Objective: This study aims to analyse the long-term trends and future projections of liver cancer burden in China from 1990 to 2035, focusing on incidence, prevalence, mortality, and disability-adjusted life years (DALYs), using data from the Global Burden of Disease Study and a Bayesian Age-Period-Cohort model. Methods: Age-standardized rates for incidence, prevalence, mortality, DALYs, and years lived with disability (YLDs) were used to assess the liver cancer burden from1990 to 2021. A Bayesian Age-Period-Cohort model was applied to project trends from2021to2035. Results: From 1990 to 2021, age-standardized liver cancer incidence slightly decreased, from 13.51 to 13.29 per 100,000, while rates for males rose from18.94to20.00 per 100,000. Mortality and DALYs significantly decreased, particularly in males, but prevalence declined modestly for both sexes. The liver cancer burden peaked in different age groups for males (50–54) and females (65–69). Future projections (2021–2035) suggest a 31.7% increase in incidence, from197,000 cases in 2021 to 259,000 in 2035, and a 31.2% rise in prevalence. DALYs and deaths are projected to increase by 30.5% and 35.9%, respectively. Conclusion: Liver cancer burden in China has decreased in terms of age-standardized incidence, mortality, and DALYs. However, absolute numbers are expected to rise by 2035, particularly among males and older populations. This highlights the need for strengthened prevention, early detection, and treatment strategies to address the growing liver cancer burden.

Background: Depression in children and adolescents poses significant public health challenges, impacting individual and societal well-being. Traditional methods for diagnosing and predicting depression often rely on symptomatic assessments post-onset, delaying intervention. Recent advancements in machine learning (ML) provide new opportunities for early prediction and prevention. Methods: This study leveraged data from the U.S. National Health Interview Survey(NHIS) from 2004 to 2014, involving 27,642 participants aged 4-17. We employed machine learning models, including XGBoost, Random Forest, Decision Tree, and Bagging Classifier, to analyze a vast array of physical activity and health behavior data. These models were evaluated for their accuracy in predicting mental health indicators. Results: The XGBoost model demonstrated notable accuracy (77.5%) in forecasting mental health scores, closely followed by the Random Forest classifier (77.4%). The models were further assessed for their ability to classify varying degrees of mental anxiety, with the redefined categories of 'Mild', 'Moderate', and 'Severe'. The retrained XGBoost model showed enhanced accuracy across these categories (81.7%- 85.7%), with AUC values indicating reliable differentiation between mental health states. Discussion: This study expands the scope of ML in predicting depression, highlighting the intricate relationship between diverse health behaviors and mental health. The predictive accuracy of the models underscores the potential of M Linearly detection and intervention for depression. Future research should focus on refining these models for broader application and exploring their utility in real-world clinical settings.

Timely and accurate identification of plant diseases is essential for increasing plant productivity. The widely used state-of-the-art CNN-based models still face challenges and limitations on leaf images under complex backgrounds due to a lack of a global receptive field and self-attention mechanism. This study proposes a lightweight transfer learning-based vision transformer architecture (TVITA) for the automatic identification of plant leaf diseases without using any convolution. Two popular PlantVillage datasets—the original dataset (OD) with 55,448 images and the augmented dataset (AD) with 61,486 images—were used for model training (70%), validation (20%), and testing (10%). The proposed TVITA model, leveraging transfer learning by fine-tuning the pre-trained VITSO model on the augmented dataset, outperformed both the VITSO and VITSA models, which were trained from scratch on the OD and AD, respectively. The TVITA achieved a recognition accuracy of 97.85%, precision of 96.58%, recall of 97.50%, F1 score of 96.95%, and AUC of 98.76% on the OD testing set, and 97.50%, 97.00%, 97.07%, 96.99%, and 98.74% on the AD testing set. The results indicate that the proposed TVITA model achieves stateof-the-art accuracy, greater robustness, and lower computational cost compared with popular stateof-the-art CNN-based architectures. This study highlights the efficacy of transfer learning in enhancing ViT models' performance for plant disease identification, suggesting potential applications in other domains requiring high classification accuracy.

