Search Results (11,109)

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Woven composite structures are inherently influenced by uncertainties across multiple scales, ranging from constituent material properties to mesoscale geometric variations. These uncertainties give rise to both spatial autocorrelation and cross-correlation among material parameters, resulting in stochastic strength performance and damage morphology at the macroscopic structural level. This study established a comprehensive multiscale uncertainty quantification framework to systematically propagate uncertainties from the microscale to the macroscale. A novel dual-correlation sampling approach, based on multivariate random field (MRF) theory, was proposed to simultaneously capture spatial autocorrelation and cross-correlation with clear physical interpretability. This method enabled a realistic representation of both inter-specimen variability and intra-specimen heterogeneity of material properties. Experimental validation via in-plane tensile tests demonstrated that the proposed approach accurately predicts not only probabilistic mechanical responses but also discrete damage morphology in woven composite structures. In contrast, traditional independent sampling methods exhibited inherent limitations in representing spatially distributed correlations of material properties, leading to inaccurate predictions of stochastic structural behavior. The findings offered valuable insights into structural reliability assessment and risk management in engineering applications. Full article

This study examines the effects of partial wakes caused by upstream turbine yaw control on the trailing-edge noise of a downstream turbine under stable and neutral atmospheric conditions. Using a combined model coupling the unsteady vortex lattice method (UVLM) with the Curl wake model, calibrated with large eddy simulation data, wake behavior and noise characteristics were analyzed for yaw angles from −30° to +30°. Results show that partial wakes slightly raise overall noise levels and lateral asymmetry of trailing-edge noise, while amplitude modulation (AM) strength is more strongly influenced by yaw control. AM varies linearly with wake deflection at moderate yaw angles but behaves nonlinearly beyond a threshold due to large wake deflection and deformation. Findings reveal that yaw control can significantly increase the lateral asymmetry in the AM strength directivity pattern of the downstream turbine, and that AM characteristics depend on the complex interplay between inflow distribution and convective amplification effects, highlighting the importance of accurate wake prediction, along with appropriate consideration of observer point location and blade rotation, for evaluating AM characteristics of a wind turbine influenced by a partial wake. Full article

Increased precipitation and extreme weather due to climate change can remobilize recent and legacy environmental contaminants from soil, sediment, and sewage overflows. Heavy metals are naturally distributed in Earth’s crust, but anthropogenic activity has resulted in concentrated emissions of toxic heavy metals and deposition in surrounding communities. Cities around the world are burdened with heavy metal pollution from past and present industrial activity. The city of Camden, NJ, represents a valuable case study of climate impacts on heavy metal mobilization in stormwater runoff due to similar legacy and present-day industrial pollution that has taken place in Camden and in many other cities. Various studies have shown that lead (Pb) and other toxic heavy metals have been emitted in Camden due to historic and recent industrial activity, and deposited in nearby soils and on impervious surfaces. However, it is not known if these heavy metals can be mobilized in urban stormwater, particularly after periods of high precipitation. In this study, Camden, NJ stormwater was collected from streets and parks after heavy rain events in the winter and spring for analysis with inductively coupled plasma-mass spectrometry (ICP-MS) to identify lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As). Lead was by far the most abundant of the four target elements in stormwater samples followed by Hg, Cd, and As. The locations with the highest Pb concentrations, up to 686.5 ppb, were flooded allies and streets between commercial and residential areas. The highest concentrations of Hg (up to 11.53 ppb, orders of magnitude lower than Pb) were found in partially flooded streets and ditches. Lead stormwater concentrations exceed EPA safe drinking levels at the majority of analyzed locations, and Hg stormwater concentrations exceed EPA safe drinking levels at all analyzed locations. While stormwater is not generally ingested, dermal contact and hand-to-mouth behavior by children are potential routes of exposure. Heavy metal concentrations were lower in stormwater collected from parks and restored areas of Camden, indicating that these areas have a lower heavy metal exposure risk. This study shows that heavy metal pollution can be mobilized in stormwater runoff, resulting in elevated exposure risk in industrial cities. Full article

