Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Parkinson's Disease (PD) is a neurodegenerative disorder that affects the motor system of the human body. Currently, the available treatments are limited and often have undesirable side effects. In this context, the aim of this study was to develop a novel combinational formulation of Apomorphine and Quetiapine and evaluate its efficacy in a rat model of PD. The formulation was prepared using the solvent evaporation technique and characterized for its physicochemical properties. The in-vitro drug release was evaluated using the Franz diffusion cell method, and the pharmacokinetics of the combination was studied in rats. The behavioural and biochemical parameters were evaluated in the rats using the Rotarod, pole test, and biochemical assays. The results showed that the developed formulation had satisfactory physicochemical properties and exhibited sustained drug release. The pharmacokinetic studies showed that the combination exhibited a longer residence time than the individual drugs. The behavioural and biochemical parameters showed that the combination was effective in improving motor function and reducing oxidative stress. In conclusion, the developed Apomorphine and Quetiapine combination formulation showed promising results in improving motor function and reducing oxidative stress in rat model of PD. Further studies are required to evaluate the safety and efficacy of the developed formulation in humans.

Cryptococcosis is one of the most serious opportunistic diseases in patients living with HIV. For this reason, it is important to have early diagnosis and appropriate treatment. Objectives. The aim of the study was to know the evolution of patients diagnosed with cryptococcosis by detection of Cryptococcus antigen in serum by lateral flow (CrAg LFA) without nervous system involvement and with treatment in accordance with the results. Materials and Methods. A retrospective, longitudinal, analytical study was performed. Seventy patients with cryptococcosis initially diagnosed by serum CrAg LFA without meningeal involvement between January 2019 and April 2022 were analyzed for medical records. The treatment regimen was adapted to the results of blood culture, respiratory material or pulmonary tomography imaging. Results. Seventy patients were included, 13 had probable pulmonary cryptococcosis, 4 had proven pulmonary cryptococcosis, 3 had fungemia and 50 had pre-emptive therapy without microbiological or imaging findings compatible with cryptococcosis. Among the 50 patients with pre-emptive therapy, none had meningeal involvement or cryptococcosis recurrences to date. Conclusion. Pre-emptive therapy avoided progression to meningitis in patients with CrAg LFA-positive patients. Pre-emptive therapy with dose adjustment of fluconazole in patients with the mentioned characteristics was useful despite the use of lower doses than recommended.

This research investigates the effect of pre-soaking treatment on plant-based aggregate using a wet grout binder to formulate a high-strength lightweight concrete (HSLWC). The surface modification utilising a novel grout soaking technique with various water-to-cement (w/c) ratios indicated a new method of approach for the recent development of lightweight plant-based aggregate (LWPA). In this experiment, the fresh and hardened properties of modified LWPA lightweight concrete were assessed by verifying their workability, densities, compressive and split tensile strengths towards the modulus of elasticity. The results showed that pre-soaking plant-based lightweight aggregate (w/c: 0.6, 0.8, 1.0, and 1.2) slightly increased the density of the samples compared to untreated LWPA. The oven-dry density of treated and untreated LWPA is controlled in the range of HSLWC. The outcomes indicated that the workability of the surface- modified LWPA has significant improvement up to 40% in 6 min for (TDS)/0.6 sample compared to the original LWPA. The mechanical properties of the LWPA concrete with surface modification method exhibit substantial increment of compressive strength, split tensile strength, and modulus of elasticity were recorded at 22%, 26%, and 34%, respectively. Significantly, the findings from this scientific method revealed that the pre-soaking treatment method on LWPA has shown to be a highly recommended technique in improving interfacial bonding while performing as one of the most promising solutions to improve the properties of lightweight concrete.

The implementation of green roofs as sustainable urban drainage systems provides benefits for stormwater control and the environment and is always more encouraged. In this paper, the estimation of the probability of vegetation survival without irrigation has been proposed as a guide to choose the proper values for the design parameters; in particular the growing medium thickness has been related to the average return interval of the water content at the end of the dry period. Moreover the study represents an improvement of the analytical probabilistic approach since a chain of consecutive rainfall events has been considered, in order to take into account the possibility that the storage capacity is not completely available at the beginning of each event because of the pre-filling from more than one previous rainfall as typically happens for green roofs. Finally, developed equations have been validated by means of their application to two case studies, respectively in northern and southern Italy.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Kersting’s groundnut is an important source of protein and essential nutrients that contribute to food security in West Africa. However, the crop is still underexploited by the populations and under-researched by the scientific community. This study aimed to investigate the genetic diversity and population structure of 217 Kersting’s groundnut accessions from five origins using 886 DArTseq markers. Gene diversity was low and ranged from 0.049 to 0.064. The number of private alleles greatly varied among populations (42–192) and morphotypes (40–339). Moderate to very high levels of selfing and inbreeding were observed among populations (s=56–85%, F_IS=0.389–0.736) and morphotypes (s=57–82%, F_IS=0.400–0.691). Moreover, little to very high genetic differentiations were observed among populations (0.006≤F_IS≤0.371) and morphotypes (0.029≤F_IS≤0.307). Analysis of molecular variance partitioned 38.5% of the genetic variation among and 48.7% within populations (P<0.001). Significant isolations by distance were detected between populations (R²=0.612, P=0.011) and accessions (R²=0.499, P<0.001). Discriminant analysis of principal components and neighbour joining consistently distinguished eight distinct clusters. These data provide a global picture of the existing genetic diversity for Kersting’s groundnut and will guide the choice of breeding strategies to increase production.

Electronic sports (esports) and research on this emerging field are interdisciplinary in nature. By extension, it is essential to understand how to standardize and structure training with the help of existing tools developed by years of research in sports sciences and informatics. Our goal in this article was to verify if the current body of research contains substantial evidence of the training systems applied to training esports players. To verify the existing sources, we have applied a framework of scoping review to address the search from multiple scientific databases with further local processing. We conclude that the current research on esports dealt mainly with describing and modeling performance metrics spanned over multiple fragmented research areas (psychology, nutrition, informatics), and yet these building blocks were not assembled into an existing well-functioning theory of performance in esports by providing exercise regimes, and ways of periodization for esports.

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols.

Frailty syndrome, a significant contributor to morbidity and mortality in older adults, has traditionally been addressed through aerobic exercises. However, recent research has highlighted the importance of anaerobic and strength training activities in mitigating frailty. This article explores the shift from aerobic to anaerobic/strength training exercises, emphasizing the benefits of the latter in improving muscle strength, balance, and overall physical function. The discussion includes the physiological mechanisms underlying this shift, the evidence supporting strength training, and practical recommendations for implementation.

Background/Objectives: Rehabilitation helps reduce disability in dementia. The Australian National Dementia Action Plan identifies a gap in clear treatment pathways post-diagnosis, affecting the quality of life for those with dementia. This study assessed the impact of a one-day dementia training course and follow-up on GPs' and practice nurses' knowledge, attitudes, and confidence regarding dementia rehabilitation. Methods: The training, led by two experienced GPs and an academic physiotherapist, covered dementia diagnosis, allied health roles, care planning, and referrals. The follow-up involved applying the learnt material and completing a reflective task. Three longitudinal surveys (Dementia Knowledge Assessment Scale - DKAS, General Practitioners’ Attitudes and Confidence towards Dementia Survey - GPACS-D, and Dementia Rehabilitation Scale) and Likert-scale statements were conducted pre-course, post-course, and at 4-month follow-up, alongside a focus group. Descriptive and regression analyses were applied to survey data, and content analysis was used for focus group data. Results: Seventeen participants (14 GPs, 3 nurses) completed the pre-post course survey, with eight (6 GPs, 2 nurses) participating in follow-up and focus group discussions. Post-course, DKAS scores increased by 12.1%, GPACS-D by 10.1%, and the dementia rehabilitation scale by 9.4%. Likert-scale statements improved by 8-79%. At the four-month follow-up, there was a slight, non-significant decline in most measures. Focus groups highlighted the training’s impacts, useful components, barriers, and suggestions for improvement. Conclusion: Training GPs and practice nurses in dementia rehabilitation enhances knowledge, awareness, and confidence. Ongoing efforts are needed to sustain benefits and address referral barriers for better access to dementia rehabilitation services.

This study aims to analyze the effectiveness of implementing the Teaching Factory (TEFA) in improving the professional competence of teachers in State Vocational Schools (SMK) in Jakarta. Teaching Factory is an educational approach that integrates the learning environment with real work practice to bridge the gap between theory and practice. This study uses a quantitative method with surveys and interviews as data collection tools. The respondents are vocational school teachers involved in the Teaching Factory program. The results show that the implementation of Teaching Factory significantly improves teachers' abilities in both technical pedagogical aspects and soft skills, contributing positively to the development of their professional competence. The study concludes that Teaching Factory is an effective method for enhancing the quality of teaching and aligning teachers' skills with industry needs.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p &lt; 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p &lt; 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Few-shot object detection (FSOD) aims to address the challenge of requiring a substantial amount of annotations for training in conventional object detection, which is very labor-intensive. However, existing few-shot methods achieve high precision with the sacrifice of time-consuming for exhaustive fine-tuning, or take poor performance in novel-class adaptation. We presume the major reason is that the valuable correlation feature among different categories is insufficiently exploited, hindering the generalization of knowledge from base to novel categories for object detection. In this paper, we propose Few-Shot object detection via Correlation-RPN and Transformer Encoder-Decoder (CRTED), a novel training network to learn object-relevant features of inter-class correlation and intra-class compactness while suppressing object-agnostic features in the background with limited annotated samples. And we also introduce a 4-way tuple-contrast training strategy to positively activate the training progress of our object detector. Experiments over two few-shot benchmarks (Pascal VOC, MS-COCO) demonstrate that, our proposed CRTED without further fine-tuning can achieve comparable performance with current state-of-the-art fine-tuned works. The codes and pre-trained models will be released.

Background: Delivery of quality family planning services depends on availability of skilled health care providers, which is often a challenge in Low- and middle-income countries. This study evaluates the effects of a targeted training program on family planning services delivered by the Lady Health Workers (LHWs) in six districts of Punjab, Pakistan. The primary goal was to enhance the quality and accessibility of family planning services, recognizing the pivotal role of LHWs in community health. Methods: Using a robust panel data difference-in-differences (DiD) approach, we analyzed the electronic medical records (EMR) of 1,410 LHWs over a nine-month period. This timeframe in-cluded both pre-training (July to December 2022) and post-training (April to June 2023) phases. Our assessment focused on the impact of training on five outcome variables: the number of follow-up clients, the number of Intrauterine Contraceptive Device (IUCD) clients, the number of condom clients, the number of modern method clients, and the number of injection clients. We also con-sidered control variables such as LHW age, LHW education, the number of eligible couples in each LHW's catchment area, and a categorical variable for the districts. Results: The positive and statistically significant (p<0.05) coefficients across various service cat-egories – Follow Up Clients [Coeff: 1.00, CI: (0.30 - 1.70)], IUCD Clients [Coeff: 0.23, CI: (-0.01 - 0.46)], Condom Clients [Coeff: 1.41, CI: (0.97 – 1.86)], Modern Method Clients [Coeff: 1.82, CI: (0.96 – 2.68)], and Injection Clients [Coeff: 0.32, CI: (0.14 – 0.50)]. These findings indicate enhanced productivity among LHWs who have received training, with results significant at the 95% con-fidence interval (CI). Conclusion: The study highlights the effectiveness of targeted training programs in boosting family planning services at the community level and offers insightful implications for policy-makers and health practitioners. While focused on six districts in Punjab, these promising results emphasize the need for ongoing training and support for frontline health workers to bolster family planning initiatives.

