Search Results (232)

Achieving food security for a growing population under a changing climate is a key concern in Senegal, where agriculture employs 77% of the workforce with a majority of small farmers who rely on the production of crops for their subsistence and for income generation. Moreover, due to the underproductive soils and variable rainfall, Senegal depends on imports to fulfil 70% of its food requirements. In this research, we considered four crops that are crucial for Senegalese agriculture: millet, sorghum, peanuts and rice. We used crop simulation models to explore existing yield gaps and optimal agronomic practices. Improving the N fertilizer management in sorghum and millet resulted in 40–100% increases in grain yields. Improved N symbiotic fixation in peanuts resulted in yield increases of 20–100% with highest impact in wetter locations. Optimizing irrigation management and N fertilizer use resulted in 20–40% gains. The best N fertilizer strategy for sorghum and millet included applying low rates at sowing and in early development stages and adjusting a third application, considering the expected rainfall. Peanut yields of the variety 73-33 were higher than Fleur-11 in all locations, and irrigation showed no clear economic advantage. The best N fertilizer management for rainfed rice included applying 30 kg N/ha at sowing, 25 days after sowing (DAS) and 45 DAS. The best combination of sowing dates for a possible double rice crop depended on irrigation costs, with a first crop planted in January or March and a second crop planted in July. Our work confirmed results obtained in field research experiments and identified management practices for increasing productivity and reducing yield variability. Those crop management practices can be implemented in pilot experiments to further validate the results and to disseminate best management practices for farmers in Senegal. Full article

Background: In this work, we examine determinants of staff departure rates in the NHS, a critical issue for workforce stability and continuity of care. High turnover, particularly among clinical staff, undermines service delivery and incurs substantial replacement costs. Methods: Here, we analyse a unique dataset derived from Electronic Staff Records at Ashford and St. Peter’s NHS Foundation Trust, using a machine learning approach to move beyond traditional survey-based methods, to assess propensity to leave. Results: In addition to established predictors such as salary and length of service, we identify drivers of increased risks of staff exits, including the distance between home and workplace and, especially for medical staff, cost centre vacancy rates. Conclusions: These findings highlight the multifactorial nature of staff retention and suggest the potential of local administrative data to improve workforce planning, for example, through hyperlocal recruitment strategies. Whilst further work will be required to assess the generalisability of our findings beyond a single Trust, our analysis offers insights for NHS managers seeking to stabilise staffing levels and reduce attrition through targeted interventions beyond pay and tenure. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Construction delays are a repeated problem in government buildings projects in Kuwait, always leading to increased costs and schedule slippage. This pilot study investigates key delay factors and corresponding solutions strategies by analyzing the responses from 60 construction professionals representing project management consultants (PMCs), contractors, and consultants. Using a structured questionnaire and confirmatory factor analysis (CFA), the study identifies and validates critical delay constructs and explores useful solutions measures from stakeholders’ perspectives. The findings provide foundational data to refine the main study and enhance model validity for structural equation modeling (SEM). The top of the delay factors are poor contractor monitoring, weakness of consultant project management team, and design faults. Recommended solutions strategies include establishing a monitoring system to track subcontractor progress and addressing potential delays proactively, ensuring timely approval for the required workforce, and establishing clear delivery schedules. The results validate the questionnaire’s reliability (Cronbach’s alpha = 0.920) and provide insights into urgency areas for delay mitigation in the Kuwaiti governmental building construction sector. Full article

The adoption of automation technologies across various industries has significantly increased in recent years. Despite the widespread integration of robotics in many sectors, the construction industry remains predominantly reliant on manual labour. This study is motivated by the need to accurately recognise construction worker activities in labour-intensive environments, leveraging deep learning (DL) techniques to enhance operational efficiency. The primary objective is to provide a decision-support framework that mitigates productivity losses and improves time and cost efficiency through the automated detection of human activities. To this end, sensor data were collected from eleven different body locations across five construction workers, encompassing six distinct construction-related activities. Three separate recognition experiments were conducted using (i) acceleration sensor data, (ii) position sensor data, and (iii) a combined dataset comprising both acceleration and position data. Comparative analyses of the recognition performances across these modalities were undertaken. The proposed DL architecture achieved high classification accuracy by incorporating long short-term memory (LSTM) and bidirectional long-term memory (BiLSTM) layers. Notably, the model yielded accuracy rates of 98.1% and 99.6% for the acceleration-only and combined datasets, respectively. These findings underscore the efficacy of DL approaches for real-time human activity recognition in construction settings and demonstrate the potential for improving workforce management and site productivity. Full article

