Search Results (162)

Forestry projects have long faced the inherent tension between stringent conservation objectives and the enhancement of human well-being, making it increasingly important to assess the sustainable livelihoods of participating households. The Natural Forest Management Project in the Northeast and Inner Mongolia state-owned forest region (NSFR) aims to transform high-quality ecological products and services into inclusive public benefits while providing reasonable compensation for ecological conservation and restoration efforts. This approach seeks to achieve synergies among ecological protection, economic development, and livelihood improvement. Drawing on six consecutive years (2017–2022) of longitudinal micro-level household survey data, this study quantifies the sustainable livelihood levels of households participating in natural forest management. A Natural Forest Involvement (NFI) index was constructed to measure their degree of participation. Furthermore, the well-being effects of frontline participants in natural forest management activities were investigated. The findings indicate that the overall sustainable livelihood capital of these households shows a steady upward trend across NSFR, significant disparities exist among different areas. Moreover, approximately half of forestry worker households are deeply embedded in the natural forest management system, and this engagement pattern negatively affects households’ sustainable livelihood capital. These results not only enrich the empirical literature on forestry project effectiveness but also offer relevant insights for forestry project design and policy formulation in other developing countries. Full article

Forestry operations expose workers to a high risk of health constraints, accidents, and injuries. We are trying to protect them and implement many effective countermeasures; nevertheless, the development of new forestry machines remains a long process, with limited safety and ergonomic feedback, usually provided only at a late stage in the design process. In this study, we propose a practical digital ergonomics workflow that combines inertial motion capture, standardized risk scoring, and digital human modelling to improve and shorten human-centered and safer design of forestry machinery. We validated the approach in a field pilot on a prototype milling–spraying device for standing trees. Two experienced operators performed a full work-cycle (carry → install → operate → dismantle → return), during which their whole-body kinematics were captured in real forest conditions. These were then evaluated using kinematic metrics, RULA, OWAS, and a heart-rate-based load index. Based on these ergonomical and risk findings, we translate motion-derived risk ‘hotspots’ into real redesign targets (grip/handle geometry, weight distribution, support elements, and control layout), outlining an updated forestry-specific DHM/HDT (digital human modeling; human digital twin) framework that explicitly incorporates terrain and environmental constraints to accelerate the iteration of safer prototypes. The updated digital modeling framework will be used in the design of the new, more complex machine—“Semi-autonomous system for optimizing degraded soils by deep injection”. This machine contains a much more complex and advanced structure, including a tractor with an attachment tool for specialized deep soil injection. We suppose that using motion capture data, human digital twins, and digital human models can effectively support designing and the development process to avoid human-related construction nonconformities of this complex machine even before the final machine prototype is produced for functional field testing. Full article

High-risk working conditions in construction, such as working at height, may elicit elevated mental stress in workers and pose significant safety challenges. This study aims to physiologically assess construction workers’ mental stress under high-risk working conditions using heart rate variability (HRV) features derived from electrocardiograph (ECG) signals. An experimental study in the field was conducted, where inexperienced scaffolding workers’ (n = 20) ECG signals were collected when working at three different heights corresponding to low, medium, and high levels of mental stress. Supervised machine learning algorithms, including Support Vector Machine (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF), were applied for model development. The results show that the HRV features obtained good prediction accuracy. The classification accuracy is up to 85.00% between low and medium stress levels, 92.50% for differentiating low and high stress levels, and 87.50% for classifying medium and high stress levels. These findings demonstrate the potential of ECG-derived HRV features for differentiating the mental stress responses of construction workers under high-risk working conditions and provide empirical evidence supporting the feasibility of physiological monitoring of workers’ mental stress in real construction environments. Full article

