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Interpersonal touch plays a crucial role in human communication, development, and wellness. Mediated interpersonal touch (MIT), a technology to distance or virtually simulated interpersonal touch, has received significant attention to counteract the negative consequences of touch deprivation. Studies investigating the effectiveness of MIT have primarily focused on self-reporting or behavioral correlates. It is largely unknown how MIT affects neural processes such as interbrain functional connectivity during human interactions. Given how users exchange haptic information simultaneously during interpersonal touch, interbrain functional connectivity provides a more ecologically valid way of studying the neural correlates associated with MIT. In this study, a palm squeeze task is designed to examine interbrain synchrony associated with MIT using EEG-based hyperscanning methodology. The phase locking value (PLV) index is used to measure interbrain synchrony. Results demonstrate that MIT elicits a significant increase in alpha interbrain synchronization between participants' brains. Especially, there was a significant difference in the alpha PLV indices between no MIT and MIT conditions in the early stage (130-470 ms) of the interaction period (t-test, p < 0.05). Given the role that alpha interbrain synchrony plays during social interaction, a significant increase in PLV index during MIT interaction seems to indicate an effect of social coordination. The findings and limitations of this study are further discussed, and perspectives on future research are provided.

IntroductionThermal feedback technologies have been explored in human-computer interaction to provide secondary information and enhance the overall user experience. Unlike fast-response haptic modalities such as vibration and force feedback, the human brain's processes associated with thermal feedback are not fully understood.MethodsIn this study, we utilize electroencephalography (EEG) brain imaging to systematically examine the neural correlates associated with a wide range of thermal stimuli, including 9, 15, 32, and 42°C, during active touch at the fingertip. A custom experimental setup is developed to provide thermal stimulation at the desirable temperature levels. A total of 30 participants are recruited to experience the four levels of thermal stimulation by actively touching a thermal stimulation unit with the index finger while recording brain activities via EEG. Time-frequency analysis and power spectral density (PSD) of the EEG data are utilized to analyze the delta, theta, alpha, beta, and gamma frequency bands.ResultsThe results show that the delta, theta, and alpha PSDs of 9 and 15°C stimuli are significantly higher than the PSDs of 32 and 42°C in the right frontal area during the early stage of the stimulation, from 282 ms up to 1,108 ms (One-way ANOVA test, Holm-Bonferroni correction, p < 0.05). No significant differences in PSDs are found between 9 and 15°C thermal stimuli or between 32 and 42°C thermal stimuli.DiscussionThe findings of this study inform the development of thermal feedback system in human-computer interaction.

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts.Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts’ assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet.Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models.Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.

Climatic conditions intricately influence agriculture, impacting optimal crop growth and reproduction through environmental factors. This study introduces a technological solution leveraging the Internet of Things (IoT), with Raspberry Pi 3 as the central controller. The system systematically collects crucial data on temperature, humidity, and soil moisture, ensuring the quality of crops. Subsequently, this information is used to formulate systematic crop watering plans. By efficiently using Raspberry Pi, the system regulates and monitors crops, contributing to water conservation by directing it precisely to the crops. This technology offers a practical and efficient approach to crop management, especially during challenging agricultural periods.

The ubiquity of Internet of Things (IoT) devices has prompted security concerns, particularly in the face of evolving botnet attacks. This paper investigates the impact of botnet attacks on IoT devices and proposes a network-based detection and prevention system employing signature and anomaly-based mechanisms. Notably, our methodology extends beyond traditional detection, focusing on proactively impeding bot creation. Leveraging a Linux-based distributed system, Security Information and Event Management (SIEM) tools, and custom rules, our approach encompasses distinct phases Preprocessing, Network Security Monitoring, Rule-based IDS System, and Analysis. Experimental results with diverse PCAP files demonstrate the efficacy of custom rules, significantly enhancing alert counts for various security aspects, including network trojan detection and privacy violations. The significant finding is the substantial increase in alert counts after the integration of custom rules, exemplified in the 1.1 GB PCAP file scenario. Network trojan detection surged from 585 to 988, emphasizing the heightened efficacy of rule-based measures. Privacy breaches and bad traffic alerts also experienced significant increments, showcasing the system’s improved sensitivity and responsiveness. This finding reinforces the pivotal role of custom rules in fortifying IoT network security comprehensively.

