Search Results (36,959)

Despite existing research on child undernutrition in the tropics, a comprehensive understanding of how weather patterns impact childhood growth remains limited. This study summarizes and estimates the effect of rainfall and temperature patterns on childhood linear growth among under-fives in the tropics. A total of 41 out of 829 studies were considered based on preset inclusion criteria. Standardized regression coefficients (β) were used to estimate effect sizes, which were subsequently pooled, and forest plots were generated to visually represent the effect size estimates along with their 95% confidence intervals. Of the total reports, 28 and 13 research articles were included in the narrative synthesis and meta-analysis, respectively. The studies establish that patterns in rainfall and temperature either increase or decrease childhood linear growth and the risk of stunting. An increase in every one standard deviation of rainfall results in a 0.049 standard deviation increase in linear growth (β = 0.049, 95% CI: 0.024 to 0.073). This positive association is likely mediated by various factors. In countries where agriculture is heavily dependent on rainfall, increased precipitation can lead to higher crop yields which could in turn result in improved food security. The improved food security positively impacts childhood nutrition and growth. However, the extent to which these benefits are realized can vary depending on moderating factors such as location and socio-economic status. Temperature pattern showed a negative correlation with linear growth, where each standard deviation increase resulted in a decrease in linear growth by 0.039 standard deviations, with specific impacts varying by regional climates (β = −0.039, 95% CI: −0.065 to −0.013). Additionally, our meta-analysis shows a small but positive relationship of childhood stunting with temperature pattern in western Africa (β = 0.064, 95% CI: 0.035, 0.093). This association is likely due to temperature patterns’ indirect effects on food security and increased disease burden. Thus, the intricate interaction between weather patterns and childhood linear growth requires further research to distinguish the relationship considering other factors in the global tropics. While our findings provide valuable insights, they are primarily based on observational studies from sub-Saharan Africa and may not be generalizable to other tropical regions. Full article

While Kerala’s transition from an agrarian to a service-oriented economy is widely acknowledged, discussions are most often confined to material domains, overlooking overall life satisfaction, which is critical to pursue any profession. This state-wide community-based cross-sectional survey was conducted to gather data from farmers residing in three geographical zones of Kerala, India, North, South and Central, to understand their life satisfaction and how it correlates to their access to resources and social support. From each zone, we randomly chose two districts, and from each district, two panchayats. From each panchayat, we chose one ward to identify a total of 580 eligible farmers. Structured interviews were conducted using door-knock surveys to elicit information from the respondents using a set of standardized questionnaires. The results suggest that the respondents had a mean age of 54.5, with 19.8% being over 65, indicating demographic ageing in the farming sector. Only 46% chose farming as their full-time occupation. Farmers with higher levels of education who owned large plots of land experienced life satisfaction. Interpersonal relationships and social support were significant determinants of life satisfaction, as these factors were pivotal in their access to formal and informal services. With social support being pivotal in life satisfaction, it is imperative to change the social mindset towards farming. Additionally, the government should promote advanced technologies and high-yielding agricultural practises to transform the economic landscape of Kerala in favour of agriculture, which is imperative for the food security of the state. Full article

Financial inclusion is essential in achieving sustainable development goals in emerging countries. Goal number five of the sustainable development agenda, “Gender equality”, depends on women’s financial empowerment. It enhances women’s ability to make decisions, access resources and income, and control benefits from economic gains and power. The paper investigates the relationship between financial inclusion and women’s financial worries, using the 2021 Global Financial Inclusion Survey recently published by the World Bank for the Kingdom of Saudi Arabia. This paper examines the effects of several aspects of inclusive finance, access, usage, and quality on women’s financial worries. The research implements an ordered probit analysis and contributes to the literature on women’s financial empowerment and inclusion by demonstrating that having access to financial services empowers women to make decisions about their lives, which greatly improves their welfare and sense of security. These findings provide policymakers with different targeted policies and strategic management practices to substantially improve financial inclusion and alleviate financial concerns within the population, especially among women. Full article

