Search Results (165)

Effective in situ H₂S removal is essential for the utilization of small, remote natural gas wells, where centralized treatment is often unfeasible. This study presents an integrated environmental–economic assessment of two such processes, LO-CAT^® and triazine-based absorption, using a scenario-based framework. Environmental impacts were assessed via the Waste Reduction Algorithm (WAR), considering both Potential Environmental Impact (PEI) generation and output across eight categories, while economic performance was analyzed based on equipment, chemical, energy, environmental treatment, and labor costs. Results show that the triazine-based process offers superior environmental performance due to lower toxic emissions, whereas LO-CAT^® demonstrates better economic viability at higher gas flow rates and H₂S concentrations. An integrated assessment combining monetized environmental impacts with economic costs reveals that the triazine-based process becomes competitive only if environmental impacts are priced above specific thresholds. This study contributes a practical evaluation framework and scenario-based dataset that support sustainable process selection for decentralized sour gas treatment applications. Full article

The identification and detection of planting holes, combined with UAV technology, provides an effective solution to the challenges posed by manual counting, high labor costs, and low efficiency in large-scale planting operations. However, existing target detection algorithms face difficulties in identifying planting holes based on their edge features, particularly in complex environments. To address this issue, a target detection network named YOLO-PH was designed to efficiently and rapidly detect planting holes in complex environments. Compared to the YOLOv8 network, the proposed YOLO-PH network incorporates the C2f_DyGhostConv module as a replacement for the original C2f module in both the backbone network and neck network. Furthermore, the ATSS label allocation method is employed to optimize sample allocation and enhance detection effectiveness. Lastly, our proposed Siblings Detection Head reduces computational burden while significantly improving detection performance. Ablation experiments demonstrate that compared to baseline models, YOLO-PH exhibits notable improvements of 1.3% in mAP50 and 1.1% in mAP50:95 while simultaneously achieving a reduction of 48.8% in FLOPs and an impressive increase of 26.8 FPS (frames per second) in detection speed. In practical applications for detecting indistinct boundary planting holes within complex scenarios, our algorithm consistently outperforms other detection networks with exceptional precision (F1-score = 0.95), low computational cost, rapid detection speed, and robustness, thus laying a solid foundation for advancing precision agriculture. Full article

The accurate detection of surface defects in bamboo is critical to maintaining product quality. Traditional inspection methods rely heavily on manual labor, making the manufacturing process labor-intensive and error-prone. To overcome these limitations, TEB-YOLO is introduced in this paper, a lightweight and efficient defect detection model based on YOLOv5s. Firstly, EfficientViT replaces the original YOLOv5s backbone, reducing the computational cost while improving feature extraction. Secondly, BiFPN is adopted in place of PANet to enhance multi-scale feature fusion and preserve detailed information. Thirdly, an Efficient Local Attention (ELA) mechanism is embedded in the backbone to strengthen local feature representation. Lastly, the original CIoU loss is replaced with EIoU loss to enhance localization precision. The proposed model achieves a precision of 91.7% with only 10.5 million parameters, marking a 5.4% accuracy improvement and a 22.9% reduction in parameters compared to YOLOv5s. Compared with other mainstream models including YOLOv5n, YOLOv7, YOLOv8n, YOLOv9t, and YOLOv9s, TEB-YOLO achieves precision improvements of 11.8%, 1.66%, 2.0%, 2.8%, and 1.1%, respectively. The experiment results show that TEB-YOLO significantly improves detection precision and model lightweighting, offering a practical and effective solution for real-time bamboo surface defect detection. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

Background/Objectives: European healthcare systems are increasingly adopting automation technologies to improve efficiency. This study evaluates the economic viability of hospital automation and medication management digitalization. Methods: An economic evaluation was based on a standardized hospital model comprising 561 beds, representative of an average acute care hospital across EU27 + UK. For each technology, several cost items were estimated using country-specific parameters such as labor costs, medication error rates, healthcare expenditure, and money discount rate. The financial metrics (Return On Investment—ROI, Net Present Value—NPV, Payback Time—PBT) were first calculated at the hospital level. These results were then extrapolated to the national level by scaling the per-hospital estimates according to the total number of hospital beds reported in each country. Finally, national results were aggregated to derive the overall European impact. Results: The analysis estimated a total European investment of EUR 3.55 billion, with an average PBT of 4.46 years and annual savings of 1,96 billion. ROI averaged 167%, and the total NPV was 8.21 billion. A major saving driver was the reduction in Medication Administration Errors that has an impact of 37.2% on the total savings. Payback times ranged from 3 years in high-GDP countries, to 7 years in lower-GDP nations. Conclusions: These findings demonstrate how providing structured data on hospital automation benefits could support decision-making processes, highlighting the organizational and economic feasibility of the investment across different European national contexts. Full article

