Search Results (74,428)

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments. Full article

Fading in communication channels presents eminently stochastic characteristics and is a significant challenge, especially at millimeter wave (mmW) frequencies, where the need for lines of sight and the high attenuation of obstacles complicate transmission. This article presents a model based on Bayesian fundamentals intended to improve the description and simulation of stochastic fading effects in these channels. It also includes the use of signal processing techniques to simulate and reconstruct the received signal, simulating the communication channel with an FIR filter. The results obtained by simulating the model show its ability to efficiently capture rapid and profound variations in the signal, typical of those that occur in urban and suburban environments and transmissions in the mmW spectrum. It also provides greater uniformity in signal reconstruction compared to the traditional models that are in use. Using Bayesian fundamentals, which allow dynamic adaptation to change in channel behavior, can improve the efficiency and reliability of networks, especially modern smart networks. Compared to traditional models, the proposed model offers improved signal reconstruction and fading mitigation accuracy, with prospects for future integration in smart communication systems. The better capacity in signal reconstruction presents itself as a differentiator of the model, suggesting greater precision in data transmission. Full article

Visualizing magnetic fields is essential for studying the operation of electromagnetic systems and devices that use permanent magnets or magnetic particles. However, commercial devices for this purpose are often expensive due to their complex designs, which may not always be necessary for specific research needs. This work presents a method for designing an automated laboratory setup for magnetic cartography, utilizing a 3D printer to produce structural plastic components for the scanner. The assembly process is thoroughly described, covering both the hardware and software aspects. Spatial resolution and mapping parameters, such as the number of data points and the collection time, were configured through software. Multiple tests were conducted on samples featuring flat inductive coils on a printed circuit board, providing a reliable model for comparing calculated and measured results. The scanner offers several advantages, including a straightforward design, readily available materials and components, a large scanning area (100 mm × 100 mm × 100 mm), a user-friendly interface, and adaptability for specific tasks. Additionally, the integration of a pre-built macro enables connection to any PC running Windows, while the open-source microcontroller code allows users to customize the scanner’s functionality to meet their specific requirements. Full article

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally. Full article

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality. Full article

In electrospinning, nanofibers are frequently produced in nonwoven web form. Their poor mechanical properties (below 100 MPa) and difficulty in tailoring the fibrous structure have restricted their applications. However, advanced materials must be highly resistant to both deformation and fracture. By combining electrospinning technology with stretching, we have overcome this disadvantage and demonstrated a polyacrylonitrile nanofiber yarn with a tensile strength of 743 ± 20 MPa. The nearly perfect uniaxial orientation of the fibrils under the stretching process is crucial for the remarkable mechanical properties of the yarn. Additionally, the nanofiber yarn was functionalized by a dip-coating process with silver nanowires (AgNWs), imparting conductive properties. This conductive, high-strength nanofiber yarn demonstrates practical applications in flexible and wearable devices. The presented strategy is versatile and can be adapted to create other high-performance nanofiber yarns, with potential uses in fields such as biomedicine and smart textiles. Full article

Through the coffin set of Tanethereret—dated to the first half of the 21st Dynasty—this article aims to underline the importance of analysing the masks and human features of ancient Egyptian yellow coffins and their value in disclosing new and important information about the Third Intermediate Period society. By moving between different visualisations, overlapping layers, measuring, and comparing, the sculpted human forms can be, for example, further indices of the quality of the production/”workshop”/artist and of the socio-economic power of the client. The possibility of making a three-piece set—coherent not only in decoration but also in form—suggests the existence of workshops capable of producing high-quality coffins and, consequently, that some people could still economically afford such coffin sets. Gaining access to such “workshops” and this type of production may indeed represent a further attempt to “manufacture social power” for the middle or high elites. Moreover, this specific case study also shows the dynamism of ancient Egyptian artistic production in a period of crisis, with artists able not only to re-adapt and re-commodify an ancient object but also to create possible new compositions with a balanced mix of styles between tradition and innovation. The study of this “invisible” part of the yellow coffins thus represents a new way of reconstructing the history of the people “hidden behind” the yellow coffins and the socio-economic sphere of ancient Egyptian society in the Third Intermediate Period, manifested through the resulting art and material culture. Full article

The issue of Out-of-School Children (OOSC) presents a significant policy challenge in Sindh, Pakistan, where 44% of 5–16-year-olds are not attending school. This study examines how different leadership strategies in government and Public-Private Partnership (PPP) schools address this crisis. Drawing on transformational leadership theory and institutional theory, this research investigates the influence of school governance structures—specifically government schools, Education Management Organizations (EMOs), and Sindh Education Foundation (SEF) schools—on school leaders’ abilities to increase student enrollment and retention. Through a multiple case study design, this study collects qualitative data from three school heads, augmented by perspectives from vice principals and teachers. The findings indicate that leadership agency is constrained in government schools due to centralized decision-making, while PPP schools, particularly SEF schools, offer more autonomy, enabling more adaptive and community-centered leadership strategies. This study concludes that decentralized governance in SEF schools is more effective in addressing the OOSC issue, particularly in marginalized areas. These findings offer valuable insights for policymakers seeking to enhance educational access in regions with similar socio-economic challenges. The implications of this research underscore the need for flexible, context-specific leadership models to combat the OOSC crisis. Full article