(1) Background: This text emphasizes concerns regarding the safety of Spanish police officers at-tributed to inadequate training, employing biomechanics for the analysis and mitigation of risks during Use of Force training. The elevated global prevalence of musculoskeletal disorders under-scores the imperative for effective occupational health measures. The study concentrates on evalu-ating injury risks in novice police officers during Use of Force training. (2) Research method: The movements of police officers were captured using a Rokoko Smartsuit Pro equipped with 19 sen-sors. The Biomechanics of Bodies software processed the data, providing REBA and Marras risk scores. Statistical tests were conducted, and Operational Tactical Procedures (OTP) were inte-grated into police training. A diverse group of officers participated, categorized by training level and equipped with the Rokoko Smartsuit. (3) Results: The study evaluates police force training us-ing REBA and Marras methods. REBA scores indicate a moderate to high injury risk, suggesting effective technique design. ANOVA reveals no significant differences between training levels. Marras analysis, characterized by non-normal distribution, unveils varied risk perceptions among groups via the Kruskal-Wallis test. (4) Conclusions: The analysis of both the REBA and Marras methods implies the effectiveness of the OTP technique in reducing injury risks across dif-ferent training levels. The ANOVA test indicates no significant differences in REBA Maximum Scores between the groups. However, the Kruskal-Wallis test for Marras Probability of high-risk values suggests distinctions in medians between training levels. The combined analysis visually illustrates variations in membership regions between expert, intermediate, and novice groups.

Keywords: Аrtificial neural network, electric drive system, database, stability, training algorithm, technological mechanisms.

Social communication skills, especially eye contact and joint attention, are highly impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied Behavior Analysis is one of the best evidence-based treatments for ASD, but it is not accessible to most families in low- and middle-income countries (LMICs) as it is a costly and intensive and needs to be delivered by highly specialized professionals. Parental training has emerged as an effective alternative. The aim of this study was to test the efficacy of a parental intervention group via video modeling to acquire eye contact and joint attention. Four graded measures of eye contact and joint attention (full physical prompt, partial physical prompt, gestural prompt, and independent) were assessed in 34 children with ASD and intellectual disability (ID). There was a progressive reduction in the level of prompt required over time to acquire eye contact and joint attention, and a positive correlation between the time of exposure to the intervention and acquisition of the abilities. This parent training using video modelling to teach eye contact and joint attention skills to children with ASD and ID is a low-cost intervention that can be applied in low- resource settings.

Nurses in disaster response require comprehensive understanding, training, and col-laboration among educators, researchers, and practitioners to overcome challenges and improve their capabilities. This study evaluates the impact of simulation-based training on improving nursing students' knowledge and performance in crisis management and triage during mass casualty incidents in Saudi Arabia, aiming to enhance existing pro-tocols in disaster management. This quantitative interventional pre-post study aimed to assess the impact of a training intervention on the disaster nursing skills of nursing students at Taif University in Saudi Arabia. The study involved a random sample of 101 nursing students and utilized a realistic train accident simulation with a response team comprising healthcare professionals and emergency specialists. A detailed questionnaire was used to measure emergency management skills and knowledge, and pre-test and post-test evaluations were conducted. Data analysis was performed using SPSS, and the study was conducted on a voluntary basis with necessary approvals obtained. The findings have the potential to enhance disaster management protocols and improve the preparedness of nursing professionals in Saudi Arabia. The posttest analysis revealed that a significant portion of participants achieved excellent, very good, and good levels of performance, indicating the effectiveness of the training program. In contrast, the pretest grades showed a higher percentage of participants receiving fail level grades, high-lighting the need for improvement prior to the training intervention. This study high-lights the importance of comprehensive training and education in disaster nursing for improving emergency response and patient outcomes

Contrary to the long-held belief that the effects of vaccines are specific for the disease they were created; compelling evidence has demonstrated that vaccines can exert positive or deleterious non-specific effects (NSEs). In this review, we compiled research reports from the last 40 years, showing that live vaccines induce positive NSEs, whereas non-live vaccines induce several negative NSEs, including increased female mortality associated with enhanced susceptibility to other infectious diseases, especially in developing countries. These negative NSEs are determined by the vaccination sequence, the antigen concentration in vaccines, the type of vaccine used (live vs. non-live), and also by repeated vaccination. We do not recommend stopping using non-live vaccines, as they have demonstrated to protect against their target disease, so the suggestion is that their detrimental NSEs can be minimized simply by changing the current vaccination sequence. High IgG4 antibody levels generated in response to repeated inoculation with mRNA COVID-19 vaccines could be associated with a higher mortality rate from unrelated diseases and infections by suppressing the immune system. Since most COVID-19 vaccinated countries are reporting high percentages of excess mortality not directly attributable to deaths from such disease, the NSEs of mRNA vaccines on overall mortality should be studied in depth.

Poor hygiene in the food services industry is a critical public health concern in the Maldives, with an average of 35 public complaints per month regarding the poor hygienic conditions of the restaurants in Male’, the capital of Maldives. Thus, this study aimed to identify the level of knowledge, attitude, and the gap of self-reported with the observed practice (KAP) of food handlers as well as the potential association between KAP. 290 food handlers from restaurants were surveyed, demonstrating moderate knowledge (55.5%), moderate attitude (3.62±0.51) and good practice (4.18±0.54). However, it is critical the result shows the food handlers in Male’ have poor practice on the time and temperature control where especially on thawing and storage resulting from a lack of training in the temperature danger zone. The work experience impacting the level of knowledge, education impacts the level of attitude and age, and education and work experience impacting practice. Male’ restaurants mostly employed the foreigners mostly from Bangladesh and the food handlers were identified to have low level of education. Association analysis identified significant relationships between knowledge, attitude, and practice, while the principle component analysis shows categories that are related to each of KAP. This study is fundamental for policymakers to review and improve law enforcement to address the current food safety situation in the Maldives.

To create normative scores for all CrossFit® Open (CFO) workouts and compare male and female performances, official scores were collected from the official competition leaderboard for all competitors of the 2011-2022 CFO competitions. Percentiles were calculated in athletes (18 – 54 years) who completed all workouts within a single year ‘as prescribed’ and met minimum scoring thresholds. Independent t-tests revealed significant (p &lt; 0.05) sex differences for 56 of 60 workouts. In workouts scored by repetitions completed, men completed more repetitions in 18 workouts by small-to-large differences (d = 0.22-0.81), whereas women completed more repetitions in 6 workouts by small-to-medium differences (d = 0.36-0.77). When workouts were scored by time-to-completion, men were faster in 10 workouts by small-to-large differences (d = 0.23-1.12), while women were faster in three workouts by small differences (d = 0.46). In three workouts scored by load lifted, men lifted more weight by large differences (d = 2.00-2.98). All other differences were either trivial or not significant. Despite adjusted programming for men and women, the persistence of performances differences across all CFO workouts suggest resultant challenges are not the same. These normative values may be useful to training and research in male and female CrossFit® athletes.

Background: Physical activity has been considered as a promising approach to slow down the disease process in Multiple Sclerosis (MS) patients. While the functional impairments of MS have been studied in detail, but there is limited evidence of the efficacy of exercise interventions on the Multiple Sclerosis functional composite (MSFC) score in these patients. The study aim was to in-vestigate the improvement in MSFC score by multi-function swing suspension training (MFSST) in the women with MS. Methods: The patients were divided into the intervention and control groups. A total of 47 MS patients completed the MSFC components at baseline and after the intervention. Z scores were created for each test based on control means. Results: The MSFC components showed a significant increment in comparison with the baseline levels in the four, six, and eight weeks following the first exercise session (all p<0.05). These dif-ferences in the control group were not significant. The improvement in the MSFC score and the component Z-scores in the intervention groups was found from the fourth week onwards. Conclusions: The study findings highlight that MFSST can be used as an effective treatment method in MS patients due to its positive effects on physical parameters, such as muscles strength, functional impairments, and cognitive problems.

An increasing number of studies have shown that safety culture factors have a substantial influence on safety performance in a variety of industrial sectors. These factors' impact on safety performance is unclear, especially at the public service and statutory authorities. On the other hand, the understanding of indicators for safety performance in every working sector in Malaysia is on the continuing progress. Hence, this study's contribution is to explore the influence of safety culture factors (i.e., management commitment and supervision in safety, safety system) and safety competence on safety performance in government paramedic training institutes. IPMA (importance-performance map analysis) is a technique used in Smart PLS to determine the significance and performance of each of these factors. The study was conducted via an online survey and involved 258 safety and health committee members in Ministry of Health paramedic training institute. As a matter of relevance, the IPMA's empirical data study revealed that management commitment and supervision in safety were the predominant factors in determining safety performance. Meanwhile, for performance, the findings showed that worker involvement, safety system, and safety competence perform well in determining the safety performance.

The analysis of microscopy images has always been an important yet time consuming process in in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad-hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points.

The present research is embedded in the professional development and research line and in the needs of secondary education and first-year university teachers. We focussed on evidencing the importance of teachers’ professional training to include some specific adaptation and skills in intercultural dialogue and understanding -often called Intercultural Competence- because of its direct impact on the sustainable development of human beings, groups, and ecosystems. We investigated the role played by each of the main competencies linked to the following intercultural dimensions: Professional Identity, Ethics and Axiology, Methodology, and Inclusive Education. We used an integrated methodology and a cross-study of data, performed after the obtention of a three-cornered evaluation of results collected in focus groups, interviews, and questionnaires. We were able to show the impact of intercultural dialogue and understanding in the education for a sustainable development pattern. This is fundamental to set up a new ecology of forms, knowledge, attitudes, and educational meanings, further used to update teachers and students’ training in sustainable ecology and cultural diversity. Progress made in these complementary competencies -Professional Identity, Ethics and Axiology, Methodology- were appraised by teachers participating in the present study; the latter showing an increased interest and demand for the intercultural competence, after increasing their proficiency in the other complementary competencies.

This study aims to explore the effect of real-time visual feedback (VF) information of the pres-sure of center (COP) provided by intelligent insoles on balance training in a one leg stance (OLS) and tandem stance (TS) posture. Thirty healthy female college students were randomly assigned to the visual feedback balance training group (VFT), non-visual feedback balance training group (NVFT), and control group (CG). The balance training includes: OLS, tandem Stance (dominant leg behind, TSDL), tandem stance (non-dominant leg behind, TSNDL). The training lasted 4 weeks, the training lasts 30 minutes at an interval of 1 days. There was a sig-nificant difference in the interaction effect between Groups*Times of the COP parameters (p<0.05) for OLS. There was no significant difference in the interaction effect between Groups*Times of the COP parameters (p>0.05) for TS. The main effect of the COP parameters was a significant difference in Times (p<0.05). The COP displacement, velocity, radius, and area in VFT significantly decreased after training (p < 0.05). Therefore, the visual feedback technology of intelligent auxiliary equipment during balance training can enhance the benefit of training. The use of smart wearable devices in OLS balance training may improve the visual and physical balance integration ability.