AbstractDrawing upon dynamic capability theory, this article tested high performance organization (HPO) framework with relation to organizations’ performance and explore causes of tested results in public sector of Pakistan. An explanatory sequential mix method was applied whereby the partial least square structural equation modeling method was used to test the direct effect of the HPO framework on organizations’ performance while qualitative method was used to explore causes of poor factors resulted from HPO framework. The results show that the HPO Framework is valid for PSOs. However, this study found weak predictive relevance and small effect size of HPO framework in public organizations. The overwhelming and compelling causes identified are mechanistic structure, lack of commitment and resources, and lack of ethical and visionary leadership in qualitative strand of the study. This study has several contributions; firstly, it explains whether HPO framework is valid to be applied in public organizations, secondly, this study answer question: how five factors criteria of HPO framework influence public organization performance? Thirdly, this study explains HPO framework application to improve organization status from low performance to high performance through ethical and visionary leadership under the lens of dynamic capability theory. This study provides a clue for public managers of developing countries to produce models of high performance and to generate new ideas and information to realize the power of high performance. High performance factors and models will promote sustainable performance of organizations which will benefit employees, organizations and society overall.

While text passwords are still a pervasive authentication tool, their inadequacies are well recognized. Poorly chosen and weak passwords are the main reason behind security breaches. Multiple authentication techniques such as biometric, token-based, and knowledge-based authentication have been developed to overcome data leaks. However, acceptance of these authenticating techniques is complicated, and users find them hard to use. Microbusinesses, defined as having less than two employees, usually have very limited resources including budget, information security expertise and updated computer systems to fulfil the security requirements. Many microbusiness owners use the same information technology as in the home but for more sophisticated commercial reasons. An effective and easy way for microbusinesses to add an extra protection layer to their systems and passwords is through the use of password managers. This paper examines the useability and ease of use of the password manager software. We extended the Technology Acceptance Model (TAM) and tested the mediating role of self-efficacy on TAM's relationship with computer security usage. A sample of 420 microbusiness owners was taken to test the relationships among the variables through an online web-based survey. The results confirmed that self-efficacy plays a vital role in the user acceptance of password managers and reported its mediating role between perceived ease of use, perceived usefulness, and computer security usage.

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance.

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Cryptocurrency has revolutionized the economic system of the world. It provides a new and innovative means of exchange that has speedily invaded the financial market trends and changed the traditional cash world. However, consumers have low acceptability for blockchain-based cryptocurrency due to increasing online scams and the absence of a regulatory framework. There is also a misconception about its usage on many platforms, which has created a clear gap in the literature to address this issue. Therefore, the current study intends to investigate the effect of technology awareness on the behavioral intention of crypto users through perceived factors (usefulness, ease of use, risk). It also empirically examines the moderating role of government support on these indirect paths. The underlying framework is investigated by surveying 333 respondents from the Z generation. Results revealed that perceived factors (usefulness, ease of use, risk) mediate the relationship between technology awareness and behavioral intention. Furthermore, government support strengthens the indirect relationship of technology awareness on behavioral intention through technology acceptance determinants, such that the effect of technology awareness on behavioral intention through perceived factors (usefulness, ease of use, risk) is more assertive when government support is high. The findings will provide a new dimension to different financial bodies implementing monetary policy and highlight the need to adopt innovative digital technologies in Pakistan.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes’ scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target’s service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes.

