Browse Articles

Forward-backward translation, content validity, face validity, construct validity, criterion validity, test-retest reliability, and internal consistency of a questionnaire on patient acceptance of orthodontic retainer

This study aimed to assess the validity and reliability of a questionnaire on patient acceptance of orthodontic retainers. The original questionnaire was forward- and backward-translated, followed by four validity tests (content validity, face validity, construct validity, criterion validity) and two reliability tests (test-retest reliability, internal consistency). Content validity was assessed by nine orthodontists who appraised the questionnaire’s representativeness, relevance, clarity, and necessity. Face validity was established through semi-structured in-depth interviews with 35 English-literate participants currently wearing orthodontic retainers. Construct validity was established through Exploratory Factor Analysis (EFA). For criterion validity, 107 participants concurrently answered the questionnaire and the Retainer-modified Malaysian Oral Health Impact Profile questionnaire. Test-retest reliability was verified by 34 subjects who responded to the questionnaire again after a two-week interval. Six revised items passed the threshold value of 0.78 for Item-Content Validity Index and Content Validity Ratio and were revised based on findings from the face validity test. Principal Component Analysis of EFA extracted information on only one component, and all items were positively correlated with the component matrix. Spearman’s rho value (rs = 0.490 and rs = 0.416) indicated a moderate correlation between the two questionnaires for criterion validity. Intraclass Correlation Coefficient ranged from 0.687 to 0.913, indicating moderate to excellent test-retest reliability. Cronbach’s alpha ranged from 0.687 to 0.913 indicating that none of the questionnaire items showed unacceptable or poor internal consistency. The questionnaire on patient acceptance of orthodontic retainers has been validated and can be used in both clinical and research settings.

An exploratory review of resiliency assessments after brain injury

Background People with brain injury can have lower resiliency compared to the general public. Yet, resiliency facilitates positive processes to negotiate adversity after brain injury. Therefore, measuring resiliency after a brain injury is important. Objective The review aimed to (1) identify self-report resiliency outcome measures for use with people after acquired brain injury, using the process-based Traumatic Brain Injury Resiliency Model as the guiding conceptual framework, and (2) summarize the psychometric properties of the identified outcome measures and the utility of these measures in clinical rehabilitation. Method The COSMIN guidelines for systematic reviews were followed to ensure appropriate reporting for each measure. Databases CINAHL, EMBASE, Medline, and PsychINFO were searched and independently reviewed by two people. Articles providing data on psychometric properties for measures of resilience for people with brain injury were retrieved. Psychometric properties and clinical utility (number of items, scoring details) were summarized. Results Thirty-two articles were retrieved, including nine measures of resiliency: Acceptance and Action Questionnaire–Acquired Brain Injury, Confidence after Stroke Measure, Connor-Davidson Resilience Scale, Daily Living Self-Efficacy Scale, General Self-Efficacy Scale, Participation Strategies Self-Efficacy Scale, Resilience Scale, Robson Self-Esteem Scale, and the Stroke Self-Efficacy Questionnaire. All measures have acceptable to excellent psychometric properties in accordance with the COSMIN guidelines. Conclusion There are established measures of resiliency in brain injury rehabilitation. Future work may explore use of these measures in a clinical context and implementation of rehabilitation goals for improving resiliency after brain injury.

A weak edge estimation based multi-task neural network for OCT segmentation

Optical Coherence Tomography (OCT) offers high-resolution images of the eye’s fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation. Yet, these methods still encounter two primary challenges. Firstly, deep learning methods are sensitive to weak edges. Secondly, the high cost of annotating medical image data results in a lack of labeled data, leading to overfitting during model training. To tackle these challenges, we introduce the Multi-Task Attention Mechanism Network with Pruning (MTAMNP), consisting of a segmentation branch and a boundary regression branch. The boundary regression branch utilizes an adaptive weighted loss function derived from the Truncated Signed Distance Function(TSDF), improving the model’s capacity to preserve weak edge details. The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. Our method surpasses other cutting-edge segmentation networks on two widely accessible datasets, achieving Dice scores of 84.09% and 93.84% on the HCMS and Duke datasets.

