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The maximum power point tracking (MPPT) algorithms are required to deliver the optimal energy from solar photovoltaic cells/array (PV) under numerous weather conditions. Therefore, MPPT circuits driven by defined rules called algorithms are designed. These algorithms range from simple to complex in design and implementation and are selected based on the scenarios of the surroundings. However, the incremental conductance (InC) MPPT algorithm is one of the market's most simple, easy to implement, and demanding algorithms. The drawback associated with the InC algorithm is its tracking speed. To overcome this weakness various researchers have made multiple improvements. Although the performance became better but was not satisfied. Further, the improvements introduce steady-state oscillations of the operating point around the MPP. So, the user needs to pick and choose based on demand. Keeping the target in focus, we have introduced a couple of modifications in the structure of INC. that remain fruitful. The proposed structure named the augmented InC algorithm has shown marvelous improvement in tracking speed and steady-state oscillations. The results have been compared with the conventional InC algorithm, where the proposed augmented InC algorithm has outperformed the conventional InC algorithm in tracking speed and steady-state oscillations. We have used the MATLAB script to code the conventional InC algorithm and proposed augmented InC algorithms based on their designed flowchart. Both algorithms have been applied to the standalone solar photovoltaic system composed of a solar photovoltaic array, DC/DC boost converter, illumination and temperature inputs, MPPT algorithm, and a DC load. The model is designed in Simulink/MATLAB.

Abstract In a dynamic financial ecosystem, digitalisation is supporting banks in revamping their business processes to be more efficient, reducing costs, coping with customers’ evolving demands, and keeping them abreast of market competition. This two-stage research aims to investigate three issues in Pakistan’s banking industry from 2006 to 2020: (i) banking efficiency; (ii) the impact of digitalisation on banking efficiency; and (iii) banking efficiency’s absolute and conditional convergence. In our first-stage analysis, bootstrap data envelopment analysis has been applied, which exhibits bias-corrected overall, pure, and scale efficiencies of 74, 77, and 96%, respectively. In the second-stage analysis, we executed Tobit and two-step dynamic panel data system generalised method of moments (DPDSYS-GMM) models, and the results uncover that digitalisation has a positive influence on banking efficiency. Findings confirm that return on assets, bank size, interest rate, and gross domestic product growth rate have a positive association with banking efficiency. Our research reveals that state-controlled banks outperform their private sector and special-purpose counterparts. Our DPDSYS-GMM findings validate β and σ-convergence, implying that initially, low-efficient banks caught up (converged) to the more efficient opponents, reducing cross-sectional efficiency dispersion and attaining common equilibrium. The findings of banking efficiency conditional β-convergence assert that the adoption of digital technology has played a critical role in the convergence process and that digitalisation has acted as a catalyst for less efficient banks to catch up significantly faster to their more efficient rivals. This study displays digitalisation’s disruptive influence on the overall transformation of Pakistan’s banking system in the modern era, resulting in significant efficiency gains.

The geometry, electronic structure, and adsorption properties of halogen molecule X2(X = F, Cl) on arsenene were investigated using first-principles calculations. The adsorption of molecules was considered at various sites and in various orientations on the pristine arsenene (p-As) surface. Both molecules show chemisorption and the crystal orbital Hamiltonian population (COHP) analysis reveals the formation of strong X-As bonds. In particular, the adsorbed molecules spontaneously dissociate into atomic halogen atoms, with a diffusion barrier of 1.91 (1.72) eV for F2(Cl2). The adsorbed X2 molecules induced distortions in the local geometry due to strong interaction with arsenene. Importantly, the formation of X-As bonding remarkably changed the electronic properties, evidenced by the decrease of the actual band gap due to the emergence of defect states within the band gap. For instance, the F2 adsorbed arsenene system (F2-As) exhibited an average band gap of 1.17 eV, and Cl2 adsorbed arsenene (Cl2-As) showed an average band gap of 0.83 eV. In particular, indirect to direct band gap transition was observed for some adsorption configurations. The reduction in band gap resulted in the enhancement of electrical conductivity. These findings suggest that the electronic properties of arsenene can be tuned by halogen decoration.

