Agricultural intensification in East Africa, particularly within the Enkare Narok Catchment in Kenya, poses significant threats to water quality and hydrological stability, yet the efficacy of conservation practices is often complicated by legacy nutrients and water balance trade-offs. This study aims to use a SWAT model to simulate and calibrate hydrology and water quality, quantify the impacts of various land management scenarios on sediment, nitrogen (N), and phosphorus (P) loads, and evaluate the trade-offs between water quality improvements and water yield, with a specific focus on legacy nitrogen dynamics. The rigorously calibrated model (NSE = 0.67, R 2 = 0.73, PBIAS = 2.97%) simulated a Baseline scenario, a Conservation scenario (featuring no-till, filter strips, and riparian buffers), and an Intensification scenario. Results demonstrated that conservation practices dramatically reduced sediment and phosphorus yield by 80% and 76%, respectively, but were markedly less effective for dissolved nitrogen, achieving only an 8% reduction due to legacy N in groundwater. Furthermore, the Conservation scenario significantly reduced water yield (Q/P from 0.37 to 0.11) by enhancing evapotranspiration, revealing a critical trade-off between water quality and quantity. Conversely, intensification increased all pollutant loads by 30-50% and resulted in a flashier hydrological regime. We conclude that while surface-level best management practices are effective for sediment and phosphorus control, sustainable watershed management in such regions requires a dual strategy that couples these practices with targeted subsurface interventions to address the legacy nitrogen problem and carefully managed water use.

：Low-permeability reservoirs, characterized by their low porosity and permeability, often present significant challenges in oil and gas extraction. To enhance recovery efficiency in such reservoirs, this study examines the performance and field application of three active emulsion systems developed by Jiangsu Oilfield. Laboratory tests were conducted to assess key indicators, including emulsification performance, interfacial tension, wettability, swept pore volume, and other relevant factors. The results reveal that the S3 active emulsion delivers the best overall performance, with an interfacial tension of approximately 10 -2 mN/m, a droplet size of 0.72 μm, and the persistence of a middle-phase microemulsion layer even after 72 hours of standing. Wetting reversal was achieved at a concentration of 0.05%, promoting efficient use of small pores, making it highly suitable for low-permeability reservoir development. To validate the practical effectiveness of the S3 emulsion system, field trials were conducted in the Cui 17 block. The results demonstrated substantial improvements in oil production and water reduction, with a cumulative increase of 2,091 tons of oil and an average reduction in water cut of 13.05%, highlighting the system’s excellent field adaptability. Combining laboratory and field data, the study concludes that this active emulsion system holds significant potential for application in low-permeability reservoirs, providing valuable technical support for enhanced oil recovery in such fields.

The aim of this research is to analyze the effects of magnetic field on tangent hyperbolic Casson nanofluid flow past a porous stretching surface with first-order chemical reaction and extra stress. The mathematical model for the present flow has been developed in terms of partial differential equations which then were non-dimensionalized and later expressed in finite difference form. Central differences have been used for spatial partial derivatives and forward differences for the temporal partial derivatives. Simulations were conducted in MATLAB for the governing equations and the results indicated that velocity profiles decrease with rise in suction parameter, angle of inclination for the applied magnetic field but increases with a rise in Reynolds number, thermal and mass Grashof numbers and Casson fluid factor. Temperature of the nanofluid in the floe region reduces with an increase in suction parameter and Prandtl number but it increases with increase in Reynolds and magnetic numbers. Magnetic induction profiles have direct relationship with variation in Reynolds number but inversely proportional to magnetic Prandtl number. Then increase in chemical reaction parameter leads to reduction in species concentration profiles. A rise in γ, Sc, M, Pr and β increases skin friction coefficient. Increasing Pr increases heat transfer while the opposite happens with increase in γ, M, Sc and β. Increasing γ, Sc, M and β while Pr does the opposite. Applications of this present nanofluid flow problem include hydromagnetic generators, dynamos for generation of electricity and also in the design of electrochemical sensor systems for detection of heavy metals such as copper, Arsenic, Chromium and Zinc.