Employing foreign caregivers: A qualitative study of the perspectives of older stroke survivors

Background Global populations are aging, and the numbers of stroke survivors is increasing. Consequently, the need for caregiver support has increased. Because of this and demographic and socioeconomic changes, foreign caregivers are increasingly in demand in many developed countries. Therefore, the perspectives of older adult care recipients regarding foreign caregivers warrants attention. This study explored the experiences of older stroke survivors receiving care from foreign caregivers in Taiwan, examining their expectations, needs, and challenges. Methods This study employed a descriptive qualitative approach, conducting in-depth interviews with 23 older stroke survivors (mean age, 73.4 years; women, 47.8%). Thematic analysis was applied to transcribed data, with reflective memos aiding in meaning derivation. Methodological rigor was ensured through member checking, triangulation, and auditing. Results Three major themes emerged: the motivations for hiring a foreign caregiver, expectations of stroke survivors toward foreign caregivers, and challenges related to employing foreign caregivers. Stroke survivors expected foreign caregivers to be obedient, embrace the local language and culture, and be proficient in caregiving and homemaking. Various challenges, including communication difficulties, cultural differences, skill gaps, and unfavorable attitudes and behaviors among caregivers, were noted. Conclusions Foreign caregivers are a major part of the long-term care workforce and play a crucial role in stroke rehabilitation in aging Asian societies. Older stroke survivors often hire foreign caregivers to alleviate caregiving burdens, and they typically expect foreign caregivers to conform to their needs. However, employing foreign caregivers can be frustrating and stressful. Government intervention and open dialogue is necessary to improve care quality and prevent recurring caregiving problems and conflicts.

Unveiling chemical industry secrets: Insights gleaned from scientific literatures that examine internal chemical corporate documents—A scoping review

Objective Examine peer-reviewed scientific articles that used internal industry documents in the chemical sector to reveal corporate influence. Summarize sources of internal documents used in prior scientific papers to identify ongoing corporate strategies within the chemical field. Compare the corporate strategies identified in the chemical sector with the ones identified already identified in the pharmaceutical sector. Propose a theoretical framework for categorizing and examining the different form of corporate capture at play. Design Performed a scoping review to pinpoint scientific papers employing internal industry/corporate documents within the chemical sector. Methods We conducted a systematic search using broad and case study-derived keywords, detailed in the S1 Appendix. This resulted in 351 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo’s ghost management framework. Results The final results included and analyzed 18 scientific papers. Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n = 16), regulatory capture (n = 15), professional capture (n = 7), civil society capture (n = 6), media capture (n = 4), legal capture (n = 4), technological capture (n = 3), and market capture (n = 2). Comparative analysis The limited scientific literature meeting our criteria confirms early findings by Wieland et al, highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, shows a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong’s pharmaceutical corporate capture review, which identified 37 papers before 2022, our chemical industry findings reveal a lower count, with only 18 papers identified. Notably, the vast majority of the papers in both sectors shows an emphasis on analyzing strategies used for scientific capture. However, the area of regulatory capture reveals a significant distinction: only 6 of the 37 articles related to the pharmaceutical industry analyzed this dimension, as compared to 15 of the 18 articles related to the chemical industry. This body of work suggests that existing research on the chemical industry is particularly concerned with analyzing how the sector navigates and circumvents regulatory oversight. Both industries employ strategies involving conflicts of interest and the legitimization of their actions to shield themselves from public policy scrutiny and protect their interests. However, their goals seem to be significantly different. The scientific literature analyzing the pharmaceutical industry’s internal document tends to identify strategies maximizing profits through the biased promotion of health products, whereas the scientific literature analyzing the chemical industry’s internal documents is more inclined in identifying strategies institutionalizing ignorance about existing risks, evading accountability, and preventing regulatory actions. Strengths Our scoping review shows how internal documents can reveal how the chemical industry strategically institutionalizes ignorance to manage business risks. It exposes intentional efforts by chemical corporations to promote ignorance and foster conflicts of interest, thereby legitimizing their business models and safeguarding corporate interests. We shared our research findings on the Dataverse/ Borealis platform (https://doi.org/10.5683/SP3/EOIOAU), making them accessible for future studies to apply the same analytical framework seamlessly. Limitations We excluded papers that did not meet our research criteria, prioritizing those that analyzed internal corporate documents for uncovering covert ghost management captures. Beyond scientific literature, various grey literature sources have conducted quality investigations on ghost management strategies in the chemical industry, and many leaked internal documents in the chemical industry, often available through toxicdocs.org, were not analyzed in the scientific literature. Also, market concentration and other corporate captures can be investigated using publicly available resources. Despite searching scientific papers in various languages, no relevant publications were found outside of English. This presents an opportunity for future research to conduct a separate scoping review.