Purpose of the study. The aim of the study was to evaluate the differences in morphometric parameters of the kidneys in rats with longterm administration of nonsteroidal anti-inflammatory drugs and new derivatives of cyanothioacetamide to assess nephrotoxicity. Materials and methods. The new cyanothioacetamide derivatives synthesized at the “ChemEx” Research Laboratory were preliminarily subjected to virtual bioscreening. We selected 5 heterocyclic compounds of cyanothioacetamide derivatives with potential analgesic activity, which was confirmed in various pharmacological tests. To study the chronic effect of new cyanothioacetamide derivatives on the kidneys, rats were preliminarily administered the new compounds intragastrically at a dose of 5 mg/kg for 14 days. The comparison drugs were nimesulide (5 mg/kg), indomethacin (7 mg/kg), acetylsalicylic acid and paracetamol at a dose of 50 mg/kg. The stereometric indices of the kidneys were assessed in each group. Results. The study revealed that new cyanothioacetamide derivatives with laboratory codes d02–123, d02–133, d02–139, d02–172, d02–149 have lower nephrotoxicity compared to reference drugs. In particular, the compound with the code d02–149 showed minimal changes in the structure of the proximal and distal tubules and the absence of hemorrhages, indicating its low risk for the kidneys. Derivatives d02–123 and d02–133 showed adaptive changes, such as an increase in the area of the distal tubules and a decrease in the volume of normal glomeruli, which may be associated with compensatory mechanisms during long–term exposure. Conclusions. New cyanothioacetamide derivatives demonstrate potentially less nephrotoxic effects compared to existing NSAIDs. These results highlight the need for further study of new cyanothioacetamide derivatives to develop safer drugs with analgesic activity, especially given the ability of some of them to have less damaging effects on the kidneys.

Introduction: Reports that examined the students' knowledge, attitudes, and practices (KAP)toward Corona Virus Disease-19 (COVID-19) in Saudi Arabia; particularly the western region; are limited. This study examined COVID-19-related KAP levels among Taif University(TU)students. Methods: A cross-sectional study was conducted among 794 students using an Arabic language questionnaire that contained demographic data and 36 questions to measure COVID-19-related KAP. The KAP scores were classified as poor (<50%), fair (50 to <75%) and good(>75%). Data were analysed at a 0.05 significance level. Results: The study included794students, of whom 34% (n=270) were males, at least18-years old and mainly aged 20to<25years (76.2%). Most of the participants were Saudi citizens (95.6%; n=734), and from Western Saudi Arabia (76.4%; n=587). The overall average COVID-19-related KAP scores were73.1±12.3%, 72.0%±8.6%, and 79.8%±13.8%, respectively. Besides, fair/good KAP scores were reported as 89.7%, 65.5%, and 78.7%, respectively. A higher knowledge score was significantly related to college type (p=0.008) and having a healthcare worker in the family (p=0.041). Also, a higher attitude score was significantly related to gender (p=0.008) and electively gettingCOVID-19 vaccine (p=0.010). In addition, a higher practice score was significantly related to gender, age, marital status, college, academic level, vaccination period and having a COVID-19-infected family member (p<0.05). Conclusions: COVID-19-related knowledge among the participants was high, and their practices and attitudes aimed at preventing and controlling infection with COVID-19. These findings are helpful for health professionals and policy makers for further minimizing COVID-19 infection among TU students and all similar university settings.

The reaction of pyrolysis is a promising technique for the conversion of plastic waste. Studying the kinetics of plastic waste degradation can provide useful information for conducting and optimizing the pyrolysis process. A common method to investigate polymer degradation kinetics is thermogravimetric analysis (TGA). In this study, a non-isothermal regime of heating at five different heating rates was employed. The rate of mass loss of a sample with and without catalyst ZSM-5 as a function of temperature and time in a controlled N2 atmosphere was performed. The determination of the kinetic parameters was done using two different model free methods. According to the Kissinger and Dynamic methods, the kinetic parameters, such as activation energy (Ea) and the preexponential factor, were calculated. The analysis is based on the fact that plastic decompositions are typically heterogeneous. The thermal decomposition has greater values of the activation energy in the range 298 – 309 kJ mol^(-1) , than the activation energy of catalytic decomposition in the range 260 – 303 / kJ mol^(-1).

The present work was conducted to assess the physiological response of onion plant to GA3 at Sher-e-Bangla Agricultural University, Bangladesh in the open environment under natural sunlight during the period from October 2014 to March 2015. GA3 at levels of 0, 20, 40 and 60 ppm were used with the onion by laid out in a Randomized Complete Block Design with three replications. The factor levels of GA3 which were applied during transplanting by root soaking, and foliar spray at 30 and 60 days after transplanting. GA3 has a great effect on increasing vegetative growth, shoot biomass, bulb biomass and also dry matter accumulation in onion plant compared to control. Insignificant effect by all concentration of GA3 was found in bulb length, fresh root biomass, and dry root biomass. Thus, the use of 60 ppm GA3 was better compared to the other three concentrations and significantly increased the fresh bulb biomass about 42.96 % over control. It is concluded from this study, GA3 @ 60 ppm can be used to bring the improvement of onion production from the economic point of view.

The effectiveness of on-the-job training has been the focus of many researches in the area of training. It has been known as important to many business organizations. The focus of the present study is to find out if OJT is used in Saudi business organizations as training type to improve the KAS of employees. Thirty general managers and thirty HR managers were part of the sample group. The following research questions were studied: (1) Do companies in Saudi Arabia conduct on-the-job training to their employees as an organizational HR policy? (2) Do on-the-job training efforts of some companies in Saudi Arabia have shown effectiveness as perceived by the HR managers and general managers? (3) What are these perceived changes? and (4) What factors brought about the perceived success of on-the-job training in Saudi Arabia? The findings show that Saudi business organizations conduct on-the-job training to its employees as an organizational HR policy; HR managers and general managers perceive OJT as effective in effecting positive changes in the knowledge, attitudes and skills (KAS) of employees; changes include: increased knowledge, improved skills, and developed right attitudes toward work of trainees; and there are factors in the training that help in making the OJT successful such as: designing training that have appropriate objectives and assessment measures to the needs of the trainees and the organization, skilled trainers, appropriate and useful methods or processes, encouraging training preview and the trainee’s willingness to undergo training.

