Search Results (244)

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

To better understand the characteristics and causes of acid rain pollution in Huangshan City, China, in the context of reduced atmospheric pollutant emissions, this study systematically analyzes precipitation monitoring data from Huangshan City for the period 2013–2025. The analytical methods included volume-weighted mean, neutralization factor, and linear regression analysis. The results indicate that, with 2017 as a turning point, acid rain in Huangshan City transitioned from high-level fluctuations to a stabilization phase at medium-to-low levels. However, the annual mean pH remained below 5.6, indicating that the acid rain problem persists. Regarding pollutant emission reductions, sulfur dioxide (SO₂) control has achieved significant results, but nitrogen oxide (NO_x) pollution remains prominent due to factors such as a sharp increase in vehicle ownership. Analysis of the chemical composition of precipitation shows that the SO₄²⁻/NO₃⁻ ratio decreased from 4.09 to 0.92, and the acid rain type has shifted from sulfate-dominated to mixed sulfate-nitrate-dominated. In precipitation, highly specific ion pairings are observed: Ca²⁺ with SO₄²⁻ (r = 0.989) and NH₄⁺ with NO₃⁻ (r = 0.839). These two ion pairs together account for 81.4% of the total cations, forming two independent neutralization mechanisms—below-cloud and in-cloud—which explains the relative stability of precipitation pH despite a decline in total ion concentration. Furthermore, interannual variability in precipitation amount, particularly extreme wet events, is a key external factor driving fluctuations in acid rain frequency under stable emission conditions. The dominant driver of acid rain frequency variability has shifted from emission-dominated to precipitation-dominated. Full article

Compared with gaseous hydrogen at ambient temperature, liquid hydrogen (LH₂) possesses a higher volumetric energy density and is therefore regarded as one of the most economically viable hydrogen storage and transportation options. However, the extremely large temperature difference between the storage temperature of LH₂ and the ambient environment may give rise to serious safety hazards once a leakage accident occurs. Focusing on an integrated hydrogen production and refueling station (IHPRS), this study investigates the suppression effect of a novel synergistic protection system—combining a barrier wall and an air curtain—on LH₂ leakage and dispersion. By comparing the dispersion distances of hydrogen clouds under different barrier wall–air curtain configurations, the optimal synergistic structure was identified as a barrier wall with a planar size of 36 m × 12 m and a height of 3 m, combined with an air curtain velocity of 40 m/s. The reliability of this structure is further evaluated under practical influencing factors: under varying natural wind conditions, the maximum downwind dispersion distance is reduced by up to 58.02%; at a flash evaporation mass fraction of 20%, horizontal dispersion is suppressed by 42.18% and 33.17% in the X- and Z-directions, respectively; and at a leakage mass flow rate of 5.15 kg/s, the X-direction dispersion distance is reduced by 33.88% with a 40.14% increase in cloud height. The results show that the proposed barrier wall–air curtain synergistic protection structure can effectively alter the dispersion path of the FHC (refers to the hydrogen cloud with a volume concentration within the flammable range between 4 and 75% vol) formed by LH₂ leakage, shorten the hazardous downwind distance, and enhance the vertical dispersion of the FHC. These findings provide theoretical support and safety guidance for the risk control of LH2 leakage accidents in IHPRS. Full article

Iron (Fe) is an essential micronutrient that constrains primary productivity across approximately 50% of the global ocean, thereby regulating ocean–atmosphere carbon exchange and climate. Atmospheric deposition dominates the external supply of Fe to the open ocean, directly impacting marine biogeochemical cycles. This review systematically synthesizes current knowledge on the sources of total and soluble aerosol Fe and on the key factors and mechanisms governing Fe solubility, including proton- and ligand-promoted dissolution, photoreduction, cloud processing, and their spatiotemporal variability. We critically evaluate the methodologies used to measure Fe solubility across studies, highlighting persistent uncertainties that arise from inconsistent extraction solutions, filter pore sizes, and leaching protocols. By identifying these challenges and integrating field observations, laboratory experiments, and model results, we aim to clarify the controls on atmospheric Fe solubility and provide a more robust assessment of its contribution to marine primary productivity and biogeochemistry. Full article

