Search Results (404)

The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article

Material Extrusion (MEX) process is increasingly used to fabricate components for structural applications, driven by the availability of advanced materials and greater industrial adoption. In these contexts, understanding the mechanical performance of printed parts is crucial. However, conventional methods for assessing anisotropic elastic behavior often rely on expensive equipment and time-consuming procedures. The aim of this study is to evaluate the applicability of the impulse excitation of vibration (IEV) in characterizing the dynamic mechanical properties of a 3D-printed composite material. Tensile tests were also performed to compare quasi-static properties with the dynamic ones obtained through IEV. The tested material, Nylon 12CF, contains 35% short carbon fibers by weight and is commercially available from Stratasys. It is used in the fused deposition modeling (FDM) process, a Material Extrusion technology, and exhibits anisotropic mechanical properties. This is further reinforced by the filament deposition process, which affects the mechanical response of printed parts. Young’s modulus obtained in the direction perpendicular to the deposition plane (E₃₃), obtained via IEV, was 14.77% higher than the value in the technical datasheet. Comparing methods, the Young’s modulus obtained in the deposition plane, in an inclined direction of 45 degrees in relation to the deposition direction (E₄₅), showed a 22.95% difference between IEV and tensile tests, while Poisson’s ratio in the deposition plane (v₁₂) differed by 6.78%. This data is critical for designing parts subject to demanding service conditions, and the results obtained (orthotropic elastic properties) can be used in finite element simulation software. Ultimately, this work reinforces the potential of the IEV method as an accessible and consistent alternative for characterizing the anisotropic properties of components produced through additive manufacturing (AM). 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

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. 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

This study presents an exhaustive analysis of LGBTQIA+ audiovisual production available on the main streaming platforms in Spain, covering both Spanish and international content. Using a sample of 1490 works from ten video-on-demand services (Apple TV+, Disney+, Filmin, FlixOlé, Max, Movistar Plus+, Netflix, Prime Video, Rakuten, and SkyShowtime), this study examines how the offered catalogues are configured and structured in response to the commercial dynamics of the LGBTQIA+ production market. Using quantitative methodology, the research addresses the industrial production models, the agents involved and the characteristics of the most widely offered narrative genres and formats, highlighting distribution patterns and visibility in the catalogues. The findings include a marked international abundance, a reflection of the global market guidelines and the hegemony of narratives aimed at transnational audiences. National productions, although less numerous, are a significant contribution to the audiovisual landscape, incorporating cultural identities with an LGBTQIA+ representation that is more aligned with local realities. The central role of independent producers is observed in production models where international agreements are outlined as a key strategy. In addition, it highlights the prevalence of genres such as drama and comedy, together with that of the film format. The visibility and representation of sexual and gender diversity indicates a positive commercial response, although with considerable challenges. 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

Although, the use of sensors is increasing in a wide range of fields with great success (e.g., food, environment, pharma, etc.), their uptake is slow and lower than other innovations. While the uptake is low, some users, producers, and service industries are continuing to benefit from the incorporation of technology in their business. Among these technologies, vibrational spectroscopy has demonstrated its benefits and versatility in a wide range of applications. Both conventional near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) systems are two of the main techniques utilized in a wide range of applications in different fields. These techniques use the NIR region of the electromagnetic spectrum (750–2500 nm). Specifically, NIR-HSI systems provide spatial information and spectral data, while conventional NIR spectroscopy provides spectral information from a single point. Even though there is a clear distinction between both techniques in terms of their benefits, confusion still exists among users about their similarities and differences. This paper provides a critical discussion of the main advantages and limitations of both techniques, focusing on food science applications. Full article

This study aims to implement the concept of education for sustainable development by 2030, which can be applied in the context of hospitality education in the Asia–Pacific region. Specifically, this study focuses on achieving Sustainable Development Goal 12, which pertains to responsible consumption and production, particularly in relation to food. A case study was conducted using a learner-centered approach, wherein students, as active agents, can solve problems using professional skills such as cooking, baking, and beverage preparation. Through participant observations, students learn about sustainability, starting from natural farming and extending to banquet planning and entrepreneurship simulation in a green restaurant. The program was designed as a farm-to-table process for sustainability learning. A conceptual framework of a hospitality–health supply chain was constructed to understand how the program supports the goal of education for sustainable development for 2030—societal transformation. The study has several important implications. Students are trained to be responsible producers in a green dining setting, starting from practical classroom experiences in the kitchen of a green restaurant, which will enhance their becoming the critical human resources in the hospitality industry. This program offers a successful vocational education opportunity, teaching students how to responsibly run an enterprise with low-carbon products and services. 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

Background: Large language models (LLMs) have the potential to enhance information processing and clinical reasoning in the healthcare industry but are hindered by inaccuracies and hallucinations. The retrieval-augmented generation (RAG) technique may address these problems by integrating external knowledge sources. Methods: We developed a RAG-based chatbot called Thyro-GenAI by integrating a database of textbooks and guidelines with LLM. Thyro-GenAI and three service LLMs: OpenAI’s ChatGPT-4o, Perplexity AI’s ChatGPT-4o, and Anthropic’s Claude 3.5 Sonnet, were asked personalized clinical questions about thyroid disease. Three thyroid specialists assessed the quality of the generated responses and references without being blinded, which allowed them to interact with different chatbot interfaces. Results: Thyro-GenAI achieved the highest inverse-weighted mean rank for overall response quality. The overall inverse-weighted mean rankings for Thyro-GenAI, ChatGPT, Perplexity, and Claude were 3.0, 2.3, 2.8, and 1.9, respectively. Thyro-GenAI also achieved the second-highest inverse-weighted mean rank for overall reference quality. The overall inverse-weighted mean rankings for Thyro-GenAI, ChatGPT, Perplexity, and Claude were 3.1, 2.3, 3.2, and 1.8, respectively. Conclusions: Thyro-GenAI produced patient-specific clinical reasoning output based on a vector database, with fewer hallucinations and more reliability, compared to service LLMs. This emphasis on evidence-based responses ensures its safety and validity, addressing a critical limitation of existing LLMs. By integrating RAG with LLMs, it has the potential to support frontline clinical decision-making, especially helping first-line physicians by offering reliable decision support while managing thyroid disease patients. Full article

This study examined the trends and key themes of artificial intelligence in the field of tourism and hospitality research. On 5 March 2025, a search was performed using “artificial intelligence” and related terms in the “Title, Abstract, and Keywords”, focusing on tourism and hospitality journals indexed in Scopus. The identified documents were subjected to performance analysis and science mapping techniques. The search yielded 921 documents, comprising 882 articles and 39 reviews. The number of documents increased from 3 in 1987 to 277 in 2024. R. Law from the University of Macau was the most prolific author, while the Hong Kong Polytechnic University recorded the highest publication count. Chinese researchers produced the most documents, totaling 262 articles and reviews. A keyword co-occurrence analysis revealed four key themes: “machine learning and sentiment analysis of online reviews”, “adoption of AI including robots and ChatGPT in the hospitality industry”, “artificial neural networks for tourism management and demand analysis”, and “random forest models in travel”. Additionally, the study noted a shift in research focus from tourism demand forecasting and sentiment analysis to using service bots and applying artificial intelligence to enhance service quality, with a recent emphasis on generative AI tools like ChatGPT. Full article