Search Results (89)

This study investigates the spatial dimensions of labour market polarization in Hungary by examining the widening gap between developed agglomerations and lagging peripheral regions. It explores how educational inequality, technology-driven risks, and constrained mobility affect the spatial aspects of labour market polarization. It covers all 197 districts of Hungary on the LAU-1 level. Using cluster analysis and OLS regression models, we shall explore relationships between employment rates, educational attainment, automation exposure—as based on occupation-level data—and a composite mobility index. From the data, we detected distinct labour market zones, which are dynamic agglomerations, industrial transition zones, and peripheral lagging. The data confirms that the “triple trap” is clearly experienced by the peripheral regions, with lower educational attainment, high exposure to automation impacting nearly 50%, and mobility constraints keeping the workforce bound to local public works employment. These results provide evidence that labor market polarization is a self-reinforcing spatial process. It implies that successful policy interventions should be comprehensive, addressing the interrelated elements of transport infrastructure, skill development, and regional economic diversification in one stroke to break the vicious circle of immobility. Full article

This article examines graduate employability challenges in the tourism and hospitality sector of Marrakech, a major tourism destination and strategic regional labour market in Morocco, characterised by strong seasonality, high labour turnover, and persistent education–employment mismatches. Rather than focusing exclusively on technology, the study analyses employability as a multidimensional and context-dependent process, in which digitalisation and artificial intelligence (AI) constitute one influencing factor among others. The research adopts a qualitative, purposive design based on semi-structured interviews conducted between August and October 2025 with 20 stakeholders directly involved in recruitment, training, or early career integration. These include five-star hotel general managers and HR officers, riad managers, travel agencies, recruitment intermediaries, representatives of Morocco’s public employment service (ANAPEC—National Agency for the Promotion of Employment and Skills) and private, regional tourism authorities, academics and young tourism graduates. Interview transcripts were thematically analysed using NVivo to identify recurrent patterns in recruitment practices, skill expectations, and the impact of AI in employability. The results, reflecting stakeholders’ perceptions within this local labour market, show that employability is shaped by six interrelated dimensions: (1) the structure and functioning of the tourism labour market (segmentation, turnover, mobility); (2) partial misalignment between training provision and operational service realities; (3) recruitment standards that prioritise behavioural and relational competences alongside formal qualifications, particularly for frontline positions; (4) language proficiency, especially English and French, as a baseline employability condition; (5) growing expectations regarding digital literacy linked to tourism operations (property management systems, reservation platforms, online reputation management); and (6) the perceived impact of AI-enabled tools (automation of routine tasks, decision-support systems, chatbots), which is seen less as a source of job destruction than as a driver of task reconfiguration and skill upgrading. By situating employer and graduate perceptions within the broader Moroccan employment and training context, the study contributes a place-based understanding of employability in tourism. It highlights the shared responsibility of individuals, employers, and education and training institutions in supporting skill development. The article concludes by discussing policy and practice-oriented levers to strengthen graduate employability, including co-designed curricula, structured internships and mentoring schemes, employer-supported upskilling in tourism-specific digital and AI-related competences, and reinforced labour-market intermediation through ANAPEC and regional governance actors. Full article

Russia’s 2022 invasion precipitated one of Europe’s largest episodes of forced academic mobility, compelling universities to shift from emergency access to durable inclusion. This article investigates how Ukrainian students are integrated into Romanian higher education through a qualitative case study at Ovidius University of Constanta, undertaken within the Erasmus+ EIUS project. We analysed a participatory focus-group workshop (“Building Bridges,” May 2024) involving 72 participants (15 Ukrainian students, 31 Romanian students, 26 academic staff). Transcripts were coded via reflexive thematic analysis and interpreted through a SWOT lens to connect lived experience with institutional strategy. Findings indicate that integration generates tangible pedagogical and social value—diversity enriches coursework, empathy strengthens peer collaboration, and exposure to multilingual classrooms catalyses instructional innovation. Yet systemic fragilities persist: language anxiety (“translation silence”), fragmented support pathways, and limited access to counselling shift emotional labour onto faculty and peers. Opportunities cluster around Erasmus+ infrastructures, bilingual materials, and co-created projects that transform access into participation; threats include latent prejudice, social isolation, compassion fatigue, and policy discontinuity as crisis attention wanes. We advance the concept of institutionalised solidarity—a multi-level inclusion model that couples emotional infrastructures (mentoring, trauma-informed pedagogy, counselling) with organizational infrastructures (integration offices, linguistic scaffolding, adaptive assessment). The study contributes an empirically grounded framework for moving from humanitarian reaction to sustainable academic inclusion and offers actionable guidance for European universities seeking resilience under protracted disruption. Full article