Energy-intensive processes such as flexographic printing, extrusion coating, slitting, compressed air generation, and chilled water production make liquid carton packaging manufacturing a major electricity consumer, increasing the need for cost-effective and sustainable energy solutions. This study evaluates the real-world performance of a 679 kWp grid-tied solar photovoltaic (PV) system integrated at the 11 kV level in a liquid carton packaging factory in Nairobi, Kenya, operating under regulatory export control constraints that require full on-site consumption of PV generation. Using measured operational data from energy monitoring platforms, including Sunny Portal, 1.31.8 Schneider EcoStruxure, and Sphera Cloud 8.17.2, system performance was assessed in accordance with IEC 61724-1, focusing on final yield, capacity utilization factor, grid offset contribution, and carbon emissions reduction. The results show that the system generated 617 MWh over the assessment period, corresponding to an average daily final yield of 2.49 kWh/kWp·day and a capacity utilization factor of 10.38%. On-site PV generation supplied approximately 17% of the plant’s annual electricity demand and avoided about 277.7 t CO₂ emissions. Performance benchmarking against comparable installations in Kenya, Morocco, Malaysia, Senegal, and Uzbekistan indicates that the lower observed yield is primarily driven by curtailment and industrial load-matching limitations rather than inadequate solar resource or component inefficiency. The findings demonstrate that meaningful electricity cost savings and emissions reductions can be achieved in energy-intensive manufacturing environments despite export restrictions while highlighting the importance of improved load alignment and data-driven operational strategies to enhance PV utilization. Full article

While hardware selection is widely recognized as a key factor in machine learning performance, systematic and reproducible evaluation across heterogeneous and accessible platforms remains limited, particularly when jointly considering execution time, energy consumption, stability, and cost-efficiency. This work presents a unified and fully reproducible benchmarking framework for supervised learning, designed to enable controlled and comparable evaluation across diverse hardware environments. The proposed methodology enforces consistent training pipelines, fixed hyperparameter configurations, and repeated executions to ensure statistical reliability, while incorporating performance metrics such as execution time, power consumption, and energy usage, as well as performance-per-dollar. The framework is validated on a representative set of platforms, including CUDA-enabled GPUs, Apple Silicon (CPU/GPU), x86 processors, ARM-based embedded systems, and cloud-based environments, using convolutional, recurrent (RNN, LSTM, BiLSTM), and tree-based (XGBoost) models. The results reveal that hardware efficiency is strongly model-dependent. GPUs provide the highest computational performance and stability for parallel workloads, whereas Apple Silicon achieves superior energy efficiency with competitive execution times, particularly for recurrent architectures. The batch size analysis shows that performance can vary significantly depending on workload configuration, especially on CPU-based platforms, while epoch-based evaluation confirms that the measured performance reflects steady-state behavior rather than initialization overhead. In contrast, conventional CPUs and embedded systems exhibit significant scalability limitations for deep learning training, although they remain competitive for tree-based methods such as XGBoost, which demonstrates near hardware-independent predictive performance. These findings highlight the limitations of generalized hardware selection criteria and emphasize the need for model-aware and hardware-aware benchmarking. The proposed framework offers a practical and extensible foundation for reproducible, hardware-aware evaluation of machine learning systems, supporting informed decision-making in research, deployment, and cost-constrained scenarios. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

Traffic congestion in urban areas poses substantial challenges to transportation management, urban planning, and environmental sustainability. This study introduces an explainable artificial intelligence (XAI) framework for predicting traffic congestion in Casablanca, Morocco, by integrating gradient boosting models with lightweight XAI techniques that are suitable for edge deployment. Employing SUMO-simulated traffic data comprising 30,000 data points across 30 scenarios, we implemented a GradientBoostingRegressor (scikit-learn) enhanced with native feature importance analysis, permutation importance, and partial dependence plots, achieving $R^2=0.9939$, MAE = 0.015, and RMSE = 0.019. The XAI analysis reveals that lag features (32.0%), temporal patterns (35.0%), and infrastructure features (15.0%) are the primary contributors to congestion prediction, with culturally relevant factors, such as Friday prayers, accounting for 8.7% of the total feature importance. The model was deployed through a knowledge-distillation TinyML pipeline, achieving 31× compression (2.4 MB → 76 KB) on ESP32 microcontrollers with 2.1 ms inference latency and a 667× reduction in per-inference energy consumption compared to cloud-based deployment. This lightweight XAI approach directly addresses the gap between interpretability requirements and edge deployment constraints, facilitating sustainable intelligent transportation systems in developing countries with limited infrastructure and energy resources. The proposed framework is transferable to other rapidly urbanizing cities in the Global South, offering a replicable template for data-driven interpretable traffic management that can directly inform infrastructure investment prioritization, adaptive signal-control policy design, and culturally aware urban mobility planning strategies. Full article

