Search Results (13,994)

The broad-spectrum insect growth regulator (IGR) and insecticide 2-((1-(4-Phenoxyphenoxy)propan-2-yl)oxy)pyridine (MPEP; also known as pyriproxyfen) is increasingly being used to address public health programs for vector control, initiated by the spread of Zika virus in 2015–2016. While considered relatively safe for humans under normal conditions, limited toxicology data are available. Current studies were undertaken to address the data gap regarding potential immunotoxicity of MPEP, with particular emphasis on host resistance to viral infection. Hsd:Harlan Sprague Dawley SD^® rats were treated for 28 days by oral gavage with doses of 0, 62.5, 125, 250 or 500 mg/kg/day of MPEP in corn oil. There was a dose-dependent increase in liver weights which is consistent with the liver playing a dominant role in MPEP metabolism. However, no histological correlates were observed. Following treatment, rats were subjected to a battery of immune tests as well as an established rat model of influenza virus infection to provide a comprehensive assessment of immune function and host resistance. While several of the immune tests showed minor exposure-related changes, evidenced by negative dose–response trends, most did not show significant differences in any of the MPEP treatment groups relative to vehicle control. Most notable was a negative trend in pulmonary mononuclear cell phagocytosis with increases in dose of MPEP. There was also a positive trend in early humoral immune response (5 days after immunization) to keyhole limpet hemocyanin (KLH) as evidenced by increased serum anti-KLH IgM antibodies which was followed later (14 days following immunization) by decreasing trends in anti-KLH IgM and IgG antibody levels. However, MPEP treatment had no effect on the ability of rats to clear the influenza virus nor the T-dependent IgM and IgG antibody response to the virus. The lack of effects of MPEP on host resistance to influenza suggests the immune effects were minimal and unlikely to present a hazard with respect to susceptibility to respiratory viral infection. Full article

Smart cities leverage digital marketing to promote sustainability and build a distinctive global branding. Despite its growing significance, the role digital marketing in smart city development remains underexplored. This study aims to fill this gap by employing bibliometric analysis of 1908 articles indexed in the Scopus database (2000–2024), using the Bibliometrix R-Studio (version 1.4.1743) and VOSviewer (version 1.6.20). The analysis reveals two thematic clusters: (1) “Digital Innovation and Sustainability”, which emphasizes technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data for energy efficiency and green urban development; and (2) “Governance and Policy”, which highlights digital marketing’s role in enabling participatory governance, citizen engagement, and inclusive urban policies. Findings underscore that digital marketing is not only a strategic communication channel but also a driver of sustainable urban transformation. By synthesizing insights from urban planning, technology, and sustainability, this paper provides a novel perspective on the intersection of digital marketing and smart cities. The results provide valuable guidance for policymakers, city planners, and researchers to harness digital marketing in promoting sustainability and further develop the smart city concept. Full article

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

Background: Micronutrient deficiencies, particularly among pregnant women and children under five years old, remain a significant public health challenge in Nigeria. Despite existing policies and programmes, national data on prevalence and risk factors are fragmented. Objective: To synthesise the current evidence on the prevalence of key micronutrient deficiencies and associated risk factors among pregnant women and children under five years old in Nigeria. Methods: A systematic review and meta-analysis were conducted using peer-reviewed studies that were published between 2008 and 2024. The databases searched included PubMed, Scopus, and African Journals Online. After screening 1207 studies, 37 studies were included: 27 were conducted among pregnant women and 10 were among children. A meta-analysis was conducted to estimate the anaemia prevalence using a random-effects model. A narrative synthesis was conducted to synthesise evidence on other micronutrients (i.e., magnesium, copper, and vitamins C and E) due to the limited data and risk factors. Results: The pooled prevalence of anaemia was 56% among children and 54% among pregnant women. The prevalence of other micronutrient deficiencies varied widely, with a high prevalence of zinc (86.4%), magnesium (94%), and vitamin D (73.3%) deficiencies in certain regions. The identified risk factors included poor dietary diversity, lower socioeconomic status, low maternal education, infection burden, and early or high parity. Most studies were facility-based and sub-national, limiting the generalisability. Conclusions: This review highlights a high prevalence of anaemia and micronutrient deficiencies among pregnant women and children in Nigeria. Key risk factors included a poor diet, low maternal education, infections, and reproductive health challenges. Targeted, multisectoral policies are urgently needed to address these gaps and improve health outcomes. Full article

