Search Results (36,517)

Observing pig behavior, such as feed intake, water intake, and resting behavior, is essential for improving the well-being of these animals. However, monitoring such behaviors by traditional methods can be exhausting for both humans and animals, interfering with their development. The research aimed to identify behavioral patterns of pigs in an air-conditioned environment through computer vision. Microcameras were installed in the animals’ stalls to generate videos over an experimental period of 92 days and the temperature and humidity of the air were simultaneously recorded. The physiological variables of the animals were collected to identify whether they were under heat stress. To recognize the drinking, eating, standing and lying behavior of pigs, YOLOv5 was trained and then the model was used to detect the animals. Regions in the images corresponding to the feeders and drinkers were established. To identify feeding behavior and water intake, criteria based on the occupation of the feeding zone by pigs detected in the standing position were established. The results showed that the trained model achieved an average accuracy rate of 97.3% and an average recall of 96.1% in animal detection. The model exhibited 97.5% accuracy and 97.0% recall rates in recognizing the feeding behavior and water consumption of pigs. The proposed method can be used in videos or images and minimizes the need for manual intervention, offering an efficient means of monitoring pig behavior in agricultural environments and contributing to the productivity of pig farming operations. Full article

Dry rooms used in battery and semiconductor research facilities require ultra-low dew-point environments, which demand significant thermal energy for desiccant rotor regeneration. In steam-regenerated systems, condensate discharged through steam traps partially evaporates due to pressure reduction, generating flash steam that is typically released into the atmosphere, resulting in substantial energy losses. This study investigates the generation and recovery potential of flash steam in dry room HVAC systems. Field measurements were conducted for 18 steam-regenerated desiccant air handling units installed in a medium-scale research facility (total floor area: 43,000 m²) in southern Gyeonggi Province, Korea. Boiler operation data—including feedwater flow rate, pressure, and operating time—were analyzed over a six-month period from March to August 2025. The results showed that the average flash steam generation rate was approximately 1.16 ton/h, corresponding to 8.56% of the average feedwater flow rate. Two recovery methods were evaluated: a steam jet thermocompressor (SJT) and an exhaust vapor condenser (EVC). The analysis revealed that the EVC system provides a more practical solution for medium-scale dry rooms because it does not require high-pressure primary steam. By recovering flash steam using three EVC units, an average heat recovery of 724 kW was achieved. The recovered heat can produce 86 °C hot water, which can be utilized as a driving heat source for an absorption chiller, generating approximately 507 kW of cooling capacity. This configuration partially offsets the cooling load of existing centrifugal chillers, thereby reducing electrical energy consumption. In addition, the proposed system eliminates atmospheric discharge of flash steam, mitigating the visible white plume phenomenon commonly observed in industrial facilities. The results demonstrate the technical feasibility of integrating flash steam recovery with absorption cooling to enhance energy efficiency in medium-scale dry room HVAC systems. Full article

The long-term success of dental implants is significantly influenced by primary stability, which is commonly assessed through insertion torque (IT) and removal torque (RT) measurements in vitro. While polyurethane (PU) blocks are accepted by the American Society for Testing and Materials (ASTM) as the standard bone analog material for biomechanical testing, the use of polyethylene (PE) as a bone model material for dental implant research remains limited and not well established. This operator-blinded, in vitro study compared the IT and RT values of dental implants placed in PE and PU blocks of identical density (60 pounds per cubic foot [pcf]; 0.96 g/cm³). A total of 60 tapered dental implants (4.2 × 12 mm, RBM surface, platform switching) were placed into PE (n = 30) and PU (n = 30) blocks by a calibrated operator blinded to the material type. Implant sockets were prepared by an independent surgeon following the manufacturer’s drilling protocol. IT and RT values were recorded using a physiodispenser with torque measurement capability (5–80 N·cm). Statistical analysis was performed using Student’s t-test (α = 0.05), with Mann–Whitney U tests reported as a sensitivity analysis for non-normally distributed variables. No statistically significant difference was observed in IT between PE and PU groups (58.50 ± 8.42 vs. 58.17 ± 9.60 N·cm; p = 0.887; Cohen’s d = 0.04; 95% CI of mean difference: −4.33 to 5.00 N·cm). However, RT was significantly higher in the PU group compared to the PE group (71.17 ± 7.15 vs. 64.33 ± 9.17 N·cm; p = 0.002; Cohen’s d = 0.83; 95% CI: −11.08 to −2.58 N·cm; Mann–Whitney U sensitivity analysis p = 0.004). Under the specific high-density (60 pcf) conditions tested, the absence of a statistically significant IT difference does not constitute formal evidence of equivalence or non-inferiority, and the significantly higher RT in PU indicates that PE and PU are not interchangeable bone analogs. Further studies across a range of densities, implant macrogeometries, and using formal equivalence testing are required before PE can be considered for in vitro dental implant stability research. Full article

