Search Results (26)

Drones can improve the efficiency of point-to-point logistics and distribution and reduce labor costs; however, the complex three-dimensional airspace environment poses significant challenges for flight paths. To address this demand, this paper proposes a hybrid algorithm that integrates the Sparrow Search Algorithm (SSA) with the African Vulture Optimization Algorithm (AVOA). Firstly, the algorithm introduces Sobol sequences at the population initialization stage to optimize the initial population; then, we incorporate SSA’s discoverer and vigilant mechanisms to balance exploration and exploitation and enhance global exploration capabilities; finally, multi-guide differencing and dynamic rotation transformation strategies are introduced in the first exploitation phase to enhance the direction of local exploitation by fusing multiple pieces of information; the second exploitation phase achieved a dynamic balance between elite guidance and population diversity through adaptive weight adjustment and enhanced Lévy flight strategy. In this paper, a three-dimensional model is built under a variety of constraints, and SAVOA (Sparrow-Enhanced African Vulture Optimization Algorithm) is compared with a variety of popular algorithms in simulation experiments. SAVOA achieves the optimal path in all scenarios, verifying the efficiency and superiority of the algorithm in UAV logistics path planning. Full article

Background/Objectives: Systemic sclerosis (SSc) is a multisystemic chronic autoimmune disease, which leads to disability and possibly early retirement. The objective of our study was to explore the associations between employment status (ES) and demographic, clinical and functional features in a single-center EUSTAR cohort. Methods: Consecutive patients with SSc examined between November 2011 and June 2023, who were under the age of retirement in our country (62 years for women, 65 for men at the time), were included. All patients underwent a comprehensive clinical assessment and filled in a work assessment questionnaire as well as two validated health-related questionnaires: the Scleroderma Health Assessment Questionnaire (SHAQ) and the Duruoz Hand Index (DHI). Associations between ES and potential predictors (education level, disease characteristics, work conditions, SHAQ and DHI) were tested using logistic regression adjusted for age and gender. Results: Ninety-one patients (mean ± SD age 53.7 ± 11.8 years, twenty-two with diffuse skin involvement, fifty-six with a history of digital of digital ulcers (DUs)), were included. Only 22 patients were still employed, while 69 were retired, of which 38 retired because of SSc. Among the employed, nine performed manual labor, nine spent many hours standing and three had to work in a cold environment. When potential predictors were tested separately, adjusted for age and sex, patients with higher education (OR (95% CI) 11.36 (2.03–63.36), p = 0.006) and no history of digital ulcers had higher odds of being employed. The presence of joint contractures and weightlifting as a work demand were associated with unemployment. In a multivariable model, higher education (OR 5.91, 95% CI 0.97–36.09, p = 0.054 and younger age (OR 0.90, 95% CI 0.85–0.96, p = 0.001) were independently associated with continued employment. High school education did not show a significant effect (OR 0.089, 95% CI 0.015–0.530, p = 0.008). Patients with a history of digital ulcers had the lowest employment rates compared to those with no digital ulcer history. No significant associations were found between employment status and SHAQ or DHI scores. Conclusions: SSc is associated with significant work disability and early retirement. Higher education, the lack of Dus and younger age were highly associated with staying employed. Given the rarity of SSc, we consider that our good sample size (n = 91) reflects disease prevalence, but results should be tested in other studies and the single center should be considered when interpreting generalizability. Full article

Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions. Full article

Urban logistics are facing growing sustainability challenges, particularly in last-mile delivery operations, which contribute significantly to traffic congestion, emissions and operational inefficiencies. The COVID-19 pandemic further exposed the vulnerabilities in traditional logistics systems, accelerating interest in innovative solutions such as electric vehicles (EVs) and autonomous vehicles (AVs) for last-mile delivery. This study investigates the potential of EV and AV technologies to enhance sustainable urban logistics by integrating cleaner, smarter transportation into delivery networks. Drawing on survey data from logistics professionals and consumers in Italy, the findings highlight the key benefits of EV and AV adoption, including reduced emissions, improved delivery efficiency and increased resilience during global disruptions. Autonomous delivery robots and EV fleets can reduce labor costs, traffic congestion and carbon footprints while meeting evolving consumer demands. However, barriers such as limited charging infrastructure, range constraints, and technological readiness remain critical challenges. By addressing these issues and aligning EV and AV strategies with urban mobility policies, last-mile delivery systems can play a crucial role in advancing cleaner, more efficient and sustainable urban logistics. This research emphasizes the need for continued investment, policy support and public–private collaboration to fully realize the potential of EVs and AVs in reshaping future urban delivery systems. Full article