This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1−𝜎 uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out.

Agricultural productivity plays a vital role in global economic development and growth. When crops are affected by diseases, it adversely impacts a nation’s economic resources and agricultural output. Early detection of crop diseases can minimize losses for farmers and enhance production. In this study, we propose a new hybrid deep learning model, PLDPNet, designed to automatically predict potato leaf diseases. The PLDPNet framework encompasses image collection, pre-processing, segmentation, feature extraction and fusion, and classification. We employ an ensemble approach by combining deep features from two well-established models (VGG19 and Inception-V3) to generate more powerful features. The hybrid approach leverages the concept of vision transformers for final prediction. To train and evaluate PLDPNet, we utilize the public potato leaf dataset: early blight, late blight, and healthy leaves. Utilizing the strength of segmentation and fusion feature, the proposed approach achieves an overall accuracy of 98.66%, and F1-score of 96.33%. A comprehensive validation study is conducted using Apple (4 classes) and tomato (10 classes) datasets achieving impressive accuracies of 96.42% and 94.25%, respectively. These experimental findings confirm that the proposed hybrid framework provides more effective and accurate detection and prediction of potato crop diseases, making it a promising candidate for practical applications.

Microglia migrate to the cerebral cortex during early embryonic stages. However, the precise mechanisms underlying microglia migration remain incompletely understood. As an extracellular matrix protein, Netrin-1 is involved in modulating the motility of diverse cells. In this paper, we found that Netrin-1 promoted microglial BV2 cell migration in vitro. Mechanism studies indicated that the activation of GSK3β activity contributed to Netrin-1–mediated microglia migration. Furthermore, Integrin α6/β1 might be the relevant receptor. Single-cell data analysis revealed the higher expression of Integrin α6 subunit and β1 subunit in microglia in comparison with classical receptors, including Dcc, Neo1, Unc5a, Unc5b, Unc5c, Unc5d, and Dscam. Microscale thermophoresis (MST) measurement confirmed the high binding affinity between Integrin α6/β1 and Netrin-1. Importantly, activation of Integrin α6/β1 with IKVAV peptides mirrored the microglia migration and GSK3 activation induced by Netrin-1. Finally, conditional knockout (CKO) of Netrin-1 in radial glial cells and their progeny led to a reduction in microglia population in the cerebral cortex at early developmental stages. Together, our findings highlight the role of Netrin-1 in microglia migration and underscore its therapeutic potential in microglia-related brain diseases.

Energy is the primary concern of the modern era and the requirement of energy is being increased day by day; energy resources are not sufficiently available for sustainable development. It is crucial to generate affordable and pollution-free sources of energy to meet this required demand. Walking is a common daily activity for humans; the kinetic energy from walking is converted into mechanical energy. Moreover, this energy is converted into electrical power using a rack-and-pinion mechanism which is simply a non-conventional method of producing electric current. In this research study, a simple and low-cost rack-and-pinion mechanism with a flywheel is introduced to enhance the performance and efficiency of energy conversion from kinetic energy to mechanical energy and subsequently into electrical energy. The results showed that the proposed footstep floor tile generated an average power of 3 watts for a 0.5 s duration with a peak load of 60 kg. The electrical energy produced per step was noted as 1.8 Joules. A percentage of 75% of the total potential energy theoretically accessible was transmitted by the energy-harvesting paver, and 50% of it was successfully converted into electricity. The generated energy is stored in a backup battery bank system and can be used to charge smart devices, providing a cost-effective and pollution-free solution.

