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ICTs and access to Internet use are considered vital for the achievement of sustainable development goals. So, this study explored the effect of the global digital divide, trade openness, renewable energy consumption, and forestation on greenhouse gas (GHG) emissions in 42 high-income countries (HICs) and high-middle-income (HMICs), low-income countries (LICs), and low-middle-income countries (LMICs) of Africa from 1990 to 2018. TheDumitrescu-Hurlin causality results confirmed a unidirectional causality from GHG emissions to the global digital divide (HICs and HMICs), global digital divide to GHG emissions (LICs), and GHG emission to trade openness (LICs and LMICs). Moreover, the long-run results of the autoregressive distributed lag (ARDL) model showed an increase in GHG due to an increase in the global digital divide in all three panels. Further, ARDL results showed reduced GHG emissions due to increased trade openness in LIC and LMICs, renewable energy consumption, and forestation in all three panels. Thus, to encounter pollution from Internet use, the government should start environment-friendly projects through public and private investment in smart and modern environment-friendly technology and reduce the taxes and tariffs on them. Moreover, the governments of African countries should create public awareness through print and electronic media for raising the forestation area.

The well-known Hermite-Hadamard inequality has attracted the attention of several researchers due to the fact that Hermite-Hadamard inequality has many important applications in mathematics as well as in other areas of science. In this article, the authors present new Hermite-Hadamard inequality of the Mercer type containing Riemann-Liouville k-fractional integrals. For these inequalities, we give integral identity for differentiable functions. With the help of the identity and Hermite-Hadamard-Mercer type inequalities, we derive several results for the inequalities. We establish bounds for the difference of the obtain results by applying Hölder's inequality and power-mean inequality. We hope that the proposed result will invigorate further interest in this direction.

Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks. Given this background, we designed a solution to detect intrusions using the Convolutional Neural Network (CNN) for Enhanced Data rates for GSM Evolution (EDGE) Computing. We created two separate categories to handle the attack and non-attack events in the system. The findings of this study indicate that this approach was significantly effective. We attempted both multiclass and binary classification. In the case of binary, we clustered all malicious traffic data in a single class. Also, we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD. Principal Component Analysis (PCA) was implemented to decrease the size of the feature vector based on feature extraction and engineering. The approach proposed in the current investigation obtained accuracies of 99.34% and 99.13% for binary and multiclass classification, respectively, for the NSL-KDD dataset. The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network (PCCNN) approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.

Ecometrics is the study of the relationship between organismal traits and environments. This study used Monte Carlo methods to assess the effects of extinction, extirpation, and exotic species on ecometric correlations at the continental scale. These potentially confounding processes arise from anthropogenic activities, taphonomic biases in fossil assemblages, and selective mass extinctions. Random, independent local extinctions introduced a predictable downward bias in ecometric correlations, which can be corrected by rarefaction if correlations are being estimated from fossil assemblages. Random global extinctions on species have a less predictable effect on ecometric correlations and introduce pronounced effects if more than 25% of the continental fauna is affected; however, global extinctions do not bias the estimation of R2 even though they increase its uncertainty. Selective extinction and introduction of exotic species had little impact on ecometric correlations, though caution is urged in generalizing this result.

Semantics describe how language and its constituent parts are understood or interpreted. Semantic analysis is the computer analysis of language to derive connections, meaning, and context from words and sentences. In English language learning, dynamic content generation entails developing instructional materials that adjust to the specific requirements of each student, delivering individualized and contextually appropriate information to boost understanding and engagement. To tailor instructional materials to the various requirements of students, dynamic content creation is essential in English language learning (ELL). This work is a unique method for automatic and adaptive content production in ELL that uses Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) together. The suggested approach uses BERT enabling content selection, adaption, and adaptive educational content production, and GRU for semantic extraction of features and contextual information captured from textual input. The article presents a novel approach to creating automated and adaptable educational tools for ELL that uses GRU for semantic feature extraction. Using persuasive essays collected in the PERSUADE 2.0 corpus annotated with discourse components and competency scores, this is an extensive dataset. After extensive testing, this approach shows outstanding outcomes, with high accuracy reaching 97% when compared to the current Spiking Neural Network (SNN) &Convolutional Neural Network (CNN), Logistic Regression (LR), and Convolutional Bidirectional Recurrent Neural Network (CBRNN). Python is used to implement the suggested work. The suggested strategy improves ELL engagement and understanding by providing individualized, contextually appropriate learning resources to each student. In addition, the flexibility of the system allows for real-time modifications to suit the changing needs and preferences of the learners. By providing instructors and students in a variety of educational contexts with a scalable and effective approach, this study advances automated content development in ELL. The model architecture will be improved in the future, along with the application's expansion into other domains outside of ELL and the investigation of new language aspects.