Cash dividends, as a tangible form of monetary distribution, serve as a fundamental mechanism for remunerating investors for their capital commitments. Beyond manifesting a firm’s commitment to fulfilling its social responsibilities toward shareholders, such distributions potentially shape corporate deliberations regarding accountability toward a broader spectrum of stakeholders. Drawing on behavioral explanations of corporate decision-making, this study examines the association between cash dividend payouts and environmental protection tax burdens among Chinese A-share listed companies from 2018 to 2023. The empirical results indicate a significant and robust negative association between corporate cash dividend payouts and environmental protection tax burdens. Mechanism analysis suggests that this cross-domain behavioral consistency is primarily channeled through the proactive fulfillment of corporate environmental responsibilities. Further inquiry reveals that both government environmental subsidies and media coverage exert positive moderating effects on this relationship. Notably, this observed negative association is particularly pronounced in firms characterized by lower executive environmental awareness, those operating in regions with lenient environmental regulations, companies navigating economic downturns, and those situated within low-pollution industries. This research provides novel evidence for the “governance complementarity” hypothesis, suggesting that financial accountability and environmental stewardship are mutually reinforcing rather than mutually exclusive. Furthermore, it offers a pioneering micro-behavioral perspective on how firms in emerging economies can harmonize shareholder wealth distribution with green transition objectives. Full article

This article examines two-state proportional hazard rate models with unobserved heterogeneity specific to each state, a framework that is especially relevant for labor market transitions. To make estimation feasible in large longitudinal datasets, we implement hshaz2s, a Stata routine that uses analytical expressions for the gradient vector and Hessian matrix of the log-likelihood function through the dual second-order moment (d2 ml) method. The empirical application estimates a discrete-time duration model for transitions between employment and unemployment using Spanish labor market microdata for young low-skilled workers over 2000–2019. The results show that apprenticeship contracts are associated with lower exit rates from employment than other temporary contracts, but not with faster transitions from unemployment back into employment. The estimates also reveal substantial state-specific unobserved heterogeneity, with a large latent group characterized by persistent spells in both states. Analytical second-order information also markedly reduces convergence time under richer heterogeneity structures. Overall, the article makes this class of two-state hazard models operational for applied research and provides new evidence on apprenticeship and temporary contracts in Spain. Full article

Intermodal container terminals play a critical role in modern logistics systems, where operational efficiency and energy consumption are strongly influenced by container stacking strategies. Inefficient yard organization leads to increased reshuffling operations, which negatively affect handling time and resource utilization. Despite extensive research, the relationship between operational performance and energy consumption remains insufficiently explored under dynamic terminal conditions. This study applies a discrete-event simulation framework to evaluate the impact of alternative container stacking strategies on both operational efficiency and energy consumption. The model represents container arrivals, storage decisions, retrieval processes, and reshuffling operations over a multi-day simulation horizon. Three stacking strategies—FIFO, balanced distribution, and departure-time clustering—are analysed under identical and dynamically evolving conditions using performance indicators related to reshuffling intensity, handling efficiency, and energy consumption. The results show that stacking strategies significantly affect terminal performance, but their effectiveness depends on the structure of container flows. While FIFO achieves the lowest reshuffling intensity and energy consumption under high-load conditions, departure-time clustering improves performance in outbound-dominated scenarios. The findings also reveal a structural discrepancy between operational and energy-related performance, as non-productive movements account for a higher share of operations than of total energy consumption. The study demonstrates that container stacking should be treated as a multi-criteria decision problem, where minimizing reshuffles does not directly correspond to minimizing energy consumption. The proposed simulation-based framework provides a consistent environment for evaluating trade-offs between operational and energy-related performance under controlled dynamic conditions. Full article

