NATULAR®SC, mosquito larvicide, was laboratory and field-tested against Aedes albopictus to determine the application rate in metric units (µl/liter). The objective was to estimate in microliters (µl) the amount of formulated NATULAR SC to apply per liter of habitat water. Replicated dose response experiments were conducted on lab-reared third instar Ae. albopictus under controlled laboratory conditions followed by probit analysis. The median lethal dose, LD50, was estimated to be 0.07 µl/liter (95% C.L. 0.054,0.094). Control mortality was less than 1%. The LD95 was estimated to be 0.54 µl/liter (95% C.L. 0.323,1.265). According to the World Health Organization Pesticide Evaluation Scheme (WHOPES) doubling the LD95 approximates the diagnostic dose and is an estimate of the field application rate. The next step consisted of outdoor field trials in 5-liter buckets. Twelve application rates ranging from 0.1 µl/liter to 2.0 µl/liter were tested. Results indicated 1 µl/liter was an optimum application rate. A major conclusion is no single best application rate will kill 100% of susceptible mosquito larvae. An optimal field rate balances efficacy with minimizing the concentration of pesticide. In every treatment a small probability of larval survival is always present. This is a compelling argument for rotating active ingredients according to their mode of action.

A simplified standardized ultrasound-guided plane block for the Intercostobrachial nerve: Effective volume of 0.5% ropivacaine determined by Dixon's up-and-down method.

Integrating photon with proton dose-response data alters a pulmonary complication model-based patient selection in esophageal cancer.

Frequency of indoor tobacco smoking and its determinants in Madagascar.

ABSTRACT Objective: To investigate the antifungal properties and potential toxicity of Graptophyllum pictum leaves extract (GPLE) as a candidate for herbal biomaterial-based denture cleanser. Material and Methods: The research was conducted through an in silico study, phytochemical tests, antifungal activity tests, and brine shrimp lethality test (BSLT). An in silico study determined the grid score values of GPLE’s ligands and Candida albicans’ protein complexes. Antifungal activity was evaluated on Aquadest, Polident, and GPLE at 30%, 40%, 50%, 60%, and 70%. The BSLT test groups consisted of aquadest and groups of 1000, 500, 250, 100, 50, and 25 ppm of GPLE. Primary data analysis was performed using the Kruskal-Wallis test with a different significance value of p<0.05. A probit regression test was performed for BSLT results with a value of LC50 > 1000 μg/mL. Results: The in silico evaluation demonstrated GPLE’s antifungal efficacy, which is attributed to its high binding affinity to DHFR, hsp90, and NMT protein. Phytochemical analysis confirmed the presence of antifungal compounds such as flavonoids, saponins, alkaloids, tannins, terpenoids, and steroids. GPLE exhibits fungicidal properties at a concentration of ≥50%. Additionally, BSLT findings indicate an LC50 value of 4780.735 μg/ml with a 95% confidence interval, suggesting no acute toxicity potential. Conclusion: Graptophyllum pictum leaves extract has antifungal and nontoxic properties as a candidate for herbal biomaterial-based denture cleansers.

Do patients who underestimate their health decline rely more on doctors? Insights from patients in their last six months of life.

Despite an increasing number of mapping studies being conducted in China, there is an absence of a systematic reviews, which makes it difficult to inform the applications and further assess the methodological consistency, accuracy, and applicability of existing mapping studies. The objective of this review is to consolidate existing evidence, identify methodological gaps, and provide recommendations for improving mapping studies conducted among the Chinese population. A systematic literature search was conducted in 14 databases from inception to May 31, 2025 to identify studies that developed mapping algorithms to estimate health utility values, specifically among Chinese populations. A data template was applied to extract dataset information, source and target measures, mapping types (direct vs indirect), models used, goodness-of-fit indicators, validation methods, and the optimal mapping algorithms selected. Potential challenges for future related studies were further discussed. A total of 33 studies was included. Most studies (87.9%) focused on mapping disease-specific non-preference-based measures (PBMs) to generic PBMs. The studies covered a broad range of disease areas, including oncology (36.4%), musculoskeletal disorders (15.2%), metabolic diseases (15.2%), cardiovascular diseases (9.1%), and neurological conditions (6.1%). All studies used direct mapping, with the ordinary least squares model (n = 37) being used most frequently, followed by Tobit model (n = 32) and Beta model (n = 22). Eleven studies explored indirect mapping, with the Ordered Logit and Ordered Probit models being the most employed techniques. Thirty-two studies conducted internal validation, with the N-fold cross-validation being the most used method-no study conducted external validation. The sample size ranged from 133 to 3320, with a median sample size of 553. Conducted conceptual analysis was performed in 81.8% of the studies to assess the degree of overlap between the source measure and target measure; 72.7% of the studies reported the utility/score distributions, and 15.2% of studies further reported the response distributions. This systematic review provides insights into methodologies employed in mapping studies in China and identifies key areas for improvement. Addressing issues related to sample size, conceptual overlap, model selection, and validation methods will enhance the quality and applicability of mapping algorithms, ultimately supporting more robust cost-utility analyses in the Chinese healthcare system.

