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Porphyromonas gingivalis (P. gingivalis) is a Gram-negative anaerobic bacterium that plays an important role in the development and progression of periodontitis. Hyaluronic acid (HA) is a naturally occurring glycosaminoglycan that has previously demonstrated antibacterial potential in vitro against multiple bacterial species, including P. gingivalis. The purpose of this systematic review is to evaluate the effectiveness of HA as an adjunctive topical antibacterial agent to non-surgical mechanical therapy of periodontitis in reducing the prevalence of P. gingivalis in subgingival biofilms. Five clinical studies were identified that satisfied the eligibility criteria. Only three trials were suitable for the meta-analysis as they provided data at three and six months. Data on the prevalence of P. gingivalis in each study were collected. The odds ratio (OR) for measuring the effect size with a 95% confidence interval (CI) was applied to the available data. The results did not favor the use of HA during non-surgical mechanical therapy to reduce the prevalence of P. gingivalis in subgingival biofilm (odd ratio = 0.95 and 1.11 at three and six months, consecutively). Within their limitations, the current data do not indicate an advantage for using HA during mechanical periodontal therapy to reduce the prevalence of P. gingivalis. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. Objective: This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. Methods: In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. Results: The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. Conclusion: This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses. [ABSTRACT FROM AUTHOR]

Porphyromonas gingivalis (P. gingivalis) is a cornerstone pathogen in the development and progression of periodontal and peri-implant tissue destruction. It is capable of causing dysbiosis of the microbial biofilm and modulation of the host immune system. Hyaluronic acid (HA) is a naturally occurring glycosaminoglycan found in all living organisms. It is well known and has been used for improving tissue healing. In addition, some studies have suggested that there may be an antimicrobial potential to HA. The aim of this study was to evaluate the effect of hyaluronic acid, azithromycin (AZM), and chlorhexidine (CHX) on the expression of genes (i.e., fimA, mfa1, hagA, rgpA, rgpB, and kgp) related to the virulence and adhesion of P. gingivalis. The study groups were divided into four: (1) HA treated group; (2) AZM treated group; (3) CHX treated group; and (4) untreated group to serve as a negative control. P. gingivalis ATCC 33277 was cultured and then exposed to four different concentrations (100% MIC, 50% MIC, 25% MIC, and 12.5% MIC) of HA, AZM, and CHX for 24 h. The expression levels of the aforementioned genes were measured using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Relative fold-change values were calculated and compared between groups. The fold-change values of all genes combined were 0.46 ± 0.33, 0.31 ± 0.24, and 0.84 ± 0.77 for HA, AZM, and CHX, respectively. HA has downregulated all the genes by mostly a half-fold: 0.35 ± 0.20, 0.47 ± 0.35, 0.44 ± 0.25, 0.67 ± 0.46, 0.48 ± 0.33 and 0.35 ± 0.22 with fimA, mfa1, hagA, rgpA, rgpB and kgp, respectively. The effect of HA was significant on all genes except rgpB compared to the untreated control. Lower concentrations of HA tended to exhibit greater downregulation with 1 mg/mL being the most effective. High molecular weight (1.5 MDa) hyaluronic acid has a potent effect on P. gingivalis by downregulating fimA, mfa1, hagA, rgpA, and kgp. The effect of HA was generally less than that of AZM but greater than that of CHX. [ABSTRACT FROM AUTHOR]

The resilience of small and medium-sized enterprises (SMEs) to disruptive events is significant as this highly prevalent category of business forms the economic backbone in developed countries. This article provides an overview of the application of a computational modelling and simulation approach to evaluate SMEs' operational resilience to flooding based on combinations of structural and procedural mitigation measures that may be implemented to improve their premises' resistance to flooding and safeguard their business continuity. The approach integrates flood modelling and simulation with agent-based modelling and simulation (ABMS) within a modelled geographical environment. SMEs are modelled as agents based on findings of semi-structured interviews with SMEs that have experienced flooding or are at risk of flooding. In this paper, the ABMS has been applied to a new case study of the major flood event of 2007 in Tewkesbury. Furthermore, to enable an evaluation of the operational resilience of manufacturing SMEs in terms of the relative effectiveness of flood mitigation measures, a new coefficient based on production loss is introduced. Results indicate structural mitigation measures are more effective than procedural measures. While this result is intuitive, the approach provides a means of evaluating the relative effectiveness of combinations of mitigation measures that SMEs may implement to enhance their operational resilience to flooding. This article is part of the theme issue 'Urban flood resilience'. [ABSTRACT FROM AUTHOR]