What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition. Full article

The inspiration behind this specific research is based on addressing the growing need to improve road safety via the application of the Internet of Things (IoT) system. Although several investigations have discovered the possibility of IoT-based accident recognition, recent research remains fragmented, usually concentrating on outdated science or specific use cases. This study aims to fill that gap by carefully examining and conducting a comparative analysis of 101 peer-reviewed articles published between 2008 and 2025, with a focus on IoT systems for accident recognition techniques. The review categorizes approaches depending on the sensor used, incorporation frameworks, and recognition techniques. The study examines numerous sensors, such as Global System for Mobile Communications/Global Positioning System (GSM/GPS), accelerometers, vibration, and many other superior sensors. The research shows the constraints and advantages of existing techniques, concentrating on the significance of multi-sensor utilization in enhancing recognition precision and dependability. Findings indicate that, although substantial improvements have been made in the use of IoT-based systems for accident recognition, problems such as substantial implementation costs, weather conditions, and data precision issues persist. Moreover, the research acknowledges deficiencies in standardization, as well as the requirement for strong communication systems to enhance the responsiveness of emergency services. As a result, the study suggests a plan for upcoming developments, concentrating on the incorporation of IoT-enabled infrastructure, sensor fusion approaches, and artificial intelligence. This study improves knowledge by offering an extensive viewpoint on IoT-based accident recognition, providing insights for upcoming research, and suggesting policies to facilitate implementation, eventually enhancing worldwide road safety. Full article

Equine influenza is a highly contagious disease caused by the equine influenza virus (EIV). The occurrence of EIV outbreaks in America is associated with low levels of vaccination coverage. In Colombia, no seroprevalence evaluation has been carried out to estimate the distribution of the virus within the country. Our aim was to perform a sero-epidemiological survey of equine influenza infections and to identify associated risk factors in horses from four departments of Colombia. Serological testing was carried out by using an ELISA for the detection of IgG antibodies against the influenza A virus. The evaluation of epidemiological variables, clinical manifestations, and vaccination history was carried out through the application of a data collection instrument. Among the 385 horses analyzed, 27% of the samples tested positive, with a higher prevalence in Study 1 from horses with respiratory symptoms (40.4%) than in Study 2 from horses without clinical signs (16.1%). Only horses housed in stables had higher odds of testing positive. The study also revealed that unvaccinated horses were 68% less likely to test positive than vaccinated horses were. This research highlights a significant gap in vaccination coverage and the presence of antibodies even in asymptomatic horses. Management factors such as activity type and housing should be considered when strategies for EIV prevention are developed. Full article

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. Full article

Root reinforcement in soil plays a critical role in maintaining forest slope stability. However, accurately estimating the reinforcement provided by the entire root system of a mature tree remains a time-intensive task. Previous experimental studies on root reinforcement have predominantly focused on small trees, leaving a knowledge gap concerning larger specimens. This study integrates field pullout test data of individual roots, analyses of root geometry distribution within root systems, and theoretical frameworks, including root distribution and Root Bundle Models, to develop methods for estimating root reinforcement across varying tree sizes. The findings indicate that root system reinforcement in large trees is substantially greater than in smaller counterparts. The methodology proposed herein provides forest management professionals with a practical tool for evaluating root reinforcement in dominant forest trees, thereby facilitating improved assessment of landslide risks in forested slopes. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