Dengue and chikungunya are endemic arboviral diseases in many low- and middle-income countries, often co-circulating and presenting with overlapping symptoms that hinder early diagnosis. Timely differentiation is critical, especially in resource-limited settings where laboratory testing is unavailable. We developed and evaluated machine-learning (ML)- and deep-learning (DL) models to classify dengue, chikungunya, and discarded cases using a large-scale, real-world dataset of over 6.7 million entries from Brazil (2013–2020). After applying the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, we trained six ML models and one artificial neural network (ANN) using only demographic, clinical, and comorbidity features. The Random Forest model achieved strong multi-class classification performance (Recall: 0.9288, the Area Under the Curve (AUC): 0.9865). The ANN model excelled in identifying chikungunya cases (Recall: 0.9986, AUC: 0.9283), suggesting its suitability for rapid screening. External validation confirmed the generalizability of our models, particularly for distinguishing discarded cases. Our models demonstrate high-accuracy in differentiating dengue and chikungunya using routinely collected clinical and epidemiological data. This work supports the development of Artificial Intelligence-powered decision-support tools to assist frontline healthcare workers in under-resourced settings and aligns with the One Health approach to improving surveillance and diagnosis of neglected tropical diseases. Full article

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites. Full article

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the risks in laboratory and medical environments, particularly for professional cleaning staff. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, this gap is concerning. This study developed an exploratory simulation tool, using a simulated cohort based on key demographic and exposure patterns from foundational research, to assess the impact of chemical exposure from cleaning products in laboratory environments. Four supervised machine learning models were applied to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. High exposures to hydrochloric acid and ammonia were found to be significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in professional cleaning workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose–response relationships and the temporal dynamics of chemical exposure to further refine these models and improve understanding of long-term health risks. Full article

Unsafe behaviors among construction workers remain a leading cause of accidents in the construction industry. Previous studies have primarily relied on structural equation modeling and causal inference approaches to investigate the determinants of workers’ safety behavior. However, these methods are often limited in their ability to address confounding bias inherent in observational data and tend to focus on isolated effects of individual variables, thereby overlooking the complex interactions between organizational and individual factors. To overcome these limitations, this study applies the Categorical Boosting (CatBoost) algorithm to examine the joint organizational and individual mechanisms underlying construction workers’ safety behavior. CatBoost is particularly suitable for small- to medium-sized datasets and is capable of automatically capturing complex, nonlinear relationships among variables. Leveraging the SHAP interpretability framework, both main-effect and interaction analyses are conducted to systematically identify the most influential determinants. The results demonstrate that CatBoost outperforms eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models in predicting safety-related outcomes. Prosociality (PSO) is identified as the most influential predictor, followed by personal proactivity (PAC). Interaction analyses further reveal that organizational attributes—such as prosociality, loyalty, and mutual assistance—play a critical role in cultivating a safety-oriented organizational climate, while an optimistic personal attitude further enhances safety performance on construction sites. Overall, these findings provide meaningful theoretical insights and practical implications for improving safety management in the construction sector. Full article

Background: Influenza is a major health threat to the elderly in China. Despite this, influenza vaccination rates still remain low and vary across regions that have different funding policies. In this study, we compare the vaccination status and influencing factors among older adults under the free, partial reimbursement, and self-paid vaccination strategies. Methods: Three cities with free, partial reimbursement, and self-paid influenza vaccination policies were selected. A cross-sectional, anonymous survey was then conducted. A total of 2265 elderly individuals aged 60 years and above were recruited using probability proportionate to size sampling. A standardized questionnaire was used during face-to-face interviews to collect data regarding the influenza vaccination status and influencing factors. The statistical analyses included chi-square tests, a multivariate logistic regression, and random forest models. Results: Among the 2265 participants (free policy region: n = 426; partial reimbursement region: n = 633; self-paid region: n = 1206), vaccination rates during the 2023–2024 season were significantly higher in the free policy region (53.29%) than in the partial reimbursement (20.85%) and self-paid (13.60%) regions (p < 0.001). The intention to vaccinate for the 2024–2025 season was also highest in the free policy region (68.78%), followed by partial reimbursement (47.71%) and self-paid (37.15%) regions (p < 0.001). This result demonstrated the same trend as the vaccination behavior. Cues to action (e.g., healthcare worker or family member recommendations) positively influenced vaccinations across all of the regions. In the self-paid region, perceived barriers, such as vaccine cost and side effect concerns, significantly reduced both behaviors and the next-season intention to vaccinate. Healthcare worker recommendations were key positive factors, while misconceptions and costs were major barriers to vaccination. Conclusions: Vaccination rates varied significantly across regions with different influenza vaccine subsidy policies. The free policy region demonstrated the highest coverage rate, while the self-paid region exhibited the lowest, suggesting that financial policies are a key determinant of vaccination uptake. Furthermore, free vaccination policies were associated with improved influenza vaccine knowledge among the elderly. Analysis of other influencing factors revealed that healthcare workers’ recommendations played a crucial role across all policy regions, though their impact on current-season vaccination behavior and next-season vaccination intention differed by subsidy context. Further studies are needed to explore the best approaches for optimizing region-specific subsidy strategies for promoting influenza vaccination among the elderly in China. Full article

