Search Results (277)

The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that are not only tailored to specific administrative domains but also robust to the linguistic, contextual, and structural variability inherent in user-submitted content. This study investigates the comparative effectiveness of large language models (LLMs) alongside instruction-tuned embedding models in the task of categorizing public transportation complaints. LLMs were tested using a few-shot inference, where classification is guided by a small set of in-context examples. Embedding models were assessed under three paradigms: label-only zero-shot classification, instruction-based classification, and supervised fine-tuning. Results indicate that fine-tuned embeddings can achieve or exceed the accuracy of LLMs, reaching up to 90 percent, while offering significant reductions in inference latency and computational overhead. E5 embeddings showed consistent generalization across unseen categories and input shifts, whereas BGE-M3 demonstrated measurable gains when adapted to task-specific distributions. Instruction-based classification produced lower accuracy for both models, highlighting the limitations of prompt conditioning in isolation. These findings position multilingual embedding models as a viable alternative to LLMs for classification at scale in data-intensive public sector environments. Full article

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. Full article

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)—a massive waste generated by copper mining activities worldwide—employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO₄. CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7–388 days) and following immersion in a 5 wt.% MgSO₄ solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by ≤10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO₄ exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28–100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180–360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C–S–H into non-cementitious M–S–H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials. Full article

The new generation of robots (Industry 5.0 and beyond) is expected to be accompanied by the massive introduction of autonomous and cooperative agents in our society, both in the industrial and service sectors. Cooperation with humans will be simplified by humanoid robots with a similar kinematic outline and a similar kinematic redundancy, which is required by the diversity of tasks that will be performed. A bio-inspired approach is proposed for coordinating the redundant DOFs of such agents. This approach is based on the ideomotor theory of action, combined with the passive motion paradigm, to implicitly address the degrees of freedom problem, without any kinematic inversion, while producing coordinated motor patterns structured according to the typical features of biological motion. At the same time, since the approach is force-field-based, it allows us to integrate the computational loop parallel modules that exploit the redundancy of the system for satisfying geometric or kinematic constraints implemented by appropriate repulsive force fields. Moreover, the model is expanded to include dynamic constraints associated with the Lagrangian dynamics of the humanoid robot to improve the energetic efficiency of the generated actions. Full article

This study examines the awareness and implementation of Circular Economy (CE) principles within Chile’s mining sector, which represents the world’s leading copper producer. We employed a mixed-methods approach, combining quantitative surveys with qualitative semi-structured interviews, to evaluate perceptions and implementation levels of CE practices across diverse organizational contexts. Our findings reveal a pronounced knowledge gap: while 73.3% of mining professionals reported familiarity with CE concepts, only 57.3% could provide accurate definitions. State-owned mining companies demonstrated substantially higher CE implementation rates, with 36.5% participating in eco-industrial collaborations and 51% conducting environmental audits, compared to their private counterparts. Small enterprises (1–100 employees) exhibited particularly limited engagement, as demonstrated by 71.8% lacking established sustainability reporting mechanisms. A considerable implementation gap was also identified; although 94.8% of respondents considered CE principles integral to business ethics and 89.6% recognized CE as essential for securing a social license to operate, only 20.8% reported that their organizations maintained dedicated CE units. The research presents actionable recommendations for policymakers, including targeted financial incentives and training programs for small- and medium-sized enterprises (SMEs) in mining services, the establishment of standardized CE performance metrics for the sector, and the integration of CE principles into strategic management education to accelerate sustainable transformation within Chile’s critical mining industry. Full article

Thermally sprayed carbide cermet coatings, particularly those based on tungsten carbide (WC) and chromium carbide (Cr₃C₂) and produced with the high velocity oxygen fuel (HVOF) process, are used in tribological applications as environmentally friendly alternatives to electroplated hard chrome coatings. These functional coatings are especially prevalent in the automotive industry, offering excellent wear resistance. However, their mechanical and tribological performances are highly dependent on factors such as feedstock powders, spray parameters, and service conditions. This review aims to gain deeper insights into the above elements. It also outlines emerging advancements in HVOF technology—including in situ powder mixing, laser treatment, artificial intelligence integration, and the use of novel materials such as rare earth elements or transition metals—which can further enhance coating performance and broaden their applications to sectors such as the aerospace and hydro-machinery industries. Finally, this literature review focuses on process optimization and sustainability, including environmental and health impacts, critical material use, and operational limitations. It uses a life cycle assessment (LCA) as a tool for evaluating ecological performance and addresses current challenges such as exposure risks, process control constraints, and the push toward safer, more sustainable alternatives to traditional WC and Cr₃C₂ cermet coatings. Full article

