Search Results (8,926)

Solar panel deployment is vital to generate clean energy and reduce carbon emissions, but sustaining energy output requires regular monitoring and maintenance. This is particularly critical in countries with harsh environmental conditions, such as Qatar, where high dust density reduces solar radiation reaching panels, thereby lowering generating efficiency and increasing maintenance costs. This paper introduces a data-driven model that uses the relationship between generated and consumed energy to track changes in solar panel performance. By applying statistical analysis to real and simulated data, the model identifies when efficiency losses are within the parameters of normal variation (e.g., daily fluctuations) and when they are likely caused by dust accumulation or system ageing. The findings demonstrate that the model provides a reliable and cost-effective way to support timely cleaning and maintenance decisions. It offers decision-makers a practical tool to improve residential solar panel management, reducing unnecessary costs, and ensuring more consistent renewable energy generation. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

Miniaturised sampling and extraction are redefining therapeutic drug monitoring (TDM) by enabling low-volume sampling, simplifying collection, and improving patient acceptability, while also promoting decentralised workflows and more sustainable laboratory practices. This review critically appraises the current landscape, with emphasis on analytical performance, matrix compatibility, and readiness for clinical implementation. It examines validation requirements, the extent of alignment and existing gaps across major regulatory guidelines, and recurrent challenges such as haematocrit bias, real-world stability and transport, incurred sample reanalysis, device variability, commutability with conventional matrices, and inter-laboratory reproducibility. To make the evidence actionable, operational recommendations are distilled into a practical ten-point checklist designed to support validation and translation of miniaturised approaches into routine laboratory practice. Looking ahead, priorities include automation and portable platforms, advanced functional materials, and integration with digital tools and biosensors, alongside the development of harmonised frameworks tailored to miniaturised methods and prospective clinical studies that demonstrate impact on dosing decisions, adherence, and clinical outcomes. Overall, this review aims to equip researchers, laboratory professionals, and regulators with the knowledge to implement miniaturised bioanalysis and advance personalised medicine through TDM. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

Sustainable community development depends not only on economic and environmental factors but also on effective local leadership. This study examines the key factors shaping leadership competencies among Ecuadorian local leaders, focusing on the influence of socioeconomic conditions, individual attributes, and access to professional development opportunities. A cross-sectional survey of 60 leaders from diverse regions was analyzed using Principal Component Analysis (PCA) and biplot visualizations to uncover latent competency structures relevant to sustainable governance. The results highlight sharp disparities between urban and rural contexts: urban leaders exhibited stronger competencies, largely supported by institutional resources and training access, while rural leaders relied more on informal governance and community legitimacy. Strategic vision, decision-making, and resilience emerged as pivotal competencies for effective local leadership. Strengthening these competencies is a prerequisite for achieving socially and institutionally sustainable governance, directly supporting the implementation of the Sustainable Development Goals (SDGs), particularly Goals 11, 16, and 17. Full article

As manufacturing industries pursue net-zero emission (NZE) goals, hybrid manufacturing processes that integrate additive manufacturing (AM) with traditional casting techniques are gaining traction for their sustainability potential across the globe. Therefore, this work presents a “gate-to-gate” life cycle assessment (LCA) comparing AM-assisted sand casting (AM-SC) and AM-assisted investment casting (AM-IC), for Al-Si5-Cu3 alloy as a case material, under various energy scenarios including a conventional grid mix and renewable sources (wind, solar, hydro, and biomass). This study compares multiple environmental impact categories based on the CML 2001 methodology. The outcomes show that AM-SC consistently outperforms AM-IC in most impact categories. Under the grid mix scenario, AM-SC achieves 31.57% lower GWP, 19.28% lower AP, and 21.15% lower EP compared to AM-IC. AM-SC exhibits a 90.5% reduction in “Terrestrial Ecotoxicity Potential” and 75.73% in “Marine Ecotoxicity Potential”. Wind energy delivers the most significant emission reduction across both processes, reducing GWP by up to 98.3%, while AM-IC performs slightly better in HTP. These outcomes of the study offer site-specific empirical insights that support strategic decision-making for process selection and energy optimisation in casting. By quantifying environmental trade-offs aligned with India’s current energy mix and future renewable targets, the study provides a practical benchmark for tracking incremental gains toward the NZE goal. This work followed international standards (ISO 14040 and 14044), and the data were validated with both foundry records and field measurements; this study ensures reliable methods. The findings provide practical applications for making sustainable choices in the manufacturing process and show that the AM-assisted conventional manufacturing process is a promising route toward net-zero goals. Full article

