Search Results (297)

Growers rarely use the grafting method on a double-root system due to limited information on the added advantages for increased plant vigour and yield of soilless-grown cucumber (Cucumis sativus L.). The study aimed to investigate the effect of combining rootstock cultivar and the grafting method on the growth, yield, and quality of soilless-grown cucumber in a non-temperature-controlled (NTC) tunnel. Two rootstock cultivars, Flexifort (Flex) (Cucurbita maxima × Cucurbita moschata) and Ferro (Fer) (C. maxima × C. moschata), were grafted with scion cultivar Hoplita (H) to either single- (1R) or double- (2R) root systems, and the original scion root system was combined with either a Flexifort or Ferro rootstock (O1R) to two root systems and a non-grafted plant (Hoplita). Plants were grown in 10 L containers filled with sawdust as a growing medium. The leaf number was higher in ‘HO1RFlex’ combinations, while the non-grafted plants had a significantly lower leaf number. Thicker stem diameter was obtained from non-grafted plants. The tallest plants were obtained from HO1Fer combinations at 39, 53, and 101 days after transplanting (DAT), while non-grafted plants at 25 and 101 DAT were the shortest plants. Plants grafted to single- or double-root systems, regardless of rootstock cultivar, had higher early, marketable, and total yield compared to non-grafted cucumber. Many medium-sized fruits were obtained in ‘HO1RFlex’ combinations during the early harvest. The total soluble solids (TSSs) of cucumber juice were higher in ‘H1RFer’ while fruit mineral content was not affected by the combined rootstock cultivar and grafting method. Grafting to a double-root system using the original scion roots combined with rootstock or double rootstock had a limited effect compared to plants grafted to a single-root system. It is recommended that scion be grafted to a single-root system of either rootstock Ferro or Flexifort compared to a double-root system, particularly for the cost effectiveness of seeds and labour used in grafting, as well as for improved vegetative growth, including early marketable and total yield of cucumber. The growing containers of various sizes need further investigation to allow for the root extension and growth of grafted plants. Full article

The year 2024 has been recorded as the warmest year on record, with global temperatures temporarily exceeding the 1.5 °C threshold owing to rising anthropogenic greenhouse gas emissions. This has intensified global attention on heatwaves, which are a major public health threat linked to increased morbidity and mortality rates. This study conducted a bibliometric analysis of 901 Web of Science-indexed journal articles (2004–2024) using the term “heat wave health.” The findings revealed a significant increase in global temperatures, with an increasing frequency, intensity, and duration of extreme heat events. Heatwaves have been linked to higher rates of injuries, mental health disorders, and mortality, particularly in urban areas, due to ozone pollution, atmospheric contaminants, and the urban heat island effect, leading to increased emergency hospitalisation. Rural populations, especially outdoor labourers, face occupational heat stress and a higher risk of fatality. Adaptation measures, including early warning systems, heat indices, air conditioning, white and green roofs, and urban cooling strategies, offer some mitigation but are inadequate in the long term. Significant knowledge gaps persist regarding regional vulnerabilities, adaptation effectiveness, and socio-economic disparities, underscoring the urgent need for interdisciplinary research to inform heat-resilient public health policies and climate adaptation strategies. This study highlights the urgent need for further interdisciplinary research and targeted policy interventions to enhance heatwave resilience, particularly in under-researched and highly vulnerable regions of the world. Full article