Pneumonia is a highly transmissible disease in children. According to the World Health Organization (WHO), the most affected regions include south Asia and sub-Saharan Africa. Worldwide, 15% of pediatric deaths can be attributed to pneumonia. Computing techniques have a significant role in science, engineering, and many other fields. In this study, we focused on the efficiency of numerical techniques via computer programs. We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques. We discuss two types of analysis: dynamical and numerical. The dynamical analysis included positivity, boundedness, local stability, reproduction number, and equilibria of the model. We also discuss well-known computing techniques including Euler, Runge Kutta, and non-standard finite difference (NSFD) for the model. The non-standard finite difference (NSFD) technique shows convergence to the true equilibrium points of the model for any time step size. However, Euler and Runge Kutta do not work well over large time intervals. Computing techniques are the suitable tool for crosschecking the theoretical analysis of the model.

[...]at this year's Grammy Awards, singer Adele wowed audiences in an olive green, floor-skimming Givenchy Haute Couture gown, with a checkered bodice and delicate beaded sleeves. Modest wear is being introduced to add variety to the UK fashion industry and doesn't just mean the introduction of hijabs and burkinis - it also means clothing that offers more coverage for a wide range of women from different cultures and religions.

UK high streets are also buying into the trend with Debenhams soon to become the first major UK department store to sell hijabs as part of a new range of Muslim clothing. [...]at this year's Grammy Awards, singer Adele wowed audiences in an olive green, floor-skimming Givenchy Haute Couture gown, with a checkered bodice and delicate beaded sleeves. The burkini first appeared in the UK limelight when TV chef Nigella Lawson wore the head-to-toe three-piece garment on holiday in Australia in 2011. Modest wear is being introduced to add variety to the UK fashion industry and doesn't just mean the introduction of hijabs and burkinis - it also means clothing that offers more coverage for a wide range of women from different cultures and religions. The London-based public speaker champions the freedom to wear modest fashion for all women and wants to become a positive role model, regardless of religion.

Objective Diabetes distress typically causes depressive symptoms; common comorbidity of diabetes unpleasantly affects patients’ medical and psychological functions. Psychotherapeutic interventions are effective treatments to treat depressive symptoms and to improve the quality of life in many chronic diseases including diabetes. The present study investigated the efficacy of cognitive behavior therapy (CBT) to treat depressive symptoms in patients with type 2 diabetes mellitus (T2DM) using experimental and waitlist control conditions. Materials and Methods A total of 130 diagnosed patients with T2DM were taken from outdoor patients services of different hospitals in Faisalabad. Ninety patients met the eligibility criteria and were randomly assigned to experimental ( n  = 45) and waitlist control (n = 45) conditions. All the patients completed clinical interviews and assessment measures at pre-and post-assessment stages (16 weeks intervals). Medical consultants at the respective hospitals diagnosed the patients on the base of their medical reports and then referred those patients to us. Then we used different scales to assess primary and secondary outcomes: Diabetes Distress Scale (DDS) and Patient Health Questionnaire (PHQ) to assess primary outcomes, and a Short Health Anxiety Inventory (SHAI), a Revised Version of the Diabetes Quality of Life Questionnaire (DQLQ), and a General Medication Adherence Scale (GMAS) were used to investigate secondary outcomes. Repeated measure ANOVA was used to analyze the results. Results The findings indicated that patients who received CBT got a significant reduction in their diabetes distress F(1,60) = 222.710, P  < 0.001, η 2  = .788), depressive symptoms F(1,60) = 94.436, P  < 0.001, η 2  = .611), health anxiety F(1,60) = 201.915, P < . 0.001, η 2  = 771), and a significant improvement in their quality of life F(1,60) = 83.352, P <  0.001, η 2  = .581), treatment adherence F(1,60) = 67.579, P <  0.001, η 2  = .566) and physical activity schedule F(1,60) = 164.245, P < .0.001, η 2  = .736 as compared to the patients in waitlist control condition. Conclusion It is concluded that cognitive behavior therapy is an effective and promising intervention for depressive symptoms, diabetes distress, and health anxiety which also helps the person to promote quality of life, treatment adherence and physical activity.