Search Results (4,531)

The article presents a method that supports the evaluation of design manufacturability in the area of input material selection, enabling the reduction in material diversity under single-piece and small-batch production conditions. The proposed approach combines the analysis of alternative materials with activity-based costing (ABC) and data concerning actual and planned material requirements. The method enables the assessment of the impact of semi-finished product substitution on material costs, processing costs, production organisation, and material-management costs before order execution is launched. In the conducted case study, it was demonstrated that effective management of material diversity can significantly reduce the range of materials and decrease total manufacturing costs. For the analysed period, the number of material items was reduced from 32 to 19 (a 41% reduction), resulting in cost savings of approximately 11,000 PLN. In addition to total cost, the approach supports the assessment of operational benefits associated with reduced material diversity, such as a lower number of material items, fewer suppliers, reduced inbound inspection and receipt operations, and decreased inventory levels and capital tied up in stock. Material substitution may decrease or increase direct material costs and may increase machining time when larger dimensions are used; therefore, the method jointly evaluates cost and lead-time impacts prior to order release. The results confirm that integrating design, technological, and logistics data is an effective approach to rationalising material management in machinery manufacturing enterprises. Full article

Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of risks allow resources to be allocated where they have the greatest effect, thereby optimizing overall project outcomes. However, conventional methods such as expert judgment and probabilistic modeling often struggle to process extensive datasets and complex interdependencies among risk factors. This study explores the potential of an AI-based framework for risk identification, utilizing artificial intelligence to analyze project documentation and generate a preliminary set of identified risks. The proposed methodology is implemented on the ‘Trg pravde’ judicial infrastructure project in Zagreb, Croatia, applying AI models (GPT-5, Gemini 2.5, Sonnet 4.5) to identify phase-specific risks throughout the project lifecycle. The approach aims to improve the efficiency of risk identification, reduce human bias, and align with established project management methodologies such as PM². Initial findings suggest that the use of AI may broaden the range of identified risks and support more structured risk analysis, indicating its potential value as a complementary tool in risk management processes. However, human expertise remains crucial for prioritization, contextual interpretation, and mitigation. The study demonstrates that AI augments, rather than replaces, traditional risk management practices, enabling more proactive and data-driven decision-making in construction projects. Full article

The purpose of this paper is to examine the application and development of CAD/CAM technologies in the modern metal cutting industry, with a focus on their role in increasing production accuracy, efficiency, and sustainability. The study presents an industrial case of laser cutting of AISI 304 stainless-steel sheets, in which two approaches are compared under identical material and technological parameters: conventional manual nesting and automatic nesting based on algorithms implemented in a CAD/CAM environment. The methodology evaluates both layouts using clear technical and economic indicators, including number of parts per sheet, material utilization, cutting time, weight of scrap, and cost per sheet. For the analyzed batch, automatic nesting increases the number of parts per sheet from 44 to 76 (≈73%), reduces the unused sheet area from 61% to 39%, and shortens the cutting time from 12 to 9 min (≈25%), which leads to a reduction in material waste by about 36% and cost savings of approximately 314 EUR per sheet. As a result, the process becomes more efficient and reliable, supporting sustainable and digital manufacturing goals. The findings confirm the importance of algorithmic optimization in CAD/CAM systems for enhancing industrial competitiveness, enabling effective resource management, and facilitating the transition towards Industry 5.0. Full article

Achieving the ambitious EU goals of zero-waste manufacturing requires innovative tools and methodologies that address both efficiency and environmental sustainability. This study presents a comprehensive methodology for assessing the efficiency and eco-efficiency of industrial processes, in order to support zero-waste manufacturing strategies. The proposed approach assesses critical performance metrics while integrating environmental-impact analysis to provide a holistic view of process optimization. The methodology was applied to two industrial use cases in the composites sector, a field with significant environmental impact due to the resource-intensive nature of composite manufacturing and challenges associated with the end-of-life management. By implementing this dual assessment, the study identifies key areas for improvement in operational performance and sustainability, offering actionable insights for process optimization and waste reduction. The results reveal that labor costs emerged as the primary contributor to the total costs for both use cases, more than 50%. On the other hand, the resin infusion phase accounts for the majority of the environmental impacts, accounting for more than 70% of the total impacts. This analysis highlights that eco-efficiency assessments, integrating environmental and cost data, allow the identification of inefficiencies, helping industries to prioritize improvement areas. In this specific case, the high environmental impact of resin infusion needs enhanced waste monitoring and process optimization, while the labor-intensive operations need streamlined workflows to reduce operational time and associated costs. The present methodology intends to serve as a practical tool for industries aiming to balance high-performance manufacturing with reduced environmental impact. Full article

