Search Results (420)

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Reliable air pollution monitoring remains a major challenge in Sub-Saharan Africa (SSA), limiting the assessment of population exposure and the development of effective mitigation strategies. Recent advances in low-cost (LC) sensors offer promising opportunities, but their deployment in low-infrastructure settings still faces significant technical and logistical challenges. This study presents the experience gained from deploying LC sensor networks in Burkina Faso, Niger, and the Republic of Guinea, focusing on the practical challenges of installing and maintaining these systems under demanding conditions. In Burkina Faso, an LC station was co-located with a reference-grade instrument, enabling field calibration. In Niger, factory-calibrated LC sensors were deployed across urban, semi-urban, and rural settings, while in Guinea they were installed in a remote area. Several practical issues and challenges emerged, including unstable power supplies, limited internet connectivity, safety, and logistical constraints. Careful planning and involvement of local expertise proved essential for the long-term sustainability of LC sensors. Knowledge transfer to local partners supported ongoing maintenance and strengthened data ownership. Overall, this study demonstrated that the reliability of LC air quality networks in SSA depends not only on technology, but also on adaptive strategies, robust calibration, and strong local engagement, offering practical guidance for future scalable and sustainable implementations in resource-limited settings. Full article

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options. Full article

Wildlife is a common asset to which the local community has the right to consume. To achieve sustainable management of this resource, a community forest (CF) with a wildlife vocation has been set up as part of the “Sustainable management of wildlife and the bushmeat sector in Central Africa” project. Given the constraints faced by these community forests (CFs), we conducted a study to assess their governance in Gabon. Our objective was to examine whether their current mode of operation would allow them to survive in the long term, with a view to integrating sustainable hunting practices. To do this, we constructed a SWOT matrix (strengths, weaknesses, opportunities and threats) to determine their strengths and weaknesses, from which we carried out a factorial correspondence analysis (FCA) to identify potentially viable CFs. This enabled us to understand that most of the difficulties encountered by these CFs stem from the low level of appropriation of this concept by local communities, which is due to the low level of intervention by the forestry administration in raising awareness of CF management. This study shows that local communities must first take ownership of how CFs work so that they can better apply their success factors. Full article

Sustainable agricultural development in rural sub-Saharan Africa increasingly depends on coordinated governance of energy and water resources. Despite the growing deployment of solar photovoltaic water pumping systems (SPVWPS), little is known about how the institutional framework shapes SPVWPS effectiveness for productive agricultural use in rural Tanzania. Drawing on systems thinking concepts, specifically hierarchy, interaction, and interconnectedness, this study analyses the institutional frameworks governing energy and water provision for irrigation and livestock keeping across three rural Tanzanian communities. A mixed-methods design was employed, with qualitative inquiry as the primary mode; 65 household surveys, nine semi-structured interviews with community leaders, SPV developers, and local officials, and seven focus group discussions with farmers and livestock keepers were conducted across the three study areas. National energy and water policy documents, reports, and strategic plans were also reviewed to contextualise the institutional frameworks governing energy and water delivery in rural areas. Findings reveal limited coordination among stakeholders, particularly between NGOs, government agencies (REA, RUWASA, and NIRC), and local communities in the planning and implementation of SPVWP projects. Top-down delivery mechanisms marginalised community feedback, undermining local ownership and limiting the productive use potential of installed systems. This study proposes an integrated institutional framework that combines systems thinking with bottom-up and top-down approaches, explicitly embedding structured feedback mechanisms and aligning stakeholder roles across all governance levels. The framework was validated through interviews with experts in the rural energy and governance field, confirming its practical relevance and applicability to rural energy–water governance. The framework offers actionable guidance for policymakers and development practitioners seeking to strengthen institutional coordination in rural energy–water–agriculture governance, contributing to progress towards SDG 7 and SDG 2 across sub-Saharan Africa. Full article

