Search Results (848)

Soil organic carbon (SOC) plays a critical role in the global carbon cycle and serves as a sensitive indicator of climate change impacts, with its dynamics significantly influencing regional ecological security and sustainable development. This study focuses on the Songnen Plain in Northeast China—a key black soil agricultural region increasingly affected by water erosion, primarily through surface runoff and rill formation on gently sloping cultivated land. We aim to investigate the spatiotemporal dynamics of SOC stocks across different cultivated land erosion zones under projected future climate change scenarios. To quantify current and future SOC stocks, we applied a boosted regression tree (BRT) model based on 130 topsoil samples (0–30 cm) and eight environmental variables representing topographic and climatic conditions. The model demonstrated strong predictive performance through 10-fold cross-validation, yielding high R² and Lin’s concordance correlation coefficient (LCCC) values, as well as low mean absolute error (MAE) and root mean square error (RMSE). Key drivers of SOC stock spatial variation were identified as mean annual temperature, elevation, and slope aspect. Using a space-for-time substitution approach, we projected SOC stocks under the SSP245 and SSP585 climate scenarios for the 2050s and 2090s. Results indicate a decline of 177.66 Tg C (SSP245) and 186.44 Tg C (SSP585) by the 2050s relative to 2023 levels. By the 2090s, SOC losses under SSP245 and SSP585 are projected to reach 2.84% and 1.41%, respectively, highlighting divergent carbon dynamics under varying emission pathways. Spatially, SOC stocks were predominantly located in areas of slight (67%) and light (22%) water erosion, underscoring the linkage between erosion intensity and carbon distribution. This study underscores the importance of incorporating both climate and anthropogenic influences in SOC assessments. The resulting high-resolution SOC distribution map provides a scientific basis for targeted ecological restoration, black soil conservation, and sustainable land management in the Songnen Plain, thereby supporting regional climate resilience and China’s “dual carbon” goals. These insights also contribute to global efforts in enhancing soil carbon sequestration and achieving carbon neutrality goals. Full article

Study develops and field-validates a SCADA-based real-time monitoring system to reduce unplanned dilution and hanging-wall over-break in underground long-hole stoping at a Zimbabwean gold mine. The objectives were to detect and constrain drilling deviation in real time, quantify the impact on stope stability and dilution, and evaluate operational and economic effects. The system integrates IMU inclinometers (hole angle), rotary encoders (depth), and LiDAR (collar spacing) with a Siemens S7 PLC (RS Americas, Fort Worth, TX, USA) and AVEVA™ InTouch HMI 2023 R2. Field trials across three production stopes (12L, 14L, 15L) compared baseline manual monitoring to SCADA control. Mean angular deviation fell from 0.8–1.6° to 0.2–0.3°, length deviation from 0.8–1.1 m to 0.05–0.08 m, and positional error from 0.25–0.32 m to 0.04–0.06 m; major collapses were eliminated, and ELOS dropped (e.g., 0.20 m to 0.05 m). Dilution decreased from 25% (typical 21–26%) to 16–18%, with mill feed grade rising from 1.90 to 2.25 g/t; production rates were maintained, with brief auto-stops in 5% of holes and rapid operator correction. Real-time drilling control materially reduces unplanned dilution and improves wall stability without productivity penalties, yielding compelling economics. Full article

