Search Results (15,963)

Stripe rust is a severe disease affecting wheat (Triticum aestivum L.) production in the United States Southeast region, necessitating the identification of resistant sources. The study was conducted at the University of Arkansas, Fayetteville, and utilized a Randomized Block Design over three years. A total of 1130 lines were inoculated annually, and their responses for stripe rust were recorded. The results revealed that 11%, 79%, and 77% of the lines showed resistance in the first, second, and third years, respectively, with an overall 50% of the whole population. Linear Mixed Model and Generalized Estimating Equation analyses highlighted environmental influences, with cooler, humid conditions in 2021 favoring stripe rust, while warmer, variable conditions in 2022 and stable weather in 2023 contributed to lower disease severity. Data analysis of infection rates and disease development indicated that the newer generations of wheat lines tested in 2022 and 2023 exhibited higher resistance lines, lower infection rates, and slower disease progression. The findings support targeted breeding strategies for durable stripe rust resistance, emphasizing the importance of multi-environment testing and selection of lines with adult-plant resistance traits. This research offers valuable insights for breeders, agronomists, and farmers aiming to mitigate stripe rust impact through improved cultivars and informed management practices. Full article

Cities concentrate many people, and studies have shown that resultant urban heat islands can be intense. Urban parks can function as “cool islands” that mitigate heat island effects. This study used the microclimate model ENVI-met 5.1 to assess the cooling effect of Panyu Park in the center of Shanghai, China. The primary objectives were to increase the diversity of data sources and to conduct a microclimate analysis. Two scenarios were examined: the actual park and no park. The results indicated that (1) the integration of satellite technology enhanced the data sources for ENVI-met and thereby increased the efficiency of urban modeling and (2) the simulated results for the park correlated well with the actual data observed at weather stations. The presence of the park resulted in a decrease in the maximum air temperature by 0.1 °C at 1.4 m above ground, a decrease in the wind speed by 1.67 m/s, a maximum increase of 0.2% in relative humidity, and a reduction of 1.94 in the Predicted Mean Vote. The results demonstrated the applicability of multi-source satellite data in microclimate research, saved time on data collection, and provided valuable information for studies undertaken in areas where the collection of field data is challenging and/or historical data are unavailable. Full article

This study explores the trajectory of interest in and use of Extractum Cannabis (hemp extract, i.e., extract of Cannabis sativa L.) for the symptomatic treatment of minor gastrointestinal distress and dyspepsia in nineteenth- and early twentieth-century Barcelona (Catalonia, Spain) prior to 1939, through a review of primary sources. The objective of this paper is to present a historical pharmaceutical and applied review of the medical use of the hemp genus (Cannabis L.) prior to its prohibition, thereby contributing to its recognition as a medicinal product. The information provided demonstrates evidence of the medicinal use of cannabis within the historical context studied. The interactions between this legacy medical use and the contemporary body of pharmacological and toxicological knowledge (on hemp, its constituents, and the endocannabinoid system in gastrointestinal and stomach disorders) are discussed, providing new possible clinical perspectives. Within its limitations—including the scope, limited accessibility to, and varying quality of archives—this research contributes to a more granular understanding of the historical embeddedness of psychoactive hemp medicines in northeastern Spain, suggesting that medical and pharmaceutical traditions could play a role in informing contemporary approaches to “medical marijuana”. Full article

As a clean unconventional energy source, shale gas reservoirs are increasingly important globally. Accurate prediction methods for shale gas production capacity can bring significant economic benefits by reducing construction and operating costs. Decline curve analysis (DCA) is an efficient method that uses mathematical formulas to describe production trends with minimal reliance on geological or engineering parameters. However, traditional DCA models often fail to capture the complex production dynamics of shale gas wells, especially in complex environments. To overcome these limitations, this study proposes an Improved DCA method that integrates multiple base empirical DCA models through ensemble learning. By combining the strengths of individual models, it offers a more robust and accurate prediction framework. We evaluated this method using data from 22 shale gas wells in region L, China, comparing it to six traditional DCA models, including Arps and the Logistic Growth Model (LGM). The results show that the Improved DCA model achieved superior performance—with an mean absolute error (MAE) of 0.0660, an mean squared error (MSE) of 0.0272, and an $R^2$ value of 0.9882—and exhibited greater stability across various samples and conditions. This method provides a reliable tool for long-term production forecasting and optimization without extensive geological or engineering information. Full article

