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Model evaluation for automated scoring of electropenetrography waveform data from mosquitoes
Evaluation and comparison of the sustainability of autumn and spring sugar beet production systems using emergy indicators
Dual-stream feature fusion network for landslide detection
Synergistic optimization and allocation of water-energy-carbon-pollution nexus in Zhengzhou’s water supply system under uncertainty
Abstract Urban water supply systems are increasingly challenged by water scarcity, rising energy consumption, carbon emissions, and water pollution under rapid urbanization and climate change. Existing studies have mainly focused on optimizing individual objectives such as water allocation or energy efficiency, while limited attention has been paid to the coordinated optimization of the Water-Energy-Carbon-Pollution (WECP) nexus under uncertainty. This study develops an integrated multi-objective optimization framework for the WECP nexus using Zhengzhou, China, as a representative case. First, a coupling coordination degree model is established to evaluate the interactions among water conservation, energy saving, carbon reduction, and pollution control. Subsequently, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), integrated with interval parameters and dynamic constraints, is employed to optimize multi-source water allocation while balancing economic, social, and environmental objectives. Furthermore, an elastic storage mechanism is introduced to improve system adaptability under uncertain water demand and climate variability.The results indicated that the optimized allocation significantly enhanced system coordination, with the coupling coordination degree increasing from 0.67 to 0.87. Groundwater extraction decreased by 18.5%, reclaimed water utilization increased to 14.7%, and annual carbon emissions were reduced by approximately 57,000 t CO2, while maintaining economic benefits and reducing water shortages. Compared with conventional optimization methods, the proposed framework provided a more balanced trade-off among competing objectives and improved decision-making flexibility through Pareto-optimal solutions. This study demonstrated the potential of integrated WECP optimization for promoting sustainable urban water management and provided practical guidance for low-carbon transformation of water supply systems in Zhengzhou and other water-scarce cities.
A gradient-enhanced physics-informed neural network with adaptive loss weighting for high-dimensional non-linear sine-Gordon problems
Accelerometer-in-the-loop safe learning control of mesh-order vibrations in cycloidal drives
Structure-activity relationship of pyrazole-based corrosion inhibitors for carbon steel in HCl: combined experimental and theoretical study
Abstract The corrosion inhibition performance of two newly synthesized pyrazole-based derivatives, P6 (bis-pyrazole) and P8 (pyrazolyl-thiadiazine), toward carbon steel in 1.0 M HCl was systematically investigated using combined experimental and theoretical approaches. Electrochemical and gravimetric results revealed high inhibition efficiencies exceeding 95% at 5 × 10⁻⁴ M, with P6 exhibiting superior performance. This enhanced performance is attributed to its stronger adsorption affinity and more stable interaction with the steel surface. Polarization measurements revealed predominantly anodic inhibition behavior, although both anodic and cathodic reactions were affected, while impedance analysis confirmed the formation of a protective adsorbed film. Surface characterization (SEM, EDX, and AFM) further supported the development of a compact and homogeneous protective layer. Thermodynamic analysis suggested a mixed adsorption mechanism involving electrostatic and chemical interactions. Computational studies (DFT and Monte Carlo simulations) corroborated the experimental findings, demonstrating strong adsorption on the Fe (110) surface.
High-capacity reversible hydrogen storage in Na-decorated Al–N–O nanocluster: a DFT-D3 study
Zoo housed meerkats do not recognise human emotions
Abstract Domestic mammals can categorically process and respond functionally to human emotional cues. However, to date, it is unclear whether this ability is underlaid by shared mammal emotional processing, associative learning through exposure to humans, or has arisen through the domestication process. To disambiguate these hypotheses, we investigated whether zoo-housed meerkats ( Suricata suricatta ), who have not undergone the process of domestication but are regularly exposed to human contact, recognise human emotions, and whether this is modulated by human interaction levels. 35 meerkats were exposed to human emotional expressions (happy, sad, fearful and neutral) to assess their behavioural responses, and a cross-modal paradigm was used to examine expectancy violation responses when presented with emotionally incongruent signal pairs. While isolated findings suggest human interaction level modulated some behavioural responses to the test paradigms, no patterns of functional responses, lateralised processing, or cross-modal integration consistent with those documented in domestic mammals emerged, either for the whole study population or for those meerkats with a history of close interaction with humans. Thus, zoo-housed meerkats do not show strong evidence of human emotion recognition. Our findings support the hypothesis that the capacity for human emotion recognition in domestic species has arisen through artificial selection for attention to human communicative cues.
Pioneer vegetation shapes soil microbial community assembly and ecosystem functions in riparian sand mining sites
Abstract Pioneer vegetation initiates the recovery of degraded ecosystem; However, its influence on soil microbial communities in riparian sand mining sites remains poorly understood. Four naturally established pioneer plant species ( Artemisia scoparia , Imperata cylindrica , Saccharum arundinaceum , and solitary trees) were selected in a representative sand mining site along the Huai River, and systematically investigated soil microbial community structure, assembly processes, co-occurrence network stability, and functional potential to elucidate the regulatory mechanisms of pioneer vegetation on soil microbial communities and ecosystem functions. The results showed that pioneer vegetation types shaped distinct soil microbial communities, with bacterial communities more responsive than fungi. Total carbon (TC), total phosphorus (TP), and nitrate nitrogen (NO 3 -N) collectively explaining 76.8% of the variation in bacterial community composition. Microbial community composition exhibited strong vegetation-specific patterns. A. scoparia plots significantly enriched Actinobacteria , whereas solitary tree plots significantly enriched Basidiomycota . Network stability varied markedly with vegetation type; Functional prediction revealed that bacterial nitrogen cycling potential was significantly modulated by vegetation type, with nitrification potential being significantly lowest in I. cylindrica . Structural equation modeling demonstrated that vegetation type indirectly influenced soil multifunctionality via regulating soil physical properties, with contrasting pathways in bacterial and fungal models. Our findings suggest potential ecological mechanisms by which pioneer plants influence early ecosystem recovery in degraded riparian zones through the “plant-soil-microbe” continuum, thus providing a scientific basis for functional trait-based ecological restoration.