The Mercosur countries (also known as the Southern Common Market countries) and the European Union (EU) Member States are two major global agri-food exporters whose production structures, patterns of specialisation and sustainability standards differ significantly. The upcoming entry into force of the EU–Mercosur Partnership Agreement (EMPA) may alter competitive conditions in the EU agri-food markets, as its most important component—the EU–Mercosur Interim Trade Agreement—provides for tariff liberalisation. The aim of this article is therefore to compare the agricultural production potential of Mercosur and the EU countries using a set of indicators grouped into production factors (land, labour and capital), productivity, production structure, and qualitative sustainability-related factors. The analysis employs comparative and dynamic statistical methods (including compound annual growth rates and measures of variability). The study is based on FAOSTAT data for 2018–2023, complemented by information on regulatory frameworks and EMPA provisions. The results show that agriculture in Mercosur is land-abundant, cost-efficient, and oriented toward export-driven livestock and commodity production, while the EU is characterised by higher capital intensity and significantly higher land and labour productivity. These structural asymmetries, reinforced by lower input costs and less stringent production standards in Mercosur, suggest increased competitive pressure in the EU market after EMPA implementation, particularly in beef, poultry, sugar and ethanol. The findings highlight the need for continuous monitoring of market dynamics and, where necessary, the activation of safeguard mechanisms. The study provides also an updated evidence base to support policymakers in assessing the implications of the EMPA. Full article

Timely and accurate inspection of concrete bridges is critical to ensuring structural integrity and public safety. Traditional visual inspections conducted by human inspectors are labour-intensive, inconsistent, and often limited in their ability to access all structural components, particularly in hazardous or inaccessible areas. Image-based inspection techniques have emerged as a safer and more efficient alternative, and recent advancements in deep learning have significantly enhanced their diagnostic capabilities. This systematic review critically evaluates 77 studies that applied deep learning approaches to the detection and classification of surface defects in concrete bridges using 2D images. Relevant publications were retrieved from major scientific databases, screened for eligibility, and analyzed in terms of model type, training strategies, and evaluation metrics. The reviewed works encompass a wide spectrum of algorithms—spanning classification, object detection, and image segmentation models—highlighting their architectural features, strengths, and trade-offs in terms of accuracy, computational complexity, and real-time applicability. Key findings reveal that transfer learning, data augmentation, and careful dataset composition are pivotal in improving model performance. Moreover, the review identifies emerging research trajectories, such as integrating deep learning with Building Information Modeling (BIM), leveraging edge computing for real-time monitoring, and developing rich annotated datasets to enhance model generalizability. By mapping the current state of knowledge and outlining future research directions, this study provides a foundational reference for researchers and practitioners aiming to deploy deep learning technologies in bridge inspection and infrastructure monitoring. Full article

This study presents a systematic literature review of critical risk factors affecting the time and cost performance of highway construction projects. Drawing from 83 peer-reviewed studies across multiple geographic regions, the paper identifies recurrent drivers of project delay and cost overrun in highway construction. The most frequently reported risks include (1) financial constraints, (2) political regulatory issues; (3) land-acquisition and right-of-way delays; (4) design and scope changes; (5) utilities relocation/conflicts; (6) materials and equipment shortages; (7) contractor-related issues; (8) planning and scheduling weaknesses; (9) labour and personnel issues; and (10) weather conditions. These risk factors collectively highlight the importance of robust planning, proactive stakeholder coordination, and the integration of risk-informed decision-making tools. The findings emphasize the need for targeted risk mitigation during early project stages and provide a foundation for refining risk assessment frameworks and future research directions in transport infrastructure development. Full article

This entry presents the history, geography, business, regulations, and the roles of gig workers, platform/algorithms, and employers, focusing primarily on the USA and the EU. The gig economy is informally referred to also as the fourth industrial revolution or the 1099 economy, emphasising sharing, freelance, or platform work; it is a complex and changing business model and regulatory environment. In practice, the gig economy refers to a tripartite relation between workers, platforms/apps, and employers, leading to a two-sided market, where algorithms match supply and demand for paid labour and clients. It is only recently that the gig economy has started to be conceptualised, and its implications, challenges, and impacts are captured in economic law and society, including the power dynamics related to the interplay between economics, technology, regulation, and communities. Conceptually, the gig economy is important, as small paid work has always been present in society for all types of workers and beneficiaries. This new business model of on-demand work has some perceived advantages, such as freedom of work, under-regulation, efficient use of capital, driving down costs, and improving services. However, there is a dualisation of anti-power between workers and non-employers that may lead to precarious work, less free workers, and shadow corporations that distort the market using game changers like digital management algorithms. Currently, the size of the gig economy comprises 154–435 million gig workers out of the world’s 3.63 bn workers, with a market size of USD 557 bn, and is still expanding. Full article