This paper predicts offered wage rates and job requirements in the logistics industry by utilizing data from online job postings collected through two methods: search tools and web scraping. We apply conventional estimation techniques, such as ordinary least squares and kernel density estimation, to analyze the collected data. Additionally, for the first time, we employ nowcasting methods (linear regression, decision tree, and K-nearest neighbor methods) in this context to generate robust results. Our main findings are as follows: First, the average real wage derived from online job postings aligns with officially published GDP per capita data for the studied countries and regions. Second, we identify significantly positive causal effects of work experience on real wages in the logistics industry. Third, skill requirements exhibit year-over-year variations. Finally, the decision tree method generates the closest nowcasted results in line with the actual web scraped data. The proposed methodologies and their findings establish a reliable approach using search tools and web scraping to define and predict labor demand for stakeholders in this sector as well as others. Full article

With the rapid development of e-commerce, warehousing and logistics systems are facing the dual challenges of increasing order processing demand and green and low-carbon transformation. Traditional manual and single-robot scheduling methods are not only limited in efficiency, but will also make it difficult to meet the strategic needs of sustainable development due to their high energy consumption and resource redundancy. Therefore, in order to respond to the sustainable development goals of green logistics and resource optimization, this paper replaces the traditional mobile handling robot in warehousing and logistics with a composite robot composed of a mobile chassis and a robotic arm, which reduces energy consumption and labor costs by reducing manual intervention and improving the level of automation. Based on the traditional contract net protocol framework, a distributed task allocation strategy optimization method based on an improved genetic algorithm is proposed. This framework achieves real-time optimization of the robot task list and enhances the rationality of the task allocation strategy. By combining the improved genetic algorithm with the contract net protocol, multi-robot multi-task allocation is realized. The experimental results show that the improvement strategy can effectively support the transformation of the warehousing and logistics system to a low-carbon and intelligent sustainable development mode while improving the rationality of task allocation. Full article

The evolving labor environment underscores the critical role of cognitive and non-cognitive (soft) skills in fostering workforce adaptability and enhancing labor market outcomes. This study investigates the combined influence of these skills on the probability of employment, focusing on the Kenyan labor market, where high youth unemployment and job market mismatches persist despite government interventions and education sector reforms. Traditionally, emphasis has been placed on cognitive skills, with limited integration of non-cognitive skills into educational curricula, exacerbating the disconnect between youth competencies and market demands. Using binary logistic regression, this study evaluates factors influencing youth employment, highlighting the complementarity of cognitive and non-cognitive skills. Findings reveal that individuals possessing a blend of these skills have higher employment prospects, with notable improvements for young women possessing agreeableness and digital literacy. Additionally, factors such as marital status and higher education levels positively influence employability. These results underscore the equal importance of personality traits and cognitive abilities in labor market success. Policymakers are urged to prioritize curriculum reforms that integrate non-cognitive skill development and encourage employers to include assessments of these skills in hiring practices to address persistent labor market mismatches. Full article

This study aimed to identify patterns of sustainability engagement based on circular economy (CE) strategy implementation, CE-oriented collaborative relationships, and environmental, social, and governance (ESG) performance, as well as to investigate whether these dimensions predict corporate reputation. Data were collected through a survey of 235 upper-level managers in the Brazilian agribusiness sector. A two-step analytical approach was applied, with cluster analysis identifying groups exhibiting distinct patterns regarding sustainability engagement (“Very Sustainable” and “Low-Sustainable”), followed by logistic regression, which singled out six key predictors among 28 variables, namely avoiding non-sustainable materials, repurposing by-products, fostering a shared CE vision, adhering to ethical guidelines, ensuring financial transparency, and fair labor practices. The final model achieved 83.4% accuracy, underscoring how an integrated approach to sustainability enhances corporate reputation. Considering its theoretical contributions, this study extends the NRBV and RV theories by demonstrating that CE strategies, CE-oriented collaborative relationships, and ESG performance strengthen pollution prevention initiatives, sustainable product development efforts, and trust among partners, among other achievements, thereby enhancing firms’ reputation and sustainable performance. Methodologically, the study integrates cluster analysis and predictive modeling to assess sustainability’s impact on reputation. From a managerial perspective, findings emphasize that corporate reputation benefits from circularity, governance integrity, and stakeholder engagement. However, the cross-sectional design, industry-specific sample, and reliance on self-reported data limit generalizability. Future research should adopt longitudinal and cross-industry approaches, examining regulatory shifts, technological advances, and evolving stakeholder demands in the sustainability–reputation nexus while incorporating external data sources to assess variations across institutional and cultural settings. Full article

