Design and implementation of an innovative single-phase direct AC-AC bipolar voltage buck converter with enhanced control topology.

Direct AC-AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC-AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Prostate Segmentation in MRI Images using Transfer Learning based Mask RCNN.

INTRODUCTION: The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it's the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA. METHODS: These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples. RESULTS: This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate cancer detection. CONCLUSION: Lastly, limitations in current work, research findings, and prospects have been discussed.

Design of load-aware resource allocation for heterogeneous fog computing systems.

The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

A systematic review of robotic efficacy in coral reef monitoring techniques.

Coral reefs are home to a variety of species, and their preservation is a popular study area; however, monitoring them is a significant challenge, for which the use of robots offers a promising answer. The purpose of this study is to analyze the current techniques and tools employed in coral reef monitoring, with a focus on the role of robotics and its potential in transforming this sector. Using a systematic review methodology examining peer-reviewed literature across engineering and earth sciences from the Scopus database focusing on "robotics" and "coral reef" keywords, the article is divided into three sections: coral reef monitoring, robots in coral reef monitoring, and case studies. The initial findings indicated a variety of monitoring strategies, each with its own advantages and disadvantages. Case studies have also highlighted the global application of robotics in monitoring, emphasizing the challenges and opportunities unique to each context. Robotic interventions driven by artificial intelligence and machine learning have led to a new era in coral reef monitoring. Such developments not only improve monitoring but also support the conservation and restoration of these vulnerable ecosystems. Further research is required, particularly on robotic systems for monitoring coral nurseries and maximizing coral health in both indoor and open-sea settings.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

End of Affirmative Action-Who Is Impacted Most? Analysis of Race and Sex Among US Internal Medicine Faculty.

BACKGROUND: For over 50 years, the United States (US) used affirmative action as one strategy to increase diversity in higher education including medical programs, citing benefits including training future public and private sector leaders. However, the recent US Supreme Court ending affirmative action in college admissions threatens advancements in the diversity of medical college faculty. OBJECTIVE: Our study evaluated the demographic trends in Internal Medicine (IM) faculty in the US by assessing sex and race/ethnicity diversity to investigate who is likely to be impacted most with the end of affirmative action. DESIGN: Longitudinal retrospective analysis SUBJECTS: IM faculty from the Association of American Medical Colleges faculty roster from 1966 to 2021 who self-reported sex and ethnicity MAIN OUTCOMES: The primary study measurement was the annual proportion of women and racial/ethnic groups among IM faculty based on academic rank and department chairs. RESULTS: Although racial/ethnic diversity increased throughout the era of affirmative action, African American, Hispanic, and American Indian populations remain underrepresented. White physicians occupied > 50% of faculty positions across academic ranks and department chairs. Among the non-White professors, Asian faculty had the most significant increase in proportion from 1966 to 2021 (0.6 to 16.6%). The percentage of women increased in the ranks of professor, associate professor, assistant professor, and instructor by 19.5%, 27.8%, 25.6%, and 26.9%, respectively. However, the proportion of women and racial/ethnic minority faculty decreased as academic rank increased. CONCLUSION: Despite an increase in the representation of women and racial/ethnic minority IM faculty, there continues to be a predominance of White and men physicians in higher academic ranks. With the end of affirmative action, this trend has the danger of being perpetuated, resulting in decreasing diversity among IM faculty, potentially impacting patient access and health outcomes.

Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems.

In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this paper, we propose Learned-Sparse Bayesian Learning with Generalized Approximate Message Passing algorithm (L-SBL-GAMP) algorithm for designing optimal hybrid precoders/combiners suitable for mmWave Massive MIMO systems. The L-SBL-GAMP algorithm is an extension of the SBL-GAMP algorithm that incorporates a Deep Neural Network (DNN) to improve the system performance. Based on the nature of the training data, the L-SBL-GAMP can design the optimal Hybrid precoders/combiners, which enhances the spectral efficiency of mmWave massive MIMO systems. The proposed L-SBL-GAMP algorithm reduces the iterations, training overhead, and computational complexity compared to the SBL-EM algorithm. The simulation results unveil that the proposed L-SBL-GAMP provides higher achievable rates, better accuracy, and low computational complexity compared to the existing algorithm, such as Orthogonal Matching Pursuit (OMP), Simultaneous Orthogonal Matching Pursuit (SOMP), SBL-EM and SBL-GAMP for mmWave massive MIMO architectures.

Methamphetamine-induced dental caries: a review of the literature.