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. This paper introduces a novel cooperative AI model that forecasts the risk of future falls in the elderly based on behavioral and health abnormalities. Two AI models’ predictions are combined to produce accurate predictions: The ${AI}_1$ model is based on vital signs using Fuzzy Logic, and the ${AI}_2$ model is based on Activities of Daily Living (ADLs) using a Deep Belief Network (DBN). A meta-model then combines the outputs to generate a total fall risk prediction. The results show 85.71% sensitivity, 100% specificity, and 90.00% prediction accuracy when compared to the Morse Falls Scale (MFS). This emphasizes how deep learning-based cooperative systems can improve well-being for older adults living alone, facilitate more precise fall risk assessment, and improve preventive care. Full article

Minced lamb remains one of the most produced meat products in the meat industry, across both the food service and retail sectors. Tea polyphenols (TPs), renowned for their diverse biological activities, are increasingly being employed as natural food additives in research and development. Tea polyphenols at concentrations of 0.00% (CG), 0.01% (TP1), 0.10% (TP2), and 0.30% (TP3) were added to lamb which had undergone a series of freeze–thaw cycles. The presence of tea polyphenols led to a significant decrease in the number of disulfide bonds, resulting in a slower oxidation rate. In addition, the surface hydrophobicity and juice loss of the minced lamb supplemented with tea polyphenols were 91.23 ± 0.22 and 20.00 ± 0.46, respectively, representing a reduction of 1.5% and 7.59% compared to the group without the addition of tea polyphenols. However, the addition of high-dose tea polyphenols also led to a reduction in emulsification stability, alterations in protein conformation, and changes in water migration. Furthermore, the incorporation of a minimal quantity of tea polyphenols (0.01%) resulted in enhanced emulsification stability, water retention, textural properties, and microstructures in minced lamb. This suggests that tea polyphenols have the potential to improve the quality of minced lamb following freezing and thawing processes. Full article

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. The work includes three datasets: namely, insurance churn, internet service provider customer churn, and Telecom churn datasets. The implementation and comparison conducted in this study of models include XGBoost, Convolutional Neural Networks (CNNs), and Ensemble Deep Learning with the pre-trained hybrid approach. The results show that the ensemble deep learning model outperforms other models in terms of accuracy and F1-score, achieving accuracies of up to 95.96% in the insurance churn dataset and of 98.42% in the telecom churn dataset. Moreover, traditional machine learning models like XGBoost also produced competitive results for selected datasets. The proposed deep learning ensembles reveal the strength and possibility for churn prediction and provide a benchmark for future research relevant to customer retention strategies. Also, the proposed ensemble deep learning model shows stable performance across different sectors, which reflects its ability to capture the varied churn patterns of different sectors. Full article

The energy transition of the road transport sector is now a strategic priority for achieving global decarbonization targets. In particular, the highway sector offers the opportunity to integrate sustainable solutions without additional land consumption, thanks to the availability of relevant areas that are already covered by infrastructure. This study proposes a large-scale analysis of the potential photoelectric energy that can be produced within highway infrastructures, with the aim of evaluating the contribution that these assets can make to electric mobility. The analysis was conducted using geographic information systems (GISs), applied to the case study of the A3 Napoli–Pompei–Salerno highway. The processing of topographical, orographic, and solar data has made it possible to identify a total surface area of approximately 27,100 m² that is potentially suitable for the installation of photovoltaic systems, distributed among service areas, toll stations, car parks, and side sections. This result highlights the concrete possibility of making the most of the energy potential of highway infrastructure, promoting self-production and local consumption models to power the electric vehicle charging network, thus contributing directly to the reduction of emissions and the sustainability of the transport system. Full article

Amid global economic transformation, a persistent productivity gap exists between developed and developing nations in agriculture sector, shaped by technological advancements and shifting resource allocation patterns. Agricultural socialized services (ASS), defined as organized systems providing technical support, mechanization assistance, information services, market linkages, and resource optimization to farmers, have emerged as critical mechanisms for agricultural development. In developing economies, these services catalyze gains in agricultural labor productivity through the integration of advanced technologies and the mechanization of farming practices. Using panel data from 30 Chinese provinces during 2011 to 2022, this study investigates the relationship between ASS and ALP, focusing on regional heterogeneity, threshold effects, and spatial spillovers. The combination of spatial econometric methods and threshold analysis was selected for its unique capacity to capture both the geographic interdependencies and nonlinear relationships that characterize agricultural development processes. These thresholds at 5.254 and 8.478 represent critical points where the impact of ASS on ALP significantly changes in magnitude, revealing a nonlinear relationship that evolves across different stages of agricultural development. The study highlights notable regional disparities in the impact of ASS. Specifically, ASS is more effective on ALP in eastern, central and key food-producing regions, while its impact is relatively weak in western and non-food-producing regions. Spatial spillover analysis indicates that advancements in ASS create positive externalities, extending beyond their immediate implementation zones and facilitating inter-provincial agricultural cooperation and development. These findings provide crucial guidance for policymakers and agricultural service providers to optimize resource allocation and service delivery strategies. By identifying critical development thresholds and regional variations, this research offers evidence-based support for government officials designing targeted agricultural policies and enterprises developing region-specific service models to foster sustainable agricultural growth across diverse regional landscapes. Full article

