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Abstract Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model performance may lag due to the unavailability of annotated datasets. To overcome these issues, the research proposed a model for heart disease detection and classification by integrating blockchain technology with a Modified mixed attention-enabled search optimizer-based CNN-Bidirectional Long Short-Term Memory (M2MASC enabled CNN-BiLSTM) model. The novel model incorporates a pre-trained VGG16 model to enhance feature extraction and improve the overall predictive accuracy. Leveraging the continuous monitoring capabilities of IoT devices, patient data is collected in real-time, providing a dynamic source to the CNN-BiLSTM model. Blockchain integration ensures stored health data’s security, transparency, and immutability, addresses privacy concerns, and promotes trust in the predictive system. The classifier parameters are tuned using the modified mixed attention and search optimization. The M2MASC-enabled CNN-BiLSTM model performs better than traditional methods of accuracy 98.25%, precision 99.57%, and recall 97.53% for TP 80 with the MIT-BIH dataset.

Abstract Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

Abstract As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified.

Abstract Background Developmental leaf senescence (DLS) is an irreversible process followed by cell death. Dark-induced leaf senescence (DILS) is a reversible process that allows adaptations to changing environmental conditions. As a result of exposure to adverse environmental changes, plants have developed mechanisms that enable them to survive. One of these is the redirection of metabolism into the senescence pathway. The plant seeks to optimise resource allocation. Our research aims to demonstrate how epigenetic machinery regulates leaf senescence, including its irreversibility. Results In silico analyses allowed the complex identification and characterisation of 117 genes involved in epigenetic processes in barley. These genes include those responsible for DNA methylation, post-translational histone modifications, and ATP-dependent chromatin remodelling complexes. We then performed RNAseq analysis after DILS and DLS to evaluate their expression in senescence-dependent leaf metabolism. Principal component analysis revealed that evaluated gene expression in developmental senescence was similar to controls, while induced senescence displayed a distinct profile. Western blot experiments revealed that senescence engages senescence-specific histone modification. During DILS and DLS, the methylation of histone proteins H3K4me3 and H3K9me2 increased. H3K9ac acetylation levels significantly decreased during DILS and remained unchanged during DLS. Conclusions The study identified different epigenetic regulations of senescence types in barley leaves. These findings are valuable for exploring epigenetic regulation of senescence-related molecular mechanisms, particularly in response to premature, induced leaf senescence. Based on the results, we suggest the presence of an epigenetically regulated molecular switch between cell survival and cell death in DILS, highlighting an epigenetically driven cell survival metabolic response.

Abstract Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization’s fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

Abstract Cybertwin (CT) is an innovative network structure that digitally simulates humans and items in a virtual environment, significantly influencing Cybertwin instances more than regular VMs. Cybertwin-driven networks, combined with Mobile Edge Computing (MEC), provide practical options for transmitting IoT-enabled data. This research introduces a hybrid methodology integrating deep learning with Cybertwin-driven resource allocation to enhance energy-efficient workload offloading and resource management in MEC networks. Offloading work is essential in MEC networks since several applications require significant resources. The Cybertwin-driven approach considers user mobility, virtualization, processing power, load migrations, and resource demand as crucial elements in the decision-making process for offloading. The model optimizes job allocation between on-premises and distant execution using a task-offloading strategy to reduce the operating burden on the MEC network. The model uses a hybrid partitioning approach and a cost function to optimize resource allocation efficiently. This cost function accounts for energy consumption and service delays associated with job assignment, execution, and fulfilment. The model calculates the cost of several segmentation and offloading procedures and chooses the lowest cost to enhance energy efficiency and performance. The approach employs a deep learning architecture called “CNN-LSTM-TL” to accomplish energy-efficient task offloading, utilizing pre-trained transfer learning models. Batch normalization is used to speed up model training and improve its robustness. The model is trained and assessed using an extensive mobile edge computing public dataset. The experimental findings confirm the efficacy of the proposed methodology, indicating a 20% decrease in energy usage compared to conventional methods while achieving comparable or superior performance levels. Simulation studies emphasize the advantages of incorporating Cybertwin-driven insights into resource allocation and workload-offloading techniques. This research enhances energy-efficient and resource-aware MEC networks by incorporating Cybertwin-driven techniques.