Distinct genome-wide salivary DNA methylation changes following superset and repeated sprint training in youth male basketball players
Impact of crop residue burning on regional air quality over the Indo-Gangetic Plain: a comparative study during different cultivation patterns
Physics-informed multi-task learning for permeability prediction and probabilistic HFU modeling: a case study from the Lower Bahariya Reservoir, Shahd SE field Egypt
Abstract Accurate permeability prediction is essential for reliable reservoir characterization and simulation, yet remains challenging due to complex nonlinear relationships and subsurface heterogeneity. Conventional hydraulic flow unit (HFU) methods rely on discrete rock typing and fixed porosity–permeability relationships, limiting their ability to capture continuous variations. Physics-informed neural networks (PINNs) offer a data-driven alternative with embedded physical constraints, but their effectiveness is often limited by weak enforcement of physics during inference. In this study, a physics-guided multi-task neural network (MT-PINN) is proposed to simultaneously predict permeability and hydraulic flow units within a unified framework. The model integrates data-driven learning with physics-based relationships and probabilistic rock typing, enabling permeability to be estimated as a weighted combination of multiple flow units and allowing smoother transitions between facies. The proposed approach was evaluated using core and well log data and compared against conventional HFU and standard PINN methods. Within the studied dataset, the MT-PINN demonstrated improved predictive performance, with a higher correlation coefficient ( $$\:R\:=\:0.936$$ ) compared to HFU ( $$\:R\:=\:0.89$$ ) and PINN ( $$\:R\:=\:0.90$$ ), along with a reduction in log-scale error. The model also provides more continuous and stable permeability predictions along depth. In addition to permeability estimation, the MT-PINN outputs both discrete HFU classifications and associated class probabilities, which can be incorporated into 3D stochastic reservoir modeling workflows for uncertainty-aware multi-realization analysis. The proposed framework demonstrates the potential to integrate traditional petrophysical methods with modern machine learning techniques, providing a promising workflow for permeability prediction and reservoir characterization within the studied reservoir.
Fractional modeling of CD38–mediated multiple myeloma dynamics with immune interaction and therapy effects dynamics
Shape-preserving minimum trace (SP-MinT): a regularized forecast reconciliation method for hierarchical time series
Abstract Forecast reconciliation has become the standard for ensuring coherence in hierarchical time series. However, state-of-the-art methods like Minimum Trace (MinT) prioritize the minimization of error variance, often at the expense of distorting the temporal morphology of the forecast. This paper reframes forecast reconciliation as a multi-objective problem, showing that variance-optimal coherence is insufficient for operational decision-making, and proposing a shape-aware reconciler that explicitly encodes temporal structure. We introduce Shape-Preserving Minimum Trace (SP-MinT), a novel framework that regularizes the optimization process with domain-informed priors constructed from historical day-of-week profiles. We validate the method using a rigorous rolling cross-validation on real-world electricity demand data from Victoria, Australia. The results demonstrate that SP-MinT outperforms the standard MinT-WLS benchmark by reducing the Root Mean Squared Error (RMSE) by 31.94% and the Shape Error (Dynamic Time Warping) by 43.16%. By bridging the gap between statistical optimality and morphological fidelity, SP-MinT offers grid operators hierarchically coherent forecasts that respect physical ramping constraints.
Non-toxic harpagophytum procumbens (Burch.) DC. ex Meisn. dry extract exhibits repellent and antioxidant activities in nauphoeta cinerea
dual functional microwave sensor with high-gain array antenna using loaded U-resonator and asymmetric T-junction
Battery-aware approximate wireless telemetry framework for resilient wearable ECG monitoring under extreme power constraints
Abstract Wearable electrocardiogram (ECG) patches utilizing Bluetooth Low Energy (BLE) face a critical, yet under- characterized, failure mode: as battery state of charge (SoC) depletes, firmware-mandated reductions in RF transmission power elevate the bit error rate (BER) in Rayleigh-fading channels, causing conventional QRS detection to fail during the most clinically important periods of continuous cardiac monitoring. This paper presents the Battery-Aware Approximate Wireless Telemetry (BAWT) framework, a co-designed solution that jointly optimizes a Peukert-corrected Li-ion discharge model, a six-state adaptive RF power controller, and a Rayleigh-fading indoor channel model. At the receiver, the proposed Bayesian Adaptive Feature Estimator (BAFE) fuses a Wiener-optimal morphological bandpass prior with a channel-SNR-derived MMSE-Wiener weight, enabling reliable QRS extraction from severely corrupted bit streams without forward error correction hardware. Validated on the MIT-BIH Arrhythmia and PTB-XL clinical databases against the ANSI/AAMI EC57 standard, BAWT demonstrates substantial performance gains: at the clinically critical 20% SoC operating point, BAFE raises mean QRS sensitivity from 35.1% to 91.9% on MIT-BIH and from 32.9% to 82.8% on PTB-XL, with the majority of individual records satisfying the Se ≥ 95% clinical threshold. The adaptive power controller extends the critical operating window by 355% over fixed full-power operation, and a fully characterized Pareto-optimal frontier enables system designers to navigate the trade-off between battery lifetime extension (up to 144.5%) and clinical QRS detection accuracy across the full SoC range. These results establish a rigorous co-design framework for robust, power-aware wearable cardiac monitoring compliant with ANSI/AAMI EC57. .