Background: In the digital era monitoring the patient's health status is more effective and consistent with smart healthcare systems. Smart health care facilitates secure and reliable maintenance of patient data. Sensors, machine learning algorithms, Internet of things, and wireless technology has led to the development of Artificial Intelligence-driven Internet of Things models. Objective: This research study proposes an Artificial Intelligence driven Internet of Things model to monitor Alzheimer's disease patient condition. The proposed Smart health care system to monitor and alert caregivers of Alzheimer's disease patients includes different modules to monitor the health parameters of the patients. This study implements the detection of fall episodes using an artificial intelligence model in Python. Methods: The fall detection model is implemented with data acquired from the IMU open dataset. The ensemble machine learning algorithm AdaBoost performs classification of the fall episode and daily life activity using the feature set of each data sample. The common machine learning classification algorithms are compared for their performance on the IMU fall dataset. Results: AdaBoost ensemble classifier exhibits high performance compared to the other machine learning algorithms. The AdaBoost classifier shows 100% accuracy for the IMU dataset. This high accuracy is achieved as multiple weak learners in the ensemble model classify the data samples in the test data accurately. Conclusions: This study proposes a smart healthcare system for monitoring Alzheimer's disease patients. The proposed model can alert the caregiver in case of fall detection via mobile applications installed in smart devices. [ABSTRACT FROM AUTHOR]

Recently, light fidelity (Li-Fi) technology becomes important for rapid data transmission in the network. Integration of different communication technologies can be used to address the traffic limitations posed by increasing number of Internet users. Hybrid Wi-Fi/Li-Fi networks (HLWN) make use of rapid Li-Fi data transmission and Wi-Fi coverage So, the combination of them can resolve the limitations of both technologies and enhance the efficiency of the overall network. At the same time, the choice of access point (AP) remains a difficult task as the coverage regions of various networks overlapped with one another. Based on instant channel data, the traditional load balancing schemes allocate every client to a certain AP, offering the optimum solutions at a time instance. But it can results in several handoffs for clients and degrade the throughput performance. Therefore, this article develops a Mobility aware LB using Kho–Kho optimization algorithm (MALB-KKOA) for HLWN. The presented MALB-KKOA technique is investigated in two aspects such as It is permissible to have both several AP associations (MA) and a single AP association (SA) (MA). In the SA phase, every client is aided by an AP either Li-Fi or Wi-Fi where the MALB-KKOA model allocates every client to a particular network over a time duration to reduce handover. On the other hand, in the MA phase, every client is concurrently assisted by Li-Fi as well as Wi-Fi, and no vertical handover takes place. In addition, the MALB-KKOA model is mainly based on the approaches utilized by the players in a tag-team game named Kho–Kho. Moreover, the MALB-KKOA model computes an objective function for the minimization of packet loss and delay. The experimental validation of the MALB-KKOA model is tested and the results signified its promising performance over other recent approaches. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) is a generally researched approach for decentralized controlling in difficult large-scale autonomous methods. Typical features create RL system as an appropriate candidate to develop powerful solutions in variation of healthcare fields, whereas analyzing decision or treatment systems can be commonly considered by a prolonged and sequential process. This study develops a new Penguin Search Optimization Algorithm with Multi-agent Reinforcement Learning for Disease Prediction and Recommendation (PSOAMRL-DPR) model. This research aimed to use a unique PSOAMRL-DPR algorithm to forecast diseases based on data collected from networks and the cloud by a mobile agent. The major intention of the proposed PSOAMRL-DPR algorithm is to identify the presence of disease and recommend treatment to the patient. The model manages the agent container with different mobile agents and fetched data from dissimilar locations of the network as well as cloud. For disease detection and prediction, the PSOAMRL-DPR technique exploits deep Q-network (DQN) technique. In order to tune the hyperparameters related to the DQN technique, the PSOA technique is used. The experimental result analysis of the PSOAMRL-DPR technique is validated on heart disease dataset. The simulation values demonstrate that the PSOAMRL-DPR technique outperforms the other existing methods. [ABSTRACT FROM AUTHOR]

