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Abstract Avian coccidiosis, caused by the protozoan Eimeria, leads to significant economic losses for the poultry industry. In this study, bacteriophages that specifically bind to the calcium-binding protein (EtCab) of Eimeria tenella were selected using a biopanning process with a pIII phage display library. The recombinant EtCab protein served as the ligand in this selection process. The binding ability of target phages to the EtCab protein or E. tenella sporozoites was evaluated. The role of peptides corresponding to target phages in inhibiting the invasion of E. tenella sporozoites into cells was analysed using flow cytometry. Subsequently, the phages were encapsulated in sodium alginate to protect them from degradation in gastric fluid, which has a low pH value. Chickens were orally administered both microencapsulated and non-microencapsulated phages, and the protective effects against E. tenella infection were assessed. The binding mechanism of these peptides to the EtCab protein was investigated through in silico analysis. The results indicated that three specific phages (Y, G, and V) could bind effectively to recombinant EtCab protein as well as to sporozoite proteins. All three peptides, particularly Y and G, demonstrated significant inhibition of sporozoite invasion into cells in vitro. Additionally, oral administration of the encapsulated phages Y and G provided a higher level of protection against Eimeria infection compared to encapsulated phage V and the unencapsulated phages. Molecular docking studies revealed that three peptides, particularly Y and G, efficiently bind to the EtCab protein through hydrogen bonds. This study provides a reference for developing small molecular drugs targeting coccidiosis.

In this study, phthalate inulin nanoparticles (PINs) were chemically modified and characterized. The internalization of PINs into the probiotic E. faecalis, which delivering Fiber2 protein of fowl adenovirus serotype 4 (FAdV-4), was investigated. The expression of the Fiber2 protein in E. faecalis was detected using western blot analysis. To protect recombinant E. faecalis from degradation of in the gastric acid environment, sodium alginate was used to encapsulate the bacteria. The survival ratio and release of E. faecalis in simulated gastrointestinal fluid was assessed. Oral administration of microencapsulated E. faecalis loaded with PINs (Micro-E/Fiber2-PINs) or inulin (Micro-E/Fiber2-inulin) was conducted, followed by an experimental challenge with FAdV-4 in chickens to evaluate immune responses and protection. The results showed the internalization of PINs into the bacteria promoted bacteria growth, and significantly improved the expression level of Fiber2. After incubation in simulated gastric fluid, the number of viable bacteria from the Micro-E/Fiber2-PINs group was significantly higher than that from the E. faecalis/Fiber2 group. The release of bacteria from the microcapsules was completed within 30 min. Animal experiments demonstrated that oral immunization with Micro-E/Fiber2-PINs significantly enhanced humoral and cellular immune responses, relieved inflammatory injury in FAdV-targeted organs, and improved survival rate of challenged chickens. This study presents promising potential for developing oral vaccines against pathogen infection.

Abstract Introduction Acne remains one of the most common inflammatory dermatoses seen worldwide. There are significant challenges when managing acne relating to a variety of factors, including (1) lack of consensus on the use of the numerous available grading systems and outcome measures, (2) appreciation of the numerous areas that relate to severity, (3) the chronic nature of acne which requires a longitudinal approach to management (including both facial and truncal disease), and (4) the need to target acne early to avoid physical and psychosocial scarring. Consideration of these aspects when managing acne should result in improved outcomes. Acne guidelines review the available evidence based on robust clinical trials and are usually supplemented with some expert opinion when evidence is not available. Methods In this paper, the UK Acne Working Group reflects on the latest National Institute for Health and Care Excellence (NICE) acne guidelines with a goal of providing additional practical insights. Conclusion The group have identified areas where new evidence has now become available since the formulation of the NICE acne guidelines. This publication considers newly approved acne medications in the UK, guidance on assessing acne severity, approaches to managing truncal acne, acne sequelae, and adult female acne with hormonal therapies.

Distributed generation (DG) systems are becoming more popular due to several benefits such as clean energy, decentralization, and cost effectiveness. Because the majority of renewable energy sources provide DC power, power electronic inverters are necessary for their conversion from DC to AC power. To fulfill this demand, the next generation power inverter employs innovative technologies while simultaneously assuring stability and resilience. This paper highlights the limitations of current inverter technology and points the way forward to the next generation of inverters that overcome those limitations. A more efficient, trustworthy, and system-resilient inverter employs new technology such as the internet of things (IoT). However, these new technologies expose the system to cyber-physical threats. This problem is being overcome through the application of artificial intelligence and machine learning. Initially, the present state of the inverter technology with its current challenges against grid resilience has been investigated in this paper. After that, the necessity of smart inverter and their impact on the power system has been reviewed to enhance grid resilience, stability, and adaptability. Finally, a directional pathway to the next generation inverter has been proposed by addressing the features, components requirement integration challenges, and possible solutions in this paper.