(1) Background: Austria’s use of fully mechanized harvesting systems has been continuously increasing. Technical developments, such as traction aid winches, have made it possible to drive on increasingly steep terrain. However, this has led to challenges and potential hazards for the operators, resulting in higher stand damage rates and risks of workplace accidents. Since these systems and working environments involve a highly complex interplay of various parameters, the purpose of this protocol is to propose a new set of methodologies that can be used to obtain a holistic interpretation of the psychophysiological interrelationship between the working conditions and stress of harvester and forwarder drivers. (2) Methods: We developed a research protocol to analyse the (a) environmental and (b) machine-related parameters; (c) psychological and psychophysiological responses of the operators; and (d) technical outcome parameters. Within this longitudinal exploratory field study, experienced drivers were monitored for over an hour at the beginning and the end of their workday while operating in varying steep terrains with and without a traction aid winch. The analysis is based on macroscopic (collected using cameras), microscopic (eye-tracking glasses and AI-driven emotion recognition), quantitative (standardized questionnaires), and qualitative (interviews) data. This multimodal research protocol aims to improve the health and safety of forest workers, increase their productivity, and reduce damage to remaining trees. Full article

Stingless bees, particularly Tetragonula pagdeni, are vital for both ecosystems and the economy due to their pollination services and nest products. However, little is known about their nesting habits. This study investigated the nesting ecology of Tetragonula pagdeni in West Bengal, India. The species was found inhabiting a variety of landscapes, including agricultural, forest, rural, semi-urban, and urban areas, with a greater abundance in rural areas featuring mixed vegetation. Colonies, which were eusocial, perennial, and cavity-nesting, occupied diverse substrates, including tree trunks, building walls, rock crevices, electric poles, and field ridges—tree trunks and walls being the most common. Wild nests were located at heights ranging from 0 to 13.46 m, mostly around 2 m. Nest entrances varied in shape (circular, oval, slit-like, or irregular), with a longest opening axis of 10.50 ± 2.94 mm, and were oriented in multiple directions. Internally, nests measured 198.31 ± 86.36 mm in length and 142.73 ± 17.28 mm in width. Nests featured brood zones surrounded by honey and pollen pots, along with structure-supporting elements like the involucra and pillars. Brood cells were light brown and oval; those for workers and drones were similar, while queen cells were larger. Honey pots were light to dark brown, oval, dome-shaped, or irregular. Each involucrum was a thin, flat sheet, and the pillar was short, narrow, thread-like. These findings offer valuable insights into the distribution, nesting behaviour, and nest architecture of Tetragonula pagdeni, supporting its conservation and sustainable management. Full article

Background: Natural solutions, such as locally available food resources (LAFRs) and nontimber forest products (NTFPs), are recognized for their bioactive potential in addressing health challenges. Despite Mizoram’s rich biodiversity, the population faces increasing risks of noncommunicable diseases (NCDs) and micronutrient deficiencies (MNDs). Methods: This cross-sectional study assessed priority dietary preferences, food group consumption, dietary diversity score, and lifestyle practices, alongside a review of the nutraceutical potential of LAFRs and NTFPs. A three-day dietary recall was analyzed using t-tests at a 5% significance level against standards from the Indian Council of Medical Research (ICMR). One-way ANOVA was further employed to examine potential differences in food group consumption among occupational, gender, and age groups. Results: Results revealed strong cultural preferences for carbohydrate-rich breakfasts and meat-based dinners, with lunch often skipped or replaced by snacks. Over 85% of participants reported inadequate intake of milk, fruits, pulses, and nuts. Compared with older and high-income women, younger women exhibited the lowest intake of food groups and nutrient-dense foods. Occupation significantly influenced dietary patterns, with heavy workers consuming more cereals but fewer micronutrient-rich foods. A shift from traditional to modern dietary and lifestyle practices was observed, influencing overall diet quality and long-term health outcomes. The mean Dietary Diversity Score (0–10) was 5.6 ± 1.3, indicating significant gender differences in diet variety (males: 5.8 ± 1.2; females: 5.4 ± 1.4; p = 0.04). The review highlights that LAFRs and NTFPs serve as valuable sources of antioxidants, anti-inflammatory compounds, and bioactives with antidiabetic and anticancer properties while also providing essential micronutrients. Conclusions: The findings reveal a marked dietary transition in Mizoram and underscore the urgent need for food-based strategies to address nutrient gaps and the growing burden of NCDs. Full article

