommodate complex analytical models that are far beyond what is possible. Strong governance structures present significant challenges to reducing regulation compliance, modeling explanations, and data protection concerns. The advancement of these technologies should eventually influence the way in which financial risk management is conducted, and it will bring more mature financial systems that are able to respond to new threats and more specifically and effective in providing services to other populations.
Self-Healing Subscription-Based Cloud Infrastructure: A Technical Review
Enterprise cloud environments struggle with maintaining Service Level Agreements. Configuration errors happen frequently. Security threats emerge constantly. Infrastructure failures disrupt operations without warning. Manual intervention cannot keep pace with these challenges. A novel framework tackles these problems head-on. It uses automated API-level controls combined with machine learning detection. The system has an API Gatekeeper at its core. This component selectively disables specific application programming interfaces when problems occur. The isolation is granular. It prevents damage from spreading while keeping other services running. A Compliance Engine watches resource utilization patterns around the clock. Machine learning algorithms build baseline behavior profiles. They spot deviations that signal potential threats or performance problems. When anomalies show up, the framework toggles off problematic APIs right away. Automated remediation kicks in to fix root causes. No human intervention needed. After remediation succeeds and compliance is verified, the system brings everything back online. It re-enables the disabled APIs. Response times drop dramatically compared to manual processes. The framework keeps SLA compliance consistent through proactive detection and automated containment. Organizations get better service reliability. Operational overhead goes down. Security posture gets stronger. The API toggle mechanism marks real progress in cloud service management. It enables precise control at the function level instead of shutting down entire services.