Missing values in hourly outdoor air temperature series are common and can bias building energy assessments that rely on uninterrupted temperature profiles. This paper examines how the polynomial degree can be used to reconstruct incomplete temperature data from the duration curve, which affect the energy indicators of an air-source heat pump (ASHP). Using an operational dataset from Opole, Poland (1 September 2019–31 August 2020; 5.1% gaps), global polynomials of degree n = 3…11 were fitted to the sorted hourly temperatures, and the reconstructions were mapped back to time. The reconstructions drive a building–ASHP model evaluated for two supply-water regimes (L_WT, leaving water temperature = 35 °C and 45 °C). Accuracy is assessed with mean absolute error (MAE), root-mean-square error (RMSE), and R² on observed, filled, and full subsets—including cold/hot tails—and propagated to energy metrics: seasonal space-heating demand (Q_season); electricity use (E_el); seasonal coefficient of performance (SCOP); peak electrical power (P_el,max); seasonal minimum coefficient of performance (COP_min); and the share of error due to filled hours (WFE_fill). All degrees satisfy $\left|{RE}_{Q_{season}}\right|\le 2\%$. For L_WT = 35 °C, relative changes span ${RE}_{E_{el}}$ ≈ −2.22…−1.63% and ${RE}_{N_{el,max}}$ ≈ −21.6…−7.7%, with ${ER}_{SCOP}$ ≈ +0.53…+0.80%. For L_WT = 45 °C, ${RE}_{E_{el}}$ remains ≈ −0.43% across degrees. A multi-criterion selection (seasonal bias, stability of energy indicators, tail errors, and WFE_fill) identifies n = 7 as the lowest sufficient degree: increasing n beyond seven yields negligible improvements while raising the overfitting risk. The proposed, data-driven procedure makes degree selection transparent and reproducible for gap-filled temperature inputs in ASHP studies. Full article

In order to study the impact on the shock effect when a high-speed train passes over a concrete-filled steel tube (CFST) tied-arch bridge, a dynamic load test was carried out in the background of the Qinjiang River Bridge in Qinzhou, Guangxi Province, to test the bridge displacements, accelerations, and dynamic stresses. The bridge finite element model was coupled with a CRH2 train model developed in SIMPACK to perform ANSYS–SIMPACK co-simulation of vehicle–bridge interactions. Model reliability was verified by comparing simulated results with field measurements under matched operating conditions. On this basis, a parametric study was conducted for single-line operation with a mainline spacing of 4.2–5.4 m (0.4 m increments) and train speeds of 80–270 km/h (10 km/h increments), yielding 80 working conditions to evaluate hanger impact responses. The results indicate that the ANSYS–SIMPACK co-simulation provides reliable predictions. Compared with long hangers, short hangers exhibit larger stress impact coefficients. As train speed increases, the hanger impact effect shows a wavelike increasing trend. When the speed approaches 180–200 km/h, the excitation nears the bridge’s dominant natural frequency, and impact effects on bridge components peak, identifying a critical speed range that is more prone to inducing vehicle–bridge resonance; the impact coefficient of the shock effect on both sides of the train is different: the coefficient on the far side of the bridge is about 2 times of that on the near side of the bridge, so when the impact coefficient is regulated, the unevenness of the impact of the shock effect on both sides can be taken into account. Single-line operation can introduce a lateral load bias on the train, and the distance of the train from the center line is positively correlated with the impact size of the shock effect, with the stress impact coefficient of the shock effect on both sides of the bridge and span deflection increasing as the spacing of the main line increases. Full article

The objective of this study is to determine how easterly jets and associated convections interact over tropical North Africa during the Jul–Sep season, using reanalysis and satellite datasets for 1990–2024. Four indices are formed to describe mid- and upper-level zonal winds, and moist convection over the Sahel and India. Time-space regression identifies the large-scale features modulating the easterly jets. Cumulative departures are analyzed and ranked to form composites in east wind/convective phases and weak wind/subsident phases. The upper-level tropical easterly jet accelerates over the Arabian Sea during and after Pacific La Nina and the cool-west Indian Ocean dipole, and shows four year cycling aligned with thermocline oscillations. The mid-level Africa easterly jet strengthens during Atlantic Nino conditions that enhance the Sahel’s convection in the Jul–Sep season. Both jets accelerate when convection spreads west of India, whereas brief spells of decoupling suppress North African crop yields. The case of 15–20 August 2018 is analyzed, when a surge of Indian monsoon convection and tropical easterly jet penetrated the Sahel, leading to widespread uplift and rainfall. Full article