Thermal infrared remote sensing offers a cost-effective means of regional geothermal reconnaissance, yet a fundamental challenge remains: isolating the weak geothermal surface signal (typically 1–3 °C) from dominant surface noise introduced by seasonal temperature cycles (annual amplitude > 20 °C), topographic variability, land cover heterogeneity, and irregular cloud-affected satellite sampling. Conventional single-scene or arithmetic-mean approaches are highly susceptible to these confounding factors and frequently produce pseudo-anomalies that obscure genuine geothermal targets. To overcome this limitation, we propose a physics-based time-series framework in which a nonlinear annual temperature variation model, T(t) = T0 + A·sin(2πt/τ + φ), is fitted to multi-temporal Landsat 8 thermal infrared data via the Levenberg–Marquardt algorithm. Applied to ~50 cloud-free scenes (2021–2022) processed on the Google Earth Engine over the Shanxi Graben System, northern China, the model simultaneously retrieves the background temperature parameter T0 and seasonal amplitude A—two physically interpretable quantities that encode distinct geothermal signatures more robustly than simple temporal statistics. Sub-regional corrections for the elevation (−4 °C/100 m above 800 m), aspect (R² > 0.95 in piecewise linear segments), and slope further suppress topographic pseudo-anomalies prior to anomaly extraction. Over known high-temperature geothermal fields (Tianzhen and Yanggao; >100 °C at 100 m depth), the method reveals clear T0 offsets of +1–2 °C (3–5% relative) and amplitude deficits of ~2 K (5–10% relative) relative to the background, with model-fitted T0 values averaging ~2 °C higher than arithmetic means due to the correction for seasonal sampling bias. Combined with 5 km fault-proximity buffers, extracted anomaly zones align well spatially with known geothermal sites and major structural corridors of the graben system. However, deeper low-temperature systems (45–50 °C at 300–500 m depth) produce ambiguous signals below the ~1.5 K detection threshold, indicating inherent limitations for deeply buried resources. The fully reproducible, training-data-free workflow is implementable via open satellite archives and cloud computing platforms, making it a transferable low-cost tool for structurally controlled geothermal reconnaissance across extensional basins worldwide. Full article

Underground roadways are essential for personnel movement and equipment transport in coal mines, and the stability and deformation of surrounding rock are critical to mine safety. Traditional methods for monitoring surrounding rock deformation in underground coal mining are time-consuming, inefficient, and require on-site manual measurements. To improve monitoring efficiency and reduce acquisition time, a 3D laser scanning system was employed for deformation monitoring. However, in complex underground environments, 3D laser scanning is affected by multiple environmental factors. Controlled experiments were designed to simulate these conditions, and the effects of scanning resolution, object color, dust concentration, and scanner position on measurement errors were quantified to evaluate the feasibility of roadway measurements. A simulated roadway deformation environment was constructed, and point cloud data were used to monitor deformation and quantify the measurement error of the 3D laser scanner. A corresponding deformation monitoring system was developed to identify deformation patterns of surrounding rock in underground roadways. The proposed method was applied and validated at the Zouzhuang Coal Mine. The results indicate that the proposed approach can automatically acquire high-accuracy deformation data. Full article

This manuscript presents the development of an HEX platform inverse kinematics model, its numerical implementation, and experimental validation. A complete inverse-kinematics formulation is established from the geometric definition of the base and mobile joint coordinates and a z–y–x Euler rotation sequence, allowing actuator-length computation for arbitrary 6-DOF poses. The model is implemented to map the operational workspace under actuator stroke and joint-angle constraints via a two-stage deterministic search, providing dense workspace point clouds, surfaces, and quantitative translational/rotational limits for multiple stroke ranges. Experimental validation is performed on a hexapod platform controlled through an embedded inverse-kinematics layer within a cascaded position–velocity–current architecture with dual-encoder actuator feedback. For a ±25 mm actuator travel range, the experiments confirm close agreement with translation simulations with differences of the order of 2% to 3% in x, y, and z, while larger discrepancies were observed in orientation limits, i.e., the model predicts γ ≈ ±32.5° and α, β ≈ ±10–11°, whereas measurements yield γ ≈ ±30° and α,β ≈ ±14–15°, evidencing higher sensitivity of rotational capability to real mechanical and control factors. Full article

In response to escalating global environmental challenges, mitigating carbon emissions in the construction sector has emerged as a critical strategy for addressing climate change. As reported by the United Nations Environment Programme (UNEP) and the International Energy Agency (IEA), the construction industry remains a major contributor to global greenhouse gas emissions. This study investigates the influencing factors and optimization pathways for embodied carbon emissions during the materialization phase of prefabricated buildings. Through longitudinal field research at a large-scale precast component factory in western China, key carbon emission factors were identified using Min–Max normalization and Principal-Components Analysis (PCA). A cloud entropy–based evaluation model was further developed to quantify the emission weights of 32 factors. The results reveal the existence of ‘leveraging effects’ among emission factors, wherein certain low-weight factors exert disproportionate influence on systemic carbon reduction because of their cascading impacts on other variables. Prioritizing factors with greater leveraging potential is imperative for the formulation of effective emission reduction policies. This study leverages NK model simulations (10,000 iterations), to predict the reduction potential of each factor and identifies four indicators with the most significant leveraging effects. Strategic recommendations are proposed that emphasize a synergistic approach that integrates direct emission control and indirect cascading optimization. These findings provide actionable insights for achieving systemic carbon reduction in prefabricated building systems. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article