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network's energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies. [ABSTRACT FROM AUTHOR]

Dealing with imbalanced data is a common challenge in machine learning, where one class has significantly fewer examples than another. Successfully addressing this challenge requires careful consideration of the data, algorithm, and evaluation metrics to ensure that the model accurately predicts the minority class. In this study, we present a hybrid approach called Siamese‐HYNAA, which combines a Siamese network and a population‐based optimizer hypercube natural aggregation algorithm (HYNAA) to generate candidate solutions for augmenting the minority class. We collected 10 imbalanced datasets ranging from 1.81 to 8.78 imbalanced ratios and built solution pairs based on correctly predicted candidate solutions using support vector machine (SVM). We then fed these solutions to the Siamese network, which employs a one‐shot learning approach to improve predictions with fewer candidate solutions. However, we found that SVM predicted only a small number of minority class samples accurately, prompting us to optimize the number of candidate solution pairs using HYNAA to generate more synthetic samples for the Siamese network. We evaluated our proposed strategy against basic SMOTE and our previous work, SMOTE‐PSOEV, using various performance measures, including ROC‐AUC learning curves, sensitivity, specificity, accuracy, Characteristic stability index, balanced accuracy, F1‐score, informedness, markedness, and execution time. Our results indicate that Siamese‐HYNAA generates promising results for imbalanced data. [ABSTRACT FROM AUTHOR]

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has enhanced many industries in terms of smart decision-modeling and performance assessment. Numerous developments in industries such as national security and police forces have been revolutionized to interpret data regarding ubiquitous instances. The current paper introduces an IoT-inspired system for evaluating the competence of police officers' performance. The work presented in this study focuses on evaluating several actions of police officers to determine overall conduct using the Bayesian Classification Model. The probabilistic integrity estimate (PIE) has been quantified using efficient data processing for effective decision-making. Furthermore, the 2-level decision tree model has been presented to evaluate police personnel's efficiency. The presented model is tested on challenging data sets obtained from the online repository for validation purposes. Conspicuously, the presented methodology outperformed state-of-the-art decision methods in terms of classification efficiency, a temporal delay, prediction estimation, stability, and reliability. [ABSTRACT FROM AUTHOR]

Hypocapnia is known to affect patients with acute stroke and plays a key role in governing cerebral autoregulation. However, the impact of hypocapnia on brain tissue pulsations (BTPs) is relatively unexplored. As BTPs are hypothesised to result from cerebrovascular resistance to the inflow of pulsatile arterial blood, it has also been hypothesised that cerebral autoregulation changes mediated by hypocapnia will alter BTP amplitude. This healthy volunteer study reports measurements of BTPs obtained using transcranial tissue Doppler (TCTD). Thirty participants underwent hyperventilation to induce mild hypocapnia. BTP amplitude, EtCO2, blood pressure, and heart rate were then analysed to explore the impact of hypocapnia on BTP amplitude. Significant changes in BTP amplitude were noted during recovery from hypocapnia, but not during the hyperventilation manoeuvre itself. However, a significant increase in heart rate and pulse pressure and decrease in mean arterial pressure were also observed to accompany hypocapnia, which may have confounded our findings. Whilst further investigation is required, the results of this study provide a starting point for better understanding of the effects of carbon dioxide levels on BTPs. Further research in this area is needed to identify the major physiological drivers of BTPs and quantify their interactions with other aspects of cerebral haemodynamics. [ABSTRACT FROM AUTHOR]

