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Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k -space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique.

Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in developing countries. Symptoms such as chlorosis, structural deformities, and wilting, characterize these diseases. However, early identification can be challenging due to symptoms similarity. Researchers using artificial intelligence (AI) for plant disease classification, challenges like data imbalance, symptom variability, real-time performance, and costly annotation hinder accuracy and adoption. This work introduced a novel approach using the You Only Look Once (YOLO) deep learning model, chosen for its exceptional accuracy and speed. The study focuses on analyzing YOLO models, specifically YOLOv3 and YOLOv4, to identify fruit plant diseases. This work examines healthy peach and strawberry leaves, as well as peach leaves affected by bacterial spots and strawberry leaves with scorch disease. These models underwent thorough training using data from the publicly accessible Plant Village dataset. The simulation results were highly promising, numerically YOLOv3 model achieved 97% accuracy and a Mean Average Precision (mAP) of 92%, within a total detection time of 105 s. In comparison, the YOLOv4 model outperformed, with a 98% accuracy and an impressive mean average precision of 98%, all while completing the detection process in just 29 s. YOLOv4 demonstrated lower complexity, significantly faster, and more precise performance, especially in detecting multiple items. Serving as an efficient real-time detector, it holds the potential to transform plant disease diagnosis and mitigation strategies, ultimately leading to increased agricultural productivity and enhanced financial outcomes for developing nations.

Aims Major depressive disorder is a severe psychiatric disorder that afflicts ~17% of the world population. Neuroimaging investigations of depressed patients have consistently reported the dysfunction of the basolateral amygdala in the pathophysiology of depression. However, how the BLA and related circuits are implicated in the pathogenesis of depression is poorly understood. Methods Here, we combined fiber photometry, immediate early gene expression (c‐fos), optogenetics, chemogenetics, behavioral analysis, and viral tracing techniques to provide multiple lines of evidence of how the BLA neurons mediate depressive‐like behavior. Results We demonstrated that the aversive stimuli elevated the neuronal activity of the excitatory BLA neurons (BLACAMKII neurons). Optogenetic activation of CAMKII neurons facilitates the induction of depressive‐like behavior while inhibition of these neurons alleviates the depressive‐like behavior. Next, we found that the chemogenetic inhibition of GABAergic neurons in the BLA (BLAGABA) increased the firing frequency of CAMKII neurons and mediates the depressive‐like phenotypes. Finally, through fiber photometry recording and chemogenetic manipulation, we proved that the activation of BLAGABA neurons inhibits BLACAMKII neuronal activity and alleviates depressive‐like behavior in the mice. Conclusion Thus, through evaluating BLAGABA and BLACAMKII neurons by distinct interaction, the BLA regulates depressive‐like behavior. Under normal conditions, BLA neurons are in a resting state and maintained the balance in inhibition/excitation. However, stress leads to the overactivity of BLACAMKII neurons and mediates the depressive‐like phenotypes. Enhancing the inhibition in the BLA either through chemogenetic activation of BLAGABA neurons or direct inhibition of BLACAMKII neurons alleviates depressive‐like behaviors.

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

As a ubiquitous secondary messenger in plant signaling systems, calcium ions (Ca2+) play essential roles in plant growth and development. Within the cellular signaling network, the accurate decoding of diverse Ca2+ signal is a fundamental molecular event. Calcium-dependent protein kinases (CDPKs), identified commonly in plants, are a kind of vital regulatory protein deciphering calcium signals triggered by various developmental and environmental stimuli. This review chiefly introduces Ca2+ distribution in plant cells, the classification of Arabidopsis thaliana CDPKs (AtCDPKs), the identification of the Ca2+-AtCDPK signal transduction mechanism and AtCDPKs’ functions involved in plant growth regulation and abiotic stress responses. The review presents a comprehensive overview of AtCDPKs and may contribute to the research of CDPKs in other plants.

This study delves into the interconnections among corporate social responsibility, green intellectual capital, green ambidextrous innovation, and sustainable performance, particularly in the context of Industry 4.0 and sustainability. A questionnaire-based survey was conducted, and a sample of 317 small and medium enterprises was collected. Using Partial Least Squares Structural Equation Modeling in Smart-PLS v4, the findings reveal a significant relationship between corporate social responsibility and sustainable performance, with green intellectual capital and green ambidextrous innovation serving as mediating factors. Moreover, the study highlights the moderating role of Industry 4.0 among green intellectual capital and green ambidextrous innovation with sustainable performance. These findings may guide the managers in designing and implementing CSR strategies beyond compliance and contributing to competitive advantage through green intellectual capital and green ambidextrous innovation for business success in the era of Industry 4.0.