Abstract Background Whiteflies (Bemisia tabaci) are phloem sap-sucking pests that because of their broad host range and ability to transmit viruses damage crop plants worldwide. B. tabaci are now known to be a complex of cryptic species that differ from each other in many characteristics such as mode of interaction with viruses, invasiveness, and resistance to insecticides. Asia II 1 is an indigenous species found on the Indian sub-continent and south-east Asia while the species named as Middle East Asia Minor 1 (MEAM1), likely originated from the Middle-East and has spread worldwide in recent decades. The purpose of this study is to find genomic differences between these two species. Results Sequencing of the nuclear genome of Asia II 1 with Illumina HiSeq and MiSeq generated 198.90 million reads that covers 88% of the reference genome. The sequence comparison with MEAM1 identified 2,327,972 SNPs and 202,479 INDELs. In Total, 1294 genes were detected with high impact variants. The functional analysis revealed that some of the genes are involved in virus transmission including 4 genes in Tomato yellow leaf curl virus (TYLCV) transmission, 96 in Tomato crinivirus (ToCV) transmission, and 14 genes in insecticide resistance. Conclusions These genetic differences between Asia II 1 and MEAM1 may underlie the major biological differences between the two species such as virus transmission, insecticide resistance, and range of host plants. The present study provides new genomic data and information resources for Asia II 1 that will not only contribute to the species delimitation of whitefly, but also help in conceiving future research studies to develop more targeted management strategies against whitefly.

Tactile sensation largely influences human perception, for instance when using a mobile device or a touch screen. Active touch, which involves tactile and proprioceptive sensing under the control of movement, is the dominant tactile exploration mechanism compared to passive touch (being touched). This paper investigates the role of friction stimulation objectively and quantitatively in active touch tasks, in a real human-computer interaction on a touch-screen device. In this study, 24 participants completed an active touch task involved stroking the virtual strings of a guitar on a touch-screen device while recording the electroencephalography (EEG) signal. Statistically significant differences in beta and gamma oscillations in the middle frontal and parietal areas at the late period of the active touch task are found. Furthermore, stronger beta event-related desynchronization (ERD) and rebound in the presence of friction stimulation in the contralateral parietal area are observed. However, in the ipsilateral parietal area, there is a difference in beta oscillation only at the late period of the motor task. As for implicit emotion communication, a significant increase in emotional responses for valence, arousal, dominance, and satisfaction is observed when the friction stimulation is applied. It is argued that the friction stimulation felt by the participants' fingertip in a touch-screen device further induces cognitive processing compared to the case when no friction stimulation is applied. This study provides objective and quantitative evidence that friction stimulation is able to affect the bottom-up sensation and cognitive processing.

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller's bandwidth and find that the system throughput decreases when the controller's bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems. [ABSTRACT FROM AUTHOR]

In this work, the process of ethane recovery plant was simulated for the purpose of Front End Engineering Design. The main objective is to carry out a series of simulation using Aspen HYSYS to compare recovery of ethane from Joule Thomson (JT) Valve, Turbo-Expander and Twister Technology. Twister technology offers high efficiency, more ethane recovery and lower temperature than JT valve and turbo-expander process. It lies somewhere between isenthalpic and isentropic process due to its mechanical configuration. Three processes were compared in terms of recovery of ethane. To conduct the simulations, a real gas plant composition and design data were utilized to perform the study for comparison among chosen technologies which are available for ethane recovery. The same parameters were used for the comparisons. Effect of operating conditions including pressure, temperature, and flow rate as well as carbon dioxide on the recovery of ethane was examined.

Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes' scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traffic load, for instance. DoS or Distributed Denial of service (DDoS) attacks are malicious attempts to disrupt a targeted service by flooding the target's service with network packets. Constant observation of the network traffic is extremely important for the early detection of such attacks. Extended Berkeley Packet Filter (eBPF) and eXpress Datapath (XDP) are advanced technologies in the Linux kernel that perform high-speed packet processing. In the case of Kubernetes, eBPF and XDP can be used to protect against DDoS attacks by enabling fast and efficient network security policies. For example, XDP can be used to filter out traffic that is not authorized to access the Kubernetes cluster, while eBPF can be used to monitor network traffic for signs of DDoS attacks, such as excessive traffic from a single source. In this research, we utilize eBPF and XDP to build a detection and observation mechanism to filter out malicious content and mitigate a Denial of Service attack on Kubernetes. [ABSTRACT FROM AUTHOR]

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days. [ABSTRACT FROM AUTHOR]

