Search Results (1,304)

Material circularity in construction requires material information at the end of life for the trading of materials. Different digital technologies (DTs) are essential for such information management. This research aims to review key aspects of developing a blockchain-based material passports (MPs) system when integrating with key DTs used for MPs. This research is based on a critical literature review, with an integrative approach that synthesises both academic and grey literature. The literature search was initiated using chosen keywords relevant to the topic to first identify the key literature. This was followed by using a snowballing technique to expand the search with further relevant literature. Building Information Modelling (BIM), digital twin (DTw) and blockchain technology (BCT) were identified as key technologies for material information management. BIM and DTw are central to the management process as all the information created and collected is modelled, visualised, analysed and stored using BIM platforms. However, existing MP platforms utilising centralised databases to store data were found to be unreliable for managing material data in an industry like construction with a dispersed supply chain and typically longer lifecycle. BCT was realised as necessary for information management in construction, as it allows us to manage information in a more decentralised, transparent and immutable manner. Furthermore, examining current research about blockchain application for information management in construction led to the conclusion that, although the studies on blockchain-based MP platforms covering the entire industry supply chain prevail, the management of material data at the built asset level throughout its lifecycle using such MP systems is underexplored. Thus, building on the literature review, a conceptual model of blockchain-based MP system is proposed in this paper, describing integration with BIM and DTw, and with relevant processes and actors to manage MP information throughout the building lifecycle. Acknowledging the limitations of a subjective literature review, the conceptual model and the ideas are proposed as a foundation for further research and develop MP system with empirical validation. Although theoretically, this study identifies the suitability of blockchain technology for managing product lifecycle information in industry like construction and provides ground for further theoretical research for planning and policy required for blockchain-based MP development and implementation. Full article

This study investigates how financial literacy, FinTech adoption, and financial attitudes shape economic decision-making among millennials in Lebanon, a crisis-affected emerging economy. The study examines whether enhancing financial literacy can strengthen economic resilience through improved financial behavior, with financial attitudes acting as a mediator. Guided by Behavioral Finance Theory, the study employs a quantitative approach using data from 390 Lebanese millennials collected via a structured questionnaire. Structural equation modeling was applied to test direct and mediating effects. Both financial literacy and FinTech adoption were found to significantly influence millennials’ financial behavior, with financial literacy emerging as the stronger predictor. The findings also revealed that financial attitude significantly mediates the link between literacy and behavior, suggesting that financial knowledge alone is insufficient without attitudinal reinforcement. This study fills a critical empirical gap in the MENA region by offering evidence from a highly under-researched, crisis-affected emerging market. It introduces an integrated model combining technological, cognitive, and attitudinal dimensions of financial behavior. The study offers practical implications for policymakers, financial institutions, and international development actors seeking to strengthen financial inclusion and household stability in similar turbulent contexts. Full article

Religious festivals are increasingly recognized as significant cultural and tourism phenomena, yet their study from a tourism perspective remains underexplored. This systematic literature review examines the thematic focus, stakeholder involvement, research methods, and theoretical frameworks employed in the study of religious festivals. Using the PRISMA framework and the Covidence data management tool, 24 studies were selected from an initial pool of 493. The findings reveal that research on religious festivals has primarily focused on visitor experiences, motivations, perceptions, and impacts, with limited attention to stakeholder integration and theoretical diversity. Notably, religious leaders and ministers, key actors in festival organization, are underrepresented in the literature. This review identifies critical gaps, including the need for sustainability-focused research, broader stakeholder engagement, and the application of diverse theoretical frameworks. By synthesizing existing knowledge, this study provides a roadmap for advancing research on religious festivals and their intersections with tourism. Full article

The article queries the conventional interpretation of Vladimir Tatlin’s oeuvre as rational and pragmatic by focusing on more “irrational” aspects such as the visionary and unfeasible Monument to the III International, Letatlin and other, parallel projects that were never constructed or, perhaps, were never meant to be constructed. While acknowledging Tatlin’s debt to Cézanne and Picasso and referring to Formalist critics Punin and Tarabukin and to his proximity to Constructivism, the article also emphasizes the common contemporary reception of Tatlin as an actor, a buffoon and even a Holy Fool. The article concludes with copious references to Tatlin’s support of Daniil Kharms and the OBERIU group of Absurdist writers and to his illustrations for the former’s “fairy-tale” Vo-pervykh i vo-vtorykh. Full article

