A computational stochastic procedure for solving the epidemic breathing transmission system.

This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs. An activation function 'log-sigmoid' uses twenty neurons in the hidden layers. The precision of SCGGNNs is obtained by comparing the proposed and database solutions. While the negligible absolute error is performed around 10-06 to 10-07, it enhances the accuracy of the scheme. The obtained results of the breathing transmission epidemic system have been provided using the training, verification, and testing procedures to reduce the mean square error. Moreover, the exactness and capability of the stochastic SCGGNNs are approved through error histograms, regression values, correlation tests, and state transitions.

Understanding the role of galectins toward influenza A virus infection.

INTRODUCTION: Influenza A virus (IAV) is highly contagious and causes respiratory diseases in birds, mammals, and humans. Some strains of IAV, whether from human or avian sources, have developed resistance to existing antiviral drugs. Therefore, the discovery of new influenza antiviral drugs and therapeutic approaches is crucial. Recent studies have shown that galectins (Gal), a group of ß-galactose-binding lectins, play a role in regulating various viral infections, including IAVs. AREAS COVERED: This review provides an overview of the roles of different galectins in IAV infection. We discuss the characteristics of galectins, their impact on IAV infection and spread, and highlight their positive or negative regulatory functions and potential mechanisms during IAV infection. Furthermore, we explore the potential application of galectins in IAV therapy. EXPERT OPINION: Galectins were first identified in the mid-1970s, and currently, 15 mammalian galectins have been identified. While all galectin members possess the carbohydrate recognition domain (CRD) that interacts with ß-galactoside, their regulatory functions vary in different DNA or RNA virus infections. Certain galectin members have been found to regulate IAV infection through diverse mechanisms. Therefore, a comprehensive understanding of their roles in IAV infection is essential, as it may pave the way for novel therapeutic strategies.

Flow Boiling of Liquid n-Heptane in Microtube with Various Fuel Flow Rate: Experimental and Numerical Study.

The evaporation of liquid hydrocarbon n-heptane is discussed in detail with experimentation and numerical techniques. A maximum wall temperature of 1050 K was reported during an experimental process with a two-phase flow that was stable and had a prominent meniscus at a small fuel flow rate (FFR) ≤ 10 µL/min. At medium to high FFR (30-70 µL/min), the flow field was unstable, with nucleating bubbles and liquid droplets inside the microtube and the maximum temperature recorded was 850 K for 70 µL/min. For the numerical model, the temperature of the wall was used as a boundary condition. Using the numerical model, the evaporative flux at the meniscus, pressure drop, pressure oscillation, and heat transfer coefficient (HTC) were investigated. A single peak in HTC was obtained at a low fuel flow rate, while multiple peaks were obtained for high FFR. At low FFR, the pressure peak was observed to be 102.4 KPa, whereas at high FFR, the pressure peak increased to 105.5 KPa. This shows a 2% increase in pressure peak with an increase in FFR. Similarly, when the FFR increased from 5 µL/min to 70 µL/min, the pressure drop increased from 500 Pa to 2800 Pa. The high amplitude of pressure drops and a high peak of HTC were found, which depend on the mass flow rate. The coefficient of variation for pressure drop depends mainly on the fuel flow rate.

Novel Mixed-Dimensional hBN-Passivated Silicon Nanowire Reconfigurable Field Effect Transistors: Fabrication and Characterization.

This work demonstrates the novel concept of a mixed-dimensional reconfigurable field effect transistor (RFET) by combining a one-dimensional (1D) channel material such as a silicon (Si) nanowire with a two-dimensional (2D) material as a gate dielectric. An RFET is an innovative device that can be dynamically programmed to perform as either an n- or p-FET by applying appropriate gate potentials. In this work, an insulating 2D material, hexagonal boron nitride (hBN), is introduced as a gate dielectric and encapsulation layer around the nanowire in place of a thermally grown or atomic-layer-deposited oxide. hBN flake was mechanically exfoliated and transferred onto a silicon nanowire-based RFET device using the dry viscoelastic stamping transfer technique. The thickness of the hBN flakes was investigated by atomic force microscopy and transmission electron microscopy. The ambipolar transfer characteristics of the Si-hBN RFETs with different gating architectures showed a significant improvement in the device's electrical parameters due to the encapsulation and passivation of the nanowire with the hBN flake. Both n- and p-type characteristics measured through the top gate exhibited a reduction of hysteresis by 10-20 V and an increase in the on-off ratio (ION/IOFF) by 1 order of magnitude (up to 108) compared to the values measured for unpassivated nanowire. Specifically, the hBN encapsulation provided improved electrostatic top gate coupling, which is reflected in the enhanced subthreshold swing values of the devices. For a single nanowire, an improvement up to 0.97 and 0.5 V/dec in the n- and p-conduction, respectively, is observed. Due to their dynamic switching and polarity control, RFETs boast great potential in reducing the device count, lowering power consumption, and playing a crucial role in advanced electronic circuitry. The concept of mixed-dimensional RFET could further strengthen its functionality, opening up new pathways for future electronics.

