A novel stabilized artificial neural network model enhanced by variational mode decomposing.

Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series, but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues, we propose a stabilized ANNs, called SANN.

Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.

The study presents a segmented dataset comprising dental periapical X-ray images from both healthy and diseased patients. The ability to differentiate between normal and abnormal dental periapical X-rays is pivotal for accurate diagnosis of dental pathology. These X-rays contain crucial information, offering in- sights into the physiological and pathological conditions of teeth and surrounding structures.

LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.

Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents.

Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands.

An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases.

Grass-Shaped Zinc Oxide Nanoparticles Synthesized by the Sol-Gel Process and Their Antagonistic Properties towards the Biotrophic Parasite, .

The presence of spp., also known as root-knot nematodes, presents a significant danger to global agricultural progress. Since chemical nematicides have high levels of toxicity, it is imperative to develop environmentally friendly methods to manage root-knot nematodes.

Economic growth, foreign investment, tourism, and electricity production as determinants of environmental quality: empirical evidence from GCC region.

Each economic factor generates both positive and negative externalities regarding environmental quality. Owing to this, the current study aims to explore the impacts of various economic variables on the environmental quality of the Gulf Cooperation Council (GCC) region. By sampling the 24 years (1996-2019) financial statistics of six GCC region countries, we investigate the impact of economic growth, foreign investment, trade volume, tourism investment and revenue, and electricity production on CO emissions.

Phytochemical Analysis and Binding Interaction of Cotton Seed Cake Derived Compounds with Target Protein of for Nematicidal Evaluation.

The root-knot nematode is one of the most damaging plant-parasitic nematodes and is responsible for significant crop losses worldwide. Rising human health and environmental concerns have led to the withdrawal of commonly used chemical nematicides. There has been a tremendous demand for eco-friendly bio-nematicides with beneficial properties to the nematode hosting plants, which encourages the need for alternative nematode management practices.

Can learning presence be the fourth community of inquiry presence? Examining the extended community of inquiry framework in blended learning using confirmatory factor analysis.

Researchers continue to extend the community of inquiry (COI) framework, highlighting its utility in online and blended learning environments for providing a successful learning experience. Recent studies have added the learning presence dimension to the classic COI framework which contains teaching, social, and cognitive presences, to represent online students' traits of self-regulation. However, there is a need to examine whether this additional presence structurally represents relationships with other COI presences.

Marketing training strategies that pharmaceutical sales managers use to reduce unethical behavior.

Background: Some pharmaceutical company sales representatives are using bribes to encourage increasing medication prescriptions. In 2012, GlaxoSmithKline paid $3 billion on a felony charge related to bribing doctors to prescribe the company's medications. Using Hunt and Vitell's general theory of marketing ethics, the purpose of this qualitative multiple case study was to explore strategies some sales managers in the pharmaceutical industry used to improve marketing training to reduce unethical sales representative behavior.

Deep learning based model for classification of COVID -19 images for healthcare research progress.

As imaging technology plays an important role in the diagnosis and evaluation of the new coronavirus pneumonia (COVID-19), COVID-19 related data sets have been published one after another, but there are relatively few data sets and research progress in related literature. To this end, through COVID-19-related journal papers, reports, and related open-source data set websites, organize and analyze the new coronary pneumonia data set and the deep learning models involved, including computed tomography (CT) image data sets and X-ray (CXR) Image dataset. Analyze the characteristics of the medical images presented in these data sets; focus on open-source data sets, as well as classification and segmentation models that perform well on related data sets.

Isolation and optimization of extracellular PHB depolymerase producer Aeromonas caviae Kuk1-(34) for sustainable solid waste management of biodegradable polymers.

Bioplastics, synthesized by several microbes, accumulates inside cells under stress conditions as a storage material. Several microbial enzymes play a crucial role in their degradation. This research was carried to test the biodegradability of poly-β-hydroxybutyrate (PHB) utilizing PHB depolymerase, produced by bacteria isolated from sewage waste soil samples.

Evaluation of the Egyptian knowledge bank using the information systems success model.

The appearance of the coronavirus pandemic had several implications on the educational process, which caused students, at all educational levels, to resort to digital libraries as the available educational opportunity in this challenging time. This study sheds light on the Egyptian Knowledge Bank (EKB) as one of the largest digital libraries in the world. It empirically evaluates the EKB, using the updated Information Systems Success (ISS) model proposed by DeLone and McLean (2003), among postgraduate business students in the context of Egyptian universities with a sample of 245 students.

