Search Results (1,473)

This study determined the water-use efficiency for post-weaning growth performance of beef cattle of different frame sizes under intensive production systems. A total of 33 beef cattle weaners of three different frame sizes (small, medium, and large) were randomly allocated individually to metabolic pens. Feed and water were provided ad libitum. The water intake (WI), feed intake (FI), and weight were measured across different feeding phases (starter, grower, and finisher). Water consumption (WC) average daily gain (ADG), weight gain (WG), water intake efficiency (WIE), water footprint per animal (WFP/AU), and WFP/kg were computed. General Linear Model of Statical Analysis software (SAS) version 9.4 was used to analyse the data, and the means were separated using Fisher’s LSD test. The results showed that large-frame beef cattle had significantly higher (p < 0.05) WT_f. (412.73 ± 27.27 kg) and WI (3394.09 ± 156.3 L), but also the largest WFP/AU (4407 ± 197.22 L). The medium-frame cattle achieved the highest ADG (1.48 ± 0.14 kg/day) and a moderate WIE (20.15 ± 2.18 L/kg gain), indicating an optimal trade-off between productivity and water use. The small-frame beef cattle exhibited the best WCE (0.051 ± 0.005 kg/L) and the lowest WFP/AU (3822 ± 197.22 L), highlighting superior water-use adaptability. Pearson’s correlation revealed that WCE was positively associated with ADG (r = 0.499; p < 0.05) and negatively with WIE (r = −0.987; p < 0.05). These findings suggest that medium-frame beef cattle provided a balanced compromise between growth performance and resource efficiency, making them more suitable for sustainable production in water-limited environments. Full article

Instance segmentation stands as a foundational technology in real-world applications such as autonomous driving, where the inherent trade-off between accuracy and computational efficiency remains a key barrier to practical deployment. To tackle this challenge, we propose a dual-path enhanced framework based on YOLO11l. In this framework, two improved models, YOLO-SA and YOLO-SD, are developed to enable high-performance lightweight instance segmentation. The core innovation lies in balancing precision and efficiency through targeted architectural advancements. For YOLO-SA, we embed the parameter-free SimAM attention mechanism into the C3k2 module, yielding a novel C3k2SA structure. This design leverages neural inhibition principles to dynamically enhance focus on critical regions (e.g., object contours and semantic key points) without adding to model complexity. For YOLO-SD, we replace standard backbone convolutions with lightweight SPD-Conv layers (featuring spatial awareness) and adopt DySample in place of nearest-neighbor interpolation in the upsampling path. This dual modification minimizes information loss during feature propagation while accelerating feature extraction, directly optimizing computational efficiency. Experimental validation on the Cityscapes dataset demonstrates the effectiveness of our approach: YOLO-SA increases mAP from 0.401 to 0.410 with negligible overhead; YOLO-SD achieves a slight mAP improvement over the baseline while reducing parameters by approximately 5.7% and computational cost by 1.06%. These results confirm that our dual-path enhancements effectively reconcile accuracy and efficiency, offering a practical, lightweight solution tailored for resource-constrained real-world scenarios. Full article

With the rapid proliferation of social media platforms and user-generated content, audio data is frequently shared, remixed, and redistributed online. This raises urgent needs for copyright protection and traceability to safeguard the integrity and ownership of such content. Resilience to desynchronization attacks remains a significant challenge in audio watermarking. Most existing techniques face a trade-off between embedding capacity, robustness, and imperceptibility, making it difficult to meet all three requirements effectively in real-world applications. To address this issue, we propose an improved patchwork-based audio watermarking algorithm. Each audio frame is divided into two non-overlapping segments, from which mid-frequency energy features are extracted and modulated for watermark embedding. A linearly decreasing buffer compensation mechanism balances imperceptibility and robustness. Additionally, an optimization algorithm is incorporated to enhance watermark transparency while maintaining resistance to desynchronization attacks. During watermark extraction, each bit of the watermark is recovered by analyzing the intra-frame energy relationships. Furthermore, we provide a theoretical analysis demonstrating that the proposed method is robust against various types of attack. Extensive experimental results demonstrate that the proposed scheme ensures high audio quality, strong robustness against desynchronization attacks, and a higher embedding capacity than existing methods. Full article

This study examines the role of aggregators in the context of digital energy and the integration of renewable energy sources (RES). The primary economic functions of aggregators are examined, including their role in optimizing energy markets and enhancing the flexibility and resilience of electricity systems. Different business models are presented, including the Energy as a Service (EaaS) model, and the effects of aggregators’ participation in electricity markets and balancing markets are examined. Special attention is paid to models for optimizing trading strategies and energy storage management. A comparative assessment of two scenarios for the distribution of the energy mix between solar and wind energy in the period 2022–2024 is conducted, evaluating the necessary storage capacities to achieve energy sustainability. The study highlights the importance of aggregators for grid stability, the integration of RES, and achieving higher efficiency through digitalisation and decentralisation in the context of European energy policy and the transition to a low-carbon economy. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 $\mathsf{\mu }$J. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article

Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras and deep-learning-based object detection algorithms are increasingly used in search operations. This study investigates the optimal flight parameters, specifically flight speed and ground sampling distance (GSD), to maximize a search efficiency metric called effective coverage. A custom dataset of 4468 aerial images with 35,410 annotated cardboard targets was collected and used to evaluate the influence of flight conditions on detection accuracy. The effects of flight speed and GSD were analyzed using regression modeling, revealing a trade-off between the area coverage and detection confidence of trained YOLOv8 and YOLOv11 models. Area coverage was modeled based on flight speed and camera specifications, enabling an estimation of the effective coverage. The results provide insights into how the detection performance varies across different operating conditions and demonstrate that a balance point exists where the combination of the detection reliability and coverage efficiency is optimized. Our table of the optimal flight regimes and metrics for the most commonly used cameras in UAV operations offers practical guidelines for efficient and reliable mission planning. Full article

The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption remains a critical yet challenging task due to the inherent trade-off between them. Joint association, offloading, and computing resource allocation are essential to achieving satisfying system performance. However, these processes are difficult due to the highly dynamic environment and the exponentially increasing complexity of large-scale networks. To address these challenges, we introduce a carefully designed cost function to balance the latency and the energy consumption, formulate the joint problem into a partially observable Markov decision process, and propose two multi-agent deep-reinforcement-learning-based schemes to tackle the long-term problem. Specifically, the multi-agent proximal policy optimization (MAPPO)-based scheme uses centralized learning and decentralized execution, while the closed-form enhanced multi-armed bandit (CF-MAB)-based scheme decouples association from offloading and computing resource allocation. In both schemes, UDs act as independent agents that learn from environmental interactions and historic decisions, make decision to maximize its individual reward function, and achieve implicit collaboration through the reward mechanism. The numerical results validate the effectiveness and show the superiority of our proposed schemes. The MAPPO-based scheme enables collaborative agent decisions for high performance in complex dynamic environments, while the CF-MAB-based scheme supports independent rapid response decisions. Full article

This study analyzed shoreline evolution (2000–2024) at Dalian World Peace Park’s sandy tourist beach using GEE, CoastSat, and DSAS. At the same time, combined with the grain size analysis of beach sediments before and after typhoons, the impact of extreme events on the shoreline line changes was explored. The DSAS shows a spatial differentiation pattern of the southern shoreline retreat trend zone, the central shoreline dynamic balance trend zone and the northern shoreline advance trend zone. The 2008 reclamation project altered hydrodynamics, creating an artificial headland effect that triggered significant northern shoreline advancement (max 74.16 m) and southern retreat (27.14 m), demonstrating unforeseen long-term trade-offs of large-scale interventions. Subsequent cobble structures, acting as a nature-based solution, enhanced sediment retention and wave energy refraction, promoting dynamic equilibrium and shoreline resilience. However, the 2017 double typhoon caused instantaneous retreat with finer, poorly sorted sediment, highlighting persistent vulnerability to extreme events. This study underscores the critical need for adaptive management within a sustainable shoreline development framework. Full article

This study investigates the perceptions of economics students from Romania’s Central Region regarding the global phenomena of globalization, localization, and slowbalization (GLS), analyzed through the lens of environmental, economic, and educational sustainability. The research highlights a high level of awareness and understanding of globalization and localization, while the concept of slowbalization remains relatively unfamiliar and often perceived with uncertainty or neutrality. Most respondents view globalization as the most sustainable model for long-term economic development, emphasizing its contributions to international trade, market expansion, investment flows, and access to global education and research. At the same time, localization is recognized for its role in preserving cultural identity, strengthening local economies, and addressing pressing environmental issues through low-carbon solutions. Regarding educational sustainability, students support a hybrid model that balances global exposure with the appreciation of local knowledge and traditions—a glocal approach particularly endorsed by master’s students. The study also reveals statistically significant differences between undergraduate and graduate respondents, indicating more mature perspectives among those in advanced studies. The paper could help in course design and lesson engagement and concludes by recommending curricular reforms in economic education and proposing future interdisciplinary, comparative, and qualitative research to deepen understanding of GLS dynamics, particularly in the context of emerging global trends and technological transformations. Full article