Dengue overview: An updated systemic review.

Dengue is caused by the dengue virus (DENVs) infection and clinical manifestations include dengue fever (DF), dengue hemorrhagic fever (DHF), or dengue shock syndrome (DSS). Due to a lack of antiviral drugs and effective vaccines, several therapeutic and control strategies have been proposed. A systemic literature review was conducted according to PRISMA guidelines to select proper references to give an overview of DENV infection. Results indicate that understanding the virus characteristics and epidemiology are essential to gain the basic and clinical knowledge as well as dengue disseminated pattern and status. Different factors and mechanisms are thought to be involved in the presentation of DHF and DSS, including antibody-dependent enhancement, immune dysregulation, viral virulence, host genetic susceptibility, and preexisting dengue antibodies. This study suggests that dissecting pathogenesis and risk factors as well as developing different types of therapeutic and control strategies against DENV infection are urgently needed.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

Synergistic impacts of ferromanganese oxide biochar and optimized water management on reducing Cd accumulation in rice.

Ferromanganese oxide biochar composite (FMBC) is an efficient remediation material for cadmium -contaminated soils. However, the effect of FMBC under varied water managements on the remediation of Cd-polluted soil is unclear. In this study, we conducted both incubation and field experiments to investigate the combined effects of corn-stover-derived biochar modified with ferromanganese on the immobilization and uptake of Cd by rice under continuous aerobic (A), aerobic-flooded (AF), and flooded-aerobic (FA) water management regimes. The results showed that loading iron-manganese significantly increased the maximum sorption capacity (Qm) of Cd on FMBC (50.46 mg g-1) due to increased surface area, as compared to the pristine biochar (BC, 31.36 mg g-1). The results revealed that soil Eh and pH were significantly affected by FMBC and it's synergistic application with different water regimes, thus causing significant differences in the concentrations of DTPA-extractable Cd under different treatments. The lowest DTPA-extractable Cd content (0.28-0.46 mg-1) was observed in the treatment with FMBC (2.5 %) combined FA water amendment, which reduced the content of available Cd in soil by 2.63-28.4 %. Moreover, the treatments with FMBC-FA resulted the proportion of residual Cd increased by 22.2 % compared to the control. Variations in the content and fraction of Cd had a significant influence on its accumulation in the rice grains. The FMBC-FA treatments reduced the Cd concentration in roots, shoots and grains by 37.97 %, 33.98 %, and 53.66 %, respectively, when compared with the control. Predominantly because of the reduction in Cd biological toxicity and the improved soil nutrient content, the combined application increased the biomass and yield of rice to some extent. Taken together, the combination of the Fe-Mn modified biochar and flooded-aerobic water management may potentially be applied in Cd-polluted soil to mitigate the impacts of Cd on rice production.

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Non-intrusive RF sensing for early diagnosis of spinal curvature syndrome disorders.

The recent developments in communication and information ease people's lives to sit in one place and access any information from anywhere. However, the longevity of sitting and sitting in different postures raises the issues of spinal curvature. It necessitates a physical examination to identify the spinal illness in its early stages. This article aims to develop an intelligent monitoring framework for detecting and monitoring spinal curvature syndrome problems based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing actual patients. The proposed SDRF-based system identifies irregular spinal curvature syndrome and offers feedback signals when an incorrect posture is identified. We design the system using wireless university software-defined radio peripheral (USRP) kits to transmit and receive RF signals and record the wireless channel state information (WCSI) for kyphosis, Lordosis, and scoliosis spinal disorders. The statistical measures are extracted from the WCSI and apply machine learning algorithms to identify and classify the type of disorders. We record and test the system using 11 subjects with the spinal disorders kyphosis, Lordosis, and scoliosis. We acquire the WCSI, extract various statistical measures in terms of time and frequency domain features, and evaluate machine learning classifiers to identify and classify the spinal disorder. The performance comparison of the machine learning algorithms showed overall and each spinal curvature disorder recognition accuracy of more than 99%.

The role of economic growth, information technologies, and globalization in achieving environmental quality: a novel framework for selected Asian countries.