Background and Objectives: Parental beliefs strongly influence treatment adherence in pediatric allergic diseases. Concerns about corticosteroid therapy—known as corticophobia—may disrupt disease control and compromise child well-being. This study aimed to evaluate parental knowledge, beliefs, and concerns regarding topical, inhaled, and intranasal corticosteroid use in children, and to identify sociodemographic factors associated with corticophobia. Materials and Methods: This prospective survey was conducted in a tertiary pediatric allergy and immunology clinic. A structured questionnaire was anonymously completed by 110 parents of children receiving corticosteroid therapy. The survey assessed demographics, family history of atopy, corticosteroid use, perceived disease severity, knowledge level, concerns, and sources of information. Descriptive statistics and chi-square tests were applied (p < 0.05 significant). Results: The most frequent concerns were growth retardation, hormonal imbalance, and long-term side effects. Corticophobia was significantly more prevalent among university-educated parents (p = 0.043) and those with a family history of atopy (p = 0.017). Despite generally high adherence to prescribed regimens, nearly 60% of parents sought additional information, highlighting the impact of knowledge gaps on health-related parenting practices. Conclusions: Corticophobia remains a common parental concern in pediatric allergy care, with implications for adherence, family decision-making, and child well-being. Addressing misinformation and providing family-centered, tailored educational strategies—particularly for highly educated parents and those with an atopic background—may reduce fears, strengthen trust, and promote sustainable healthy behaviors. Full article

This paper presents a comprehensive design framework for Enterprise Architecture aimed at facilitating decision-driven analytics in smart factories. The motivation behind this research lies in challenges faced by manufacturing companies, such as skilled labor shortages and increasing global competition, alongside the imperative for sustainable production. This journal provides a novel approach for designing and documenting prescriptive analytics use cases in manufacturing environments. The framework addresses the need for effective integration of advanced data analytics and prescriptive analytics solutions within existing production environments, thereby enhancing operational efficiency and decision-making processes. A Design Science Research approach is used to iteratively derive a framework based on stakeholder needs and activities along the prescriptive analytics use case development cycle. The resulting framework is demonstrated and evaluated in an IoT Factory setup in a research facility. From a practical perspective, the framework supports manufacturing companies in systematically designing prescriptive analytics use cases. From a research perspective, it contributes to the body of knowledge on Enterprise Architecture Management (EAM) by operationalizing the design of prescriptive analytics use cases in manufacturing contexts. The main contributions of this study include the development of a framework that supports the planning, design, and integration of prescriptive analytics use cases. This framework fosters interdisciplinary collaboration and aids in managing the complexity of data-driven projects. Full article

Today, the right to easy access to medical care for all citizens is one of the universal rights promoted by the WHO to achieve the health goals of sustainable development. Furthermore, an intelligent reevaluation of hospital management is becoming an absolute necessity in light of the pressure that healthcare and public health establishments worldwide face. A management based on the capitalization and exploitation of vast quantities of knowledge. In the 21st century, medicine has already moved to the “in silico” phase, where healthcare professionals must use knowledge bases to make clinical decisions. Indeed, knowledge gains greater value when it’s actively engaged with through dynamic knowledge bases. There is a plethora of research on hospital management, but few studies have approached it from the angle of informational intelligence governed by knowledge management. In this article, we adopt a positivist posture, using deductive logic and the Delphi method based on expert opinion and consensus. We aim to approach hospital management from an informational intelligence perspective, inspired by knowledge representation systems and the object approach. We present an initial vision of the intelligent hospital management model, showing its strengths in relation to its predecessors, as well as its potential uses. Full article

Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi’an, simulation results show that the model reduces gas supply costs by 3.45% for residential users and 6.82% for non-residential users, increases user welfare by 4.64% and 88.87%, and decreases carbon emissions by 115.18 kg and 2156.8 kg, respectively. Moreover, non-residential users achieve an additional reduction in carbon trading costs of 183.85 CNY. The findings demonstrate the effectiveness of integrating dynamic price signals, user behavior modeling, and carbon constraints into a unified optimization framework, offering decision support for sustainable and flexible natural gas scheduling. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