Conventional energy generation technologies face unreliability due to the depletion of fossil fuels, soaring energy prices, greenhouse gas emissions, and continuously increasing energy demand. As a result, researchers are searching for reliable, cheap, and environmentally friendly renewable energy technologies. Solar photovoltaic (PV) technology, which directly converts sunlight into electricity, is the most attractive sustainable energy source due to the sun's ubiquitous presence. However, the non-linear behaviour of solar PV demands maximum power point tracking (MPPT) to ensure optimal power production. Although Hill Climbing (HC) is a simple, cheap, and efficient MPPT algorithm, it has a drawback of steady-state oscillations around MPP under uniform weather conditions. To overcome this weakness, we propose some modifications in the tracking structure of the HC algorithm. The proposed optimized HC (OHC) algorithm achieves zero steady-state oscillations without compromising the strength of the conventional HC algorithm. We applied both algorithms to an off-grid PV system under constant and changing weather conditions, and the results demonstrate the superiority of the proposed OHC algorithm over the conventional HC algorithm.

In order to simplify the analysis of complex electronic systems, they needsto be modeled accurately. Model reduction is further required to streamline the procedural and computational complexities. Further the instability caused by the model reduction techniques worstly effects the accuracy of a system. Therefore, we have proposed some improvements in the frequency limited impulse response Gramians based model order reduction techniques for discrete time systems. The propsed techniques assures the stability of the model after it get reduced. The proposed techniques provided better results than the stability preserving techniques.

This Operating the solar photovoltaic (PV) system at its maximum power point (MPP) under numerous environmental conditions to extract the maximum power is a challenging task. The challenge is to track the MPP, especially under partial shading conditions (PSC), where the formation of multiple MPP occurs in the characteristic curve of a PV array. Nevertheless, achieving this would benefit us with optimal power production, reducing the payback time and initial cost of the PV system. To perform this duty, an electronic circuit ruled by an algorithm is employed. The MPP tracking (MPPT) algorithms can be categorized into conventional and nature inspired. The conventional algorithms can successfully track the MPP under uniform weather conditions (UWC), and unable to identify the global MPP (GMPP) under PSC. However, the nature inspired algorithms possess the ability to perform efficiently under all weather conditions. Considering this strength of nature inspired algorithms, one of the top performing algorithms named as Flower pollination algorithm (FPA) is selected based on its brilliant searching strategy in adjacent and distant locations. In this paper, some structural modifications have been proposed in the FPA to further improve its searching capability and get more quick, accurate and efficient results for the MPPT of solar PV system. Results have proven the superiority of the proposed Modified FPA (MFPA) over the FPA in terms of efficiency, accuracy, tracking speed, energy conservation, economic saving, and payback time. Simulation is performed in MATLAB/Simulink.

This Extraction of the maximum electrical power from a solar photovoltaic (PV) system under numerous weather conditions is required to reduce its payback time period, per unit energy price, and to compensate for the high initial price of the solar PV system. This could only be achieved by continuously operating the solar PV system at its maximum power point (MPP) under several weather conditions. Unlike under uniform weather conditions (UWC), identification of the real MPP (Global MPP) under partial shading condition (PSC) in a reasonable time is a challenging task due to the formation of multiple local MPP in the power-voltage (P-V) characteristic curve of a solar PV array. The nature-inspired MPP tracking algorithms have been proved suitable for global MPP tracking (MPPT) under PSC. In this research paper, a renowned nature-inspired flower pollination algorithm (FPA) is deeply reviewed, modified, and integrated with the random walk filter to improve its performance in terms of tracking speed, and efficiency. A comparison of the proposed ‘Adaptive Flower Pollination Algorithm (AFPA)’ and conventional FPA algorithm has been made under zero, weak, and strong PSCs for a 4S solar PV array. The proposed algorithm has produced remarkable results in tracking speed, and efficiency, for the global MPP (GMPP) tracking under different PSCs. The simulation is performed in MATLAB/Simulink software.

Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis.

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.

The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings.

Abstract An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature‐vector space for making a final decision. Specifically, five different well‐known pre‐trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet‐19, Inception V3, Xception, and InceptionResNetV2 are fine‐tuned and utilised for feature extraction. First, the high‐level deep layers of the networks were used to get multi‐features hierarchy from sketch images. Second, an entropy‐based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU‐Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state‐of‐the‐art algorithms.