This study examines the impact of information and communication technologies (ICT), GDP growth, population, and globalization on the environmental quality of 31 Asian economies (i.e., categorized as lower middle-income, upper middle-income, and high-income groups Asian economies). This analysis employed the time series data from 1990 to 2018. The robust second-generation econometric technologies are used in this analysis. This study applied the Environmental Kuznets curve (EKC) premises under the extended "STIRPAT model" to add population and GDP (per capita) and information technologies (ICTs) by employing ecological footprint. To estimate, the estimators of this study used the CS-ARDL estimates, and for robustness check, this study used the augmented mean group (AMG) test. The co-integration test found the long-run association between ecological footprint and its main determinants. The results of CS-ARDL have confirmed the imperative role of information technologies in mitigating the ecological footprint in the higher, upper-middle, and lower-middle-income economies of Asian economies. The statistical findings of this study are robust to diagnostic tests and alternative estimation proxies and techniques. Moreover, policymakers need to identify the direction of the information technology-ecological footprint nexus through cooperation in combating climate change with financial assistance in the ICT sector.

On controlled Hamilton and Hamilton-Jacobi differential equations of higher-order.

In this paper, we investigate the nonlinear dynamics associated with controlled Lagrangians involving higher-order derivatives. More precisely, we establish the controlled higher-order Hamilton ordinary differential equations (ODEs) and Hamilton-Jacobi partial differential equation (PDE) for the considered class of Lagrangians governed by higher-order derivatives of the state variables. Moreover, we formulate and prove an invariance result with respect to the state variable. In addition, in order to validate the theoretical results and to highlight their effectiveness, some illustrative applications are presented.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization.

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

Electromagnetic Feet With Soft Toes for Adaptive, Versatile, and Stable Locomotion of an Inchworm-Inspired Pipe Crawling Robot.

Feet play an important role in the adaptive, versatile, and stable locomotion of legged creatures. Accordingly, several robotic research studies have used biological feet as the inspiration for the design of robot feet in traversing complex terrains. However, so far, no robot feet can allow legged robots to adaptively, versatilely, and robustly crawl on various curved metal pipes, including flat surfaces for pipe inspection. To address this issue, we propose here a novel hybrid rigid-soft robot-foot design inspired by the leg morphology of an inchworm. The foot consists of a rigid section with an electromagnet and a soft toe covering for enhanced adhesion to a metal pipe. Finite element analysis , performed under different loading conditions, reveals that due to its compliance, the soft toe can undergo recoverable deformation with adaptability to various curved metal pipes and plain metal surfaces. We have successfully implemented electromagnetic feet with soft toes (EROFT) on an inchworm-inspired pipe crawling robot for adaptive, versatile, and stable locomotion. Foot-to-surface adaptability is provided by the inherent elasticity of the soft toe, making the robot a versatile and stable metal pipe crawler. Experiments show that the robot crawling success rate reaches 100% on large diameter metal pipes. The proposed hybrid rigid-soft feet (i.e., electromagnetic feet with soft toes) can solve the problem of continuous surface adaptation for the robot in a stable and efficient manner, irrespective of the surface curvature, without the need to manually change the robot feet for specific surfaces. To this end, the foot development enables the robot to meet a set of deployment requirements on large oil and gas pipelines for potential use in inspecting various faults and leakages.

The role of green growth, green financing, and eco-friendly technology in achieving environmental quality: evidence from selected Asian economies.

Many countries are trying to achieve carbon neutrality targets by using environment-friendly technology and green growth. Thus, this analysis effort to identify the key role of green growth in improving the environmental quality. This study investigates the impact of green growth, income, environmental taxes, environment-friendly technology, renewable energy, and financial development in the context of 12 Asian economies over the period of 1990 to 2018. This study used the method of cross-section - augmented autoregressive distributed lag (CS-ARDL) to find out the impact of green growth and growth (GDP) on environment quality with some plausible variables under the scheme of environmental Kuznets curve (EKC). The study employed the method of CS-ARDL and for robustness the augmented mean group (AMG) method to find out the impact of green growth and GDP growth on environment quality with some plausible variables under the scheme of EKC. The results of CS-ARDL concluded that CO2 is significantly affected by GDP growth, green growth, and technological change in the context of Asian economies. The GDP square is inversely and the GDP growth is positively related to the CO2, indicating the presence of inverted U-shaped EKC in this region. But the inverse relationship between green growth and green growth square and concave EKC is observed in Asian countries. The study used the Dumitrescu and Hurlin panel test to gauge the causality between the variables. This study suggested that policymakers should focus on transforming the country's energy system in ways that will reduce energy-related CO2 emissions faster than previously expected.

Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology.

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.

Nexus between non-renewable energy production, CO2 emissions, and healthcare spending in OECD economies.

