Search Results (34,111)

Whale shark aggregations in tropical coastal systems are linked to environmental variability and prey dynamics, yet the drivers of surface sightings remain poorly understood. In Nosy Be, Madagascar, a seasonal aggregation of immature whale sharks occurs within a productive coastal ecosystem. This study investigated the relationship between daily environmental conditions and whale shark surface sighting probability while accounting for heterogeneous sampling effort. Boat-based survey data collected from 2019 to 2025 were aggregated by sampling day, and daily whale shark surface sighting probability was analysed using a bias-reduced grouped binomial Generalized Linear Model. Environmental covariates included sea surface temperature, sea surface chlorophyll-a concentration, cloud cover, wind speed, and precipitation, while El Niño–Southern Oscillation variability was assessed as an interannual climatic descriptor. Using data from 103 recorded whale shark surface sightings, chlorophyll-a emerged as the strongest predictor, showing a positive association with daily sighting probability, whereas other environmental variables exhibited weaker and inconsistent effects. These findings suggest that whale shark surface sightings in Nosy Be are primarily associated with prey aggregation processes driven by local productivity rather than with direct responses to local physical environmental conditions. By integrating environmental variability and sampling effort into ecological models, this study provides insights into whale shark habitat use and supports ecosystem-based management of sustainable whale shark tourism in tropical coastal ecosystems. Full article

The catenary support component (CSC) is a key part of the electrified railway system, and its operational status directly affects railway operational safety. These components’ images are collected using inspection equipment and detected using computer vision techniques. However, catenary network inspection faces the following issues: (1) due to limitations in the equipment’s shooting angle and changes in viewing distance, the collected images contain multi-scale and multi-class problems, and (2) the railway environment is highly variable, and adverse weather conditions such as fog, rain, and low light affect the imaging devices, leading to degraded image quality. To address these issues, this paper proposes a novel detection framework, FF-DEIM, for detecting catenary support components. First, a dual-channel fusion network (DCFNet) is introduced, which significantly improves image quality by removing foreground interferences such as fog, raindrops, and dynamic blur. Second, a pretraining framework based on contrastive learning, mask image modeling with contrastive learning (MIMCL), is designed to enhance the model’s focus on key regions of the catenary network components, optimizing feature extraction capabilities and improving model convergence speed. Then, a feature-focusing pyramid network (FFPN) is proposed, which uses the focus feature module to fuse cross-level contextual features, enhancing the ability to capture local details and improving the model’s small object detection performance. Finally, a drone-based catenary network image dataset, including scenes with fog, rain, and low light, is constructed, and experiments validate the effectiveness of the proposed method. Full article

This research examines the behavioral shifts within a discrete-time predator–prey framework, constructed by applying the forward Euler discretization to a continuous model. The system incorporates Smith’s growth dynamics for the prey population alongside a Holling type III functional response to characterize predator behavior. Through bifurcation analysis, it is demonstrated that the interior fixed point undergoes stability loss via Neimark–Sacker and period-doubling transitions, leading to the emergence of quasiperiodic oscillations and chaos. Furthermore, the application of normal-form theory verifies the nondegeneracy of these bifurcations and establishes the direction of the resulting orbits. We use phase portraits, Lyapunov exponents, and bifurcation diagrams to confirm the model’s rich dynamics. These numerical tools demonstrate how the system moves from stable equilibria to more intricate behaviors. The application of partial rank correlation coefficients reveals the most influential parameters governing the system’s asymptotic population levels, providing a global perspective on parameter sensitivity. The Ott–Grebogi–Yorke (OGY) chaos control strategy is employed to suppress unwanted bifurcations and stabilize chaotic oscillations within the system. These results underscore the role of nonlinear interactions and discrete-time frameworks in precipitating unpredictable population fluctuations while simultaneously offering a suite of mechanisms for enhancing the stability of ecological networks. Full article