Background: Community pharmacies are increasingly delivering structured services to support chronic disease management, such as Multidose Drug Dispensing (MDD). This strategy can improve adherence and safety, but evidence of its economic feasibility in Portuguese pharmacies remains limited. Objective: To estimate the cost of implementing and operating an MDD system in a community pharmacy, informing reimbursement models and policy. Methods: A micro-costing approach assessed fixed and variable expenses for serving polymedicated elderly patients. Costs were calculated in euros (2024/2025) and expressed per working day based on 253 annual preparation days. Results: First-year costs totaled €70,985.68, including €8184.00 for setup, €21,579.00 for supplies, and €41,222.68 for staff salaries. The daily operating cost was €280.58, with labour representing the major expense. A break-even analysis indicated sustainability with around 700 users at €10/month. Conclusion: Although requiring significant initial investment, MDD can become financially viable through scaling, workflow efficiency, and supportive reimbursement strategies. Full article

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs. Full article

Digital twins are increasingly being adopted to support efficient hazard assessment and predictive modelling. A key prerequisite for the development of reliable digital twins in rock slope engineering is the accurate identification and segmentation of the rock lithology, as material properties significantly influence rock mass behaviour and govern the occurrence and severity of geotechnical hazards. Traditional methods for characterising rock lithology, primarily based on visual interpretation and borehole data, can be labour-intensive, subjective, and unsuitable for large-scale, dynamic environments such as mining operations. To address this gap, this paper proposes the application of deep learning-based 3D point cloud segmentation models to automate the segmentation of rock lithology in open-pit mine highwalls. Four different segmentation models (SparseUNet, Point Transformer version 2, Point Transformer version 3, Sonata) are explored and their performance in efficient and accurate rock lithology segmentation is evaluated using high-resolution 3D point clouds. The models are trained using a mine highwall dataset consisting of 1498 point cloud segments generated using terrestrial and aerial photogrammetry of nine open-pit mine sites from Australia. The data is split into an 80:20 ratio for training and validation purposes. The results show that Point Transformer version 3 outperforms Sonata, Point Transformer version 2 and Saprse UNet by 21%, 26% and 55%, respectively, measured by mean intersection over union on the unseen validation dataset split. The outcomes of this work provide the basis for developing lithology-informed digital twins of rock slopes, enabling data-driven rockfall hazard assessment, predictive monitoring, and sustainable slope management. Full article

The objective is to evaluate the differences between the Chilean ports of Valparaíso and San Antonio and the port of Rotterdam in their journey towards smart ports, focusing on Blockchain (BC) technologies, Artificial Intelligence (AI), big data, Internet of Things (IoT), 5G networks and Digital Twins (DT), according to Port 4.0 and 5.0 models. The methodology is a qualitative assessment based on scores from the analysis of Port 4.0 technology information, including labour relations, environmental care and community integration for Port 5.0. The results confirm Rotterdam as representative of a ‘Smart Port’ for Ports 4.0 and 5.0, showing gaps with Chilean ports, which are rated as ‘incipient implementation’ in Port 4.0 and ‘in transition’ in Port 5.0. These differences are due to factors such as investment, financing, infrastructure, governance, regulation, digital human capital, organisational culture and innovation, and the characteristics of the port ecosystem. Full article