Introduction: The brachial plexus is a variable nerve plexus in its formation and branching pattern. Variations in the brachial plexus are not uncommon, and its area of supply and associations with other adjacent anatomical structures require clinical and surgical attention. Aim: To find out any anatomical variations in the formation and branching pattern of the brachial plexus in human foetuses. Materials and Methods: A morphometric prospective observational study was conducted from January 2020 to July 2022, on 30 stillborn or Intrauterine Dead (IUD) human foetuses were taken in the study. Gestation age from 28 to 40 weeks of gestation were included in the study, while those with gross anomalies or morphological anomalies of the cranium and vertebral column were excluded. The dissection includes vertical midline incision of the skin from the external occipital protuberance to the lower limit of the thoracic vertebra (T12). The skin was cut in the midline and reflected laterally. All superficial and deep muscles of the neck were dissect out to clear the cervical and thoracic parts of the vertebral column. The vertebral column was cut and opened to visualise the spinal cord and spinal nerves. Based on gestational age, the foetuses were divided into three groups: four in the first group (28-31 weeks), twelve in the second group (32-35 weeks), and fourteen in the third group (36-40 weeks) were divided for descriptive purposes. The gender of foetuses was determined based on the external genitalia. Results: A total of 60 brachial plexuses were dissected, of which 49 (81.66%) had the usual anatomical formation of the trunks: the upper trunk formed by C5 and C6 roots, the middle trunk by the C7 root, and the lower trunk by the C8 and T1 roots. Eleven (18.34%) plexuses presented variations in trunk formation. Specifically, 8 (13.3%) brachial plexuses were of the prefix type, where the upper trunk was formed by the C5 and C6 roots with an additional contribution from the C4 root; there was inter-branch communication between C6 and C7 in one (1.7%) case; the middle and lower trunks united by C7, C8, and T1 roots formed the lower trunk in another (1.7%) case, and finally, one (1.7%) case exhibited the post-fix type. Conclusion: In this study, out of the 30 foetuses examined, 11 showed variations, which is not uncommon. The prefix type of brachial plexus is quite common among the possible variations, although anatomists and neurosurgeons cannot ignore the possibility of other variations, like post-fix type or communication between the roots of the brachial plexuses.

Abstract The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.

This study investigates the mechanical properties and the water absorption behavior of Keratin fiber-reinforced epoxy composites. The Keratin fibers were used as reinforcement with epoxy. Samples were prepared using the Hand lay-up method, having different amounts of reinforcement. The samples were characterized for their mechanical behavior as per ASTM standards. The tensile strength peaks found at 30wt% declined thereafter due to weakened interfacial bonding. Enhanced tensile moduli observed with fiber loading peaked at 30wt%. Flexural strength and modulus raised to 30wt%. Izod impact strength increased consistently with higher percentages, reaching a maximum of 40wt%. Density values closely matched with theoretical values, with epoxy composites demonstrating lower densities. The positive aspect is found that even after 40 days, only 4.04% of water was absorbed by the composite containing 40wt% fiber which shows the hydrophobic nature of the composite. Certainly, the thickness swelling behavior is notably adequate, owing to the low water absorption characteristics exhibited by fiber composite. This research highlights the significance of Keratin fibers composite in the field of automobile, shed manufacturing, and Marine products manufacturing industry.

Barriers to rental housing (RH) pose significant challenges to urban development in India. This study examines critical success barriers (CSBs) to implementing RH policies, offering novel insights into this underexplored area. Despite rising demand for RH, significant obstacles hinder effective policy execution. Through expert surveys with 36 respondents and RIDIT analysis, the research identifies and prioritizes 16 CSBs, categorized into Regulatory and Institutional, Economic and Market, and Infrastructure and Development groups. Key findings reveal that the most critical barriers are inflexible rental agreements, negative social attitudes, and the absence of government incentives. These interdependent barriers impact the feasibility, attractiveness, and sustainability of RH projects. Inflexible agreements limit participation, negative social attitudes hinder inclusive development, and lack of incentives reduces private investment. The study recommends revising rental agreements, providing substantial incentives to private investors, and promoting high-density development through modified zoning regulations. Emphasizing multi-level governance, stakeholder engagement, and regular policy evaluations is crucial for effective implementation. Addressing these barriers can enhance the RH sector's sustainability and scalability. This research contributes to the existing literature by providing a quantitative framework for identifying and prioritizing CSBs in the RH, offering valuable insights for policymakers, developers, and stakeholders in urban housing.

Gynecological cancer, the most common form of cancers in women worldwide, initiates in the reproductive organs of females. More often, the common treatment measures, i.e. surgery, radiation, and medical oncology are found to be unsuccessful in the treatment of gynecological tumors. Emerging evidence indicates that extracellular vesicles (EVs) play a significant role in the pathogenesis of gynecological cancers by distinct mechanisms. The present review highlights how EVs contribute to the progression of different types of gynecological cancers such as cervical cancer, endometrial cancer, ovarian cancer, vaginal cancer, uterine sarcoma, gestational trophoblastic disease (GTD), and vulvar cancer. The primary focus is to understand how EVs’ cargo alters the phenotypic response of the recipient cells, thereby contributing to the progression of the disease, thus can be considered as a prognostic and diagnostic biomarker. A brief discussion on the role of EVs in the diagnosis and prognosis of different gynecological cancer types is also highlighted. Targeting the biogenesis of the EVs, their inside cargo, and EVs uptake by the recipient cells could be a potential therapeutic approach in the treatment of gynecological cancer beside conventional therapeutic means.