Green infrastructure (GI) provides considerable benefits, including stormwater runoff management, biodiversity conservation, and urban sustainability promotion, and thus has garnered widespread attention. However, the limited research on residents’ preferences for GI constrains further promotion in China. To address this issue, data were collected from 436 residents in Wuhan, China, through an online survey. This study employed a comprehensive analytical framework that integrates best–worst scaling (BWS) with the contingent valuation method (CVM) to assess the preferences of residents in Wuhan, China, for six types of GI and estimate their willingness to pay (WTP) for GI enhancements. The conditional model and mixed logit model results indicated that residents preferred GI facilities that offer direct benefits, such as street trees and permeable pavements, and showed a lower preference for structures less suited to a Chinese context, such as eco-roofs. Regarding heterogeneity, only permeable pavements showed significant variation in preferences. Furthermore, the average WTP for GI enhancement was 142.28 RMB/household/year. Factors including familiarity with GI, information sources, and air quality improvement perceptions positively influenced the WTP, while low income negatively impacted the WTP. These findings offer insights for urban planners to develop effective policies to enhance public support for GI and promote urban sustainability. Full article

Background: Artificial intelligence (AI) has rapidly advanced, significantly impacting medicine. ChatGPT, a new AI model, generates responses based on user input. This study evaluates ChatGPT’s ability to assist with pre- and post-kidney transplantation (KT) patient education. Methods: ChatGPT was queried about KT on 21 February 2023 and 2 March 2023. Questions were categorized into general information for pre-KT patients or donors and post-KT patient instructions. Two experts independently assessed the accuracy of ChatGPT’s responses, and the Flesch–Kincaid readability test was applied to evaluate readability. Results: ChatGPT’s responses to general pre-KT questions were clear, concise, and accurate but occasionally misleading. Post-transplant instructions were generally clear and partially concise but lacked supporting evidence. Instructions for emergency situations post-KT were typically safe and reliable, whereas medication-related directions were often inaccurate and unreliable. The mean Flesch–Kincaid readability score was 30, indicating that ChatGPT’s answers were not easy to understand. Conclusion: This study demonstrates that while ChatGPT can provide clear definitions, explain symptoms, and offer reasonable advice on managing medical situations after KT, it frequently gives misleading answers to scientific inquiries. Transplantation researchers and providers should recognize ChatGPT as a potential information source for patients but exercise caution due to its incomplete accuracy and lack of references. Full article

Background: Digital Information and Communication Technologies (ICTs) offer significant opportunities to enhance public health, particularly through their strategic use in promoting health literacy. Objectives: This systematic review protocol aims to outline the methodological steps necessary to conduct a systematic review of the effectiveness of digital interventions in improving health literacy among informal caregivers. Methods: This review will include studies involving adult informal caregivers (≥18 years) undergoing a digital health intervention promoting health literacy. Intervention, effectiveness or efficacy, RCT, quasi-experimental, and observational studies will be eligible. This review will follow the methodology for Cochrane Systematic Review and Meta-Analysis. The search strategy will identify studies published in the databases SCOPUS and Web of Science, as well as CINAHL (via EBSCO) and PubMed and gray literature sources. Two in-dependent reviewers will screen the studies, extract data, and critically appraise the selected studies. It is planned that the risk of bias will be assessed using the RoB2, Newcastle-Ottawa Scale (NOS), and the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions). From the included studies, data will be extracted on the identification of the study, the aim of the study, characteristics of the population, study method and intervention and control conditions, study variables, and significant results. Conclusions: It is anticipated that the results of the study will guide healthcare professionals and managers in incorporating digital technologies into health literacy programs for informal caregivers. Whenever possible, a statistical meta-analysis will be performed to combine study results. The PROSPERO registration number is CRD42024589465. Full article