Cellular senescence is a root cause of aging and age-related disease. Senescent cells persist in tissues, secreting inflammatory factors that fuel inflammaging and immune decline. At the subcellular level, mitochondrial dysfunction has become recognized as a central driver of the senescent state: metabolism shifts toward glycolysis, mitophagy stalls while reactive oxygen species production escalates, mitochondrial dynamics tip toward hyperfusion or fragmentation, and damaged mitochondrial DNA leaks into the cytosol to activate the cyclic GMP-AMP synthase–stimulator of interferon genes pathway, amplifying the senescence-associated secretory phenotype. Conventional drugs have struggled to address these layered defects, steering interest toward natural bioactive compounds—polyphenols, flavonoids, saponins—that can simultaneously restore mitophagic flux, boost antioxidant defenses, rebalance fission–fusion, and intercept mitochondrial DNA-driven inflammation. However, the key issue is delivery: these molecules rarely reach mitochondria in meaningful concentrations in vivo due to their poor bioavailability, rapid metabolism, and off-target distribution. Platforms using triphenylphosphonium, mitochondria-penetrating peptides, or biomimetic shells have successfully funneled therapeutic payloads into mitochondria in several models of disease. We contend that the proposed systematic integration of these delivery systems with natural senotherapeutic compounds offers a promising direction for future research. Full article

Annular trapped pressure is an important factor affecting wellbore integrity during the production of high-temperature and high-pressure oil and gas wells. In wells with multilayer casing structures, one or more enclosed annuli may form because of cement top, packer setting, wellbore structural constraints and wellhead sealing. When the temperature and pressure fields in the wellbore change during production, the annular fluid undergoes thermal expansion, compressive deformation and possible phase-state changes, causing trapped pressure to evolve continuously with time. Conventional annular pressure prediction methods are usually based on a single annulus, quasi-static assumptions or simplified fluid properties, and therefore cannot fully describe deformation transfer among annuli, thermal expansion of tubular strings and nonlinear gas–liquid compression. To address this problem, this study analyses the formation mechanism of annular trapped pressure from the perspective of thermal–fluid–solid coupling and develops a dynamic pressure evolution model that accounts for transient temperature variation, annular fluid thermal expansion and compression, elastic deformation of tubular strings and multi-annulus coupling. The analysis indicates that annular trapped pressure is essentially a pressure response produced by fluid thermal expansion under structural confinement. A multi-annulus system is not a set of independent annuli, but a pressure–deformation–volume feedback system coupled through shared casing walls. The proposed solution framework and pressure evolution analysis provide a theoretical basis for annular pressure prediction, casing safety assessment and wellbore-integrity management in high-temperature and high-pressure gas wells. Full article

Research on sharing economy platforms has expanded rapidly, yet the literature remains fragmented across studies on platform business models, gig work, algorithmic management, trust, reputation systems, artificial intelligence, and data-driven value creation. This article addresses this fragmentation through a bibliometric and systematic review of 660 documents retrieved from Scopus and Web of Science covering the period from 2010 to May 2026. A PRISMA-based protocol guided identification, deduplication, screening, eligibility assessment, and final corpus construction. The analysis combined performance indicators, co-citation analysis, keyword co-occurrence mapping, country collaboration analysis, longitudinal thematic evolution, strategic diagrams, and systematic content coding using Bibliometrix/Biblioshiny 5.4.1, VOSviewer 1.6.21, and SciMAT 1.1.04. The results show a marked acceleration of the field after 2020 and identify major research clusters around algorithmic labour and platform control, algorithmic management, trust and reputation, and dynamic pricing. The systematic coding further indicates that algorithmic management, reputation systems, dynamic pricing, surveillance, matching, and AI-enabled mechanisms recur across governance and value-creation processes. The study develops an integrative framework that interprets these patterns through four connected elements: data inputs, algorithmic mechanisms, governance functions, and value outcomes. This framework provides managers and regulators with a basis for assessing transparency, accountability, participant autonomy, value distribution, and the legitimacy of platform governance. Full article