Genomic selection (GS) is a predictive approach that was build up to increase the rate of genetic gain per unit of time in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effect. GS enables the prediction of breeding value of candidate genotypes for selection. In this work we address important issues related to GS and its implementation in tomato breeding context. Genomic constrains and critical parameters affecting the accuracy of prediction in such crop such as phenotyping, genotyping training population composition and size and statistical method should be carefully evaluated. Comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding program are also discussed. GS applied to tomato breeding has already shown to be feasible. We illustrated how GS can improve the rate of gain in elite lines selection, descendent and in backcross schemes. The GS schemes begin to be delineated and computer science can provide support for future selection strategies. A new breeding framework is beginning to emerge for optimizing tomato improvement procedures.

PcALCL mainly concerns elderly patients. It is a large CD30+ T-cell neoplasm composed of large cells with anaplastic, pleomorphic or immunoblastic morphology, with exclusively cutaneous onset and localization. The clinical course of pcALCL is predominantly indolent. Most elderly patients with lymphoma tend to have a sedentary lifestyle, which has a negative effect on their quality of life (QoL) and on their survival. Several studies indicate that exercise has a positive impact on QoL because it reduces peak oxygen consumption, improves physical capacity, increases self-esteem, reduces accumulated stress and promotes relaxation. Therefore, particularly in indolent lymphomas, it is necessary to indicate a program of physical activity to be practiced systematically. The complete surgical excision and local radiotherapy are the first line gold standard in pcALCL with solitary lesion.

Attitudes and behaviours towards the natural environment have been extensively studied in certain cultural settings during the last 40 years. In education, the teacher's ability to grasp the fundamentals of an academic subject may define his or her own attitudes towards that discipline; certainly the reverse is also valid. The correlations between affective and cognitive domains appear to play a significant role in teaching-learning dynamics. In this study we seek to assess whether the affective posture towards school sciences of a cohort of teachers in rural communities shows an association with their cognitive competence in the disciplines. The results of this study provide evidence that there is a statistically significant correlation between the cognitive and affective domains for in-service teachers. The affective domain, therefore, could serve as a predictor for cognitive competency and self-efficacy expectancies with respect to both content and career fulfilment.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

Evaluation of the patients after the surgical correction of idiopathic scoliosis in long-term follow-up with the clinical neurophysiology methods has not been presented in detail. This study aimed to compare the results of the neurophysiological studies in 45 girls with scoliosis of Lenke 1-3 types performed pre- (T0), postoperatively (T1, one week after surgery), and 6 months after the surgery (T2). Parameters of surface electromyography during the attempt of maximal contraction (mcsEMG) and transcranially evoked motor evoked potentials (MEP) recorded from anterior tibial muscles, as well as the electroneurography (ENG) of peripheral transmission in the peroneal nerve motor fibers were compared. The results indicate that efferent neural conduction function both centrally and peripherally as well as TA muscle function improve immediately after surgical correction of scoliosis (at p=0.05) and further normalization appears after six months in long-term follow-up (at p=0.03). It has been found in sEMG recordings, that the TA muscle motor unit recruitment function after half a year from surgical treatment in IS patients is comparable to the normal condition. ENG recordings results indicated the gradual retreat of the motor fibers injury symptoms mainly of the axonal type in peroneal nerves, the surgeries improved also the lumbar ventral roots’ neural transmission to the functional status considered as normal. MEP amplitude parameters recorded after the surgical scoliosis correction in T1 indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts; in long-lasting T2 observation, they reached values comparable to those recorded in healthy volunteers bilaterally. Preoperatively (T0) the results of all neurophysiological study parameters in IS patients were asymmetrical at p=0.036-0.05 and recorded as worse on the concave side, suggesting the lateralization of neurological motor deficits. One week postoperatively (T1), this asymmetry was recorded as gradually reduced, showing almost no difference between the right and left sides six months later (T2). The presented algorithm of the diagnostic proceeding pre-, early post-, and long-lasting postoperatively using mcsEMG, MEP, and ENG neurophysiological examinations can be significant not only in making the final decision regarding surgical treatment and its personalization but also helps in precise ascertaining its effects as well as in predicting the final result of IS treatment.

A community-level nutrition intervention was implemented among tribal children (3 to 6 years) in Telangana, India. The one-year intervention involved six nutrient-rich formulations of millet-pulse-groundnut based products, suited to local taste preferences. Anthropometric measurements of height, weight and Mid-Upper-Arm -Circumference (MUAC) along with haemoglobin (Hb) levels were monitored at baseline and endline. The treatment group showed considerable gain in height (3.2 cm), weight (1.68 kg) and MUAC (0.33 cm), over the control group. Paired t-test indicated significant differences (p<.01) between pre- and post-intervention anthropometric measurements. Positive shifts were observed in wasting (WHZ; -1.2 ±1.3 to -0.9± 1), stunting (HAZ; -1.8±1.6 to -0.3±1.3) and underweight (WAZ; -1.9±1.2 to -0.7±1) among treatment group. Hb levels in the treatment group also improved significantly from 9.70 ± 0.14 g/dl (moderately-anaemic) to 11.08 ± 0.13 g/dl (non-anaemic). Post-intervention Focus Group Discussions (FGDs) involving mothers and teachers confirmed the positive impact. Thus, nutritional intervention formulated using climate resilient millets, pulses and groundnut, promotes dietary diversity and improves the nutrition & health status of children.

Empowering teachers with Critical Thinking and understanding of sustainability is vital for guiding students’ informed decisions and actions in today’s world. Higher education needs innovative, student-centered methods like case-based teaching in order to promote Critical Thinking and the principles of Education for Sustainable Development. The aim of the study was to explore and evaluate pre-service teachers' Critical Thinking when engaged with case studies in a practicum preparation course centered on Education for Sustainable Development. Nine undergraduate students in Teacher Education participated in the study and answered four case studies. The case studies’ responses were analyzed through a bottom-up content analysis revealing eight higher-order categories. While approaching the case studies, student-teachers followed thinking processes similar to the processes proposed by the iterative models of Critical Thinking as perceived by Garrison (1991) and/or Brookfield (1987). Nevertheless, in some cases, pre-service teachers’ disengagement was evident. The rubric assessment highlighted that the responses fell under three profiles, namely the “Deficient Critical Thinker”, the “Emerging Critical Thinker” and the “Competent Critical Thinker”. The findings emphasize the need for targeted and individualized teaching interventions in teacher higher education programs aiming at Education for Sustainable Development complying with the learning needs of student-teachers’ Critical Thinking profiles.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

Lung cancer is the leading cause of global cancer-related mortality resulting in ~1.8 million deaths annually. Systemic, molecular targeted, and immune therapies have provided significant improvements of survival outcomes for patients. However, drug resistance usually arises and there is an urgent need for novel therapy screening and personalized medicine. 3D patient-derived organoid (PDO) models have emerged as a more effective and efficient alternative for ex vivo drug screening than 2D cell culture and patient-derived xenograft (PDX) models. In this review, we performed an extensive search of lung cancer PDO-based ex vivo drug screening studies. Lung cancer PDOs were successfully established from fresh or biobanked sections/biopsy of lung tumors, PDXs, and pleural affusion. PDOs were subject to ex vivo drug screening with chemotherapy, targeted therapy and immune therapy agents. PDOs mainly recapitulated the genomic alterations, transcriptomic landscape and drug sensitivity of primary tumors. Although sample sizes of the previous studies were limited and some technical challenges remained, PDOs showed promise to screen novel therapy drugs. With the technical advance of high throughput, tumor-on-chip, combined microenvironment, and air-liquid interface (ALI), the drug screening using PDOs would serve better for precision care of lung cancer patients.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

The novel coronavirus (COVID-19) was first reported in the U.S. on December 29, 2019 and has spread rapidly throughout the country, affecting individuals with varying severity due to their risk status. According to the Centers for Disease Control and Prevention, it is estimated that 45.4% of US adults are at higher risk for complications from coronavirus disease because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. In this study, we developed a mathematical model to assess the impact of a COVID-19 vaccine among low and high risk groups. Numerical simulations shows vaccinating both low and high risk groups simultaneously, rather than prioritizing the vaccine on high risk group only, further reduces the daily mortality. The result supports the need for an aggressive vaccination program, regardless of whether individuals are within the low or high risk population.

The basic principles of geological risk calculation through Probability of Success (PoS) are mostly applied for numerical estimation of additional hydrocarbon existence in proven reservoirs or potential hydrocarbon discoveries in selected geological regional subsurface volume. It can be tailored and validated for a comprehensive input dataset collected in the selected petroleum province, adapted by dividing up geological events into several probability categories and classes. The most applied categories are (existence of) reservoir rocks, traps and isolators, source rocks, migration pathways and preservation conditions for hydrocarbons The methodology results in unique probability values as multiplication of independent statistical events, which can also be applied in the assessment of a potential hydrocarbon discovery of desired minimal volume and its value in any virtual currency like risk-neutral dollars. Such methodology has been extensively developed in the last decades in the Croatian subsurface, mostly in the Croatian part of the Pannonian Basin System (CPBS). Through the adaptation of geological categories, it was also applied in hybrid, i.e., stochastical, models developed in the CPBS (Drava Depression). Stochastically estimation of porosity was already applied. As the robustness of this methodology is very high, it was also modified to estimate the influence of water-flooding in increasing oil recovery in some proven Neogene sandstone reservoirs in the CPBS (Sava Depression). This new modification is being presented to be applied to geological risk calculation, intending to assess the safety of geological environment in deep wells, where depleted radioactive fuel would be disposed, a subject of great importance. The case study encompassed the magmatic and metamorphic rocks in the pre-Neogene basement of the CPBS. For disposal purpose, these are regionally lithologies considered as to be the safest ones considering petrophysical values, water saturation, recent weathering and tectonic activity.

This study was aimed at investigating the protective effects of glass ionomer cement (GIC) and surface pre-reacted glass ionomer (S-PRG) fillers used as dental restorative materials on demineralization of bovine enamel. GlasIonomer FX ULTRA (FXU), Fuji IX GP Extra (FIXE), CAREDYNE RESTORE (CDR) were used as GICs. PRG Barrier Coat (BC) was used as the S-PRG filler. They were incubated in a lactic acid solution (pH = 4.0) for 6 d at a temperature of 37 °C. The mineral was etched from the enamel surface, and a large number of Ca and P ions were detected in solution. The Al, F, Na, Sr, and Sr ions were released in GICs and S-RPG fillers. The Zn ion was released only in CDR and the B ion was released only in BC. The presence of apparent enamel prism peripheries was observed after 6 d of treatment for the group containing only enamel blocks. pH values for the FXU, FIXE, CDR, BC, and enamel block groups after 6 d were 6.5, 6.6, 6.7, 5.9, and 5.1, respectively. Therefore, the observed pH neutralization effect suppressed progression of caries due to the release of several ions from the restoratives.

