Search Results (46)

Coal plays a significant role in the economies of many countries and serves as an energy source for numerous societies. However, its combustion causes various environmental problems and contributes to climate change. This article examines the efficiency of coal consumption in 26 European Union countries and its changes from 2014 to 2022. Data Envelopment Analysis (DEA) methodology was applied to measure the extent of overall technical, pure technical, and scale technical efficiency, based on data concerning three production factors (labour, fixed assets, and energy), with GDP as a desirable output and CO₂ emissions as an undesirable output. The empirical findings revealed that Cyprus, Denmark, Luxembourg, and Poland were efficiency leaders throughout the entire study period. France, Germany, Italy, and the Netherlands managed energy and non-energy resources efficiently but were found inefficient in terms of operational scale. Countries that do not use their resources at optimal levels in the production of goods and services should provide greater technical and financial support to their production processes and improve the organisation and structure of labour. Full article

Job advertisements play a key role in human resource management as they are the first contact between employers and potential employees. A well-written job advertisement communicates not only the requirements and expectations of the position but also the culture, values, and goals of the organisation. Transparent and attractive advertisements increase the number of applicants and help to select the right candidates, leading to more efficient recruitment and selection processes in the long run. From a human resource management perspective, effective job advertising can give organisations a competitive advantage. Continuous changes in the labour market and technological developments require new competencies. Digitalisation, automation, and data-driven decision-making have brought IT, analytical, and communication skills to the fore. There is a growing emphasis on soft skills such as problem solving, flexibility, and teamwork, which are essential in a fast-changing work environment. Job advertisements should reflect these expectations so that candidates are aware of the competencies and skills required for the position. The aim of the study is to carry out a cross-country comparative analysis for a few pre-selected jobs based on data extracted from the CEDEFOP database as it is assumed that there are differences between countries in the European Union in terms of the expectations of workers for the same jobs. Full article

Background: Leigh’s Disease is a rare mitochondrial disorder primarily affecting the central nervous system, with frequent secondary cardiac manifestations such as hypertrophic and dilated cardiomyopathies. Early detection of cardiac complications is crucial for patient management, but manual interpretation of cardiac MRI is labour-intensive and subject to inter-observer variability. Methodology: We propose an integrated deep learning framework using cardiac MRI to automate the detection of cardiac abnormalities associated with Leigh’s Disease. Four CNN architectures—Inceptionv3, a custom 3-layer CNN, DenseNet169, and EfficientNetB2—were trained on preprocessed MRI data (224 × 224 pixels), including left ventricular segmentation, contrast enhancement, and gamma correction. Morphological features (area, aspect ratio, and extent) were also extracted to aid interpretability. Results: EfficientNetB2 achieved the highest test accuracy (99.2%) and generalization performance, followed by DenseNet169 (98.4%), 3-layer CNN (95.6%), and InceptionV3 (94.2%). Statistical morphological analysis revealed significant differences in cardiac structure between Leigh’s and non-Leigh’s cases, particularly in area (212,097 vs. 2247 pixels) and extent (0.995 vs. 0.183). The framework was validated using ROC (AUC = 1.00), Brier Score (0.000), and cross-validation (mean sensitivity = 1.000, std = 0.000). Feature embedding visualisation using PCA, t-SNE, and UMAP confirmed class separability. Grad-CAM heatmaps localised relevant myocardial regions, supporting model interpretability. Conclusions: Our deep learning-based framework demonstrated high diagnostic accuracy and interpretability in detecting Leigh’s disease-related cardiac complications. Integrating morphological analysis and explainable AI provides a robust and scalable tool for early-stage detection and clinical decision support in rare diseases. 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