Background and aim: Phytoformulation therapy is a pioneering strategy for the treatment of metabolic disorders and related diseases. The aim of the present study was to investigate the protective effect of a phytoformulation consisting of hydroxycitric acid and capsaicin against obesity-related cardiomyopathy. Experimental procedure: Sprague-Dawley rats were fed HFD for 21 weeks, and phytoformulation (100 mg/kg body weight) was administered orally for 45 days starting at week 16. Results and conclusion: We found that HFD supplementation resulted in significant hyperglycemia and caused an increase in cardiac lipid deposition, inflammation and apoptosis in the heart. Phytoformulation therapy not only significantly decreased blood levels of glucose, cholesterol, triglycerides, free fatty acids, and inflammatory cytokines in obese rats, but also protected cardiac tissue, as shown by histological analysis. Conversely, phytoformulation therapy decreased mRNA levels for sterol regulatory element-binding factor 1, fatty acid synthase, acetyl-CoA carboxylase, and fatty acid binding protein 1 genes involved in fatty acid synthesis and absorption in obese rats. It increased the levels of lysosomal acid lipase, hormone-sensitive lipase, and lipoprotein lipase genes involved in fatty acid degradation in the heart. In addition, the phytoformulation improved cardiac inflammation and apoptosis by downregulating the genes nuclear factor kappa-light-chain enhancer of activated B cells (NF-kB), tumour necrosis factor α, interleukin-6, toll-like receptor-4 (TLR-4), BCL2-associated X and caspase-3. In conclusion, our results show that the phytoformulation improved insulin sensitivity and attenuated myocardial lipid accumulation, inflammation, and apoptosis in the heart of HFD-induced obese rats by regulating fatty acid metabolism genes and downregulating NF-kB/TLR-4/caspase-3.

Abstract Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

Abstract Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

This study explores the priority ranking and importance of indicators for effective inclusive education (IEIE) based on the perspectives of school stakeholders regarding the inclusion of children with locomotor disabilities (CwLDs) versus those with physical disabilities (CwPDs) in mainstream primary schools in Delhi. A paper-based survey collected ratings using a Likert scale. The data underwent reliability testing with Cronbach’s alpha coefficient, indicating good reliability. Then, analyzed using three Multi-Criteria Decision Making (MCDM) methods (TOPSIS, GRA, and RIDIT) to assess potential variations in indicator rankings. Further, Spearman’s rank correlation coefficients demonstrated a positive relationship within each inclusion case of CwLDs and CwPDs, but a contrasting relationship when ranking compared against each other (i.e., CwLDs versus CwPDs) among all three methods. Results highlight stakeholders’ emphasis on qualitative aspects over financial and administrative support, with certain indicators (e.g. student-teacher ratio, trained/untrained educators, awareness about UDL) deemed more significant for CwPDs due to their greater needs. The findings advocate for enhancing inclusive education facilities and raising awareness among primary school stakeholders to foster greater inclusion of children with disabilities. This study contributes valuable insights into the complex dynamics of inclusive education prioritization and underscores the importance of addressing diverse needs of inclusive learning environments.

IntroductionCardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge.MethodsThis research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model’s weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures.ResultsFar dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set.DiscussionWe have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

The use of closed-circuit television (CCTV) systems is widespread in all areas where serious safety concerns exist. Keeping an eye on things manually sounds like a time-consuming and challenging process. Identifying theft, detecting aggression, detecting explosive risks, etc., are all circumstances in which the term “security” takes on multiple meanings. When applied to crowded public spaces, the phrase “security” encompasses nearly every conceivable kind of abnormality. Detecting violent behaviour among them is challenging since it typically occurs in a group setting. Several practical limitations make it hard, though complex functional limitations make it difficult to analyze crowd film scenes for anomalous or aberrant behaviour. This article provides a broad overview of the field, starting with object identification and moving on to action recognition, crowd analysis, and violence detection in a crowd setting. By combining you only look once (YOLO) with transfer learning, the model may acquire new skills from various sources. This makes it more flexible for use in various object identification applications and lessens the time and effort required to gather large annotated datasets. This article proposes the YOLO model with transfer learning for intelligent surveillance in Internet of Thing (IoT)-enabled home environments in smart cities. Quantization concepts are being applied to optimize the YOLO model in this work. Using YOLO with quantization, the model is optimized for use on edge devices and mobile platforms, which have limited computing capabilities. Thus, even with limited technology, object detection systems may be used in various real-world applications. The proposed model has been validated on two different datasets of 7,382 images. The proposed model gains an accuracy level of 98.27%. The proposed method outperforms the conventional one. The use of the YOLO model and transfer learning with quantization has significant potential for enhancing ecological smart city monitoring, and further research and development in this area could contribute to developing more effective and efficient environmental smart city monitoring systems.

Introduction: In this communication the focus is on the summarization of the complex molecular pathways that Ginkgo biloba Linn. utilizes to treat metabolic syndromes, a major international health concern marked by interrelated risk factors like obesity, insulin resistance, dyslipidaemia, and hypertension. Method: Several databases, including the Web of Science, Google Scholar, PubMed, ScienceDirect, Scopus, and PubChem, were used while literature review was conducted. The use of suitable keywords like mechanism of Ginkgo biloba, pharmacological activities of medicinal plants, herbal remedies in metabolic syndromes etc. was made. It was attempted that all the English language articles published between 1980 and 2023 were referred. Results: Numerous molecular mechanisms contribute to the development of various metabolic syndromes and their risk factors. These mechanisms include insulin resistance, chronic low-grade inflammation, increased oxidative stress, dysregulation of lipid metabolism, impaired mitochondrial function, and others. This review summarizes the ways in which Gingko biloba, through modification of ROS formation, blocking the activation of NADPH oxidases, modifying the expression of antioxidant enzymes, downregulating MAPKs and AP-1, increasing cAMP, deactivating Stat5, activating the AMPK signalling pathway, influencing Stat3/JAK2, NF-κB, Nrf-2, mTOR, HGF/c-Met, Wnt/β-catenin, and BMP signalling pathways, modifying the mitochondrial transmembrane potential, etc., can alleviate disorders related to metabolic dysfunction. Discussion: The therapeutic properties of Ginkgo biloba Linn. and its constituents, including its anti-inflammatory, antioxidant, anti-apoptotic, and apoptotic activities, have drawn attention in recent years. In response to the growing public demand for novel therapeutics, prospective novel medicines may be produced by comprehending their molecular mechanisms and signalling networks.

