Investigating structural and optoelectronic properties of Cr-substituted ZnSe semiconductors.

The optoelectronic and structural characteristics of the Zn1-xCrxSe (0 ≤ x ≤ 1) semiconductor are reported by employing density functional theory (DFT) within the mBJ potential. The findings revealed that the lattice constant decreases with increasing Cr concentration, although the bulk modulus exhibits the opposite trend. ZnSe is a direct bandgap material; however, a change from direct to indirect electronic bandgap has been seen with Cr presence. This transition is caused by structural alterations by Cr and defects forming, which results in novel optical features, including electronic transitions. The electronic bandgap decreases from 2.769 to 0.216 eV, allowing phonons to participate and improving optical absorption. A higher concentration of Cr boosts infrared absorption and these Cr-based ZnSe (ZnCrSe) semiconductors also cover a wider spectrum in the visible range from red to blue light. Important optical parameters such as reflectance, optical conductivity, optical bandgap, extinction coefficient, refractive index, magnetization factor, and energy loss function are discussed, providing a theoretical understanding of the diverse applications of ZnCrSe semiconductors in photonic and optoelectronic devices.

Developing and evaluating the impact of a small group communication programme in improving the entrepreneurial competence and economic self-efficacy of smallholder farmers with art skills.

The researchers in Study 1 conducted interviews among experts and developed a small group communication programme to be delivered in 24 months. In Study 2, a quasi-experiment was conducted involving 540 smallholder farmers in Nigeria to test the impact of the developed programme. The result showed that smallholder farmers with art skills who received the small group communication programme reported a significant improvement in their entrepreneurial competence and economic self-efficacy compared to smallholder farmers who did not receive the programme. A follow-up assessment after two years revealed the steady effectiveness of the developed programme.

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection.

Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (gateway), thereby causing failure in the functionality of the system have been more pronounced. Various machine learning and deep learning algorithms have been used to propose intelligent intrusion detection systems (IDS) to mitigate the challenging effects of these network threats. One concern is that although deep learning algorithms have shown good accuracy results on tabular data, not all deep learning algorithms can perform well on tabular datasets, which happen to be the most commonly available format of datasets for machine learning tasks. Again, there is also the challenge of model explainability and feature selection, which affect model performance. In this regard, we propose a model for IDS that uses attentive mechanisms to automatically select salient features from a dataset to train the IDS model and provide explainable results, the TabNet-IDS. We implement the proposed model using the TabNet algorithm based on PyTorch which is a deep-learning framework. The results obtained show that the TabNet architecture can be used on tabular datasets for IoT security to achieve good results comparable to those of neural networks, reaching an accuracy of 97% on CIC-IDS2017, 95% on CSE-CICIDS2018 and 98% on CIC-DDoS2019 datasets.

A bibliometric analysis of emerging adulthood in the context of higher education institutions: A psychological perspectives.

In recent years, there has been a rise in studies aimed at better understanding the needs and traits of emerging adults and the role that higher education institutions play in their development and success. Despite the relevance of higher education institutions to the emerging adulthood development, there has been scant work done to synthesise the literature on this topic. A bibliometric method was utilised to retrieve 2484 journal articles from Web of Science (WoS). Utilizing co-citation analysis and co-word analysis, we determined the most influential publications, mapped the knowledge structure, and predicted future trends. The results of the co-citation analysis indicate five clusters, while the co-word analysis indicates four. The results could be used as a roadmap for the future of research on emerging adults by a variety of interested parties, including policymakers, university administrators, funders, and academics.

Numerical optimization and performance evaluation of ZnPC:PC70BM based dye-sensitized solar cell.

The increase in global energy consumption and the related ecological problems have generated a constant demand for alternative energy sources superior to traditional ones. This is why unlimited photon-energy harnessing is important. A notable focus to address this concern is on advancing and producing cost-effective low-loss solar cells. For efficient light energy capture and conversion, we fabricated a ZnPC:PC70BM-based dye-sensitized solar cell (DSSC) and estimated its performance using a solar cell capacitance simulator (SCAPS-1D). We evaluated the output parameters of the ZnPC:PC70BM-based DSSC with different photoactive layer thicknesses, series and shunt resistances, and back-metal work function. Our analyses show that moderate thickness, minimum series resistance, high shunt resistance, and high metal-work function are favorable for better device performance due to low recombination losses, electrical losses, and better transport of charge carriers. In addition, in-depth research for clarifying the impact of factors, such as thickness variation, defect density, and doping density of charge transport layers, has been conducted. The best efficiency value found was 10.30% after tweaking the parameters. It also provides a realistic strategy for efficiently utilizing DSSC cells by altering features that are highly dependent on DSSC performance and output.

Should I Help? Prosocial Behaviour during the COVID-19 Pandemic.

The Movement Control Order (MCO) enacted during the COVID-19 pandemic has profoundly altered the social life and behaviour of the Malaysian population. Because the society is facing huge social and economic challenges that need individuals to work together to solve, prosocial behaviour is regarded as one of the most important social determinants. Because it is related with individual and societal benefits, participating in prosocial activities may be a major protective factor during times of global crisis. Rather than focusing only on medical and psychiatric paradigms, perhaps all that is necessary to overcome the COVID-19 risks is for individuals to make personal sacrifices for the sake of others. In reality, a large number of initiatives proven to be beneficial in decreasing viral transmission include a trade-off between individual and collective interests. Given its crucial importance, the purpose of this concept paper is to provide some insight into prosocial behaviour during the COVID-19 period. Understanding prosocial behaviour during the COVID-19 pandemic is crucial because it may assist in the establishment of a post-COVID society and provide useful strategies for coping with future crises.

BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning.

Following the recent advances in wireless communication leading to increased Internet of Things (IoT) systems, many security threats are currently ravaging IoT systems, causing harm to information. Considering the vast application areas of IoT systems, ensuring that cyberattacks are holistically detected to avoid harm is paramount. Machine learning (ML) algorithms have demonstrated high capacity in helping to mitigate attacks on IoT devices and other edge systems with reasonable accuracy. However, the dynamics of operation of intruders in IoT networks require more improved IDS models capable of detecting multiple attacks with a higher detection rate and lower computational resource requirement, which is one of the challenges of IoT systems. Many ensemble methods have been used with different ML classifiers, including decision trees and random forests, to propose IDS models for IoT environments. The boosting method is one of the approaches used to design an ensemble classifier. This paper proposes an efficient method for detecting cyberattacks and network intrusions based on boosted ML classifiers. Our proposed model is named BoostedEnML. First, we train six different ML classifiers (DT, RF, ET, LGBM, AD, and XGB) and obtain an ensemble using the stacking method and another with a majority voting approach. Two different datasets containing high-profile attacks, including distributed denial of service (DDoS), denial of service (DoS), botnets, infiltration, web attacks, heartbleed, portscan, and botnets, were used to train, evaluate, and test the IDS model. To ensure that we obtained a holistic and efficient model, we performed data balancing with synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) techniques; after that, we used stratified K-fold to split the data into training, validation, and testing sets. Based on the best two models, we construct our proposed BoostedEnsML model using LightGBM and XGBoost, as the combination of the two classifiers gives a lightweight yet efficient model, which is part of the target of this research. Experimental results show that BoostedEnsML outperformed existing ensemble models in terms of accuracy, precision, recall, F-score, and area under the curve (AUC), reaching 100% in each case on the selected datasets for multiclass classification.