The classification of agricultural products is of great importance for quality control, optimized marketing, efficient logistics, research progress, consumer satisfaction, and sustainability. Dragon fruit has many varieties that need to be identified quickly and accurately for packaging and marketing. Considering the increasing demand for dragon fruit, it is obvious that an automated classification system has significant commercial and scientific value by increasing sorting efficiency and reducing manual labor costs. This study aimed to classify four commonly produced dragon fruit varieties according to their color, mechanical, and physical properties using machine learning models. Data were collected from 224 dragon fruits (53 American beauty, 57 Dark star, 65 Vietnamese white, and 49 Pepino dulce variety). Classification was performed using measurable physical and mechanical properties obtained through digital image processing, colorimetry, electronic weighing, and stress–strain testing. These methods provided objective and reproducible data collection for the models. Three models—Random Forest, Gradient Boosting, and Support Vector Classification—were implemented and their performances were evaluated using accuracy, precision, recall, Matthews’s correlation coefficient, Cohen’s Kappa, and F1-Score. The Random Forest model showed the highest performance in all metrics, achieving 98.66% accuracy, while the Support Vector Classification model had the lowest success. The superior performance of the Random Forest model can be attributed to its ability to handle complex, nonlinear relationships among multiple variables while preventing overfitting through ensemble learning. However, potential challenges in dragon fruit classification include variations due to environmental factors, genetic variation, and hybridization. Future research can focus on incorporating biochemical or genetic markers and improving real-time classification for industrial applications. Full article

In the scientific literature, it is highlighted that industrialized construction has significant comparative advantages over traditional construction, primarily in four indicators: (i) cost reduction, (ii) time reduction, (iii) increased energy performance, and (iv) reduced environmental impacts. However, there is no certainty about the range of variation of these indicators for these comparative advantages, creating uncertainty about the real impact of industrialized construction. In this work, through a systematic literature review based on PRISMA, 90 articles that met the selection criteria related to the four mentioned indicators were selected and analyzed. The results show that industrialized construction has comparative advantages over traditional construction but with a wide spectrum of variation in each of the indicators. In the cost indicator, reductions between 7% and 50% and increases between 26% and 72% are observed; in time, reductions between 9% and 50% and increases up to 32% are recorded, and reductions in energy demand between 20% and 90% are also reported. For the environmental indicators, data were only provided for projects in the design stage or for construction solutions on a scale, demonstrating the need to obtain indicators in the operational stage. It is concluded that although industrialized construction offers significant advantages, it is essential that new construction solutions consider contextual variations, initial investment, logistical challenges, and specialized labor to maximize their benefits. Full article

The prevalence of musculoskeletal disorders (MSDs) is high among older adults worldwide, significantly affecting their quality of life and overall health. Understanding the prevalence of MSDs and their associated factors is crucial to developing effective preventive and management strategies in Thailand. In this study, we aimed to investigate the prevalence of MSDs and their associated factors among older patients at Walailak University Physical Therapy Clinic. In this retrospective study, we analyzed the medical records of 396 older patients. Data on demographics, underlying diseases, career types, and treatments were collected and analyzed using descriptive statistics chi-squared tests, and logistic regression analysis to determine their associations with MSD prevalence. The overall prevalence of MSDs was 89.90%. MSD prevalence was higher among female patients than among male patients (p < 0.001). The most commonly affected body regions were the lower back, shoulders, and knees. Career type (p < 0.001) had the highest impact on the presence of MSDs after controlling for sex, age, and underlying diseases as covariates in a logistic regression model. Manual labor and heavy industry workers as well as pensioners showed an increased risk of MSDs. While older age was associated with a higher MSD prevalence using chi-squared statistics, it was removed from the logistic regression models. Pensioners were the most likely to receive treatment, indicating the need for targeted interventions for individuals with physically demanding occupations. These findings underscore the importance of targeted interventions and further research on socioeconomic factors, lifestyle behaviors, and comorbidities to manage MSDs among older adults in Thailand. Full article