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

The main objective of energy balance analysis is to guide farmers in making informed decisions that promote the efficient management of natural resources, optimise the use of agricultural inputs, and improve the overall economic performance of their farms. In addition, it supports the adoption of sustainable agricultural practices, such as crop diversification, the use of renewable energy sources, and the recycling of agricultural by-products and residues into natural energy sources or fertilisers. This paper analyses the variation in energy efficiency between 2019 and 2022 of the main crops in Angola: maize, soybean, and rice, and the forest production of eucalyptus biomass in agroforestry farms. The research was based on the responses to interviews conducted with the managers of the farms regarding the machinery used, fuels and lubricants, labour, seeds, phytopharmaceuticals, and fertilisers. The quantities are gathered by converting data into Megajoules (MJ). The results show variations in efficiency and energy balance. In corn, efficiency fluctuated between 1.32 MJ in 2019 and 1.41 MJ in 2020, falling to 0.94 MJ in 2021 due to the COVID-19 pandemic before rising to 1.31 MJ in 2022. For soybeans, the energy balance went from a deficit of −8223.48 MJ in 2019 to a positive 11,974.62 MJ in 2022, indicating better use of resources. Rice stood out for its high efficiency, reaching 81,541.33 MJ in 2021, while wood production showed negative balances, evidencing the need for more effective strategies. This research concludes that understanding the energy balance of agricultural operations in Angola is essential not only to achieve greater sustainability and profitability but also to strengthen the resilience of agricultural systems against external factors such as climate change, fluctuations in input prices, and economic crises. A comprehensive understanding of the energy balance allows farmers to assess the true cost-effectiveness of their operations, identify energy inefficiencies, and implement more effective strategies to maximise productivity while minimising environmental impacts. Full article

The transportation sector is encountering escalating issues associated with technical progress, environmental laws, economic upheavals, and deficiencies in personnel capabilities. Although prior research has examined these concerns separately, there is an absence of thorough studies investigating the interaction between these elements and their influence on the long-term sustainability of the transportation industry. This study aims to address this research vacuum by providing a comprehensive examination of the impact of technical breakthroughs, environmental requirements, supply chain obstacles, and worker skills on the advancement and sustainability of the transport sector. This research employs expert assessment and regression analysis to investigate data from important stakeholders in the transport industry, aiming to elucidate the interrelationships among these four parameters. The results demonstrate a robust association between technical progress and environmental restrictions, while also underscoring considerable economic challenges, especially supply chain disruptions that impede the adoption of new technology. The lack of trained labour is seen as a significant element that intensifies these issues. This study enhances the current literature by providing a comprehensive view of the elements influencing the sustainability of the transport industry. The report offers actionable suggestions for industry stakeholders, including measures to improve staff training, streamline supply chain management, and more efficiently incorporate new technology. These results have considerable significance for governments and transport corporations aiming to secure the industry’s long-term sustainability. Full article

The global effort to combat climate change highlights the critical role of storing organic carbon in soil to reduce greenhouse gas emissions. Traditional methods of mapping soil organic carbon (SOC) have been labour-intensive and costly, relying on extensive laboratory analyses. Recent advancements in unmanned aerial vehicles (UAVs) offer a promising alternative for efficiently and affordably mapping SOC at the field level. This study focused on developing a method to accurately predict topsoil SOC at high resolution using spectral data from low-altitude UAV multispectral imagery, complemented by laboratory data from the Nogalte farm in Murcia, Spain, as part of the LIFE AMDRYC4 project. To attain this objective, Python version 3.10 was used to implement several machine learning techniques, including partial least squares (PLS) regression, random forest (RF), and support vector machine (SVM). Among these, the random forest algorithm demonstrated superior performance, achieving an R² value of 0.92, RMSE of 0.22, MAE of 0.19, MSE of 0.05, and EVE of 0.71 in estimating SOC. The results of the RF model were then visualised spatially using GIS and compared with simple spatial interpolations of soil analyses. The findings suggest that a multispectral sensor UAV-based modelling and mapping of SOC can provide valuable insights for farmers, offering a practical means to monitor SOC levels and enhance precision agriculture systems. This innovative approach reduces the time and cost associated with traditional SOC mapping methods and supports sustainable agricultural practices by enabling more precise management of soil resources. Full article