Abstract Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

The skin is the body's largest organ. The epidermis and dermis make up the skin, and their primary purpose is to defend the body from adverse environmental factors like chemicals, allergens, toxins, and bacteria. Many different types of natural products have shown promise in the treatment of skin disorders. Multiple synthetic chemicals and environmental pollution have an impact on modern human existence. Therefore, nature has provided several essential ingredients for boosting skin health and shielding skin from environmental damage. The most significant in vivo and in vitro studies on the use of different natural products in inflammatory, cancerous, and skin infection disorders and their mechanisms of action were summarised in this review. The study also highlights the potential photoprotective effects of numerous herbal ingredients. Sunburn is caused primarily by the sun's ultraviolet rays, which can also cause cancer of the skin. Herbal ingredients with sunblocking properties can prevent the penetration of harmful ultraviolet radiation. Compared to their synthetic counterparts, herbal remedies have fewer adverse effects and are just as effective in treating chronic conditions. Flavonoids, polyphenols, carotenoids (Lycopene, carotene), and phenolic acids found in herbs rich in vitamins (A, C, and E) have antioxidant properties that increase photoprotection.

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Electric power is crucial for economic growth and the overall development of any country. The efficient planning of distribution system is necessary because all the consumers mainly rely on the distribution network to access the power. This paper focuses on addressing distribution system challenges and meeting consumers’ fundamental needs, such as achieving an improved voltage profile and minimizing costs within an environmentally sustainable framework. This work addressed the gap in the existing research by analysing the performance of both balanced and unbalanced systems within the same framework, specifically using the IEEE 33-bus and IEEE 118-bus test systems. Unlike prior studies that focused solely on either balanced or unbalanced systems, this work redistributed balanced loads into three-phase unequal unbalanced loads to create a more challenging unbalanced distribution network. The primary objective is to compare the effects of balanced and unbalanced loads on system the performances and to identify strategies for mitigating unbalanced load issues in each phase. Six optimization methods (PSO, TLBO, JAYA, SCA, RAO, and HBO) were employed to minimize losses, voltage variations, and other multi-objective function factors. Additionally, the study compared the cost of energy loss (CEL), emission factors, costs associated with distributed clean energy resources (DCER), and active and reactive power losses. Phase angle distortions due to unbalanced loads were also analysed. The results showed that among the optimization techniques tested (PSO, TLBO, JAYA, SCA, RAO, and HBO), the HBO method proved to be the most effective for the optimal allocation of distributed clean energy resources, yielding the lowest PFMO values and favourable outcomes across the technical, economic, and environmental parameters.

Abstract Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.

Introduction: Cardiovascular diseases are associated with increased morbidity and mortality among CKD (chronic kidney disease) population. Recent studies have found increasing prevalence of PH (pulmonary hypertension) in CKD population. Present study was done to determine prevalence and predictors of LV (left ventricular) systolic dysfunction, LVDD (left ventricular diastolic dysfunction) and PH in CKD 3b-5ND (non-dialysis) patients. Methods: A cross sectional observational study was done from Jan/2020 to April/2021. CKD 3b-5ND patients aged ≥15 yrs were included. Transthoracic 2D (2 dimensional) echocardiography was done in all patients. PH was defined as if PASP (pulmonary artery systolic pressure) value above 35 mm Hg, LV systolic dysfunction was defined as LVEF (left ventricular ejection fraction) ≤ 50% and LVDD as E/e′ ratio >14 respectively. Multivariate logistic regression model was done to determine the predictors. Results: A total of 378 patients were included in the study with 103 in stage 3b, 175 in stage 4 and 100 patients in stage 5ND. Prevalence of PH was 12.2%, LV systolic dysfunction was 15.6% and LVDD was 43.65%. Predictors of PH were duration of CKD, haemoglobin, serum 25-OH vitamin D, serum iPTH (intact parathyroid hormone) and serum albumin. Predictors of LVDD were duration of CKD and presence of arterial hypertension. Predictors of LV systolic dysfunction were eGFR (estimated glomerular filtration rate), duration of CKD, serum albumin and urine protein. Conclusion: In our study of 378 CKD 3b-5ND patients prevalence of PH was 12.2%, LV systolic dysfunction was 15.6% and LVDD was 43.65%. Resumen: Introducción: Las enfermedades cardiovasculares se asocian a un aumento de la morbilidad y la mortalidad entre la población con enfermedad renal crónica (ERC). Estudios recientes han encontrado un aumento de la prevalencia de la hipertensión pulmonar (HP) en la población con ERC. El presente estudio se llevó a cabo para determinar la prevalencia y los predictores de la disfunción sistólica del ventrículo izquierdo (VI), la disfunción diastólica del VI y la hipertensión pulmonar en pacientes con ERC 3b -5ND (sin diálisis). Métodos: Se realizó un estudio observacional transversal desde enero/2020 hasta abril/2021. Se incluyeron pacientes con ERC 3b -5ND de edad ≥15 años. Se realizó una ecocardiografía transtorácica 2D (bidimensional) a todos los pacientes. La HP se definió como un valor de presión sistólica de la arteria pulmonar superior a 35 mm Hg, la disfunción sistólica del VI se definió como una fracción de eyección del ventrículo izquierdo ≤ 50% y la DVL como una relación E/e′ > 14 respectivamente. Se realizó un modelo de regresión logística multivariante para determinar los predictores. Resultados: Un total de 378 pacientes fueron incluidos en el estudio con 103 en estadio 3b, 175 en estadio 4 y 100 pacientes en estadio 5ND. La prevalencia de la HP fue del 12,2%, la disfunción sistólica del VI fue del 15,6% y la DVL fue del 43,65%. Los predictores de la HP fueron la duración de la ERC, la hemoglobina, la vitamina D 25-OH en suero, la iPTH en suero (hormona paratiroidea intacta) y la albúmina en suero. Los predictores de la EVL fueron la duración de la ERC y la presencia de hipertensión arterial. Los predictores de la disfunción sistólica del VI fueron la tasa de filtración glomerular estimada, la duración de la ERC, la albúmina sérica y las proteínas en orina. Conclusión: En nuestro estudio de 378 pacientes con ERC 3b-5ND la prevalencia de la HP fue del 12,2%, la disfunción sistólica del VI fue del 15,6% y la DVL fue del 43,65%.