Understanding heavy metal behavior in arid saline soils is critical for phytoremediation in degraded lands. This study investigated metal distribution and plant enrichment in the Tarim Basin using 323 soil and 55 plant samples (Populus euphratica, Tamarix ramosissima, cotton, jujube). Analyses included redundancy analysis (RDA) and bioconcentration factor (BCF) assessments. Key findings reveal that elevated salinity (total salts, TS > 200 g/kg) and alkalinity (pH > 8.5) immobilized As, Cd, Cu, and Zn. Precipitation and competitive leaching reduced metal mobility by 42–68%. Plant enrichment strategies diverged significantly: P. euphratica hyperaccumulated Cd (BCF = 1.59) and Zn (BCF = 2.41), while T. ramosissima accumulated As and Pb (BCF > 0.05). Conversely, cotton posed Hg transfer risks (BCF = 2.15), and jujube approached Cd safety thresholds in phosphorus-rich soils. RDA indicated that pH and total salinity (TS) jointly suppressed metal bioavailability, explaining 57.6% of variance. Total phosphorus (TP) and soil organic carbon (SOC) enhanced metal availability (36.8% variance), with notable TP-Cd synergy (Pearson’s r = 0.42). We propose a dual-threshold management framework: (1) leveraging salinity–alkalinity suppression (TS > 200 g/kg + pH > 8.5) for natural immobilization; and (2) implementing TP control (TP > 0.8 g/kg) to mitigate crop Cd risks. P. euphratica demonstrates targeted phytoremediation potential for degraded saline agricultural systems. This framework guides practical management by spatially delineating zones for natural immobilization versus targeted remediation (e.g., P. euphratica planting in Cd/Zn hotspots) and implementing phosphorus control in high-risk croplands. Full article

Evidence of possible cerebellar involvement in spatial processing, place learning and other types of higher order functions comes mainly from clinical observations, as well as from mutant mice and lesion studies. The latter, in particular, have reported deficits in spatial learning and memory following surgical or neurotoxic cerebellar ablation. However, the low specificity of such manipulations has often made it difficult to precisely dissect the cognitive components of the observed behaviors. Likewise, due to conflicting data coming from lesion studies, it has not been possible so far to conclusively address whether a cerebellar dysfunction is sufficient per se to induce learning deficits, or whether concurrent damage to other regulatory structure(s) is necessary to significantly interfere with cognitive processing. In the present study, the immunotoxin 192 IgG-saporin, selectively targeting cholinergic neurons in the basal forebrain and a subpopulation of cerebellar Purkinje cells, was administered to adult rats bilaterally into the basal forebrain nuclei, the cerebellar cortices or both areas combined. Additional animals underwent injections of the toxin into the lateral ventricles. Starting from two–three weeks post-lesion, the animals were tested on paradigms of motor ability as well as spatial learning and memory and then sacrificed for post-mortem morphological analyses. All lesioned rats showed no signs of ataxia and no motor deficits that could impair their performance in the water maze task. The rats with discrete cerebellar lesions exhibited fairly normal performance and did not differ from controls in any aspect of the task. By contrast, animals with double lesions, as well as those with 192 IgG-saporin given intraventricularly did manifest severe impairments in both reference and working memory. Histo- and immunohistochemical analyses confirmed the effects of the toxin conjugate on target neurons and fairly similar patterns of Purkinje cell loss in the animals with cerebellar lesion only, basal forebrain-cerebellar double lesions and bilateral intraventricular injections of the toxin. No such loss was by contrast seen in the basal forebrain-lesioned animals, whose Purkinje cells were largely spared and exhibited a normal distribution pattern. The results suggest important functional interactions between the ascending regulatory inputs from the cerebellum and those arising in the basal forebrain nuclei that would act together to modulate the complex sensory–motor and cognitive processes required to control whole body movement in space. Full article

Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) for uncalibrated visual adaptive control of robotic arms. The algorithm improves upon the Twin Delayed Deep Deterministic Policy Gradient (TD3) network framework by adopting an architecture with one actor network and three critic networks, along with corresponding target networks. By constructing a multi-critic network integration mechanism, the mean output of the networks is used as the final Q-value estimate, effectively reducing the estimation bias of a single critic network. Meanwhile, a behavior cloning regularization term is introduced to address the common distribution shift problem in offline reinforcement learning. Furthermore, to obtain a high-quality dataset, an innovative data recombination-driven dataset creation method is proposed, which reduces training costs and avoids the risks of real-world exploration. The trained policy network is embedded into the actual system as an adaptive controller, driving the robotic arm to gradually approach the target position through closed-loop control. The algorithm is applied to uncalibrated multi-degree-of-freedom robotic arm visual servo tasks, providing an adaptive and low-dependency solution for dynamic and complex scenarios. MATLAB simulations and experiments on the WPR1 platform demonstrate that, compared to traditional Jacobian matrix-based model-free methods, the proposed approach exhibits advantages in tracking accuracy, error convergence speed, and system stability. Full article

This work investigated the effect of cerium (Ce) addition on the wear behavior of commercially pure titanium (CP-Ti) by varying the Ce content to 0.8, 1.4, and 2.0 wt.%. Alloys were fabricated using plasma arc melting, and wear resistance was evaluated under loads of 1 N and 5 N dry sliding condition. Microstructural characterization confirmed the formation of CeO₂ precipitates, whose size and distribution varied with the Ce content. The Ti-0.8Ce alloy exhibited the highest hardness (203 HV), showing a 35% increase compared to CP-Ti, and the lowest wear rate reduced by approximately 47% and 22% under 1 N and 5 N loads, respectively. In contrast, Ti-1.4Ce and Ti-2.0Ce formed coarse CeO₂ precipitates, which acted as third-body abrasives. Although these alloys showed lower average friction coefficients than CP-Ti (up to 22% reduction), the enhanced abrasive interaction promoted material removal and increased wear rates. Notably, Ti-2.0Ce exhibited the most severe degradation in wear resistance, with wear rates increases of 21% and 27% under 1 N and 5 N loads, respectively. These findings demonstrate that while CeO₂ precipitates reduce friction by suppressing direct metal–metal contact, their abrasive nature adversely affects wear resistance when the particle size and volume fraction are excessive. Therefore, 0.8 wt.% Ce was identified as the optimal composition for improving the wear resistance, achieving the best combination of high hardness, low wear rate without excessive third-body abrasion. Full article

This multidisciplinary comparative study investigates consumption patterns, health-related properties, and quality attributes of trademarked and local extra virgin olive oil (EVOO) samples. It highlights the importance of localization in promoting agricultural sustainability, strengthening regional economies, and enhancing socio-economic impacts within EVOO production and consumption systems. In terms of quality characteristics, significant differences were observed in color parameters (L*, a*, b*, Chroma, Hue angle) among EVOO samples (p < 0.05). Regarding nutritional and functional properties, total phenolic content (TPC) measured with the Folin–Ciocalteu method ranged from 58.15 to 176.29 mg of gallic acid equivalents/kg of oil, while total antioxidant capacity (TAC) measured by CUPRAC and DPPH assays varied between 3.42 and 6.54 and 8.56–10.71 µmol of Trolox equivalents/g of oil, respectively. TPC and TAC were also evaluated for their stability during in vitro gastro-intestinal digestion, demonstrating that EVOO’s bioactive potential remains stable under gastric and intestinal conditions. Local samples exhibited significantly higher TACs than trademarked products across undigested, gastric, and intestinal phases (p < 0.05). Concurrently, a face-to-face consumer survey assessed purchasing behaviors and preferences, revealing that 71.3% of consumers preferred local EVOO and showed a low tendency to purchase commercial brands (p < 0.05). Cooperatives were identified as the main distribution channel, playing a crucial role in sustaining local production systems. This study offers valuable insights into EVOO’s bioactive content and consumer behavior, providing a foundation for developing both localized and commercial products that support health outcomes. Additionally, the findings contribute to policy development concerning sustainable food systems and geographical indications. Full article