Prestress loss evaluation in prestressed strand is essential for prestressed structures. However, the sensors installed outside the duct can only measure the total prestress loss. The sensors attached on strand inside the duct also have several problems, such as inadequate durability in an aggressive environment, vulnerable damage at tensioning and so on. This paper proposes a new installation method for long-gauge fiber Bragg grating (LFBG) sensor to prevent accidental damage. Then the itemized prestress losses were determined in each stage of the pre-tensioning and post-tensioning according to the LFBG measurements. We verified the applicability of the LFBG sensors for prestress monitoring and the accuracy of the proposed prestress loss calculation method during pre-tensioning and post-tensioning. In the pre-tensioning case, the calculated prestress losses had less deviation from the true losses than those obtained from foil-strain gauges, and the durability of the LFBG sensors was better than foil-strain gauges, whereas in post-tensioning case, the calculated prestress losses were close to those derived from theoretical predictions. Finally, we monitored prestress variation in the strand for 90 days. The itemized prestress losses at each stages of post-tensioning were obtained by the proposed calculation method to show the prospect of the LFBG sensors in practical evaluation.

We propose a notion of $(q,\sigma,\tau)$-differential graded algebra, which generalizes the notions of $(\sigma,\tau)$-differential graded algebra and $q$-differential graded algebra. We construct two examples of $(q,\sigma,\tau)$-differential graded algebra, where the first one is constructed by means of generalized Clifford algebra with two generators (reduced quantum plane), where we use a $(\sigma,\tau)$-twisted graded $q$-commutator. In order to construct the second example, we introduce a notion of $(\sigma,\tau)$-pre-cosimplicial algebra.

The purpose of current study was to investigate the expression characteristic of circular RNAs (circRNAs) in peripheral blood of type 2 diabetes mellitus (T2DM) patients and their potentials as diagnostic biomarkers for pre-diabetes and T2DM. In present study, the circRNAs in the peripheral blood from 6 healthy individuals and 6 T2DM patients were collected for microarray analysis. The results indicated that there were 489 differentially expressed circRNAs, of which 78 were upregulated and 411 were downregulated in the T2DM group. Then we selected 5 circRNAs as the candidate biomarkers under a stricter screening criteria and further verified them in another cohort (control group, n=20; pre-diabetes group, n =20; T2DM group; n=20). 3 of the 5 circRNAs presented upregulated expression in the experimental groups, including 2 circRNAs of the T2DM group that had higher expression than the pre-diabetes group. Hsa_circ_0054633 was identified to have the largest area value under the carve (AUC). In another independent cohort (control group, n=60; pre-diabetes group, n=63; T2DM group, n=64), the diagnostic capacity of hsa_circ_0054633 was tested. The results showed that the AUC for the diagnosis of pre-diabetes was 0.751(95% confidence interval=[0. 666-0.835], P＜0.001) while it was 0.793 ([0.716-0.871], P＜0.001) for the diagnosis of T2DM. After including the risk factors of T2DM, the AUC increased to 0.841 ([0.773-0.910], P <0.001) and 0.834 ([0.762-0.905], P <0.001), respectively. Hsa_circ_0054633 presented a certain diagnostic capability for pre-diabetes and T2DM.

Background/Objectives: Traditional neurosurgical training involves a steep learning curve. The introduction of advanced simulation technologies, such as virtual reality (VR), provides an alternative method for skill acquisition, allowing for repeated practice and objective assessment. This study focuses on evaluating the learning curve associated with lumbar pedicle Kirschner wire insertion using a virtual fluoroscopic simulator among neurosurgical residents. Methods: Eighteen neurosurgery residents and two interns participated in this study. Participants’ initial skill levels were assessed through a questionnaire. Performance metrics, including insertion accuracy, time, and the number of virtual X-ray scans, were recorded across multiple attempts until a “practical learning plateau” was reached. Statistical analyses were conducted to model learning curves, assess the correlation between pre-training experience and performance. Results: The average number of attempts required to stabilize performance was 5.61. Learning rates varied, with some residents showing rapid improvement while others progressed more slowly. Higher accuracy in wire placement correlated with reduced usage on X-rays over time. However, experienced residents did not necessarily learn faster, indicating potential challenges in adapting to new simulation-based methods. Post-training feedback highlighted the simulator’s utility in increasing confidence and skill levels, although some limitations in anatomical accuracy were noted. Conclusions: This study demonstrates the potential of virtual simulation to enhance neurosurgical training by providing a controlled environment for repeated practice and objective feedback. Simulation-based training can effectively complement traditional methods, though individualized approaches may be necessary to accommodate varying learning rates among residents. Further refinement of simulation tools and their integration into standard training curricula are recommended.

Background: The hub-and-spoke model (HSM), offers a framework for efficient healthcare service delivery. This scoping review seeks to establish the implementation and effectiveness of the HSM in Antimicrobial Stewardship (AMS) programs. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane library and Google scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention or action, were included. No study specific filters were applied and all populations, study designs and health settings were included. Data screening and selection was achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesized. Standard appraisal tools were impractical; however, critically evaluation of data collection and outcome reporting was ensured. Results: Out of 1,438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programs, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS program success: tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers: leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusion: The review identified a literature gap in HSM use for AMS programs. Further studies are needed to assess HSM's effectiveness, feasibility, and cost-effectiveness in AMS contexts.

This study aimed to describe and distinguish shot characteristics between set winning and losing pairs in high-level male padel players from Finland. Between 2022 and 2023, we analyzed 4,469 points across 38 matches, examining factors such as match type, set outcome, effectiveness of the last shot, generator of forced error, and shot type. Results showed that set-winning players produced more winners (CSR = 7.7; CSR = 6.8) and committed fewer forced (CSR = 4.2; CSR = 2.8) and unforced errors (CSR = 4.0; CSR = 4.3) compared to set losing players, both in pressure training and competition matches. In addition, irrespective of the set outcome, these players achieved more winners and generated more forced errors with the smash, bandeja, forehand volley, and backhand volley. Nevertheless, a significant number of errors (both forced and unforced) are committed with the bandeja and volley (forehand and backhand). Furthermore, notable errors (forced and unforced) are observed in shots following one bounce and without a wall rebound, particularly in the forehand, backhand, and return. In conclusion, the ability to produce winners while minimizing forced and unforced errors proves to be crucial for securing sets in high-level men’s padel.

Background: The work and life of a junior doctor has changed dramatically over the past 50 years. Descriptions of historic working conditions are usually anecdotal and tinted with nostalgia. But presented with the facts, do heath care professionals feel working conditions things have improved or declined in the last 50 years? Methods: An interview was conducted with a retired surgeon who, in 1970, worked as a house surgeon (Foundation Year 2, FY2 equivalent) at a regional burns service. This was compared with a review of available literature to assess objectively working conditions in that period. The information generated from this produced a poster summarizing the key differences over a 53-year timeframe. This information was presented to the current medical work force and a survey was conducted to determine their preference. Statistical analysis including binary logistic regression was performed. Results: The questionnaire was completed by 76 respondent healthcare workers of mixed grades and backgrounds. The majority of respondents (64%) would choose to work in today’s burns unit. The age of the respondent was the best predictor for preference of working conditions (p = 0.043). Conclusions: Multiple changes in the working conditions were identified. The majority of respondents consider today’s working conditions to be better than that of the past. However, more senior clinicians were more likely to prefer the conditions of 1970 to present day, suggesting a generational shift in opinion.

The presence of cracks reduces egg quality and safety, which are easy to cause food safety hazards to consumers. Machine vision-based methods for crack egg detection have made significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches which rely on improving network structures or increasing training data volumes, can not effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown testing egg data. Firstly, multi-domain egg data is built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust crack egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 model trained by proposed multi-domain trained method with NSFE-MMD has a detection mAP of 86.6\% on the unknown test domain 4, and the YOLOV8 model has a detection mAP of 88.8\% on domain 4, which is an increase of 8\% and 4.4\% compared to the best performance of models trained on a single domain, and an increase of 4.7\% and 3.7\% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain trained method has a detection mAP of 87.9\% on egg data of the unknown testing domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain trained method, which can be more suitable for the large quantity and variety of egg detection production.

Numerous investigations have suggested that seismic source parameters and properties of the surrounding medium harbor valuable information regarding alterations in stress fields and medium properties at the focal depth. Monitoring the spatial and temporal evolution of these parameters can yield insights into variations in stress fields or medium properties within the seismogenic zone, offering a critical avenue to overcome the Earth's inaccessible nature. In recent years, modern seismic parameters such as seismic moment, focal mechanism solution, source stress drop, corner frequency, radiated seismic energy, rupture radius, and apparent stress have been increasingly used to characterize source characteristics. The Sichuan region is situated on the southeastern edge of the Qinghai-Tibet Plateau and is known for its strong eastward extrusion and structural deformation. The main fault zones in the area include the Xianshuihe Fault, Anninghe Zemuhe Fault, and Longmenshan Fault. This paper estimates source parameters of small to medium-sized earthquakes (ML 1.5-5.2) in the main fault zones and adjacent areas in Sichuan. A total of 4,310 earthquake stress drop measurements from 2019 to 2023 were analyzed to create a stress distribution image along the fault. This earthquake parameters training catalog provides insight into how coseismic stresses change and their relationship with other geophysical factors, as well as their spatial and temporal evolution.

Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

This article reviews the introduction of Alzheimer's Disease (AD), neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD, applications of neural networks in AD drug discovery, other brain diseases, and challenges faced by AD. First, the paper introduces the background and characteristics of AD. AD is a degenerative neurological disorder characterized by impaired memory, decreased cognitive function, and loss of neurons. These characteristics place a huge burden on the lives and families of patients. Next, the basic principle and structure of neural network are discussed. A neural network is a computational model made up of multiple neurons that can perform tasks by learning and adapting to input data. In particular, the key concepts of neural network hierarchy, activation function and weight adjustment are discussed. Then, the training and learning methods of neural networks are discussed. Common techniques such as backpropagation algorithm and gradient descent optimizer are introduced in detail, as well as the importance of data preprocessing and model evaluation. Next, the paper focuses on the application of neural network in early diagnosis of AD. By extracting features from brain image data, neural networks can automatically identify differences between AD patients and healthy subjects, enabling early diagnosis and intervention. In addition, the application of neural networks in AD drug discovery is also discussed. By analyzing and predicting a database of known drugs, neural networks can help discover potential treatments for AD and speed up the drug discovery process. The paper further explores the application of neural networks in other brain diseases and highlights the challenges faced by AD, such as the lack of reliable biomarkers, complex pathological mechanisms, etc. In summary, this paper presents a systematic overview of AD, neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD and drug discovery, and other brain diseases and challenges associated with AD.

