Search Results (995)

As agricultural labor shifted toward non-farm sectors and the farming population aged, innovative production arrangements became essential to sustain land productivity. While partial agricultural production chain management (PAPM) was widespread, the productivity impact of whole-process agricultural production chain management (WAPM)—a comprehensive model integrating all production stages—remained empirically underexplored. Using nationally representative panel data from the China Labor-force Dynamics Survey (CLDS, 2014–2018) for grain-producing households, this study estimates the differential impacts of WAPM and PAPM with a two-way fixed-effects (TWFE) model, supplemented by propensity score matching (PSM) as a robustness check. The results show that WAPM significantly enhanced land productivity. Notably, the effect size of WAPM (coefficient: 0.486) is substantially larger than that of PAPM (coefficient: 0.214), indicating that systematic integration of service chains offers superior efficiency gains over fragmented outsourcing. Mechanism analysis suggests that WAPM improves productivity primarily by alleviating labor constraints and mitigating the disadvantages of small-scale farming. Furthermore, heterogeneity analysis demonstrated that these benefits are amplified in major grain-producing regions and hilly areas. These findings support policies that facilitate a transition from single-link outsourcing toward whole-process integrated service provision. Full article

In response to recent policies on sustainable finance, nature restoration, soil protection, and biodiversity conservation, it is increasingly important for projects to assess their impacts on natural capital to safeguard Ecosystem Services (ES). Nature-Based Solutions (NBSs) are recognized as strategic tools for fostering cost-effective, nature- and people-centered development. Yet, standard economic and financial assessment methods often fall short, as many ES lack market prices. Indirect, ecosystem-based approaches—such as ES monetization and environmental cost accounting—are therefore critical. This study evaluates the feasibility of investing in NBSs by estimating their economic and financial value through indirect ES valuation. An empirical methodology is applied to quantify environmental costs relative to ES delivery, using Willingness to Pay (WTP) as a proxy for the economic relevance of NBSs. The proposed ES-Cost Accounting (ES-CA) framework was implemented across major NBS categories in Europe. Results reveal that the scale of NBS implementation significantly influences both unit environmental costs and ES provision: larger interventions tend to be more cost-efficient and generate broader benefits, whereas smaller solutions are more expensive per unit but provide more localized or specialized services. The findings offer practical guidance for robust cost–benefit analyses and support investment planning in sustainable climate adaptation and mitigation from an ES perspective. Full article

This study analyzes the provisioning strategy of the European Scops Owl (Otus scops) via continuous video monitoring of a breeding pair in a peri-urban Mediterranean forest in NE Spain (n = 724 deliveries). Invertebrates dominated numerically, with Orthoptera constituting 64.6%. Although vertebrates were scarce (1.8%), they contributed disproportionately to total biomass (20.8%), with rodents alone accounting for 20.3% of delivered energy. Parental effort followed a bimodal nocturnal rhythm, peaking at darkness onset (22:00 h) and before dawn. Crucially, we found a significant predation bias towards female orthopterans (65.6% vs. 34.3%; p < 0.001). While driven by Meconema thalassinum, selection in larger species like Tettigonia viridissima evidences a strategy focused on biomass profitability. Since Ensifera biomass scales allometrically (W $~ L^{2.797}$), selecting females yields disproportionate energetic gains. We also report the systematic removal of ovipositors prior to delivery, a behavior that optimizes intake but renders high-value females undetectable in traditional pellet analyses. These results suggest O. scops exploits artificial light sources (“streetlight traps”) to maximize foraging efficiency. Full article

Resource provisioning and scheduling are essential challenges in handling multiple workflow requests within cloud environments, particularly given the constraints imposed by limited resource availability. Although workflow scheduling has been extensively studied, few methods effectively integrate resource provisioning with scheduling, especially under cloud resource limitations and the complexities of multiple workflows. To address this challenge, we propose an innovative two-stage genetic-based optimization approach. In the first stage, candidate cloud resources are selected for the resource pool under the given resource constraints. In the second stage, these resources are provisioned and task scheduling is optimized on the selected resources. A key advantage of our approach is that it reduces the search space in the first stage through a novel encoding scheme that enables a caching strategy, in which intermediate results are stored and reused to enhance optimization efficiency in the second stage. The proposed solution is evaluated through extensive simulation experiments, assessing both resource selection and task scheduling across a diverse range of workflows. The results demonstrate that the proposed approach outperforms existing algorithms, particularly for highly parallel workflows, highlighting its effectiveness in managing complex workflow scheduling under resource-constrained cloud environments. Full article

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems. Full article

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

Existing metaheuristic approaches often struggle to maintain an effective exploration–exploitation balance and are prone to premature convergence when addressing highly conflicting time–cost–safety–risk trade-off problems (TCSRTPs) under complex construction project constraints, which can adversely affect project productivity, safety, and the provision of decent jobs in the construction sector. To overcome these limitations, this study introduces a hybrid metaheuristic called the Q-Learning Inspired Mountain Gazelle Optimizer (QL-MGO) for solving multi-objective TCSRTPs in construction project management, supporting the delivery of resilient infrastructure and resilient building projects. QL-MGO enhances the original MGO by integrating Q-learning with an opposition-based learning strategy to improve the balance between exploration and exploitation while reducing computational effort and enhancing resource efficiency in construction scheduling. Each gazelle functions as an adaptive agent that learns effective search behaviors through a state–action–reward structure, thereby strengthening convergence stability and preserving solution diversity. A dynamic switching mechanism represents the core innovation of the proposed approach, enabling Q-learning to determine when opposition-based learning should be applied based on the performance history of the search process. The performance of QL-MGO is evaluated using 18- and 37-activity construction scheduling problems and compared with NDSII-MGO, NDSII-Jaya, NDSII-TLBO, the multi-objective genetic algorithm (MOGA), and NDSII-Rao-2. The results demonstrate that QL-MGO consistently generates superior Pareto fronts. For the 18-activity project, QL-MGO achieves the highest hypervolume (HV) value of 0.945 with a spread of 0.821, outperforming NDSII-Rao-2, MOGA, and NDSII-MGO. Similar results are observed for the 37-activity project, where QL-MGO attains the highest HV of 0.899 with a spread of 0.674, exceeding the performance of NDSII-Jaya, NDSII-TLBO, and NDSII-MGO. Overall, the integration of Q-learning significantly enhances the search capability of MGO, resulting in faster convergence, improved solution diversity, and more reliable multi-objective trade-off solutions. QL-MGO therefore serves as an effective and computationally efficient decision-support tool for construction scheduling that promotes safer, more reliable, and resource-efficient project delivery. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article