Abstract Billions of gadgets are already online, making the IoT an essential aspect of daily life. However, the interconnected nature of IoT devices also leaves them open to cyber threats. The quantity and sophistication of cyber assaults aimed against Internet of Things (IoT) systems have skyrocketed in recent years. This paper proposes a next-generation cyber attack prediction framework for IoT systems. The framework uses the multi-class support vector machine (SVM) and the improved CHAID decision tree machine learning methods. IoT traffic is classified using a multi-class support vector machine to identify various types of attacks. The SVM model is then optimized with the help of the CHAID decision tree, which prioritizes the attributes most relevant to the categorization of attacks. The proposed framework was evaluated on a real-world dataset of IoT traffic. The findings demonstrate the framework's ability to categorize attacks accurately. The framework may determine which attributes are most crucial for attack categorization to enhance the SVM model's precision. The proposed technique focuses on network traffic characteristics that can be signs of cybersecurity threats on IoT networks and affected Network nodes. Selected feature vectors were also created utilizing the elements acquired on every IoT console. The evaluation results on the Multistep Cyber-Attack Dataset (MSCAD) show that the proposed CHAID decision tree can significantly predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction is essential in managing cyber attacks in real-time communication. Because of its efficiency and scalability, the model may be used to forecast cyber attacks in real time, even in massive IoT installations. Because of its computing efficiency, it can make accurate predictions rapidly, allowing for prompt detection and action. By locating possible entry points for attacks and mitigating them, the framework helps strengthen the safety of IoT systems.

Abstract The patients’ vocal Parkinson’s disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson’s individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients’ speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson’s disease.

For power systems to operate reliably and effectively, state estimate is essential. For monitoring and control applications, it offers real-time estimates of the system's state indicators, including voltage levels and orientations. In this paper, a novel catagonfly optimization (CFO) technique that aims to improve state estimation effectiveness in electrical systems is presented. The suggested strategy combines optimization techniques from cat swarm and dragonfly. The suggested strategy is evaluated using the IEEE-118 test environment under various simulated scenarios, and the findings are contrasted with those of other methods already in use. The optimization results and statistical error assessment demonstrate the suggested CFO technique's efficiency to the alternatives. The results of this study also have the potential to improve the incorporation of sustainable energy sources, microgrids, and additional emerging innovations into existing power grids.

A variety of Electric Vehicle (EV) charging algorithms provide various EV charging load profiles, when utilized together, has an impact on the electrical grid functions. Present-day charging an EV Models of demand are either based on level of charging when an EV arrives or smart charging algorithms strengthened with specific charging levels and/or procedures. In this work, a brand-new data-driven technique for calculating EV charging load is suggested. They start by introducing a mathematical model that describes an adaptability of demand for EV charging. The characteristics of several EV load models are then identified, and advanced simulation techniques are suggested to simulate EV charging demand under various power market realizations. The suggested EV load modeling technique may act as a benchmark system by simulating various EV operating schedules, charging levels, and consumer engagement. The suggested framework would also give EV charging infrastructure advice from transmission system operators development in contemporary power networks.