With the advancement of resilience concepts, enhancing resilience capacity has become an effective approach to addressing rainwater and flooding issues. Most rural planning and construction efforts adopt urban planning models from economically developed regions, often leading to surface hardening, which subsequently causes drainage difficulties and severe surface water accumulation during the rainy season. In contrast, traditional Lingnan villages, exemplified by Guangdong’s Changqi Ancient Village, continue to function normally in flood-prone areas, suggesting that their water management knowledge merits investigation. Previous research on rainwater management in traditional Chinese villages has predominantly been qualitative, lacking scientific data support. This study employs an eco-social resilience perspective, combining field surveys and interviews with villagers, and utilizes the SWMM (Storm Water Management Model) software to conduct both qualitative and quantitative analyses of Changqi Ancient Village. The findings reveal the following: (1) The SWMM effectively quantifies rainwater and flood management in traditional villages. (2) From an ecological resilience perspective, the village’s geographical location is crucial. The topography, along with a rainwater regulation system comprising rivers, ponds, ditches, and permeable pavements, significantly influences the village’s drainage performance. (3) From a social resilience perspective, community participation is vital to the long-term stable development of traditional villages. This includes post-disaster collective fundraising by villagers for the restoration of rainwater and flood management facilities, the formulation of village regulations, and the construction and restoration of spiritual sites. (4) From an eco-social resilience perspective, the eco-social resilience system exhibits adaptive cyclical characteristics, where the geographical environment and the local economy significantly shape the ecological spatial patterns of Changqi, while positive interaction between nature and human society ensures the system’s dynamic equilibrium. Full article

Microbes produce various bioactive metabolites that can influence plant growth and stress tolerance. In this study, a plant growth-promoting rhizobacterium (PGPR), strain S14, was identified as Micrococcus luteus (designated as MlS14) using de novo whole-genome assembly. The MlS14 genome revealed major gene clusters for the synthesis of indole-3-acetic acid (IAA), terpenoids, and carotenoids. MlS14 produced significant amounts of IAA, and its volatile organic compounds (VOCs), specifically terpenoids, exhibited antifungal activity, suppressing the growth of pathogenic fungi. The presence of yellow pigment in the bacterial colony indicated carotenoid production. Treatment with MlS14 activated the expression of β-glucuronidase (GUS) driven by a promoter containing auxin-responsive elements. The application of MlS14 reshaped the root architecture of Arabidopsis seedlings, causing shorter primary roots, increased lateral root growth, and longer, denser root hairs; these characteristics are typically controlled by elevated exogenous IAA levels. MlS14 positively regulated seedling growth by enhancing photosynthesis, activating antioxidant enzymes, and promoting the production of secondary metabolites with reactive oxygen species (ROS) scavenging activity. Pretreatment with MlS14 reduced H₂O₂ and malondialdehyde (MDA) levels in seedlings under drought and heat stress, resulting in greater fresh weight during the post-stress period. Additionally, exposure to MlS14 stabilized chlorophyll content and growth rate in seedlings under salt stress. MlS14 transcriptionally upregulated genes involved in antioxidant defense and photosynthesis. Furthermore, genes linked to various hormone signaling pathways, such as abscisic acid (ABA), auxin, jasmonic acid (JA), and salicylic acid (SA), displayed increased expression levels, with those involved in ABA synthesis, using carotenoids as precursors, being the most highly induced. Furthermore, MlS14 treatment increased the expression of several transcription factors associated with stress responses, with DREB2A showing the highest level of induction. In conclusion, MlS14 played significant roles in promoting plant growth and stress tolerance. Metabolites such as IAA and carotenoids may function as positive regulators of plant metabolism and hormone signaling pathways essential for growth and adaptation to abiotic stress. Full article

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications. Full article

This study examines the microstructure, crystallographic texture evolution, and mechanical properties of stainless steel 316L fabricated through electron beam melting using a stochastic scan strategy at a preheat temperature of 1123 K. X-ray diffraction confirmed the presence of a pure austenitic phase in the fabricated material. Equiaxed cellular structures were observed in the center of the melt pool regions and elongated cellular structures observed at the melt pool overlap regions. A finite element-based numerical model was employed to estimate the thermal gradients and solidification rates within the melt pool of an electron beam spot. Microstructural analysis indicated a generation of columnar grains from the bottom to the top of the build owing to high thermal gradients. A crystallographic texture investigation showed a generation of strong <110> fiber texture along the build direction of the material and reported that the stress distributions within the melt pool led to a strong crystallographic texture driven by the stress evolution observed from thermokinetic computational modelling of the electron beam-melting process. Mechanical properties were assessed using profilometry-based indentation plastometry, demonstrating strain hardening at a high temperature of 773 K. Full article