With advancement in Unmanned Aerial Vehicle (UAV) photogrammetry, productivity in construction management can now be achieved with accuracy and is less labour-intensive. In the basement construction of buildings, prudent earthwork activities are often necessary, setting the basis of the building footprint. As such, monitoring earthwork volume estimation becomes important to avoid over- or under-cutting the earth. Conventional methods by means of land surveying are time-consuming, labour-intensive, and susceptible to varying degrees of accuracy. Moreover, earthwork sites often have multiple activities ongoing that increase the complexity of volume estimation through land surveying. This study explores the use of UAV photogrammetry to estimate earthwork excavation volume in a complex urban earthwork site in Singapore over time and discusses the feasibility, challenges and productivity enhancements of integrating the technology into the construction process. In this study, the earthwork site and controlled trials show that the models reconstructed with UAV photogrammetry data can produce volume measurements that fulfil the stakeholder’s accuracy tolerance of 5% between the estimated and actual volume. The filtering of unwanted objects in the model, such as columns, cranes and trucks, was successful but was insufficient for objects that occluded large areas of the soil surface. The integration of UAV photogrammetry with a highly automated acquisition and processing workflow for earthwork monitoring brings about productivity enhancements in time and labour efforts and improves the efficiency and consistency of models. Furthermore, the digitalisation of earthwork sites into point clouds and three-dimensional (3D) models increases data visualisation and accessibility, facilitates project team collaboration, and enables cross-platform compatibility into Building Information Modelling (BIM), which can significantly aid in reporting and decision-making processes. Full article

The adoption of innovations in rice cultivation is presumed to operate in a rational manner, wherein new technologies or practices that successfully increase productivity or resource efficiency are adopted by target farmers based on cost-benefit calculations. In contrast, this paper examines a case of a public initiative to promote the system of rice intensification (SRI), wherein farmers widely disadopted the technique despite reporting increasing yields and reduced water consumption. To explain this paradox, we use the concept of the socio-ecological niche to examine a range of social and institutional factors that shape farmers’ decision-making. These included (1) access to land and labour; (2) water management capacity; (3) the quality of networks for knowledge sharing. The research suggests that small variations in these categories among otherwise similar smallholder households can markedly shape farmers’ risk perceptions and tangible outcomes with SRI. The implication is that agricultural innovations should be judged within their wider social context rather than on narrow evaluations of agronomic efficiency. Importantly, this must involve greater feedback mechanisms from smallholders with a variety of socio-economic profiles to help shape the character of agricultural innovations and extension strategies. Full article

Construction is widely acknowledged for its socioeconomic contributions, although it is also always considered as a dangerous and incident-prone industry. As a new method of working, prefabrication presents better work environments and other benefits that can potentially improve the safety and mental health of construction workers. This study compares the extent of stressors in traditional and prefabricated construction. Eighty-four construction site and factory-based workers in Australia were surveyed. Prefabricated construction respondents reported less experience of industry-related, management/organisational, and personal stressors. Specifically, the stressors found to be weakened by prefabrication were mental fatigue, work injuries, poor working conditions, unfavourable shift rosters, work overload, and poor work–life balance. Furthermore, the degree of the experience of potential mental health improvement factors such as labour effort efficiency, reduced on-site trade overlap, increased mechanised construction, and less dependence on weather conditions, among others, was significantly higher in prefabrication than in traditional construction. The influence of prefabrication on measures of poor and positive mental health is recommended for further studies, particularly by finding its links with the different groups of construction workers. Full article