Rental assistance, housing insecurity, & well-being: Spillover effects of emergency rental assistance during the COVID-19 pandemic.

Genomic and toxicity analysis of Lepidopteran toxic novel Bacillus thuringiensis strain T407.

This study integrates artificial intelligence and econometric modeling to develop an intelligent framework for identifying intercity collaborative innovation in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). A key contribution is the construction of an Institutional Cooperation Index (ICI) that quantifies institutional collaboration between cities through a hybrid expert-scoring and machine learning approach, normalized to [0,1] with an optimized threshold of 0.4 for effective cooperation classification. To reduce subjectivity, AI-based normalization and random perturbation simulation are applied, and validation shows strong correlations between the ICI, R&D intensity, and industrial upgrading. Logistic and Probit regressions are employed as AI classifiers to predict collaboration probabilities using features such as economic linkage, institutional synergy, spatial proximity, and industrial complementarity. NLP techniques extract institutional cooperation features from policy documents, and rare-event correction with network-robust validation ensures prediction stability. Results reveal that institutional cooperation significantly enhances intercity innovation, exhibiting threshold and nonlinear effects. Overall, the AI-driven modeling framework bridges institutional design, network interaction, and innovation outcomes, offering a computationally interpretable foundation for optimizing regional cooperation and intelligent policy decisions in the GBA.

With Chinas overall victory in the battle against poverty, consolidation and expansion of poverty alleviation results and preventing a large-scale relapse into poverty have become key problems of the new era. Rural households suffer especially significant poverty vulnerability issues as a result of their sole source of income and weak risk resistance abilities. How digital inclusive finance influences the vulnerability of rural households to poverty and what its mechanisms are need thorough theoretical research and empirical testing. Based on China Household Finance Survey data and Digital Inclusive Finance Index, this study examines the impact effects and mechanisms of digital inclusive finance on the poverty vulnerability of rural households by Probit model. It creates a set of overall indicators for the poverty vulnerability of rural households including financial margins, debt servicing capability, and risk insurance capacity, and divides it into four levels. From the empirical results, it can be seen that digital inclusive finance has played a positive role in poverty reduction risks of rural household, and it shows a strong anti-poverty effect. From the mechanism test results, it can be seen that financial asset diversification is a channel through which digital inclusive finance affects the rural poverty vulnerability, because digital inclusive finance helps the rural households to allocate their financial assets in a more diversified way, thus reducing their poverty vulnerability. The research conclusions can provide significant theoretical foundation and policy reference for consolidating and expanding the results of poverty alleviation, as well as preventing a large-scale return to poverty.

ABSTRACT The current adoption intensity of Climate-Smart Agriculture Technologies (CSATs) among smallholder farmers is below the desired level despite the increasing climate change challenges. This study analysed the perceptions of smallholder coffee farmers towards climate variability and how these influence the adoption intensity of CSATs. A survey was conducted with 226 randomly selected coffee farming households in Luweero district, Uganda. Multivariate regression, Multivariate probit and Poisson regression were used to assess the determinants of farmers’ perceptions of climate variability, the determinants of adoption among the different CSATs and the influence of farmers’ perceptions of climate variability on the adoption intensity of CSATs, respectively. The findings show that smallholder farmers are aware of climate variability, as their perceptions about the increase in temperature and decrease in rainfall align with the available meteorological data. Additionally, farmers’ perceptions of changes in rainfall and temperature, credit access, interaction with an extension worker and access to climate information positively influence their adoption intensity of CSATs. The study recommends that efforts to enhance the adaptive capacity of smallholder farmers should consider enhancing farmers’ climate variability awareness through the provision of climate information, enhancing farmers’ access to credit facilities, and strengthening extension service delivery to support farmers in implementing multiple healthy and environmentally friendly CSATs.