Gigabit-capable PON is a fiber-based access technology that offers high bandwidth efficiently and cost-effectively. It comprises an optical line terminal (OLT), an optical splitter, and multiple optical network units (ONUs). The passive optical splitter/combiner distributes traffic from the OLT to the ONUs downstream and from the ONUs to the OLT upstream. The paper investigates the performance and power link budget of optical transmission systems, focusing on the challenges posed by increased user numbers and power consumption in modern optical fiber communication infrastructure. It evaluates the performance of Time Division Multiplexing (TDM) in gigabit-capable Passive Optical Networks (PON) for multiple users, considering a single operating wavelength, fiber cable length, and power across various scenarios for upstream and downstream transmission. The system is evaluated using Optisystem simulation software, focusing on bit error rate, Q-factor, and eye diagram results. The analysis shows improvements in power savings, balanced against potential network performance and PON architecture lifetime degradation. However, these degradations can be managed using the power-efficient TDM-GPON access technology for high bit-rate transmission.

In this paper, a mining face of a mine in Pubai Huangling County serves as the study object. Use the numerical modelling analysis approach to compare the actual mining face (315m) with the designed mining face (200m). The results show that the large-size mining face has a larger peak abutment pressure and a larger stress concentration factor than the general-size mining face. Mining intensifies roadway stress, causing greater displacement of its roof, floor, and walls. To tackle the intensified pressure from the expanded mining face, we developed a hydraulic fracturing pressure management plan. This plan was crafted through meticulous theoretical studies and computational models, and it has since undergone rigorous field testing.

not-yet-known not-yet-known not-yet-known unknown Civil infrastructure is a system through which humans inter- act with each other and their environment, making it essen- tial for community well-being. Such systems, however, de- grade over time and become less usable, either due to pro- gressive wear or abrupt damage. Therefore, it becomes nec- essary to allocate resources to maintain the infrastructure and thereby ensure the community’s well-being. However, the relationship between infrastructure condition and com- munity well-being is unclear and challenging to quantify. This is especially true in locations that are impacted by climate change or other significant forcing factors. Current compu- tational models often struggle to predict cascading nonlin- ear events that occur during system failures. This paper in- troduces a computational method to quantitatively analyze the impact of infrastructure on community well-being, com- bining agent-based modeling (ABM) and network robustness measurement. We specifically examine the town of Utqiaġvik, Alaska, where permafrost thaw due to global warming threat- ens a substantial amount of infrastructure. The results show that the breakdown of critical infrastructure progressively weak- ens community access to essential resources, leading to much lower robustness. Additionally, our simulations indicate that smaller household sizes and redundant infrastructure design prove beneficial for sustaining resource accessibility and fos- tering close social connections within the community. These insights offer valuable guidance for understanding the com- plex systems interplay among communities, infrastructure, and the environment, thereby informing strategies to build more sustainable and resilient systems in remote areas such as Utqiaġvik.

not-yet-known not-yet-known not-yet-known unknown This work reports on the design, construction, calibration, and validation of a low-cost, clip-on extensometer for crack mouth opening displacement (CMOD) measurements in fracture mechanics specimens. The device employs readily available components and integrates a full Wheatstone bridge with instrumentation-amplifier conditioning to achieve high measurement resolution at minimal cost. A dedicated calibration protocol, combined with the compliance-based framework specified in the standard, enables direct conversion of bridge output to CMOD. The device’s performance was verified on compact tension specimens through a monotonic test, and cross-validated against a finite element simulation and analytical displacement prediction. The results demonstrated linear-elastic agreement within experimental uncertainty across a verification window, as well as a reproducible CMOD–time histories suitable for both toughness evaluation and crack-growth assessments. By providing full documentation of the hardware and manufacture procedures, this study facilitates rapid replication in contexts where commercial instruments may be cost-prohibitive. Furthermore, it reaffirms that CMOD measurement using clip-on extensometers remains a robust and essential technique, complementing modern optical methods.