As an indigenous species on the Tibetan Plateau, the yak is well adapted to the plateau hypoxic environment. The high-altitude hypoxia adaptation of the yak requires the adaptive reshaping of multiple tissues and organs, especially the lungs. To reveal the adaptive development of yak lungs under hypoxic stress at the tissue and molecular levels, we conducted histomorphological observations as well as transcriptomic and metabolomic studies of yak lungs at three ages (0.5, 2.5, and 4.5 years). The results showed that the lung tissue developed significantly with age. The mean alveolar area was higher (p < 0.01) in 4.5 and 2.5-year-old yaks than in 0.5-year-old yaks. The percentage of elastic fibers, micro-arterial wall thickness, and micro-arterial area showed an increasing trend (p < 0.01) from 0.5-year-old yaks to 2.5-year-old yaks and then to 4.5-year-old yaks. In addition, some critical differentially expressed genes related to angiogenesis (MYC, EPHA2, TNF), fiber formation (EREG), smooth muscle proliferation (HBEGF), erythropoiesis (SOCS3), and hypoxia response (ZFP36) were identified. Some metabolites associated with these genes were also found simultaneously. These findings provide a deeper understanding of the molecular strategies underlying this species’ extraordinary ability to survive normally in low-oxygen environments. In conclusion, the lungs of yaks undergo continuous adaptive development under hypoxic stress, and these findings are crucial for understanding the molecular mechanisms by which native species of the Tibetan Plateau survive in harsh environments. Full article

Objective: The objective of this study was to explore the acceptability and feasibility of a therapeutic assessment protocol for the Screening and Support of Youth (SASY). SASY provides brief but comprehensive community-based screening and support for diverse youth in the community. Methods: SASY screening evaluates symptoms, functioning and clinical risk. The Kiddie Computerized Adaptive Test was used to evaluate seven different diagnoses and symptom severity. The Weiss Functional Impairment Rating Scale-Self was used to measure functional impairment. Measures were scored according to nationally developed norms. An algorithm was developed to aggregate symptom and function ratings into an overall score for clinical risk. The results are discussed with participants in a motivational interview designed to promote insight, followed by the opportunity for the participant to engage in an online intervention. Protocol changes necessitated by social distancing during the pandemic led to innovative methods including the use of a QR code for recruitment, integration of both online and offline participation, and expansion from in-person recruitment within the schools to virtual engagement with youth throughout the community. The final sample included disproportionately more Black or African American and Hispanic youth as compared to school and community statistics, suggesting that optimization of online and offline methods in research may facilitate the recruitment of diverse populations. Qualitative interviews indicated that the screening and feedback raised youth awareness of their wellbeing and/or distress, its impact on their functioning, and engagement with options for improved wellbeing. Conclusions: The emergence of innovative methods optimizing the advantages of both online and offline methods, developed as a necessity during the pandemic, proved advantageous to the feasibility and acceptability of community-based recruitment of at-risk, minoritized youth. Full article

Bioremediation is a promising strategy to remove crude oil contaminants. However, limited studies explored the potential of bacterial consortia on crude oil biodegradation in high salinity soil. In this study, four halotolerant strains (Pseudoxanthomonas sp. S1-2, Bacillus sp. S2-A, Dietzia sp. CN-3, and Acinetobacter sp. HC8-3S), with strong environmental tolerance (temperature, pH, and salinity), distinctive crude oil degradation, and beneficial biosurfactant production, were combined to construct a bacterial consortium. The inoculation of the consortium successfully degraded 97.1% of total petroleum hydrocarbons in 10 days, with notable removal of alkanes, cycloalkanes, branched alkanes, and aromatic hydrocarbons. Functional optimization showed that this consortium degraded crude oil effectively in a broad range of temperature (20–37 °C), pH (6–9), and salinity (0–100 g/L). In salt-enriched crude-oil-contaminated soil microcosms, the simultaneous treatment of bioaugmentation and biostimulation achieved the highest crude oil degradation rate of 568.6 mg/kg/d, compared to treatments involving abiotic factors, natural attenuation, biostimulation, and bioaugmentation after 60 days. Real-time PCR targeting the 16S rRNA and alkB genes showed the good adaptability and stability of this consortium. The degradation property of the constructed bacterial consortium and the engineered consortium strategy may have potential use in the bioremediation of crude oil pollution in high-salinity soil. Full article