Fused Deposition Modeling (FDM) is a commonly used 3D printing process characterized by its versatility in material selection; however, FDM’s layer-by-layer process often leads to lower strength properties. This study explores the mechanical properties of FDM 3D-printed composite materials printed with varying nozzle diameters, specifically on the influence of Carbon Fiber-reinforced Polylactic Acid (PLA-CF) on tensile and flexural strength when reinforcing Polylactic Acid (PLA) parts. Composite samples were printed with varying ratios of PLA and PLA-CF, ranging from 0% to 100% PLA-CF in 20% increments, with layer groups stacked vertically, while also using three different nozzle diameters (0.4 mm, 0.6 mm, and 0.8 mm). Tensile testing revealed a proportional increase in strength as PLA-CF content increased, indicating that carbon fiber reinforcement significantly enhances tensile performance. However, flexural testing demonstrated a decrease in bending strength with higher PLA-CF content, suggesting a trade-off between stiffness and flexibility. Mid-range ratios (40–60% PLA-CF) provided a balance between tensile and flexural properties. Finally, atomic force microscopy was utilized to provide a better understanding of the microscale morphology and surface properties of PLA and PLA-CF thin films. The results highlight the potential of PLA-CF/PLA composites to allow for more direct control over the tensile–flexural trade-off during the printing process, as opposed to manufacturing filaments with fixed fiber percentages. These results provide a path for tailoring the mechanical behavior of printed parts without requiring specialized filaments. Full article

Rapid urbanization has intensified eco-economic trade-offs, necessitating integrated optimization frameworks that balance development with environmental conservation in land use planning. Traditional methods often fail to optimize both objectives simultaneously, highlighting the need for systematic approaches addressing competing demands. This study develops an integrated linear programming (LP) and CLUE-S modeling framework using Guangzhou, a rapidly urbanizing megacity in China, as a case study. The methodology combines LP quantitative optimization with CLUE-S spatial allocation under dual objectives: maximizing ecosystem service value and economic benefits across four policy scenarios: ecological protection, cultivated protection, economic development, and balanced development. Data inputs include the 2020 land-use database, 12 socio-economic and biophysical driving factors, and territorial planning constraints. Results show that the coupled framework effectively balances urban expansion with ecological protection, reducing habitat fragmentation and preserving key ecological corridors compared with business-as-usual scenarios. Accuracy assessments further confirm the robustness and reliability of the framework. The integrated LP-CLUE-S framework captures land use dynamics and spatial constraints, providing a robust tool for territorial spatial planning. This approach offers actionable insights for reconciling development pressures with environmental conservation, contributing a replicable methodology for sustainable land resource management with strong transferability potential for other rapidly urbanizing regions facing similar eco-economic challenges. Full article

A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article

Droughts present persistent and severe challenges to the security of regional water supplies, particularly in arid and semiarid regions such as northern China. Traditional reservoir operation models that prioritize water supply reliability or economic efficiency often fail to adequately address the risks posed by extreme drought events. In this study, we develop a novel risk-informed multiobjective reservoir operation model that incorporates three key performance indicators: reliability, resilience, and vulnerability (RRV). This model aims to improve drought response and enhance the overall stability of the water supply system. It is applied to a multisource water supply system composed of the Nierji Reservoir and various water-user sectors. Unlike traditional models, this approach explicitly balances the trade-offs among supply reliability, recovery capability, and water shortage during drought periods. Comparative analyses with conventional strategies (CSs) under both a six-year consecutive dry period and a representative single dry year demonstrate the superior performance of the RRV-based model in drought management. Specifically, the model reduces the average supply disruption duration from 8–10 to 4–6 ten-day intervals, increases water supply reliability to 90%, decreases the maximum single-event shortage depth to 22 × 10⁶ m³, and lowers the average water shortage to 221 × 10⁶ m³. Agricultural water shortages are reduced, although slight increases occur in other sectors. The results highlight resilience as the most influential objective in the model, and its inclusion or exclusion can be adjusted based on different drought response priorities. This study presents a novel and adaptive framework for reservoir operation under drought conditions, offering practical implications for improving the resilience and efficiency of regional water resource systems in the context of climate change. Full article

Polyvinyl chloride (PVC) remains one of the most widely used synthetic polymers worldwide, primarily due to its versatility, cost-effectiveness, and broad applicability across construction, healthcare, automotive, and consumer goods industries. However, its production involves hazardous chemicals, particularly vinyl chloride monomer (VCM), which requires rigorous safety assessments. In this context, the present study applies the Inherent Safety Index (ISI) methodology to evaluate the safety performance of a suspension polymerization process for PVC production that incorporates direct water recycling as a sustainability measure. The integration of water reuse reduces the fractional water consumption index from 2.8 to 2.2 and achieves a recovered water purity of 99.6%, demonstrating clear environmental benefits in terms of resource conservation. Beyond water savings, the core objective is to assess how this integration influences the inherent risks associated with the process. The key operational stages—polymerization, VCM recovery, product purification, and water recirculation—were modeled and analyzed using computer-aided process engineering (CAPE) tools. The ISI analysis yielded a score of 33, surpassing the threshold typically associated with inherently safer designs, with VCM hazards alone contributing a score of 19 due to its high flammability and carcinogenicity. These findings reveal a critical trade-off between environmental performance and inherent safety, underscoring that resource integration measures, while beneficial for sustainability, may require complementary safety improvements. This study highlights the necessity of incorporating inherently safer design principles alongside process integration strategies to achieve balanced progress in operational efficiency, environmental responsibility, and risk minimization in PVC manufacturing. Full article