As silicon scaling nears its physical limits and digital systems process ever-growing amounts of data, integrating computation directly within memory is emerging as a key strategy to overcome the constraints of conventional Von Neumann architectures. Approximate In-Memory Computation (IMC) with memristors offers a promising path toward energy-efficient processing for data-intensive applications. Recent adaptive approximate adders exploit operand magnitude to dynamically switch between exact and approximate computation, but typically ignore carry propagation across approximation boundaries, which can significantly degrade application-level robustness. This work introduces RapproX, a family of adaptive memristive approximate adders featuring a lightweight carry lookback mechanism that approximates carry interaction between exact and approximate regions. The proposed approach improves arithmetic robustness while introducing only minimal overhead and enabling resource-efficient implementations through memristor reuse. Experimental results demonstrate that the proposed approaches achieve superior arithmetic quality compared to State-of-the-Art (SoA) memristive approximate adders. More importantly, the carry lookback mechanism translates into substantial application-level benefits. In image processing, RapproX reduces energy consumption by up to 30.9% compared to the most competitive SoA design and by 50.3% compared to exact computation while maintaining roughly 43 dB Peak Signal-to-Noise Ratio (PSNR). Across a range of machine-learning workloads, including k-means, AlexNet on MNIST, and multiple CIFAR-10 models, RapproX preserves near-exact inference accuracy for the evaluated models at low-to-moderate k and maintains the energy advantages of adaptive approximation, while SoA approximations degrade markedly under the same conditions. These simulation-based results suggest that lightweight carry-aware approximation can improve the robustness of adaptive approximate in-memory computing with only marginal hardware overhead. Full article

This paper considers the problem of early detection of late blight (Phytophthora infestans) in tomatoes based on computer vision and machine learning methods. The main purpose of the study was to develop a representative dataset of images of tomato leaves and an approach to extracting informative features for classifying the stages of disease development. A new dataset was generated using tomato plants grown under greenhouse conditions, with leaf images subsequently captured under controlled laboratory conditions, including five stages of late blight progression with variability in imaging devices, lighting conditions, and temporal disease dynamics. To improve the quality of image analysis, a preprocessing stage was applied, including conversion to grayscale, median filtering, and binarization using the Otsu method. In addition to the traditional textural features, fractal analysis was used to quantify the structural complexity of the affected leaf areas. To verify the information content of the selected features, classification experiments were conducted using Random Forest, XGBoost, and Support Vector Machine models, and the quality was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of textural and fractal features contributes to a more accurate distinction between the stages of disease. The developed dataset and the proposed approach can be used in further research on plant disease diagnosis, agricultural monitoring, and precision farming systems although it should be acknowledged that the dataset is limited to greenhouse settings, and field-scale generalizability requires further validation. Full article

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances. Full article

Investigating the dynamic interplay between population shrinkage and water resource carrying capacity holds critical implications for safeguarding national food security and ecological resilience. This study systematically examines the underlying mechanisms and spatiotemporal evolution of their coupling relationship in Northeast China, thereby advancing the theoretical framework of human–land systems and delivering empirically grounded insights for sustainable regional development. Drawing on balanced panel data from 34 prefecture-level cities in Northeast China over the period 2010–2023, this study develops a population shrinkage index grounded in registered population dynamics. Building upon the DPSIR (driving forces–pressures–state–impacts–responses) conceptual framework, we construct a comprehensive evaluation system for agricultural water resource carrying capacity, explicitly operationalizing each of its five dimensions. Employing a spatial Durbin model (SDM), we rigorously estimate the direct effect of population shrinkage on carrying capacity, while simultaneously testing the mediating pathways through human capital accumulation and fiscal policy interventions. The empirical analysis reveals that (1) over the study period (2010–2023), population shrinkage in Northeast China intensified progressively, expanding spatially from initially localized pockets to widespread, regionally contiguous areas. Concurrently, agricultural water resource carrying capacity displayed pronounced spatial heterogeneity and statistically significant spatial clustering. (2) Overall, the degree of population decline significantly negatively affects agricultural water resource carrying capacity. Further heterogeneity tests showed that this negative effect exhibits significant variation across different population-contracted regions and provinces. Among the control variables, urbanization rate, per capita GDP, and total water resources availability exhibit statistically significant positive associations with agricultural water resource carrying capacity (3) Over the study period, higher human capital endowment and greater fiscal intervention intensity significantly attenuated the adverse effect of population shrinkage on agricultural water resource carrying capacity. Full article

