A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques.

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network's efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

Managing the low carbon transition pathways through solid waste electricity.

The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023-2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053.

Enhancing the driving experience of smart city users based on content delivery framework for intelligent transportation systems.

Integration of several communication technologies that facilitate user access contributes to the rapid development of the smart city notion. Intelligent transportation systems (ITS) are introduced as part of smart city development to provide drivers with enhanced communication and information-sharing capabilities. The article introduces a novel ITS content delivery framework (CDF) that addresses communication outage issues. CDF-ITS uses End-to-end decision-making system modelling to examine factors such as communication, content distribution, and vehicle features. A suitable communication slot for vehicular users is determined by processing these characteristics based on outage time and variables. By allocating time-aware communication slots according to the classification of the propagation factor, outage problems may be reduced. End-to-end decision-making is used for classification and vehicle attribute balance, allowing immediate responses to user requests. The experimental outcomes show that the latency of 0.297 s, outage time of 0.0837, distributed messages of 276, and computing complexity of 11.87 are used to assess the proposed framework's efficiency across vehicle density and velocities.

Corrigendum: Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease.

[This corrects the article DOI: 10.3389/frai.2023.1339988.].

Enhancing service availability and resource deployment in IoT using a shared service replication method.

In many real-world contexts, the Internet of Things (IoT) is valued for its capacity to facilitate the smooth operation of interoperable applications and services. It is critical to ensure the accessibility and replication of IoT resources to improve the agility of these applications. As a solution, the Network Function Virtualization (NFV) paradigm is embedded into the IoT design to leverage information from various endpoint applications better and maximize resource utilization. In this study, the Shared Replication Augmenting Method (SRAM) is proposed to increase resource usage in underutilized NFVs and maintain service availability simultaneously. The regressive decision-making learning used by SRAM enables the detection of NFV needs for data and application portability across various real-time use cases. This regression method can uncover data needs and their causes, allowing for prompt answers and more efficient use of available resources. The suggested SRAM technique dynamically modifies the procedure while considering computation-less function allocations, making it suitable for various interoperable applications. It distributes root-to-service virtualization and availability based on historical use and data replication. Therefore, SRAM improves resource usage by 7.09 % with no increase in latency or delays. It also increases service availability by 10.4 %, reduces latency by 11.89 %, eliminates backlogs by 11.1 %, and reduces data repetition by 8.97 %. This study enhances resource consumption and productivity in IoT settings by offering SRAM as a viable solution. The study's results prove its potential to reduce the occurrence of replication, delay, and queues while raising the availability of services.

Outcomes of Endoscopic Stapedectomy: Systematic Review.

Introduction Stapes surgery was traditionally performed with the use of microscopy either through postauricular, endaural or transcanal approaches. Endoscopic stapedectomy ushered a revolution as a new technique with less complications. Objective To review the outcomes of endoscopic stapes surgery with an emphasis on intraoperative and postoperative clinical and audiological results. Data Synthesis A literature review on the PubMed, Web of Science, Scopus, the Cochrane Library, and Embase databases was conducted. Endoscopic stapes surgery or stapedotomy were the main keywords used, and we searched for studies and research published from January 2015 to October 2021. Articles on endoscopic stapes surgery were included, and qualitative and descriptive analyses of the studies and outcomes data regarding audiometric changes and postoperative complications were conducted. Articles including patients with cholesteatoma were excluded. A total of 122 studies were retrieved for qualitative and descriptive analyses and to measure the outcomes of endoscopic stapedotomy; only 12 studies met the inclusion criteria, and the rest was excluded. The meta-analysis revealed a statistically significant difference in hearing improvement. The gain in air-bone gap ranged from 9 dB to 16 dB. A low rate of operative and postoperative complications was reported. Conclusions Endoscopic stapes surgery appears to be a reasonable alternative to microscopic stapes surgery, with shorter operative times, low complication rate, and significant hearing improvement. The endoscopic technique enabled a better visualization and less scutum drilling, which was confirmed by all included studies.

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things.

Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.

Outcomes of Endoscopic Stapedectomy: Systematic Review

Abstract Introduction Stapes surgery was traditionally performed with the use of microscopy either through postauricular, endaural or transcanal approaches. Endoscopic stapedectomy ushered a revolution as a new technique with less complications. Objective To review the outcomes of endoscopic stapes surgery with an emphasis on intraoperative and postoperative clinical and audiological results. Data Synthesis A literature review on the PubMed, Web of Science, Scopus, the Cochrane Library, and Embase databases was conducted. Endoscopic stapes surgery or stapedotomy were the main keywords used, and we searched for studies and research published from January 2015 to October 2021. Articles on endoscopic stapes surgery were included, and qualitative and descriptive analyses of the studies and outcomes data regarding audiometric changes and postoperative complications were conducted. Articles including patients with cholesteatoma were excluded. A total of 122 studies were retrieved for qualitative and descriptive analyses and to measure the outcomes of endoscopic stapedotomy; only 12 studies met the inclusion criteria, and the rest was excluded. The meta-analysis revealed a statistically significant difference in hearing improvement. The gain in air-bone gap ranged from 9 dB to 16 dB. A low rate of operative and postoperative complications was reported. Conclusions Endoscopic stapes surgery appears to be a reasonable alternative to microscopic stapes surgery, with shorter operative times, low complication rate, and significant hearing improvement. The endoscopic technique enabled a better visualization and less scutum drilling, which was confirmed by all included studies.