Background and Objectives: Current evidence remains insufficient to determine whether the impact of autism spectrum disorder (ASD) on dental health is primarily mediated through oral hygiene and dietary habits or through direct effects of the disorder itself. This study examined the theoretical pathways through which ASD severity and toothbrushing-related and dietary-choice-related factors influence dental health in autistic children and adolescents. Materials and Methods: A cross-sectional study was conducted with 399 mothers reporting on their autistic children (aged 2–18 years, mean = 7.8). The exclusion criterion was being older than 18 years. Data included parent-reported data about ASD severity, dental health status, willingness to brush teeth, and dietary quality (assessed using the Diet Quality Inventory). Structural Equation Modeling (SEM) was used to analyze the direct and indirect effects of ASD severity on dental health, with probit regression coefficients estimated using the WLSMV method. Results: Parent-reported variables of ASD severity, diet quality, and toothbrushing willingness together explained 37% of the variance in dental health. The direct effect of ASD severity on dental health was 0.199 (p = 0.039). The indirect effect via toothbrushing was 0.137 (p = 0.006), and via diet quality, it was 0.070 (p = 0.020). The total indirect effect of ASD on dental health was 0.207 (p = 0.026), which was approximately as strong as the direct effect. The associations among the studied variables were statistically equivalent across sex and age groups. Conclusions: Parent-reported ASD severity shows significant association with dental health outcomes, both directly and indirectly, with toothbrushing behavior emerging as the primary mediator. Interventions that promote regular brushing (and, to a lesser extent, healthier eating) may help to reduce the dental health disparities associated with autism.

This study attempts to investigate the impact of socio-demographic characteristics on the choices of job search channels used by unemployed persons when seeking jobs in the Gampaha District. A structured questionnaire was administered on a face-to-face basis to collect data from a sample of 200 unemployed job seekers who live in the Attanagalla divisional secretariat division, Gampaha in 2023. The study used a systematic sampling procedure, and four types of job search channels, such as newspaper advertisements, the Internet and social media, direct contacts with an employer, and informal networks were taken as dependent variables, while socio-demographic characteristics of the respondents were considered as independent variables in the study. Frequency analysis, descriptive statistics, and chi-square tests were used to explore the basic features of the young job seekers in the study. Results of frequency analysis indicated that 22% of the unemployed used newspaper advertisements, 10.5% of them used direct contacts with the employer, 18% of them used informal networks, and 49.5% of them used the Internet and social media as their job search channels. A multinomial probit regression model was used to investigate how socio-demographic characteristics impact the choice of job search channels used by unemployed youth. The findings of the model revealed that except for household size, all socio-demographic characteristics, such as age, civil status, level of education, knowledge in information technology, duration of job search, and whether the person previously worked or not, have a significant influence on the young job seekers' job search channels.

The emergence of resistance to synthetic (chemical) insecticides along with their harmful effects on human health, non-target organisms and the environment necessitates the development of new complementary bioinsecticides that are effective, environmentally friendly, biodegradable and target-specific. This study was undertaken to evaluate larvicidal activities of 80% methanol and n-hexane extracts of four plants that are traditionally used by communities against mosquitoes. The dried plant parts of Ocimum lamiifolium, Amaranthus hybridus, Premna schimperi, and Lepidium sativum were extracted with 80% methanol and n-hexane solvents. Bioinsecticidal activities of these extracts were evaluated under laboratory condition in the range of 62.5-2000ppm against late 3rd to early 4th instar larvae of An. arabiensis, An. stephensi and Ae. aegypti mosquitoes. Larval mortality was observed after 24h of exposure. The mortality data were subjected to probit analysis to determine LC50 and LC90 values. In the concentration ranges of 62.5-2000ppm, the LC50 and LC90 values of the most potent n-hexane extracts tested plants; Ocimum lamiifolium against An. arabiensis, An. stephensi and Ae. aegypti with a general ranges 666.07 to 1278.22, and 1920.82 to 2139.91, and Amaranthus hybridus against An. stephensi and Ae. aegypti 412 to 1426.03 and 736.150 to 1222.62, Lepidium sativum and Premna schimperi against An. arabiensis exhibited 100% larvicidal activity with LC50 and LC90 values ranges 713.25 to 1278.22, and 636.76 to 988.90, respectively. All the n-hexane extracts showed larvicidal activities. The n-hexane crude extracts of the tested plants have the potential to be used as bioinsecticides against larvae of An. arabiensis, An. stephensi and Ae. aegypti. Therefore, it is necessary to undertake studies that focus on bioassay-guided isolation, purification and structural elucidation of active compound (s) from the most active n-hexane fractions of the tested plants to develop a product that may complement the current existing vector control tools.

This study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two robust distress signals are identified: Return on Assets (threshold: 7.03%) and Total Asset Growth (threshold: −9.05%). The nonlinear impacts of financial distress on variables are analyzed, with a focus on contextual considerations in decision-making. These findings bring attention to the importance of utilizing advanced techniques like ANN for improved predictive accuracy, offering profound clarification for risk assessment and management.