Introduction: Post-COVID-19 syndrome (PCS) is a multisystemic disorder marked by impaired physical performance as one lead symptom. Since it has been suggested that endurance training as part of medical rehabilitation may be effective to improve physical performance capacity in PCS, this study aimed to compare different modes of aerobic endurance training. Methods: Hundred-ten PCS patients (49.3±11.8 years, 38% women, time after infection=260.2±127.5 days) underwent detailed clinical screening including symptom-limited cardiopulmonary exercise testing at admission and after 4-6 weeks of inpatient medical rehabilitation. Questionnaires were used to assess disease perception. Patients performed controlled isocaloric cycle ergometer training (3-5 sessions/week, 18 min) as either continuous training (CT) at 50% of maximal workload or as interval training (IT, load=60%, relief=30%). Outcomes of PCS patients were compared to coronary artery disease patients (CAD, n=96) to evaluate overall training effectiveness. Results: Training participation was comparable between both groups with no indication of training-specific exercise-induced fatigue. Overall, PCS patients improved significantly by a mean of 6.8±12.1% for W at VT1, 3.1±10.0% for VO2 at VT1, 5.5±14.7% for O2-pulse at VT1, 7.5±15.0% for W at VO2peak, 2.7±11.0% for VO2peak and 4.6±12.4% for O2-pulse at VO2peak (all p<0.05) with no significant differences between groups (p>0.05). Both groups showed reduced levels of fatigue, anxiety and depression as well as improved quality of life and wellbeing (all p<0.05). Compared to guideline-based cardiac rehabilitation, PCS patients showed a similar improvement in workload and oxygen uptake compared to CAD patients. Conclusion: PCS patients benefit from aerobic endurance training, performed as moderate continuous or interval training as part of a medical rehabilitation program in terms of improved physical exercise capacity and disease perception. Results for PCS patients are comparable to guideline-based rehabilitation of CAD patients. Clinical Trial: NCT06016192

The purpose of this study was to investigate the horizontal force and contribution of shoulder horizontal adductor and elbow extensor muscles activity during wide and narrow grip bench press (BP) in various conditions, such as resistance trained/non-trained, concentric/eccentric and muscle fatigue/non-fatigue.　We measured the horizontal force on the bar and the electromyographic (EMG) muscle activity of pectoralis major (PM) and triceps brachii (TB) during 10RM BP with wide grip (81cm) and narrow grip (40cm)　in 7　resistance trained men and 7 non-trained men. The all-reps average of the horizontal to vertical force ratio both in resistance trained and non-trained subjects was about 30% outward for the wide grip, and about 10% inward for the narrow grip. The EMG contribution ratio PM/TB shows no significant differences between narrow and wide grip in all evaluated conditions except in non-trained subjects muscle fatigue eccentric phase. Both resistance trained and non-trained subjects did not push the bar straight upward, and the EMG PM/TB was almost unchanged by hand width. The direction adjustment of the force on the bar that achieves almost the same muscle activity degree of the shoulder and elbow joints might be optimal BP kinetics.

Change detection with heterogeneous remote sensing images (Hete-CD) plays an important role in practical applications, especially when homogenous remote sensing images are unavailable. However, bitemporal heterogeneous remote sensing images (HRSIs) cannot compare directly to measure change magnitude, and many deep learning methods require large amounts of samples to train the module. Moreover, labeling many samples for land cover change detection with HRSIs is time-consuming and labor-intensive. Therefore, acquiring satisfactory performance of Hete-CD remains a challenge for deep learning networks with very small training samples. In this study, we promote a novel deep-learning framework for Hete-CD to obtain satisfactory performance while the initial samples are very small. We initially design a multiscale network with select kernel-attention module to focus on capturing different change targets with various sizes and shapes. Then, a simple yet effective non-parametric sample-enhanced algorithm based on the Pearson correlation coeffi-cient is promoted to explore potential samples around each initial sample. Finally, the proposed network and sample-enhanced algorithm are fused into one iterative framework to improve the change detection performance with very small samples. Experimental results conducted on four pairs of actual HRSIs indicated that the proposed framework can achieve competitive accuracies with very small samples for initialization when compared with some state-of-the-art methods. For example, the improvement is approximately 3.38% and 1.99% when compared with the selected traditional methods and deep learning methods, respectively.

Aerobic exercise training (AET) has been used in the management of heart disease. AET may, totally or partially, restore the activity and/or expression of proteins that regulate calcium (Ca2+) handling, optimize intracellular Ca2+ flow, and attenuate cardiac functional impairment in failing hearts. However, the literature presents conflicting data regarding the effects of AET on Ca2+ transit and cardiac function in rats with heart failure resulting from aortic stenosis. The objective of this study was to evaluate the effects of AET on calcium handling and cardiac function in rats with heart failure due to aortic stenosis. Wistar rats were distributed into two groups: control (Sham; n= 61) and aortic stenosis (AoS; n= 44). After 18 weeks, the groups were redistributed into: non-exposed to exercise training (Sham, n = 28 and AoS, n = 22) and trained (Sham-ET, n = 33 and AoS-ET, n = 22) for 10 weeks. Treadmill exercise training was performed with a velocity equivalent to the lactate threshold. Echocardiogram, isolated papillary muscle, and isolated cardiomyocyte analyzed cardiac function. During isolated papillary muscle assay and isolated cardimyocyte was evaluated Ca2+. The expression of regulatory proteins of diastolic Ca2+ was analyzed via Western Blot. AET attenuated the diastolic dysfunction and benefited the systolic function. AoS-ET animals presented better response to post-rest contraction, and SERCA2a and L-type Ca2+ channels blocked than the AoS. Furthermore, AET was able to improve aspects of the mechanical function and the responsiveness of the myofilaments to the Ca2+ of the AoS-ET animals. AoS animals presented alteration in the protein expression of the SERCA2a and NCX, and AET restored SERCA2a and NCX levels near normal values. Therefore, AET increased SERCA2a activity, improved the cellular Ca2+ influx mechanism, and increased myofilament responsiveness to Ca2+, attenuating cardiac dysfunction at cellular, tissue, and chamber levels in animals with aortic stenosis and heart failure.

The transition from undergraduate dental student to the actual practicing dentist is a crucial phase and ensuring the preparedness of graduates for the complexity and demands of contempo-rary dental practice is a challenging task. This study aimed to evaluate the self-perceived prepar-edness of undergraduate dental students and house officers in the dental colleges of Pakistan. Cross-sectional national study was planned to collect the information from dental students and new graduates in Pakistan. The pre-validated Dental Undergraduates Preparedness Assessment Scale (DU-PAS) was used. Purposive sampling technique was utilized to recruit house officers and undergraduate dental students from 27 dental schools in Pakistan. The data analysis was car-ried out using the R statistical environment for Windows (R Core Team, 2015). The total of 862 responses with 642 females and 219 males were analyzed in the study. Overall, clinical skills score was 30.56+9.08 and score for soft skills was 30.54+10.6. The mean age of the participants was 23.42+1.28. Deficiencies were reported in various soft skills and clinical skills attributes.The results highlighted the strengths and weaknesses of dental students and new graduates in Pakistani den-tal institutions. The findings may be used to further develop and strengthen teaching and training of dental students in Pakistan.

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

The issue of actualization of labor interests as a motivational driver and one of the sources of labor productivity is little-studied in applied and organizational psychology. The study given in the paper fills some “blanks” of this problem. The leading approach to research is the motivational system proposed on the basis of theoretical analysis and the developed psychological model of labor interests. Using the methods of a special survey, questioning and interview guides, the analysis and comparative assessment of the labor interests of 50 candidates for the service manager position (entertainment and restaurant industry field) was conducted; as well as of 45 employees in this industry aged 18 to 25. The main results of the paper show the connection of labor interests with the company's personnel management system, namely with the processes of selection, adaptation, and training. It was revealed that the candidates selected for the service manager position were of primary group interest and financial incentive was secondary. At the same time, financial incentive contributed to the successful passage of the adaptation period. Occupational interests of employees depended on their education and job specifics. Career interests were influenced by the time spent with the company. The recommendations necessary for employers to create the conditions corresponding to the leading labor interests of employees were substantiated.

Understanding and preventing the “great obstetrical syndromes” has been the focus of pregnancy research for decades. The combined global impact from these syndromes on maternal and perinatal morbidity and mortality is compounded by increased long-term risk of adverse health effects following pregnancy. Accumulating evidence indicates that conditions such as pre-eclampsia, fetal growth restriction, spontaneous preterm labour, and stillbirth, are syndromes with etiologic heterogeneity that share common pathophysiologic pathways involving placental dysfunction. Recently, the importance of the interaction between fetal and maternal genomes has been extended to include the epigenetic impact of pre-existing maternal pathophysiology on placental development and highlights the importance of nutritional and environmental factors in altered placentation. Validated first-trimester screening algorithms for preeclampsia can identify high-risk women and prophylactic treatment with low-dose aspirin can be initiated. High-risk women can be monitored during the second trimester using clinical assessment, ultrasound, and biochemical markers, that are able to predict complications prior to the development of clinical signs and symptoms. The integration of lifestyle interventions, pregnancy screening, and medical management represents a conceptual advance in pregnancy care. This approach has the potential to significantly reduce pregnancy complications, maternal and perinatal morbidity and mortality, adverse health outcomes later in life, and health-care costs.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Transport electrification is essential for reducing CO2 emissions, and technologies such as hybrid and range-extended electric vehicles will play a crucial transitional role. Such vehicles employ an internal combustion engine for on-board chemical energy conversion. The Wankel rotary engine should be an excellent candidate for this purpose, offering high power-to-weight ratio, simplicity, compactness, perfect balance, and low cost. Until recently, however, it has not been in production in the automotive market, due, in part, to relatively low combustion efficiency and high fuel consumption and unburnt hydrocarbon emissions, which can be traced to constraints on flame speed, an elongated combustion chamber, and relatively low compression ratios. This work uses large-eddy simulation to study the in-chamber flow in a peripherally ported 225cc Wankel rotary engine, providing insight into these limitations. Flow structures created during the intake phase play a key role in turbulence production but presence of the pinch point inherent to Wankel engine combustion chambers inhibits flame propagation. Two efficiency-enhancement technologies are introduced as disruptive solutions: (i) pre-chamber jet ignition and (ii) the two-stage rotary engine. These concepts overcome the traditional efficiency limitations and show that Wankel rotary engine design can be further enhanced for its role as a range extender in electrified vehicles.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

This study was designed to extend the literature on multicomponent interventions for the children of deaf adults (CODA) population. This study used two evidence-based behavior intervention strategies: pre-session pairing (PSP) and independent group contingency (GC). These strategies have been successfully used to decrease off-task behavior in elementary school children with various abilities and diverse backgrounds. However, the research on multicomponent interventions has yet to examine their effects on the CODA population in inclusive classrooms. This study used a concurrent multiple baseline design across participants with an ABC sequence to examine teacher-implemented PSP with independent GC in reducing the off-task behavior of three CODAs in an inclusive classroom at a public elementary school. The results show that the intervention successfully decreased off-task behavior for all participating CODAs. The results suggest that CODAs can adapt to classroom rules and expectations when PSP and independent GC are systematically applied to them in classrooms.

Spiral steroids (SS) are a newly discovered type of steroid. Originally, they were described as cardiotonic glycosides because they cross-reacted with digoxin-specific antibodies (Digoxin-Like-Materials-DLM). SS differ from the classical steroids in several ways: [a] they are steroid phosphodiester conjugates and displace digoxin from digoxin-specific antibodies, [b] in contrast to all other steroids, cholesterol is not a precursor, [c] SS have more than 21 carbon atoms; the extra atoms form a 5th ring which includes a lactone, [d] SS bind to NaK-ATPase in membranes rather than receptors in the nucleus. SS have been identified by HPLC- mass spectroscopy in extracts from mammals, birds, and oysters but were not detected in plant extracts. Overall, SS seem to be key components in regulating potassium electrolytes during pregnancy and its related diseases – specifically pre-eclampsia and necrotizing enterocolitis.