The region along the Great Wall is a typical dryland agricultural zone, serving as both a potential area for staple grain production and a key region for specialty crops like coarse grains and cool-climate vegetables. Studying the characteristics of drought during the spring sowing period is crucial for developing diversified planting strategies and ensuring food security. This study analyzes the drought conditions along the Great Wall from 1960 to 2023, revealing the spatial and temporal distribution of drought in the region and quantifying the impact of climate change on drought frequency and intensity. By doing so, it fills a gap in the existing drought research, which often lacks the long-term, multi-dimensional analysis of spring sowing drought characteristics. Using daily meteorological data from April 20 to May 20 during the spring sowing period between 1960 and 2023, the study employs the Meteorological Composite Drought Index (MCI) to quantitatively identify drought conditions and examine the spatial and temporal evolution of drought in the region. The results show that, on a daily scale, the frequency of mild and moderate droughts is 60.45% and 25.19%, respectively, with no occurrences of severe or extreme drought. On an annual scale, the intensity of drought and the ratio of affected stations show an increasing trend, with a decrease in mild drought frequency and an increase in moderate and severe drought occurrences. Additionally, the spatial distribution of drought frequency follows a pattern of “higher in the east than in the west” and “higher in the north than in the south”. The study also finds that the migration of drought frequency centers shows a clear temporal evolution, with the center shifting southwestward from the 1960s to the 2000s, and then moving northeastward from the 2000s to 2023. These findings provide critical data support for optimizing agricultural drought resistance strategies and offer new insights for future research on the relationship between drought and climate change. It is suggested that agricultural practices and water resource management policies should be adjusted according to the spatial migration of drought centers, with a particular focus on optimizing drought mitigation measures during the spring sowing period. Full article

The increasing volume of motorized traffic not only negatively impacts the structural preservation and overall planning of individual buildings within the block but also disrupts the originally harmonious and pleasant spatial environment of the area. Walking, as a primary mode of urban transportation, plays a crucial role in preserving the unique characteristics of historical blocks, enhancing the quality of the urban environment, and achieving long-term sustainable urban development. This study takes the Five Avenues historical block as a case, assessing the current walking environment from the perspective of Post-Occupancy Evaluation (POE). Machine learning techniques (including web scraping, the TF-IDF algorithm, and the LDA model) were employed to collect and analyze user feedback data, assisting in constructing walking environment satisfaction indicators. A total of 19 key factors affecting walking satisfaction were identified. Paired sample t-tests, ANOVA, and reliability and validity analyses were applied to examine the feasibility and practicality of the questionnaire content. Finally, using Importance–Performance Analysis (IPA), the improvement priorities for walking environment indicators were clearly defined. Although the overall satisfaction index of the Five Avenues is comparatively high, unobstructed pathways have the greatest impact on walking environment satisfaction, followed by the rationality of guiding signage facilities, and then by public security management and facility maintenance. Furthermore, visitors prioritize factors such as the cultural recognizability of the area, travel convenience, green space accessibility, and the sidewalk width proportion; they are less focused on the functional aspects of the walkways. Based on the analysis results from POE and machine learning, targeted strategies for improving the walking environment in historical blocks were proposed, aiming to provide a more comprehensive basis for improving the walking environments of similar blocks. Full article

In Pakistan, the majority of people have access to energy supplies. However, people who are underprivileged, below the extreme poverty line, or part of the middle class often spend disproportionate portions of their income on energy supplies and services, to some extent because of higher upfront prices for energy supplies, expensive products, and expensive imported appliances. The nonavailability of low-cost energy supplies is mainly affecting underdeveloped regions that have mostly low-income households. We used the dynamic ordinary least squares method to look at the impact of the energy crisis on income inequality from 1997 to 2021. The results show that the energy crisis exacerbates income inequality as low-income groups end up spending more significant shares of their income on energy products, supplies, and services than higher-income groups. Fair and equal access to energy supplies and services is less likely to reduce income inequality if prices are not cost-efficient. Cautious deliberation regarding the structure of energy tariffs is inevitable; at the same time, safety nets and social security programs for the poorest groups need to be expanded. At this stage, the aim is to target energy prices that will achieve the objectives of reducing polarity and increasing real income. Full article