The PEMFCs are considered to be promising clean energy technology for electric transportation and distributed generation owing to their high efficiencies and low emissions; however, their nonlinear electrochemical dynamics and their sensitivity to load variations make a challenge to delivering stable power especially under dynamic operating conditions. This research aims to solve these issues by building a detailed mathematical model of PEMFCs based on electrochemical principles, voltage loss mechanisms, hydrogen consumption, and dynamic load behavior. One of the main objectives is to come up with a nonlinear control strategy and then verify the same so that it can be utilized to regulate output voltage, improve transient response, and enhance fuel utilization efficiency. Validation of the proposed Lyapunov-based nonlinear design of the controller was tested through MATLAB/Simulink. The performance under step, ramp, and fluctuating load conditions was then compared with that of classical PID and fuzzy-PID control methods. Simulation results demonstrate that the nonlinear controller achieves about 30% faster settling time, 40% less overshoot, and up to a 12% enhancement in hydrogen utilization efficiency than conventional methods. This instance testifies the robustness and better adaptability of the controller under real-world disturbances. The incremental research presents a scalable control framework for PEMFCs, bearing immense practical relevance in electric vehicles, microgrids, and futuristic smart energy systems.

Premature failure of waterproofing layers is a critical issue in the aggressive corrosive environment of intelligent pig houses. To overcome this challenge, a novel multiphase composite waterproofing and anticorrosion system was developed. The system comprised aliphatic anionic waterborne polyurethane as the polymer matrix, emulsified asphalt as the flexible phase, and heavy calcium carbonate as the inorganic reinforcing phase. The structure–property relationship of waterborne polyurethane was systematically investigated, and the optimal stable state of anionic aliphatic polyurethane in manure solution was identified for the first time. Then, a “polyurethane–emulsified asphalt–heavy calcium carbonate” multiphase synergistic design strategy was proposed. The composite exhibited optimal performance when the polyurethane content was 15% and heavy calcium carbonate, which was introduced with a 1:1 ratio of 400- and 800-mesh particles, accounted for 20%–30% of the total mass. The bonding strength reached 1.35 MPa, and the material retained adequate performance after simulated corrosion testing. Microscopic analysis revealed that a synergistic barrier mechanism, which combined crosslinking densification, flexible buffering, and inorganic passivation, effectively blocked the penetration of corrosive media.

Medium-term electricity demand forecasts (days–weeks ahead) are essential for scheduling, maintenance, and hedging, yet challenging due to multi-scale seasonality and weather-driven nonstationarity. We propose a Multi-Scale Transformer (MSTr) that fuses diurnal, weekly, and seasonal context via scale-specific pooling and learned softmax gating. The model is trained in a leak-safe, direct- H protocol for horizons H∈{24 ,168 ,336 ,672} using only information available at decision time. On hourly data, MSTr consistently outperforms strong baselines (LightGBM, LSTM, and Seasonal/Naive) across RMSE, WAPE, and sMAPE, with the largest gains at 168–336 h, where weekly/seasonal signals dominate. Explainability analyses (SHAP, partial dependence, and temperature–load sensitivity) show that MSTr captures meteorological and calendar effects more faithfully, while ablations confirm the utility of positional encoding, multi-scale pooling, and learned gates. A block-bootstrap evaluation and Diebold–Mariano tests indicate that MSTr’s improvements are statistically significant at key horizons. The approach is efficient, integrated into existing pipelines, and supports transparent reporting through gate weights and seasonal slices.

not-yet-known not-yet-known not-yet-known unknown This commentary critically reviews the 2024 article ”Kirchhoff meets Johnson: In pursuit of unconditionally secure communication” by Ertugrul Basar, focusing on claims regarding the originality, security, and energy efficiency of thermal noise-based secure communication systems. The article presents the Kirchhoff-law-Johnson-noise (KLJN) key exchange scheme and wireless noise modulation as breakthroughs in security; this review clarifies their historical foundation, practical limitations, and security vulnerabilities. Key issues discussed include proper attribution to pioneering work by Kish (2005–2006), feasibility of unconditional security for wireless adaptations, and exaggerated claims of low-power operation. The aim is to contextualize this field’s evolution for engineering audiences and stimulate more precise scholarship and realistic expectations for future research directions.