The modern logistics industry is grappling with significant challenges brought about by rapid market transformations. These challenges encompass workforce shortages, the increasing complexity of diverse logistics systems, environmental concerns arising from unregulated warehouse construction, and the scarcity of available land for development. In response to these pressing issues, this study introduced an automated logistics warehouse system that incorporated stackable bidirectional infinite-loop modules and employed a pre-transfer method. Extensive simulations rigorously validated the feasibility and effectiveness of this system, affirming its substantial capacity to enhance spatial utilization and operational efficiency within warehouses. Consequently, this study not only offers solutions to streamline transfer device routes, optimize storage space, reduce workforce demands, and contribute to economic gains, but also addresses labor shortages and land scarcity challenges. The overarching objective of this study was to provide pragmatic strategies for effectively tackling the multifaceted challenges confronting the logistics industry. Full article

This study examines Cambodian farmers’ demand for weather index insurance (WII), an innovative financial product, for managing climate change-related risks. Rice and cassava farmers in Battambang Province of Cambodia were interviewed to understand their preferences for WII. We applied a binary logistic model to quantify the factors that influence farmers’ WII demand. We discovered that farmers’ marital status and off-farm labor are crucial factors that impact the demand for WII. More importantly, we also investigated gender differences, considering the critical role of women in the agricultural sector and personality differences between men and women. Our findings indicated that for male respondents, being married and having an additional off-farm laborer increase the probability of demand for WII by 72.6% and 36.8%, respectively. For female respondents, the education level is the most significant factor in making purchase decisions. An additional year of education increases the probability of WII demand by 5.0%. Generally, our results are consistent with some prior studies but inconsistent with others. This suggests that further research is necessary to understand the barriers associated with WII schemes and how to overcome them. Regardless, our study provides valuable insights for various stakeholders in implementing WII schemes, including financial professionals, insurance companies, communities, and governments, for designing more flexible WII products, improving farmers’ financial literacy, and providing effective post-event support to enhance farmers’ resilience to climate change. Full article

Given energy costs for gestating and caring for male offspring are higher than those of female newborns, external environmental conditions might be regarded as likely to affect the timing of delivery processes differentially depending on the sex of the newborn calf to be delivered. The aim of the present paper is to evaluate the association between environmental stressors such as the moon phase and weather-related factors and the onset of labor in female dromedaries. A binary logistic regression model was developed to find the most parsimonious set of variables that are most effective in predicting the probability for a gravid female dromedary to give birth to a male or a female calf, assuming that higher gestational costs and longer labor times are ascribed to the production of a male offspring. Although the differences in the quantitative distribution of spontaneous onset of labor across lunar phases and the mean climate per onset event along the whole study period were deemed nonsignificant (p > 0.05), a non-negligible prediction effect of a new moon, mean wind speed and maximum wind gust was present. At slightly brighter nights and lower mean wind speeds, a calf is more likely to be male. This microevolutionary response to the external environment may have been driven by physiological and behavioral adaptation of metabolic economy and social ecology to give birth to cooperative groups with the best possible reduction of thermoregulatory demands. Model performance indexes then highlighted the heterothermic character of camels to greatly minimize the impact of the external environment. The overall results will also enrich the general knowledge of the interplay between homeostasis and arid and semi-arid environments. Full article

The phenomenon of migration among medical personnel from less developed countries is a large one, with negative effects on the origin country, but more worrying is graduates’ propensity to migrate during or immediately after university studies. The analysis of the labor market in the health sector from the last two decades shows us greater attractiveness of employment in (more) economically developed states compared to the demand from the health sector in graduates’ origin countries. This research’s purpose is to identify the determinants of the propensity to study and work abroad of medical students as a defined factor for better employment, and to identify the push factors in the origin country. As a result of the dichotomous nature of the dependent variables, logistic regression was applied. The independent variables (gender, residence, medical specialization, grades and perceived economic status) were used to identify the odds of the intention to migrate for studies. The results highlighted a higher propensity to migrate for studies among medical students, with opportunities offered by universities differing across countries and geographical areas. Moreover, students with a lower level of household income have openness to migrate, the tuition fees being managed through part-time/temporary employment during studies. Full article