In recent years, technical advancements in high-heat-flux devices (such as high power density and increased output performance) have led to immense heat dissipation levels that may not be addressed by traditional thermal fluids. High-heat-flux devices generally dissipate heat in a range of 100–1000 W/cm2 and are used in various applications, such as data centers, electric vehicles, microelectronics, X-ray machines, super-computers, avionics, rocket nozzles and laser diodes. Despite several benefits offered by efficient spray-cooling systems, such as uniform cooling, no hotspot formation, low thermal contact resistance and high heat transfer rates, they may not fully address heat dissipation challenges in modern high-heat-flux devices due to the limited cooling capacity of existing thermal fluids (such as water and dielectric fluids). Therefore, in this review, a detailed perspective is presented on fundamental hydrothermal properties, along with the heat and mass transfer characteristics of the next-generation thermal fluid, that is, the hybrid nanofluid. At the end of this review, the spray-cooling potential of the hybrid nanofluid for thermal management of high-heat-flux devices is presented.

Quality antenatal care is a window of opportunity for improving maternal and neonatal outcomes. Numerous studies have shown a positive effect of women empowerment on improved coverage of maternal and reproductive health services, including antenatal care (ANC). However, there is scarce evidence on the association between women's empowerment and improved ANC services both in terms of coverage and quality. Addressing this gap, this paper examines the relationship between multi-dimensional measures of women empowerment on utilization of quality ANC (service coverage and consultation) in Pakistan. We used Pakistan Demographic and Health Survey 2017-18 (PDHS) data which comprises of 6,602 currently married women aged between 15-49 years who had a live birth in the past five years preceding the survey. Our exposure variables were three-dimensional measures of women empowerment (social independence, decision making, and attitude towards domestic violence), and our outcome variables were quality of antenatal coverage [i.e. a composite binary measure based on skilled ANC (trained professional), timeliness (1st ANC visit during first trimester), sufficiency of ANC visits (4 or more)] and quality of ANC consultation (i.e. receiving at least 7 or more essential antenatal components out of 8). Data were analysed in Stata 16.0 software. Descriptive statistics were used to describe sample characteristics and binary logistic regression was employed to assess the association between empowerment and quality of antenatal care. We found that 41.4% of the women received quality ANC coverage and 30.6% received quality ANC consultations during pregnancy. After controlling for a number of socio-economic and demographic factors, all three measures of women's empowerment independently showed a positive relationship with both outcomes. Women with high autonomy (i.e. strongly opposed the notion of violence) in the domain of attitude to violence are 1.66 (95% CI 1.30-2.10) and 1.45 (95% CI 1.19-1.75) and times more likely to receive antenatal coverage and quality ANC consultations respectively, compared with women who ranked low on attitude to violence. Women who enjoy high social independence had 1.87 (95% CI 1.44-2.43) and 2.78 (95% CI 2.04-3.79) higher odds of quality antenatal coverage and consultations respectively, as compared with their counterparts. Similarly, women who had high autonomy in household decision making 1.98 (95% CI 1.60-2.44) and 1.56 (95% CI 2.17-1.91) were more likely to receive quality antenatal coverage and consultation respectively, as compared to women who possess low autonomy in household decision making. The quality of ANC coverage and consultation with service provider is considerably low in Pakistan. Women's empowerment related to social independence, gendered beliefs about violence, and decision-making have an independent positive association with the utilisation of quality antenatal care. Thus, efforts directed towards empowering women could be an effective strategy to improve utilisation of quality antenatal care in Pakistan.

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

Cysteine and homocysteine (Hcy), both sulfur‐containing amino acids (AAs), produced from methionine another sulfur‐containing amino acid, which is converted to Hcy and further converted to cysteine. This article aims to highlight the link between cysteine and Hcy, and their mechanisms, important functions, play in the body and their role as a biomarker for various types of diseases. So that using cysteine and Hcy as a biomarker, we can prevent and diagnose many diseases. This review concluded that hyperhomocysteinemia (elevated levels of homocysteine) is considered as toxic for cells and is associated with different health problems. Hyperhomocysteinemia and low levels of cysteine associated with various diseases like cardiovascular diseases (CVD), ischemic stroke, neurological disorders, diabetes, cancer like lung and colorectal cancer, renal dysfunction‐linked conditions, and vitiligo. Cysteine and homocysteine, both sulfur‐containing amino acids (AAs) produced from methionine another sulfur‐containing amino acid, which is converted to homocysteine and further converted to cysteine. The aim of this article is to highlight the link between cysteine and homocysteine, and their mechanisms, important functions, play in the body and their role as a biomarker for various different types of diseases. Hyperhomocysteinemia (elevated levels of homocysteine) is considered as toxic for cells and is associated with different health problems. Not only hyperhomocysteinemia but also low levels of cysteine in most cases is used as a biomarker for various diseases like cardiovascular diseases (CVD), ischemic stroke, neurological disorders, diabetes, cancer like lung and colorectal cancer, renal dysfunction‐linked conditions, and vitiligo.