The issue of sustainability has long been the subject of interest of the architecture engineering and construction sector. All three aspects of sustainability - economic, environmental and social - can be affected through appropriate construction waste management. Construction and demolition waste (CDW) is one of the largest worldwide waste streams, therefore it is given great attention by all stakeholders (investors, contractors, authorities, etc.). Researches have shown that one of the main barriers to insufficiency CDW recovery is inadequate policies and legal frameworks to manage CDW. It is also one of the EU's environmental priorities. Aim of the article is to confirm the economic potential of construction and demolition waste audit processing through case study. A pre-demolition waste audit has been processed for unused building of shopping center in the town Snina in Slovakia. Subsequently, a comparison of economic parameters (waste disposal costs and transport costs) of recommended CDW management was performed. This comparison confirmed the economic benefits of environmentally friendly construction waste management methods according to the waste audit results, which will also increase the sustainability of construction projects. In addition, the cost parameters of selected waste disposal methods could be another dimension of building information modelling.

Glioblastoma (GBM) is the most lethal form of brain tumor, characterized by rapid growth and surrounding tissue invasion. The current standard treatment is surgery followed by radiotherapy, and concurrent chemotherapy, typically with temozolomide. Although extensive research has been performed over the past years to develop an effective therapeutic strategy for the treatment of GBM, efforts have not provided major improvements in the overall survival of patients with GBM. Thus, new therapeutic approaches are urgently needed. A major challenge in the development of therapies for central nervous system (CNS) disorders is overcoming the blood–brain barrier (BBB). In this context, the intranasal (IN) route of drug administration has been proposed as a non-invasive alternative route to directly targeting the CNS. In fact, this route of drug administration may bypass the blood-brain barrier and reduce systemic side effects. Recently, formulations have been developed to further enhance nose-to-brain transport, mainly with the use of nano-sized and nanostructured drug delivery systems. The focus of this review will be on the strategies developed to deliver a number of anticancer compounds for the treatment of GBM using the nasal administration. In particular, the specific properties of nanomedicines proposed for the nose-to-brain delivery will be critically evaluated. The number of preclinical and clinical data reviewed support the idea that nasal delivery of anticancer drugs might represent a breakthrough advancement in the fight against GBM.

Background: Despite procedural advancements in the management of univentricular heart patients palliated with a Fontan-type circulation, their exercise capacity and quality of life remain limited. Postoperative exercise training programs offer potential benefits, yet practical implementation and adherence to guidelines are often inadequate. This systematic review and meta-analysis aimed to evaluate the safety, efficacy, and optimal exercise modalities for Fontan patients. Method: A systematic search was conducted on August 24, 2023, using PubMed, Scopus, Web of Science, and Cochrane Library. Studies were screened via Rayyan QCRI based on pre-set exclusion criteria, and their quality was assessed using the Cochrane RoB Tool 2 and the STROBE checklist. A meta-analysis employed a continuous random-effects model to assess the efficacy of cardiac rehabilitation interventions. Results: The study included twenty-six studies (7 RCTs, 19 cohorts) representing 22 individual cohorts. The key findings were that aerobic exercise, lower-extremity resistance training, and inspiratory muscle training significantly improved VO2, enhancing exercise capacity in Fontan patients. Conclusion: While the results are promising, it's crucial to remember that training programs varied widely. Exercise training shows promise for postoperative management, potentially improving exercise capacity, cardiac biomarkers, lung function, and lower limb strength. However, further research is not just a suggestion, but a necessity. It's necessary to refine protocols, assess long-term impacts, and investigate underlying mechanisms, especially for Fontan patients with severe clinical conditions. This emphasizes the importance and urgency of further research in this area.

Despite copious research, the methods for assessing upper- and lower-limb motor coordination are limited. We devised a simplified evaluation method based on upper- and lower-limb motor coordination jumping and conducted a practical examination of the characteristics of the motion and applicability of the evaluation method. Overall, 88 high-school athletes with comparable competitive abilities were included in this study. The participants were divided into two groups based on their prior experience with motor coordination training (24 in the experienced group; 64 in the inexperienced group). First, a population proportion test was employed to assess the success rate of the four upper- and lower-limb motions in each task. This revealed significant difference between all tasks, except for the fourth motion, indicating that different motions have varying degrees of difficulty and that the combination of motions with differing difficulty coefficients can result in the creation of entirely new tasks with varying degrees of difficulty. Second, the experienced group demonstrated significantly superior performance compared to the inexperienced group by a t-test. These results confirmed that athletes (with the same level of competitive ability) trained in upper and lower limb motor coordination were able to excel in this evaluation method, confirming its validity.

COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

Objective: This study aims to investigate the relationship between sports psychological capital, coach leadership behavior, and satisfaction among middle school students in the 2023 sports college entrance examination in Chongqing. Methods: Using the structural questionnaire design, this paper carries out a questionnaire survey on the candidates who come to participate in the volleyball specialty college entrance examination in Chongqing, the corresponding database was established and the data were statistically processed by spss21.0 and amos18.0. Results: 1) there was a significant positive correlation between sports psychological capital, coach leadership behavior and examinees satisfaction; 2) sports psychological capital and coach leadership behavior had a certain influence on five dimensions of examinees' personal treatment, personal performance, team integration, team interaction and support from relatives and friends; 3) The sense of self-efficacy in sports psychological capital and coach's leading behavior are the most important to examinees' satisfaction; 4) Both coaches' leadership behavior and sports psychological capital can directly affect examinees' satisfaction, but sports psychological capital is the most important. It can not only directly affect examinees' satisfaction, but also act as an intermediary between coaches' leadership behavior and examinees' satisfaction. Conclusion: Sports College Entrance Examination Students can improve training satisfaction and sports performance by strengthening sports psychological capital training and perceiving coach's leadership behavior; In the leadership behavior of coaches, we should emphasize training and appropriately weaken authoritative teaching, while the training of sense of hope and toughness should be highlighted in sports psychological capital.

This review article seeks to systematically identify appropriate ways to measure managerial skills. The systematic literature review was conducted according to the criteria in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, and the eligibility criteria were declared through the PICOS tool (population, interventions, comparators, outcomes and study); a tool based on 6,803 records identified bibliometrically in the Web of Science database. The literature review stages determined a reduced set of articles that presented valid and reliable measurement scales covering some determinant constructs on managerial skills for employability such as fundamental, emotional, managerial, leadership, decision-making, communication and teamwork skills.

Objective The aim of this study was to examine the impact of a modern lifestyle on college female students' health, specifically focusing on their hip and knee joint mobility. The objective of this study was to assess the influence of hydraulic resistance training with the self-myofascial release technique on improving joint mobility in female college students. The training also aimed to compare the effectiveness of hydraulic resistance exercise with and without the self-myofascial release technique on hip and knee joint mobility.Methods The study used a randomized controlled trial design to recruit 30 female college students aged 18-25 through campus advertisements and announcements. Medical conditions or injuries that might have affected participation was screened. Participants were indiscriminately allocated to Group A (Hydraulic Resistance Training with Self-Myofascial Technique) or Group B (Hydraulic Resistance Training without Self-Myofascial Technique). The baseline assessments included joint range of motion (ROM) measurements using a goniometer. Certified trainers supervised the training sessions, and the data were analyzed using SD and mean using SPSS. ResultsThis study aimed to evaluate the impact of hydraulic resistance training with self-myofascial release on hip and knee joint mobility in female college students. The results showed that both groups showed improved hip and knee flexion with and without the self-myofascial release technique. However, the Group A using the self-myofascial release technique (Group A) showed a more significant enhancement, as indicated by the smaller p value compared to Group B. It has been suggested that the self-myofascial release technique may improve hip and knee joint mobility. ConclusionThis study concluded that Hydraulic Resistance Training with Self-Myofascial Release (HSMR) could improve hip and knee joint mobility among female college students. These findings suggest that integrating the self-myofascial release technique into training regimens can improve joint mobility. However, further research with larger sample sizes and extended intervention periods was needed to understand the long-term effects and sustainability of these improvements.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Pacing strategy refers to the distribution of effort and speed throughout the race to achieve optimal performance. The study aims to understand whether the choice of pacing strategy in swimming depends on the length of competitions and how sex, age, and performance level influence this strategy. Participants were the finalists of the 800-meter and 1500-meter freestyle events at the elite and junior world championships in 2022-2023. Race outcomes and pacing parameters were compared between the two distances and across different groups of swimmers. Swimmers in both distances did not break world records. Pacing strategy generally followed a U-shape with significant differences in the frequency and duration of speed changes between the two distances. The 800-meter exhibited more frequent changes in acceleration while the 1500-meter events generally followed a more consistent time series pattern. There were differences in pacing strategies between males and females and between junior and elite swimmers. Swimmers closer to world records showed more consistent pacing patterns compared to those farther from records. The study suggests that pacing strategies are influenced by race distance, sex, age, and performance level. The research highlights the complex interplay between physiological and psychological factors that shape a swimmer's decision-making during a race.

The practice of teaching and scientific research on cadaveric material remains crucial for medical education, especially in surgical disciplines. However, in Italy, this practice has been neglected due to legislative insufficiency and financial constraints. Although innovative methods and tools like simulators and e-learning have been adopted, direct hands-on experience with human cadavers remains irreplaceable for medical and surgical education. The absence of clear legislation governing cadaveric dissection has limited availability for teaching and research, resulting in economic burdens for universities and individuals seeking proper surgical training. To address this issue, the Law No. 10/2020 and the recent implementing decree were introduced in Italy, providing detailed legislation on the donation of bodies for educational and research purposes. The law emphasizes the importance of respecting the donor's specific choices and aligns with constitutional principles promoting culture, research, and health protection. However, some critical issues related to consent procedures, duration of body availability, and preservation of anatomical parts remain. Additionally, the law's dissemination among the population needs improvement. Future optimization could include allowing donors to choose the timing of body donation and considering different timeframes for body availability. Furthermore, the implementation of consent procedures could be simplified to increase donations. The law should also address the need for appropriate reception centers and allocate resources for effective dissemination. Despite these challenges, Law No. 10/2020 represents a significant step forward in enhancing medical-surgical training, scientific research, and the overall quality of patient care in Italy.

The field of applied behavior analysis has been directly involved in both research and applications of behavioral principles to improve the lives of captive zoo animals. Thirty years ago, Forthman and Ogden (1992) wrote one of the first papers documenting some of these efforts. Since that time, considerable work has been done using behavioral principles and procedures to guide zoo welfare efforts. The current paper re-examines and updates Forthman and Ogden’s original points, with attention to the five categories they detailed: (1) promotion of species-typical behavior, (2) reintroduction and repatriation of endangered species, (3) animal handling, (4) pest control, and (5) animal performances. In addition, we outline three current and future directions for behavior analytic endeavors: (i) experimental analyses of behavior and the zoo, (ii) applied behavior analysis and the zoo, and (iii) within-subject methodology and the zoo. The goal is to provide a framework that can guide future behavioral research in zoos, as well as create applications based on these empirical evaluations.

