Search Results (83)

As global environmental challenges intensify, there is an increasing need to equip younger generations with the knowledge, values, and sense of responsibility necessary for a sustainable future. This study explores how environmental citizenship education (ECE), implemented through a nature-based learning program within a Nature and Science School (NSS) in Türkiye, was experienced and interpreted by primary school pupils in relation to their development of understanding of sustainability and environmental citizenship. NSSs, integrated into the formal education system by the Turkish Ministry of National Education, offer inquiry-driven and experiential learning in natural settings. The study took place in Talat Göktepe Grove, a biodiverse site including forest and marine ecosystems, where a four-month ECE program was conducted. A holistic single-case study design was employed, drawing on pupil diaries and semi-structured interviews. A total of 88 pupils engaged in structured outdoor activities addressing biodiversity, sustainability, and the climate crisis. Initially, pupils described environmental citizenship through individual actions. Over time, their perspectives expanded to include civic participation, environmental rights, and collective responsibility. Their reflections also revealed a more nuanced understanding of sustainability, encompassing concepts such as ecosystem balance, renewable energy, and environmental justice. The study provides insight into how nature-based education may support meaning-making around environmental citizenship and sustainability in early education. Full article

Amid urbanization, many forest farmers have migrated for work, leading to a shortage of young labor in forestry. Socialized prevention and control (SPC) measures have emerged as a new forestry model. By integrating forestland property rights theory, SPC economic principles, and collaborative disaster governance, this study compares the econometrics methods of seemingly unrelated regression (SUR) and structural equation models (SEMs) to address correlation and endogeneity issues. It aims to understand forest farmers’ willingness to pay for SPC services, purchase forest insurance, and join as forest rangers. The findings offer theoretical and practical insights that address current challenges and rationalize SPC promotion costs, filling gaps in the existing literature. The results indicate that high-quality foresters with more home-planted forests are more inclined to hire SPC companies, while better-educated farmers are less likely to purchase forest insurance. Western forest farmers, highly reliant on forests, show greater willingness to become rangers under village committee organization. Farmers organized by committees or with prevention experience suggest SPC costs around USD $65/ha and forest premiums at USD $5/ha, with high-quality farmers proposing a ranger salary of USD $190/month. Recommendations include collecting SPC funds from farmers and supplementing through local finances; enhancing the forest insurance system; monitoring SPC companies; and recruiting young, skilled rangers. Full article

China’s population is aging rapidly, with a large proportion of elderly individuals “aging in place”. In central areas of large cities, the amount of community and home-based elderly care services provided by the government and for-profit organizations are insufficient to meet the demands of these “aging in place” elderly. Taking the core area of Beijing as the spatial scope, this empirical study collects the demand on services of the main types of elderly residents in community and home-based dwelling through questionnaires (n = 242) and employs a mixed-methods approach for analysis. Descriptive statistics and exploratory factor analysis are used to determine the categories and levels of those demands, and machine learning methods (random forest regression model) are used to calculate the importance of various influencing factors (features of the elderly and subdistricts’ built environment) on them. It is shown that elderly residents have a higher demand for psychological and physical condition maintenance services (mean = 3.40), and a lower demand for reconciliation and rights defense services (mean = 3.08). The results also show that the built environment factors are very important for the elderly on choosing demands, especially mean distance of CECSs (community elderly care stations) to downtown landmarks and main roads in subdistricts, and characteristics of CECS. The elderly’s own features also have a relatively important impact, especially their living arrangements, caregivers, and occupations before retirement. This study applies machine learning techniques to sociological survey analysis, helping to understand the intensity of elderly people’s demand for various community and home-based elderly care services. It provides a reference for the allocation of such service resources. Full article

As a kind of proof of the rights and interests of forest resources, the implementation of forest ticket is an important measure for China to revitalise collective forest land resources and promote the transformation of forest resources into economic development advantages, which is of great significance to the sustainable development of forestry. Based on the dynamic evolutionary game method of multiple subjects, this paper constructs an evolutionary game model of state-owned forest farms, village collective economic organisations, and forest farmers; analyses their strategy choices and the stability of the equilibrium point of the game system; and examines the influence of different parameter values on the model strategy evolution through combination with numerical simulation methods. The results show that the level of knowledge and participation in the forest ticket system significantly influence the optimal equilibrium strategy of each subject of forest cooperative management. The optimal strategy is only when the evolution of the game model is stable at a high level of input, participation, and high willingness to participate. In addition, the forest market environment and the level of inputs from the agents also affect the rate of stabilisation of behavioural strategies. Full article

