Abstract: The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth generation (5G) networks, with their high bandwidth and low latency, are poised to support the massive connectivity requirements of UAVs. However, the high mobility of drones presents significant challenges for handover management, leading to frequent service interruptions and degraded performance. This paper proposes a novel, first-of-its-kind framework that integrates multi-UAV trajectory prediction with proactive handover optimization in 5G networks. Our approach utilizes a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to predict the future flight path of each UAV. The predicted trajectories are then fed into a Deep Reinforcement Learning (DRL) agent, which makes optimal handover decisions to ensure seamless connectivity and high Quality of Service (QoS). Unlike existing solutions that primarily rely on simulated data, our framework is validated using a real-world drone trajectory dataset. The experimental results demonstrate that our proposed method significantly outperforms traditional and existing machine learning-based handover schemes in terms of handover success rate, average Signal-to-Interference-plus-Noise Ratio (SINR), and handover delay. The proposed framework paves the way for more reliable and efficient drone operations in 5G and beyond networks.

Abstract: Retrofitting existing residential buildings is a critical strategy for achieving urban decarbonization while addressing public health disparities, particularly in communities disproportionately affected by environmental and socioeconomic stressors. This study presents a scalable urban building energy modeling framework that integrates physics-based simulations with machine learning to evaluate and prioritize health-driven retrofit strategies across residential building stocks. Synthetic datasets were generated through parametric simulations of representative building archetypes and retrofit scenarios, capturing variations in envelope performance, HVAC systems, infiltration rates, and ventilation strategies. Machine learning models were trained as surrogate predictors of building energy performance, enabling rapid evaluation of retrofit impacts. A range of algorithms—including decision trees, random decision forests, gradient boosting machines, support vector machines, k-nearest neighbors, and artificial neural networks—were evaluated. An artificial neural network implemented as a multilayer perceptron was selected for further analysis due to its strong predictive performance (R² = 0.94) and ability to capture complex nonlinear relationships among retrofit variables. The final model used the Port optimization algorithm for stable convergence and improved generalization. The framework is applied to Seattle’s Duwamish Valley, a community experiencing disproportionate environmental and health burdens. The results highlight retrofit priorities—particularly infiltration reduction, HVAC upgrades, and improved envelope performance—that deliver co-benefits for energy efficiency, indoor environmental quality, and occupant health. The results demonstrate that machine learning–enhanced physics-based UBEM can significantly accelerate retrofit evaluation while preserving the interpretability of simulation-based approaches. The proposed framework provides a scalable approach for identifying health-informed retrofit pathways that support equitable urban decarbonization.

Abstract: A computational model of anisotropic biomass particle pyrolysis was used to study the influence of particle properties and process conditions. The model couples multicomponent CRECK kinetics with intraparticle heat and mass transport. Particle size and lignocellulosic composition significantly affect conversion time and product yields; aspect ratio was also found to be important for larger-diameter particles. Larger particles (8 mm diameter, 4:1 aspect ratio) showed conversion times more than twice those of 3 mm particles, and char yield increased from about 16% to 23% when comparing small and large particles. Lignin-rich materials (e.g., palm shell) produced higher char and lower volatile yields than cellulose-rich biomass (wood, sugarcane bagasse); for 3 mm particles, char changed from 16% (oak) to 23% (palm shell). Higher reactor temperatures and heating rates substantially shortened particle conversion time—by up to 75%—and noticeably affected product yields. Analysis of the Biot and Pyrolysis numbers indicates millimeter-scale particles operate in a transition regime where internal conduction, external convection, and chemical kinetics occur on comparable timescales, so models must include these phenomena to accurately predict conversion times and final yields for reactor design and optimization.