Smartphones are now deeply embedded in daily life, but excessive dependence may increase the risk of nomophobia, which is associated with anxiety, sleep disruption, and reduced productivity. Existing screening methods mainly rely on self-reported questionnaires, which are subjective and difficult to scale for continuous monitoring. This study proposes a privacy-preserving federated deep learning framework for three-level nomophobia risk prediction (Normal, Mild, and Severe) using smartphone usage logs while keeping raw user data on local devices. The proposed pipeline uses a publicly available secondary dataset with 1000 original records and expands it to 100,000 records through constraint-aware synthetic augmentation. A continuous risk score is computed from standardized smartphone usage indicators and then converted into three classes using tertile-based thresholds. Several local architectures, including CNN, MLP, ResMLP, Wide & Deep, and a lightweight TabNet-style gated model, are evaluated under FedAvg. In the reported experiments, differential privacy is enabled through DP-SGD with gradient clipping and Gaussian noise. To protect update transmission, the framework applies protected update sharing through encrypted transport of masked updates. Each client masks its local update and encrypts the masked payload before transmission. This mechanism improves communication confidentiality and reduces the direct exposure of client updates. Under a fixed federated setup with five clients and 25 communication rounds, tabular models achieved near-ceiling performance on the constructed test set. The MLP achieved 99.12% accuracy, 99.12% F1-score, 0.9868 MCC, and 0.9997 AUC, while Wide & Deep achieved 98.95% accuracy, 98.95% F1-score, 0.9843 MCC, and 0.9997 AUC. In contrast, sequential models such as RNN and LSTM showed near-random performance, suggesting that the current aggregated feature representation is better suited to tabular learning than temporal modeling. These results indicate that the proposed federated pipeline can effectively learn the constructed nomophobia risk labels while preserving local data ownership. However, because the labels are derived from usage features rather than clinical or psychometric assessment, the findings should be interpreted as proof-of-concept results for constructed risk labels rather than evidence of clinical diagnostic validity. Full article

This paper explores the importance of local food in tourism development in the Republic of Srpska by analyzing the perceptions of hospitality employees in relation to the characteristics of the food service establishments in which they work. The aim of this study is to determine how local food influences tourism development and whether such effects are conditioned by specific factors. Although previous studies have extensively examined local food through the lens of consumer behavior, there remains a significant research gap regarding the internal perspective of hospitality employees as co-creators of the gastronomic experience. This study addresses that gap by applying Social Exchange Theory (SET) to explain how employees’ perceptions of economic, social, and environmental benefits shape their willingness to support the integration of local food. By placing employees at the center of the analysis, the paper provides insight into the mechanisms through which authentic ingredients are transformed into symbolic capital and strengthen destination identity. In this context, the analytical Local Food model was adapted and applied to a sample of 480 respondents, evenly distributed across the mesoregions of the Republic of Srpska. Using exploratory factor analysis (EFA), three key dimensions of influence were identified—economic, environmental, and social. In addition, independent-samples t-tests and one-way analysis of variance (ANOVA) confirmed that employees’ perceptions vary significantly depending on the production capacity of the establishments, whereas the type and location of the establishments were not identified as significant determinants of these differences. The findings further indicate that the intensity of these factors varies according to location, production capacity, and ownership type, while other characteristics of the hospitality establishments in which the respondents were employed were not found to be significant. A strong interrelationship among the identified factors was confirmed, with the social factor emerging as the most dominant. Overall, the findings highlight the importance of local food in strengthening the tourism attractiveness and sustainability of the hospitality sector in the Republic of Srpska. Full article

Village chickens are essential for rural livelihoods and food security in Sub-Saharan Africa; however, their productivity is constrained by seasonal feed scarcity and suboptimal feeding management. This study evaluated household-level feeding practices and their seasonal variation across three wards in Central Tanzania (Sanza, Majiri, and Iwondo). Data were collected from 852 randomly selected households using structured questionnaires covering flock ownership, feeding frequency, feed types, seasonal feed availability, and gender roles. Feeding practices exhibited marked seasonal variation, with supplementary feeding peaking during the rainy and post-harvest periods because of the increased availability of crop residues and natural feed resources. In contrast, supplementary feeding declined during the dry season, accompanied by increased reliance on scavenging. Feed types varied according to local cropping systems, with millet and sorghum predominating in different wards. Women were primarily responsible for daily poultry management activities, including feeding, but had limited involvement in decision-making related to resource allocation. Flock sizes were small and relatively uniform across the study areas. These findings highlight the importance of seasonally adaptive feeding strategies, improved feed resource management, and gender-responsive extension services for enhancing the productivity and sustainability of village chicken production systems. Full article