Patterns and predictors of mortality in the first 24 hours of admission among children aged 1–59 months admitted at a Regional Referral Hospital in South Western Uganda

Most deaths among children under 5 years occur within the first 24 hours of hospital admission from preventable causes such as diarrhea, pneumonia, malaria, and HIV/AIDS. The predictors of these deaths are not yet well documented in our setting. This study aimed to describe the patterns and predictors of these mortalities among children aged 1–59 months at a regional hospital in South Western Uganda. We conducted a prospective cohort study among 208 children aged 1–59 months admitted to Mbarara Regional Referral Hospital. The mortality rate within the first 24 hours was 7.7% (95% CI 4–12) and the median time to death was 7.3(2.62–8.75) hours. Most deaths occurred in infants, with severe pneumonia, severe acute malnutrition, and malaria as leading causes. Factors predicting mortality included admission during the night (AHR: 3.7, 95% CI 1.02–13.53, p-value 0.047) and abnormal neutrophil count(AHR: 3.5, 95% CI 1.10–11.31, p-value 0.034). The study highlights the importance of timely interventions, particularly for infants, and suggests extra monitoring for those admitted at night or with abnormal neutrophil counts.

Why the gaze behavior of expert physicians and novice medical students differ during a simulated medical interview: A mixed methods study

Human cognition is reflected in gaze behavior, which involves eye movements to fixate or shift focus between areas. In natural interactions, gaze behavior serves two functions: signal transmission and information gathering. While expert gaze as a tool for gathering information has been studied, its underlying cognitive processes remain insufficiently explored. This study investigated differences in gaze behavior and cognition between expert physicians and novice medical students during a simulated medical interview with a simulated patient, drawing implications for medical education. This study employed an exploratory sequential mixed methods design. During the simulated medical interview, participants’ gaze behavior was measured across five areas: the patient’s eyes, face, body trunk, medical chart, and medical questionnaire. A hierarchical Bayesian model analyzed differences in gaze behavior between expert physicians and novice medical students. Then, a semi-structured interview was conducted with participants to discern their perceptions during their gaze behavior; their recorded gaze behavior was presented to them, and analyzed using a qualitative descriptive approach. Model analyses indicated that experts looked at the simulated patient’s eyes less frequently compared to novices during the simulated medical interview. Expert physicians stated that because of the potential for discomfort, looking at the patient’s eyes was less frequent, despite its importance for obtaining diagnostic findings. Conversely, novice medical students did not provide narratives for obtaining such findings, but increased the number of times they did so to improve patient satisfaction. This association between different perceptions of gaze behavior may lead to new approaches in medical education. This study highlights the importance of understanding gaze behavior in the context of medical education and suggests that different motivations underlie the gaze behavior of expert physicians and novice medical students. Incorporating training in effective gaze behavior may improve the quality of patient care and medical students’ learning outcomes.