Abstract Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree’s minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a ‘Ranker method’ to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model’s performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

This study explored the combined administration of docetaxel (DOC) and erlotinib (ERL) using nanostructured lipid carriers (NLCs), with folic acid (FA) conjugation to enhance their synergistic anticancer efficacy against triple-negative breast cancer. NLCs were developed through hot melt homogenization–ultrasound dispersion, and optimized by a quality-by-design (QbD) approach using Plackett–Burman design and Box–Behnken design. Plots were generated based on maximum desirability. Spherical, nanosized dispersions (

Heavy metal contamination has emerged as a global environmental concern, with tannery effluents serving as a significant source of these pollutants. The discharge of tannery effluents (TEs) into natural ecosystems has given rise to a spectrum of catastrophic risks, exacerbating concerns related to public health, safety, and environmental integrity. This current study focuses on the mycoremediation of the heavy metals present in TE, employing the mycelia of Pleurotus opuntiae, an environmentally sustainable solution. The toxicity of TE was rigorously characterized by evaluating a range of physicochemical parameters in accordance with the American Standard and Testing Methods. Subsequently, various diluted concentrations of effluent (25%, 50%, 75% and 100%) were incorporated into MDA media to assess the tolerance index (TI) of P. opuntiae. Notably, the highest TI was observed in the 25% and 50% TE concentrations, while no growth was observed in the 75% and 100% groups due to the exceptionally elevated heavy metal content. P. opuntiae demonstrated remarkable efficacy in heavy metal removal, with the most substantial reductions recorded in the 25% diluted effluent (91.3% Pb, 72.2% Cr and 66.5% Zn), closely followed by the 50% diluted effluent. The highest intracellular bioaccumulation was observed for Pb (17.2 µg/g), outperforming Cr (14.5 µg/g) and Zn (8.5 µg/g) in mycelia grown in 25% diluted effluent. To elucidate the detoxification mechanisms underlying metal removal, various characterizations of the mycelium were conducted, including SEM, FTIR, and XRD analyses. Furthermore, LC–MS analysis shed light on the pivotal role of metabolites in regulating heavy metals within the physiological metabolism of P. opuntiae. Moreover, an upsurge in the concentration of the stress marker, metallothionein, and augmented activity of antioxidant enzymes, like SOD, CAT, LPO and GSH, collectively suggested the significant role of antioxidants in mitigating reactive oxygen species (ROS) and heavy metal toxicity. These comprehensive findings provide a solid foundation for understanding the mechanisms responsible for heavy metal removal by P. opuntiae and pave the way for the development of effective remediation strategies for decontaminating the effluents discharged by the leather industry, contributing to the preservation of our environment and to public well-being.

Abstract Fruits significantly impact everyday living, i.e., Citrus fruits. Numerous fruits have a solid nutritious value and are packed with multivitamins and trace components. Citrus fruits are delicate and susceptible to many diseases and infections. Many researchers have suggested deep and machine learning-based fruit disease detection and classification models. This research presents a precise fruit disease identification model based on context data fusion with Faster-CNN in an edge computing environment. The goal is to develop an accurate, efficient, and trustable fruit disease detection model, a critical component of autonomous food production in a robotic edge platform. This research examines and explores four different diseases of Citrus fruits using CNN deep learning models to be adopted as edge computing solutions. Identification of citrus diseases such as cankers black spot, greening, scab, melanosis, and healthy citrus fruits are implemented using the proposed sequential model without pruning, with pruning having different sparsity levels followed by post quantization. Through the transfer learning method, this model is optimized for the assignment of fruit disease detection employing visuals from two patterns: Near-infrared (NIFR) and RGB. Early and late data fusion techniques for integrating multi-model (NIFR and RGB) facts are evaluated. The accuracy obtained from the proposed model for the canker disease is 97%, scab 95%, melanosis 99%, Greening 97%, Black spot 97% and healthy 97%. In this paper, the results of the proposed model are compared and evaluated with the sparsity levels of 50–80%, 60–90%, 70–90%, and 80–90% pruning and also obtained the results of post-quantization on each level. The results show that the model size with 60–90% pruning can be counteracted to the 47.64 of the baseline model without significant loss of accuracy. Moreover, post-quantization can reduce the 60–90% pruning from 28.16 to 8.72. In addition to enhanced precision, the above initiative is much faster to implement for new fruit diseases because it needs bounding box annotation instead of pixel-level annotation.

Abstract Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT-enabled environments focusing on proposing an ML-based model for predicting available parking space. The study evaluates the performance of various models including K-nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1-score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1-score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT-enabled environment. On the other hand, models such as K-nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT-enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real-time scenarios, and better user satisfaction in the IoT-enabled environment.

The interface between nanostructured materials and plant cell organelles, such as chloroplasts, and has been recently found to have potential to impart organelles with new functions and enhanced performances. The plant nanobionics-based technologies can be implemented to provide the precise quantity of nutrients and pest control systems to improve the crop productivity as the concerns are growing regarding various agricultural difficulties such as poor nutrient use, stagnant yields, nutrient deficiencies, climate change, and water scarcity. The creation of novel nanomaterial (NM) based-fertilizers and -pesticides has encouraged the assimilation of mineral nutrients as well as to control pests without harming the environment. These nanostructured materials are more effective in releasing nutrients in a site-specific manner, increasing plant uptake efficiency and decreasing nutrient loss, and targeting specific pests than conventional fertilizers and pesticides. This article discusses about recent advancement of innovative nanostructured materials that could transport nutrients, such as carbon-based nanoparticles (NP) and metal-based NP: Iron (Fe), Copper (Cu), Zinc (Zn), Silver (Ag), and Cerium (Ce) etc. We explored the potential development and implementation challenges for these NPs in this article and highlighted the importance of using a systems approach when creating nano bionics-based technology in the near future.