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies. [ABSTRACT FROM AUTHOR]

As the "Internet of Medical Things (IoMT)" grows, healthcare systems can collect and process data. It is also challenging to study public health prevention requirements. Virus transmission can be prevented by wearing a mask. The World Health Organization (WHO) recommends wearing a facemask to protect against the COVID‐19 pandemic—the levels of a pandemic rise across almost all regions of the world. By following the WHO rules, we support the development of face mask‐detecting technologies and determine whether or not people are using masks in public locations. The proposed paradigm in this paper will work in three stages. Firstly, we use an Image data generator to import the images. In addition to using a Haar cascade (HC) classifier for detecting faces, residual learning (ResNet152V2) trains a model that detects whether someone is wearing a face mask. Detection and classification are carried out in real‐time with high precision. Compared with other recently proposed methods, the model achieved 99.65% accuracy during training and 99.63% during validation. [ABSTRACT FROM AUTHOR]

Brain tumors are still diagnosed and classified based on the results of histopathological examinations of biopsy samples. The existing method requires extra effort from the user, takes too long, and can lead to blunders. These limitations underline the need of employing a fully automated deep learning system for the multi‐classification of brain tumors. In order to facilitate early detection, this study employs a convolutional neural network (CNN) to multi‐classify brain tumors. In this research, we present three distinct CNN models for use in three separate categorization tasks. The first CNN model can correctly categorize brain tumors 99.74% of the time. The second CNN model is 96.27% accurate in differentiating between normal, glioma, meningioma, pituitary, and metastatic brain tumors. The third CNN model successfully distinguishes between Grades II, III, and IV brain tumors 99.18% of the time. The Hybrid Particle Swarm Grey Wolf Optimization (HPSGWO) technique is used to quickly and accurately determine optimal values for all of CNN models most important hyperparameters. An HPSGWO algorithm is used to fine‐tune all the necessary hyperparameters for optimal classification performance. The results are compared with standard existing CNN models across a range of performance measures. The proposed models are trained using publicly available large clinical datasets. To verify their initial multi‐classification of brain tumors, clinicians and radiologists might use the proposed CNN models. [ABSTRACT FROM AUTHOR]

It is well known that the brain pulses with each cardiac cycle, but interest in measuring cardiac-induced brain tissue pulsations (BTPs) is relatively recent. This study was aimed at generating BTP reference data from healthy patients for future clinical comparisons and modelling. BTPs were measured through the forehead and temporal positions as a function of age, sex, heart rate, mean arterial pressure and pulse pressure. A multivariate regression model was developed based on transcranial tissue Doppler BTP measurements from 107 healthy adults (56 male) aged from 20-81 y. A subset of 5 participants (aged 20-49 y) underwent a brain magnetic resonance imaging scan to relate the position of the ultrasound beam to anatomy. BTP amplitudes were found to vary widely between patients (from ∼4 to ∼150 µm) and were strongly associated with pulse pressure. Comparison with magnetic resonance images confirmed regional variations in BTP with depth and probe position. [ABSTRACT FROM AUTHOR]

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and cloud computing are found to be effective in providing on-time medical support for patients by storing and accessing their clinical data on servers. There are some issues, such as communication delay, security, and energy loss in IoT sensors, that affect the quality of the service. In this research, a remote patient monitoring system using energy-efficient IoT sensors is proposed to meet security demands. The optimization of network clustering and cluster head selection is performed based on trust, inter- and intra-cluster distance, and energy level by the proposed SS-ROW algorithm, which is formulated by integrating the characteristics of the squirrel and sparrow with Ciphertext Key 2 Policy (CK2P) encryption on data. Further, an effective trust mechanism based on CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and KP-ABE (Key-Policy Attribute-Based Encryption) is established to attain better results in terms of the number of alive nodes, energy left, and throughput. [ABSTRACT FROM AUTHOR]