Context: For decades, maize monoculture practices dominated Northeast China, causing significant damage to the local soil and ecological environment. Crop rotation has, in recent years, been promoted as an environmentally friendly and sustainable technology in China. Despite its numerous benefits for the environment and crop productivity, farmers’ willingness to adopt crop rotation remains low. Objective: This study aims to investigate the social–psychological factors influencing farmers’ intentions to adopt maize–soybean rotation, with the goal of informing strategies for promoting sustainable agricultural practices. Methods: Based on a farm-level survey of 298 rural households in Northeast China, this study integrates value orientation into the Theory of Planned Behavior and employs structural equation modeling to investigate the social–psychological factors that affect farmers’ willingness to adopt soybean-based rotation. Results and Conclusions: The findings confirm the applicability of the extended Theory of Planned Behavior in explaining farmers’ decision-making. Farmers’ attitudes (0.384) and perceived behavioral control (0.323) had significant positive effects on adoption intentions, whereas subjective norms (0.018) were not significant. More favorable attitudes and greater perceived behavioral control, reflecting higher risk tolerance and better access to external support, promoted adoption. Value orientations strongly shaped farmers’ attitudes: altruism (0.148) and biospheric values (0.180) had positive effects, while egoism (0.044) showed no significant impact. These results offer guidance for policymakers to design targeted interventions promoting sustainable crop rotation. Significance: These results can help policymakers better understand what factors influence farmers’ adoption of rotation and what targeted measures can be taken to popularize the improved agricultural system. To foster farmers’ adoption of rotation, it is important to go beyond traditional supporting policies and to leverage innovative approaches to promote value orientation on sustainable farming practices. Full article

Bacterial cellulose (BC) is a type of biomass composed entirely of cellulose. This characteristic favors the presence of a multitude of active sites, which facilitate the exchange of heavy metals present in polluting effluents. Upon contact with water contaminated with metals such as chromium, arsenic, and lead, among others, this biomass offers a potential solution to the environmental problem of industrial pollutants in water. This is particularly pertinent given the well-documented harmful effects of heavy metals in aquatic ecosystems. In this context, the objective is to determine the impact of temperature on Cr (IV) adsorption using bacterial cellulose biomass as an adsorbent, under different temperature scenarios, similar to the conditions of discharge of contaminated effluents into rivers, lagoons, and wetlands. In this study, the biomass was previously characterized through FTIR and SEM images, and isothermal models were subsequently evaluated along with batch adsorption kinetics. The findings demonstrate that bacterial cellulose biomass has great potential for Cr (VI) removal at various temperatures, with an adsorption capacity of 140 mg/g at high temperatures and a reduction of up to 125 mg/g at low temperatures. The findings of this study constitute a valuable contribution to decision-making when considering the expansion of these treatment processes, facilitating this task by offering a comparative analysis of effluent discharge conditions in relation to various scenarios involving contaminated liquid temperatures. The use of this biomaterial in an environmental sustainability initiative focused on water resource conservation is a very promising prospect. Full article

Using used serviceable material (USM), recycled and upcycled, for aircraft is environmentally and financially beneficial in helping the aviation industry achieve sustainability. This study aims to identify determinants of aircraft USM value and assess their significance using the Fuzzy Analytic Hierarchy Process (FAHP) to gain insights for making the USM market more active. Sixteen factors in four categories are selected based on literature and focus group interviews. A survey to analyze factor priority is conducted with 118 industry experts. The results show that maintenance requirements, airworthiness directive status, and maintenance status from the technical category are the most critical determinants of aircraft USM value, followed by traceability, former operator, and former aviation authority from the operational category and new part value. The technical category corresponds to “must-be” traits in the Kano model, requiring compliance by sellers, whereas new part value information can help buyers’ decisions. The implementation of an internationally agreed mutual accreditation system for approved maintenance organizations and a standard for aircraft dismantling is proposed to improve technical and operational determinants to achieve fewer uncertainties in USM valuation. This study aims to offer a new guideline for evaluating USM value to market participants. Price modeling of USM is left for future studies. Full article

Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO₃⁻), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices ($WQIs$), while useful, suffer from subjectivity in assigning weights, which can lead to misinterpretations. This study addresses these limitations by developing a novel, objective Groundwater Quality Index ($GWQI$) through the seamless integration of Machine Learning (ML) models. Utilizing a database of 775 wells from the Mexican National Water Commission (CONAGUA), Principal Component Analysis (PCA) was applied to achieve significant dimensionality reduction. We successfully reduced the required monitoring parameters from 13 to only three key indicators: total dissolved solids (TDSs), chromium (Cr), and manganese (Mn). This reduction allows for an 87% decrease in the number of indicators, maximizing efficiency and generating potential savings in monitoring resources without compromising water quality prediction accuracy. Six $WQI$ methods and six ML models were evaluated for quality prediction. The Unified Water Quality Index (${WQI}_u$) demonstrated the best performance among the $WQIs$ evaluated and exhibited the highest correlation (R² = 0.85) with the traditional $WQI$ based on WHO criteria. Furthermore, the ML Support Vector Machine with polynomial kernel (svmPoly) model achieved the maximum predictive accuracy for ${WQI}_u$ (R² = 0.822). This robust $GWQI$-ML approach establishes an accurate, objective, and efficient tool for large-scale groundwater quality monitoring across Mexico, facilitating informed decision-making for sustainable water management and enhanced public health protection. Full article