Abstract Background Left ventricular ejection fraction falls when the myocardium has already lost a significant portion of its functional capacity. There are conflicting data on whether diastolic dysfunction precedes systolic dysfunction after cardiotoxic chemotherapy. We aimed to study systolic and diastolic dysfunction after cardiotoxic chemotherapy and whether diastolic dysfunction can predict subsequent risk of systolic dysfunction. It was an observational prospective cohort study, and patients receiving cardiotoxic chemotherapy were included. Baseline, demographic, and clinical details were recorded. Echocardiographic measurements of left ventricular systolic function, global longitudinal strain, and diastolic function were noted at baseline, three months, and 6 months. Results We included eighty patients. The mean age of the patients was 54.92 ± 7.6 years, predominantly females (80%). The mean left ventricular ejection fraction fell from 64.92 ± 1.96 to 60.97 ± 4.94 at 6 months. Low ejection fraction was seen in 8 (10%) patients at 6 months. The mean global longitudinal strain (GLS) at baseline was − 18.81 ± 0.797 and fell to − 17.65 ± 2.057 at 6 months, with 12 (15%) patients having low GLS (

Abstract Background Fragile histidine triad (FHIT) has been documented to play a vital role in various cancers including acute lymphoblastic leukemia (ALL). Keeping in view the plausible role of FHIT gene, we aimed to examine DNA promoter hypermethylation and mRNA expression in ALL cases in Kashmir (North India). Methods A total of 66 cases of ALL were analyzed for FHIT mRNA expression and promoter methylation by qRT-PCR and Methylation Specific-PCR (MS-PCR) respectively. Results FHIT mRNA expression showed significantly decreased expression in ALL cases with mean fold change of 9.24 ± 5.44 as compared to healthy controls (p = 0.01). The pattern of FHIT deregulation in ALL cases differed significantly between decreased and increased expression (p

Introduction: Bloodstream Infections (BSI) are known to be responsible for significant mortality and morbidity. Cancer patients, being immunosuppressed, are more vulnerable to developing infections and often present with Febrile Neutropenia (FN). Aim: To determine the epidemiology, microbiology, and antibiotic susceptibility pattern among paediatric cancer patients admitted with FN. Materials and Methods: This ambispective study was conducted over a period of 5 years, from April 2018 to April 2023, in the Department of Paediatric Oncology in coordination with the Department of Microbiology at Sher-i-Kashmir Institute of Medical Sciences, Srinagar, Jammu and Kashmir, India. Isolates collected from April 2018 to April 2022 were retrospectively evaluated with respect to gender, age, and severity of neutropenia, whereas those analysed prospectively were from May 2022 to April 2023. Data from 328 patients presenting with 420 episodes of FN were obtained, including information on age, gender, the organism isolated, and antibiotic susceptibility pattern. Blood samples were collected in blood culture bottles and incubated at 35±2°C in the automated BACT/ALERT blood culture system. Positive bottles were further subjected to manual identification and antibiotic susceptibility testing of the bacterial isolates using the Kirby Bauer disk diffusion method. The results were interpreted according to the Clinical and Laboratory Standards Institute guidelines of 2023. All statistical analyses were performed using Statistical Package for Social Sciences (SPSS) software v.23. Results: A total of 328 patients were admitted over the five-year period, with 420 episodes of FN. The culture positivity rate was 81 out of 420 (19.2%). Among the patients, 50 (61.7%) were males, and 36 (45%) had moderate neutropenia at the time of admission. The most common malignancy was Acute Lymphoblastic Leukaemia (ALL), accounting for 25 cases (30.8%), followed by neuroblastoma with 13 cases (16%) and retinoblastoma with 1 case (1.2%). Coagulase-negative Staphylococci (CoNS) were isolated in 21 cases (25.9%). Furthermore, 12 (75%) of the Staphylococcus isolates and 12 (57.14%) of the CoNS were found to be methicillin-resistant. Among the gram-negative bacteria, Acinetobacter baumanii showed the highest resistance to the tested antibiotics. Conclusion: The majority of cases were moderately neutropenic, and CoNS was found to be the most common pathogen in BSI. None of the isolates were pan drug-resistant. Empiric antibiotic treatment for FN should be tailored to the locally prevalent pathogens and their susceptibility patterns. This approach will aid in appropriate infection control practices, ultimately reducing mortality and morbidity in affected patients.