Survey and detection of the spotted lanternfly, Lycorma delicatula (White), rely either on traps that exploit the insect’s behavior as it navigates its environment, or on visual surveys of either its mobile life stages or egg masses. A recently described egg mass trap, coined the “lampshade” trap, can assist with early detection in newly infested areas, provide egg masses for researchers, and potentially facilitate spotted lanternfly population reduction by removal of egg masses from the environment. Here, we describe a modified lampshade trap, the sconce trap, that uses less material, can be pre-cut prior to deployment, and can be deployed by one person, representing potential cost, labor, and time savings. Both traps were comparable at detecting populations of spotted lanternflies, and while females deposited more eggs on the larger lampshade traps, they deposited more eggs on sconce traps as a function of trap area. Full article

We investigated the application of artificial intelligence (AI) technology for the inspection of semiconductor process equipment to address key issues such as low production efficiency and high equipment failure rates. The semiconductor industry, being central to modern technology, requires complex and precise processes where even minor defects lead to product failures, negatively impacting yield and increasing costs. Traditional inspection methods are not adequate for modern high-precision, high-efficiency production demands. By integrating advanced AI technologies, such as machine learning, deep learning, and pattern recognition, large volumes of experimental data are collected and analyzed to optimize process parameters, enhance stability, and improve product yield. By using AI, the identification and classification of defects are automated to predict potential equipment failures and reduce downtime and overall costs. By combining AI with automated optical inspection (AOI) systems, a widely used defect detection tool has been developed for semiconductor manufacturing. However, under complex conditions, AOI systems are prone to producing false positives, resulting in overkill rates above 20%. This wastes perfect products and increases the cost due to the need for manual re-inspection, hindering production efficiency. This study aims to improve wafer inspection accuracy using AI technology and reduce false alarms and overkill rates. By developing intelligent detection models, the system automatically filters out false defects and minimizes manual intervention, boosting inspection efficiency. We explored how AI is used to analyze inspection data to identify process issues and optimize workflows. The results contribute to the reduction in labor and time costs, improving equipment performance, and significantly benefitting semiconductor production management. The AI-driven method can be applied to other manufacturing processes to enhance efficiency and product quality and support the sustainable growth of the semiconductor industry. Full article

This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering. Full article

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article

The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0. Full article

The construction of high-rise buildings necessitates efficient and reliable material transport systems to improve productivity and reduce labor-intensive tasks. Traditional methods such as cranes and elevators are widely used but are often constrained by high costs and spatial limitations. Manipulator-based robotic systems have been explored as alternatives; however, they require complex control algorithms and struggle with confined construction environments. To address these challenges, we propose a lifting robot designed for repetitive inter-floor material transport in construction sites. The proposed system integrates a gear-connected double parallelogram linkage with a crank-rocker mechanism, enabling one-degree of freedom (1-DOF) operation for simplified control and precise positioning. Additionally, a spring-cable-based gravity compensation mechanism is implemented to reduce actuator torque, enhancing energy efficiency and structural stability. A prototype was fabricated, and experimental validation was conducted to evaluate torque reduction, positioning accuracy, and structural performance. Results demonstrate that the proposed system effectively minimizes driving torque, improves load-handling stability, and enhances overall operational efficiency. This study provides a foundation for developing automated lifting solutions in construction, contributing to reduced worker strain and increased productivity. Full article

This study evaluates the impact of grape variety and harvesting method—manual versus mechanized—on must composition, wine quality, and economic performance in the Târnave viticultural area of Romania. Four grape varieties—Pinot Noir, Sauvignon Blanc, Fetească Regală, and Muscat Ottonel—were analyzed. Manual harvesting increased reducing sugars by 4.3–5.1 g/L and decreased titratable acidity by 0.6–0.8 g/L, particularly in Pinot Noir and Muscat Ottonel. Alcohol content was higher by 0.4–0.6 vol% in manually harvested samples, and dry extract increased by 1.0–1.3 g/L. Mechanized harvesting raised catechin concentrations by 15–19 mg/L due to enhanced skin maceration, but also slightly elevated volatile acidity (by ~0.1 g/L). From an economic perspective, labor cost was reduced from 480 lei/ton (approx. EUR 96) for manual harvesting to 120 lei/ton (approx. EUR 24) with mechanization. Fuel and maintenance costs for mechanized equipment averaged 85 lei/ha (EUR 17), and equipment depreciation was estimated at 100 lei/ton (EUR 20). The total harvesting cost per ton decreased from 480–520 lei to 300–320 lei (approx. EUR 96 to EUR 64), representing a ~38% reduction. The study supports a hybrid approach: manual harvesting for sensitive or premium cultivars, and mechanization for cost-efficient, large-scale production, aligning wine quality goals with economic sustainability. Full article

