Search

Agricultureis the golden thread that fastens all the sustainable development goals globally. However, the massive population explosion and ecosystem degradation have pressurized various auxiliaries of agriculture, primarily food security, crop protection, and disease identification. Although the penetration of digital technologies brings new opportunities to modern agriculture, the environmental facet has been neglected. Given this, the potential of sustainable computing and deep learning is investigated to handle critical agricultural technology impediments, lower resource expenditure, and propel sustainable agrarian developments. This research analyzes the relationship between Smart Agriculture and Sustainable Computing to balance the three pillars of Sustainable Agriculture practices—socio-economic–environment. Motivated by the analysis, the proposed work presents a deep learning-based lightweight, computation-efficient, performance-optimized, and explainable crop protection model to classify plant diseases. The proposed model reports accuracy, precision, recall, and F1-score of 99.4%, 99.4%, 99.5%, and 99.6%, respectively, outperforming state-of-the-art models. Further, the F1-score is improved by 15%, using 6.29 × fewer trainable parameters and 1.88 × fewer FLOPs that facilitate seamless deployment of the model on embedded devices, particularly for automated in situ plant disease classification. Moreover, to confirm the applicability of the proposed model across various crops, validation is conducted on additional crops, showcasing the model’s efficacy. The proposed model serves as a sustainable and innovative technological solution, aiding in the preservation of agricultural yields, enhancement of quality, and reduction of pesticide usage to safeguard the environment, achieved through energy-efficient resource utilization.

Forest cover prediction plays a crucial role in assessing and managing natural resources, biodiversity, and environmental sustainability. Traditional optimization algorithms have been employed for this task, but their effectiveness and efficiency in handling complex forest cover prediction problems are limited. This paper presents a novel approach, Annealing Lévy Quantum Inspired Particle Swarm Optimization (ALQPSO) that combines principles from quantum computing, particle swarm optimization; annealing, and Lévy distribution to enhance the accuracy and efficiency of forest cover prediction models by significant feature selection. The proposed algorithm utilizes quantum-inspired operators, such as quantum rotation gate, superposition, and entanglement, to explore the search space effectively and efficiently. By leveraging the principle of Lévy distribution and annealing, ALQPSO facilitated the exploration of multiple potential solutions simultaneously, leading to improved convergence speed and enhanced solution quality. To evaluate the performance of ALQPSO for forest cover prediction, experiments are conducted on the forest cover dataset. Initially, exploratory data analysis is performed to determine the nature of features. Thereafter, feature selection is performed through the proposed ALQPSO algorithm and compared with Quantum-based PSO (QPSO) and its variants. The experiments are conducted on all potential fields to identify the best among them. The experimental analysis demonstrates that ALQPSO outperforms traditional algorithms in terms of prediction accuracy, convergence speed, and solution quality (in terms of a number of features), highlighting its efficacy in addressing complex forest cover prediction problems.

In this study, we explore the impact of uniaxial strain on phonon characteristics of V2CTxMXene through a combination of Raman spectroscopy and first-principles calculation. The Raman spectrum of V2CTxMXene reveals in-plane E1gand E2gmodes at approximately 283 cm–1and 407 cm–1, respectively, stemming from the vibration of vanadium atoms. Through the application of mechanical strain, up to 3.9% along the uniaxial direction, noteworthy alterations are observed in the Raman active optical phonon modes of V2CTxMXene. Particularly, the degenerate phonon mode E1gundergoes a split into two sub-bands when the crystal symmetry is disrupted with minimal strain. The resulting sub-bands, denoted as E1g′ parallel to the strain direction and E1g″ perpendicular to the strain direction, display a blue shift in their respective modes under the influence of strain. The first-principles calculations demonstrate the consistent trend of splitting and blue shift in the E1gphonon mode under strain. Additionally, the Grüneisen parameter (γ) and shear deformation potential have been calculated through Raman spectroscopy and the phenomenological method, considering the diatomic chain model. This comprehensive study not only enhances our comprehension of the light–matter interaction in few-layer 2D V2CTxMXene but also provides valuable insights into the potential applications of this material in flexible optoelectronic devices.