This study addresses second-home ownership not merely as a form of tourism accommodation or real estate investment, but as a home-building process intersecting with local life, belonging, daily practices, and sustainable destination governance. While the economic, environmental, and community impacts of second-homes have been extensively discussed in the literature, how individuals perceive their primary and secondary homes differently in terms of the bodily, material, vibrant, imaginary, and emotional dimensions of home has been examined in a limited number of studies. This research analyzes paired data obtained through a two-stage online questionnaire from 223 British participants who own a secondary home in the Mugla–Ortaca region and a primary home in the United Kingdom. The 18-item Home Scale was used as the measurement tool. Confirmatory factor analysis, reliability–validity analyses, measurement invariance, and paired-samples t-tests were applied. The findings show that the bodily home difference was not statistically significant at the conventional 0.05 threshold, whereas primary-home scores were significantly higher in the material, vibrant, imaginary, and emotional home dimensions. The small to small-medium effect sizes suggest that the results should be interpreted cautiously as an asymmetrical home-building process rather than as evidence of a hierarchical superiority of the primary home. The study proposes a planning approach that does not view second home owners as merely transient consumers in sustainable coastal–rural destinations, but rather considers social sustainability, service planning, seasonality management, and local community engagement channels together. Full article

Traditional wooden granaries in rural Türkiye are disappearing at an accelerating rate due to agricultural abandonment, rural depopulation, and the absence of systematic documentation and conservation frameworks. In the Sındırgı district of Balıkesir, one of the richest concentrations of vernacular granary architecture in the Marmara Region, these structures remain largely unprotected and unstudied within a sustainable design framework, constituting an urgent conservation challenge. This study aims to assess the current preservation status of Sındırgı granaries, classify their typological diversity, and evaluate their sustainability performance against a defined set of ecological design criteria. A mixed methods approach was employed, combining a systematic literature review with extensive fieldwork across 33 neighborhoods. In total, 1411 granaries were identified and grouped into five typologies: evli, Simav, kabak, sandık, and üstü örtülü sandık. These typologies were systematically compared to five parameters: spatial distribution across neighborhoods, plan and section geometry, construction system and structural elements, material selection and condition, and preservation status. This comparison revealed that typological variation is not incidental but directly reflects differences in land ownership, agricultural production capacity, topography, and distance from the district center. Representative examples from each typology were documented through onsite measurements, photogrammetry, technical drawings, and interviews with local craftsmen. The sustainability performance of the granaries was then assessed across seven ecological design criteria: spatial organization, building form design, structural element design, material use and conservation, design with nature, urban design area planning, and nature interaction. The findings demonstrate that the long-term durability of these structures depends on an interrelated system of climate-responsive design decisions rather than any single factor. The study concludes by proposing a holistic conservation model comprising typology-based inventory, roof water moisture-focused intervention, periodic monitoring, and transmission of vernacular building knowledge, a framework applicable to comparable rural granary heritage across the region. Full article

Predictive maintenance enables early detection of machine failures and reduces unexpected production downtime. However, conventional approaches typically rely on centralized data collection and model training which introduce challenges related to data sovereignty, communication overhead and data ownership. To address these challenges, this research proposes a collaborative federated learning framework for predictive maintenance that can be deployed in distributed smart manufacturing systems. The proposed data-sovereign federated learning approach allows multiple factories to collaboratively train a machine failure prediction model while maintaining data locality. In the framework, each factory trains a local multilayer perceptron (MLP) model using its own machine operational data, while a central server aggregates local model parameters using the Federated Averaging (FedAvg) algorithm to construct a global predictive model. The proposed framework was evaluated using the publicly available AI4I 2020 predictive maintenance dataset, where multiple factories are simulated by partitioning the dataset into distributed clients. Experimental results show that the federated learning model achieves competitive performance compared to centralized machine learning baselines, attaining an accuracy of 97.17%, precision of 0.6000, recall of 0.5000, and F1-score of 0.5455. These results demonstrate that federated learning can enable effective predictive maintenance while maintaining data sovereignty in distributed manufacturing environments. Full article

