Vermicomposting technology as a dynamic strategy to mitigate environmental crisis: a bibliometric study of last three decades.

Efficient recycling of resources forms the cornerstone of sustainable development. Among multiple options in stock for waste recycling, vermicomposting technology is regarded as a futuristic strategy, being tested in every part of the globe due to easy accessibility. Hence, a bibliometric study was planned to set a sight on global scientific trends encompassing vermicomposting research in last three decades. The data were retrieved from Google Scholar, Scopus and PubMed. Publications from different search engines were filtered out and 2064 unique documents were collected and illustrated in MS Excel and Vos-viewer. Inferences were drawn on significant aspects, such as publication growth trend, journal analysis and co-occurrence of keywords. The study revealed that the number of publications increased from 3 in 1992 to 166 in 2021. The number of citations also increased and peaked at 4314 in 2015. Following this, we clustered keywords using principle component analysis and worked out links between domains of vermicomposting. Vermicomposting conjoined to words substrate manipulation, quality improvement, heavy metal adsorption, and yield parameters. This implies that vermicompost is being explored for many alternate uses in addition to its use as a fertiliser. We concluded that vermicomposting is one of the promising technologies for waste recycling. It modulates plant growth and subdues stress in plants. Additionally, being an efficient adsorbent, it serves bioremediation of contaminated sites. Therefore, the future of this technology lies in synthesising nano-formulations, integrating into biosensor technology, simulating for predicting timelines under different conditions and making efforts to improve their adsorption.

In vivo effects of a selected thiourea derivative 1-(2-chlorobenzoyl)-3-(2,3-dichlorophenyl) against nociception, inflammation and gastric ulcerogenicity: Biochemical, histopathological and in silico approaches.

The current study was designed to investigate the potential of a synthetic therapeutic agent for better management of pain and inflammation, exhibiting minimal to non-existent ulcerogenic effects. The effect of 1-(2-chlorobenzoyl)-3-(2,3-dichlorophenyl) thiourea was assessed through model systems of nociception and anti-inflammatory activities in mice. In addition, the ulcerogenic potential was evaluated in rats using the NSAID-induced pyloric ligation model, followed by histopathological and biochemical analysis. The test was conducted on eight groups of albino rats, comprising of group I (normal saline), groups II and III (aspirin® at doses of 100 mg/kg and 150 mg/kg, respectively), groups IV and V (indomethacin at doses of 100 mg/kg and 150 mg/kg, respectively), and groups VI, VII, and VIII (lead-compound at 15 mg/kg, 30 mg/kg and 45 mg/kg doses, respectively). Furthermore, molecular docking analyses were performed to predict potential molecular target site interactions. The results showed that the lead-compound, administered at doses of 15, 30, and 45 mg/kg, yielded significant reductions in chemically and thermally induced nociceptive pain, aligning with the levels observed for aspirin® and tramadol. The compound also effectively suppressed inflammatory response in the carrageenan-induced paw edema model. As for the ulcerogenic effects, the compound groups displayed no considerable alterations compared to the aspirin® and indomethacin groups, which displayed substantial increases in ulcer scores, total acidity, free acidity, and gastric juice volume, and a decrease in gastric juice pH. In conclusion, these findings suggest that our test compound exhibits potent antinociceptive, anti-inflammatory properties and is devoid of ulcerogenic effects.

Implementation of frailty screening for older people living with HIV in Brighton, UK.

OBJECTIVES: To evaluate the implementation of frailty screening in people living with HIV (PLWH) in a large urban cohort of patients in Brighton, UK. METHODS: Focus group discussions with HIV professionals and PLWH interviews helped inform the design and implementation of the frailty screening pathway in the clinic. Data were collected from PLWH aged over 60 years attending their HIV annual health check from July 2021 to January 2023 (n = 590), who were screened for frailty by nurses using the FRAIL scale. We assessed the proportions of PLWH who screened as frail, prefrail or robust and compared patient characteristics across groups. All PLWH identified as frail were offered a comprehensive geriatric assessment delivered by a combined HIV geriatric clinic, and uptake was recorded. RESULTS: A total of 456/590 (77.3%) PLWH aged over 60 years were screened for frailty. Median age and time since HIV diagnosis (range) for those screened were 66 (60-99) years and 21 (0-32) years, respectively. In total, 56 (12.1%) of those screened were identified as frail, 118 (25.9%) as prefrail and 282 (61.8%) as robust. A total of 10/56 (18%) people identified as frail declined an appointment in the geriatric clinic. Compared with non-frail individuals, frail PLWH had been living with HIV for longer and had a greater number of comorbidities and comedications but were not chronologically older. CONCLUSIONS: Implementing frailty screening in PLWH over 60 years old is feasible in a large cohort of PLWH, as recommended by the European AIDS Clinical Society. More research is needed to determine if frailty screening can improve clinical outcomes of older PLWH and the use of the comprehensive geriatric assessment within HIV services.

Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the "ECG Images dataset of Cardiac Patients", comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method's main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management.

Effects of Exercise-based Management on Motor Symptoms in Parkinson's Disease - A Meta-analysis.

Parkinson's disease (PD) is the second most common neurological illness after Alzheimer's disease. According to research, medication alone can give palliative alleviation; however, freezing of gait (FOG) and balance can be treated with physical therapy. This meta-analysis aims to bridge gaps about exercise-based therapy's impact on balance and FOG in patients with PD. Google Scholar, CINHAL, Medline, PubMed, and PEDro were searched for 2016-2021 citations using the PIOD paradigm. Pooled effect size mean and SD were analysed using a fixed and random effects model. A total of 21 trials were included in this review, with SMD=0.60 and p=0.0007 utilising BBS. The pooled analysis revealed statistically significant impacts on exercise-based management in the experimental group. With SMD=0.87 and p<0.00001 using Mini-BESTest, the pooled analysis revealed that exercise-based management was also effective on balance in the experimental group. The fixed effect model of FOG in terms of SMD was used to draw the pooled effects of FOG in terms of SMD and FOG in terms of SMD (0.21; 95 percent CI -0.01 to 0.44; p=0.06). According to this research, several physiotherapy approaches such as exergaming, gamepad systems, virtual reality, gait exercises, and core training, help Parkinson's patients regain balance and FOG. Key Words: Parkinson's disease, Physical therapy techniques, Neurological rehabilitation, Balance, Freezing of gait, Motor symptoms.

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique.

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.

Seismic performance evaluation of plastered cellular lightweight concrete (CLC) block masonry walls.

The current research presents a novel and sustainable load-bearing system utilizing cellular lightweight concrete block masonry walls. These blocks, known for their eco-friendly properties and increasing popularity in the construction industry, have been studied extensively for their physical and mechanical characteristics. However, this study aims to expand upon previous research by examining the seismic performance of these walls in a seismically active region, where cellular lightweight concrete block usage is emerging. The study includes the construction and testing of multiple masonry prisms, wallets, and full-scale walls using a quasi-static reverse cyclic loading protocol. The behavior of the walls is analyzed and compared in terms of various parameters such as force-deformation curve, energy dissipation, stiffness degradation, deformation ductility factor, response modification factor, and seismic performance levels, as well as rocking, in-plane sliding, and out-of-plane movement. The results indicate that the use of confining elements significantly improves the lateral load capacity, elastic stiffness, and displacement ductility factor of the confined masonry wall in comparison to an unreinforced masonry wall by 102%, 66.67%, and 5.3%, respectively. Overall, the study concludes that the inclusion of confining elements enhances the seismic performance of the confined masonry wall under lateral loading.

Case of a WHO Grade II Atypical Meningioma in a 16-Year-Old Female.

Meningiomas have a high frequency of occurrence as primary intracranial tumors. We report the case of a 16-year-old female who presented with a three-week history of persistent headache, vomiting, and photophobia. Imaging studies revealed the presence of a meningioma in the right occipital lobe of the brain. The patient underwent surgical resection, and histopathological analysis confirmed the diagnosis of an atypical WHO grade 2 meningioma. The patient experienced a significant improvement in her symptoms postoperatively and had no evidence of recurrence on follow-up imaging. This case highlights the importance of considering meningioma in the differential diagnosis of relatively young patients presenting with chronic headaches, and the favorable prognosis associated with atypical WHO grade 2 meningiomas following complete surgical resection.

Aortic valve surgery in the pediatric population: pathophysiology, clinical features and management.

There are several conditions where the function of the aortic valve can be compromised in the pediatric population. The aortic valve is composed of three leaflets which are thin and mobile and are attached to the aortic sinuses. Each leaflet is made up of connective tissue, forming a highly ordered network of extracellular matrix components. Together, this enables the aortic valve to open and close more than 100,000 times throughout the day. However, there are conditions where the structure of the aortic valve can be compromised resulting in its function being affected. Conditions such as congenital valvular aortic stenosis and abnormal valve morphology including bicuspid valves often necessitate intervention to improve symptoms and quality of life in children. Other conditions which result in requiring surgical intervention include infective endocarditis and trauma. In this article, we present the common forms of aortic valve disease in the pediatric population and the clinical presentation and pathophysiology of these. We also discuss the range of management options including medical management and percutaneous intervention. Surgical interventions such as Aortic annular enlargement techniques, the Ross procedure and the Ozaki procedure will also be discussed. The effectiveness, complications and long-term outcomes associated with these methods will be explored.

Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data's characteristics. The proposed novel approach model RFMT analyzed Pakistan's largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky-Harabasz, Davies-Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

Current investigative modalities for detecting and staging lung cancers: a comprehensive summary.

This narrative review compares the advantages and drawbacks of imaging and other investigation modalities which currently assist with lung cancer diagnosis and staging, as well as those which are not routinely indicated for this. We examine plain film radiography, computed tomography (CT) (alone, as well as in conjunction with positron emission tomography (PET)), magnetic resonance imaging (MRI), ultrasound, and newer techniques such as image-guided bronchoscopy (IGB) and robotic bronchoscopy (RB). While a chest X-ray is the first-line imaging investigation in patients presenting with symptoms suggestive of lung cancer, it has a high positive predictive value (PPV) even after negative X-ray findings, which calls into question its value as part of a potential national screening programme. CT lowers the mortality for high-risk patients when compared to X-ray and certain scoring systems, such as the Brock model can guide the need for further imaging, like PET-CT, which has high sensitivity and specificity for diagnosing solitary pulmonary nodules as malignant, as well as for assessing small cell lung cancer spread. In practice, PET-CT is offered to everyone whose lung cancer is to be treated with a curative intent. In contrast, MRI is only recommended for isolated distant metastases. Similarly, ultrasound imaging is not used for diagnosis of lung cancer but can be useful when there is suspicion of intrathoracic lymph node involvement. Ultrasound imaging in the form of endobronchial ultrasonography (EBUS) is often used to aid tissue sampling, yet the diagnostic value of this technique varies widely between studies. RB is another novel technique that offers an alternative way to biopsy lesions, but further research on it is necessary. Lastly, thoracic surgical biopsies, particularly minimally invasive video-assisted techniques, have been used increasingly to aid in diagnosis and staging.

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization.

The extractive summarization approach involves selecting the source document's salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.

Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.

Physical and electrochemical properties of new structurally flexible imidazolium phosphate ionic liquids.

New structurally flexible 1-methyl- and 1,2-dimethyl-imidazolium phosphate ionic liquids (ILs) bearing oligoethers have been synthesized and thoroughly characterized. These novel ILs revealed high thermal stabilities, low glass transitions, high conductivity and wide electrochemical stability windows up to 6 V. Both the cations and anions of 1-methyl-imidazolium ILs diffuse faster than the ions of 1,2-dimethyl-imidazolium ILs, as determined by pulsed field gradient nuclear magnetic resonance (PFG-NMR). The 1-methyl-imidazolium phosphate ILs showed relatively higher ionic conductivities and ion diffusivity as compared with the 1,2-dimethyl-imidazolium phosphate ILs. As expected, the diffusivity of all the ions increases with an increase in the temperature. The 1-methyl-imidazolium phosphate ILs formed hydrogen bonds with the phosphate anions, the strength of which is decreased with increasing temperature, as confirmed by variable temperature 1H and 31P NMR spectroscopy. One of the representative IL, [EmDMIm][DEEP], presented promising elevated temperature performance as an electrolyte in a supercapacitor composed of multiwall carbon nanotubes and activated charcoal (MWCNT/AC) composite electrodes.

Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network's reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay.

The Moderating Effect of Learning Experience on Learning Motivation and Learning Outcomes of International Students.

With the increasing level of internationalization in higher education, the number of international students in mainland China is rapidly increasing. However, limited research has considered that student results may be affected by a reduced motivation to learn. Therefore, the aim of this research is to explore the effect of the learning motivation on the learning outcomes of international students and the moderating role of learning experience. A sample of 130 international students from 23 countries studying in mainland China was analyzed. The study found a significant correlation between the learning motivations and international students' learning outcomes. It was also determined that learning experience has significantly enhances the relationship between learning motivation and the learning outcomes of international students. This study contributes to the higher education literature on learning motivation by students and learning outcomes.

IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare.

Accurate classification of brain tumors is vital for detecting brain cancer in the Medical Internet of Things. Detecting brain cancer at its early stages is a tremendous medical problem, and many researchers have proposed various diagnostic systems; however, these systems still do not effectively detect brain cancer. To address this issue, we proposed an automatic diagnosing framework that will assist medical experts in diagnosing brain cancer and ensuring proper treatment. In developing the proposed integrated framework, we first integrated a Convolutional Neural Networks model to extract deep features from Magnetic resonance imaging. The extracted features are forwarded to a Long Short Term Memory model, which performs the final classification. Augmentation techniques were applied to increase the data size, thereby boosting the performance of our model. We used the hold-out Cross-validation technique for training and validating our method. In addition, we used various metrics to evaluate the proposed model. The results obtained from the experiments show that our model achieved higher performance than previous models. The proposed model is strongly recommended to be used to diagnose brain cancer in Medical Internet of Things healthcare systems due to its higher predictive outcomes.

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network.

Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13-17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.