Abstract: We investigate the problem of stable signal estimation under irregular observation conditions characterized by missing samples, heteroscedastic noise, and reportability constraints. In such environments, the primary engineering challenge arises from the severe curvature distortion of the objective landscape, which renders traditional Euclidean gradient methods numerically unstable. To address this, we propose a layered information-geometric framework for stable estimation on a distorted statistical manifold.The architecture consists of three integrated components: (i) a deterministic basin-safe front-end that resolves global navigation on the non-convex information landscape; (ii) a diagonal-Fisher local refiner that performs local metric normalization to correct for geometric scaling under irregular weighting; and (iii) a PT-even gatekeeper that acts as an engineering feasibility constraint by restricting the optimization trajectory to the reportable subspace. As a concrete structured path within this layered estimator, the QFRT branch—a Quaternionic Fourier–Ramanujan representation used here as an arithmetic-periodicity anchor under shared whitening and weighting—serves as a structured source of robustness under gappy observations without altering the bounded scope of the present technical claim.Across a locked stress matrix, the resulting hybrid estimator exhibits a two-layer gain structure. First, a general refinement gain in RMSE_ω is achieved via curvature-aware updates in the classical-tangent regime. Second, a specific PT-sensitive gain emerges when nuisance-coupled sectors become observable, effectively suppressing non-reportable "ghost-mode" leakage (|z| ≈ 0) where unprojected baselines suffer from substantial parameter drift. Mechanism diagnostics support a seed-path shielding interpretation: the classical front-end resolves the ω-dominant basin selection problem, shielding the downstream PT-aware refinement from unfavorable seed geometry. The resulting contribution is a technical methods framework for auditable stable estimation under missing and heteroscedastic observations.

Abstract: Stepped spillways are engineered structures designed to mitigate the erosive effects of floodwaters on downstream riverbeds. Despite extensive research on spillways with slopes exceeding 26.6°, there is a notable gap in understanding energy loss mechanisms for slopes between 3.4° and 26.6°. This study aims to develop predictive models for energy dissipation in stepped spillways within this slope range, eliminating the need for complex friction factor calculations. Experimental data from air-water flow experiments on a large-scale stepped spillway facility inform the development of these models. The proposed models demonstrate strong correlation with experimental data (R = 0.79 - 0.99), offering a simplified and accurate approach to estimating energy losses.

Abstract: Predictive maintenance has become an essential component of smart manufacturing systems because it enables early detection of machine failures and reduces unexpected pro-duction downtime. However, conventional predictive maintenance approaches typically rely on centralized data collection and model training which may raise concerns regarding data privacy, communication overhead and data ownership in manufacturing environments. To address these challenges, this research proposes a privacy-preserving collaborative federated learning framework for predictive maintenance that can be deployed in distributed smart manufacturing systems. The proposed approach allows multiple factories to jointly train a machine failure prediction model without sharing raw data. In the framework, each factory trains a local multilayer perceptron (MLP) model using its own machine operational data, while a central server aggregates local model parameters using the Federated Averaging (FedAvg) algorithm to construct a global predictive model. The proposed framework was evaluated using the publicly available AI4I 2020 predictive maintenance dataset where multiple factories are simulated by partitioning the dataset into distributed clients. Experimental results show that the federated learning model achieves performance comparable to centralized machine learning baselines, reaching an accuracy of 99.93%, precision of 1.000, recall of 0.980 and F1-score of 0.990 while still preserving data privacy and IP protection across distributed participants.

Abstract: Automotive electronic control units increasingly rely on resource-constrained microcontrollers to implement authentication and message validation for in-vehicle networks such as CAN. Although cryptographic primitives may be mathematically secure, their software implementations on low-cost ECUs can leak sensitive information through timing side-channels. This paper presents a practical timing side-channel evaluation of an embedded authentication routine implemented on Arduino-class microcontrollers representative of entry-level automotive ECUs. A Python-based measurement framework interacts with the target device over a serial interface, repeatedly triggers authentication operations, and records execution time under controlled conditions. Experimental results show statistically distinguishable timing distributions correlated with secret-dependent execution paths, enabling an attacker with CAN-adjacent access to infer partial information about authentication checks. Lightweight countermeasures, including constant-time comparisons and control-flow normalization, are implemented and evaluated. The mitigated design reduces observable timing leakage with minimal execution-time and memory overhead, highlighting the need for implementation-level timing analysis in automotive embedded systems.