This study analyzes the influence of natural resource rents, governance indicators, and their interactions on economic growth in twelve oil-producing countries in the MENA region from 2002 to 2021. Various versions of a panel ARDL model are estimated using PMG, MG, and DFE estimators. The results suggest that natural resource rents in MENA oil-producing countries positively affect long-term economic growth when accompanied by good governance. Government effectiveness and control of corruption also contribute positively to economic growth in the long run. Furthermore, financial development is found to enhance long-term economic growth. These findings highlight the potential of natural resources to drive economic growth when supported by strong institutions. To maximize natural resource rent benefits, MENA countries should improve governance indicators such as government effectiveness, control of corruption, and rule of law. This includes enhancing civil service competence, decision implementation, and managing political pressure. Key factors include revenue mobilization, infrastructure quality, policy consistency, and penalties for corruption. Ensuring equality under the law, transparent legal processes, an independent judiciary, and access to legal remedies are crucial for effective rule of law. Additionally, MENA countries should prioritize developing non-oil sectors like tourism, industry, technology, entertainment, transportation, and communication. Full article

The concern for sustainability, understood as the responsible use of natural resources to ensure the well-being of future generations, has grown across various sectors. One of the main drivers of environmental degradation is the use of fossil-fuel-based transportation, which produces pollutant emissions and contributes to climate change. In this context, electric cars have emerged as a smart and sustainable alternative, as they do not emit polluting gases and, when powered by renewable energy sources such as photovoltaics, can significantly reduce the carbon footprint. Based on this concept, it is noted that electric cars are an intelligent alternative to reduce the degradation caused by fossil fuels. The generation of electricity from renewable sources, such as photovoltaics, biogas and others, combined with the low maintenance costs and long service life of these technologies, represents an extremely sustainable solution. In this work, methodologies are applied for sizing and analysis of the cost of electricity generation through photovoltaic energy. The operational cost of the electric car being fueled by electricity provided by this source and by energy from the utility grid compared to the operational cost of an internal combustion engine car is also considered. The (CO₂)eq emitted by the use of the photovoltaic plant, energy from the grid and the gasoline used in the internal combustion engine car is also determined. It is concluded that the return on investment for the energy generated by photovoltaic energy is approximately 5 years. The annual cost for an electric car is 76.49% lower when using electricity provided by energy concessionaires in Brazil and 81.35% lower for energy from photovoltaic plants compared to an internal combustion engine vehicle, also considering that the harm to the environment is low for this technology. These data emphasize the importance of looking for technological and sustainable solutions that adapt energy production systems, reduce costs and, above all, help to mitigate the impact on the environment, reflecting a commitment to the future of our planet and the quality of life of future generations. Full article

Measuring the contributions of the bioeconomy depends on the concept and method used. The concept of bioeconomy linked to biomass is widely used, and estimating the bio-based share in sectors, activities, or products is a limitation and a challenge. Therefore, the present study aims to propose a method for measuring the contributions of biomass-based bioeconomy (BmBB) by tracking the direct and indirect flows of biomass embodied in goods and services for intermediate and final demand. Our analysis focused on measuring the impact of BmBB on the gross value of production (GVP) and the value added to biomass through incremental improvements to the input–output models. The development and application of the method used data from Brazil’s input–output matrices from 2010 to 2018. The results suggest that the BmBB’s GVP shared 5.75% of the GDP, on average, between 2010 and 2018 and more than 6% in recent years. The BmBB accounted for 4.87% of the Brazilian economy’s added value. The ‘Biomass’ aggregate, comprising agriculture, livestock, and forestry, contributes 78.3% of GVP and 8.0% of BmBB’s added value. The opposite occurs with the ‘BioAgroindustry’ aggregate, whose GVP was only 12.3% but contributed 81.5% of the BmBB’s value added. The significant volume of direct sales of ‘’Biomass’ in the final demand of households and the foreign market may explain this situation. We concluded that the proposed method contributes to measuring the BmBB, capturing the biomass share involved in producing, manufacturing, and consuming goods and services. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article