Background: Antimicrobial resistance (AMR) is a global health threat. AMR surveillance is crucial for understanding and controlling the spread of AMR. Surveillance systems can inform clinicians, guide health policymakers, and support effective AMR interventions. AMR surveillance data from former Soviet region countries are often limited, resulting in gaps in up-to-date knowledge. Methods: This systematic review was registered under the PROSPERO protocol CRD42024537799 and followed the PRISMA guidelines. Data from five databases (PubMed, MEDLINE, Embase, CINAHL, and CyberLeninka) and official sources were searched according to the inclusion criteria. Results: In total, 30 publications describing AMR surveillance and National Action Plans (NAPs) were included. Among the 15 countries, 14 (93.3%) have either developed a NAP or are in the process of implementing one; and 7 (46.7%) countries have a standardized AMR surveillance system. Almost all countries have reference laboratory centers, but nine (60%) countries have established standard testing methods in all sites. Only three (20%) countries have fully implemented quality assessment. There is an increasing tendency to involve and report AMR data to international networks, and countries will strengthen their AMR systems by adhering to international standards. The subgroup analysis revealed that Central and Western Asian countries are less developed in terms of AMR surveillance, which may encourage proactive engagement in AMR governance in these regions. Conclusions: This review is crucial for understanding the current efforts and improving AMR surveillance in former Soviet countries. The findings are promising and indicate that AMR surveillance is established in all reviewed countries, although at different levels. Full article

Background: The “copycat effect” is a psychological phenomenon in which a person’s actions influence the behavior of others, leading to imitations of behaviors, events, or ideas. It is often observed in contexts such as crimes, suicides, or violent behaviors, where media coverage of an event can trigger similar reactions in other people. Usually, many studies associate the copycat phenomenon with homicide or serial crimes. Little attention is paid to the phenomenon of emulation in suicide and, therefore, to the copycat phenomenon in this context. Methods: In our study, a systematic review of the literature was carried out using keywords related to copycat and suicide. Subsequently, each study was read and analyzed. Results: The results were compared with each other and demonstrated how it is essential to mitigate the resonance of suicidal events, especially through the media and social networks. In particular, this risk of emulation is greater among young people who are exposed to a continuous external source of information through news from the web in an incessant and constant way. This risk increases most immediately after the publicity of the suicidal event in a limited period of time. Conclusions: Unfortunately, little attention is given to the analysis of the copycat phenomenon, which causes an underestimation of the data related to suicide emulation, especially from the preventive perspective of a phenomenon that is still widespread. Full article

As an important data source, the Building Information Model (BIM) plays an important role in modern building safety management. Numerous studies have closely examined automatic compliance inspections for building safety and the safety management of dangerous projects. However, the value of the BIM has not been fully exploited in evidence-based practices of building safety. To address this limitation, this paper proposes an ontology-based Evidence/Risk-Based Safety (ERBS) management framework for divisional and subdivisional works with high risk, which includes: (1) BIM data extraction based on dynamo; (2) creation of an ontology based on building information and the ERBS management process model; (3) converting BIM data and evidence into ontology individuals; and (4) integrating the ontology through semantic web technology and using the Semantic Web Rule Language (SWRL) to conduct rule-based reasoning on the ontology. A case study shows that the framework is effective for the ERBS management of divisional and subdivisional works with high risk. The framework proposed in this study provides effective safety management methods for high-risk projects that can be applied in wider engineering practice in the future. Full article

Aiming at the problem of estimating the speed of M-ary Phase Shift Keying (MPSK) communication radiated sources and their carrying platform targets, this paper proposes a data-assisted and inter-symbol spectrum analysis-based speed estimation method for MPSK communication radiated sources. The method first demodulates a signal-carrying message symbol from the received MPSK signal; then segments the signal according to the symbol synchronization information and the symbol period; and then compensates the phase of the symbol waveform corresponding to the message data according to the demodulated message symbol; finally combines the phase-compensated symbol waveform data into a two-dimensional matrix and finds the Doppler frequency of the data at the same sampling moment of different symbols using the vertical Fourier transform to obtain the moving target speed. The speed measurement accuracy and anti-noise performance of the method are analyzed through simulation experiments, and the simulation results show that the speed measurement accuracy of the method is 98.5%. Full article