Abstract: To overcome the high labor intensity and severe vegetative damage inherent in the mechanized picking of Osmanthus fragrans at the full bloom stage, a selective separation mechanism utilizing a flexible roller-brush was proposed. First, biomechanical thresholds for selective detachment were quantitatively established, requiring 1.2 N for corolla-pedicel abscission and >5.0 N for petiole-branch retention. Rigid-flexible coupled transient dynamic simulations verified that flexible polyurethane (PU) bristles effectively attenuate impact forces via a "flexible unloading effect." This mechanism precisely transfers targeted detachment kinetic energy (~1.3 N) to fragile pedicels while restricting forces on robust petioles (~2.5 N) below the damage threshold. Furthermore, a terrain-adaptive picker (CZD-01) with a three-degree-of-freedom contour-following arm was developed. A mixed I-Optimal response surface design was employed to optimize the operational parameters. ANOVA revealed that bristle material is the absolute dominant factor determining the leaf detachment rate (P<0.0001). Under the globally optimized configuration-a roller brush rotational speed of 161 r/min, a circumferential rotation speed of 8 r/min, and smooth PU bristles-field validations demonstrated an average flower picking rate of 84.0% and a strictly controlled leaf detachment rate of 6.5%. The developed flexible picking technology achieves an optimal balance between productivity and canopy protection, offering a robust mechanization paradigm for fragile floral crops.

Abstract: Groundwater is a major source of water supply in Diyala Governorate, Iraq. This study proposes an integrated framework for groundwater quality assessment and forecasting by combining the Entropy Weighted Water Quality Index (EWQI) with machine learning algorithms. A total of 855 groundwater samples were collected, of which 853 were retained after preprocessing. The EWQI was calculated using ten hydrochemical parameters for each sample, namely pH, total dissolved solids (TDS), K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, and NO3−. Based on the Iraqi Standard Drinking Water Limits (IQS), EWQI values were categorized into five water quality classes: Excellent, Good, Medium, Poor, and Extremely Poor. For predictive analysis, both regression and classification models were developed using support vector machine (SVM), random forest (RF), backpropagation multilayer perceptron (BP-MLP), and one-dimensional convolutional neural network (1D-CNN) algorithms under three feature scenarios: (1) hydrochemical variables only, (2) hydrochemical variables combined with well properties, and (3) hydrochemical variables, well properties, and spatial coordinates. Model hyperparameters were optimized using grid search with 10-fold cross-validation on the training set, and model performance was evaluated using an 80/20 stratified split. Among the tested models, SVM demonstrated the best performance for EWQI prediction in the first scenario (hydrochemical variables only), achieving RMSE = 4.14, MAE = 0.93, and R2 = 0.999. It also produced the highest classification performance, with an accuracy of 0.971 and a macro-F1 score of 0.973. Meanwhile, RF showed consistently strong and balanced classification performance across all scenarios, reaching a macro-F1 score of 0.961. These findings highlight the effectiveness of integrating EWQI with machine learning techniques for reliable groundwater quality assessment and prediction, thereby supporting sustainable water resource management in Diyala Governorate.

Abstract: The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was developed by coupling the Muskingum method with multiple machine-learning algorithms (Ridge, LASSO, RF, and LSTM) to predict and correct Muskingum residuals. Global Muskingum parameters were identified using the L-BFGS-B algorithm to represent basin-scale routing characteristics. For rolling forecast, a multidimensional feature space was constructed by integrating routing gradients and hydraulic interaction terms. The results indicated that all hybrid models outperformed the traditional Muskingum method across lead times. The Ridge-based hybrid model achieved the best performance at short lead times, with the Nash–Sutcliffe efficiency (NSE) at a 4-h lead time increasing from 0.56 for the physical baseline to 0.977. For longer lead times (12–24 h), the LASSO-based hybrid model demonstrated higher robustness, which was attributed to L1-regularization-based feature selection. Overall, the proposed framework alleviates systematic underestimation during high-flow periods and provides a predictive scheme for arid-region rivers that preserves physical interpretability while improving forecasting accuracy.