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.

Bupropion is an atypical antidepressant indicated for the treatment of major depressive disorder (MDD), seasonal affective disorder (SAD), and smoking cessation. It is also used off-label for attention deficit hyperactivity disorder (ADHD). Its mechanism of action includes the selective norepinephrine and dopamine reuptake inhibitor (NDRI). The drug is available in immediate-release (IR), sustained-release (SR), and extended-release (XL) formulations. Common side effects are typically mild and include anxiety, insomnia, headache, dizziness, constipation, and nausea. Rarely, cutaneous hypersensitivity reactions may occur. We describe a 23-year-old man who developed severe and diffuse urticaria and angioedema 4 weeks after initiation of bupropion XL for MDD and ADHD. The bupropion was stopped, and he was treated with levocetirizine, diphenhydramine (oral and topical), and methylprednisolone with complete resolution of his symptoms within 2 weeks. Due to a good initial therapeutic response to the medication, a trial of bupropion SR was initiated. The patient again had a favorable therapeutic response without any dermatologic side effects.

Precision Agriculture (PA) techniques employing Internet of Things (IoT) can significantly improve crop yields and enhance productivity. Radio frequency identification (RFID) based IoT systems can enhance precision livestock farming by enabling livestock traceability and identification in large farms, but congestion is a major challenge. In such conditions, livestock estimation using RFID is a complex task because of high chances of tag collision due to significant increase in interference at the gates. Several anti-collision algorithms have been explored in recent past to address this issue with the aid of a single antenna. However, the throughput gains from a single antenna are limited particularly for dense scenarios like gates at a small ruminant farm. Therefore, we propose a multi-antenna RFID reader system with a dynamic frame slotted ALOHA (DFSA) anti-collision algorithm to address the tag collision problem by exploiting the spatial diversity of the multiple inputs and multiple outputs (MIMO) backscattering channel. The proposed technique is simulated in MATLAB for several tag population figures both with and without interference actions of various intensities. Results have shown that significant throughput gains can be achieved, in comparison with gains attained by anti-collision algorithms based on a single antenna. The simulation testbed enables us to evaluate the proposed technique for multiple receive antenna configurations.

The quickening propensity of growth within the areas of information and communications technology and energy networks has triggered the emergence of a central idea termed as Internet of Energy (IoE). Such a concept relates the internet of things, which is the term used in describing the usage of sophisticated digital control devices with the capability of transmitting data via IT systems. The concept of an interconnected energy network is being formed in the upcoming days through upgrades in the field of intelligent energy systems to control real-time energy optimization and management. The energy industry has had a sustained expansion, reaching the IoE milestone and continuing to this cutting-edge power system, which is the next generation of IoE. A directional pathway from the traditional power system to the IoE has been conducted in this research work by addressing the importance of integrating the smart transmission and communication infrastructure, smart metering, pricing and energy management scheme. A detailed investigation of the IoE with respect to technical angles, such as communication architecture, IoE on the supply & demand side, and IoE protocol based on fundamental elements and essential technologies has been carried out to indicate the blueprint and jurisdiction complexity. The integration, security and energy management challenges may deviate the performance of the IoE technology that has been focused with proper control issues and solutions. Finally, a directional framework to establish the next-generation IoE system has been constructed with future scopes to insure higher resiliency, cyber-security and stability.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot–human interactions will depend on soft robotics. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials’ fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.