Companies underestimate the cost of digitalization when introducing new technologies and practices in existing work environments. Although the technologies are well developed and on the market for many decades, their application and integration into companies’ operations require the necessary time and workforce skills set to be successfully implemented. The present study proposes a framework that integrates the European Technology Readiness Level (TRL) scale with the Industry 4.0 Maturity Index (MI). The authors suggest a two-dimensional map that incorporates both technologies and the Industry 4.0 maturity level. This tool aids in evaluating alternative scenarios based on internal expertise and available resources. Full article

The transmission of vibrations generated by high-powered machines to the hands can lead to serious health problems and various work-related difficulties for the operators. These issues result in a loss of workforce and increased operational costs due to compensation payments made to affected workers. Exposure to hand–arm vibration can be controlled through the use of vibration damping gloves. In this study, the hand–arm vibration exposure of operators using a jackhammer in three different mines was measured with and without gloves, and the vibration damping ratio of each glove was calculated. One-way analysis of variance was performed to determine the difference between the vibration damping ratios (%) obtained from three separate field measurements of 12 different gloves, and significant differences were detected. In addition, vibration exposure was measured with and without gloves for operators using a vibrating sieve set with standard vibration in a laboratory environment. From both the field and laboratory measurements, the gloves made of chloroprene rubber provide the most effective protection. Full article

This paper examines the costs associated with installing PIP (Pile-in-Pile) slip joints compared to traditional tubular joints, focusing on investment, installation processes, and long-term benefits. Previous studies have indicated that the structural performance of PIP slip joints is superior to that of traditional joints. By utilizing the frictional interfaces between conventional structural steel components and the simplest installation methods, PIP slip joints maximize structural integrity and ease of maintenance. As a result, they can lead to lower lifecycle costs, provided they are installed correctly. Quantitatively, the PIP slip joint achieved the highest internal rate of return (IRR) at 43.42%, the lowest Levelized Cost of Energy (LCOE) at 0.013589 EUR/kWh, and the shortest payback period at 2.92 years—outperforming grouted and bolted flange joints across all key financial metrics. The analysis also addresses logistical challenges and workforce requirements, highlighting that significant economic benefits can be realized when implemented appropriately. Furthermore, the PIP slip joint promotes sustainability goals by minimizing material usage, which ultimately leads to reduced carbon emissions through more efficient fabrication and installation, as well as enabling faster deployment. A comprehensive financial assessment of these joint systems in offshore wind monopiles reveals that PIP slip joints are the most cost-effective and financially advantageous option, outperforming key metrics like IRR, LCOE, and payback period due to lower initial investments and operational costs. As PIP slip joints yield a higher net present value (NPV), a shorter payback period, and a lower LCOE, they can enhance profitability and reduce financial risk, and are suitable for streamlined implementation. While grouted and bolted flange joints exhibit similar financial performance, PIP slip joints’ minimal expenditure and consistent superiority make them the optimal choice for sustainable and economically viable offshore wind projects. 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

High-Speed Rail (HSR) has long been proposed as a transformative infrastructure project for Australia; yet, despite multiple feasibility studies and significant government expenditure, it remains unrealized. This study investigates the key barriers preventing HSR implementation. To achieve this, a novel mixed-methods approach that triangulates a comprehensive literature review, in-depth expert interviews, and broad stakeholder survey was employed. The Analytic Hierarchy Process (AHP) was used to quantify the relative importance of the identified barriers. Simultaneously, qualitative insights were gathered through interviews with industry leaders, government officials, and infrastructure experts. This dual approach provided a comprehensive understanding of the challenges. The findings highlight the importance of external factors. These include political uncertainty, financial constraints, and systemic logistical challenges, which go beyond technical feasibility. Based on these insights, this research identifies critical early-stage risks and contributes to a re-evaluation of HSR not solely as a transport solution but also as a vital tool for regional development. Refining cost and time estimation methodologies using reference class forecasting, fostering proactive political engagement to secure bipartisan support, enhancing private sector collaboration through early contractor involvement and risk-sharing mechanisms, and developing a national upskilling framework to address workforce shortages were also key findings. The study has garnered industry recognition and support, with experts acknowledging its contribution to the ongoing discourse on HSR implementation in Australia. Full article