In this paper, we analyze the confidentiality of a hybrid radio frequency (RF)/free-space optical (FSO) system with regard to physical layer security (PLS). In this system, signals are transmitted between the source and destination using RF and FSO links, with the destination employing the maximal-ratio combining (MRC) scheme. A non-cooperative target (NCT) is assumed to have eavesdropping capabilities for RF and FSO signals in both collusion and non-collusion strategies. The Nakagami-m distribution models fading RF links, while FSO links are characterized by the Málaga (M) distribution. Exact closed-form expressions for the system’s secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived based on the generalized Meijer G-function with two variables. Asymptotic expressions for the SOP are also obtained under high-signal-to-noise-ratio (SNR) regimes. These conclusions are validated through Monte Carlo simulations. The superiority of the hybrid RF/FSO system in improving the communication security of a single link is confirmed in its comparison with conventional means of RF communication. Full article

Utilizing groundwater offers a promising solution to alleviate water stress in Ethiopia, providing a dependable and sustainable water source, particularly in regions with limited or unreliable surface water availability. However, effective decision-making regarding well drilling and placement is essential to maximize groundwater resource potential, enhancing agricultural productivity, reducing hunger, and bolstering food security in Ethiopia. This study concentrates on the development of two-stage stochastic mixed integer programming (SMIP) models to optimize well placement for sustainable agricultural irrigation, considering uncertain demand scenarios. Additionally, a deterministic mixed integer programming model is formulated for comparison with the two-stage SMIP. Experiments are conducted to explore various demand scenario distributions, revealing that the optimized total cost for the two-stage SMIP generally exceeds that of a deterministic setting, aligning with the two-stage SMIP’s focus on long-term benefits. Moreover, slight differences are observed in well layouts under different assumption scenarios. The study also examines the impact of selected parameters, such as fixed construction costs, per-meter drilling costs, and demand scenarios. The out-of-sample performance shows that the stochastic model is more flexible and resilient, with 11% and 4% lower costs than deterministic cases 1 and 3, respectively. This flexibility provides a more robust long-term strategy for well placement and resource allocation in groundwater management. Full article

Pesticides are essential to modern agriculture, significantly enhancing crop yields and quality to ensure global food security. Their critical role in preventing hunger is highlighted by the notable increase in global pesticide trade over the past decade. In Indonesia, pesticide usage has surged, with a 24% rise in registered pesticide formulations between 2017 and 2021. However, this increase presents challenges, particularly in the disposal of empty pesticide containers (EPC), which pose substantial environmental and health risks if improperly managed. To address this, a closed-loop supply chain (CLSC) is proposed as an effective, eco-friendly solution for the management of EPC waste. This study evaluates the implementation of a CLSC for EPC in Indonesia, employing rich picture analysis and stakeholder interviews to identify key challenges, opportunities, and strengths. Notable challenges include regulatory gaps, financial and operational feasibility, and limited farmer engagement. However, opportunities exist in enhancing environmental sustainability, generating economic benefits, and gaining support from industry groups. One key strength is the widespread practice of triple rinsing among farmers, with 62.63% of respondents already adopting this method. This study underscores the important of establishing clear and enforceable regulations, introducing financial incentives and supportive policies, promoting public–private partnerships, creating targeted behavior change interventions, and ensuring organizational commitments and training programs. These insights are crucial in developing a sustainable CLSC, ensuring both environmental and economic benefits. Full article

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images—IV-YOLO—to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond. Full article

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time. Full article

This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P₂O₅), potassium (K₂O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security. Full article