This study aims to investigate the determinants of labour productivity across European non-financial entities using aggregated data from the Bank for the Accounts of Companies Harmonized (BACH) database. Focusing on six European Union countries (Belgium, France, Italy, Portugal, Poland, and Spain). Annual information from 2010 to 2023 is used (the last available year), including three size classes (small, medium-sized and larger entities) per division (two-digit code) by year and by country, totalling 14,188 observations. The combination of sectors and class sizes varies from 191 to 208 by country. It uses gross value added per employee as a proxy for labour productivity. Using a fixed-effects estimator and panel data regression techniques, the analysis reveals that labour productivity explanatory factors, particularly firm size, profitability, financialisation, leverage, and tangibility, have heterogeneous and sometimes contradictory effects across countries, sectors, and size classes. Larger firms generally tend to have higher levels of labour productivity, although this feature is not consistent among countries. Size and profitability more consistently exert a strong positive influence, whereas financialisation and leverage typically show negative or nonlinear effects. The results highlight the structural diversity of the European corporate landscape and challenge the adequacy of one-size-fits-all policy measures, contributing to the literature on productivity and offering further insights to policymakers by integrating cross-sectional, sectoral, and size-specific perspectives on labour efficiency within the EU context. Full article

This article argues that sustainability governance in small-scale regenerative aquaculture arises less from formal regulation than from the relational, ethical, and temporal labour of practitioners. Based on an ethnographic study of Câr-y-Môr, Wales’s first community-owned regenerative ocean farm, the research combines over 250 h of participant observation, 25 interviews, and document analysis with transdisciplinary humanities-informed sustainability science (THiSS). The study shows how technocratic environmentalism, reliant on auditing, reporting, and standardised procedures, often clashes with the shifting rhythms of tides, weather, and the embodied work of marine labour. Ethnography uniquely reveals the embodied knowledge, improvisation, and moral commitment through which practitioners continually remake governance, translating bureaucratic rules into ecologically and socially meaningful practice. The findings demonstrate that adaptive governance requires recognition of local and experiential expertise, proportionate regulatory frameworks, and protected spaces for experimentation and learning. Seen in this way, sustainability shifts from a fixed goal to a relational process. When governance learns from practice and care is recognised as a form of knowledge, it becomes more adaptive, situated, and responsive, revealing both the constraints of technocratic control and the possibilities of care-based policy and practice. Full article

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain. Full article

Pasture systems, typically composed of grasses, legumes, and forage crops, are vital livestock nutrition sources. The quality of these pastures depends on various factors, including species composition and growth stage, which directly impact livestock productivity. Remote sensing (RS) technologies offer powerful, non-invasive means for large-scale pasture monitoring and classification, enabling efficient assessment of pasture health across extensive areas. However, traditional supervised classification methods require labelled datasets that are often expensive and labour-intensive to produce, especially over large grasslands. This study explores unsupervised clustering as a cost-effective alternative for identifying pasture types without the need for labelled data. Leveraging spatiotemporal data from the Sentinel-2 mission, we propose a clustering framework that classifies pastures based on their temporal growth dynamics. For this, the pasture segments are first created with quick-shift segmentation, and spectral time series for each segment are grouped into clusters using time-series distance-based clustering techniques. Empirical analysis shows that the dynamic time warping (DTW) distance measure, combined with K-Medoids and hierarchical clustering, delivers promising pasture mapping with normalised mutual information (NMI) of 86.28% and 88.02% for site-1 and site-2 (total area of approx. 2510 ha), respectively, in New South Wales, Australia. This approach offers practical insights for improving pasture management and presents a viable solution for categorising pasture and grazing systems across landscapes. Full article

The COVID-19 pandemic exacerbated labor market inequalities, disproportionately impacting workers based on age, gender, and sector. In Turkey, the pandemic-induced economic crisis resulted in a substantial increase in unemployment, with youth (ages 15–24) encountering the most significant challenges. Young women, in particular, experienced more severe outcomes, increasing their vulnerability in the labor market. This study examined the factors contributing to the intensified challenges faced by young women during the pandemic. Using official data from the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD), and the Turkish Statistical Institute (TurkStat), comparative analyses were conducted on labor market indicators by age and gender, focusing on unemployment rates, informal employment, and sectoral distribution. This study considers both narrow and broad definitions of unemployment, including underemployment and the potential labor force. The findings indicate that young women suffered the most severe employment losses, exacerbated by their concentration in low-wage, precarious jobs and informal work, with gendered occupational segregation further intensifying these disparities. Full article