Abstract: Background: Airport security demands sub-second, high-throughput identity verification while increasingly stringent privacy regulation prohibits the centralized accumulation of passenger data. Existing deployments copy complete passenger profiles to every checkpoint terminal, multiplying the data breach surface at each journey touchpoint and conflicting with GDPR data minimization requirements. Methods: This paper presents BIPV (Blockchain-based Identity and Privacy Verification), a system that resolves this tension through programmable zero-knowledge proofs. BIPV anchors only cryptographic references on a Hyperledger Fabric consortium blockchain; passengers prove eligibility at checkpoints via Circom-compiled Groth16 zk-SNARKs that confirm policy compliance without disclosing any underlying personal attributes. We detail the Circom circuit design for airport policy predicates (AgeVerifier, NationalityChecker, DocumentValidator), a proof pre-computation and caching strategy that eliminates gate-lane latency, and a Hyperledger Fabric consortium governance model that anchors verification keys without recording passenger movement. Results: Our prototype achieves 0.42 s mean verification latency, 2,380 passengers per checkpoint per hour, and a 94.7% reduction in PII exposure relative to centralized baselines, evaluated across 1,000 simulated verification sessions. Security analysis confirms resistance to credential forgery, replay attacks, and consortium collusion under standard cryptographic assumptions. Conclusions: BIPV satisfies GDPR data minimization requirements, ICAO Annex 17, and IATA One ID guidelines. Beyond aviation, the BIPV model generalizes to any domain requiring high-assurance, high-throughput identity verification under privacy obligations.

Abstract: The construction (AEC) industry has consolidated Building Information Modelling (BIM) as the standard for producing and managing project information, yet its analytical exploitation in Business Intelligence (BI) environments remains manual, ad hoc and dependent on proprietary platforms. Existing literature addresses partial aspects of the problem —IFC extraction, dashboards, semantic approaches, data quality— without articulating a coherent architecture that integrates dimensional modelling, open-standard-based ETL, granular lineage and pre-ingestion validation. This work proposes BIM2BI, a BIM-BI integration architecture organised into four functional layers (data sources, transformation and orchestration, analytical storage and exploitation) that formalises a separation of responsibilities, explicit data contracts between layers and an extensibility-without-redesign principle. The architecture is grounded in the openBIM standards IFC, IDS and BCF, adopts the IfcGlobalId as a technical key for end-to-end lineage and uses IDS as a pre-ETL quality gate with a staged three-level validation strategy. The proposal is validated empirically through an open-source reference implementation (MIT licence) applied to ten representative use cases grouped in three complexity profiles. The results demonstrate the viability of the architecture in terms of data quality, traceability, reproducibility and scalability, and document empirical findings on the real behaviour of openBIM standards within automated analytical workflows.

Abstract: Cement manufacturing is one of the major sources of carbon dioxide (CO2) emissions globally. Cement replacement materials are increasingly used to minimize the environ-mental impact of concrete production. In the present study, the mechanical and envi-ronmental performance of concrete mixtures containing fly ash and recycled glass powder as partial cement replacements at levels of 10%, 20%, and 30% were investigated. Workability, unit weight, compressive strength, and water permeability tests were con-ducted to evaluate the effect of replacements on the behavior of concrete. Carbon emissions and performance-normalized indicators were applied to evaluate environmental performance. It was observed that as the replacement level increased, the carbon emis-sions decreased. The highest reduction was observed at the 30% replacement level, as 28.9%. Compressive strength varied between 21.9 and 27 MPa. This indicates that all mixtures met the targeted strength range. The mixture with 30% fly ash demonstrated the highest environmental efficiency, with a carbon intensity of 10.84 kg CO₂/MPa. This in-dicates a 19.2% reduction compared to the control mixture. The sensitivity analysis re-vealed that changes in emission factors did not alter the order of the mixture.