Correlation analysis between micro and macro indicators of high modulus modified asphalt for asphalt pavement

The relationship between the micro technical indexes and the macro road performance of high modulus asphalt (HMA) is helpful for understanding its mechanism and performance, and promoting its application. To explore the relationship, two kinds of high modulus asphalt (HMA), LLDPE/SBS composite modified asphalt and rubber/PPA composite modified asphalt were prepared according to the HMA requirements. Secondly, Molecular models of two kinds of HMA were established through molecular dynamics (MD) simulations, and the high temperature parameters of LLDPE/SBS composite modified asphalt were obtained with the two methods, namely the micro molecular dynamics simulation and high temperature rheological test, respectively. Then, through correlation analysis and regression calculation, the estimation formula was established between the results of molecular dynamics simulation and high temperature rheological test. Finally, in order to evaluate and verify the rationality of the estimation formula, the two methods were carried out on the other HMA (rubber/PPA composite modified asphalt). The results show that the shear modulus obtained by molecular dynamics simulation has a good correlation with the high temperature rheological properties. The estimation formula based on molecular dynamics simulation can be used to estimate the high temperature shear modulus of high modulus asphalt, and the relative error is less than 7%, which means that the formula can be used to effectively predict the high temperature performance of high modulus asphalt.

Birth prevalence and determinants of neural tube defects among newborns in Ethiopia: A systematic review and meta-analysis

Background Neural tube defects (NTDs) are complex multifactorial disorders in the neurulation of the brain and spinal cord that develop in humans between 21 and 28 days of conception. Neonates with NTDs may experience morbidity and mortality, with severe social and economic consequences. Therefore, the aim of this systematic review and meta-analysis is to assess the pooled prevalence and determinants for neural tube defects among newborns in Ethiopia. Methods The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO Number: CRD42023407095). We systematically searched the databases PubMed, Science Direct, Cochrane Library, Google Scholar and Research Gate. Grey literature was searched on Google. Heterogeneity among studies was assessed using the I2 test statistic and the Cochran Q test statistic. A random effects model was used to estimate the birth prevalence of neural tube defects. Result Twenty-five articles were included in the meta-analysis to estimate the prevalence and determinants of neural tube defects in Ethiopia. A total of 611,354 newborns were included in the analysis. The pooled birth prevalence of neural tube defects was 83.40 (95% CI: 60.78, 106.02) per 10,000 births. The highest and lowest prevalence rates were 130.9 (95% CI: 113.52, 148.29) in Tigray and 28.60 (95% CI: 18.70, 38.50) per 10,000 births in Amhara regional states. Women’s intake of folic acid supplements and planned pregnancy were identified as protective factors for NTDs, while stillbirth history, use of any drugs during pregnancy, exposure to radiation, and pesticides during pregnancy were risk factors for neural tube defects. Conclusion The pooled birth prevalence of neural tube defects in Ethiopia was found to be high. Effective prevention interventions, especially focusing on periconceptional folic acid supplementation as well as folate fortification, should be prioritized alongside nutrition education, maternal health care, and environmental safety measures.

Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms

Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045 samples, factors like Humidity, Ambient temperature, Wind speed, Visibility, Cloud ceiling and Pressure serve as inputs for constructing these machine learning models in forecasting solar energy. Model accuracy is meticulously assessed and juxtaposed using metrics such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. SHAP analysis reveal that ambient temperature and humidity have the greatest influences on the value solar energy generated from photovoltaic panel.

Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos

Background and purpose The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry. Methods This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score. Results The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video’s content and mGQS, was 0.66 indicated moderate correlation. Conclusion YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content.

A GPU-accelerated fuzzy method for real-time CT volume filtering

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be utilized to reduce radiation without losing diagnosis capabilities. The objective in this work is to obtain an implementation of a filter capable of processing medical images in real-time. To achieve this we have developed several filter methods based on fuzzy logic, and their GPU implementations, to reduce mixed Gaussian-impulsive noise. These filters have been developed to work in attenuation coefficients so as to not lose any information from the CT scans. The testing volumes come from the Mayo clinic database and consist of CT volumes at full and at simulated low dose. The GPU parallelizations reach speedups of over 2700 and take less than 0.1 seconds to filter more than 300 slices. In terms of quality the filter is competitive with other state of the art algorithmic and AI filters. The proposed method obtains good performance in terms of quality and the parallelization results in real-time filtering.