This contribution delivers theoretical analysis of the process and relevant findings related to the above process. Design solutions on used metal sheet holders for forming are presented in this article. In the experimental part we conducted laboratory tests using sheet metal samples. This contribution is complemented with discussion on the presented issue.

The aim of this paper is to analyze, following a theoretical framework, the scientific language used in the DSM-5 to diagnose “mental disorders”. In particular, the concepts of “science of mental disorders” and “scientific progress” are investigated and the epistemological and operative implications of their use are introduced, covering the fields of psycho-social sciences. Arguing from a interactionist theoretical perspective, for the first set of implications, the concepts of a-theoretical, objective and neutral, evidence-based research and the tools used in order to pursue diagnostic validity and reliability are analyzed. For the second set, some consequences of the use of such concepts in the work of mental health professionals with their patients, are highlighted. In conclusion, some considerations about the relationship between the object of study, epistemological references and practical instruments are expressed.

The growth performance and intracellular lipid production potentiality of two identified yeast stains Candida tropicalis (S5) and Issatchenkia orientalis (D5) in N-limited medium under different carbon, nitrogen sources, carbon/nitrogen ratio and temperature was studied . Among all carbon sources, glucose was the best in lipid weight, lipid content, lipid yield and lipid productivity and recorded a large values (5.73&6.51gl-1) of cell biomass in both strains. The increases in cell biomass reached its maximum value by use of yeast extract in both strains (8.62 and 8.78gl-1, respectively). However, urea was the preferable N source for lipid production in both strains. On the other hand, KNO3 proved to be the lowest values treatment among all parameters. The interaction treatments yeast extract + (NH4)SO4 and Urea + peptone were unfavorable for cell biomass, but it was convenient for oil yield parameters in Candida. The data showed that the maximum cell biomass was obtained at 54 and 100 C/N ratio for Candida and Issatchenkia, respectively. The data also exhibited that designated sharp increases in lipid weight, lipid content and lipid productivity were developed by the two yeast strains reached their maximum values at C/N ratio 115 and 100, respectively. Further increase in C/N ratio resulted in drop in lipid weight; this drop was slight in Candida above 115, whereas it was drastically in Issatchenkia after C/N 100.

Abstract: In currently, the revolution in a high-speed broadband network is the requirement and also endless demand for high data rate and mobility. To achieve above requirement, the 3rd Generation Partnership Project (3GPP) has been established the Long Time Evolution (LTE). LTE has established an improved LTE radio interface named LTE-Advanced (LTE-A) and it is a promising technology for providing broadband, mobile Internet access. But, better Quality of Service (QoS) to provide for customers is the main issue in LTE-A. To reduce the above issue, the packets should be utilized by using one of the most significant function of packet scheduling to upgrading system performance via determines the throughput performance. In existing scheme, the user with poor Channel Quality Indicator (CQI) has smaller throughput issue is not focused. In this paper, a Hybrid Weighted Round Robin with Shortest Job First (HWRR-SJF) Scheduling technique is proposed to enhance efficient throughput and fairness in LTE system for stationary and mobile users. In this proposed scheduling, to schedule users according to a different criterion like fairness and CQI. HWRR-SJF Scheduling has been proposed for scheduling of the users and it produces increased throughput for various SNR values simulated alongside Pedestrian and Vehicular moving models. The proposed method also uses a 4G-LTE filter or Digital Dividend (DD) in order to align the incoming signal. The digital dividend is used to remove white spaces, which refer to frequencies assigned to a broadcasting service but not used locally. The proposed model is very effective for users in terms of the performance metrics like packet loss, throughput, packet delay, spectral efficiency, fairness and it has been verified through MATLAB simulations

Abstract: In Unstable Mobile nodes in network does not maintain accuracy of data transmission as maximum level, since nodes characteristics are updated, then nodes receive data’s are intruded, its packet information is missed. For that time, congestion is made for current routing path, so consider that path is failure, also provide re transmission. It occupies more energy, and packet drop rate. In proposed Enhanced data Accuracy based Path Discovery (EAPD) technique is used to provide transmitting and receiving data has higher accuracy. It verifies the every node communication in routing path have maximum data accuracy, they are selected, otherwise communication data have minimum data accuracy is rejected. Backing route selection algorithm is constructed to avoid intrusion for communication period, it discovery the path, which are not loss the data from packets, since congestion is easily identified. It reduces energy consumption, and packet drop rate.