As remote sensing technology continues to evolve, the integration of multi-view data, including HSI and LiDAR data, has emerged as a pivotal research area aimed at enhancing the precision of classification. However, most current multimodal data fusion methods follow a process of first extracting features from each modality, then combining these features using a fusion strategy, and finally performing classification. This approach may result in the diminution of original information during the feature fusion process and relies heavily on the performance of the Softmax function during classification, without adequately considering the trustworthiness of the results. To address the above issues, this paper presented a hybrid feature and trusted decision fusion (HFTDF) method for dual-view remote sensing data classification. In terms of the research method, the approach first performs preliminary feature extraction on dual-view data using shallow CNN models, while implementing a shallow fusion strategy to integrate original information from different data sources at an early stage. Next, it leverages the proficiency of CNNs in learning localized characteristics and the potential of the Transformer in terms of its handling of overarching information, conducting hybrid feature learning on data from each view. Additionally, a deep fusion strategy serves to investigate the intricate interrelations among diverse perspectives. Finally, evidence theory is applied to model the uncertainty of classification results, generating trusted vectors, and a trusted decision fusion strategy is employed to merge the trusted information from each modality at the decision level, thereby enhancing the reliability of the results. HFTDF achieves overall classification accuracies of 94.68%, 99.17%, and 82.05% on the Houston 2013, Trento, and MUUFL datasets, respectively, when only 20 samples of each class are used for training. The classification results of the experiments reveal that HFTDF outperforms in the classification of dual-view data. Full article

Background: HPV represents one of the most common sexually transmitted infections worldwide, with significant adverse clinical consequences such as cervical cancer. However, the knowledge and awareness regarding HPV and its vaccination, particularly among Saudi women, are still under debate. Our study aims to investigate the knowledge, awareness, and attitude toward HPV and its vaccines among women in the Medina region. Methods: A descriptive cross-sectional study was conducted from July 2024 to September 2024 in women aged more than 18 years and residing in Medina. The collected data included information regarding women’s demographics, knowledge, awareness, and attitudes toward HPV and its vaccination. All the statistical analyses were executed using SPSS. Results: A total of 721 participants enrolled in our study after adequate completion of the online questionnaire. The majority of the population (45.2%) is aged 18–25 years, with 51.3% of participants being single regarding marital status. A total of 59.4% of the participants have heard about HPV, 37% know that it is sexually transmitted, and 37.4% know about its ability to cause cervical cancer. Social media and the internet were the primary sources of information regarding the HPV vaccine, with 41.6% thinking that the HPV vaccine can be effective against HPV infection. Around 40% of the participants showed an attitude toward receiving the HPV vaccine as they feel they are at risk. The educational status and monthly income were notably associated with the overall knowledge and awareness regarding HPV and its vaccine. Conclusions: Our descriptive cross-sectional study highlights the significant gap in knowledge and awareness regarding HPV and its vaccine, with a lack of awareness identified as the commonest barrier preventing people from receiving the HPV vaccination. Hence, enhancing the knowledge and awareness level is essential to increase vaccination rates. Full article

The use of conventional farming methods, excessive reliance on fertilizers and inputs, and abrupt shifts in climate have raised significant concerns regarding global agricultural production, particularly in developing countries like Nepal. Agriculture products such as rice hold significant importance in Nepal’s agriculture and economy, serving as a staple food and a crucial source of livelihood for its population. Sustainable cultivation and enhancing productivity are imperative for ensuring food security and economic stability in the country. Adoption of climate-smart agriculture (CSA) practices can minimize detrimental effects, promote sustainability, and enhance resilience towards climate change. We surveyed 200 farmers across four municipalities in the Chitwan District of Nepal to explore the prevalence and socio-economic drivers of the adoption of CSA practices, which include stress-tolerant varieties, efficient water management, and diversified cropping, among others. The results revealed that the adoption of pest-resistant plant varieties was a common CSA practice in the study area. Logistic regression results revealed that the adoption of CSA practices increases with an increase in the education of farmers and membership of climate-related organizations. Similarly, the adoption of CSA practices is negatively associated with an increase in farm size, farmers’ farming experience, and their access to credit facilities. Short-term courses and training could be initiated as a complement to formal education to maximize the adoption of CSA practices. Similarly, climate and farmer-related organizations should be further strengthened to maximize their capacity to facilitate more farmers and provide need-based, timely information flow. This study highlights the potential of CSA to promote sustainability and enhance resilience to climate change, but also identifies barriers such as credit access and the need for tailored policy interventions. Our findings contribute to understanding the dynamics of CSA adoption in vulnerable agricultural settings and can guide future strategies to promote sustainability and climate resilience in smallholder farming communities in developing countries. Full article

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0. Full article