The present study investigates the dynamic relationships between non-renewable energy production from fossil resources, healthcare expenditures, and carbon dioxide (CO2) emissions in the OECD region. This study has used the balanced panel of 38 OECD countries spanning from 2008 to 2018. This study is employing panel vector auto-regression econometric approach based on generalized method of moment. The study reveals the following interesting outcomes: The response of energy production from fossil resources to healthcare expenditures is positive; energy production has a positive unidirectional causal relationship with CO2 emissions, whereas CO2 emissions have insignificant relation with energy production. There is a positive bidirectional relationship between healthcare spending and CO2 emissions, but there is no evidence that healthcare spending causes energy production. Furthermore, the outcomes present the essential policy consequences.

Mid- and far-infrared localized surface plasmon resonances in chalcogen-hyperdoped silicon.

Plasmonic sensing in the infrared region employs the direct interaction of the vibrational fingerprints of molecules with the plasmonic resonances, creating surface-enhanced sensing platforms that are superior to traditional spectroscopy. However, the standard noble metals used for plasmonic resonances suffer from high radiative losses as well as fabrication challenges, such as tuning the spectral resonance positions into mid- to far-infrared regions, and the compatibility issue with the existing complementary metal-oxide-semiconductor (CMOS) manufacturing platform. Here, we demonstrate the occurrence of mid-infrared localized surface plasmon resonances (LSPR) in thin Si films hyperdoped with the known deep-level impurity tellurium. We show that the mid-infrared LSPR can be further enhanced and spectrally extended to the far-infrared range by fabricating two-dimensional arrays of micrometer-sized antennas in a Te-hyperdoped Si chip. Since Te-hyperdoped Si can also work as an infrared photodetector, we believe that our results will unlock the route toward the direct integration of plasmonic sensors with the on-chip CMOS platform, greatly advancing the possibility of mass manufacturing of high-performance plasmonic sensing systems.

Biochar from constructed wetland biomass waste: A review of its potential and challenges.

Constructed wetland is considered a promising approach for water remediation due to its high efficiency, low operation costs, and ecological benefits, but the large amounts of wetland plant biomass need to be properly harvested and utilized. Recently, wetland plant derived biochar has drawn extensive attention owing to its application potential. This paper provides an updated review on the production and characteristics of wetland plant derived biochar, and its utilization in soil improvement, carbon sequestration, environmental remediation, and energy production. In comparison to hydrothermal carbonization and gasification, pyrolysis is a more common technique to convert wetland plant to biochar. Characteristics of wetland plant biochars varied with plant species, growth environment of plant, and preparation conditions. Wetland plant biochar could be a qualified soil amendment owing to its abundant nutrients. Notably, wetland plant biochar exhibited considerable sorption capacity for various inorganic and organic contaminants. However, the potentially toxic substances (e.g. heavy metal and polycyclic aromatic hydrocarbons) retained in wetland plant biochar should be noticed before large-scale application. To overcome the drawbacks from the scattered distribution, limited productivity, and seasonal operation of constructed wetlands, the economic feasibility of wetland plant biochar production system could be improved via using mobile pyrolysis unit, utilizing local waste heat, and exploiting all the byproducts. Future challenges in the production and application of wetland plant derived biochar include the continuous supply of feedstock and proper handling of potentially hazardous components in the biochar.

Fuzzy-interval inequalities for generalized preinvex fuzzy interval valued functions.

In this paper, firstly we define the concept of h-preinvex fuzzy-interval-valued functions (h-preinvex FIVF). Secondly, some new Hermite-Hadamard type inequalities (H-H type inequalities) for h-preinvex FIVFs via fuzzy integrals are established by means of fuzzy order relation. Finally, we obtain Hermite-Hadamard Fejér type inequalities (H-H Fejér type inequalities) for h-preinvex FIVFs by using above relationship. To strengthen our result, we provide some examples to illustrate the validation of our results, and several new and previously known results are obtained.

Controlled Silicidation of Silicon Nanowires Using Flash Lamp Annealing.

Among other new device concepts, nickel silicide (NiSix)-based Schottky barrier nanowire transistors are projected to supplement down-scaling of the complementary metal-oxide semiconductor (CMOS) technology as its physical limits are reached. Control over the NiSix phase and its intrusions into the nanowire is essential for superior performance and down-scaling of these devices. Several works have shown control over the phase, but control over the intrusion lengths has remained a challenge. To overcome this, we report a novel millisecond-range flash lamp annealing (FLA)-based silicidation process. Nanowires are fabricated on silicon-on-insulator substrates using a top-down approach. Subsequently, Ni silicidation experiments are carried out using FLA. It is demonstrated that this silicidation process gives unprecedented control over the silicide intrusions. Scanning electron microscopy and high-resolution transmission electron microscopy are performed for structural characterization of the silicide. FLA temperatures are estimated with the help of simulations.