As the core region of global steel production and consumption, the zero-carbon transition of China, Japan, and South Korea is crucial for global climate goals and industrial chain sustainability. Hydrogen-based direct reduction iron (H-DRI) production, powered by renewable energy, is a promising pathway for reducing carbon emissions. This study compares the competitive dynamics of hydrogen-based steel production in China, Japan, and South Korea, with a particular focus on the levelized cost of energy (LCOE), levelized cost of hydrogen (LCOH), and levelized cost of steel (LCOS) as key metrics for evaluating the economic viability of green hydrogen-based steel production. And then compares and analyzes the competitiveness of China, Japan, and South Korea in hydrogen-based steel production, focusing on the role of green hydrogen and renewable energy in shaping the future steel industry. This study examines the impact of technological advancements, resource endowments, and policy support on H-DRI production. It highlights the importance of offshore wind power in Japan and South Korea, where its development plays a key role in reducing the cost of green hydrogen production and providing a stable electricity supply for H-DRI production. However, the high capital expenditures (CAPEXs) and labor costs associated with offshore wind power in these countries make importing relevant technologies and products from China a more cost-effective option. This study also explores the strategic importance of international cooperation and technology transfer, emphasizing the potential for China, Japan, and South Korea to strengthen bilateral collaboration in green hydrogen and H-DRI technologies. Such cooperation supports the region’s steel decarbonization efforts and enhances its global competitiveness. The integration of offshore wind power and hydrogen production technologies offers new opportunities for energy cooperation in East Asia, with China playing a key role in providing low-cost green energy solutions. Full article

In the context of China’s accelerated “dual transition” towards industrial intelligence and green development, this paper investigates how local government decarbonization pressures affect urban industrial intelligence in China. Using the Low-Carbon City Pilot policy as a quasi-natural experiment, a staggered difference-in-differences approach and Causal Forest model reveal the following findings: (1) Local government decarbonization pressures significantly boost urban industrial intelligence. (2) Local government decarbonization pressures foster intelligent development by encouraging the introduction of intelligent policies, which motivate enterprises to adopt proactive strategies. Meanwhile, the pressures compel enterprises to engage in source-based environmental governance, resulting in a passive intelligent response. Together, these approaches enhance urban industrial intelligence. (3) Fiscal pressure negatively moderates the relationship between local government decarbonization pressures and urban industrial intelligence. (4) There is an inverted U-shaped relationship between openness to foreign trade and the Conditional Average Treatment Effect (CATE), while CATE is higher for cities with higher urban labor costs. (5) Finally, urban industrial intelligence effectively channels local government decarbonization pressures into measurable emission reductions. These findings have significant policy relevance for building a low-carbon, intelligent society. Full article

During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low sorting efficiency, and high sorting costs. In addition, the hardware performance is poor in actual production, and the model is not suitable for deployment. To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. A custom dataset containing four categories of impurities was created. This dataset was employed to evaluate various YOLOv8 models, ultimately leading to the selection of YOLOv8n as the base model. Initially, four loss functions were compared in the experiment, and Focaler_mpdiou was chosen as the final loss function. Subsequently, this loss function was applied to other YOLOv8 models, leading to the selection of YOLOv8m-Focaler_mpdiou as the teacher model. The model was then pruned to achieve a lightweight model at the expense of detection accuracy. Finally, knowledge distillation was applied to enhance its detection performance. Compared to the base model, it showed advancements in P, R, mAP, and FPS by margins of 0.0051, 0.0120, and 0.0094 and an increase of 72.2 FPS, respectively. Simultaneously, it achieved a reduction in computational complexity with GFLOPs decreasing by 2.3 and parameters shrinking by 860350 B. Afterwards, we further demonstrated the model’s generalization ability in black tea samples. This research contributes to the technological foundation for sophisticated impurity classification in tea. Full article