Several strategies and approaches have been reported for improving the resilience and optoelectronic properties of perovskite films. However, fabricating a desirable and stable perovskite absorber layer is still a great challenge due to the optoelectronic and fabrication limitations of the materials. Here, we introduce diethylammonium bromide (DABr) as a post-treatment material for the pre-deposited methylammonium lead iodide (MAPbI3) film to fabricate a high-quality two-dimensional/three-dimensional (2D/3D) stacked hetero-structure perovskite film. The post-treatment method of DABr not only induces the small crystals of MAPbI3 perovskite secondary growth into a large crystal, but also forms a 2D capping layer on the surface of the 3D MAPbI3 film. Meanwhile, the grains and crystallization of 3D film with DABr post-treatment are significantly improved, and the surface defect density is remarkably reduced, which in turn effectively suppressed the charge recombination in the interface between the perovskite layer and the charge transport layer. The perovskite solar cell based on the DABr-treatment exhibited a significantly enhanced power conversion efficiency (PCE) of 19.10% with a notable improvement in the open circuit voltage (VOC) of 1.06 V and good stability, advocating the potential of this perovskite post-treatment approach.

One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and the sharing of the data is restricted under global privacy laws. Furthermore, the sharing and collection of biomedical data have a significant network communication cost and lead to delayed heart disease prediction. To address the training latency, communication cost, and single point of failure, we propose a hybrid framework at the client end of HSP consisting of modified artificial bee colony optimization with support vector machine (MABC-SVM) for optimal feature selection and classification of heart disease. For the HSP server, we proposed federated matched averaging to overcome privacy issues in this paper. We tested and evaluated our proposed technique and compared it with the standard federated learning techniques on the combined cardiovascular disease dataset. Our experimental results show that the proposed hybrid technique improves the prediction accuracy by 1.5%, achieves 1.6% lesser classification error, and utilizes 17.7% lesser rounds to reach the maximum accuracy.

Modern-day world is facing problems such as, electricity generation deficiency, mounting energy demand, GHG (Greenhouse Gas) emissions, reliability and soaring prices. To resolve these issues, sustainable and renewable energy resources like SPV (Solar Photovoltaic) would be quite helpful. In this regard, the extraction of maximum power from SPV array in PSC (Partial Shading Weather Conditions) remains a challenge. Creation of multiple power peaks in the P-V (Power-Voltage) curve of a PV array due to partial shading, makes it difficult to track GMPP (Global Maximum Power Point) out of multiple power peaks known as LMPP (Local Maximum Power Points). Conventional algorithms are not able to perform in any condition other than UWC (Uniform Weather Condition). Nature inspired SC (Soft Computing) algorithms efficiently track the GMPP in PSC. The top performing SC algorithm named, FPA (Flower Pollination Algorithm) presents an efficient solution for GMPP tracking in PSCs. In this paper, the efficiency, accuracy and tracking speed of FPA algorithm is optimized. Comparison of the proposed OFPA (Optimized Flower Pollination Algorithm) and the existing FPAs is performed for zero shading condition, weak PSC, strong PSC, and changing weather conditions. In zero shading conditions, improvement of 0.7% in efficiency and 33% in tracking speed is achieved. In weak shading conditions, improvement of 0.97% in efficiency and 32.2% in tracking speed is achieved. In strong shading conditions, improvement of 0.24% in efficiency and 30.6% in tracking speed is achieved. OFPA is also tested for changing weather conditions (entering from Case-1 to Cae-3) and it retains its outstanding performance in the changing weather conditions. Simulations are performed in MATLAB/Simulink.

Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a time-consuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community.

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.