As a central organizing principle of biology, bacteria and archaea are classified into a hierarchical structure across taxonomic ranks from kingdom to subspecies. Traditionally, this organization was based on observable characteristics of form and chemistry but recently, bacterial taxonomy has been robustly quantified using comparisons of sequenced genomes, as exemplified in the Genome Taxonomy Database (GTDB). Such genome-based taxonomies resolve genomes down to genera and species and are useful in many contexts yet lack the flexibility and resolution of a fine-grained approach. The Life Identification Number (LIN) approach is a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. Utilizing LINgroup as an organizational concept helps resolve some of the confusion and unforeseen negative effects resulting from nomenclature changes of microorganisms that are closely related by overall genomic similarity (often due to genome-based reclassification). Our experimental results demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework. They also reveal the robustness of LIN assignment to hyper-parameter changes when considering within-species taxonomic groups.

Nanotechnology, with its distinctive characteristics and versatile applications, demonstrates considerable potential in detecting, removing, and degrading pollutants such as heavy metals, organic compounds, and microplastics. The utilization of nanotechnology for environmental remediation presents a promising and innovative approach to tackle the growing environmental challenges. This review article offers a thorough examination of recent advancements in nanotechnology and its application in addressing various types of environmental pollutants and highlights the potential of nanomaterials, such as metal-based nanoparticles, carbon nanotubes, and nanofibrous membranes, in various remediation processes. The article investigates the underlying mechanisms of nanomaterials in pollutant adsorption and degradation, highlighting the significance of their high surface-to-volume ratio, enhanced reactivity, and customizable surface properties. Nanoadsorbents demonstrate high adsorption capacities and selectivity for the removal of pollutants from water and soil. Nanofiltration membranes offer precise separation capabilities for the removal of contaminants from water sources. Photocatalysis using nanomaterials shows promise for air pollution remediation. Additionally, the development of nanosensors enables real-time monitoring and detection of pollutants. Overall, nanotechnology offers innovative and efficient solutions for addressing environmental pollution.

In the competitive landscape of the telecom sector, a Communication Service Provider's success hinges on its ability to offer compelling mobile plans tailored to diverse customer needs. This not only boosts company profits but also enhances metrics like average revenue per user (ARPU), customer lifecycle value, and reduces customer churn. Striking a balance between these objectives presents a formidable task. To address this challenge, we propose a novel approach called Genetic Algorithm Mobile Plan Generation (GA-MPG). The proposed method stands out for its deterministic approach that equally focuses on minimizing customer churn. This is done by providing them with the best-suited plans without making them pay extra for features they would use. The efficient mobile plan generation using GA-MPG is accomplished by the combination of the AdaBoost classifier and the Fuzzy model. The AdaBoost is utilized for feasible mobile plan generation and predicting the optimal solution amongst the various plans. Additionally, a fuzzy model recommends personalized plans based on customers' typical service usage. This also maximizes company profits, contrasting with existing strategies employed by various telecom companies which focus on one of the two problems. The proposed GA-MPG algorithm demonstrated promising results on a prominent US-based telecom dataset encompassing around 7000 customers, with a substantial 44% reduction in customer churn. These findings are based on the simulation results. The algorithm also shows improvements of 13% and 18% in ARPU and company profit, respectively, over a defined period.

Background:Various inotropes and inodilators have been utilized to treat low cardiac output syndrome after the arterial switch operation. The use of levosimendan, a calcium sensitizer has been limited in this setting. This study compares the effects of levosimendan with milrinone in managing low cardiac output after the arterial switch operation. Methods:A retrospective, comparative study was conducted in a tertiary care hospital on patients weighing up to 3 kg undergoing the arterial switch operation between January 2017 and January 2022. Patients received a loading dose followed by continuous infusion of either levosimendan or milrinone. Echocardiographic, hemodynamic and biochemical parameters were compared. Results:Forty-three patients received levosimendan and 42 patients received milrinone as the primary test drug. Cardiac index of less than 2.2 L/min/m2on postoperative day 1 and 2 was found in 9.3% and 2.3% of patients receiving levosimendan versus 26.2% and 11.9% in those receiving milrinone, respectively (P= .04 and .08, respectively). Early lactate-clearance and better central venous oxygen saturations were noted in the levosimendan group. Prevalence of acute kidney injury was higher in the milrinone group (50% vs 28%; P= .03). Use of peritoneal dialysis in the milrinone group versus levosimendan was 31% and 16.3%, respectively (P= .11). There was no difference in hospital mortality between the groups (milrinone, 3; levosimendan, 2, P= .62). Conclusions:Levosimendan is safe and as effective as milrinone to treat low cardiac output syndrome occurring in neonates after the arterial switch operation. In addition we found that levosimendan was renal protective when compared with milrinone.