This study investigates the multifractal characteristics and nonlinear cross-correlations between two major carbon credit indices—S&P Global Carbon Index and EEX Global Carbon Index—and key global financial assets: the Euro/US Dollar exchange rate, Dow Jones Industrial Average, gold, Western Texas Intermediate, and Bitcoin. Using daily data from August 2020 to June 2025, we apply the Asymmetric Multifractal Detrended Cross-Correlation Analysis framework to examine the strength, asymmetry, and persistence of interdependencies across varying fluctuation magnitudes. Our findings reveal consistent multifractality in all asset pairs, with stronger multifractal spectra observed in those linked to Bitcoin and Western Texas Intermediate Crude Oil price. The analysis of generalized Hurst exponents indicates higher persistence for small fluctuations and antipersistent behavior for large fluctuations, particularly in pairs involving the S&P Global Carbon Index. We also detect significant asymmetry in the cross-correlations, especially under bearish trends in Bitcoin and Western Texas Intermediate. Surrogate data tests confirm that multifractality largely stems from fat-tailed distributions and temporal correlations, with genuine multifractality identified in the S&P Global Carbon Index–Dow Jones Industrial average pair. These results highlight the complex and nonlinear dynamics governing carbon markets, offering critical insights for investors, policymakers, and regulators navigating the intersection of environmental and financial systems. Full article

In the pursuit of zero-emission mobility, hydrogen represents a promising fuel for internal combustion engines. However, its low volumetric energy density poses challenges, especially for high-performance applications where compactness and lightweight design are crucial. This study investigates the feasibility of an innovative hydrogen-fueled two-stroke opposed-piston (2S-OP) engine, targeting a specific power of 130 kW/L and an indicated thermal efficiency above 40%. A detailed 3D-CFD analysis is conducted to evaluate mixture formation, combustion behavior, abnormal combustion and water injection as a mitigation strategy. Innovative ring-shaped multi-point injection systems with several designs are tested, demonstrating the impact of injector channels’ orientation on the final mixture distribution. The combustion analysis shows that a dual-spark configuration ensures faster combustion compared to a single-spark system, with a 27.5% reduction in 10% to 90% combustion duration. Pre-ignition is identified as the main limiting factor, strongly linked to mixture stratification and high temperatures. To suppress it, water injection is proposed. A 55% evaporation efficiency of the water mass injected lowers the in-cylinder temperature and delays pre-ignition onset. Overall, the study provides key design guidelines for future high-performance hydrogen-fueled 2S-OP engines. Full article

Overcoming high draft forces has long been a primary challenge in conventional subsoiling. To better utilize this agronomically advantageous technique, it is necessary to substantially reduce the draft. Inspired by the digging behaviors of fossorial animals, a low-draft up-cutting subsoiling method was proposed in this study. Discrete element method (DEM) simulations were employed to study the draft-reducing performance of up-cutting tools compared with regular tools. The results showed that the up-cutting motion reduced the draft by 63.07%, 63.84%, and 58.92%, respectively, at rake angles of 45°, 60°, and 75%, and by 79.73%, 63.84%, and 45.22%, respectively, at advancement velocities of 0.5 m·s⁻¹, 1 m·s⁻¹, and 1.5 m·s⁻¹. An increase in up-cutting velocity reduces the draft. Soil disturbance, particle velocity distribution, and soil deformation and movement patterns change in ways that contribute to this reduction. The draft-reducing performance of a chain subsoiler developed based on the principle of soil-breaking by animal digging was verified using field tests, exhibiting a draft-reduction amplitude approaching or greater than 30%. This study shows the great application potential of the up-cutting method in reducing subsoiling drafts and provides a theoretical basis for future research. Full article

With the expected increase in drone operations, inter-operator fairness issues and congestion problems are expected to arise due to the strategic authorization approach mandated in European regulation. As an alternative, the proposed authorization method is based on a deferred authorization decision with multiple-priority classes that are gate-kept by a series of scarce flight tokens. In it, operators can guide the aerial traffic deconfliction process by indicating the criticality of each operation (i.e., selected priority class) based on their business logic and the available flight tokens. Scarce token distribution is performed by a centralized service following a fairness- or congestion-management policy defined by authorities. Also, geographical and temporal incentives can be considered using a 4D-dependent temporal airspace cost to compute the required number of tokens per flight. Results based on several simulation scenarios demonstrate the validity of the approach and its capability in prioritizing different operators’ behaviors (fairness management) or avoiding flight hotspots (congestion management). Overall, it is concluded that the proposed method is an efficient, fair, simple and scalable novel authorization process that can be integrated into the U-space ecosystem. Full article

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance. Full article

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations. Full article