This article examines the failure of the two most recent temporary housing programs implemented in Spain following two major disasters: the 2011 Lorca earthquake and the 2021 La Palma volcanic eruption. Despite differing hazard typologies, both cases resulted in incomplete and ultimately unsuccessful housing programs, with only 13 of the 60 planned units built in Lorca and 121 of the 200 planned units delivered in La Palma. Using a qualitative comparative case study approach, the research analyzes governance decisions, housing design, and implementation processes to assess their impact on the sustainability of post-disaster temporary housing. The analysis adopts the five dimensions of sustainability—environmental, economic, social, cultural, and institutional—as an integrated analytical framework for evaluating public management performance in post-disaster temporary housing. The findings show that early decision-making, shaped by political urgency, technical misjudgments, and the absence of adaptive governance, led to severe delays, cost overruns, inadequate and energy-inefficient construction, and the formation of marginalized settlements. This study concludes that the lack of regulatory frameworks, legal instruments, and operational protocols for temporary housing in Spain was a determining factor in both failures, generating vulnerability, prolonging recovery processes, and undermining sustainability across all five dimensions. By drawing lessons from these cases, this article contributes to debates on resilient and sustainable post-disaster recovery and highlights the urgent need for integrated regulatory frameworks for temporary housing in Spain. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

This study examines the environmental implications of energy-intensive cryptocurrency mining activities within the broader sustainability debate surrounding blockchain technologies. Focusing specifically on Bitcoin’s proof-of-work–based mining process, the analysis investigates the long-run relationship between greenhouse gas emissions, network-specific technical variables, and climate policy uncertainty using advanced cointegration and asymmetric causality techniques. The findings reveal a stable long-run association between mining-related activity and emissions, alongside pronounced asymmetries whereby positive shocks amplify environmental pressures more strongly than negative shocks mitigate them. Importantly, these results pertain to the mining process itself rather than to blockchain technology as a whole. While blockchain infrastructures may support sustainable applications in areas such as green finance, transparency, and energy management, the evidence presented here highlights that energy-intensive mining remains a significant environmental concern. Accordingly, the study underscores the need for active regulatory frameworks—such as carbon pricing and the polluter-pays principle—to reconcile the environmental costs of crypto mining with the broader sustainability potential of blockchain-based innovations Full article

With accelerating urbanization, rivers have been severely polluted, resulting in widespread black and odorous waterways. The coagulation–sedimentation and contact oxidation bypass treatment process is characterized by low operational cost and simple operation and management. In this study, a coagulation–sedimentation–contact oxidation biofilter process was developed to treat heavily polluted malodorous blackwater. Among the tested biofilm carriers, rigid aramid fiber exhibited the fastest biofilm formation and the best pollutant removal performance. Based on a comprehensive evaluation of effluent quality and treatment capacity, the optimal operating conditions of the proposed process were identified as a PAC dosage of 50 mg/L, an air-to-water ratio of 7:1, and a hydraulic retention time (HRT) of 2 h. Under these conditions, the effluent concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N), and suspended solids (SSs) were consistently maintained below 30, 5, and 5 mg/L, respectively. Moreover, the optimized system demonstrated strong resistance to shock loading, maintaining stable operation at influent COD and SS concentrations of approximately 150 mg/L and 40 mg/L, respectively, while complying with the Class A Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants. This study provides an efficient treatment strategy for malodorous blackwater remediation. Full article

The growing economic pressures on healthcare systems have heightened the need for effective and sustainable cost management strategies. This study presents a PRISMA-based systematic review of 210 peer-reviewed articles published between 1974 and 2024, retrieved from the Scopus and Web of Science databases. Following a structured selection and screening process, the articles were analysed to identify dominant cost control tools, contextual applications, and methodological trends across diverse health systems. The findings highlight a strong prevalence of Activity-Based Costing (ABC), Diagnosis-Related Groups (DRG), and benchmarking practices, predominantly in public hospital settings. However, significant thematic gaps remain, particularly concerning low-income countries, interdisciplinary integration, and the evaluation of digital technologies for financial optimisation. This review provides a comprehensive thematic synthesis of international research, consolidating knowledge in healthcare cost management and offering evidence-based recommendations to guide future empirical research, policy design, and strategic planning in health finance. Full article

Desalination is a vital solution to global water scarcity, yet its substantial energy demand persists as a major challenge. As the core energy-consuming components, pumps are fundamental to both membrane and thermal desalination processes. This review provides a comprehensive analysis of renewable energy source (RES)-driven pumping systems for desalination, focusing on the integration of solar photovoltaic and wind technologies. It examines the operational principles and efficiency of key pump types, such as high-pressure feed pumps for reverse osmosis, and underscores the critical role of energy recovery devices (ERDs) in minimizing net energy consumption. Furthermore, the paper highlights the importance of advanced control and energy management systems (EMS) in mitigating the intermittency of renewable sources. It details essential control strategies, including maximum power point tracking (MPPT), motor drive control, and supervisory EMS, that optimize the synergy between pumps, ERDs, and variable power inputs. By synthesizing current technologies and control methodologies, this review aims to identify pathways for designing more resilient, energy-efficient, and cost-effective desalination plants, supporting a sustainable water future. Full article

