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This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT with a classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and a 1D convolutional residual network (1D conv ResNet). This architecture effectively captures both spatial and temporal dependencies, enhances noise resilience, and extracts discriminative features from nonstationary and nonlinear vibration signals. The model is initially trained on a controlled laboratory bearing dataset and further validated on real and artificial subsets of the Paderborn bearing dataset, demonstrating strong generalization across diverse fault conditions. t-SNE visualizations confirm clear separability between fault categories, supporting the model’s capability for precise and reliable feature learning and strong potential for real-time predictive maintenance in complex industrial environments.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Climate change and environmental degradation are critical global challenges, and the G-20 nations play a pivotal role in addressing these issues due to their substantial contributions to global GDP and carbon emissions. Transitioning toward renewable energy sources is imperative for mitigating CO2 emissions and achieving sustainable development. This study investigates the impact of technological innovation, gross domestic product (GDP), renewable energy consumption, economic freedom, and financial advancement on renewable energy use and environmental pollution levels in G-20 countries from 1995 to 2022. Utilizing the PMG-ARDL dynamic panel method, the research analyzes both long-term and short-term relationships among the variables. The findings reveal that technological innovation significantly boosts renewable energy adoption, with a 1% increase in technological innovation leading to a 0.33% rise in renewable energy use in the long run and a 0.17% increase in the short run. Additionally, increased renewable energy consumption is strongly associated with reductions in CO2 emissions, highlighting its critical role in promoting environmental sustainability. The study emphasizes the importance of policies designed to enhance technological innovation to foster renewable energy usage and reduce environmental pollution. It recommends expanding and reforming the technological sector to align international and local resources with renewable energy initiatives, providing a workable framework for supporting the green growth of institutions and achieving a more sustainable future for G-20 nations. This research contributes to understanding the intricate dynamics of renewable energy transitions, offering actionable insights for policymakers and stakeholders in addressing global environmental challenges.

Antibiotics used to be supplemented to animal feeds as growth promoter and as an effective strategy to reduce the burden of pathogenic bacteria present in the gastro-intestinal tract. However, in-feed antibiotics also kill bacteria that may be beneficial to the animal. Secondly, unrestricted use of antibiotics enhanced the antibiotic resistance in pathogenic bacteria. To overcome above problems, scientists are taking a great deal of measures to develop alternatives of antibiotics. There is convincing evidence that probiotics could replace in-feed antibiotics in poultry production. Because they have beneficial effects on growth performance, meat quality, bone health and eggshell quality in poultry. Better immune responses, healthier intestinal microflora and morphology which help the birds to resist against disease attack were also identified with the supplementation of probiotics. Probiotics establish cross-feeding between different bacterial strains of gut ecosystem and reduce the blood cholesterol level via bile salt hydrolase activity. The action mode of probiotics was also updated according to recently published literatures, i.e antimicrobial substances generation or toxin reduction. This comprehensive review of probiotics is aimed to highlight the beneficial effects of probiotics as a potential alternative strategy to replace the antibiotics in poultry.

In this study, 2 symmetrical and 3 unsymmetrical thioureas were synthesized to evaluate their antioxidant, antibacterial, antidiabetic, and anticholinesterase potentials. The symmetrical thioureas were synthesized in aqueous media in the presence of sunlight, using amines and CS as starting material. The unsymmetrical thioureas were synthesized using amines as a nucleophile to attack the phenyl isothiocyanate (electrophile). The structures of synthesized compounds were confirmed through H NMR. The antioxidant potential was determined using DPPH and ABTS assays. The inhibition of glucose-6-phosphatase, alpha amylase, and alpha glucosidase by synthesized compounds was used as an indication of antidiabetic potential. Anticholinesterase potential was determined from the inhibition of acetylcholinesterase and butyrylcholinesterase by the synthesized compounds. The highest inhibition of glucose-6-phosphatase was shown by compound (03.12 mg of phosphate released). Alpha amylase was most potently inhibited by compound with IC value of 62 µg/mL while alpha glucosidase by compound with IC value of 75 µg/mL. The enzymes, acetylcholinesterase, and butyrylcholinesterase were potently inhibited by compound with IC of 63 µg/mL and 80 µg/mL respectively. Against DPPH free radical, compound was more potent (IC = 64 µg/mL) while ABTS was more potently scavenged by compound with IC of 66 µg/mL. The antibacterial spectrum of synthesized compounds was determined against Gram-positive bacteria ( ) and Gram-negative bacteria ( and ). Compound and compound showed maximum activity against with MIC values of 4.02 and 4.04 µg/mL respectively. Against , compound was more active (MIC = 8.94 µg/mL) while against . , compound was more potent with MIC of 4.03 µg/mL. From the results, it was concluded that these compounds could be used as antibacterial, antioxidant, and antidiabetic agents. However, further in vivo studies are needed to determine the toxicological effect of these compounds in living bodies. The compounds also have potential to treat neurodegenerative diseases.

Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players’ brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player’s expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

Background Globally, substance abuse has been identified as a major public health issue. The aim of the study was to determine and compare the prevalence, pattern, and predictors of substance abuse among adolescents in public and private day secondary schools in Katsina State. Methods A cross-sectional comparative study was employed to investigate 1126 adolescents obtained through multistage sampling technique in selected public and private day secondary schools across geopolitical zones spanning both rural and urban LGAs in Katsina State. Data was collected over eight weeks with the aid of pretested interviewer-administered questionnaire and was analysed using IBM SPSS version 25. Ethical approval was obtained from Katsina State Ministry of Health. Results Overall, majority (25.1%) of respondents were 18 years of age (majority, 28% in public and 25.2% in private schools were 17 and 18 years of age respectively. Overall mean age of the study population was 16.98 ± 1.27 years (Public; 16.97 ± 1.237 years and Private;16.99 ± 1.309 years). Overall, most of the respondents were in SS3 (44.1%), (Public; 47.4% and Private; 40.8%). Proportion of adolescents who ever used any substance at least once was 22.02% (7.99% public, 14.03% private). Factors independently associated with substance abuse were being in SS3 class ( p  = 0.022), coming from monogamous family ( p  = 0.014) and peer substance abuse ( p  = 0.017). The logistic regression model reveals that current users in SS3 class, from monogamous setting and whose peers abuse substances are 7 times more likely (aOR = 7.12), 5 times more likely (aOR = 5.4) and 20% more likely (aOR = 0.209) to be in private than in public schools, respectively. Conclusion Prevalence of substance abuse was high. Major predictor was peer substance abuse. Consequently, the state Ministry of Education in collaboration with Ministry of Health and NDLEA should design a substance abuse prevention programme with a view to reducing the menace of substance abuse in the state.