This study employs a dynamic stochastic general equilibrium model with Bayesian estimation to rigorously evaluate China’s macroeconomic responses to cost-push, monetary policy, and foreign income shocks. This analysis leverages quarterly data from 2000 to 2024, focusing on critical variables such as the output gap, inflation, interest rates, exchange rates, consumption, investment, and employment. The results demonstrate significant social welfare losses primarily arising from persistent inflation and output volatility due to domestic structural rigidities and global market dependencies. Monetary policy interventions effectively moderate short-term volatility but induce welfare costs if overly restrictive. The findings underscore the necessity of targeted structural reforms to enhance economic flexibility, balanced monetary policy to mitigate aggressive interventions, and diversified economic strategies to reduce external vulnerability. These insights contribute novel policy perspectives for enhancing China’s macroeconomic stability and resilience. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

(1) Background: The provision of monofunctional or inadequately distributed services in urban park green spaces often constrains residents’ opportunities and diversity for outdoor activities, particularly limiting access and participation for specific age groups or activity preferences. However, functional nodes with temporal and spatial flexibility demonstrate high-quality characteristics of resilient and shared services through integrated development. Accurately identifying user demand provides a solid basis for optimizing the functional configuration of urban parks. (2) Methods: This study took the old city area of Zhengzhou, Henan Province, China, as a case study. By collecting and integrating various types of data, such as geographic spatial data, field investigation data, and behavioral observations, we developed a population demand quantification method and a modular analysis approach for park service functions. This framework enabled correlation analysis between diverse user needs and park services. The study further classified and combined park functions into modular units, quantifying their elastic and shared service capabilities—namely, the adaptive flexibility and shared utilization capacity of park services. Additionally, we established a demand-responsive evaluation system for identifying and diagnosing problem areas in park services based on multi-source data. (3) Results: The demand response index and diagnostic results indicate that the supply of fitness facilities—particularly equipment-based installations—is insufficient within the old urban district of Zhengzhou. Among the three user groups—children, young and middle-aged adults, and the elderly—the elderly population exhibited the lowest demand response index, revealing a significant gap in meeting their specific needs. (4) Conclusions: Based on the research findings, a three-tier optimization strategy is proposed: A. improve green space connectivity to expand the service coverage of parks; B. implement multifunctional overlay and coordinated integration in spatial design based on site characteristics and demand diagnostics; and C. increase the total supply of facilities to enhance spatial efficiency in parks. By integrating the demand assessment data and diagnostic results, this approach enabled a data-driven reorganization of service types and targeted allocation of resources within existing park infrastructure, offering a practical tool and reference for the planning of urban outdoor activity spaces. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

The thermal environment problem is one of the main focuses of current urban environment research. At present, there are various methods used in urban space thermal environment (USTE) research. As a simulation method to quantify the USTE, the urban weather generator (UWG) has undergone great development and achieved many progressive results. It is necessary to establish and review its current research status by synthesizing UWG multi-scale applications. This review adopts a literature review approach, leveraging the Web of Science Core Collection to obtain previous relevant publications from 2010 to 2025 using “urban weather generator” and “thermal environment” as keywords. The literature is categorized by research themes, including model development, parameter optimization, and application cases. Through innovative analyses of spatio-temporal-scale classification, parameter optimization, the integration of anthropogenic heat emissions, and the multi-domain simulation potential of the UWG, this review synthesizes the application outcomes of the UWG model in multi-scale research, addressing gaps in current urban climate studies. The paper aims to elaborate and analyze the model’s current research status considering the following six aspects. First, the basic parameters in UWG simulation are introduced, including the data and parameter determination settings used in such simulations. Secondly, we introduce the simulation model and its basic principles, the simulation process, and the main steps of this process. Third, we classify and define UWG simulations of spatial thermal environments at different time scales and spatial scales. Fourth, regarding how to improve the accuracy of the UWG model, the deterministic parameters and uncertainty parameters settings are analyzed, respectively. Then, the impacts of anthropogenic heat during the simulation process are also discussed. Fifth, the applications of the UWG model in some major fields and its possible future development directions are addressed. Finally, the existing problems are summarized, the future development trends are prospected, and research on possible expected mitigation measures for the USTE is described. Full article