Abstract: Non-Saccharomyces yeasts (NSY) are increasingly investigated as biotechnological tools to diversify wine profiles and modulate fermentation outcomes. This study evaluated the enological behavior of two Chilean isolates, Starmerella bacillaris (SB) and Hanseniaspora uvarum (HU), in Sauvignon Blanc must from the Casablanca Valley under monoculture and sequential inoculation (NSY → Saccharomyces cerevisiae) at laboratory (500 mL) and microvinification (10 L) scales. In synthetic medium (150 g/L sugars), SB and HU showed incomplete sugar consumption, producing 4.25% and 8.50% v/v ethanol, respectively, compared with 9.16% v/v for S. cerevisiae. In laboratory-scale fermentation in real must, both strains completed fermentation in monoculture, with moderate reductions in ethanol production relative to the control. At the microvinification scale, monocultures yielded lower ethanol concentrations (11.90–12.50% v/v) than S. cerevisiae (13.50% v/v), whereas sequential fermentations converged toward control values. NSY treatments showed higher relative abundances of medium-chain ethyl esters associated with fruity and floral sensory attributes while maintaining acetic acid concentrations ≤0.50 g/L. These findings indicate that the effects of SB and HU depended primarily on fermentation strategy and process scale under the evaluated conditions.

Abstract: Temperature control during hot-mix asphalt production and paving is critical to construction quality and emissions. However, conventional thermometry is susceptible to dust and vibration. This study proposes an indirect temperature monitoring system using electronic nose technology, exploiting the nonlinear correlation between asphalt VOC odor fingerprints and temperature. The system integrates a MOS sensor array with IoT feedback, with a proof-of-concept study involving three asphalt types at five temperature levels. Leave-One-Out Cross-Validation (LOOCV) was employed to mitigate overfitting. The hybrid modeling framework significantly outperformed the Multiple Linear Regression (MLR) baseline, achieving 88.9% three-class accuracy and a regression RMSE of ±6.2℃. Drift compensation improved accuracy by 16.4%. These results show the feasibility of multi-dimensional odor patterns for quantitative temperature prediction, offering a new paradigm for closed-loop, non-contact temperature control in smart, low-carbon pavement engineering.

Abstract: The control of highly nonlinear, open-loop unstable dynamics is a prevalent engineering challenge, often benchmarked through Magnetic Levitation (Maglev) systems. While continuous-time adaptive neural networks are commonly used to reject disturbances, their direct digital implementation often induces closed-loop instability due to unaccounted sampling effects. To address this, this paper proposes a Discrete-Time Fourier Series Neural Network (FSNN) control architecture for nonlinear single-input single-output (SISO) systems that can be transformed into the Brunovsky canonical form. The parameter adaptation laws are synthesized strictly in the discrete-time domain using Lyapunov stability theory. This approach yields an explicit upper bound for the digital sampling period, ensuring a proper implementation. Furthermore, it guarantees the Uniform Ultimate Boundedness (UUB) of the tracking error in the presence of bounded unmodeled dynamics and periodic disturbances. Numerical simulations of Maglev dynamics validate the theoretical bounds, demonstrating that the FSNN controller achieves rapid learning and generates a smooth control effort, offering a robust and practical framework for digital control.