In today’s world, it is anticipated that DTw and IoRT will transform the idea of digital healthcare and advancement of it what was previously unknown. DTw is (physical or virtual) machines or computer-based models capable of mimicking or “twinning” the lives of actual entities such as objects, humans, or human-related features. Since the advent of digital and smart healthcare, the world has swiftly integrated several technologies in healthcare to support improved patient service, extend life expectancy, and lower healthcare expenses.DTw and IoRT are promising technologies and great improvements in this field. In recent years, the concepts of IoRT and DTw have captured the interest of the surgical sector. The flawless integration of data between a physical and virtual machine in either direction is the best way to conduct smart surgery. Artificial intelligence, nanotechnology, various kinds of robotic equipment, and the Internet of Things (IoT) are our pillars of completing the task. Using the DTw technological framework and IoRT infrastructure, this review paper proposes a comparative overview of traditional and IoRT collaborated DTwT and executes an adaptive perspective, opportunities, and challenges in the healthcare sector, namely surgical advances. This is an important addition to digital surgeries and to improve healthcare operations. Our literature review has sorted them according to research areas: the technological innovation, the prospective and opportunities we have, problems facing the surgical industry today and in the future, and offers an evaluation of the enabling technologies for the betterment of employing DTw and IoRT for the developing and secure surgical sector for both patients and doctors.

The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry’s sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we’ll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.

Marek’s disease virus (MDV) causes a deadly lymphoproliferative disease in chickens, resulting in huge economic losses in the poultry industry. It has been suggested that MDV suppresses the induction of type I interferons and thus escapes immune control. Cholesterol 25-hydroxylase (CH25H), a gene that encodes an enzyme that catalyses cholesterol to 25-hydroxycholesterol (25-HC), is an interferon-stimulating gene (ISG) known to exert antiviral activities. Other oxysterols, such as 27-hydroxycholesterols (27-HC), have also been shown to exert antiviral activities, and 27-HC is synthesised by the catalysis of cholesterol via the cytochrome P450 enzyme oxidase sterol 27-hydroxylase A1 (CYP27A1). At 24 h post infection (hpi), MDV stimulated a type I interferon (IFN-α) response, which was significantly reduced at 48 and 72 hpi, as detected using the luciferase assay for chicken type I IFNs. Then, using RT-PCR, we demonstrated that chicken type I IFN (IFN-α) upregulates chicken CH25H and CYP27A1 genes in chicken embryo fibroblast (CEF) cells. In parallel, our results demonstrate a moderate and transient upregulation of CH25H at 48 hpi and CYP27A1 at 72hpi in MDV-infected CEF cells. A significant reduction in MDV titer and plaque sizes was observed in CEFs treated with 25-HC or 27-HC in vitro, as demonstrated using a standard plaque assay for MDV. Taken together, our results suggest that 25-HC and 27-HC may be useful antiviral agents to control MDV replication and spread.

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

The line segments of multiple (and nearly parallel) planar laser sources appearing in monocular video are objectively associated to their respective laser sources. Such association is a precursor to 3D surface digitization where each laser plane develops a 3D surface. In this work, the detected laser line segments are modeled as a weighted directed acyclic graph (wDAG), in which the graph nodes represent the line segments and their maximum path hopes from any source node represents the laser source label. However, due to noise and occlusions, the structure of the wDAG will normally vary from that expected in the ideal cases. Furthermore, it is essential to robustness to incorporate the association information of the previous frames in the new frame associations. The proposed work objectively modifies the current frame's wDAG under these constraints so the resulting wDAG becomes in an appropriate structure for the labeling of the detected line segments. Branch and bound (BnB) optimization has been a good choice for global optimization of combinatorial problems. Rather than approaching the optimization as a pure (or full) combinatorial problem, the proposed prioritizations of the branch modification actions are nearly optimal. The high priority actions will very likely lead to an optimal modification. This aspect is exploited by the proposed sparse variant of BnB (SBnB) optimization. It significantly reduces the number of the branches to be considered, in addition to those contributed by the bounding part of the BnB optimization. The proposed approach was tested on numerous monocular videos and plausible association results were efficiently achieved.

Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations.

This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fixed neural network trying to find the pattern in feature space. These strategies have successfully been applied to many applications for euclidean space data. Since graph data in a non-euclidean space does not follow any kind of order, these solutions can be applied to exploit the node relationships. Graph Neural Networks (GNNs) solve this problem by exploiting the relationships among graph data. Recent developments in computational hardware and optimization allow graph networks possible to learn the complex graph relationships. Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints. To encapsulate the importance of graph models, in this paper, we formulate a systematic categorization of GNN models according to their applications from theory to real-life problems and provide a direction of the future scope for the applications of graph models as well as highlight the limitations of existing graph networks.