The optimal search strategy for foraging animals can vary based on environmental parameters, which can include information about the spatial distribution of prey. We tested the hypothesis that natural populations of foraging desert grassland whiptails (Aspidoscelis uniparens) structure their search strategies according to resource distribution. We experimentally provisioned prey in uniform, aggregated, and random distributions to characterize search effort (moves per minute and percent time moving) and search path (turn angles, movement duration, path straightness, step length, and two-step sequences). The search effort did not vary with treatment but animals adjusted their search path based on the presence and distribution of supplemental prey. With uniformly distributed prey, foragers took longer step lengths and more frequently engaged in two-step sequences that included long step lengths. When prey were randomly distributed, foragers made more moves of long duration and fewer straight moves, often pairing short step lengths with large turns. With an aggregated prey distribution, foragers had more moves of very short duration. Examining detailed search path characteristics can identify responses to environmental changes. Under experimental conditions, the search strategies of A. uniparens indicated behavioral responses to food distribution that could improve search efficiency. Full article

Public transit demand forecasting is a foundational component of sustainable urban mobility, enabling efficient operation, equitable service provision, and planning of public transit systems. Urban imagery, such as aerial images, contains rich information about urban sociodemographic characteristics and the built environment, offering particular value for data-scarce regions where conventional datasets are limited or outdated. However, there is limited research on using these images for public transit demand forecasting. This study introduces a deep learning approach for predicting transit ridership using aerial images. The method employs an encoder–decoder architecture to functionally separate image-derived latent representations into sociodemographic and physical environment vectors, which are subsequently used as inputs to a neural network for ridership prediction. Using data from Seoul, South Korea, the effectiveness of the proposed method is evaluated against three baseline configurations. The results show that the sociodemographic latent vector captures spatially organized residential characteristics, while the physical environment vector encodes distinct urban landscape patterns such as dense housing, traditional street grids, open spaces, and natural environments. The proposed model, which uses only imagery-derived latent features, substantially outperforms the pure image baseline and narrows the performance gap with census-informed models, reducing sMAPE by 25–60% depending on the mode. Combining imagery with census variables yields the highest accuracy, confirming their complementary nature. These findings highlight the potential of imagery-based approaches as a scalable, cost-efficient, and sustainable tool for data-driven transit planning. Full article

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO₂ absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O₂ (MAE = 1.28) and CO₂ (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article

This study deals with the successful exploitation of easily available and renewable potato peel waste (PPW) as an excellent feedstock in the production of PHA using Ralstonia eutropha. The process entailed the extraction of bioactive components from PPW by use of solvent-based procedures and screening of their antioxidant and antidiabetic activity. The extracted PPW biomass was subject to acid hydrolysis using different concentrations of sulfuric acid for hydrolysis and solubilization of sugar components. The obtained liquid (acid) hydrolysates were initially assessed to biosynthesize PHA. Activated charcoal-based detoxification of acid hydrolysates was observed to be more efficient in promoting bacterial growth and accumulation of PHA. Acid-pretreated PPW biomass was further enzymatically hydrolysed to accomplish full saccharification and used to produce PHA. The effects of provision of nutrients and employing stress state conditions were assessed to improve bacterial growth and PHA accumulation. In both hydrolysates under optimal conditions, R. eutropha demonstrated the highest biomass productivity of 7.41 g/L and 7.75 g/L, PHA accumulation of 66% and 67% and PHA yield of 4.85 g/L and 5.19 g/L, respectively. XRD, FT-IR, TGA and DSC analysis of produced PHA were studied. The results showed that the produced PHA displayed similar physicochemical and thermal properties to commercially available PHB. Overall, this work illustrates the possibilities of abundantly available PPW, which can be transformed into bioactive compounds and high-value bioplastics via a coupled bioprocess. This approach can develop process economics and sustainability within a cyclic biorefinery system and serve further industry applications. Full article

Degrowth scholars emphasize the importance of the foundational economy (FE) for ‘living well within planetary boundaries’. The foundational economy describes the provision and regulation of everyday goods and services needed for the satisfaction of basic needs, such as housing, care, education, energy, food and mobility. However, there is a lack of conceptual models linking FE production and consumption to biodiversity conservation and restoration. This paper develops an ecological–economic model of ecosystem services, biodiversity conservation, and the foundational economy. It embeds FE sectors in the whole economy and provides economic arguments both on the supply side (e.g., economies of scale, scope and density; transaction costs) as well as on the demand side (e.g., trust in institutions; universal basic services; willingness to accept changes) in favor of resource efficiency. Compared to extractive and financialized business models, the FE production has major environmental advantages, especially if connected to public and not-for-profit economic activities. Though FE production is certainly a necessary condition for biodiversity conservation, it is not per se a sufficient strategy. The foundational economy is also embedded in natural processes; thus, respective institutional, legal and economic frameworks are needed to limit the environmental impacts of FE. Full article