Microbiomes play a pivotal role in sustaining plant function and broader ecosystem processes. Leguminous plants host vast populations of intracellular bacteria within specialized root organs known as nodules. The intricate mutualism between legumes and rhizobia ensures a stable supply of biologically fixed nitrogen (N) essential for plant growth. While rhizobia remain the central actors in this symbiosis, recent discoveries reveal the presence of non-rhizobial endophytes within nodules, suggesting a complex interplay shaped by host selection and compatibility with rhizobial partners. Understanding the structure and dynamics of crop nodule-associated microbial communities is thus critical for optimizing host responses to rhizobia and for leveraging beneficial plant–microbe interactions. This review explores the dualistic nature—both facilitative and inhibitory—of the nodule microbiome in relation to nodulation. We examine the diversity of soil bacteria that stimulate nodulation and those that ultimately colonize nodule tissues, questioning whether these functional groups overlap. Furthermore, we discuss the molecular dialogs and counter-signaling mechanisms that regulate endophyte ingress into nodules, and evaluate how nodule endophytes contribute to plant performance and soil fertility. Full article

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time. Full article

As sustainability marketing research advances, conceptual debates persist regarding relevant stakeholders, motivations, and the integration of sustainability into marketing. However, these debates are not always consistently reflected in empirical work. Given the urgency of sustainability, a unifying framework is crucial to synthesize four decades of research. We aim to provide a durable framework to ensure that critical research progresses efficiently by supporting meaningful knowledge generation in sustainability marketing research. We conduct a scoping review of 48 conceptual articles, resulting in the A-VO-S Map. Its three dimensions are: Actors (Consumers, Businesses, Institutions), Value Orientations (Self-, Societally-, and Environmentally-Oriented), and Scope of Sustainability (Peripheral to Central). We then present an evidence map of empirical sustainability marketing research based on a content analysis of 191 empirical articles from 19 top journals. Findings reveal that Consumers are overrepresented, Societal-Orientation lacks supporting evidence, and sustainability is studied with a Moderate Scope. We conclude the paper with a practitioner-informed research agenda and an interactive version of our evidence map. Thus, we offer unique contributions over prior reviews through a meta-framework that functions as an evidence map, with high valorization potential due to its interactive tool and practitioner-informed perspectives. Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

In response to the deterioration of path-tracking accuracy and driving stability encountered by intelligent vehicles under dynamically varying operating conditions, a multi-objective optimization strategy integrating soft actor-critic (SAC) reinforcement learning with variable-parameter Model Predictive Control (MPC) is proposed in this paper to achieve online adaptive adjustment of path-tracking controller parameters. Based on a three-degree-of-freedom vehicle dynamics model, a linear time-varying (LTV) MPC controller is constructed to jointly optimize the front wheel steering angle. An SAC agent is developed utilizing the actor-critic framework, with a comprehensive reward function designed around tracking accuracy and control smoothness to enable online tuning of the MPC weighting matrices (lateral error weight, heading error weight, and steering control weight) as well as the prediction horizon parameter, thereby realizing adaptive balance between tracking accuracy and stability under different operating conditions. Based on the simulation results, it can be concluded that under normal operating conditions, the proposed integrated SAC-MPC control scheme demonstrates superior tracking performance, with the maximum absolute lateral error and mean lateral error reduced by 44.9% and 67.2%, respectively, and the maximum absolute heading error reduced by 23.5%. When the system operates under nonlinear conditions during the transitional phase, the proposed control scheme not only enhances tracking accuracy—evidenced by reductions of 43.4% and 23.8% in the maximum absolute lateral error and maximum absolute heading error, respectively—but also significantly improves system stability, as indicated by a 20.7% reduction in the sideslip angle at the center of gravity. Experimental validation further confirms these findings. The experimental results reveal that, compared with the fixed-parameter MPC, the maximum absolute value and mean value of the lateral error are reduced by approximately 36.2% and 78.1%, respectively; the maximum absolute heading angle error is decreased by 24.3%; the maximum absolute yaw rate is diminished by 19.6%; and the maximum absolute sideslip angle at the center of gravity is reduced by 30.8%. Full article