Propylene hydroformylation is a typical large-scale gas–liquid reaction. Nevertheless, exorbitant costs and theoretical studies lagging far behind practical industry have prevented advancements in reactor efficiency. In this work, the flow, mass transfer, and reaction processes within the gas–liquid reactor were simulated using a three-dimensional CFD-PBM coupled model. The coupling processes between the flow field, mass transfer, and reaction in the gas–liquid reactor are clarified in this study. It provides precise direction for further process optimization by introducing the Hatta number as a quantitative criterion to determine the reaction’s controlling step (mass transfer-controlled or reaction-controlled). The constraints of conventional single-point analysis were overcome by visualizing a Ha number distribution contour, which showed that about 65% of the volume inside the propylene hydroformylation reactor is in a mass transfer-limited state. Based on this, operational parameter optimization was carried out, and the findings show that reaction efficiency may be successfully increased by reasonably raising the superficial gas velocity and system pressure within a certain range. The conversion rate increased by 23% when the superficial gas velocity doubled and by two times when the pressure doubled. Additionally, the effects of the stirring device and rotational speed were investigated, resulting in a 19% increase in conversion rate after optimization. The design and process optimization of similar hydroformylation gas–liquid reactors can benefit from this research. Full article

MoA (mathematics of arrays) is a theory of parallel operations on arrays that can describe all known algorithms in linear algebra, signal processing, and HPC because they are based on primitive recursion and array shapes. Mapping parallel algorithms to computer architectures remains more of an art than a science, and specific mathematical techniques are needed to provide a basis for performance evaluation at a level abstract enough to constitute an experimental science. In this paper we present a methodology for parallel code generation from MoA expressions. Then, we relate the MoA operators to the linear space of memory elements in computer architecture. Finally, we define a theory of execution costs that is based on classical operations research and is formally related to MoA-based parallel code generation. This constitutes a formalized and mechanizable approach to performance prediction, portability and optimization. Full article

Toxoplasma gondii, an obligate intracellular protozoan infecting approximately one-third of the global population, poses a significant yet underappreciated threat to reproductive health in both sexes. Although this parasite has long been linked to birth defects caused by infection during pregnancy, new research shows that it also reduces fertility in both sexes through different but related mechanisms. This review synthesizes knowledge on T. gondii-induced reproductive pathology across females and males, examining shared mechanistic themes while respecting tissue-specific differences, and evaluates emerging therapeutic strategies. In females, the parasite establishes persistent uterine reservoirs, triggers decidual immune dysregulation characterized by NK cell cytotoxicity, M1 macrophage polarization, Treg apoptosis, and inflammasome-mediated pyroptosis, while disrupting estrogen and progesterone signaling through both host receptor modulation and intrinsic parasite steroidogenic enzymes (TgCYP450mt, TgMAPR, Tg-HSD). In males, T. gondii breaches the blood–testis barrier, induces germ cell and Leydig cell apoptosis via ER stress and caspase pathways, impairs sperm quality parameters across acute and chronic infection, and disrupts the hypothalamic–pituitary–gonadal axis. Conserved molecular mechanisms—including NLRP3 inflammasome activation, PERK/eIF2α/ATF4/CHOP-mediated ER stress, and oxidative stress—operate in both reproductive tissues. The parasite’s intrinsic steroidogenic capability and bidirectional hormonal manipulation represent a paradigm shift in understanding host–parasite interactions. Conventional antiparasitics face limitations due to poor reproductive sanctuary penetration. Immunomodulatory approaches targeting Trem2, Tim-3, and the NLRP3 inflammasome show promise, along with natural products including Inonotus obliquus polysaccharide and ginseng polysaccharide. Nanomedicine platforms and mRNA vaccine candidates offer new directions for overcoming tissue barrier limitations. Toxoplasma gondii represents a fundamental threat to fertility and pregnancy outcomes rather than merely a risk for congenital infection. Integrated therapeutic strategies addressing direct parasitism, immunopathology, and endocrine disruption are needed. Longitudinal cohort studies, strain-specific mechanistic comparisons, and clinical trials of immunomodulatory adjuncts are urgently required. Full article