Background. Denture stomatitis has been linked to the adhesion and proliferation of Candida albicans (C. albicans) on denture bases, which is a common and recurrent problem in denture wearers. The current study aimed to evaluate the effect of incorporating caffeic acid phenethyl ester (CAPE) into autopolymerized polymethyl methacrylate (PMMA) acrylic resin on C. albicans adhesion, surface roughness, and hardness as well as the correlation between tested properties. Methods. Autopolymerized acrylic resin discs (N = 100, 50/C. albicans adhesion; 50/C. albicans surface roughness and hardness test) were fabricated in dimensions 15 × 2.5 mm, samples were categorized into 5 groups (n = 10) based on CAPE concentrations; unmodified (control), 2.5, 5, 10 and 15% wt of acrylic powder. Specimens were stored in distilled water for 48 h at 37°C. C. albicans adhesion was evaluated via direct culture method. Profilometer and Vickers hardness tester were used for surface roughness and hardness measurement. Post hoc Tukey’s HSD with ANOVA test was performed to compare the difference of means amongst groups. P values were statistically significant at ≤0.05. Results. The addition of 2.5% of CAPE to PMMA has significantly reduced C. albicans counts in comparison to higher CAPE concentrations (p

The development of the world demands on the progress of electricity operation and quality enhancement. Microgrid (MG) is a solution that manages the additional need of electricity. The MG generates electricity mostly from renewable energy sources, e.g. solar, wind and hydro. Most renewable energy sources require energy storage system as they are highly unpredictable in nature. An inverter is needed to convert that stored DC power to AC. The presence of a capacitor within the grid provides a galvanic connection that varies the common mode (CM) voltage of the inverter. Thus, the leakage current as well as the total harmonic distortion (THD) of the grid voltage and current increases. The increase of the leakage current hampers the safe operation of the MG. Now-a-days transformerless inverter is gaining more attention because of high efficiency, low cost, less weight and the capability of eliminating the high frequency component from the inverter CM voltage. In addition, a controller inclusion with a transformerless inverter can maintain a near-seamless performance. This paper proposes a blended technique of an improved H5 topology with an Integral Linear Quadratic Gaussian (ILQG) controller for grid-connected MG operations, that reduces the inverter leakage current by keeping the CM voltage constant. Thus it has the capability to reduce the THD of voltage and current. A comparative analysis has been carried out between this technique and existing topologies with different modulation techniques that helps to find out the most optimum inverter package to eliminate the leakage current and reduce the THD of grid voltage and grid current to almost zero.

This paper proposes a hand free human-machine interaction (HMI) system to establish a novel way for communication between humans and computers. A regular interaction system based on the computer mouse puts the user's hand for too long in a pronation posture that increases inflammation in the wrist and hand. Additionally, the need for hand obstructs the use of computers for handicap people. In this paper, we develop a new pointing device for differently able people based on open and closed human eyes with inertia measurement that restrict to deal with carpal tunnel syndrome (CTS) for regular people and enables a novel way to interact with computers for the handicap people. The proposed system carries the human head gesture and eyes to perform the movement and clicking event of the mouse cursor. A combined three-axis accelerometer and gyroscope is used to detect the head gesture and translate it into the position of the mouse cursor on the computer monitor. To perform the left and right-clicking event, the user needs to shut down the left and right eye for a moment while opening another eye. This paper is also carried out the design of a deep learning approach to classify the individual openness and closeness of both human eyes with quite a high accuracy of 95.36% that ensures the comprehensive control over the clicking performance. The use of complementary filter removes the noise and drift from the obtained performance and confirms the smooth and accurate operation of the proposed device. An experimental validation is added to show the effectiveness of the proposed HMI system. The experimental details along with the performance evaluation prove that the proposed HMI system has extensive control over its performance for differently able people.

In this paper, we review the highly promising silica glass, fabricated as doped and undoped optical fiber for intended use in radiation dosimetry. The dosimetry techniques reviewed here, underpinned by intrinsic and extrinsic defects in silica glass, focus on Thermoluminescence (TL), Optically Stimulated Luminescence (OSL) and Radioluminescence (RL), with occasional references to the much more established Radiation Induced Attenuation (RIA). The other focus in this review is on the various materials that have been reported earlier as dopants and modifiers used in silica glass optical fiber radiation dosimeters. This article also elaborates on recently reported optical fiber structures, namely, cylindrical fibers, photonic crystal fibers and flat fibers, as well as dimensions and shapes used for optimization of dosimeter performance. The various types of optical fiber radiation dosimeters are subsequently reviewed for various applications ranging from medical dosimetry such as in external beam radiotherapy, brachytherapy and diagnostic imaging, as well as in industrial processing and space dosimetry covering a dynamic dose range from μGy to kGy. Investigated dosimetric characteristics include reproducibility, fading, dose response, reciprocity between luminescence yield to dose-rate and energy dependence. The review is completed by a brief discussion on limitations and future developments in optical fiber radiation dosimetry.