Obesity is linked to an inflammatory process, with adipocyte release triggering insulin resistance (IR) and autonomic imbalance. A cardiovagal trainning protocol has shown favorable results in autonomic balance and decrease of inflammatory markers. The aim of this study was to analyze the autonomic behavior related to inflammatory and metabolic parameters in obese people after a cardiovagal exercise protocol. Twenty people with obesity, were distributed by their HOMA-IR value: obese without IR (n/IR) (n=8) and obese with IR (y/IR) (n=12). The cardiovagal training program was carried out in both groups for 8 weeks at a frequency of 5 times per week. A blood sample was obtained to determine insulin, leptin, TNF alpha and IL6 levels, in addition to determining the HOMA-IR index and autonomic function was measured by heart rate variability. Changes were recorded in the OB-IR group, a decrease in inflammatory markers, glycemia and a reduction of sympathetic activity after the cardiovagal training intervention. In addition, significant differences between the y/IR and n/IR groups were shown in insulin, leptin, TNFa and IL6 values. It can be concluded that after 4 weeks of intervention with a cardiovagal trainning protocol, parasympathetic modulation increased and inflammatory markers decreased in obese subjects.

Universities have increasingly incorporated a third mission into their strategic planning. In addition to teaching and research, they have emphasised the training of entrepreneurs. However, there is still a lot of work to be done, as this process is facing resistance. The Entrepreneurial University covers all disciplines, including faculties of education. However, it has been shown that entrepreneurship tends to be more related to the faculties of economics and engineering, with a lesser presence in the faculties of education for various reasons: they consider entrepreneurship to be alien to their teaching role, there is a lack of entrepreneurial culture, and the objective of the Entrepreneurial University is unknown. The aim of this study is to analyse the level of entrepreneurship in Spanish faculties and schools of education. Forty deans and heads of education faculties in Spain took part in the survey. The results indicated a sufficient level of entrepreneurship; the dimensions related to active methodologies, and mission and strategy were the most developed, whereas entrepreneurship funding and entrepreneurship training for faculty employees were the least developed areas. Some deans noted that entrepreneurship was alien to their professional performance, although courses and good practices for the development of entrepreneurial initiative are gradually being implemented.

Annotating training data is a time consuming and labor intensive process in deep learning, especially for images with many objects present. In this paper, we propose a method to allow deep networks to be trained on data with reduced numbers of annotations (per image) in heatmap regression tasks (e.g. object detection and counting), by applying an asymmetric loss function. In a real scenario, this reduction of annotations can be imposed by the researchers (e.g. ask the annotators to label only 50% of what they see in each image), or can potentially counteract unintentionally missing labels from the annotators. To demonstrate the effectiveness of our method, we conduct experiments in two domains, crowd counting and wheat spikelet detection, using different deep network architecture. We drop various percentages of instance annotations per image in training. Results show that an asymmetric loss function is effective across different models and datasets, even in very extreme cases with limited annotations provided (e.g. 90% of the original annotations reduced). Whilst tuning of the key parameters are required, we find that setting conservative parameter values can help more realistic situations, where only small amounts of data have been missed by annotators.

Primocane-fruiting (PF) blackberries are adaptable to different production systems. To increase yields in PF blackberries, their primocanes are typically tipped or topped in summer to encourage branch formation from axillary buds below the cut. In this study, we determined in PF ‘Prime-Ark Traveler’ whether early emerging primocanes were more productive than those that emerged later in the season and the effect of primocane bending and defoliation on flowering. The primocanes that emerged in April produced 64% more flower shoots than those that emerged after May. Also, these findings indicate the alternative primocane management practices of selecting the early emerging primocanes and bending to orient primocanes horizontally and leaf removal increase budbreak and flower shoot emergence. The present work contributes toward a better understanding of primocane emergence time and orientation-flowering relations and how they mediate crop performance in PF blackberry.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

Data from environmental satellites are underutilized in many branches of operational oceanography. Several challenges exist for potential users of satellite data that may impede or preclude employing satellite products in their work. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access confusing and inefficient. NOAA’s CoastWatch Program is designed to be a Value Added Provider, whose mission is to help make satellite data easier for users to access. With this objective the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using ArcGIS or R software. These tools leverage upon the data services provided by ERDDAP, a data distribution system designed to make data access easier for both humans and machines. The course has been offered annually since 2006, and over 350 participants have taken it. Results of post-course surveys are analyzed to measure course effectiveness. Lessons learned from conducting these courses are summarized.

Background and objectives: The inadequate knowledge of complementary and alternative medicine (CAM) among health professionals may put their clients in risky situation because they then would find information about CAM from unreliable sources. Clinical psychologists (CPs), as health professionals, have also the opportunity to provide psychoeducation on the latest CAM scientific research to their clients. The current study aimed to explore knowledge and educational needs of CAM among CPs in Indonesia because previous studies on exploring CAM knowledge and educational needs of CAM were primarily conducted in Western countries. Materials and Methods: Data were collected through semi-structured face-to-face interviews with 43 CPs in public health centers (PHCs) in Indonesia. Most interviews were held at the PHCs where participants worked and interviews lasted for 55 minutes, on average. The interview recordings were transcribed and were analyzed using deductive thematic analysis. Results: Five main themes emerged within participants’ responses regarding CAM knowledge and educational needs. First (CAM understanding), participants’ responses ranged from those with little or no prior knowledge of CAM treatments and uses, to those with much greater familiarity. Second (source of knowledge), participants’ access ranged widely in terms of references, from popular to scientific literature. Third (why is it important?), participants identified CAM essentially as part of Indonesian culture and it was therefore crucial to have this cultural knowledge. Fourth (the challenges and what is needed?), the challenges for improving participants’ knowledge came from personal and institutional levels. Fifth (what and how to learn?), participants advised that only CAM treatments that fit in brief psychotherapy sessions should be introduced in professional training. Conclusions: This qualitative study discovered that CAM was neither well-known nor understood widely. Participants advised that professional associations and health institutions should work together in enhancing knowledge of CAM and incorporating CAM education into psychology education.

Misuse and poor handling of chemical pesticides in agriculture is hazardous to the health of farmers, consumers and the environment. We studied the pest and disease management practices and the type of pesticides used in four root, tuber and banana (RTB) crops in Burundi and Rwanda through in-depth interviews with a total of 811 smallholder farmers. No chemical pesticides were used in banana in either Rwanda and Burundi, whereas the use of insecticides and fungicides in potato is quite frequent. Nearly all insecticides and about one third of the fungicides used are moderately hazardous. Personal protective equipment is used by less than a half of the interviewed farmers in both countries. Reported cases of death due to self- or accidental poisoning among humans and domestic animals in the previous 12 months were substantial in both countries. Training of farmers and agrochemical retailers in safe use of pesticide and handling, and use of integrated pest management approaches to reduce pest and disease damage is recommended.

Simulator training for image-guided surgical interventions may benefit from artificial intelligence systems that control the evolution of task skills in terms of time and precision of a trainee's performance on the basis of fully automatic feed-back systems. At the earliest stages of training, novice trainees frequently focus on getting faster at the task, and may thereby compromise the optimal evolution of the precision of their performance. For automatically guiding them towards attaining an optimal speed-accuracy trade-off, an effective control system for the reinforcement/correction of strategies must be able to exploit the right individual performance criteria in the right way, reliably detect individual performance trends at any given moment in time, and alert the trainee, as early as necessary, when to slow down and focus on precision, or when to focus on getting faster. This article addresses several aspects of this challenge for speed-accuracy controlled simulator training before any training on specific surgical tasks or clinical models should be envisaged. Analyses of individual learning curves from the simulator training sessions of novices and benchmark performance data of one expert surgeon, who had no specific training in the simulator task, validate the suggested approach.

Measuring host communities' perceptions and attitudes towards tourism development has been increasingly common since the mid-1970s. The greater the growth of the tourism industry worldwide, the more relevant it has become to assess the impact of tourism activity on the local residents. In this paper we present the results of a study carried out in two EU countries highly dependent on tourism act though at a different stage of development. Our findings are based on qualitative and quantitative data obtained via a questionnaire survey designed in pre-covid 19 times to analyze the perceptions and attitudes of tourism college students from Croatia and Spain, today’s tourism employers and employees, towards tourism development in sun and sea desti-nations. Our aim is to contrast the hypothesis that the nationality of our sample is a significant variable when evaluating the socioeconomic and environmental impact of the development of the tourism industry. Croatia’s workforce, the most highly dependent on the EU travel and tourism industry show to be much more aware of the negative impacts of tourism development than their Spaniards counterparts; showing a tourism workforce more reluctant to further tourism development.

It is axiomatic to underscore that a well-managed education system is deemed globally as one of the crucial prerequisites for not only the growth and development of every society or geopolitical space but also one of the veritable hallmarks of a diversified and functional economy that will, in the long run, culminate in economic emancipation of the people. The education industry faces enormous challenges in several countries in Africa, and Nigeria is not an exception. Even though education remains the bedrock or superstructure on which other societal structures such as politics, culture, religion, economics, etc. are laid; it is quite disheartening that education in Nigeria is not effectively and efficiently managed for economic diversification and emancipation of the recipients of such education. Indeed, the industry faces innumerable challenges, especially in contemporary times than in the pre-colonial era when the education system was essentially functional. The hydra-headed nature of the challenges with Nigeria’s educational system has inevitably resulted in different dimensions of economic quagmires entangling the country and making most of its citizens find it extremely difficult to eke out and or survive. The level of dysfunctionalities in various sections of the education system has made it seemingly intractable to palliate the economic predicaments that have been confronting the country let alone emancipate it from the monoculture of its overdependence on oil. The author of this treatise is diagnostic in his approach to understanding the intricacies of the challenges that frontally battle the Nigerian education system and deprive it of being a veritable tool for a functional economy as well catalyst for the economic emancipation of its people. The paper, through the analyses of relevant literature, amplifies the conceptual and operational dimensions of the identified challenges, offers probable suggestions on the ways by which the challenges can be tackled and finally draws a conclusion.

Scaffold guided breast tissue regeneration (SGBTR) can both transform reconstructive and cosmetic breast surgery. Implant-based surgery is the most common method. However, there are inherent limitations as there is replacement of tissue rather than regeneration. Regenerating autologous soft tissue has the potential to provide a more like-for-like reconstruction with minimal morbidity. Our SGBTR approach regenerates soft tissue by implanting additively manufactured bioresorbable scaffolds filled with autologous fat graft. A pre-clinical large animal study was conducted implanting 100 ml breast scaffolds (n=55) made from medical-grade polycaprolactone in 11 minipigs for 12 months. Various treatment groups were investigated where immediate or delayed autologous fat graft, as well as platelet rich plasma were added to scaffolds. Computed Tomography and Magnetic Resonance Imaging was performed on explanted scaffolds to determine volume and distribution of regenerated tissue. Histological analysis was performed to confirm tissue type. At 12 months we were able to regenerate and sustain a mean soft tissue volume of 60.9 4.5 ml (95% CI) across all treatment groups. There was no evidence of capsule formation. There were no immediate or long-term post-operative complications. In conclusion, we were able to regenerate clinically relevant soft tissue volumes utilizing SGBTR in a pre-clinical large animal model.