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring. Full article

Monitoring the curing process of concrete remains a challenging and critical aspect of modern construction, often hindered by labour-intensive, invasive, and inflexible methods. The primary aim of this study is to develop an integrated IoT-enabled platform for automated, real-time monitoring of concrete curing, using a combination of LoRa-based sensor networks and an LTE-M backhaul. The resulting ConMonity system employs embedded multi-sensor nodes—capable of measuring strain, temperature, and humidity–connected via an energy-efficient, TDMA-based LoRa wireless protocol to an LTE-M gateway with cloud-based management and analytics. By employing a robust architecture with battery-powered embedded nodes and adaptive firmware, ConMonity enables multi-modal, multi-site assessments and demonstrates stable, autonomous operation over multi-modal, multi-site assessment and demonstrates stable, autonomous operation over multi-month field deployments. Measured data are transmitted in a compact binary MQTT format, optimising cellular bandwidth and allowing secure, remote access via a dedicated mobile application. Operation in laboratory construction environments indicates that ConMonity outperforms conventional and earlier wireless monitoring systems in scalability and automation, delivering actionable real-time data and proactive alerts. The platform establishes a foundation for intelligent, scalable, and cost-effective monitoring of concrete curing, with future work focused on extending sensor modalities and enhancing resilience under diverse site conditions. Full article

The migration of Ethiopian women to the Middle East has primarily been studied in connection with domestic labour and the related vulnerabilities. Due to assumptions about the low educational levels of women entering this sector, as well as the precarity and temporality the sector entails, opportunities for social mobility have been largely overlooked. This article examines changes in Ethiopian women’s labour market participation in the United Arab Emirates (UAE). It demonstrates that, over time, women who enter the workforce as maids may transition into better-paid work or establish their own business ventures. It further depicts an evolving pattern of well-educated Ethiopian women entering the skilled labour market. Based on ethnographic findings from the UAE, the article offers a critical re-engagement with prevailing narratives of victimhood and severely restricted social mobility opportunities. Drawing on recent conceptualisations of mobilities, trajectories, and temporalities, the article critiques the tendency to portray Ethiopian female migrants as a homogeneous group with similar paths, thereby concealing the diversity of their experiences. Second, it questions the essentialization of women migrant workers as passive victims. By highlighting developments in women’s aspirations and agency over time, the article contributes new knowledge on the potential for social mobility within transnational labour markets. 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

Intersectoral labour mobility is a key driver of economic resilience and innovation in Europe. The redistribution of workers across sectors and regions enables economies to adapt to shocks, create flexibility and increase the rate of structural change. However, the dynamics of mobility have not been adequately investigated across varying scales of sectoral granularity and spatio-temporal dimensions. This paper applies the Intersectoral Mobility Index (MI) to all European NUTS-2 areas from 2008 to 2020, utilising Eurostat Structural Business Statistics. Two levels of sectoral aggregation (NACE Rev. 2, 1-digit and 2-digit) are employed to compute MI, capturing both broad and fine-grained reallocations. Classical indices of structural change (NAV, Krugman, Shorrocks) are combined with spatio-temporal modelling in ArcGIS Pro, employing Space–Time Cubes, time-series exponential smoothing forecasts, time-series clustering and emerging hot spot analysis. Results indicate that MI distributions are positively skewed and heavy-tailed, with peaks coinciding with systemic crises (2009–2011, 2020). At the 2-digit level, MI values are significantly higher, revealing intra-sectoral changes obscured in aggregated data. A statistically significant downward trend in mobility suggests an increasing structural rigidity following the global financial crisis. Regional clustering highlights heterogeneity: a small number of regions, such as Bremen, Madeira and the Southern Great Plain, have sustained high or unstable mobility, while most exhibit convergent mobility and low reallocation. This paper contributes to the conceptualisation of MI as a dual measure of resilience and innovation preparedness. It underscores the importance of multi-scalar and spatio-temporal methods in monitoring labour market flexibility. The findings have policy implications, including the design of targeted reskilling programmes, proactive labour market policies and just transition plans to maintain regional resilience during the EU’s green and digital transitions. Full article