Hybrid Control of the DC Microgrid Using Deep Neural Networks and Global Terminal Sliding Mode Control with the Exponential Reaching Law.

The direct current (DC) microgrid is one of the key research areas for our advancement toward carbon-free energy production. In this paper, a two-step controller is designed for the DC microgrid using a combination of the deep neural network (DNN) and exponential reaching law-based global terminal sliding mode control (ERL-GTSMC). The DC microgrid under consideration involves multiple renewable sources (wind, PV) and an energy storage unit (ESU) connected to a 700 V DC bus and a 4-12 kW residential load. The proposed control method eliminates the chattering phenomenon and offers quick reaching time by utilizing the exponential reaching law (ERL). In the two-step control configuration, first, DNNs are used to find maximum power point tracking (MPPT) reference values, and then ERL-based GTSMC is utilized to track the reference values. The real dynamics of energy sources and the DC bus are mathematically modeled, which increases the system's complexity. Through the use of Lyapunov stability criteria, the stability of the control system is examined. The effectiveness of the suggested hybrid control algorithm has been examined using MATLAB simulations. The proposed framework has been compared to traditional sliding mode control and terminal sliding mode control to showcase its superiority and robustness. Experimental tests based on the hardware-in-the-loop (HIL) setup are then conducted using 32-bit TMS320F28379D microcontrollers. Both MATLAB and HIL results show strong performance under a range of environmental circumstances and system uncertainties.

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera's field of view. A single monocular sensor's sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system's dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera's field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera's field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

Transcatheter Mitral Valve Repair via MitraClip in Patients Aged <65 Years: Multicentre 2-year Outcomes.

Background: Transcatheter mitral valve repair (TMVR) using the MitraClip has become a well-established interventional therapy and is usually performed in elderly patients. The objective of this study was to assess 2-year clinical outcomes of TMVR in patients aged <65 years at three heart centres with severe mitral regurgitation (MR) and no surgical options. Methods: A retrospective study analysed data of 36 patients aged <65 years treated with TMVR . All patients were refused surgery by Heart Team decision. Baseline MR was assessed by biplane vena contracta width in two perpendicular views (mean 8.35 ± 1.87 mm). Degenerative MR was detected in 11 patients (30.6%); functional MR was detected in 25 patients (69.4%). Results: Acute procedural success was accomplished in 88.9% of patients. No procedure-related mortality during the first 30 days was detected. Over an average of 2 years of follow-up, all-cause mortality was 19.4% and cardiovascular death was 11.1% owing to advanced heart failure. The average follow-up period was 25.8 months (median was 20 months). Statistically significant difference (p-value <0.01) was detected for N-terminal prohormone of brain natriuretic peptide (pg/ml) at baseline (mean 9,870 ± 10,819; median 7,748) compared to follow-up visits (mean 7,645 ± 11,292; median 3,263). New York Heart Association functional class improvement was achieved in 69% of patients. A second intervention (reclipping) was required in two patients to correct recurrent significant MR. Conclusion: TMVR in patients aged <65 years refused surgical repair provides satisfactory clinical outcomes at 2 years. Future studies should evaluate the outcomes of MitraClip in this population in a larger cohort.

Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease.

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets.

The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios.

A Rare Case of Gastrointestinal Stromal Tumor with a Liver Metastasis Infiltrating the Inferior Vena Cava and Extending to the Right Atrium with an Early Recurrence after Surgical Extraction.

Gastrointestinal stromal tumors (GISTs) are mesenchymal tumors of the gastrointestinal tract. The major cause of GIST is the presence of an abnormal form of tyrosine protein kinase (KIT) protein also known as CD117, which causes uncontrollable growth of the gastrointestinal cells. Most studies report incidences between 10 and 15 cases of GISTs per million. Metastases to the liver and peritoneum are the most frequent. We report a case of advanced GIST with a liver metastasis infiltrating the inferior vena cava (IVC) and extending to the right atrium in the form of a large, floating, isolated intracardiac liver metastasis with diastolic prolapsing through the tricuspid valve. This is a very rare manifestation. One week after heart surgery and removal of a 5 × 6 cm tumor mass from the right atrium and the IVC, echocardiography depicted an early recurrence.