Surgical robots have been used for many years in the field of medical sciences and engineering. With sophisticated technology governing the world in recent years, surgical robots have benefited from the addition of sensors, actuators, and intelligent control systems, allowing them to function at high precision along with more efficiency. The focus of this study is to present a brief review of sensors and components utilized in robotic surgery that have been studied in recent literature. In the present study, initially, a brief history of robotic surgery is presented, followed by a review on sensors and components in robotic surgery that can be informative for engineers or researchers while designing surgical robots for specific applications. Consequently, highlights of recent trends in the technological advancements in surgical robots have been presented. Further, the paper concludes with recent advances and new challenges in the development of surgical robots.

Abstract By extending the standard holographic principle to a cosmological framework and combining the non-flat condition with the Kaniadakis entropy, we construct the non-flat Kaniadakis holographic dark energy (KHDE) model. The model employs Kaniadakis parameter K and a parameter c. Derivation of the differential equation for KHDE density parameter to describe the evolutionary behavior of the universe is obtained. Such a differential equation could explain both the open as well as closed universe models. The classification based on matter and dark energy (DE) dominated regimes show that the KHDE scenario may be used to specify the universe’s thermal history and that a quintom regime can be encountered. For both open and closed, we find the expressions for the deceleration parameter and the equation of state (EoS) parameter. Also, by varying the associated parameters, classical stability of the method is established. On considering the curvature to be positive, the universe favors the quintom behavior for substantially smaller values as opposed to the flat condition, when only quintessence is attained for such K values. Additionally, we see a similar behavior while considering the negative curvature for such K values. Therefore, adding a little bit of spatial geometry that isn’t flat to the KHDE enhances the phenomenology while maintaining K values at lower levels. To validate the model parameters, the most recent $$30\;H(z)$$ 30 H ( z ) dataset, in the redshift range $$0.07 \le z \le 1.965$$ 0.07 ≤ z ≤ 1.965 are utilized. In addition, the distance modulus from the current Union 2.1 data set of type SNIa are employed.

Osteoarthritis (OA) is the most prevalent age-related degenerative disorder, which severely reduces the quality of life of those affected. Whilst management strategies exist, no cures are currently available. Virtually all joint resident cells generate extracellular vesicles (EVs), and alterations in chondrocyte EVs during OA have previously been reported. Herein, we investigated factors influencing chondrocyte EV release and the functional role that these EVs exhibit. Both 2D and 3D models of culturing C28I/2 chondrocytes were used for generating chondrocyte EVs. We assessed the effect of these EVs on chondrogenic gene expression as well as their uptake by chondrocytes. Collectively, the data demonstrated that chondrocyte EVs are sequestered within the cartilage ECM and that a bi-directional relationship exists between chondrocyte EV release and changes in chondrogenic differentiation. Finally, we demonstrated that the uptake of chondrocyte EVs is at least partially dependent on β1-integrin. These results indicate that chondrocyte EVs have an autocrine homeostatic role that maintains chondrocyte phenotype. How this role is perturbed under OA conditions remains the subject of future work.

Chitosan is a naturally occurring linear biopolymer made of partially deacetylated acetyl and N-acetyl glucosamine. Its biocompatible physiochemical and biochemical properties are unmatched. Chitosan is transformed to nanopowder for use in agriculture and associated industries as nanocarriers for existing agrochemicals, ensuring the delayed release of chemicals with better solubility. Chitosan nanopowder applied to leaves or soil can activate a plant's natural defences against insects and pathogens. These studies were carried out because there is a potential for toxicological risk linked with products created utilizing nanotechnology, such as chitosan nanopowder, and therefore researchers felt the need to investigate this. The egg parasitoides Trichogramma Japonicum Ashmead was used as a low-cost biomarker to determine the potential toxicity of chitosan nanopowder. This study looked into the possibility that the adult stage of the egg parasitoids, Trichogramma Japonicum Ashmead might be negatively impacted by chitosan nanopowder (80–100 nm). Unpaired t-test statistical analysis has been carried out. According to the statistical analysis, host eggs exposed to chitosan nanopowder showed noticeably greater parasitization than the control group. As a natural supply of carbohydrate polymers chitosan nanopowder promotes the parasitization of T. Japonicum. The findings showed that T. Japonicum favoured chitosan nanopowder. Through Y dual choice, eight-arm multiple choice, and no-choice olfactometer experiments, as well as images from a stereozoom microscope and a scanning electron microscope (SEM), the data was thoroughly supported. Future agricultural applications of chitosan nanopowder will benefit from a deeper understanding of our findings.

Klippel-Feil syndrome is a rare congenital bone disorder characterised by a triad of short neck, low posterior hairline and limited lateral bending of the neck with an annual incidence of 1 in 40,000 live births. It has remained an obscure term in the medical literature because of its variability in presentation and wide spectrum of anomalies involving multiple organ systems. It is unusual to find a case that has all three classical triad features. Here, we present a case of a 9-month-old infant who manifests not only all three classical triad features associated with Klippel-Feil syndrome but also demonstrates the presence of congenital heart disease, scoliosis, and renal ectopia. An early comprehensive evaluation of a suspected case is essential for diagnosis and counselling which impacts its prognosis, helps minimize social stigma and affords parents the opportunity to consider cosmetic surgery as an option, should they choose to pursue it.

