Search Results (3,069)

We analyze the variation in groundwater budget by modeling an aquifer in a semi-arid region in southern Italy, using different good pumping scenarios. This aquifer is overexploited due to the agricultural vocation of the area. We propose an integrated method to assess the distribution of hydrogeological parameters and the recharge rates. The hydrogeological parametrization is performed through a hydrostratigraphic approach using the geostatistical tool. Recharge rates are computed through a soil water balance application, using different monitoring stations over the area for the whole period of interest. Integrating the results of this analysis with pumping scenarios based on the water irrigation requirement of the main crops in the area, different water budgets are estimated. The results show how different pumping scenarios affect the availability of water resources and thus underline the importance of management. This integrated hydrogeological model can be applied to other areas with similar hydrogeological characteristics, and it can be considered a valuable tool for evaluating sustainable groundwater management strategies, considering land use practices and socio-economic factors. Full article

Waste and its environmental impact have driven the search for sustainable solutions across various industries, including construction. This study explores the incorporation of solid waste in the production of eco-friendly structural concrete, aiming to reduce pollution and promote ecological and sustainable construction practices. In this context, two types of eco-friendly concrete were produced using marine shells and recycled rubber as waste materials and compared with conventional concrete through experimental and computational approaches. The results demonstrated that the concrete with marine shells achieved a compressive strength of 32.4 MPa, 26.5% higher than conventional concrete, and a 1% reduction in weight. In contrast, the recycled rubber concrete exhibited a compressive strength of 22.5 MPa, with a 2 MPa decrease compared to conventional concrete, but a 4.3% reduction in density. Computational analysis revealed that porosity affects Young’s modulus, directly resulting in a reduction in the maximum achievable strength. This work demonstrates that it is feasible to produce eco-friendly structural concrete through the proper integration of industrial waste, contributing to decarbonization and waste valorization. Full article

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies. Full article

One of the most widely researched fields within the automotive industry is the effect vehicles place on the environment. To achieve a sustainable transport system, reducing the pollution of vehicles is an essential issue. The aim of this paper is to examine how weather conditions influence a vehicle’s operation. The study examines potential methods to evaluate the effect of different weather conditions on the aerodynamic parameters of a vehicle. Aerodynamic properties can be measured with the help of computational fluid dynamics (CFD), a wind tunnel and test-track measurements. On-board diagnostics are also examined to collect data on aerodynamics. These methods can monitor several parameters to measure and visualize the effects of weather conditions. The theoretical background to the related aerodynamic parameters is summarized. Full article

This study investigates the impact of tightening torque (preload) and the friction coefficient on stress generation and fatigue resistance of a Ti-6Al-4V abutment screw with an internal hexagonal connection under dynamic multi-axial masticatory loads in high-cycle fatigue (HCF) conditions. A three-dimensional model of the implant–abutment assembly was simulated using ANSYS Workbench 16.2 computer aided engineering software with chewing forces ranging from 300 N to 1000 N, evaluated over 1.35 × 10⁷ cycles, simulating 15 years of service. Results indicate that the healthy range of normal to maximal mastication forces (300–550 N) preserved the screw’s structural integrity, while higher loads (≥800 N) exceeded the Ti-6Al-4V alloy’s yield strength, indicating a risk of plastic deformation under extreme conditions. Stress peaked near the end of the occluding phase (206.5 ms), marking a critical temporal point for fatigue accumulation. Optimizing the friction coefficient (0.5 µ) and preload management improved stress distribution, minimized fatigue damage, and ensured joint stability. Masticatory forces up to 550 N were well within the abutment screw’s capacity to sustain extended service life and maintain its elastic behavior. Full article

This study seeks to explore the relationship between science teachers’ computational thinking skills, collaborative learning attitudes, and their creativity in the context of sustainable education. The study adopted an explanatory sequential design, which is one of the designs used in mixed-method research. A total of 369 science teachers were included in the quantitative phase of the study. Quantitative data were collected using three different scales. These scales included the “Computational Thinking Scale”, “Online Cooperative Learning Attitude Scale (OCLAS)”, and “Creative Self-Efficacy Scale”. Structural Equation Modelling (SEM), confirmatory factor analysis, and path analysis were conducted to analyze the quantitative data. The qualitative phase of the study consisted of nine science teachers. Data were collected with a semi-structured interview form by considering the scores obtained from the scales. Qualitative data were analyzed through descriptive analysis. It was found that science teachers’ computational thinking skills and collaborative learning attitudes significantly predicted their creativity within the framework of sustainable education. As a result of the interviews conducted, it was concluded that science teachers lacked computational thinking skills. It is critical to provide teachers with guidance on how to integrate computational thinking skills into their subject areas. Science teachers’ knowledge of computational thinking skills can be enhanced, and computational thinking skills can be included in all teacher education programs. Full article

Ultraviolet shortwave (UV-C) is a technology for postharvest fruit disinfection. This study aimed to use computational fluid dynamics (CFD) based on the discrete ordinate (DO) radiation model to analyze the distribution of UV-C intensity on whole and minimally processed mangoes within a disinfection chamber and to predict treatments against foodborne pathogens. The mango spears were oriented both parallel and perpendicular to the lamp and positioned at varying distances from the radiation source (50, 75, and 100 mm for spears and 100 mm for whole fruit). CFD simulations integrated with in vitro kinetic parameters enabled predictions of the time and doses needed to inactivate one to three logarithmic units of pathogens on the fruit surface. The highest average radiation intensity values were recorded for the whole mango oriented parallel to the lamp at 100 mm and the spears oriented normally to the lamp at 50 mm. The estimated times to achieve inactivation of one to three logarithmic units of microorganisms ranged from approximately 15 to 6540 s, while the doses necessary for this inactivation were, on average, 1.854, 5.291, and 10.656 kJ/m², respectively. CFD simulations are valuable for optimizing UV-C treatments in large-scale designing from both microbicide and sustainable perspectives. Full article