Clinical picture of patients taking methamphetamine for long duration includes rampant caries of the smooth surfaces of the whole dentition. The increasing use of methamphetamine in homosexuals is leading to the spread of HIV (human immunodeficiency virus). Easy availability and rapidly spreading nature of this drug (methamphetamine) results in worldwide increase of patients with medical and dental problems. Its effect on human dentition is highly damaging as patients with a beautiful smile begin to present a horrible picture of black, broken, and painful teeth within one year of methamphetamine use. Restoration of aesthetics and function of these teeth is not an easy task, and usually the first step to deal with this condition is counselling the patient to stop using this drug. Knowledge of methamphetamine-induced undesirable effects on the human body is important for the general dental practitioner as referral to mental health services is necessary in this condition.

Development of neutrosophic cubic hesitant fuzzy exponential aggregation operators with application in environmental protection problems.

The population growth and urbanization has caused an exponential increase in waste material. The proper disposal of waste is a challenging problem nowadays. The proper disposal site selection with typical sets and operators may not yield fruitful results. To handle such problems, the exponential aggregation operators based on neutrosophic cubic hesitant fuzzy sets are proposed. For appropriate decisions in a decision-making problem, it is important to have a handy environment and aggregation operators. Many multi attribute decision making methods often ignore the uncertainty and hence yields the results which are not reliable. The neutrosophic cubic hesitant fuzzy set can efficiently handle the complex information in a decision-making problem, as it combines the advantages of neutrosophic cubic set and hesitant fuzzy set. In this paper first we establish exponential operational laws in neutrosophic cubic hesitant fuzzy sets, in which the exponents are neutrosophic cubic hesitant fuzzy numbers and bases are positive real numbers. In order to use neutrosophic cubic hesitant fuzzy sets in decision making, we are developing exponential aggregation operators and investigate their properties in the current study. In many multi expert decision-making methods there are different decision matrices but same weighting vector for attributes. The results of a multi expert decision-making problem becomes more reliable if every decision expert has its own decision matrix along with his own weighting vector for attributes. In this study, we are developing multi expert decision-making method that uses different weights for an attribute corresponding to different experts. At the end we present two applications of exponential aggregation operators in environmental protection multi attribute decision making problems.

First report of Fusarium equiseti causing leaf spots of Bitter gourd (Momordica charantia) in Pakistan.

Bitter gourd (Momordica charantia L.) is an important vegetable crop of the Cucurbitaceae family widely cultivated in Pakistan and around the world. In October 2020, a nutrition management trial of Bitter gourd cv. Seminis-200) was conducted on an area of 10,860 sq. ft. (99×110 feet) at the Agricultural Research farm of Bahauddin Zakariya University, Multan (30.2601° N, 71.5158° E), Pakistan. Symptoms of large, brown necrotic leaf spots were observed on the leaves of bitter gourd vines. The disease started from the yellowing of leaves within the reticulate venation and turned brown. Irregular brown leaf spots coalesced to form large necrotic areas followed by foliar chlorosis then wilting that occurred very late. There were no crown rot symptoms although there was slight discoloration of roots and when cut longitudinally, browning of tissues was observed. The disease was assessed visually with 37% incidence which resulted in poor quality and yield in terms of reduced size and yellowing of fruit. Infected vines along with the roots were collected for the isolation of pathogen. A total of 34 leaves and 22 root samples were collected from the field for isolation. The leaf, collar and root portions were cut into 0.5 to 1 cm in length and surface disinfected with 1% sodium hypochlorite (NaOCl) for 2-3 minutes followed by washing twice with autoclaved distilled water and after drying, placed on potato dextrose agar (PDA) medium, and incubated at 25±2 °C for one week. The fungal colonies of fluffy white growth with light orange pigment were isolated. For morphological characterization, a total of 4 pure cultures were isolated from leaves, collar region and root by single spore technique on carnation leaf agar (CLA) medium after 15 days of incubation at 25±2℃. Curved and thick-walled macroconidia with elongated or pointed apical characteristic foot-shaped basal cells were produced in sporodochia. Macroconidia with 5-7 septa measured 22.50-41.80 µm × 2.90-4.20 µm (n = 60). Thick, brown with roughened walls and subglobose ellipsoidal chlamydospores were observed in clumps or chains with the dimension of 5.8 to 10.8 µm (n = 20). On morphological characteristics, the fungus was identified as Fusarium equiseti (Corda) Sacc. according to Leslie and Summerell (2006). Two single spore isolates were used for molecular identification by amplifying ribosomal DNA of the internal transcribed spacer (ITS) region with ITS1/ITS4 primers (White et al. 1990) and for ß-tubulin gene region, primers T1/Bt-2b (O'Donnell and Cigelnik, 1997) were used. The obtained sequences were deposited in GenBank with accession numbers MW880179 and MW880198 from the ITS region and BLAST search in GenBank showed 100 and 98.11% alignment with previously published sequences of F. equiseti with accessions OM992323.1and MT558569.1 respectively. Accession number OM867571from the ß-tubulin region showed 100% sequence similarity with F. equiseti with accession MN653163.1. For pathogenicity, macroconidia from 2-week-old cultures on CLA medium were harvested to prepare spore suspension (1 × 106 conidia/ml). Koch's postulates were confirmed on nine bitter gourd plants (cv. Seminis-200) by applying spore suspension of fungal inoculum at 3-4 leaf stage separately on leaves by automizer, on collar region after making incision spore suspension was applied and in the root zone, 20ml spore suspension was added whereas distilled water was used as a control with three replications. Plants were kept under controlled conditions in the greenhouse with 65% to 75% humidity and the temperature was maintained at 32±2 °C for one week. After 7-8 days, inoculated plants began to exhibit symptoms of brown, necrotic leaf spots on the leaves of bitter gourd vines followed by yellowing of leaves that eventually turned brown. Roots showed slight discoloration and browning of vascular bundles and finally, the plants wilted after four weeks. while control plants remained symptomless. The symptoms resembled those noticed in the field. The fungus was re-isolated from leaves, collar region and roots, followed by morphological identification, and finally confirmed as F. equiseti. To the best of our knowledge, this is the first report of a leaf spot caused by F. equiseti in a bitter gourd from Pakistan. If the disease is not managed properly, it may cause a drastic effect on yield under favorable environmental conditions. The pathogen may also damage other cucurbitaceous crops cultivated in the area.

Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms.

Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges.

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare.

Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.

Microfluidic device for the Separation of non-metastatic (MCF-7) and non-tumor (MCF-10A) breast cancer cells using AC Dielectrophoresis.

Dielectrophoretic devices are capable of the detection and manipulation of cancer cells in a label-free, cost-effective, robust, and accurate manner using the principle of the polarization of the cancer cells in the sample volume by applying an external electric field. This article demonstrates how a microfluidic platform can be utilized for high-throughput continuous sorting of non-metastatic breast cancer cells (MCF-7) and non-tumor breast epithelial cells (MCF-10A) using hydrodynamic dielectrophoresis (HDEP) from the cell mixture. By generating an electric field between two electrodes placed side-by-side with a micron-sized gap between them in an HDEP microfluidic chip, non-tumor breast epithelial cells (MCF-10A) can be pushed away, exhibiting negative DEP inside the main channel, while the non-metastatic breast cancer cells follow their course unaffected when suspended in cell medium due to having conductivity higher than the membrane conductivity. To demonstrate this concept, simulations were performed for different values of medium conductivity, and the sorting of cells was studied. A parametric study was carried out, and a suitable cell mixture conductivity was found to be 0.4 S/m. By keeping the medium conductivity fixed, an adequate AC frequency of 0.8 MHz was established, giving maximum sorting efficiency, by varying the electric field frequency. Using the demonstrated method, after choosing the appropriate cell mixture suspension medium conductivity and frequency of the applied AC, maximum sorting efficiency can be achieved.

Identification and primary distribution of Citrus viroid V in citrus in Punjab, Pakistan.

BACKGROUND: Citrus plants are prone to infection by different viroids which deteriorate their vigor and production. Citrus viroid V (CVd-V) is among the six citrus viroids, belongs to genus Apscaviroid (family Pospiviroidae) which induces symptoms of mild necrotic lesions on branches and cracks on trunk portion. METHODS AND RESULTS: A survey was conducted to evaluate the prevalence of CVd-V in core and non-core citrus cultivated areas of Punjab, Pakistan. A total of 154 samples from different citrus cultivars were tested for CVd-V infection by RT-PCR. The results revealed 66.66% disease incidence of CVd-V. Citrus cultivars Palestinia Sweet lime, Roy Ruby, Olinda Valencia, Kaghzi lime, and Dancy were identified as new citrus hosts of CVd-V for the first time from Pakistan. The viroid infection was confirmed by biological indexing on indicator host Etrog citron. The reported primers used for the detection of CVd-V did not amplify, rather showed non-specific amplification, which led to the designing of new primers. Whereas, new back-to-back designed primers (CVd-V AF1/CVd-V AR1) detected CVd-V successfully and obtained an expected amplified product of CVd-V with 294 bp. Sequencing analysis confirmed the new host of CVd-V showing 98-100% nucleotide sequence homology with those reported previously from other countries while 100% sequence homology to the isolates reported from Pakistan. Based on phylogenetic analysis using all CVd-V sequences in GenBank, two main CVd-V groups (I and II) were identified, and newly identified isolates during this study fall in the group I. CONCLUSION: The study revealed that there are some changes in the nucleotide sequences of CVd-V which made difficult for their detection using reported primers. All isolates of Pakistan showed high sequence homology with other isolates of CVd-V from Iran and USA whereas; the isolates from China, Japan, Tunisia, and Africa are distantly related. It is evident that CVd-V is spreading in all citrus cultivars in Pakistan.

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient's outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.