Abstract This paper describes the usefulness of renewable energy throughout the world to generate power. Renewable energy adds a remarkable scope in power system. Renewable energy sources act as the prime mover of a microgrid. The Microgrid is a small network of power system with distributed generation (DG) units connected in parallel. The integration challenges of renewable energy sources and the control of microgrid are described in this paper. The varied nature of DG system produces voltage and frequency deviation. The unknown nature of the load produces un-modeled dynamics. This un-modeled dynamic introduces measurable effects on the performance of the microgrid. This paper investigates the performance of the microgrid against different scenarios. The voltage of the microgrid is controlled by using different controllers and their results are also investigated. The performance of controllers is investigated using MATLAB/Simulink SimPowerSystems.

Background: The pathophysiology of keloid scars is still not fully understood and a universally reliable effective treatment has not been identified. Pharmacogenetics explores how drug response to a particular therapy can relate to genetic variations. Purpose: To investigate how pharmacogenetics could be applied to keloid scars and the relevance of this to clinical practice. Methods: We reviewed the literature and discuss our current knowledge of pharmacogenomics in the treatment of keloid scars. A literature search was performed using the terms ‘Pharmacogenetics’, ‘Pharmacogenomics’, ‘Keloid’ and ‘Scar’. We searched the PubMed, MEDLINE and EMBASE databases to find the relevant articles. Only articles in English were chosen. The level of evidence was evaluated and selected accordingly listing the studies with the highest level of evidence first. Results: Treatments including corticosteroid injections and 5-fluorouracil can be effective in some patients, but less so in others. Polymorphisms of the glucocorticoid receptor and variants of CCL2, YAP1 , miR-21-5p and NF-κβ might be responsible for different responses to treatments used in keloid scars such as 5-fluorouracil. Small molecule inhibitors might be utilised to target other implicated genes. Conclusion: Pharmacogenetics aims to produce the most efficacious patient outcomes while reducing adverse effects. Understanding the pharmacogenetics of keloid scars could lead to a new era of personalised medicine in the treatment of keloid scars. At present, there is some evidence (level 3b/4) to suggest genetic variations that are responsible to drug response in keloids, but further research in this field is required. Lay summary The varied response to similar therapeutic treatments in keloids has prompted the consideration of the role of genetic variants on response in the form of pharmacogenetics. Pharmacogenetics refers to drugs and their metabolism and action based on genetic influences. The ideal scenario would involve the selection of treatment based on the individual’s specific genetic variants to ensure maximum efficacy with minimal toxicity. Some evidence currently points to genetic variations in some keloid patients that might be of relevance to the treating clinician.

The invention of the nanotechnology adds a new branch to investigate and control the physical properties of matters at atomic level. The aim of this technology is to image the characteristics of metals, biological organs, and polymers. Scanning probe microscopy (SPM) opens a new branch to analysis the atomic properties of the matters. Atomic force microscopy (AFM), a branch of SPM, is a versatile tool of nanotechnology to image both conductive and non-conductive matters with high resolution. Commercial AFM uses raster scanning technique to produce image of the matters that is responsible for low scanning speed and image quality. The performances of AFM are hampered due to low bandwidth of the scanning unit and vertical Proportional-Integral (PI) controller and may damage the surface of the samples. Different non-raster scanning techniques such as sinusoidal, rotational, spiral, cycloid, and lissajous scanning have been proposed to overcome the limitations of raster scanning method by providing high scanning speed, image quality, and resolution. This paper presents a survey of raster and non-raster scanning methods for high speed AFM and provides a compression between them in term of scanning speed, bandwidth and highest achievable scanning frequency. The control techniques applied to the AFM for improving raster, sinusoidal, spiral, cycloid, and lissajous scanning methods are studied in this paper to find most optimum scanning technique for AFM.