Cattle behavior is closely related to their health status, and monitoring cattle behavior using intelligent devices can assist herders in achieving precise and scientific livestock management. Current behavior recognition algorithms are typically executed on server platforms, resulting in increased power consumption due to data transmission from edge devices and hindering real-time computation. An edge-based cattle behavior recognition method via Graph Neural Network (GNN) compression is proposed in this paper. Firstly, this paper proposes a wearable device that integrates data acquisition and model inference. This device achieves low-power edge inference function through a high-performance embedded microcontroller. Secondly, a sequential residual model tailored for single-frame data based on Inertial Measurement Unit (IMU) and displacement information is proposed. The model incrementally extracts deep features through two Residual Blocks (Resblocks), enabling effective cattle behavior classification. Finally, a compression method based on GNNs is introduced to adapt edge devices’ limited storage and computational resources. The method adopts GNNs as the backbone of the Actor–Critic model to autonomously search for an optimal pruning strategy under Floating-Point Operations (FLOPs) constraints. The experimental results demonstrate the effectiveness of the proposed method in cattle behavior classification. Moreover, enabling real-time inference on edge devices significantly reduces computational latency and power consumption, thereby highlighting the proposed method’s advantages for low-power, long-term operation. Full article

The farmland protection compensation system plays a pivotal role in addressing the dual global crises of food insecurity and ecological degradation, as well as in overcoming persistent challenges in China’s agricultural governance. By internalizing the opportunity costs borne by stakeholders fulfilling statutory obligations for farmland protection, this mechanism offers effective incentives for their active engagement, thereby establishing a societal-level interest-balancing framework conducive to sustainable land management. Existing research in China has mainly concentrated on empirical analyses of implementation models, regional disparities, and policy effectiveness evaluations of farmland protection compensation schemes. Nevertheless, systematic exploration of the normative construction and improvement pathways of the compensation rules themselves remains relatively underdeveloped. Based on the practical requirements and institutional constraints of China’s current farmland protection compensation regime, this study adopts an integrated approach that combines comparative legal analysis, textual review of regulatory documents, and empirical research to critically examine feasible paths for institutional improvement. The research findings emphasize that the optimization of China’s farmland protection compensation rules should be guided by three core principles: market orientation, ecological sustainability, and precision-based targeting. Specifically, the establishment of scientifically sound methods for calculating compensation amounts is crucial for reconciling the interests of conservation actors with inter-regional development disparities. Meanwhile, the compensation mechanism should be strategically utilized to strengthen positive incentives for ecosystem conservation. Ultimately, such institutional improvement aims to ensure the sustainable utilization of farmland resources while safeguarding global food security and maintaining the Earth’s ecological balance. Full article