Early diagnosis and rapid treatment of respiratory abnormalities such as many lung diseases including pneumonia, TB, cancer, and other pulmonary problems depend on accurate and fast classification of chest X-ray images. Delayed diagnosis and insufficient treatment lead to the subjective, labour-intensive, error-prone features of current manual diagnosis systems. To tackle this pressing healthcare issue, this work investigates many deep convolutional neural network (CNN) architectures including VGG16, VGG19, ResNet50, InceptionV3, Xception, DenseNet121, NASNetMobile, and NASNet Large. LungVisionNet (LVNet) is an innovative hybrid model proposed here that combines MobileNetV2 with multilayer perceptron (MLP) layers in a unique way. LungVisionNet outperformed previous models in accuracy 96.91%, recall 97.59%, precision, specificity, F1-score 97.01%, and area under the curve (AUC) measurements according to thorough examination on two publicly available datasets including various chest abnormalities and normal cases exhibited. Comprehensive evaluation with an independent, real-world clinical dataset from King Hussein Cancer Centre (KHCC), which achieved 95.3% accuracy, 95.3% precision, 78.8% recall, 99.1% specificity, and 86.4% F1-score, confirmed the model’s robustness, generalizability, and clinical usefulness. We also created a simple mobile application that lets doctors quickly classify and evaluate chest X-ray images in hospitals, so enhancing clinical integration and practical application and supporting fast decision-making and better patient outcomes. Full article

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture. Full article

Labour migration from Central and Eastern Europe plays a significant role in the labour market of the European Union, yet few studies examine the direction and extent of occupational mobility triggered by migration. This study introduces a new analytical tool, the Career Benefit Index, which measures the direction of change in occupational status between the labour markets of the country of origin and the host country. The tool also enables the assessment of sociological factors that explain these changes. The index was developed using data from Hungarian workers living in Austria and Germany. The analysis revealed that educational attainment has no significant impact on career mobility. In contrast, demographic factors such as gender, age, and particularly very high-level German language proficiency strongly influence career trajectories. The index demonstrates that labour market capacities play a limited role in shaping migrants’ career paths, as the host labour markets tend to “evaluate” migrant workers primarily based on their linguistic and demographic attributes. The index and the findings contribute to a deeper understanding of labour market integration among Central and Eastern European migrants and may offer new directions for migration and employment policy analysis. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article

This study investigates the determinants of Romanian workers’ migration intentions towards Italy, integrating economic, social, and psychological perspectives. Based on a sample of 358 respondents, four hypotheses were tested concerning perceived living standards, working conditions, quality of public services, and anticipated integration difficulties. Data were analysed using descriptive statistics, Spearman’s rho correlation, Mann–Whitney U, Chi-square, ANOVA, and ordinal logistic regression. The results confirm that higher perceived living standards and better working conditions in Italy significantly increase the likelihood of expressing migration intentions, while favourable evaluations of healthcare and education act as additional pull factors. Conversely, anticipated integration difficulties, particularly language barriers and cultural adaptation, reduce migration intentions, indicating that socio-psychological obstacles can counterbalance economic incentives. By combining non-parametric and multivariate analyses, the study demonstrates that migration is a multidimensional process shaped not only by structural opportunities but also by behavioural and psychological appraisals. These findings are consistent with recent research on European labour mobility and contribute to the literature by highlighting the role of subjective perceptions in shaping migration decisions. Implications for policy include the need to address both economic disparities and integration barriers to support more balanced mobility within the European space. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Soft skills are increasingly recognised as decisive factors for employability and career advancement in the global labour market. This study examines their role in the professional trajectories of university graduates in Ecuador, analysing both the competencies supplied by higher education and the structural demand of the labour market. Based on institutional surveys applied to 3358 graduates from the Salesian Polytechnic University (Cuenca campus), the results show that more than $90\%$ of graduates remain in operational positions, while only $5\%$ reach tactical or managerial levels. To address this phenomenon, five key soft skills—leadership, effective communication, teamwork, problem-solving, and adaptability—were evaluated through a structured questionnaire using Likert-type items. The findings reveal a persistent concentration of professionals in lower organisational levels and heterogeneous perceptions of the applicability of academic training. These outcomes highlight both individual skill gaps and structural limitations of the Ecuadorian labour market, such as the scarcity of managerial positions and the prevalence of family-based business structures. In response, the study proposes a sector-based curricular improvement strategy that systematically incorporates soft skills into university programmes, differentiated by economic sectors such as education, health, commerce, public administration, industry, and primary activities. Grounded in empirical evidence, this approach provides a practical framework to enhance graduates’ career progression, foster more equitable professional mobility, and strengthen the relevance of higher education. The model can be replicated across other Latin American universities facing similar challenges, while also aligning with international standards for competency-based education. Full article