Rock bursts remain among the most destructive and unpredictable disasters in mining operations, yet existing deep learning methods face significant challenges in engineering practicality, noise robustness, and representing complex inter-event relationships for accurate prediction. To address these limitations, this paper proposes DynamiXFormer, a novel Transformer-based rock burst prediction model. Unlike traditional temporal prediction paradigms, DynamiXFormer establishes a direct mapping from working face advancement to rock burst risk, thereby linking predictions to mining-induced disturbances. The model integrates three innovative modules: an Adaptive Frequency Denoising module that suppresses noise while enhancing salient information from a frequency-domain perspective; a Relative Event Encoding module that constructs inter-event correlation graphs to capture physical attribute correlations and spatio-temporal dependencies; and a Dynamic Sparse Attention mechanism that introduces a strong inductive bias, enabling attention to focus on both local precursory patterns and global critical shifts. Experiments on real-world microseismic monitoring data demonstrate that DynamiXFormer significantly outperforms six baseline models across all prediction horizons and evaluation metrics. In short-term prediction tasks, it achieves a Mean Squared Error as low as 0.000518 and a Recall of up to 97.85%. Ablation studies further validate the individual effectiveness and synergistic effects of the proposed modules. This research provides a new methodology for rock burst early warning, with strong potential to enhance mine safety monitoring and engineering applications. Full article

Interferometric Synthetic Aperture Radar (InSAR) enables regional monitoring of ground deformation, but operational geohazard analysis remains challenged by atmospheric artefacts, temporal decorrelation, and the need for scalable interpretation of multi-temporal products. A systematic review was conducted through searches in Scopus and Web of Science, resulting in 135 peer-reviewed scientific articles on the integration of Machine Learning (ML) and Deep Learning (DL) with multi-temporal InSAR (MT-InSAR). The literature is dominated by applications to landslides and land subsidence, with additional studies addressing volcanic unrest and other deformation-related hazards. Persistent Scatterer (PS) and Small-Baseline Subset (SBAS) approaches are frequently used to derive deformation time series, which are then coupled with ML/DL for the detection and mapping of active phenomena and for short-horizon forecasting. Convolutional architectures, such as Convolutional Neural Networks (CNNs), are commonly reported for spatial recognition tasks, while recurrent models like Long Short-Term Memory (LSTM) networks are often applied to time-series prediction. Reported benefits include improved automation and predictive performance, although sensitivity to noise sources remains a challenge. Overall, the evidence supports AI-enabled InSAR workflows for scalable geohazard monitoring, while highlighting the need for standardized benchmarks and systematic transferability assessment. This review provides a roadmap for transitioning from research prototypes to operational early-warning systems. Full article

Multi-robot collaboration and marine robotics constitute key research directions in intelligent autonomous systems. In this context, multi-ROV cooperative operations are increasingly deployed for sunken ship search missions. A central technical challenge in such applications is to ensure efficient, non-redundant coverage while maintaining accurate formation tracking. This scenario confronts two principal difficulties. First, overlapping operational regions among multiple ROVs tend to produce both redundant coverage and search blind zones. Second, trajectory tracking accuracy is significantly degraded by the combined effects of hydrodynamic disturbances and inherent actuator constraints in ROVs. To address these challenges, an improved dynamic window approach (DWA), incorporating a search distance penalty mechanism, is proposed for multi-ROV trajectory planning. Concurrently, a cascaded tracking control architecture is constructed, wherein a model predictive kinematic controller generates constrained velocity references, while an adaptive sliding mode dynamic controller augmented with an extended state observer provides robust disturbance rejection. Collaborative search is conducted using a three-ROV leader–follower formation. Simulation results indicate that regional search coverage is effectively improved and areas of repeated detection are significantly reduced by the proposed planning algorithm. Real-time trajectory tracking is achieved by the designed controller under two typical extreme strong disturbance conditions, namely, time-varying disturbances and abrupt disturbances, on the premise of satisfying thruster thrust constraints. The proposed scheme enables all three ROVs to successfully complete the tracking task under time-varying disturbances while reducing the frequency of thrust saturation events by up to seven times. In contrast, under the conventional MPC–ASMC controller, one ROV deviates from the formation and fails to complete the tracking task. Under abrupt disturbances, the proposed approach reduces the trajectory tracking error by up to six times and decreases the frequency of thrust saturation events by up to four times. Full article