Exploring the interrelationships and influencing factors of the multifunctionality of cultivated land is crucial for achieving its multifunctional protection and sustainable use. In this paper, we take the Min River basin as a case study to construct a multifunctional evaluation system based on “agricultural production, social security, ecological service, and cultural landscape” using multi-source data. We analyze the spatial and temporal characteristics of the multifunctionality of cultivated land through kernel density estimation (KDE) and visual mapping. Subsequently, we assess the trade-off strength between the multifunctional aspects of cultivated land using the root mean square error (RMSD). Finally, we identify the drivers of the multifunctional trade-off intensity of cultivated land and analyze their influencing mechanisms using Geographic Detectors. The results show that (1) from 2010 to 2020, the multifunctional structure of cultivated land in the study area underwent significant changes: the levels of agricultural production, social security, and ecological service functions first increased and then decreased, while the levels of cultural landscape function and comprehensive function continued to increase. The spatial distribution is characterized, respectively, by “high in the east and low in the west”, “high in the west and low in the east”, “high in the north and low in the south”, “high in the whole and sporadically low in the northeast”, and “high in the middle and low in the surroundings”. (2) During the study period, the trade-off strengths related to social security functions increased, while the trade-off strengths of the remaining multifunctional pairs of cultivated land showed a weakening trend, with high values of trade-off strengths among functions particularly prominent in the Nanping Municipal District. (3) Both natural and human factors significantly affect the multifunctional trade-off strength of cultivated land. Among the specific factors, elevation, slope, average annual temperature, and per capita GDP are the key factors influencing the strength of the trade-offs between functions. The results of this study provide empirical support for enriching the understanding of the multifunctionality of cultivated land and offer a decision-making basis for promoting the differentiated management of cultivated land resources and the synergistic development of its multifunctionality. Full article

Against the backdrop of urban–rural integration development in China, the government has further strengthened its support for rural migrant workers’ urban employment and entrepreneurship, as well as their urbanization. Nevertheless, influenced by the current urban–rural development environment and public policies, a portion of rural migrant workers have evolved into two distinct groups: those with clear intentions to settle in cities or return to their hometowns and those trapped in a “neither-nor” situation, who are unable to fully integrate into urban life or seamlessly return to the countryside. This study, based on field investigations conducted in 21 cities across seven major geographical regions in China, reveals a ternary differentiation in migration intentions among rural migrant workers: “Retain in the cities”, “Return flow”, and “Blind direction”. In advancing the urban–rural integration strategy, the “clearly-oriented” groups, choosing to stay or return, can serve as dynamic forces in reconciling resources between urban and rural areas, thereby fostering closer urban–rural ties. Conversely, the “aimless wandering” group, characterized by stagnation, confusion, frequent mobility, and recurrent unemployment, poses numerous negative impacts on urban–rural interaction and coordination, hindering the in-depth integration of urban and rural areas to some extent. Drawing upon the internationally recognized Push-Pull Theory and Social Integration Theory within the field of population migration, this study conducts an empirical analysis of large-scale sample data to explore the characteristic factors and formation mechanisms underlying the “staying”, “returning”, and “blind direction” intentions, with a particular focus on the internal dynamics shaping and evolving the “aimless wandering” group. Finally, grounded in the empirical findings, this study advocates a collaborative approach between the Chinese government and various sectors of society to address the issue by promoting employment guidance, enhancing security measures, and other initiatives that encourage rural migrant workers to either stay in cities or return to their hometowns. It aims to provide policy recommendations for a definitive resolution of the rural migrant worker issue during the crucial period of urban–rural integration development. Full article

Reducing emissions from energy production and enhancing the capacity of land use systems to store carbon are both important pathways towards greenhouse gas neutrality. Expanding photovoltaics (PV) contributes to the former, while the rewetting of drained peatlands preserves the peat soil as long-term carbon store, thus contributing to the latter. However, both options are usually considered separately. This study analyses Peatland PV, defined as the combination of open-space PV with the rewetting of peatlands on the same site, and has an explorative and field-defining character. Due to a lack of empirical data, we used expert interviews to identify the strengths and weaknesses, opportunities, and threats of Peatland PV in the sparsely populated and peatland-rich state of Mecklenburg-Western Pomerania in North-East Germany. The material was analysed using a qualitative content analysis and compiled into SWOT and TOWS matrices. Besides the ecological and technological dimensions, this study focuses on the economic and legal framework in Germany. We found that Peatland PV may mitigate land use conflicts by contributing to climate and restoration targets, energy self-sufficiency, and security. Continued value creation can incentivize landowners to agree to peatland rewetting. Technical feasibility has, however, a significant influence on the profitability and thus the prospects of Peatland PV. Although Peatland PV has recently been included in the Renewable Energy Sources Act (EEG), several specialised legal regulations still need to be adapted to ensure legal certainty for all stakeholders. Pilot implementation projects are required to study effects on vegetation cover, soil, peatland ecosystem services, biodiversity, hydrology, and water management, as well as to analyse the feasibility and profitability of Peatland PV. Full article