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. Full article

Poland is one of the leading milk producers in the EU, being the fifth largest after countries such as Germany, France, Italy, and the Netherlands. From Poland’s accession to the European Union in 2004 up to 2022, Polish milk production experienced dynamic development. In this, there occurred a strong decline in the number of dairy farms (by −78%) and the number of cows (by −21%), an increase in dairy herd size (3.5 times) and increase in milk production (+60%) and milk yield per cow (by +62%). These were among the highest growth dynamics among the analysed countries. As a result of this significant transformation, Poland maintained an important position in milk exports, with a 31% export share in production in 2022. The scale of milk production was the basic factor determining the efficiency and competitiveness of dairy farms in Poland. Milk yield, farmland productivity, labour productivity, milk price, and the Corrected Competitiveness Index (based on labour and land opportunity costs) all showed a positive relationship with cow herd size on the farm. Milk production is highly uncompetitive for smaller farms (<15 cows). Despite substantial public support, the smaller farms, where subsidies equal up to 47% of total production value, could not earn sufficient income to cover the cost of capital, risk, and management in 2008, and even more so in 2021. This is because the farm income is too small to cover the extremely high opportunity cost of labour. The larger farms (with 30 cows and more) are competitive and responsible for the majority (~60–70%) of milk produced and delivered to the market. The most challenging from the sectoral policy point of view are medium farms (10–29 cows), whose share in production and deliveries is still important. To survive as economically viable units, these farms have to increase in scale and improve productivity. Otherwise, they will be gradually supplanted by larger farms. Full article

Heavy nuclides like uranium and their decay products are commonly found in nuclear industries and can pose a significant health risk to humans due to their alpha-emitting properties. Traditional alpha detectors require close contact with the contaminated surface, which can be time-consuming, labour-intensive, and put personnel at risk. Remote detection is urgently needed but very challenging. To this end, a candidate detection mechanism is alpha-induced radio-luminescence. This approach uses the emission of photons from radio-ionised excited nitrogen molecules to imply the presence of alpha emitters from a distance. Herein, the use of this phenomenon to remotely image various alpha emitters with unparalleled levels of sensitivity and spatial accuracy is demonstrated. Notably, the system detected a 29 kBq Am-241 source at a distance of 3 m within 10 min. Furthermore, it demonstrated the capability to discern a 29 kBq source positioned 7 cm away from a 3 MBq source at a 2 m distance. Additionally, a ‘sandwich’ filter structure is described that incorporates an absorptive filter between two interference filters to enhance the ambient light rejection. The testing of the system is described in different lighting environments, including room light and inside a glovebox. This method promises safer and more efficient alpha monitoring, with applications in nuclear forensics, waste management and decommissioning. Full article

Insurance companies play a pivotal role in the financial systems of developing countries, wielding substantial influence on systemic financial stability. Thus, understanding their efficiency, performance, and sustainability is paramount for policymakers and stakeholders alike. The aim of this paper is to evaluate the relative efficiency of insurance companies within the North Macedonian market spanning the years 2018 to 2022. Employing the input-oriented BCC DEA model, the study integrates capital and labour as inputs, while assessing risk-pooling/bearing services and intermediate function as outputs. Our findings underscore the fluctuating efficiency levels within North Macedonia’s insurance sector. Notably, the sector exhibited its peak efficiency in 2018 at 83.62%, dipping to its lowest point of 73.81% in 2020. Moreover, discerning between life and non-life insurers, we observe an average relative efficiency of 0.8067 for non-life insurers, contrasted with a higher average efficiency score of 0.9011 for life insurance companies over the examined period. This study contributes significantly on multiple fronts. Firstly, it pioneers empirical investigation of the efficiency on the North Macedonian insurance market, encompassing pre- and post-COVID efficiency metrics. This fills a notable gap in the literature, particularly within the context of emerging European markets. Secondly, our comprehensive approach facilitates a holistic evaluation of the insurance sector’s performance across a five-year span, offering insights into its overarching dynamics and efficacy. Thirdly, the implications of our findings extend to policymakers, regulators, and insurance company management, aiding in informed decision-making and strategic planning. Full article