Schizophrenia is a psychological disorder, way tougher to diagnose than other psychological disorders, as it shares similar symptoms with psychosis. As we know, schizophrenia occurs due to chemical imbalance in the brain; identifying the role of neurotransmitters in schizophrenia poses a vital area to study. Neurotransmitters being the sole carrier of different brain activities, researchers have already initiated studies to investigate their role and effect in disorder. Firstly, this paper performs a critical review of the literature that dealt with different neurotransmitters in schizophrenia. Secondly, we identify the most important neurotransmitters and broadly elaborate on their functional roles and effects on the disorder. Finally, we have successfully identified various gaps and unexplored research questions to investigate these neurochemicals' role. Studies show that neurotransmitters like dopamine, glutamate, GABA, serotonin, and oxytocin are majorly responsible for schizophrenia, among which dopamine contributes the most. To the best of our knowledge, this paper encapsulates all the neurotransmitters, enzymes, and chemicals for the first time and explores their related literature. This study also identifies the most responsible chemicals involved in schizophrenia and unfolds the research community's unsolved problem.

Background:Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods:We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results:This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions:As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Involvement of small and medium-scale enterprises is important for developing countries like India. Henceforth, enhancing their competency with the adoption of a suitable enterprise resource planning system (ERPS) is a vital way to enhance their competitiveness. The choice of an appropriate ERPS can be considered as a complex multicriteria decision-making (MCDM) problem where several alternatives (ERPS packages) are assessed based on certain types of characteristics, namely domain knowledge of the supplier, system reliability, service and support, functionality, compatibility, information security, cross-module integration, etc. One critical issue associated with the evaluation of ERPSs is the handling of uncertain information. In this article, we first develop a new methodology to resolve the issue of MCDM problems. In this methodology, the weights of decision experts are systematically calculated with the extended variance approach on intuitionistic fuzzy sets. For calculating the criteria weights, an optimization model based on cross-entropy is proposed, and for the aggregation of the criteria values, a new model called intuitionistic fuzzy improved measurement alternatives and ranking based on the compromise solution is developed, which allows both the vector and linear normalizations and having the advantages of comprising two kinds of aggregation models. Next, an ERPS cloud vendor assessment problem is deliberated with three alternatives (A1: Systems, Applications, and Products (SAP), A2: Oracle Corporation, A3: Microsoft Corporation) and 16 criteria to interpret the reasonableness of the introduced framework. According to the outcomes, SAP is the most suitable alternative. Next, a sensitivity analysis is exhibited with diverse parameter values to inspect the permanence of the presented approach. The advantage of the presented approach is discussed with the comparative investigation.

Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While smart traffic monitoring and advanced parking distribution systems have eased urban pressures, optimizing mobility remains pivotal in the context of burgeoning smart cities. However, the seaport industry grapples with formidable issues in the efficient transport of containers. In light of this, the fusion of AI and CV technology holds promise as a solution. This study pioneers a systematic review, representing a novel exploration, delving into a comprehensive evaluation of the existing literature. It scrutinizes the profound advantages AI and CV offer in constructing sustainable, efficient parking ecosystems within seaports. Our methodological approach encompasses data collection, rigorous quality assessment, and meticulous exploration of the application of CV and AI in the realm of smart parking management. The findings underscore the pivotal role of AI and CV technologies in the development of efficient, sustainable transportation systems, particularly for optimizing container movement within seaports. This research presents a comprehensive analysis of the literature in the area of the application of AI and CV technologies in optimizing parking management at seaports, shedding light on the potential for sustainable transportation solutions in this critical domain. As these technologies usher in enhancements in traffic management, parking space allocation, and container logistics within seaports, this study represents a vital and timely contribution to the field, serving as a cornerstone for future innovations in seaport management and the broader context of smart cities.