Plastics are essential in modern life but accumulate as waste. Mechanical reprocessing reduces material quality, whereas thermochemical routes require harsh conditions and are costly to upgrade. Together, these factors hinder the large-scale recovery of plastics into equivalent materials. Metal–organic frameworks provide a programmable platform where reticular design fixes porosity and positions well-defined Lewis, Brønsted, redox, and photoredox sites that can preconcentrate oligomers and align scissile bonds for activation. These attributes enable complementary pathways spanning hydrolysis, alcoholysis, aminolysis, photo-oxidation, electrocatalysis, and MOF-derived transformations, with adsorption-guided capture-to-catalysis workflows emerging as integrative schemes. In this review, we establish common figures of merit such as space–time yield, monomer selectivity and purity, energy intensity, site-normalized turnover, and solvent or corrosion footprints. These metrics are connected to design rules that involve active-site chemistry and transport through semi-crystalline substrates. We also emphasize durability under hot aqueous, alcoholic, or oxidative conditions as essential for producing polymer-grade products. Full article

Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

Cardiac arrhythmias are among the leading causes of morbidity and mortality worldwide, and accurate classification of electrocardiogram (ECG) beats is critical for early diagnosis and follow-up. Supervised deep learning is effective but requires abundant labels and substantial computation, limiting practicality. We propose a simple, efficient framework that learns self-supervised ECG representations with SimCLR and uses a lightweight Multi-Layer Perceptron (MLP) for classification. Beat-centered 300-sample segments from MIT-BIH Arrhythmia are used, and imbalance is mitigated via SMOTE. Framed from a symmetry/asymmetry perspective, we exploit a symmetric beat window (150 pre- and 150 post-samples) to encourage approximate translation invariance around the R-peak, while SimCLR jitter/scale augmentations further promote invariance in the learned space; conversely, arrhythmic beats are interpreted as symmetry-breaking departures that aid discrimination. The proposed approach achieves robust performance: 97.2% overall test accuracy, 97.2% macro-average F1-score, and AUC > 0.997 across five beat classes. Notably, the challenging atrial premature beat (A) attains 94.1% F1, indicating effective minority-class characterization with low computation. These results show that combining SMOTE with SimCLR-based representations yields discriminative features and strong generalization under symmetry-consistent perturbations, highlighting potential for real-time or embedded healthcare systems. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

This paper investigates soliton solutions of the time-fractional Biswas–Arshed (BA) equation using the Extended Simplest Equation Method (ESEM). The model is analyzed under two distinct fractional derivative operators: the $\beta$-derivative and the M-truncated derivative. These approaches yield diverse solution types, including kink, singular, and periodic-singular forms. Also, in this work, a nonlinear second-order differential equation is reconstructed as a planar dynamical system in order to study its bifurcation structure. The stability and nature of equilibrium points are established using a conserved Hamiltonian and phase space analysis. A bifurcation parameter that determines the change from center to saddle-type behaviors is identified in the study. The findings provide insight into the fundamental dynamics of nonlinear wave propagation by showing how changes in model parameters induce qualitative changes in the phase portrait. The derived solutions are depicted via contour plots, along with two-dimensional (2D) and three-dimensional (3D) representations, utilizing Mathematica for computational validation and graphical illustration. This study is motivated by the growing role of fractional calculus in modeling nonlinear wave phenomena where memory and hereditary effects cannot be captured by classical integer-order approaches. The time-fractional Biswas–Arshed (BA) equation is investigated to obtain diverse soliton solutions using the Extended Simplest Equation Method (ESEM) under the $\beta$-derivative and M-truncated derivative operators. Beyond solution construction, a nonlinear second-order equation is reformulated as a planar dynamical system to analyze its bifurcation and stability properties. This dual approach highlights how parameter variations affect equilibrium structures and soliton behaviors, offering both theoretical insights and potential applications in physics and engineering. Full article

Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management. Full article

The cowpea (Vigna unguiculata (L.) Walp.) is well adapted to high temperatures, water deficits and low fertility soils, being widely cultivated in regions less favorable to common beans. Its grains are rich in proteins, vitamins and minerals, representing an important food source and a promising alternative for producing protein at low cost, in a short space of time, given the precocity of its cycle. However, in the state of Minas Gerais there is only a recommendation for one cowpea cultivar, the Poços de Caldas cultivar. In addition to being quite old, it is no longer found in crop production fields. Our objective was to provide local farmers with new cultivar options that exhibit high yield potential, appropriate plant architecture for mechanized cultivation, and superior grain health and quality. The experiments were conducted in Passos city, Brazil, during the second cropping season of the 2021, 2022, and 2023 years. Ten commercial cowpea cultivars were assessed in a randomized block design with five replications, considering morphophysiological traits and phytotechnical yield components. Treatment effects were analyzed using the Scott-Knott test, a statistical method that compares treatments and identifies significant differences among them. The thousand-seed weight and grain index showed a positive correlation with grain yield. The least productive cultivars had the longest pods and, consequently, the highest number of grains per pod. The 2022 and 2023 years provided the most favorable morphophysiological conditions for cowpea cultivation, which significantly enhanced productivity. Among the tested cultivars, BRS Xique-Xique, BRS Novaera, BRS Tumucumaque, and BRS Pajeú were the most suitable for a second cropping season cultivation in the Southwest region of Minas Gerais, while BRS Marataoã, BRS Itaim, and BRS Rouxinol were the least. We emphasize the need for further studies to support the establishment and expansion of cowpea cultivation in this region. Full article

The present study investigates the psychological impact of lighting color on spatial impressions within indoor settings, drawing on Mehrabian and Russell’s PAD model. The purpose of this study is to explore potential variations in spatial impressions, encompassing affectivity, tranquility, and thermality, across six different lighting colors (i.e., red, green, blue, yellow, orange, and purple). A controlled laboratory experiment was conducted with 101 participants, utilizing a color-changing LED lighting fixture to expose participants to actual lighting conditions rather than simulated images. The findings revealed significant differences in spatial impressions among the six lighting colors, indicating that the choice of lighting color has an impact on how people perceive space impressions. Blue lighting elicited the most favorable affective responses, while red lighting was perceived most negatively. Although purple lighting yielded the highest tranquility mean, it was not statistically different from other cool hues and was also associated with sleepiness and dullness. By incorporating secondary colors and employing real-time lighting exposure, this study offers a novel contribution to existing research on color and lighting. Full article

Background/Objectives: Synaptic plasticity is fundamental to learning and memory, yet classical models such as Hebbian learning and spike-timing-dependent plasticity often overlook the distributed and wave-like nature of neural activity. We present a computational framework grounded in Scale Relativity Theory (SRT), which describes neural propagation along fractal geodesics in a non-differentiable space-time. The objective is to link nonlinear wave dynamics with the emergence of structured memory representations in a biologically plausible manner. Methods: Neural activity was modeled using nonlinear Schrödinger-type equations derived from SRT, yielding complex wave solutions. Synaptic plasticity was coupled through a reaction–diffusion rule driven by local activity intensity. Simulations were performed in one- and two-dimensional domains using finite difference schemes. Analyses included spectral entropy, cross-correlation, and Fourier methods to evaluate the organization and complexity of the resulting synaptic fields. Results: The model reproduced core neurobiological features: localized potentiation resembling CA1 place fields, periodic plasticity akin to entorhinal grid cells, and modular tiling patterns consistent with V1 orientation maps. Interacting waveforms generated interference-dependent plasticity, modeling memory competition and contextual modulation. The system displayed robustness to noise, gradual potentiation with saturation, and hysteresis under reversal, reflecting empirical learning and reconsolidation dynamics. Cross-frequency coupling of theta and gamma inputs further enriched trace complexity, yielding multi-scale memory structures. Conclusions: Wave-driven dynamics in fractal space-time provide a hypothesis-generating framework for distributed memory formation. The current approach is theoretical and simulation-based, relying on a simplified plasticity rule that omits neuromodulatory and glial influences. While encouraging in its ability to reproduce biological motifs, the framework remains preliminary; future work must benchmark against established models such as STDP and attractor networks and propose empirical tests to validate or falsify its predictions. Full article