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Recent studies have demonstrated that nano-patch antennas formed by metallic nanocubes placed on top of a metallic film largely enhance the spontaneous emission rate of quantum emitters due to the confinement of the electromagnetic field in the small nanogap cavity. The popularity of this architecture is, in part, due to the ease in fabrication. In this contribution, we theoretically demonstrate that a dimer formed by two metallic nanocubes embedded in a dielectric medium exhibits enhanced emission rate compared to the nano-patch antenna. Furthermore, we compare the directivity and radiation efficiency of both nanoantennas. From these characteristics, we obtained information about the "material efficiency" and the coupling mismatch efficiency between a dipole emitter and the nanoantenna. These quantities provide a more intuitive insight than the Purcell factor or localized density of states, opening new perspectives in nanoantenna design for ultradirective light emission. [ABSTRACT FROM AUTHOR]

Remobilization of dry matter during the grain filling period in wheat is capable of helping the plant recover its grain yield under drought stress. In this study, the genotypic variation of different traits related to dry matter remobilization were measured in seven genotypes of wheat under the three different environment conditions of well-watered, drought stress at heading stage with application of extra nitrogen fertilizer (30%), and drought stress in Isfahan, Iran. Analysis of variance showed that the genotypes were different not only in their dry matter remobilization from the spike, the stem, the peduncle, and the leaf sheath but also in their current photosynthesis. Different environmental conditions were found to affect dry matter remobilization from the leaves and sheath, current photosynthesis, grain yield, and the relative contributions by the stem and the spike to grain yield. The highest values of spike and stem contribution to grain yield were obtained under drought stress while current photosynthesis was found to be the sole supplier for grain filling in normal conditions. Application of extra nitrogen fertilizer under drought stress was found to reduce the loss of grain yield in some genotypes as a result of enhanced vegetative growth, reserve accumulation, and dry matter remobilization to the grain. [ABSTRACT FROM AUTHOR]

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller. [ABSTRACT FROM AUTHOR]

Background and aims: Serotonin (5-hydroxtryptamine, 5-HT) is an important factor in gut function, playing key roles in intestinal peristalsis and secretion, and in sensory signalling in the brain-gut axis. Removal from its sites of action is mediated by a specific protein called the serotonin reuptake transporter (SERT or 5-HTT). Polymorphisms in the promoter region of the SERT gene have effects on transcriptional activity, resulting in altered 5-HT reuptake efficiency. It has been speculated that such functional polymorphisms may underlie disturbance in gut function in individuals suffering with disorders such as irritable bowel syndrome (IBS). The aim of this study was to assess the potential association between SERT polymorphisms and the diarrhoea predominant IBS (dIBS) phenotype. Subjects: A total of 194 North American Caucasian female dIBS patients and 448 female Caucasian controls were subjected to genotyping. Methods: Leucocyte DNA of all subjects was analysed by polymerase chain reaction based technologies for nine SERT polymorphisms, including the insertion/deletion polymorphism in the promoter (SERT-P) and the variable tandem repeat in intron 2. Statistical analysis was performed to assess association of any SERT polymorphism allele with the dIBS phenotype. Results:A strong genotypic association was observed between the SERT-P deletion/deletion genotype and the dIBS phenotype (p = 3.07 ×10-5; n = 194). None of the other polymorphisms analysed was significantly associated with the presence of disease. Conclusions:Significant association was observed between dIBS and the SERT-P deletion/deletion genotype, suggesting that the serotonin transporter is a potential candidate gene for dIBS in women. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a significant global health challenge, characterized by high incidence and mortality rates on a global scale. With the rapid advancement of computer-aided diagnosis (CAD) tools in recent years, CAD has assumed an increasingly crucial role in supporting TB diagnosis. Nonetheless, the development of CAD for TB diagnosis heavily relies on well-annotated computerized tomography (CT) datasets. Currently, the available annotations in TB CT datasets are still limited, which in turn restricts the development of CAD tools for TB diagnosis to some extent. To address this limitation, we introduce DeepPulmoTB, a CT multi-task learning dataset explicitly designed for TB diagnosis. To demonstrate the advantages of DeepPulmoTB, we propose a novel multi-task learning model, DeepPulmoTBNet (DPTBNet), for the joint segmentation and classification of lesion tissues in CT images. The architecture of DPTBNet comprises two subnets: SwinUnetR for the segmentation task, and a lightweight multi-scale network for the classification task. Furthermore, to enhance the model's capacity to capture TB lesion features, we introduce an improved iterative optimization algorithm that refines feature maps by integrating probability maps obtained in previous iterations. Extensive experiments validate the effectiveness of DPTBNet and the practicality of the DeepPulmoTB dataset., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Zhuoyi Tan reports financial support was provided by Malaysia Ministry of Higher Education and Universiti Putra Malaysia., (© 2024 The Author(s).)

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore ® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.

Topological insulator lasers (TILs) are a recently introduced family of lasing arrays in which phase locking is achieved through synthetic gauge fields. These single frequency light source arrays operate in the spatially extended edge modes of topologically non-trivial optical lattices. Because of the inherent robustness of topological modes against perturbations and defects, such topological insulator lasers tend to demonstrate higher slope efficiencies as compared to their topologically trivial counterparts. So far, magnetic and non-magnetic optically pumped topological laser arrays as well as electrically pumped TILs that are operating at cryogenic temperatures have been demonstrated. Here we present the first room temperature and electrically pumped topological insulator laser. This laser array, using a structure that mimics the quantum spin Hall effect for photons, generates light at telecom wavelengths and exhibits single frequency emission. Our work is expected to lead to further developments in laser science and technology, while opening up new possibilities in topological photonics.

Affective disorders including depression and anxiety are among the most prevalent behavioral abnormalities in patients with Alzheimer's disease (AD), which affect the quality of life and progression of the disease. Dysregulation of the hypothalamic-pituitary-adrenal-(HPA) axis has been reported in affective disorders and AD. Recent studies revealed that current antidepressant drugs are not completely effective for treating anxiety- and depression-related disorders in people with dementia. ATP-sensitive-potassium-(K ATP ) channels are well-known to be involved in AD pathophysiology, HPA axis function and the pathogenesis of depression and anxiety-related behaviors. Thus, targeting of K ATP channel may be a potential therapeutic strategy in AD. Hence, we investigated the effects of intracerebroventricular injection of Aβ25-35 alone or in combination with glibenclamide, K ATP channel inhibitor on depression- and anxiety-related behaviors as well as HPA axis response to stress in rats. To do this, non-Aβ25-35- and Aβ25-35-treated rats were orally treated with glibenclamide, then the behavioral consequences were assessed using sucrose preference, forced swim, light-dark box and plus maze tests. Stress-induced corticosterone levels following forced swim and plus maze tests were also evaluated as indicative of abnormal HPA-axis-function. Aβ25-35 induced HPA axis hyperreactivity and increased depression- and anxiety-related symptoms in rats. Our results showed that blockade of K ATP channels with glibenclamide decreased depression- and anxiety-related behaviors by normalizing HPA axis activity in Aβ25-35-treated rats. This study provides additional evidence that Aβ administration can induce depression- and anxiety-like symptoms in rodents, and suggests that K ATP channel inhibitors may be a plausible therapeutic strategy for treating affective disorders in AD patients., (Copyright © 2018 Elsevier Inc. All rights reserved.)