Savannah State University

Conceptually based on Ajzen’s (1991) theory of planned behavior, the study examined relationships among societal entrepreneurship attitude, democratic rights, criticism of entrepreneurial discourse, social justice, and entrepreneurial intention in young adults in the Eastern European part of the Russian Federation. Study revealed that the entrepreneurship attitude and social justice were strongly positively related to the intention to become an entrepreneur. Entrepreneurship intention existed regardless of the presence of democratic rights and absence of criticism of entrepreneurship because college and university students saw the entrepreneurship as a solution to some societal problems and a means to support themselves and their families.

Abstract This study evaluated the effectiveness of sepiolite nano-adsorbents (Sep) and Sep impregnated with manganese-oxide nanoparticles (Sep-MnO2) as cost-effective adsorbents for removing levofloxacin (LEV). Various analyses, including XRD, FTIR, and FESEM with EDS, were used to characterize the adsorbents. The Taguchi L25 experimental design was employed to optimize the adsorption process parameters (pH, contact time, adsorbent dose, initial concentration, and temperature) with the aim of maximizing LEV removal efficiency. Under optimal operating conditions, the maximum removal efficiencies for Sep and Sep-MnO₂ were 85.8% and 98.2%, respectively. The initial pH was identified as the most sensitive parameter, exhibiting peak performance at a pH of 3. The adsorption equilibrium data fit the Redlich–Peterson isotherm model more accurately. The maximum adsorption capacity was 11.46 and 30.98 mg/g for Sep and Sep-MnO2 adsorbents, respectively. The pseudo-second-order kinetic model provided the most accurate representation of the kinetic data. Thermodynamic analysis indicated that LEV adsorption on Sep is exothermic, whereas adsorption on Sep-MnO2 is endothermic and spontaneous. The adsorption mechanism was further explored through pH studies. The results showed that LEV adsorption involves cation exchange in acidic conditions, charge neutralization in neutral conditions, and proton transfer in alkaline conditions.

Predicting the transpiration stream concentration factor (TSCF) and other concentration factors is essential in understanding the plant uptake of organic contaminants. Traditional mechanistic and numerical modeling methods often fail to reliably predict the TSCF. This study developed a hybrid deep model to predict TSCF by integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN–LSTM model used eight physicochemical properties of organic contaminants to predict TSCF. The training procedure for this hybrid model was successful. The results indicated the training and test losses for predicting TSCF were both in the same order and close to zero. This study showed that the hybrid CNN–LSTM model can outperform mechanistic models and have higher performances compared to classical machine learning models. Feature importance analysis using extreme gradient boosting highlighted the role and importance of lipophilicity in predicting uptake and translocation of organic contaminants.

Epstein-Barr virus (EBV), a member of the herpesvirus family and the causative agent of infectious mononucleosis, affects over 90% of the global population. Ongoing scientific investigations continue to reveal its wide-ranging health implications. While the association between EBV and lymphoma, as well as nasopharyngeal cancer, has been well-established for several decades, its connection to gastric cancer was only identified approximately 15 years ago. Furthermore, EBV has been implicated in various autoimmune conditions, including systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis.

Presentamos registros recientes y novedosos (a través de fototampeo) del pecarí de labio blanco (Tayassu pecari) en una localidad del Magdalena Medio (DRMI Ciénaga de Barbacoas, Yondó, Colombia) donde la especie se reporta como extinta localmente. Los registros provienen de una única localidad a lo largo de tres muestreos, sin embargo, no se reportaron más sitios con presencia de la especie en nuestros trabajos a lo largo de diferentes localidades del Magdalena Medio o en otros estudios publicados. Esto sugiere que la especie tiene baja abundancia y ocupación en el paisaje altamente transformado de este valle interandino. Una búsqueda de registros adicionales en plataformas digitales arrojó colectas históricas en el Caribe Colombiano y un registro reciente en el valle del rio Cauca en Antioquia (Colombia), el cual debe ser confirmado en campo. La presencia de pecarí de labio blanco, junto con otras especies amenazadas como el Mono Araña Café, la Danta colombiana, el Jaguar y el Oso Hormiguero Gigante, resaltan la necesidad de restaurar el paisaje fragmentado del Magdalena Medio para mantener su diversidad a largo plazo.

The development of an ecosystem that balances consumer convenience and security is imperative given the expanding market for electric vehicles (EVs). The vast amount of data that EV charging station management systems (EVCSMSs) give is powered by the Internet of Things (IoT) ecosystem. Intrusion Detection Systems (IDSs), which track network traffic to spot potentially dangerous data exchanges in IT and IoT contexts, are constantly improving in terms of efficacy and accuracy. Intrusion detection is becoming a major topic in academia because of the acceleration of IDS development caused by machine learning and deep learning techniques. The goal of the research presented in this paper is to use a machine-learning-based intrusion detection system with low false-positive rates and high accuracy to safeguard the ecosystem of EV charging stations (EVCS).