Sugarcane bagasse is rich in cellulose and lignin, and the recycling of bagasse has become an important research field with the increasing global concern for sustainable development and environmental protection. In this paper, TG-MS-FTIR equipment was used to analyze the pyrolysis characteristics of bagasse from Guangxi under different heating rates and different atmospheres, which is conducive to the reuse of bagasse from the waste gas produced in the sugar plant. The results showed that the pyrolysis rate of sugarcane bagasse in the air atmosphere was faster than that in the nitrogen atmosphere and showed a double-peak trend, and the Coats–Redfern computational model could more accurately simulate the process of pyrolysis. The lower heating rate could overcome the heat transfer hysteresis phenomenon in the process of pyrolysis. In the air atmosphere, the contact time between oxygen and volatile products was shorter due to the high heating rate, and more and more complex species were precipitated at 10 °C/min than at 20 °C/min. In the nitrogen atmosphere, it was favorable to produce more kinds and quantities of gas products, because it did not react with oxygen. FTIR detected CH₄, CO, H₂O, CO₂, C-O-C, and C=O during pyrolysis in nitrogen, and some of C-O-C and C=O were cracked into small molecule compounds at high temperature. Full article

The closed architecture and stand-alone operation model of traditional industrial control systems limit their ability to leverage ubiquitous infrastructure resources for more flexible and intelligent development. This restriction hinders their ability to rapidly, economically, and sustainably respond to mass customization demands. Existing proposals for open and networked architectures have failed to break the vicious cycle of closed architectures and stand-alone operation models because they do not address the core issue: the tight coupling among the control, infrastructure, and actuator domains. This paper proposes a reconfigurable architecture that decouples these domains, structuring the control system across three planes: control, infrastructure, and actuator. The computer numerical control (CNC) system serves as a primary example to illustrate this reconfigurable architecture. After reviewing open and networked architectures and discussing the characteristics of this reconfigurable architecture, this paper identifies three key challenges: deterministic control functionality, the decoupling of control modules from infrastructures, and the management of control modules, infrastructures, and actuators. Each challenge is examined in detail, and potential solutions are proposed based on emerging technologies. Full article

Energy-efficient human–robot collaboration poses significant challenges to the sustainable operation of production systems. Therefore, our work proposes novel robotic edge intelligence to address the issue. First, robotic edge intelligence is proposed to fully utilize the embedded computing capabilities of edge robots, and the state transition diagrams are developed for jobs, humans, and robots, respectively. Second, a multi-objective model is designed for the energy-efficient human–robot scheduling problem to evaluate the production performance and energy efficiency as a whole. Third, a heuristic algorithm is developed to search for the optimal solutions based on an artificial plant community, which is lightweight enough to be run on edge robots. Finally, a benchmark data set is developed, and a series of benchmark experiments are implemented to test the proposed system. The results demonstrate that the proposed method can effectively enhance energy efficiency and production performance with satisfying solution performance. Full article

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study. Full article

Timber–concrete composite (TCC) systems join the positive aspects of engineered wood products (good seismftaic behaviour, low thermal conductivity, environmental sustainability, good behaviour under fire if appropriately designed) with those of concrete (high thermal inertia, durability, excellent fire resistance). TCC facades are typically composed of an internal insulated timber-frame wall and an external concrete slab, separated by a ventilated air cavity. However, there is very limited knowledge concerning the performance of TCC facades, especially concerning their thermal behaviour. The present paper deals with the development and optimization of a 2D Computational Fluid Dynamic (CFD) model for the analysis of TCC ventilated façades’ thermal behaviour. The model is calibrated and validated against experimental data collected during the annual monitoring of a real TCC ventilated envelope in the north of Italy. Also, a new solver algorithm is developed to significantly speed up the simulation (i.e., 45 times faster simulation at an error below 3.5 °C compared to a typical CFD solver). The final model can be used for the time-efficient analysis (simulation time of approximately 23 min for a full day in real-time) and the optimization of the thermal performance of TCC ventilated facades, as well as other ventilated facades with external massive cladding. Our simulation strategy partially avoids the expensive and time-consuming construction of mock-ups, or the use of comparably slow (conventional) CFD solvers that are less suitable for optimization studies. Full article

The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions. Full article

Reputation is shaped depending on factors such as the quality of products and services offered by a brand to its stakeholders, its reliability, and its innovative aspect in the eyes of stakeholders. The sustainability of a brand reputation depends on the brand creating a positive perception by fulfilling its social responsibilities and maintaining this perception in the long term. In this study, the brand reputation of Hewlett-Packard (HP) computers is evaluated through customer reviews. The data set in the study consists of 2012 customer reviews obtained from Hepsiburada, one of the most widely used e-commerce platforms in Turkey. Sentiment analysis and text mining artificial intelligence methods were used in the study. For sentiment analysis, the Naive Bayes method, which is one of the machine learning methods, was used, and the comments were divided into three groups as positive, negative, and neutral. In the study, 82% of the customer comments were positive, 11% were negative, and 7% were neutral. The fact that most of the comments consist of positive sentiments shows that HP Computer has a positive reputation in the eyes of stakeholders consisting of customers. Comments consisting of negative and neutral emotions show the aspects that the brand needs to improve. In the study, the text mining method emphasises the prominent features of the brand in the comments. This study makes an important contribution to the reputation assessment of brands and to ensuring sustainable brand reputation. Full article

Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes. Full article