Entrepreneurship education has become essential in recent years. This education system may not be unconnected with the global agitation for value creation, employability skills and job creation. Engaging in entrepreneurial training provides students with the skills needed to enhance their ability to create marketable and profitable solutions to emerging problems. To do this, many emerging entrepreneurs rely on technology to engage in entrepreneurship education. This study presents a machine learning technique to predict the adaptability level of students in online entrepreneurship education. The suitability of different algorithms like Random Forest, C5.0, CART and Artificial Neural Network was examined using the Kaggle Educational dataset. The algorithms recorded a high accuracy rate and affirmed machine learning techniques' ability to forecast students' adaptation to online entrepreneurship training. The findings of this research contribute to the field of online entrepreneurship education by providing a reliable and efficient approach for predicting students' adaptability. The proposed modified ensemble machine learning model can assist educators and administrators in identifying students who may require additional support, tailoring instructional strategies, and designing targeted interventions to enhance their adaptability and overall learning experience in online entrepreneurship education.

Modern legal proceedings heavily rely on digital evidence as a basis for decisions in a variety of contexts, including criminal investigations and civil lawsuits. However, factors like data alteration, unauthorised access, or flaws in centralised storage can threaten the security and integrity of digital evidence. We suggest a decentralised methodology for using smart contracts to safeguard digital evidence in order to overcome these issues. The decentralised model makes use of smart contracts and blockchain technology to guarantee the integrity, transparency, and immutability of digital evidence. The approach does not require a centralised authority because it makes use of a distributed ledger, which lowers the possibility of data loss or manipulation. Multiple parties participating in the evidence lifecycle can build confidence and accountability thanks to smart contracts’ programmable rules and automated enforcement mechanisms. In our study, we show the decentralised model’s architecture and describe its essential elements, such as the blockchain network, smart contracts, and decentralised storage. We go over the advantages of employing this architecture, including enhanced auditability, decreased dependency on centralised institutions, and increased data security. Additionally, we discuss potential difficulties and constraints, like scalability and interoperability. We run a few simulations and experiments to test the suggested model’s viability and effectiveness while comparing it to conventional centralised methods. The outcomes show that our decentralised paradigm offers improved security for digital evidence, guaranteeing its reliability, usability, and tamper-proofness. We also go through how the model is used in actual legal systems, law enforcement organisations, and digital forensics investigations.

Abstract There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication.

The process of assigning bugs to particular programmers is called bug assignment in software engineering. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs precisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre-processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., functionality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recommendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more optimistic outcomes than existing ones.

The Travel Satisfaction Scale (TSS) was created to gauge public opinion on Delhi Metro travel. It has two affective dimensions and one cognitive dimension. This study leverages data from the Delhi Metro commuter trips to undertake new tests because there has been little research on its reliability and structure in the past. Differences in the TSS's reliability and structure – notably for the Delhi Metro and the demographics of the region – are also considered. Finally, the outcomes of this study imply that a single dimension of the affective dimension, rather than the two sub-dimensions, provides a better fit for the Delhi Metro, as well as other public transportation infrastructures in developing countries like India. Individual objects do not load on the two emotional dimensions as intended in a three-dimensional structure, which is more suited for public transportation. Two of the scale's elements – enthusiastic/bored and relaxed/hurried – were associated with the other items in a previous study differing from ours. Researchers should adapt the structure of the TSS in the future by adding or replacing some items with alternate options, which will make it easier to collect data and reduce the burden on the respondent, as well as increase the reliability of the data while maintaining the TSS's consistency and balance.

Background Tumor budding (TB) has been identified in many solid cancers and thought to be involved in invasion and is the initial step in the metastatic process. Limited information is available documenting the role of tumor budding in breast carcinoma. With this aim, the present study evaluates the association of tumor budding, tumor microenvironment, and its correlation with clinicopathologic parameters. Materials and Methods A total of 102 cases were archived and evaluated for peripheral and intra tumoral budding along with tumor microenvironment on hematoxylin and eosin (H&E) slides. Statistical Analysis Correlation between tumor budding, tumor microenvironment, and other classical clinicopathological parameters was studied by Chi-square test. A p-value less than 0.05 was considered significant. Results Females constituted 99 cases out of 102 and 3 were males. We found 55.9% and 44.1% of patients in the age group less than or equal to 50 and greater than 50, respectively. Also, 65.6% of cases presented with small tumor size less than or equal to 5 cm, 80.39% with lymph node metastasis, and 76.4% with lympho-vascular emboli. High peripheral tumoral budding (PTB) was seen in 45.10%, low peripheral tumoral budding in 54.9%, high ITB in 53.9%, and low ITB in 46.1%. Necrosis was found only in 39.21%. Significant statistical association of PTB was found with lymph node metastasis, lymphovascular emboli, and tumor necrosis, whereas ITB with tumor grade, lymph node metastasis, lympho-vascular emboli, and necrosis. Both PTB and ITB showed no statistically significant correlation with age and size of the tumor. Conclusion Tumor budding is an independent adverse prognostic factor in invasive breast carcinoma. However, further work is needed to establish a standard method for the quantification of this parameter, which will help in effective stratification of patients in terms of disease-free survival and likely outcome.