In forested watersheds, suspended sediment concentration (SSC) is an important parameter that impacts water quality and beneficial use. Water quality also has impacts beyond the stream channel, as elevated SSC can violate Indigenous sovereignty, treaty rights, and environmental law. To address elevated SSC, watershed partners must understand the dynamics of the sediment regime in the basins they steward. Collection of additional data is expensive, so this study presents modeling and analysis techniques to leverage existing data on SSC. Using data from the South Fork Clearwater River in Idaho County, Idaho, USA, we modeled SSC over water years 1986–2011 and we applied regression techniques to evaluate correlations between SSC and natural disturbances (channel-building flow events) and anthropogenic disturbances (timber harvesting, hazardous fuel management, controlled burns, and wildfire). Analysis shows that SSC did not change over the period of record. This study provides a monitoring program design to support future decision making leading to reductions in SSC. Full article

Forest management plans are the bibles of forest management. On the basis of these plans, farmers play essential roles in forest cultivation, protection, and utilization. After the forest tenure reform in the 2000s in China, the status of farmers has changed. For example, collective management has decreased and household management has become a leading structure of operation and management. Farmer’s dependence on income from forests has increased, which is reflected in their increased participation in management. However, insights into farmers’ perceptions of and willingness to participate in the formulation of forest management plans are insufficient. This study analyzes the factors influencing farmers’ participation by using an econometric model based on 1752 samples of farmer households from 10 counties. The empirical results reveal that according to farmers, forest type and property rights influence their willingness to participate in the formulation of forest management plans. In addition, whether there is a village leader, the village distance from town, the circulation of forest land, the area of woodland, timber price, and forestry income have a significant positive impact on farmers’ willingness to participate in forest management plan formulation; the level of education and non-agricultural income have a significant negative impact on farmers’ willingness to participate in forest management program development. Finally, this study proposes to improve and deepen the reform of the forest ownership system, encourage land circulation, and give play to the role of village leaders in promoting the participation of farmers in forest management plans, so as to improve the efficiency of forest management. Full article

Humans often cross their legs unconsciously while sitting, which can lead to health problems such as shifts in the center of gravity, lower back pain, reduced blood circulation, and pelvic distortion. Detecting unconscious leg crossing is important for promoting correct posture. In this study, we investigated the detection of leg-crossing postures using machine learning algorithms applied to data from body pressure distribution sensors. Pressure data were collected over 180 s from four male subjects (25.8 ± 6.29 years old) under three conditions: no leg crossing, right-leg crossing, and left-leg crossing. Seven classifiers, including support vector machine (SVM), random forest (RF), and k-nearest neighbors (k-NN), were evaluated based on accuracy, recall, precision, and specificity. Among the tested methods, k-NN demonstrated the highest classification performance, suggesting it may be the most effective approach for identifying leg-crossing postures in this study. Full article

As a new driving force for economic growth, the digital economy has had a profound impact on the labor market. While the existing research has explored the role of the digital economy in job substitution, creation, and polarization effects, the research on the impact on the social insurance fund income is relatively scarce. In view of this, based on the provincial panel data from 2011 to 2020, this paper analyzes the effect and mechanism of the digital economy on the pension income by using the moderated chain intermediary model and random forest regression. The results show that: (1) the employment scale, labor income, industrial structure, and government intervention are the important factors affecting the income of urban pension insurance; (2) the development of the digital economy has a negative impact on the income of the basic pension insurance fund for urban employees, and the chain intermediary effect that indirectly affects the employment scale and labor income through promoting the upgrading of the industrial structure has a negative impact on the income of the pension insurance fund. The employment scale and employment income of the industries with high and low substitution rates have a significant impact; (3) government intervention can regulate the negative impact of the digital economy development on the pension fund income. Furthermore, taking the transformation and reform of social security collection and payment institutions in July 2018 as an opportunity, the analysis using the event study method found that the average level of the pension income in the regions where the tax department was fully responsible increased significantly compared with the regions where the social security department collected it. Therefore, in order to maintain the sustainability of the pension fund income and effectively prevent the problem of old-age poverty caused by the “silver wave” and the lack of protection of workers’ rights and interests, institutional innovation should be promoted, the current tax policy should be adjusted, and the inclusiveness and flexibility of the pension security system should be improved. Digital technology should be used to improve the government’s intervention capacity and management level, and promote the positive interaction between the digital economy and the pension insurance system. Full article