Electron Transfer-Mediated Photodegradation of Phototoxic Antipsychotic Drug Quetiapine.

Quetiapine (QTP) (), a psychotropic agent belonging to a chemical class, dibenzothiazepine derivatives, is photosensitive and photolabile. Its photochemistry was studied in the presence of an electron donor ,-dimethylaniline (DMA) and an electron acceptor 1,4-dicyanobenzene (DCB) under anaerobic conditions. This resulted in photoinduced electron transfer-mediated transformation of drug QTP.

Identifying effective retention strategies for front-line nurses.

Background: Nurse retention is a significant issue worldwide, with many nurses planning to leave the profession. Therefore, it is crucial that healthcare organisations and senior leaders identify effective strategies for retaining their employees, who are their most valuable assets.

Aim: To explore strategies to reduce the voluntary turnover of front-line nurses from the perspective of senior healthcare leaders.

The developing trends and driving factors of environmental information disclosure in China.

Environmental Information Disclosure (EID) is a new tool for environmental governance in the era of big data and information. Based on the Pollution Information Disclosure Index (PITI) of 120 cities in China from 2003 to 2019, spatial data exploratory analysis and dynamic spatial panel model were adopted to analyze the spatial-temporal evolution characteristics and influencing factors of EID in China. The results show that (1) great progress of China's EID has been made in legislation and practice and its ways and channels are gradually becoming diversified, while it is accompanied by the problem of inadequate and unbalanced development; (2) EID shows the "superposition effect" promoted by previous accumulation has the "peer effect" of mutual imitation and learning and presents "demonstration effect", which shows significant agglomeration distribution pattern of spatial "club", while the spillover effect within the region is significant while the radiation effect between regions is weak.

Anti-quorum Sensing and Anti-biofilm Activity of Zinc Oxide Nanospikes.

This study evaluates the impact of two separate incubation periods (4 and 6 weeks) on the morphology of sol-gel-fabricated ZnO nanospikes (ZNs), that is, ZN1 and ZN2, respectively. We further analyzed the inhibitory effects of ZN1 and ZN2 on quorum sensing (QS) and biofilm formation in (PAO1) and (strains 12472 and CVO26). The size of the synthesized ZNs was in the range of 40-130 nm, and finer nanoparticles were synthesized after an incubation period of 6 weeks.

Retaining Generation Y Nurses: Preferred Characteristics of Their Nurse Managers.

Objective: The purpose of this study was to describe generation Y nurses' perceptions of their nurse managers' servant leadership characteristics as well as the relationship between these characteristics and intent to stay on the job.

Background: To attract and retain generation Y nurses, organizational leaders need to develop an understanding of this generation's perspectives around workplace loyalty and commitment.

Method: This study had a quantitative correlational research design.

Readmission rate reduction strategies in general hospital setting.

Hospital readmissions are vital to patients, families, healthcare providers, and policymakers throughout the world. However, limited information is available about strategies to prevent readmissions in Qatar and the Middle East region in general; hence, there is a gap in business practice. The objective of this study was to explore the strategies used by hospital leaders and administrators in managing and reducing patient readmissions.

Frontier View on Nanotechnological Strategies for Neuro-therapy.

Background: Nanotechnology exploits materials and devices with a functional organization that has been engineered at the nanometre scale. The application of nanotechnology in neuroscience involves specific interactions with neurons and glial cells. This property is used for delivering drugs and other small molecules (such as genes, oligonucleotides and contrasting agents) across the blood brain barrier (BBB), an important requirement for delivering the drug successfully to the brain.

Relationship between CNS and Immunology: Correlation with Psychology.

Background: Higher animals, especially the human beings have the privilege of employing advanced central nervous system (CNS) as well as the evolved immune system to ward off various onslaughts throughout their life. Alterations in inflammatory and neural regulatory pathways lead to several disorders that are now becoming the cause of concern across the world. Deregulation in bidirectional network, particularly in aging population, leads to several neurodegenerative diseases such as dementia as a one of the major characteristics.

PeMtb: A Database of MHC Antigenic Peptide of Mycobacterium tuberculosis.

Background: For design of a subunit vaccine for tuberculosis, identification of antigenic Tcell epitope is of utmost importance. Several MHC prediction server are available that can accurately predict antigenic peptide of variable lengths. However, peptides predicted from one server not necessarily are predicted form another server, thus creating a confusing situation for scientists to choose a best epitope.