This work presents the vibration attenuation by four spring damping attached to a Electrodynamic Exciter (EDE) new concept. The device was developed to assist in the design of small dynamic actuators which operate between 1 Hz and 200 Hz. Its structure was manufactured with materials that concentrate the magnetic field in the dynamic actuator to improve movement efficiency. To collect results data, specific electronic instrumentation was used for signal generation, amplification and data acquisition. Dynamic characteristics results from the new electrodynamic exciter under springs load are presented, highlighting operating regions frequencies and damping phenomena. As a scientific contribution, the work highlights the different variations in acceleration, force, and displacement influenced by spring damping, and as operational device results these characteristics indicate possible applications in vibration tests.

The van der Waals heterostructure (vdWH) of Stanene/Hexagonal Boron Nitride (Sn/h-BN) nanosheet has drawn attention due to its promising electro-thermal properties. Understanding the interfacial thermal resistance (ITR) between layers is essential for optimal nanodevice design. Various internal and external factors, such as in-plane tensile strain, defect percentage, interlayer coupling strength (contact pressure), and system temperature, significantly influence thermal transport at the Sn/h-BN bilayer interface. While molecular dynamics (MD) simulations offer insights, they are computationally expensive, especially for low-dimensional materials with complex thermal behaviors. To address this, machine learning (ML) and deep learning (DL) models were developed to rapidly predict ITR with high accuracy. A total of 1000 training datasets were generated using high-throughput MD simulations. Five deep learning models (ANN-10, ANN-20, DNN-10-10, DNN-20-20, and DNN_7) and five machine learning models (Linear Regression, Polynomial Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest) were trained and hyperparameter-optimized. Performance was evaluated using R 2 score, MSE, and MAPE. Among DL models, DNN_7 achieved the best results with an R 2 of 0.97, a Mean Absolute Percent Error (MAPE) of 0.041, and a Mean Square Error (MSE) of 1.897. Among ML models, Random Forest performed best with an R 2 of 0.96. Correlation analysis revealed that interlayer coupling strength is the most influential factor in modulating ITR. This study demonstrates the effectiveness of data-driven models in predicting thermal properties at material interfaces, offering a fast and reliable alternative to traditional simulations. These findings provide foundational insights for designing high-performance nanoelectronic devices by enabling precise thermal management through interface engineering.

Spiking Neural Networks (SNNs) offer a biologically plausible and energy-efficient paradigm for processing temporal data, particularly suited for neuromorphic computing platforms. However, their deterministic nature limits their deployment in safety-critical applications where reliable uncertainty quantification is essential. This paper introduces a novel Bayesian Spiking Neural Network (BSNN) framework that integrates variational inference with surrogate gradient learning to enable robust uncertainty estimation while preserving the efficiency of SNNs. We formulate a scalable Bayesian framework using mean-field variational approximation over network weights and biases, enabling full predictive uncertainty quantification through Monte Carlo sampling. To address the non-differentiability of spiking dynamics, we employ a smooth surrogate gradient method based on sigmoidal derivatives during backpropagation through time. A tailored Poisson encoding scheme ensures rich temporal input representation, while a KL annealing strategy stabilizes training by gradually increasing the regularization pressure from prior distributions. Comprehensive experiments on a synthetic dataset demonstrate competitive classification accuracy (77.78%) alongside well-calibrated uncertainty estimates, as evidenced by strong calibration curves and high AUROC scores (> 0.9). The model exhibits stable training dynamics, with controlled gradients and balanced loss components. Our approach bridges the gap between efficient neuromorphic computation and trustworthy AI, offering a pathway toward deployable, uncertainty-aware edge intelligence systems.