Existing image-to-image translation methods usually adopt an encoder-decoder structure to generate images. The encoder extracts the features of input images using a sequence of convolution layers until a bottleneck, and then, the intermediate features are decoded to the target image. However, the existence of bottleneck layer in such structure may lead to blurry and bad quality of the translated images, since different domain translations may be related to the global or local region in the input image or even in an abstract level. To prevent these problems, we propose the channel attention networks for image translation in this paper. It is a novel model that supports the multi-domain image-to-image translation using one single model. Conditioning on the target domain label, an auto-encoder-like network with multiple attention connections is trained to translate the input image into the target domain. The attention connections better shuttle the low-level information in the encoder to the decoder, which helps to preserve the structure. A multi-level attention mechanism is also designed in the proposed model to further improve the performance of our model. More specially, the feature maps in the encoder are first squeezed by average pooling and used to output a channel-wise attention mask. The attention mask softly determines which channels of the feature maps are translated and which channels are kept. By enforcing the model to learn a cyclic domain transformation during training, our model does not require paired training data, which greatly improves the versatility to different kinds of data. We experimentally demonstrated the effectiveness of our proposed model on the facial and clothing image translation tasks. The extensive ablations are also conducted to further validate the contribution of the proposed attention module used in our model.

In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively.

In this study, we have presented an optimal microgrid design that ensures the uninterrupted energy supply to Mirpur University of Engineering and Technology (MUST), Azad Jammu and Kashmir AJK, Pakistan at the cheapest price by using reliable energy resources. The availability of energy resources, environmental viability, and economic feasibility are the key parameters of design. The available resources for the MUST site include the National grid, Solar photovoltaic (SPV), Battery bank, and Diesel generator. The data of electrical load, solar illumination, atmospheric temperature at the university, diesel fuel cost, SPV module lifetime, SPV degradation factor, SPV efficiency, SPV cost, battery cost, battery life, national grid energy price, load shedding and toxic emissions have been considered valuables in designing the hybrid micro-grid. The difference in net present cost (NPC) of the optimal design and the worst design is calculated by considering the above parameters. The proposed optimal microgrid design supplies energy to the load using SPV, Diesel generator, and battery bank with NPC of $250,546 and the renewable fraction of 99%. Whereas the worst design includes the Diesel generator and battery bank as energy supplying sources with the NPC of $2.14 M and a renewable fraction of 0%. Simulations performed using HOMER Pro software (HOMER Energy, HOMER Pro-3.11, Boulder, CO, USA) proved that after considering all the data and requirements mentioned above, out of 979 feasible designs, the proposed hybrid microgrid design is best suitable for MUST.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

Wind energy is a useful and reliable energy source. Wind turbines are attracting attention with the dependency of the world on clean energy. The turbulent nature of wind profiles along with uncertainty in the modeling of wind turbines makes them more challenging for prolific power extraction. The pitch control angle is used for the effective operation of wind turbines at the above-nominal wind speed. To extract stable power as well as to keep wind turbines in a safe operating region, the pitch controller should be intelligent and highly efficient. For this purpose, proportional–integral–derivative controllers are mostly used. The parameters for the proportional–integral–derivative controller are unknown and calculated by numerous techniques, which is a quite cumbersome task. In this research, the particle swarm optimization technique is used but the conventional particle swarm optimization technique cannot tackle the system’s nonlinearity and uncertainties. Hence, the proposed particle swarm optimization algorithm is employed for the calculation of the controller’s optimal parameters. The proposed technique is implemented on a 5-MW wind turbine, which is designed using the Bladed software. Simulation is performed using MATLAB/Simulink to validate the effectiveness of the proposed technique. A variable wind profile is fed as input into the system and the proposed controller provides satisfactory results for the power, rotor speed, and torque. The system is stable and the settling time is reduced.