Simulator training offers a safe and cost-effective approach to providing new operators opportunities to become familiar with operating modern machinery. However, in Korea, the current programs are insufficient in training skilled operators capable of handling advanced forestry machinery. Consequently, these programs fall short of developing the required technical expertise, leading to difficulties in workforce employment. We compared the performance of simulator-trained participants with that of machine-trained participants by testing operators on real equipment and assessing their stress levels. Participants were categorized as those with and without excavator certificates. Within each category, participants were further divided into those receiving training via simulators or those who were trained using actual equipment. Although we detected no significant differences in the overall performance of simulator- and machine-trained participants, compared with real machine training, simulator training promoted better performance, lower levels of frustration, and a reduced mental workload due to the safer and more controlled virtual environment. These findings can be used to develop more effective training programs by incorporating simulator-based modules that enhance skill acquisition whilst reducing risks. They can also inform policy decisions to improve workforce training in industries dependent on the operation of advanced machinery, thereby ensuring that operators achieve higher levels of competence and safety. Full article

Metal casting foundries present hazardous working conditions, making traditional training methods costly, time-consuming, and potentially unsafe. To address these challenges, this study presents a Virtual Reality (VR) training framework developed for the Tennessee Tech University (TTU) Foundry. The objective is to enhance introductory training and safety education by providing an immersive, interactive, and risk-free environment where trainees can familiarize themselves with safety protocols, equipment handling, process workflows, and machine arrangements before engaging with real-world operations. The VR foundry environment is designed using Unreal Engine, a freely available software tool, to create a high-fidelity, interactive simulation of metal casting processes. This system enables real-time user interaction, scenario-based training, and procedural guidance, ensuring an engaging and effective learning experience. Preliminary findings and prior research indicate that VR-based training enhances learning retention, improves hazard recognition, and reduces training time compared to traditional methods. While challenges such as haptic feedback limitations and initial setup costs exist, VR’s potential in engineering education and industrial training is substantial. This work-in-progress study highlights the transformative role of VR in foundry training, contributing to the development of a safer, more efficient, and scalable workforce in the metal casting industry. Full article

The adoption of artificial intelligence by enterprises in the EU countries increased significantly between 2021 and 2024, but the recorded values were uneven and very small. This study analyzed the main characteristics of the artificial intelligence adoption process, its dynamics and patterns using principal component analysis and K-means clustering. The results highlighted a shift from using technologies for process automation to more advanced ones like natural language generation. The process was extended and gradually covered almost all business areas. The lack of relevant expertise, high costs and gaps in regulation of the development and use of artificial intelligence are the important barriers identified by 2024. The cluster analysis of EU countries highlighted the existence of two permanent clusters, one containing the leading countries and one containing the countries lagging behind, showing a large gap between them. The increasing dependence on externally developed solutions has characterized a maturing market for artificial intelligence. The equitable adoption of artificial intelligence at the level of EU countries must be based on specific workforce training, investments in infrastructure, financial incentives and, last but not least, on clear regulations. Only in this way can the gap in this area at the EU level be reduced. Full article

As nonprofits operate in a competitive environment with limited resources, they constantly seek new ways to optimize their resources. This study investigates factors influencing nonprofits’ decision to integrate flexible labor, such as independent contractors, into their workforce. Using longitudinal data from 2008 to 2018 in the arts and cultural sector in the United States, this study tests hypotheses related to the impact of an organization’s financial health, cost of permanent employment, reliance on government funding and donations, organizational size, and service demand variations on flexible labor use. The findings confirm that nonprofits offering higher fringe benefits and facing greater service demand fluctuations rely more on flexible labor. However, contrary to our expectations, this study also finds that nonprofits with stronger long-term financial health are more inclined to adopt flexible labor, while larger nonprofits use less flexible labor than their smaller counterparts. This study advances our understanding of the organizational and sector-level factors behind flexible labor adoption in nonprofits and offers practical implications for managing it. Full article

This study applies data envelopment analysis (DEA) to evaluate the economic sustainability of U.S. airlines by examining labor efficiency as a pivotal component of cost management and long-term sustainability. Focusing on five key employee categories—pilots, flight attendants, ground staff, maintenance staff, and management—the analysis uses data from the MIT Airline Data Project spanning 2007 to 2020 to calculate relative efficiency scores for fifteen major airlines. The findings reveal significant disparities in labor efficiency across different airline sectors, particularly highlighting challenges in managing cost-intensive roles, such as ground, maintenance, and management staff. Notably, Southwest Airlines consistently demonstrates strong economic sustainability through its efficient labor practices, while carriers including United, jetBlue, Alaska, and Hawaiian Airlines exhibited marked inefficiencies in 2020, indicating a critical need for operational improvements. This research contributes to the field of airline management by linking labor efficiency metrics with broader economic sustainability objectives, thereby offering strategic insights for enhancing cost-effectiveness and ensuring the long-term financial health of the industry. Full article