Correction: Moving towards a core measures set for patient safety in perioperative care: An e-Delphi consensus study

An Extenics-TRIZ integrated RFPS model for different object of design requirements

Extending product life is one of the effective ways to reduce the waste of resources. However, many unsatisfactory products are scrapped because of a lack of adequate performance. The product should be improved and upgraded innovatively, and the existing upgradable products may create more economic benefits for the longer product life cycles. This paper proposed a product innovative design and product upgrade employing an Extenics-TRIZ Integrated requirement-function-principle-structure (RFPS) model, which aims at complex requirement flexibility with easy-to-use design process when the product needs a redesign. Here, the requirement flexibility refers to the ability of a design object to adapt its design levels. There are two design strategies: the extension analysis methods are utilized to map the top-level requirements to functions, principles, and structures requirements, and then the TRIZ is used to handle the design problems according to the objects on different levels. This design knowledge is summarized as RFPS, and it can be reused in computer-aided innovation further. A case study for a cutting table is illustrated to the innovation and upgrade, and it indicates the effectiveness for designers to implement the design methodology.

Predicting noncontact injuries of professional football players using machine learning

Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters. A sample of 34 male professional players from a Portuguese first-division team was analyzed, combining GPS data from Catapult receivers with descriptive variables for machine learning models—Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), and Adaptive Boosting (AdaBoost)—to predict injuries. These models, particularly the SVMs with cost-sensitive learning, showed high accuracy in detecting injury events, achieving a sensitivity of 71.43%, specificity of 74.19%, and overall accuracy of 74.22%. Key predictive factors included the player’s position, session type, player load, velocity and acceleration. The developed models are notable for their balanced sensitivity and specificity, efficiency without extensive manual data collection, and capability to predict injuries for short time frames. These advancements will aid coaching staff in identifying high-risk players, optimizing team performance, and reducing rehabilitation costs.

It’s dark under the lamp? The moderating role of executives’ accounting competence on relationship between goodwill impairment signal and goodwill impairment

This study examines the influence of executives’ accounting competence on relationship between goodwill impairment signal and goodwill impairment, considering the perspective of performance compensation commitment. The research employs an empirical research method and utilizes a sample of A-share listed companies in China that have signed performance compensation commitment agreements from 2007 to 2022. I found that the executives’ accounting competence weakens the relationship between goodwill impairment signal and goodwill impairment. The stronger the executives’ accounting competence is, the weaker the goodwill impairment signal is, that is, the probability of the goodwill impairment is lower and the scale of goodwill impairment is smaller. Further research shows that the negative effect of executives’ accounting competence is more significant when the performance compensation commitment of the target assets is between 80% and 100%. Through heterogeneity analysis, it is found that the negative effect of executives’ accounting competence will be suppressed when the sample is in the period of equity incentive or the auditor audits the key issues of goodwill impairment. The research in this paper not only enriches the relevant literature on the characteristics of executives, but also discusses the subsequent measurement of goodwill, which is also conducive to promoting the formulation of accounting standards and assisting the supervision of capital markets, and has important theoretical and practical significance.

Development and validation of trigger tools in primary care: A scoping review

In primary care, trigger tools have been utilized to evaluate and identify patient safety events. The use of trigger tools could help clinicians and patients detect adverse events in a patient’s medical record. Due to a lack of research on the process development of trigger tools in primary care, the purpose of this scoping review is to investigate the trigger development and validation process in primary care settings. A scoping review methodology was used to map the published literature using the Joanna Briggs Methodology of performing scoping review. We considered only studies published in English in the last five years and included both qualitative and quantitative study designs. The final review included five articles. The primary care and combined primary-secondary care studies are included to gain more knowledge in the process development and validation of trigger tools. The trigger tool development process begins with clearly defining the triggers, which are then programmed into a combined computerized algorithm. The validation process was then carried out in two steps by both physician and non-physician experts for content and concurrent validity. The sensitivity, specificity, and positive predictive value (PPV) of the final algorithm were critical in determining the validity of each trigger. This study provided a comprehensive guide to developing trigger tools, emphasizing the importance of precisely defining triggers through a thorough literature review and dual validation process. There were similarities in the development and validation of trigger tools across primary care and hospital settings, allowing primary care to learn from hospital settings.