In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder−decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

Extraction of maximum power from PV (Photovoltaic) cell is necessary to make the PV system efficient. Maximum power can be achieved by operating the system at MPP (Maximum Power Point) (taking the operating point of PV panel to MPP) and for this purpose MPPT (Maximum Power Point Trackers) are used. There are many tracking algorithms/methods used by these trackers which includes incremental conductance, constant voltage method, constant current method, short circuit current method, PAO (Perturb and Observe) method, and open circuit voltage method but PAO is the mostly used algorithm because it is simple and easy to implement. PAO algorithm has some drawbacks, one is low tracking speed under rapid changing weather conditions and second is oscillations of PV systems operating point around MPP. Little improvement is achieved in past papers regarding these issues. In this paper, a new method named ?Decrease and Fix? method is successfully introduced as improvement in PAO algorithm to overcome these issues of tracking speed and oscillations. Decrease and fix method is the first successful attempt with PAO algorithm for stability achievement and speeding up of tracking process in photovoltaic system. Complete standalone photovoltaic system?s model with improved perturb and observe algorithm is simulated in MATLAB Simulink

The title complex salt, [Ni(C3H8N2S)6](NO3)2, consists of an [Ni(Dmtu)6]2+ (Dmtu is N,N′-dimethylthiourea) dication and two nitrate counter-anions. The NiII atom (site symmetry overline{3}) is coordinated by the S atoms of six Dmtu ligands within a slightly distorted octahedral environment. The crystal structure is characterized by weak intramolecular N—H...S interactions and by intermolecular N—H...O hydrogen bonds involving the nitrate anion (site symmetry 3.). These intermolecular interactions lead to the formation of two-dimensional networks lying parallel to the ab plane. The networks are linked via non-classical intermolecular C—H...O hydrogen bonds, forming a three-dimensional arrangement.

Low user participation and less knowledge contribution seriously threaten the sustainability of online question and answers communities. Although researchers studied the different aspects of knowledge contribution and proposed useful suggestions, there is still no thorough study on the knowledge contribution pattern of consistent geeks' that can help improve low participation. According to social cognitive and self-determination theory, peers follow credible sources or role models in their participation patterns and are influenced by the community environment. Based on social cognitive and self-determination theory, we have studied the most consistent geeks of StackOverflow for the period between 2010–2020 to employ the results to activate dormant users. Two-step system GMM results revealed that most users take a free ride and hesitate to reciprocate; knowledge-seeking negatively influences the quantity of contributed knowledge. Peer recognition and repudiation positively influence the knowledge contribution of active geeks, whereas reputation scores and badges negatively influence the contributed knowledge's quantity and quality. Social interaction's role as moderator is also different for quantity and quality of knowledge contributed. Study results improve the existing literature and provide comprehensive managerial implications to improve low participation and create a progressive knowledge contribution environment. [ABSTRACT FROM AUTHOR]

Purpose: To identify self-medications used by patients as initial treatment for various eye problems and to analyze associated complications. Study Design: Cross sectional. Place and Duration of Study: Al-Khidmat teaching hospital, (University of Lahore) from October 2021 to September 2022. Method: This study included 117 patients using traditional eye medications (TEM) and over the counter (OTC) ophthalmic drugs before presenting in ophthalmic outdoor. A semi structured questionnaire was used as a tool to collect the data. All patients aged 18 years and above were directly questioned. For patients below 18 years, responses were collected from the patients themselves when possible; otherwise, the accompanying parent provided the information. Age, gender, educational status and area of residence were recorded. The symptoms compelling the use of TEM/OTC or both, the source and type of medication, diagnosis and any complications that resulted due to self-medication were documented. Complete ocular examination was done. MS Excel was used to record and analyze data. Results: There were 48% males and 52% females. Rosewater was the most frequently used TEM by 54.7%. Steroids-antibiotic combination eyedrops/ointments were used by 31.6%. Symptoms for which self-medication was done, were redness (64.1%), watering (35.9%), itching (32.5%) and discharge (26.5%). Symptoms did not improve in 54.7%, 26.5% required ophthalmic consultation and only 18.8% had temporary relief. Twelve percent developed complications. Conclusion: Self-medication with TEM or OTC drugs should be discouraged as these can cause detrimental effects on eyes. [ABSTRACT FROM AUTHOR]