Distributed Power Generation and Energy Storage Systems (DPG-ESSs) are crucial to securing a local energy source. Both entities could enhance the operation of Smart Grids (SGs) by reducing Power Loss (PL), maintaining the voltage profile, and increasing Renewable Energy (RE) as a clean alternative to fossil fuel. However, determining the optimum size and location of different methodologies of DPG-ESS in the SG is essential to obtaining the most benefits and avoiding any negative impacts such as Quality of Power (QoP) and voltage fluctuation issues. This paper's goal is to conduct comprehensive empirical studies and evaluate the best size and location for DPG-ESS in order to find out what problems it causes for SG modernization. Therefore, this paper presents explicit knowledge of decentralized power generation in SG based on integrating the DPG-ESS in terms of size and location with the help of Metaheuristic Optimization Algorithms (MOAs). This research also reviews rationalized cost-benefit considerations such as reliability, sensitivity, and security studies for Distribution Network (DN) planning. In order to determine results, various proposed works with algorithms and objectives are discussed. Other soft computing methods are also defined, and a comparison is drawn between many approaches adopted in DN planning. [ABSTRACT FROM AUTHOR]

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion‐based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre‐trained CNN model named EfficientNetB0 is fine‐tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. [ABSTRACT FROM AUTHOR]

Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast the impact of EPS on macroeconomic indicators from 1980 to 2020. The analysis is carried out by employing the Vector Auto-Regression (VAR) approach. Impulse Response Functions (IRFs) results indicate that EPS decreases Gross Domestic Product (GDP). They exist in the short run and the long run. This research study's overall findings suggest that high EPSs have a negative impact on GDP. The study implies that policymakers should develop, adopt, and initiate some imperatives to control the unanticipated volatility and movements in EP. The study highlights that policy should be designed to prevent fluctuations in sustainable EP and plan conservative energy policies that motivate discovering alternative energy sources to meet increasing energy demand and improve economic growth. [ABSTRACT FROM AUTHOR]

Abstract Severe flooding has caused major damage and disruption to households, communities, businesses, and organizations in many parts of the world. In the United Kingdom (UK), flooding has been responsible for significant losses to the economy due to its impact on businesses, 99.9% of which are Small and Medium-sized Enterprises (SMEs). This paper reports on how agent-based modeling and simulation has been developed and used to assess the effectiveness of a range of physical/structural and social preparedness adaptation measures that can be implemented by manufacturing SMEs to reduce the impact of and expedite recovery from a major flood event. Results indicate the effectiveness of combinations of these adaptation measures in relation to a one in 1000 year flood event that has been modeled and simulated in a key industrial area of the UK which, in addition to having experienced severe flooding, has a high concentration of SMEs. Highlights • Agent based approach to assess flood preparedness measures on SMEs' recovery from flooding. • Over 100 semi-structured interviews used to develop agents representing manufacturing SMEs. • A one in 1000 year flood event modeled and simulated to enable SMEs affected to be identified. • Physical measures more effective than social measures in reducing damage and disruption to SMEs. [ABSTRACT FROM AUTHOR]

The Internet of Drones (IoD) allows for coordinated control of airspace for Unmanned Aerial Vehicles (UAVs), also known as drones. The decreasing costs of processors, sensors, and wireless connectivity have made it possible to use UAVs in many variety of military to civilian applications. While most applications utilizing the drones in the IoD have been real-time related, users are now interested in obtaining real-time services from drones that are tailored to a specific fly zone. This study develops a Sea Turtle Foraging Algorithm with Hybrid Deep Learning-based Intrusion Detection (STFA-HDLID) as a algorithm that recognizes and categorizes intrusions in the IoD environment. For this purpose, it is necessary to implement data pre-processing to standardize the input data via min-max normalization. Additionally, the feature selection process is also based on the STFA. Finally, a Deep Belief Network (DBN) with a Sparrow Search Optimization (SSO) algorithm is used for classification. A comprehensive experimental analysis is performed on a benchmark dataset to demonstrate the performance of the STFA-HDLID, which achieves maximum accuracy of 99.51% and 98.85% on the TON_IoT and UNSW-NB15 datasets, respectively, outperforming other techniques. [ABSTRACT FROM AUTHOR]