This longitudinal study examined the development of executive function and calibration accuracy in pre-adolescents. Two hundred sixty two students(127 females) from grades 4 (n = 91), 5 (n = 89) and 6 (n = 82) took measures of executive function and performance calibration in a sport task, three times within a period of 20 months. A latent growth curve modeling analysis showed a significant relation between the rates of change of executive function and calibration. Further, the results showed a dynamic interplay in the development of executive function and calibration. There were significant inter-individual differences in the estimated population means both in executive function and calibration and in the rate of change of executive function, but not in the rate of change for calibration. The age of the participants had a positive effect only on the estimated population mean of executive function.

Urbanization influences food culture, particularly in low- and middle-income countries, where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labeling standards are crucial for regulating manufacturing practices and aiding consumers in making healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labeling standards in Dar es Salaam, Tanzania. A cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labeling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size. The majority of the snacks demonstrated partial adherence to labeling standards. Imported products showed significantly better full adherence to both Tanzanian and International standards. Notably, over half of the products used English for labeling, and infrequently used the recommended World Health Organization Front-of-Pack nutrition Labeling. Product category, origin, and package size were significantly associated with higher levels of International standard adherence (p<0.05). The inadequate adherence to mandatory labeling standards, scarce use of Swahili and FoPL highlight the need to strengthen labeling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labeling practices is of urgency as we seek to address diet-related Non-communicable diseases.

With the targets of petroleum exploration transferred to the deep and ancient strata, abundant oil and gas resources have been found in the Lower Paleozoic and older strata in central and western China. Due to complex evolutionary processes including multiple episodes of hydrocarbon accumulation and ubiquitously accompanied by secondary alterations, significant uncertainties were remained for the generation time and accumulation processes of these revealed petroleum. In this paper, relative pure Re and Os elements existing in the asphaltene fractions of Lower Cambrian solid bitumen collected from the Guangyuan area, western Sichuan Basin, SW China and another Middle–Lower Ordovician heavy oils in the Aiding area of the Tahe oilfield in the Tarim Basin, NW China were successfully obtained by the sample pre-treatments, and Re–Os isotopic analysis was subsequently carried out for the dating of these two petroleum. The Re–Os isotopic composition indicates a generation time of Guangyuan bitumen to locate between 572 Ma and 559 Ma, corresponding to the late Sinian period of Neoproterozoic era. By the means of Re–Os isochron ages, initial 187Os/188Os ratios, and carbon isotopic compositions, the Lower Cambrian bitumen is supposed to be originated from source rocks of the Doushantuo Formation in the Sinian strata and subsequently migrated into the reservoirs of Dengying Formation. These previously reserved petroleum had been transformed to present bitumen by the destruction of reservoirs caused by tectonic uplift. The Re–Os dating results of Middle–Lower Ordovician heavy oil of Tarim Basin supposed that it was formed between 450 Ma to 436 Ma, corresponding to the Late Ordovician–Early Silurian system, and the generated petroleum is likely to migrate into the Middle–Lower Ordovician karst reservoirs to form early oil reservoirs. With tectonic uplift, these oil reservoirs were degraded and reformed to be present heavy oil reservoirs.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

Artificial groundwater recharge is commonly used for drinking water supply. The resulting water quality is highly dependent on the raw water quality. In many cases, pre-treatment is required. Pre-treatment improves the drinking water quality, although how and to what extent it affects the subsequent pond water quality and infiltration process, is still unknown. We evaluated two treatment systems by applying different pre-treatment methods for raw water from a eutrophic and temperate lake. An artificial recharge pond was divided into two parts, where one received raw water, only filtered through a micro-screen with 500 µm pores (control treatment), while the other part received pre-treated lake water using chemical flocculation with polyaluminium chloride (PACl) combined with sand filtration, i.e. continuous contact filtration (contact filter treatment). Water quality such as cyanobacterial biomass, microcystin-LR as well as organic matter and nutrients were measured in both treatment processes. We found cyanobacterial biomass and microcystin-LR level after the contact filter treatment was significantly different from the control treatment and also significantly different in the pond water. In addition, with contact filter treatment, total phosphorus (TP) and organic matter removal were significantly improved in the end water, TP was reduced by 96 % (< 20 µg/L) and the total organic carbon (TOC) was reduced by 66 % instead of 55 % (TOC content around 2.1 mg/L instead of 3.0 mg/L). This full-scale onsite experiment demonstrated effective pre-treatment would benefit a more stable water quality system, with less variance and lower cyanotoxin risk. In a broader drinking water management perspective, the presented method is promising to reduce cyanotoxin risk, as well as TP and TOC, which are all predicted to increase with global warming and extreme weather.

Pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab in immunocompromised patients has reduced the risk of breakthrough infection, disease, hospitalization, and COVID-19 related mortality. However, the advent of the Kraken variant (XBB.1.5) has limited the use of this monoclonal antibody, based on poor efficacy in in vitro studies The objective of the study was to evaluate the risk of breakthrough infection, symptomatic disease, hospitalization, intensive care admission and COVID-19 related death in kidney transplanted recipients receiving pre-exposure prophylaxis with Tixagevimab/Cilgavimab for COVID-19 in the era of the Kraken variant (XBB.1.5). In a prospective, observational study, we enrolled kidney transplant patients undergoing pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab at the Division of Infectious Diseases of Federico II University of Naples between February 2023 and August 2023. Each patient subsequently underwent a six-month follow-up with symptom monitoring and surveillance nasopharyngeal swab for SARS-CoV-2 RNA detection every 30 days, regardless of symptoms. Thirty-four kidney transplant patients were enrolled and in the follow-up period, only one tested positive for the nasopharyngeal swab for SARS-CoV-2 research with asymptomatic infection and virological recovery on the eighth day from the diagnosis of infection. Therefore, no patient developed disease and no patient needed hospitalization and no death occurred. No adverse drug reaction to Tixagevimab/Cilgavimab occurred. Our data, although derived from a limited and uncontrolled sample, show the potential of Tixagevimab/Cilgavimab as a valid and viable therapeutic strategy in pre-exposure prophylaxis for immunocompromised patients. These findings highlight the importance of conducting clinical studies on this topic.

Serum albumin (ALB), one of the most important proteins in human physiology, has the main functions of maintaining plasma oncotic pressure and plasma volume, transporting hormones, vitamins, oligominerals and drugs, and exerting a powerful antioxidant-antiinflammatory role. Its prognostic value in liver and malabsorption syndromes is well known. In this narrative review, an analysis of the most important studies evaluating the prognostic significance of low serum ALB levels in hospitalized patients was performed. Specifically, the risk in emergency medicine, cardiovascular diseases, COVID-19 infection, nephrology, oncology and autoimmune rheumatic diseases has been examined to fully explore its clinical value. ALB is a negative acute-phase reactant and the reduction of its serum levels represents a threatening parameter for long-term survival in several clinical settings, and a strong poor prognostic biomarker in most diseases. Therefore, clinicians should consider serum ALB as a valuable tool to assess the efficacy of specific therapies, both in hospitalized patients and in chronic follow-up.

OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Background: There is an exponential increase in caesarean sections with epidemic proportions worldwide, which is considered as a serious public health issue that influences negatively both maternal and childhood health. Aims: This study aimed to evaluate the associations between pre-dominant maternal risk factors and the prevalence of caesarean section in a representative sample of women from Greece. Methods: This is a cross-sectional study conducted on 5182 healthy, re-productive-aged women from 11 geographically diverse Greek areas after applying specific inclu-sion and exclusion criteria. Statistical analysis was applied to evaluate the impact of maternal so-ciodemographic, anthropometric, lifestyle and perinatal factors in the risk of caesarean section de-liveries. Results: A high prevalence of 56.4% of caesarean section deliveries was recorded in the study population. The incidence of caesarean sections reached to 51.5% in private hospitals in which an increasing rate of 47.5% for planned caesarean sections were noticed. Older maternal age, pre-pregnancy overweight and obesity, excess gestational weight gain, high rates of preterm birth, better economic status, smoking habits, and private type of birth hospital were significantly related with enhanced risk of caesarean section, independently of multiple confounding factors. Conclu-sions: Our study revealed that caesarean section rates are continuously increase, while several maternal risk factors, including especially pre-pregnancy overweight/obesity and excess gestational weight gain, cumulatively raise its prevalence which further increase the risk for postnatal adverse outcomes for both the mothers and their children. Emergent public health policies and strategies should be promoted to confront the predominant maternal risk factors-related with caesarean sec-tion and inform future mothers how to be prevented from them by applying healthier nutritional and lifestyle habits and to select caesarean section only due to emergency medical reasons. Alarmingly enough, targeted nutritional intervention strategies are strongly recommended to ef-fectively reduce the prevalence of maternal pre-pregnancy overweight/obesity and the excess gestational weight gain, which in turn may minimize the risk of childhood overweight/obesity.

Background: Pre-pregnancy overweight and obesity of women of childbearing ages, has alarmingly a growing trend in developed countries. This study aimed to evaluate potential associations between maternal pre-pregnancy excess body weight and childhood demographic and anthropometric characteristics, as well as perinatal and postnatal outcomes. Methods: This is a cross-sectional study conducted on 5198 pre-school children aged 2-5 years old and their matched mothers that were enrolled from nine different Greek regions. Maternal and childhood anthropometric and demographic data, perinatal and postnatal outcomes were collected from medical history records or validated questionnaires. Results: A prevalence of 24.4% and 30.6% of overweight/obesity was recorded for the enrolled children and their mothers 2-5 years postpartum. In multivariate analysis, childhood overweight and obesity was significantly more frequently observed when maternal pre-pregnancy BMI status was overweight or obese [OR: 2.11 (1.80-2.42) p=0.0001]. A high prevalence of caesarean section delivery and childhood diabetes type I was also significantly associated with maternal pre-pregnancy overweight/obesity [OR:1.71 (1.33-2.19) p=0.0175) and OR: 1.27 (1.04-1.53) p=0.0014, respectively]. Conclusions: Maternal pre-pregnancy overweight and obesity rates were related with increased childhood weight status at birth and 2-5 years postpartum, highlighting the necessity of encouraging healthy lifestyle promotion, including healthier nutritional habits, and focusing on obesity population policies and nutritional interventions among women of reproducible age.

The rise in prevalence of obesity in women of reproductive age in both developed and developing countries might propagate intergenerational cycles of detrimental effects on metabolic health, contributing to substantial economic burden on society. Placental lipid metabolism might be disrupted by maternal obesity, which possibly affects the life-long health of the offspring. Here, we investigated placental lipid metabolism and handling from women with pre-gestational obesity as a sole pregnancy complication and compared to placental responses of lean women. Open profile and targeted lipidomics were used to assess placental lipids and oxidized products of docosahexahenoic acid (DHA), neuroprostanes, and arachidonic acid (AA), isoprostanes. Placental fatty acid transporters FABP1, FABP3 and endothelial lipase protein were measured. Despite no signs of overall alterations in lipid content, increased contents of DHA, AA, DHA-derived neuroprostanes and AA-derived isoprostanes and decreased content of FABP1 protein were found in placentas from obese women. Multivariate analyses suggested that these oxidised fatty acids are associated with maternal and placental inflammation and also with birth weight. These results might shed light on the molecular mechanisms associated with altered fatty acid metabolism and lipid handling in maternal pre-gestational obesity, placing these oxidized fatty acids as novel mediators of placental function.