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article

Purpose: Timeliness of systemic therapy initiation for advanced lung cancer is highly dependent on pathology and molecular pathology laboratory services. Here, we aimed to prospectively evaluate liquid biopsy as a potential strategy to expedite systemic therapy decision-making in lung cancer management. Patients and Methods: This prospective cohort study included consecutive patients with suspected lung cancer seen at the time of initial specialist consultation who underwent both liquid and solid tumour biopsy (group A) and patients with confirmed lung malignancy who underwent solid tumour biopsy alone (group B), between 1 February 2022 and 31 May 2023. Due to laboratory factors, liquid biopsies were processed in batches of 13, whereas solid tumour biopsies were processed individually upon receipt, as per standard practices. Co-primary endpoints included the time from solid versus liquid biopsies to biomarker reporting and palliative systemic therapy initiation. Results: A total of 324 patients were included in the study. The median time from date of blood draw to date of liquid biopsy result was 78 days. For group A (n = 50), the median time from date of solid tumour biopsy to biomarker reporting was 22 days, and the median time from date of solid tumour biopsy to palliative systemic therapy was 42 days. The median time from date of liquid biopsy blood draw to palliative systemic therapy initiation was 56 days. For group B (n = 274), the median times from date of biopsy to biomarker reporting and to palliative systemic therapy initiation in all patients were 22 and 47 days, respectively. Conclusions: While we did not demonstrate improvement in timeliness of biomarker reporting or systemic therapy initiation with liquid biopsy, several barriers leading to delay in liquid biopsy reporting were identified due to unexpected COVID-19-related supply chain disruption and the cost-limiting need to batch sample analysis. Further studies that address the identified barriers are warranted to assess the potential improvement in timeliness of care, should liquid biopsy analysis be implemented in real-time. Full article

The increasing global demand for clean hydrogen necessitates production methods that minimize greenhouse gas emissions while being scalable and economically viable. Hydrogen has a very high gravimetric energy density of about 142 MJ/kg, which makes it a very promising energy carrier for many uses, such as transportation, industrial processes, and fuel cells. Methane pyrolysis has emerged as an attractive low-carbon alternative, decomposing methane (CH₄) into hydrogen and solid carbon while circumventing direct CO₂ emissions. Still, the process is very endothermic and has always depended on fossil-fuel heat sources, which limits its ability to run without releasing any carbon. This review examines the integration of geothermal energy and methane pyrolysis as a sustainable heat source, with a focus on Enhanced Geothermal Systems (EGS) and Closed-Loop Geothermal (CLG) technologies. Geothermal heat is a stable, carbon-free source of heat that can be used to preheat methane and start reactions. This makes energy use more efficient and lowers operating costs. Also, using flared natural gas from remote oil and gas fields can turn methane that would otherwise be thrown away into useful hydrogen and solid carbon. This review brings together the most recent progress in pyrolysis reactors, catalysts, carbon management, geothermal–thermochemical coupling, and techno-economic feasibility. The conversation centers on major problems and future research paths, with a focus on the potential of geothermal-assisted methane pyrolysis as a viable way to make hydrogen without adding to the carbon footprint. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

Currently, with the widespread popularization of e-commerce systems, enterprises have increasingly high requirements for the timeliness of order fulfillment. It has become particularly critical to enhance the operational efficiency of heterogeneous robotic “goods-to-person” (G2P) systems in book e-commerce fulfillment, reduce enterprise operational costs, and achieve highly efficient, low-carbon, and sustainable warehouse management. Therefore, this study focuses on determining the optimal storage location assignment strategy and order batching method. By comprehensively considering the characteristics of book e-commerce, such as small-batch, high-frequency orders and diverse SKU requirements, as well as existing system issues including uncoordinated storage assignment and order processing, and differences in the operational efficiency of heterogeneous robots, this study proposes a joint optimization framework for storage location assignment and order batching centered on a multi-objective model. The framework integrates the time costs of robot picking operations, SKU turnover rates, and inter-commodity correlations, introduces the STCSPBC storage strategy to optimize storage location assignment, and designs the SA-ANS algorithm to solve the storage assignment problem. Meanwhile, order batching optimization is based on dynamic inventory data, and the S-O Greedy algorithm is adopted to find solutions with lower picking costs. This achieves the joint optimization of storage location assignment and order batching, improves the system’s picking efficiency, reduces operational costs, and realizes green and sustainable management. Finally, validation via a spatiotemporal network model shows that the proposed joint optimization framework outperforms existing benchmark methods, achieving a 45.73% improvement in average order hit rate, a 48.79% reduction in total movement distance, a 46.59% decrease in operation time, and a 24.04% reduction in conflict frequency. Full article