Residual neural ordinary differential equations (NODEs) of the form $\widehat{f}=f+h_{\theta }$ can attain small one-step prediction error yet diverge under autonomous long-horizon rollout. A recent diagnostic attributes this to the one-sided Lipschitz (OSL) constant—the supremum over visited states of the logarithmic norm ${\mu }_2(J_{\widehat{f}})={\lambda }_{\max }(\frac{1}{2}(J_{\widehat{f}}+J_{\widehat{f}}^{\top }))$—which, when positive, signals local expansion and amplifies persistent approximation error. In this work we convert this post hoc diagnostic into a train-time method by augmenting the one-step objective with a contraction penalty $\lambda {\mathbb{E}}_x[{({\mu }_2(J_{\widehat{f}}\left(x\right))-c)}_{+}]$, and we study when this improves robust forecasting across stable, expansive, marginal, and chaotic regimes under realistic sensor-corruption noise. We prove that, at the penalty’s minimizer, the empirical OSL constant is controlled on the training set. A sample-to-domain covering condition then yields, via a Gronwall-type comparison, a conditional uniform-in-time rollout-error bound, with a time-averaged variant that justifies penalizing the mean rather than the maximum log-norm. Empirically, on a six-system, four-noise benchmark the penalty reliably drives the OSL constant down by one-to-two orders of magnitude, but whether this helps long-horizon accuracy is strongly regime-dependent and $\lambda$-sensitive: a common default ($\lambda =0.1$) over-damps and degrades rollout, whereas a calibrated $\lambda \approx 0.01$ helps only for measurably expansive baselines. Under a seed-decoupled re-evaluation with 15 seeds, the benefit is robust on a near-unstable, rotation-dominated oscillator—a $2.3\times$ lower 100-step rollout error ($p=0.018$) at matched one-step error—but the apparent 5-seed improvement on a six-dimensional chemical reaction network does not replicate: with model and dataset seeds decoupled it reverses to a significant degradation, identifying the original effect as a seed-coupling artifact. The method thus yields a single robust positive result, and degrades already-contractive, conservative, and chaotic systems; for chaotic systems this is unavoidable, because enforced contraction suppresses the positive Lyapunov exponents that define the attractor. Finally, while a contraction-aware spectral penalty matches the log-norm penalty, standard ${\parallel J\parallel }_2\le 1$ spectral normalization fails on rotation-dominated dynamics ($8\times$ worse rollout, $p=0.0005$, 15 seeds), confirming that the rotation-invariance of ${\mu }_2$ is the operative property. Full article

The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and analyze real-time environmental data using specialized IoT sensors capable of monitoring critical variables, including carbon dioxide concentration (CO₂), pH, air temperature, and relative humidity in hydroponic production systems. The proposed framework integrates advanced machine-learning algorithms, including Random Forest Regressor and Long Short-Term Memory (LSTM) neural networks, to process large volumes of environmental data and support crop management. In addition, the platform incorporates Vapor Pressure Deficit (VPD) and Growing Degree Days (GDD) analyses to provide crop-specific recommendations and support informed decision-making. This platform establishes a benchmark for smart agriculture in Mexico’s Laja–Bajío region, facilitating informed decision-making and maximizing the sustainability of food systems. Experimental validation was conducted under both controlled and semi-controlled environments using Swiss chard (Beta vulgaris subsp. cicla L.) and lettuce (Lactuca sativa L.) cultivated in hydroponic systems. These environments represented contrasting climatic conditions, allowing evaluation of platform stability and forecasting performance under varying thermal regimes. The Random Forest Regressor model, trained using growth chamber data consisting of 19,836 valid observations, reproduced the deterministic VPD relationship with a coefficient of determination (R²) of 0.90 and a root mean square error (RMSE) of 0.08 kPa, confirming internal consistency and identifying temperature as the dominant contributing variable rather than predicting an independent outcome. The dynamic alarm system, integrated with crop phenological stages, demonstrated greater effectiveness than conventional static-threshold approaches by generating alerts according to crop developmental requirements. Furthermore, the web-based visualization platform enabled users to interpret environmental conditions through intuitive graphical representations, facilitating decision-making without requiring specialized technical expertise. The results demonstrate the feasibility of the IoP platform as a comprehensive environmental management tool for protected agricultural systems. The proposed framework provides a scalable solution for precision agriculture applications in the Laja–Bajío region of Mexico and in other regions with similar production systems. Full article