Solar-assisted combined cooling, heating, and power (CCHP) systems offer a sustainable solution to address the escalating energy demand resulting from economic and population growth. This advancement has facilitated a reduction in excessive fuel consumption, thereby mitigating the associated impacts of climate change and global warming. These multi-generation systems undeniably possess the advantage of fuel efficiency, leading to enhanced overall system efficiency compared to individual cooling, heating, and power generation systems. However, optimizing these systems is imperative to realize optimal system design configurations that enhance their thermodynamic performance indicators. Therefore, this study proposed the antlion multi-objective optimization approach to optimize the performance of a solar-assisted CCHP system. The aim was to create a mathematical programming model that would ascertain the ideal ratio for compression, the difference in temperature at the pinch point, the temperature of the inlet turbine, and the temperature of the combustion chamber. This would enable the optimization of net power and energy efficiency while reducing carbon dioxide emissions. The results obtained emphasized that optimized net power, exergy efficiency, and CO 2 emission values could be realized by a reduced compression ratio and inlet combustion chamber temperature. A relatively lower CO 2 emission rate of at least 6.5% and better exergy values were realized, outperforming the response surface method. The suggested optimization method produced 100 Pareto optimum solutions, giving decision-makers a variety of options and insightful information for improving systems and making decisions in the power production industry.

This study quantifies deep neural network robustness to physically induced distortions using MNIST and Mechanical MNIST (Step 5), a displacement-field variant. A custom 3-layer Convoluted Neural Network, CNN (32–256 filters) attains 98.17% accuracy on Mechanical MNIST, surpassing ResNet-18, which falls from 98.95% (MNIST) to 83.46%. Grad-CAM exposes saliency degradation under mechanical stress, notably for curved digits (‘3’, ‘7’); t-SNE and noise sensitivity analyses reveal distorted embeddings and diminished class separability. These findings highlight the fragility of deep, generic architectures in mechanically perturbed domains, advocating compact, domain-adaptive models for resilient classification in applications like tactile sensing or structural analysis.

This study investigates the mechanical strength and interfacial behavior of adhesively bonded single-side strap joints (ABSSSJ) consisting of steel and hybrid sisal-glass reinforced high-density polyethylene (HDPE) composite for automotive body panel applications.The objective of the study to investigate the mechanical strength and failure behavior of adhesively bonded steel-to-hybrid sisal-glass reinforced HDPE composite joints using a combined variational and CZM-based FEM approach. A hybrid computational framework is developed that integrates variational methods with cohesive zone modeling (CZM)-based finite element analysis (FEA) to predict stress distributions, failure initiation, and propagation across the adhesive layer. The model is validated against experimental results under varying adhesive thicknesses, overlap lengths, and fracture toughness values. Parametric studies reveal the influence of geometric and material parameters on the joint’s ultimate load-carrying capacity, peel and shear stress concentrations, and energy absorption capabilities. The results confirm that appropriate tailoring of adhesive parameters and hybrid composite layups significantly enhances bonding performance and failure resistance. The proposed approach provides a robust design methodology for hybrid fiber-reinforced polymer–metal joints in lightweight vehicle structures, ensuring both structural integrity and material sustainability.

Industrial steel slag (SS) was converted into efficient adsorbents for petroleum wastewater treatment via acid activation coupled with organic modification. Acid activation exposed a hydroxylated silica-rich matrix (SRS), enabling quaternary ammonium surfactant grafting to create organo-SRSs with enhanced hydrophobicity. Among the prepared materials, BC-SRS exhibited superior performance, achieving a maximum naphthalene (NA) adsorption capacity of 505.9 mg/g due to synergistic π–π stacking, hydrophobic interactions, and weak electrostatic forces. Adsorption followed pseudo-second-order kinetics and Freundlich isotherms, with thermodynamic analysis indicating spontaneous and endothermic behavior for BC-SRS. The material demonstrated excellent reusability and selectivity under complex water chemistries. This work presents a sustainable strategy for converting industrial waste into high-efficiency adsorbents, advancing green remediation and circular resource utilization.