The genus Proceratium comprises rare but ecologically significant cryptobiotic predators of temperate and tropical forest litter. The group is known for its abdomen which can curl dorsally >90° relative to the body axis. Proceratium includes 130 described species worldwide. In China, seven species have been recorded, yet recent surveys repeatedly reveal morphologically distinctive undescribed taxa, indicating a still-underestimated diversity of Proceratium. In this study, seven known Chinese species of the ant genus Proceratium Roger, 1863 are reviewed: P. bruelheidei, P. itoi, P. japonicum, P. kepingmai, P. longmenense, P. shohei and P. zhaoi. The species P. longigaster is removed from the ant fauna of China. Additionally, seven new species are described: P. crassopetiolum sp. nov., P. digitospinum sp. nov., P. planodorsum sp. nov., P. quandratinodum sp. nov., P. rugiceps sp. nov., P. shanyii sp. nov., and P. spinosubum sp. nov. An illustrated key to Chinese species based on workers is also provided. Full article

Background: Scientific research on fly-in/fly-out (FIFO) workers has identified a gap in understanding the dynamics of job stress parameters among forest workers throughout the shift cycle. Methods: This study investigated the relationship between psychological and psychophysiological parameters of job stress and work capacity among loggers. The research was conducted during two simultaneous scientific expeditions in July 2024, involving 47 loggers from two teams with differing socio-psychological characteristics. Data were collected daily (morning and evening) using a battery of psychophysiological and psychological tests. Teams’ socio-psychological characteristics were assessed five times during the 15-day fly-in period. Results: The adaptation (beginning) and fatigue (end) phases of the shift were significantly more stressful than the middle period. During these critical phases, assessments of functional state showed greater consistency but were less favorable. Key findings indicate a psychological mobilization effect at the period’s start, where high subjective comfort coexisted with physiological strain. By the end, functional capabilities were maintained despite high fatigue. Furthermore, loggers in teams with a positive socio-psychological climate exhibited a more favorable functional state throughout the shift. Conclusions: The study’s novelty lies in its comprehensive mapping of the dynamic interplay between job stress and work capacity across the FIFO cycle, using both instrumental and questionnaire-based methods. The results underscore the critical influence of the team’s socio-psychological climate on worker well-being and highlight specific high-stress phases that warrant targeted interventions. Full article

As a typical enclosed engineering microenvironment, tunnel construction sites exert a profound influence on workers’ unsafe behaviors. This impact is particularly significant in cold regions, where extreme environmental conditions are more likely to trigger unsafe behavior among construction workers. This study utilized two exemplary tunnels in cold regions of China as case studies. During the construction period, microenvironmental data were systematically collected, encompassing temperature, humidity, noise, and dust concentration. In parallel, data on workers’ unsafe behaviors were integrated to construct a nonlinear relationship model, and the importance of each microenvironmental variable was assessed using the random forest algorithm. The results indicate that various microenvironmental factors exhibit significant nonlinear effects on unsafe behavior. Among them, dust concentration had the strongest impact (22.56%), followed by noise (17.40%), humidity (15.02%), and temperature (9.21%). Specifically, the maintenance of temperature control close to 0 °C, humidity levels maintained at 60% to 65%, noise levels not exceeding 82 dB, and dust concentrations below 12 mg/m³ contributed to a significant reduction in unsafe behavior scores. The present study investigates the mechanism of the microenvironment of cold-region tunnel construction on personnel behavioral risk. The study’s findings provide a threshold reference and strategy support for safety optimization and engineering site management of cold-region tunnel construction environments. Full article

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R² values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers. Full article