From adaptation to building effective resilience to climate change is critical for transforming West and Central Africa (WCA) agricultural system. Climate-Smart Agriculture (CSA) is an approach initiated by leading international organizations to ensure food security, increased adaptation to climate change and mitigation. Its application spans from innovative policies, practices, technologies, innovations and financing. However, CSA initiatives lack scientific-based assessment prior to implementation to ensure their effectiveness. To fill this gap, future interventions should not only be assessed using rigorous methodology but should also be built on lessons learned from previous initiatives. Although there are a lot of climate related agricultural initiatives in WCA, most of them have not been analyzed through a CSA lens and criteria to capitalize on their experiences to improve future interventions. In this study we mapped previous climate-related initiatives in WCA, highlighted their gaps and lessons learned to accelerate the implementation of CSA in the region. The study covered 20 countries in WCA: Benin, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Chad, Côte d’Ivoire, Congo, Gabon, Gambia, Ghana, Guinea, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo. CSA initiatives were reviewed using a three-steps methodology: (i) national data collection, (ii) regional validation of the national database, (iii) data analysis including spatial mapping. Data was collected from the websites of international, regional and national organizations working in the field of agricultural development in the region. Each initiative was analyzed using a multicriteria analysis based on CSA principles. A total of 1629 CSA related initiatives were identified in WCA. Over 75% of them were in the form of projects/programs with more of a focus on the first CSA pillar (productivity and food security), followed by adaptation. The mitigation pillar is less covered by the initiatives. Animal production, fisheries, access to markets, and energy are poorly included. More than half of these initiatives have already been completed, calling for more new initiatives in the region. Women benefit very little from the implementation of the identified CSA initiatives, despite the substantial role they play in agriculture. CSA initiatives mainly received funding from technical and financial partners and development partners (45%), banks (22%), and international climate financing mechanisms (20%). Most of them were implemented by government institutions (48%) and development partners (23%). In total, more than 600 billion EUR have been disbursed to implement 83 of the 1629 initiatives identified. These initiatives contributed to reclaiming and/or rehabilitating almost 2 million ha of agricultural land in all countries between 2015 and 2025. Future initiatives should ensure the consideration of the three CSA pillars right from their formulation to the implementation. These initiatives should consider investing in mixed production systems like crop-animal-fisheries. Activities should be built around CSA innovation platforms to encourage networking among actors for more sustainability. Full article

Ground autonomous mobile robots are increasingly critical for reconnaissance, patrol, and resupply tasks in public safety and national defense scenarios, where global path planning in 3D uneven terrains remains a major challenge. Traditional planners struggle with high dimensionality, while Deep Reinforcement Learning (DRL) is hindered by two key issues: (1) systematic overestimation of action values (Q-values) due to function approximation error, which leads to suboptimal policies and training instability; and (2) inefficient exploration under sparse reward signals. To address these limitations, we propose DSAC-ICM: a Distributional Soft Actor–Critic framework integrated with an Intrinsic Curiosity Module (ICM). Our method fundamentally shifts the learning paradigm from estimating scalar Q-values to learning the full probability distribution of state-action returns, which inherently mitigates value overestimation. We further integrate the ICM to generate dense intrinsic rewards, guiding the agent toward novel and unvisited states to tackle the exploration challenge. Comprehensive experiments conducted in a suite of realistic 3D uneven-terrain environments demonstrate that DSAC-ICM successfully enables the agent to learn effective navigation capabilities. Crucially, it achieves a superior trade-off between path quality and computational cost when compared to traditional path planning algorithms. Furthermore, DSAC-ICM significantly outperforms other RL baselines in terms of convergence speed and return. Full article

Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak institutional reporting, and the dominance of informal sector activity. We present the first nationwide assessment of e-waste generation and Random Forest-based national forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires across all 16 districts, targeting households, institutions, enterprises, and informal actors. The study documented devices in use, storage, and disposal across the following six categories: ICT, appliances, lighting, batteries, medical, and other electronics. Population growth and device adoption simulations were combined with lifespan distributions and a Random Forest model trained on survey and simulated historical data to construct e-waste flows and forecast quantities through to 2050, including disposal fate probabilities for repurposing versus discarding. The results showed sharp spatial disparities, with Western Urban (Freetown) averaging about 10 kg per capita compared to 1.8 kg per capita in rural areas. Long-term district patterns were highly concentrated: 50-year annual averages indicated that Western Area Urban contributes 15.3% of national totals, followed by Bo (12.7%) and Western Area Rural (12.1%), with the top five districts contributing 59.1%. By 2050, total national e-waste entering reuse and disposal pathways was projected to reach 23.4 kilo tons per year (kt yr⁻¹) with a 95% uncertainty interval (UI) of 11–42 kt yr⁻¹ (and a 99% interval extending to 50 kt yr⁻¹), corresponding to 0.9–3.4 kg/capita/year. Household appliances dominated total mass, ICT devices exhibited high reuse rates, and batteries showed minimal reuse despite high hazard potential. These findings provide critical evidence for e-waste policy, regulation, and infrastructure planning in data-scarce regions. Full article