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies. Full article

To provide consistent air purification benefits from urban forests, it is crucial to identify common characteristics that allow for similar experimental setups. This study aimed to analyze $\mathrm{P}{\mathrm{M}}_{10}$ concentrations in urban forests near pollution sources and understand their mitigation effects. Data from the Asian Initiative for Clean Air Networks, Korea, were used, focusing on three urban forests adjacent to road and industrial pollution sources in Korea, with $\mathrm{P}{\mathrm{M}}_{10}$ concentrations collected during 2021. Considering high $\mathrm{P}{\mathrm{M}}_{10}$ concentrations during winter and spring, these seasons were divided into two sub-periods, resulting in six seasonal periods for analysis. To address the right-skewed $\mathrm{P}{\mathrm{M}}_{10}$ distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” $\mathrm{P}{\mathrm{M}}_{10}$ levels were lowest in early spring, increasing to a peak in summer before declining. High $\mathrm{P}{\mathrm{M}}_{10}$ events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median $\mathrm{P}{\mathrm{M}}_{10}$ concentrations in urban forests compared to pollution sources in the latter half of the year, while no significant median differences were found in the first half. Distribution visualizations further confirmed that even during high $\mathrm{P}{\mathrm{M}}_{10}$ periods, all urban forests showed lower $\mathrm{P}{\mathrm{M}}_{10}$ values compared to pollution sources. In conclusion, $\mathrm{P}{\mathrm{M}}_{10}$ concentrations in urban forests were consistently lower than in pollution sources across all seasons, demonstrating their effectiveness in air purification at both road and industrial pollution sources. Future research should consider additional variables, such as $\mathrm{P}{\mathrm{M}}_{2.5}$, to further explore differences between pollution sources. Full article

Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism. Full article

Although collective forest tenure reform (CFTR) has improved the legal tenure security of forestland, its impact on increasing farmers’ income is unsustainable. This study used a multiple linear regression model to empirically analyze data from 505 farmers in Jiangxi Province, examining the impact of legal, actual, and perceived tenure security on rural household income, and incorporating migration into the framework. The findings indicate that both actual and perceived tenure security have a substantial positive impact on the total rural household income and forestry income. However, it is worth noting that legal tenure security only has a positive effect on forestry income. Furthermore, outside-of-county labor migration can serve as a mediator for the income effects of actual and perceived tenure security. However, the mediating effect of intra-county labor migration is not considerable. The study found that the increase in income due to the security of actual tenure security is significant for the group of people who own less than 50 mu of forestland. However, both actual and perceived tenure security have a significant impact on income for the group of people who own more than 50 mu of forestland. The aforementioned findings indicate that, in the ongoing extensive advancement of collective forest right reform, it is crucial to prioritize the execution of forest reform policies at the local level and enhance farmers’ awareness and comprehension of said policies. In addition, the government should enhance the monitoring system for policy implementation and intensify efforts in publicizing these policies, in order to fully utilize the benefits of CFTR. Full article

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions. Full article

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing. Full article

Peatlands play an important role in the global environment and the well-being of humans by providing valuable ecosystem services. Yet, anthropogenic activities pose significant hazards for peatland management, including low levels of community participation due to lack of awareness and financial incentives. Understanding the social–cultural and economic value of these ecosystems will raise awareness to protect these important ecosystems. Here, we estimated a total economic value (TEV) of peatland ecosystem services and examined relationships between the TEV and landscape characteristics in Riau province, Indonesia. A questionnaire was used to investigate household socioeconomics, perception of peatland importance, peatland product collection, and willingness to pay for habitat and biodiversity protection from May to June 2023. A total of 200 household individuals (92% confidence) in five villages across distinct landscapes in the Sungai Kiyap-Sungai Kampar Kiri Peatland Hydrological Unit participated in the survey. The respondents obtained numerous advantages from the peatlands with an estimated TEV of USD 3174 per household per year (about 1.3 times their annual income). Approximately 81% showed a use value, especially food provisioning from fish and soil fertility. To a lesser extent, non-use values included a habitat for endemic and endangered species, biodiversity conservation for future generations, and community bonds with sacred forests. The landscape characteristics, illustrating habitat types, biophysical conditions, and property rights regimes, interplay with the relative benefits derived from the peatlands. Proximity to secondary peat swamp forests and riparian zones, especially within protected areas, enhanced economic value. Protected area co-management is essential to balance peatland conservation with sustainable livelihoods. Primary forests need restrictive protection. Meanwhile, buffer zone designation and agroforestry practices, especially in the peatland–farm interface, reduce land use tensions and promote local stewardship. This study can be used as a reference by planners and policymakers to recognize factors that promote effective peatland management, especially those that balance ecosystem protection and livelihood maintenance. Full article