The core objective of this paper is to examine the relationship between the Shari’ah perceptions and the customers’ switching intentions in both Takāful and Conventional Insurance. In today’s world, most of the businesses are focusing on customer retention rather than the customer acquisition. However, our current knowledge of insurance systems is unable to address the crucial issues of target achieving marketing tactics. Takāful is firming its roots in Muslim communities across the globe. As per customer satisfaction been considered as an important factor of customer switching, we have gone further and investigated the mediating role of customer satisfaction to customer switching intentions. The sample size of 400 customers of major life Insurance and family Takāful companies was taken using convenient sampling method. Mediation analysis was performed to test the mediating role of customer satisfaction in Family Takāful and Life Insurance. The impact of Shari’ah perceptions in life insurance and family Takāful was found significant. Furthermore, customer switching intentions were partially mediated by customer satisfaction. This study has some important implications for the insurance/Takāful practitioners and policymakers. It urges the Insurance/Takāful operators to maintain and develop strong long-term relationships with the customers to reduce their switching intentions. For Takāful practitioners, there is a need to develop clear understanding of Takāful in the minds of customers as well as general public.

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic. [ABSTRACT FROM AUTHOR]

Over the past decade RNA interference (RNAi) technology has emerged as a successful tool not only for functional genomics, but in planta expression of short interfering RNAs (siRNAs) that could offer great potential for insect pest management. The diet of insects feeding exclusively on phloem sieves contains water and sugars as main components, and the uptake of the liquid food greatly depends on the osmotic pressure within the insect body. Based on this physiological mechanism, transgenic plants of Nicotiana tabacum were generated expressing double stranded RNA (dsRNA) against both aquaporin (AQP) and a sucrase gene, alpha glucosidase (AGLU). These two genes are involved in osmotic pressure maintenance particularly in sap sucking insects, and the aim was to disrupt osmoregulation within the insect ultimately leading to mortality. Real time quantitative PCR (RT-qPCR) was performed to assess the suppression of gene expression in Bemisia tabaci (B. tabaci) and mortality was recorded during transgenic tobacco feeding bioassays. Feeding of insects on plants expressing dsRNA significantly reduced the transcript level of the target genes in B. tabaci after six days of feeding and more than 70% mortality was observed in B. tabaci fed on transgenic plants compared to the control plants. Our data shows that down-regulation of genes related to osmoregulation may find practical applications for the control of this important pest in cotton and other crops.

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent. [ABSTRACT FROM AUTHOR]

This article proposes a highly efficient current-fed half-bridge resonant converter over a wide range of the output voltages. By adding a bidirectional switch across center nodes of the voltage doubler and applying corresponding switching modulation, the converter accomplishes almost zero-voltage switching at the turn-off instant under the wide output voltage variations. Moreover, the circuit experiences very little instantaneous reactive current even when the output voltage varies widely. Primary-side duty cycle fixed at 0.5 balances the voltages across the capacitors at the primary side and at the secondary side each irrespective of the output voltage variation; it also limits the voltage stress at the active power components. Therefore, the proposed converter delivers high efficiency and compact component size. We fabricated a 400-W experimental prototype to validate the superior properties of the proposed converter. [ABSTRACT FROM AUTHOR]

Cervical spondylotic myelopathy can result from degenerative cervical spondylosis, herniated disk material, osteophytes, redundant ligamentum flavum, or ossification of the posterior longitudinal ligament. Surgical intervention for multi-level myelopathy aims to decompress the spinal cord and maintain stability of the cervical spine. Laminoplasty was major surgical advancement as laminectomy resulted in kyphosis and unsatisfactory outcomes. Hirabayashi popularised the expansive open door laminoplasty which was later modified several surgeons. Laminoplasty has changed the way surgeons approach multilevel cervical spondylotic myelopathy.