In this paper, we briefly look at the latest state-ot-the-art in the domain of multi-input multi-output (MIMO) radio frequency identification (RFID) systems while detailing the work done in the domain of anti-collision, range enhancements, bit error rate (BER) improvements and security. Various passive ultra-high frequency (UHF) RFID implementations are considered that employ multiple antennas at the reader and single or multiple antennas at each tag. We look at several recent works those explored MIMO for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the BER as well as range extension. With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems under-perform in such settings causing low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision schemes. We provide a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved.

Background: Insomnia is a major medical problem that is often associated with low health status and increased health-care utilization. Therefore, we conducted this study to determine the frequency of insomnia in a population presenting to the primary healthcare (PHC) clinics for health problems other than sleep disorders. Methods: We interviewed 336 consecutive patients attending PHC face-to-face by trained medical students. Validated questionnaires were used to evaluate insomnia, sleep quality, and daytime sleepiness. The insomnia questionnaire classifies patients into three categories: (1) no insomnia, (2) Level I insomnia with the absence of daytime dysfunction, and (3) Level II insomnia with the presence of daytime dysfunction. Results: Level I insomnia was reported by 19.3% and Level II by 57.1%. Patients with insomnia were older and had worse sleep quality. Apart from a higher prevalence of hypertension among patients with insomnia, there was no difference in other comorbidities between those with insomnia and no insomnia. None of the included patients has reported his/her complaint of insomnia to the treating general practitioner (GP), and none of them was diagnosed with insomnia by the GP. Conclusion: Insomnia and daytime dysfunction are very common in primary care population. Despite the frequent visits of these patients to PHC, none of them has reported that he/she complains to his/her GP, and therefore, did not receive any treatment for insomnia. Education of GPs is necessary to improve recognition, diagnosis, and treatment of insomnia.

Abstrak Penelitian ini bertujuan untuk mengetahui korelasi antara pertumbuhan ekonomi dan tingkat kemiskinan di Provinsi Sumatera Utara. Metode analisis yang digunakan adalah analisis deskriptif dengan memanfaatkan software SPSS v.21. Penelitian ini menggunakan data PDRB, data pertumbuhan ekonomi dan angka kemiskinan daerah Provinsi Sumatera Utara periode 2006-2015. Data yang disajikan Badan Pusat Statistika (BPS) Sumatera Utara, mengindikasikan bahwa pertumbuhan ekonomi di provinsi Sumatera Utara dari tahun 2006-2015 mengalami fluktuasi, dimana mulai naik pada tahun 2007, dan kemudian mengalami penurunan dua tahun berikutnya. Pada tahun 2010 sampai tahun 2011 kembali mengalami kenaikan namun sampai tahun 2015 terus mengalami penurunan. Sedangkan, angka kemiskinan provinsi Sumatera Utara secara umum terus menurun dari tahun 2006 sampai dengan tahun 2012. Setelah mengalami kenaikan pada tahun 2013, turun dan meningkat kembali pada tahun 2015 penurunan terus terjadi. Hasil uji hipotesis yang dilakukan terhadap kedua variabel (Petumbuhan dan Kemiskinan) memperlihatkan bahwa nilai rtabel dengan df=(10-2) adalah 0.6319. Dan apabila dibandingkan dengan Pearson Corrrelation maka hasilnya adalah rhitung < rtabel. Dengan demikian, peneitian ini menyimpulkan bahwa hipotesis yang diterima adalah tidak ada hubungan yang signifikan antara pertumbuhan ekonomi dengan angka kemiskinan di provinsi Sumatera Utara.