Differential Liberation Expansion (DLE) and viscosity tests are core elements of the Pressure–Volume–Temperature (PVT) laboratory suite used to characterize reservoir oils under depletion and to support compositional modeling and reservoir simulation. Nevertheless, both DLE and viscosity testing remain expensive and time-consuming due to specialized equipment, strict operating procedures, and the need for experienced laboratory personnel. Building on our prior work that introduced the proximity-informed Local Interpolation Model (LIM) framework for Constant Composition Expansion (CCE), this study demonstrates how the same end-to-end, neighborhood-based workflow is applied to DLE and viscosity test data. A target fluid is embedded in a compositional–thermodynamic descriptor space and paired with a small set of thermodynamically similar fluids drawn from a PVT data archive. Within this locality, LIM is used to infer DLE behavior by combining local interpolation for key scalar quantities (e.g., saturation-point and endpoint PVT values) with shape-preserving reconstruction of pressure-dependent curves. For viscosity, the same approach reconstructs the oil viscosity curve ${\mu }_o\left(p\right)$ across the undersaturated and saturated regions. Evaluation on a proprietary database of DLE and viscosity tests shows strong agreement across diverse fluids for both DLE and oil viscosity trends. For example, across Tier 1–3 fluids, the mean curve mean absolute percentage error (MAPE) is 1.01% for $B_o$, 0.51% for ${\rho }_o$, and 1.32% for the liberated-gas $Z$-factor, while the conditioned baseline viscosity workflow yields a mean diphasic-branch MAPE of 7.75%. This supports reducing reliance on new DLE and viscosity measurements while maintaining engineering-grade fidelity in reservoir engineering and simulation workflows. This approach has been fully automated through software so it can be set up and directly utilized by the field operators on their own databases to significantly reduce their fluid sampling and laboratory analysis costs. Moreover, the proposed (artificial intelligence) AI model does not use others’ data, respecting data privacy and data ownership. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

Electric vehicle (EV) adoption in South Africa remains constrained by high upfront purchase costs, limited charging infrastructure, and policy uncertainty, creating a need for lower-cost and locally relevant pathways to transport decarbonisation. This study evaluates the feasibility of converting a legacy light commercial pickup platform from internal combustion engine (ICE) propulsion to battery-electric propulsion through integrated component sizing, longitudinal vehicle simulation, and techno-economic assessment. A retrofit architecture comprising a traction battery, inverter-controller, electric motor, and DC-DC converter was developed using first-principles vehicle dynamics and energy-demand analysis. The resulting configuration employed a 40 kW AC induction motor, an approximately 28 kWh battery pack, a 40–60 kW inverter with 60 kW peak capability, and a 0.75–1.2 kW auxiliary DC-DC converter. Simulation over a representative 1000 s drive cycle showed stable speed tracking, sustained vehicle motion over approximately 10 km, and peak battery currents exceeding 300 A during acceleration, while regenerative braking reduced net cumulative energy consumption relative to gross demand. The economic analysis indicated that the retrofit pathway yielded the lowest cumulative total cost of ownership over most of a 10-year horizon, with breakeven relative to the used ICE baseline occurring at approximately 3.4 years. Lifecycle analysis further showed that the retrofit configuration achieved the lowest combined production and operational carbon burden among the compared vehicle pathways. These findings indicate that ICE-to-EV retrofitting of legacy light commercial vehicles can provide a technically feasible, economically competitive, and environmentally advantageous electrification strategy for South Africa and comparable emerging markets. Full article

In the context of China’s rapid urbanization and rural land marketization reforms, the entry of rural collectively owned commercial construction land into the market (ERCCCLM) coexists with the traditional government-led land expropriation, forming a dual land supply system. China’s dual-structure land ownership system—where urban land belongs to the state and rural land to rural collectives—aims to balance land market allocation efficiency with government regulation for public interests. However, significant differences exist between the two patterns in terms of revenue distribution, risk-bearing, and institutional constraints. Consequently, stakeholders including rural collective economic organizations, farmers, local governments, and development companies face dilemmas in selecting land supply patterns, thereby limiting land resource allocation efficiency. The research employs multidimensional economic analysis to systematically compare the ERCCCLM and land expropriation patterns, establishing a land supply pattern selection mechanism with land market price and compensation for expropriation as key variables. First, the expenditure and revenue of stakeholders in both patterns were clarified based on relevant documents, and investment revenue models were constructed. Second, through comparative analysis of revenue formation mechanisms across land supply patterns and sensitivity analysis of multi-scenario calculations, the land market price and compensation for expropriation are identified as key variables determining economic revenue. The findings indicate that when the land market price exceeds compensation for expropriation, ERCCCLM generates higher economic revenue for the rural collective economic organization and farmer. Conversely, when the land market price is equal to or lower than the compensation for expropriation, land expropriation provides more stable revenue. The land expropriation and ERCCCLM examined in this research represent a unique land expropriation and utilization system exclusive to China. The proposed selection mechanism improves land market distribution efficiency and informs policy discussions on optimizing land supply patterns, ensuring a balance between market efficiency and stakeholder equity. Full article