Research on Urban Comprehensive Carrying Capacity (UCCC) from the perspective of Production–Living–Ecological Spaces (PLES) can provide new insights for enhancing carrying capacity and offer decision-making references for sustainable urban development. Taking China’s three northeastern provinces as an example, this study, based on the PLES perspective, constructs an evaluation indicator system for UCCC. Combining the MEREC method and Grey Clustering Analysis, it assesses the UCCCs of the three northeastern provinces and analyzes the spatiotemporal evolution characteristics and coupling coordination patterns. On this basis, a Two-Way Fixed-Effects model (TW-FE) and a threshold panel model are employed to identify the key factors influencing the UCCCs and analyze the mechanism through which these factors affect the UCCCs. The research findings indicate the following: (1) From 2014 to 2023, the UCCCs in the three northeastern provinces showed an overall steady increase. The UCCC of Liaoning Province consistently remained higher than those of Jilin and Heilongjiang Provinces. In terms of the internal structure of the PLES, the Living Space Carrying Capacity (LSCC) was higher than the Production Space Carrying Capacity (PSCC) and Ecological Space Carrying Capacity (ESCC), serving as the primary driving force for the UCCC. (2) The UCCCs in the three northeastern provinces exhibited significant spatial heterogeneity. The UCCCs of Shenyang, Dalian, Changchun, and Harbin were higher than those of surrounding cities. The global Moran’s I indicated that strong spatial dependence had not yet formed, and regional synergy mechanisms remained weak. The local Moran’s I revealed that the spatial aggregation pattern displayed dynamic changes. (3) The coupling coordination degrees of the carrying capacities of the PLES in the three northeastern provinces and each of their prefecture-level cities still did not reach the “Barely Coordinated” stage during the study period. (4) The key influencing factors for the overall UCCCs in the three northeastern provinces were the Degree of Openness to the Outside World (DOOW), Financial Development Level (FDL), and Economic Development Level (EDL). The key influencing factors for the UCCCs differed among Liaoning, Jilin, and Heilongjiang Provinces, and these key factors exhibited a nonlinear effect on the UCCCs. Full article

Most Transmission System Operators (TSOs) rely on seasonally static line rating models based on extreme weather conditions to determine the transmission capacity of power lines. These conservative rating approaches constrain grid capacity, limiting the integration of new renewable energy sources and delaying the transition to a more sustainable power system. Furthermore, they restrict cross-border transmission capacity between market zones, leading to “false” congestion and unnecessary market splitting. Market splitting can result in economic losses for market participants due to price differences between market zones and the potential curtailment of renewable generation. The adoption of Dynamic Line Rating (DLR) models can help avoid the need for new transmission infrastructure, reduce market splitting and false congestion, and mitigate line degradation in a cost-effective manner. The OptiRES.Lines tool integrates several DLR models, enabling their simulation and visualization through a Geographic Information System (GIS) interface. These dynamic rating models are combined with an Optimal Power Flow (OPF) model to assess: (1) the long-term potential for integrating new power plants at different grid locations; (2) the available cross-border transmission capacity between market zones; and (3) short-term grid congestion. The tool was tested in two regions of Portugal and demonstrated a significant increase in the grid’s capacity to accommodate additional renewable generation, thereby contributing to a more sustainable power system. The results showed that, although DLR alone indicated an increase in transmission line capacity during approximately 70% of the analysed period, the inclusion of OPF analysis revealed that DLR reduced line loading factors during 95% of the time analysed, highlighting its broader system-level benefits. Full article

Hydrogen has emerged as a promising energy carrier for sustainable, low-carbon energy systems because of its high energy density and compatibility with fuel cell technologies. This study presents a comprehensive investigation of hydrogen production through the integration of steam methane reforming (SMR), solar photovoltaic (PV) power generation, proton exchange membrane (PEM) electrolysis, hydrogen storage, and PEM fuel cells. A three-dimensional computational fluid dynamics (CFD) model was developed to analyze fluid flow, heat transfer, species transport, and chemical reactions within a catalytic steam methane reformer. The simulation predicted a methane conversion of 94.71%, a hydrogen yield of 3.75 mol H₂/mol CH₄, and an overall efficiency of 63.35%, indicating highly efficient hydrogen production. Sensitivity analyses identify catalyst temperature, inlet temperature, and residence time as the dominant parameters affecting hydrogen yield. Integration with renewable energy systems demonstrated that a hybrid configuration consisting of a 120 kW PV array, a 50 kW PEM electrolyzer, a 6 kW PEM fuel cell, and 6–8 kg hydrogen storage can effectively support sustainable hydrogen production and auxiliary power demands. The proposed framework provides a practical pathway for integrating thermochemical and renewable hydrogen technologies into future energy applications worldwide. Full article