The Scania Accelerated Intermittent Theta-burst Implementation Study (SATIS)–Lessons from an accelerated treatment protocol

Background and objective The Scania Accelerated Intermittent Theta-burst Implementation Study (SATIS) aimed to investigate the tolerability, preliminary effectiveness, and practical feasibility of an accelerated intermittent theta burst stimulation (aTBS) protocol in treating depression. Methods We used an open-label observational design, recruiting 20 patients (aged 19–84 years) from two public brain stimulation centers in Sweden. During the five-day treatment period and at a follow-up visit after 30 days we closely monitored adverse events and collected self-rated side effect data. Objective (MADRS, CGI) and subjective (MADRS-S) measures of symptoms and functioning (EQ-5D) were also assessed. Feasibility was evaluated using direct patient ratings combined with a qualitative approach evaluating staff experience. Results All patients reported adverse events at some point, the most common being headache (18/20 patients), but they were generally transient. MADRS scores decreased from 28.4 (min = 17, max = 38. SD = 6.9) at baseline to 20.0 (min = 1, max = 42. SD = 11.6) after the last day of treatment. 25% (n = 5) met the response criteria, with a mean time to response of 2.2 days (min = 1, max = 3. SD = 1.1). The practical arrangements surrounding this new treatment proved challenging for the organization, but patients reported few practical problems. Conclusion SATIS provided further insights into the potential benefits and challenges associated with aTBS protocols. Effectiveness and drop-out rates were comparable to national data of conventional iTBS, but with a markedly faster time to response. More resources were required than anticipated, increasing the clinical workload.

Research on the risk spillover effect between China’s national carbon emissions trading market and crude oil futures market

The development of China’s National Carbon Market has strengthened the inherent link between the carbon market and the broader energy market, providing a potential for cross-market risk transmission resonance. Studying the risk spillover effects between China’s National Carbon Market and the crude oil futures market is of significant practical importance, both in terms of carbon market development and carbon risk management. Based on the Maximal Overlap Discrete Wavelet Transform (MODWT), the price series are decomposed across multiple scales, and the risk spillover effects between the carbon market and the crude oil futures market are examined from both the time domain and the frequency domain. Methods such as wavelet energy decomposition, wavelet correlation, lead-lag analysis, and wavelet coherence are used to explore the mean spillover effects and volatility spillover effects (collectively referred to as risk spillover effects) across various scales. The study finds that China’s National Carbon Market exhibits a clear compliance-driven effect, with relatively low market liquidity. The crude oil futures market experiences frequent price fluctuations, primarily driven by long-term factors. In the time domain, the risk transmission resonance between the carbon market and the crude oil futures market is high, with significant positive correlations observed at the D1 to D4 scales, and noticeable mean spillover effects. In the frequency domain, at the D3 to D4 scales, the carbon market and the crude oil futures market exhibit similar volatility frequencies, indicating strong volatility spillover effects. Based on these findings, it is recommended that the trading volume of the carbon market be gradually increased to improve market liquidity. Furthermore, the risk monitoring and early warning mechanisms of China’s National Carbon Market should be improved. For carbon-emitting companies, enhancing awareness of carbon asset management and making informed investment and hedging decisions based on the correlation between the two markets is crucial.

The impact of village heads’ educational levels on adolescent academic performance: Evidence from rural China

This study investigates the relationship between the educational level of village heads and the academic performance of adolescents, using data from the China Family Panel Studies (CFPS). The analysis reveals that village chiefs with well-educated significantly enhance the academic outcomes of adolescents within their communities. This positive effect remains robust even after controlling for endogeneity through instrumental variables and conducting various robustness checks. Further investigation shows that these well-educated village leaders contribute to an increased provision of public goods, thereby improving the village’s external environment, which in turn supports academic performance. Additionally, well-educated village chiefs serve as role models within the community’s social network, positively influencing parental educational aspirations and enhancing adolescents’ academic results. Notably, the impact of well-educated village chiefs is more pronounced among girls and adolescents from low-income families, underscoring its significance in promoting gender equity in education and breaking cycles of intergenerational poverty.