This article proposes a high step-up quasi-resonant converter that minimizes switching loss over a wide range of input voltages. By employing a bidirectional switch and corresponding switching modulation, the proposed converter achieves the main switch with almost zero voltage, and therefore incurs very little switching loss at all active switches. Moreover, no instantaneous reactive current flows through the circuit under wide variations of input voltages and loads. The duty cycle of primary-side switches is fixed to 0.5; then, the input and clamp capacitor voltages become identical, and thereby the following resonant capacitor voltages remain in balance. As a consequence, the proposed converter becomes compact and achieves high boost-ratio and high efficiency over a wide range of input voltages. The operating principle of the converter is presented along with its design procedure. 400-W/380-V prototype of the proposed converter has been implemented and tested at 40–60 V to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This article presents a bidirectional soft-switching push–pull resonant converter that is highly efficient over a wide range of battery voltages. It is derived by integrating a current-fed push–pull circuit and an active voltage doubler circuit with a bidirectional switch. The converter operates as a pulsewidth modulation (PWM) current-fed push–pull resonant-boost converter in the forward direction, but as a PWM half-bridge resonant converter in the backward direction. The most attractive feature of the proposed converter is that, by employing the bidirectional switch and corresponding switching modulation, it can achieve almost ZVS at turn-off instants under wide variations of battery voltages. Therefore, the proposed converter achieves high efficiency even during high-frequency operation. Moreover, no instantaneous reactive current flows through the circuit under wide variations of battery voltages and loads. Furthermore, the proposed bidirectional resonant converter has a high conversion ratio in both power flow directions without using a transformer that has high turns-ratio. The operating principles and characteristics of the proposed bidirectional resonant converter are presented in detail. Experimental results using a 1-kW prototype verified the feasibility and efficiency of the proposed converter. [ABSTRACT FROM AUTHOR]

Over the past decade RNA interference (RNAi) technology has emerged as a successful tool not only for functional genomics, but in planta expression of short interfering RNAs (siRNAs) that could offer great potential for insect pest management. The diet of insects feeding exclusively on phloem sieves contains water and sugars as main components, and the uptake of the liquid food greatly depends on the osmotic pressure within the insect body. Based on this physiological mechanism, transgenic plants of Nicotiana tabacum were generated expressing double stranded RNA (dsRNA) against both aquaporin (AQP) and a sucrase gene, alpha glucosidase (AGLU). These two genes are involved in osmotic pressure maintenance particularly in sap sucking insects, and the aim was to disrupt osmoregulation within the insect ultimately leading to mortality. Real time quantitative PCR (RT-qPCR) was performed to assess the suppression of gene expression in Bemisia tabaci (B. tabaci) and mortality was recorded during transgenic tobacco feeding bioassays. Feeding of insects on plants expressing dsRNA significantly reduced the transcript level of the target genes in B. tabaci after six days of feeding and more than 70% mortality was observed in B. tabaci fed on transgenic plants compared to the control plants. Our data shows that down-regulation of genes related to osmoregulation may find practical applications for the control of this important pest in cotton and other crops. [ABSTRACT FROM AUTHOR]

The whitefly Bemisia tabaci (Genn.) is a pest and vector of plant viruses to crop and ornamental plants worldwide. Using RNA interference (RNAi) to down regulate whitefly genes by expressing their homologous double stranded RNAs in plants has great potential for management of whiteflies to reduce plant virus disease spread. Using a Tobacco rattle virus-derived plasmid for in planta transient expression of double stranded RNA (dsRNA) homologous to the acetylcholinesterase (AChE) and ecdysone receptor (EcR) genes of B. tabaci, resulted in significant adult whitefly mortality. Nicotiana tabacum L. plants expressing dsRNA homologous to B. tabaci AChE and EcR were constructed by fusing sequences derived from both genes. Mortality of adult whiteflies exposed to dsRNA by feeding on N. tabacum plants, compared to non-dsRNA expressing plants, recorded at 24-hr intervals post-ingestion for three days, was >90% and 10%, respectively. Analysis of gene expression by real time quantitative PCR indicated that whitefly mortality was attributable to the down-regulation of both target genes by RNAi. Results indicated that knock down of whitefly genes involved in neuronal transmission and transcriptional activation of developmental genes, has potential as a bio-pesticide to reduce whitefly population size and thereby decrease virus spread. [ABSTRACT FROM AUTHOR]