Abstract: The performance of grain storage silos is strongly influenced by discharge flow patterns, hopper geometry, and material properties such as moisture content and impurity levels. However, the combined effects of these factors on flow behavior, discharge rate, and segregation are not yet fully understood. This study experimentally investigated the integrated effects of moisture content, prismatic hopper geometry (hopper angle β), and impurity addition on flow behavior, segregation, and mass flow rate in reduced-scale silos. Experiments were conducted using three prismatic silos with hopper angles of β = 15°, 33°, and 45°, filled with maize at moisture contents of 13.6%, 20.2%, and 26.0% (wet basis), under both clean conditions and with the addition of 10% impurities (fraction passing through a 5 mm sieve). The discharge rate was determined by direct mass–time measurements, flow patterns were inferred from video analysis, and segregation was quantified based on the mass fraction of impurities in samples collected during discharge. The results indicate that moisture content was the most influential factor, reducing the discharge rate by up to 22.8% when increasing from 20.2% to 26.0% w.b. (p < 0.05). Hopper geometry also had a significant effect, with performance differences among configurations becoming more pronounced under high-moisture conditions. The addition of 10% impurities increased the discharge rate under all tested conditions, with gains of up to 29.0% at 26.0% w.b. and β = 15°. Segregation intensified with increasing moisture content, leading to a progressive accumulation of impurities toward the end of discharge. The stick‑slip phenomenon was observed under a critical condition (26.0% w.b., β = 15°, with impurities), resulting in a 23.0% reduction in average discharge rate compared to the equivalent stable condition. These findings demonstrate that granular flow behavior in silos is governed by the interaction between moisture, hopper geometry, and material composition. The results also suggest that operational strategies such as pre-cleaning should be evaluated in conjunction with expected moisture conditions, as pre‑cleaning may adversely affect flow performance under high‑moisture scenarios.

Abstract: Deep soil cementation is widely used for strengthening weak soils; however, the effectiveness of injection mortars largely depends on their penetration ability and interaction with in-situ soil conditions. This study aims to evaluate the performance of an injection mortar through large-scale model testing under conditions as realistic as possible. The experiments were conducted at a scale of 1:25 using loamy soils representative of Astana’s engineering-geological conditions. The model soil was prepared by reproducing natural density and moisture parameters, including an average natural density of 1.85 g/cm³ and moisture content of 13.3%, while overburden pressure was simulated using a rigidly fixed cover. Laboratory tests determined consistency limits (plastic limit 14.42%, liquid limit 19.75%), indicating a semi-solid to solid soil state. A strong correlation (R = 0.99) between added water and achieved moisture content was established, with an optimal water volume of 18,900 cm³ required to achieve target conditions. Compaction tests showed that the required density was achieved with 20-50 roller passes depending on layer depth, resulting in final densities of 1.85-1.88 g/cm³. The results confirm that the proposed modeling approach reliably reproduces in-situ conditions and can be effectively used to assess injection mortar performance in weak soil stabilization.

Abstract: Gel electrolyte batteries have prominent advantages in safety, service life and environmental adaptability, leading to their increasing application in various fields. Compared with liquid electrolyte batteries, gel batteries exhibit excellent internal resistance consistency, which contributes to more stable current, gentler voltage changes and lower temperature rise during charging and discharging processes. However, traditional methods for estimating the SOC of gel batteries based on electrical and temperature parameters fail to achieve satisfactory accuracy due to the aforementioned characteristics. To address this issue, this paper considers the changes in the physical properties of the electrolyte during the charging and discharging of gel batteries, investigates their multi-layer structure and analyzes the feasibility of SOC estimation using ultrasonic technology. Through ultrasonic experiments, the characteristic parameters in ultrasonic time-frequency signals that are highly correlated with the SOC of gel batteries are extracted and analyzed. Subsequently, based on machine learning algorithms, a SOC estimation method for gel batteries using multi-dimensional ultrasonic time-frequency features is proposed. The verification experiments show that the RMSE and MAE of this estimation results are both within 1% which confirm the effectiveness and high accuracy of the proposed method.

Abstract: This study presents a hybrid controller that integrates fuzzy logic control and the incremental conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a step-down DC-DC converter. The study evaluates the impact of the number of membership functions (three, five, and seven) and their distribution using the integral performance indices: Integral Square Error (ISE) and Integral Absolute Error (IAE). The results demonstrate that the seven-function configuration achieved optimal values of 0.1155 and 7.365 ×10−4, for ISE and IAE respectively. In addition, this configuration achieved an energy efficiency of 99.7%, which is superior to the 98.9% efficiency obtained with three functions in the worst-case scenario. The optimal configuration was subjected to changes in irradiance and temperature to assess its dynamic response robustness. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller requires a lower computational load for rule evaluation than three controllers from recent literature. These findings demonstrate the structural efficiency and optimization capability of the proposed system to maximize energy harvesting in photovoltaic panels at a low computational cost.