This study examines the determinants of Islamic financial inclusion in Indonesia, focusing on internet access effects through integrated Unified Theory of Acceptance and Use of Technology (UTAUT) and Diffusion of Innovations (DOI) frameworks. Probit regression analysis was conducted on 6,606 respondents from the 2018-2019 Financial Inclusion Insights survey. The model includes internet access, demographic characteristics, and socio-economic factors, with comprehensive robustness checks including multicollinearity assessment, goodness-of-fit measures, and sensitivity analyses. Internet access is the strongest predictor of Islamic financial inclusion, with a marginal effect of 2.71 percentage points (p<0.001), representing a 160% relative increase. Marital status shows a 2.24 percentage point effect (p<0.001), while primary education contributes 0.88 percentage points (p<0.10). Unexpectedly, being male decreases adoption by 0.73 percentage points (p<0.05). Urban residence, age, higher education, and employment status show no significant effects. This research is the first to integrate UTAUT-DOI frameworks for Islamic financial services, extends UTAUT to Sharia-compliant behavior, and demonstrates that digital infrastructure can override traditional geographic barriers. The findings reveal context-dependent gender moderation and establish internet access as the highest-leverage policy intervention for expanding Islamic financial inclusion in developing countries.

ABSTRACT In Burkina Faso, smallholder farmers rely heavily on rain‐fed agriculture, which is affected by climate change. The adoption of climate‐smart practices is essential to strengthen the resilience of agricultural systems to climate change and improve household food security and, consequently, global food security. Despite the great potential of these practices to combat the effects of climate change on agriculture, their adoption by farmers remains low or limited. The reasons for this low adoption are varied, suggesting that the factors are largely contextual. This research analyzes the determinants of the adoption of climate‐smart practices among farmers in Burkina Faso in the context of innovation diffusion. To do this, a multivariate probit regression model was used on survey data from 48,159 plots owned by farmers in the country. The results show that age, gender, access to credit, access to extension services, property rights, livestock ownership, and education are the main determinants of the adoption of climate‐smart practices in Burkina Faso. Large‐scale awareness‐raising, training, and promotion, while promoting access to credit and land ownership documents, are necessary for better adoption of climate‐smart practices.

This study investigates the determinants of post-harvest losses (PHL) and the intensity of post-harvest technology adoption among smallholder vegetable farmers in Zimbabwe’s Mashonaland West Province. Utilising a multi-stage sampling technique, primary data were collected from 200 farmers across the Hurungwe and Makonde districts. The research employed a Multinomial Logit (MNL) model to analyse PHL determinants at four value chain stages—farm gate, transportation, storage, and marketing—and a Probit model to identify factors influencing the adoption of mitigation practices. Findings reveal that 70% of losses occur during storage, followed by farm-level and transportation stages. The MNL analysis highlights that farm gate sales and modern technology significantly decrease the likelihood of storage and marketing losses. The Probit model identifies farmer age, production volume, cooperative membership, and access to diverse information sources as significant positive predictors of technology adoption. Notably, radio ownership showed a negative correlation, suggesting a potential deficiency in broadcast technical advice. While limited to cabbage, rape, and tomatoes in two districts, the results advocate for a policy shift toward integrated post-harvest management. Recommendations include establishing decentralised, solar-powered cold storage, strengthening cooperatives for collective investment, and providing specialised technical training via extension services. Additionally, restructuring credit facilities to target post-harvest infrastructure, such as plastic crates and drying tools, is essential to minimise waste and enhance profitability.

This study analyzes the causal relationship between the adoption of Mobile Financial Services (MFS) and the subsequent improvements in household welfare and savings behaviors within Nigeria’s rural zones spanning the period 2010 to 2024. Despite significant national efforts to boost financial inclusion, rural populations remain disproportionately excluded. Utilizing a review of micro-level studies, the research validates the application of Probit and Logit regression models controlling for critical socioeconomic variables such as income level, age, gender, and access to credit to estimate the propensity of MFS usage influencing positive welfare outcomes. Results from a Logit model indicate that MFS use increases the odds of formal savings participation by 1.87 times. Findings consistently indicate that mobile banking enhances financial access, contributes positively to household consumption expenditure, and significantly increases the propensity for formal savings among rural residents. Furthermore, the paper investigates the heterogeneous impact of the digital shift accelerated by the COVID-19 pandemic, finding that while MFS adoption surged nationally, infrastructural deficits limited its transformative effects in remote rural settings. The study concludes with policy recommendations aimed at bolstering digital literacy and infrastructural resilience to maximize the pro-poor potential of mobile finance.