BACKGROUND:Arboviruses are a cause of acute febrile illness and outbreaks worldwide. Recent outbreaks of Chikungunya virus (CHIKV) in dengue endemic areas have alarmed clinicians as unique clinical features differentiating CHIKV from Dengue virus (DENV) are limited. This has complicated diagnostic efforts especially in resource limited countries where lab testing is not easily available. Therefore, it is essential to analyse and compare clinical features of laboratory confirmed cases to assist clinicians in suspecting possible CHIKV infection at time of clinical presentation. METHODOLOGY:A prospective point prevalence study was conducted, with the hypothesis that not all patients presenting with clinical suspicion of dengue infections at local hospitals are suffering from dengue and that other arboviruses such as Chikungunya, West Nile viruses, Japanese Encephalitis virus and Zika virus are co-circulating in the Sindh region of Pakistan. Out-patients and hospitalized (in-patients) of selected district hospitals in different parts of Sindh province of Pakistan were recruited. Patients with presumptive dengue like illness (Syndromic diagnosis) by the treating physicians were enrolled between 2015 and 2017. Current study is a subset of larger study mentioned above. Here-in we compared laboratory confirmed cases of CHIKV and DENV to assess clinical features and laboratory findings that may help differentiate CHIKV from DENV infection at the time of clinical presentation. RESULTS:Ninety-eight (n = 98) cases tested positive for CHIKV, by IgM and PCR and these were selected for comparative analysis with DENV confirmed cases (n = 171). On multivariable analysis, presence of musculoskeletal [OR = 2.5 (95% CI:1.6-4.0)] and neurological symptoms [OR = 4.4 (95% CI:1.9-10.2)], and thrombocytosis [OR = 2.2 (95% CI:1.1-4.0)] were associated with CHIKV infection, while atypical lymphocytes [OR = 8.3 (95% CI:4.2-16.7)] and thrombocytopenia [OR = 8.1 (95% CI:1.7-38.8)] were associated with DENV cases at time of presentation. These findings may help clinicians in differentiating CHIKV from DENV infection. CONCLUSION:CHIKV is an important cause of illness amongst patients presenting with acute febrile illness in Sindh region of Pakistan. Arthralgia and encephalitis at time of presentation among patients with dengue-like illness should prompt suspicion of CHIKV infection, and laboratory confirmation must be sought.

Cyberattacks (CAs) on modern interconnected power systems are currently a primary concern. The development of information and communication technology (ICT) has increased the possibility of unauthorized access to power system networks for data manipulation. Unauthorized data manipulation may lead to the partial or complete shutdown of a power network. In this paper, we propose a novel security unit that mitigates intrusion for an interconnected power system and compensates for data manipulation to augment cybersecurity. The studied two-area interconnected power system is first stabilized to alleviate frequency deviation and tie-line power between the areas by designing a fractional-order proportional integral derivative (FPID) controller. Since the parameters of the FPID controller can also be influenced by a CA, the proposed security unit, named the automatic intrusion mitigation unit (AIMU), guarantees control over such changes. The effectiveness of the AIMU is inspected against a CA, load variations, and unknown noises, and the results show that the proposed unit guarantees reliable performance in all circumstances.

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

Osteoarthritis (OA) is a major public concern as it is one of the leading causes of morbidity and lays a huge medical and economic burden on health resources. Early detection of OA has been a clinical challenge as early signs of joint inflammation are often not evidently identifiable on routine radiographic images. This presents a dire unmet medical need for a biomarker, which could detect early signs of joint inflammation much before irreversible joint damage and radiographic changes set in. Besides, the treatment of OA has remained mainly symptomatic. A disease modifying OA drug (DMOAD), which can act as targeted anti-OA therapy has not been able to receive regulatory approval yet. The clinical development of a DMAOD too warrants the need of a biomarker; which can act as a surrogate clinical endpoint used to monitor therapeutic efficacy and to validate a clinically meaningful change within the restricted time frame of a clinical study. In this regard, the current review focuses on cartilage oligomeric matrix protein (COMP), a potential OA biomarker which has shown significant clinical promise as a tool for early detection, therapeutic monitoring, prognostication and drug development for OA. This brief review is pivoted around the findings of selected relevant publications from PubMed indexed journals.

The utilization of high accuracy sensors in harsh environments has been limited by the temperature constraints of the control electronics that must be co-located with the sensor. Several methods of remote interrogation for resonant sensors are presented in this paper which would allow these sensors to be extended to harsh environments. This work in particular demonstrates for the first time the ability to acoustically drive a silicon comb drive resonator into resonance and electromagnetically couple to the resonator to read its frequency. The performance of this system was studied as a function of standoff distance demonstrating the ability to excite and read the device from 22 cm when limited to drive powers of 30 mW. A feedback architecture was implemented that allowed the resonator to be driven into resonance from broadband noise and a standoff distance of 15 cm was demonstrated. It is emphasized that no junction-based electronic device was required to be co-located with the resonator, opening the door for the use of silicon-based, high accuracy MEMS devices in high temperature wireless applications.

The incidence of melanoma continues to increase year on year. With better surgical techniques and medical management, greater numbers of organ transplants are being performed annually with much longer graft survival. The authors review our current understanding of the incidence of melanoma amongst organ transplant recipients, outcomes compared to the immunocompetent population, and management strategies in this burgeoning group.