This paper presents a portable toolkit, SwitchNet, for extracting relations from textual input. We summarize four data protocols for relation extraction tasks, including relation classification, relation extraction, triple extraction, and distant supervision relation extraction. This neural architecture is modular, so it can take as input data at different stages of the information extraction process (simple text, text and entities or entity pairs as relation candidates) and compute the rest of the process (named entity recognition and relation classification). We systematically design four information flows to integrate the above protocols by sharing network building blocks and switching different information flows. This framework can extract multiple triples (subject, predicate, object) in one pass. This framework enhances the use of relation classification models in end-to-end triple extraction by inferring pairs of entities of interest and using the shared representation mechanism. [Display omitted] • Dividing the information extraction process into modular neural networks. • 4 information flows for integrating 4 relation extraction data protocols. • Integrating NER and RE subtasks through the POEOI inference. • Performance improvements for 4 relation extraction tasks. [ABSTRACT FROM AUTHOR]

With Deep Learning (DL) outperforming previous Machine Learning (ML) techniques in classifying images, the remote sensing community has recently shown an increased interest in using these algorithms to classify Land Use and Land Cover (LULC) using multispectral and hyperspectral data. Land Use (LU) and Land Cover (LC) are two types of cartographic data that are used to develop smart cities and monitor the environment. LULC classification can benefit greatly from successfully applying remote sensing Image Classification (IC) using high spatial resolution data. The acquisition of spatiotemporal data for LULC classification has been made more accessible because of recent improvements in spatial analysis and Deep Learning (DL) technology. Considering the quality of Deep Neural Networks (DNN) in related Computer Vision (CV) tasks and the enormous volume of remotely sensed data accessible, DL methods appear to be particularly promising for modelling many remote sensing problems. However, there are several issues with ground-truth, resolution, and the nature of data that have a significant impact on categorization performance. We propose a Reversible Residual Network (RAVNet), a hybrid residual attention sensitive segmentation approach, to precisely categorize LULC in this study. The suggested network is based on the VNet model, which extracts relevant information by mixing low-level and high-level Feature Maps (FM). The attention-aware features change adaptively to the integration of residual modules. Our system was tested on the National Agriculture Imagery Program (NAIP) dataset, and the findings demonstrate that our architecture is competitive against other learning models. • RAVNet, a hybrid residual attention-aware LULC extraction NN inspired by ResNet. • A residual VNet architecture allowing for DCNN optimization and performance enhancement. • ResNet tested on NAIP and demonstrates that it outperforms existing learning models. [ABSTRACT FROM AUTHOR]

Healthy brain tissue pulsates with the cardiac cycle, but whether brain tissue pulsations (BTPs) are impaired by tissue ischemia due to ischemic stroke is currently unclear. This study is the first to explore the clinical potential of measuring BTPs using ultrasound in acute ischemic stroke patients. BTPs were measured in 24 healthy volunteers (aged 52–82 years) and 14 acute ischemic stroke patients (aged 51–86 years) using a novel Transcranial Tissue Doppler (TCTD) method. Measurements were quick to perform and were well tolerated by all subjects. A mixed-methods approach was used for blinded analysis of recordings. This identified qualitative disruption of BTPs in acute stroke patients, which were used to create an analysis checklist. Blinded BTP analysis by novices using the checklist resulted in high sensitivity but low specificity for stroke detection. Quantitative analysis also identified differences between stroke and healthy participants, including weaker BTPs in stroke patients. This first study reporting BTP characteristics in acute ischemic stroke revealed weaker brain tissue pulsations and waveform disruption in acute stroke patients. However, further clinical evaluation using a larger sample size is required to confirm these findings and to explore whether TCTD monitoring might be beneficial for clinical neuromonitoring. • Brain tissue pulsation bulk amplitude is reduced in stroke patients. • Differences in waveform shape exist between stroke and non-stroke participants. • Brain tissue pulsation measurements may improve existing prediction scores. [ABSTRACT FROM AUTHOR]