This paper presents a systematic, data-driven literature review of research on Artificial Intelligence (AI) agents based on the top 100 Google Scholar publications related to the search terms “AI agent” and “AI agents”. The rapid advancement of AI agents, driven in particular by recent progress in Large Language Models, has resulted in a diverse and fragmented research landscape that lacks comprehensive quantitative overviews. To address this gap, we implement and apply a fully automated, AI-driven analysis pipeline to the domain of AI agents. The collected publications are processed using a Large Language Model accessed via a Python-based Application Programming Interface (API), enabling an automated analysis of the literature without manual categorization. Based on this approach, the publications are grouped into data-driven thematic clusters reflecting dominant research perspectives in the field. Specifically, the identified clusters comprise “Architecture & Frameworks”, “Multi-Agent Systems”, “Applications”, “Safety” and “Ethics, Accountability & Governance”. By synthesizing the literature in a structured and automated manner, this work provides a consolidated overview of central research patterns, identifies key operational and structural challenges and highlights fragmentation across AI agent research. The findings support a more systematic understanding of AI agents and provide a foundation for future research on robust, scalable and trustworthy AI agent systems. Full article

Accurate detection of ship targets in complex marine environments is fundamental to ensuring maritime security and safeguarding maritime rights. With the increasing diversity of vessel types and configurations, achieving precise identification of multiple ship classes amidst dynamic interference and cluttered backgrounds has emerged as a formidable challenge in marine surveillance. To address three pervasive issues in ship target detection—namely, high false-negative rates for small targets, inadequate feature discrimination, and imprecise localization—this paper proposes AK-DSAM-YOLOv13, a multi-scale detection algorithm specifically tailored for complex marine scenarios. Built upon the YOLOv13n architecture, the proposed algorithm implements integrated optimizations across the backbone network, neck structure, and loss function. First, a lightweight cross-scale feature extraction module, AKC3k2, is constructed by incorporating Alterable Kernel Convolutions (AKConv) to reconstruct the feature extraction path, thereby significantly enhancing the representation of multi-scale targets. Second, a Dynamic Up-Sampling Dual-Stream Attention Merging (DyDSAM) structure is designed, which integrates the DySample operator with a Dual-Stream Attention Mechanism (DSAM) to effectively suppress background clutter and improve feature fusion accuracy. Third, an Accuracy-Intersection-over-Union (AIoU) loss function is introduced to jointly optimize overlap area, center distance, and aspect ratio, enhancing localization robustness for small-scale objects. Experimental results on the self-built CM-Ships dataset, as well as the public SeaShips and McShips datasets, demonstrate that AK-DSAM-YOLOv13 significantly outperforms baseline models in detection accuracy, recall, and generalization capability while maintaining a low computational overhead. This research provides an efficient and reliable technical framework for intelligent maritime visual monitoring in complex environments. Full article

The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a comprehensive review of driver behavior in mixed traffic with autonomous vehicles, with emphasis on the sociotechnical nature of human–machine coexistence. The review synthesizes recent evidence on behavioral adaptation in car-following and tactical decision-making, trust calibration, situational awareness, takeover performance, internal and external human–machine interface design, surrogate safety metrics, vehicle-to-vehicle communication, operational design domains, and data-driven scenario generation. The literature shows that drivers do not respond to autonomous vehicles uniformly. Instead, behavior varies by driving style, perceived predictability of the automated vehicle, interface transparency, and traffic context. The review also emphasizes that these interaction patterns are context-dependent and may differ substantially across regions, particularly in dense mixed traffic environments. While some adaptations can improve stability and safety, others can encourage opportunistic maneuvers, overtrust, confusion, or degraded takeover quality. The review also highlights that crash data alone are insufficient to assess safety in mixed traffic, and that near-miss analysis, surrogate conflict metrics, and scenario-based evaluation are essential for understanding safety-critical interactions. Across the literature, a central inference emerges: adaptation to autonomous vehicles is real, but it is not automatically stabilizing. Safe deployment therefore depends not only on technical vehicle performance but also on behavioral legibility, transparent communication, calibrated trust, and robust evaluation under diverse real-world conditions. The paper concludes by identifying major research gaps, including the lack of longitudinal studies, incomplete standardization of surrogate metrics, limited understanding of vehicle conspicuity effects, and the need for integrated frameworks that jointly assess driver behavior, system design, and scenario-based safety. Full article