Chronic periodontitis and type 2 diabetes mellitus (T2DM) are associated with cognitive decline when examined individually. To gain deeper insight into the combined effects of these conditions on cognitive decline, the present study aimed to examine the cognitive status of individuals with co-occurring T2DM and chronic periodontitis.

Sebaceous carcinoma is a rare cancer, and little is known about its current epidemiology and treatment. This is particularly true for sebaceous carcinomas of the trunk and extremities.

Serial cardiovascular magnetic resonance evaluation of children and young adults with SCD who underwent hematopoietic cell transplantation showed mean ECV, representing diffuse myocardial fibrosis, decreased 3.4% from baseline to 12 months posttransplantation. This trial was registered at www.clinicaltrials.govas #NCT04362293.

This study examines how well a heterogeneous dual-hop terrestrial radio frequency and underwater acoustic downlink system maintain confidentiality. It includes an RF source, an intermediate decode-and-forward relay, an underwater multi-hydrophone destination, and a nearby eavesdropper. Eavesdropper overhears underwater acoustic signal transmitted by the relay. RF and underwater acoustic links undergo Nakagami-m and Rayleigh fading, respectively. Due to the propagation delay of underwater scenario, both destination and eavesdropper links are associated with outdated channel state information which also employs maximal-ratio combining. The closed form expressions of secrecy outage probability, strictly positive secrecy capacity, and intercept probability are derived. Monte-Carlo simulations are obtained to certify the analytical expressions.

Nine out of 19 genes encoding GABAAreceptor subunits have been linked to monogenic syndromes characterized by seizures and developmental disorders. Previously, we reported the de novo variant p.(Thr300Ile) in GABRA4in a patient with epilepsy and neurodevelopmental abnormalities. However, no new cases have been reported since then. Through an international collaboration, we collected molecular and phenotype data of individuals carrying de novo variants in GABRA4. Patients and their parents were investigated either by exome or genome sequencing, followed by targeted Sanger sequencing in some cases. All variants within the transmembrane domain, including the previously reported p.(Thr300Ile) variant, were characterized in silico and analyzed by molecular dynamics (MD) simulation studies. We identified three novel de novo missense variants in GABRA4(NM_000809.4): c.797 C > T, p.(Pro266Leu), c.899 C > A, p.(Thr300Asn), and c.634 G > A, p.(Val212Ile). The p.(Thr300Asn) variant impacts the same codon as the previously reported variant p.(Thr300Ile) and likely arose post-zygotically as evidenced by sequencing oral mucosal cells. Overlapping phenotypes among affected individuals included developmental delay (4/4), epileptiform EEG abnormalities (3/4), attention deficits (3/4), seizures (2/4), autistic features (2/4) and structural brain abnormalities (2/4). MD simulations of the three variants within the transmembrane domain of the receptor indicate that sub-microsecond scale dynamics differ between wild-type and mutated subunits. Taken together, our findings further corroborate an association between GABRA4and a neurological phenotype including variable neurodevelopmental, behavioral and epileptic abnormalities.

The presence of oxidative stress in bone defects leads to delayed regeneration, especially in the aged population and patients receiving cancer treatment. This delay is attributed to the increased levels of reactive oxygen species (ROS) in these populations due to the accumulation of senescent cells. Tissue-engineered scaffolds are emerging as an alternative method to treat bone defects. In this study, we engineered tissue scaffolds tailored to modulate the adverse effects of oxidative stress and promote bone regeneration. We used polycaprolactone to fabricate nanofibrous mats by using electrospinning. We exploited the ROS-scavenging properties of cerium oxide nanoparticles to alleviate the high oxidative stress microenvironment caused by the presence of senescent cells. We characterized the nanofibers for their physical and mechanical properties and utilized an ionization-radiation-based model to induce senescence in bone cells. We demonstrate that the presence of ceria can modulate ROS levels, thereby reducing the level of senescence and promoting osteogenesis. Overall, this study demonstrates that ceria-infused nanofibrous scaffolds can be used for augmenting the osteogenic activity of senescent progenitor cells, which has important implications for engineering bone tissue scaffolds for patients with low regeneration capabilities.

During this digital era, substantial visual data is being generated by various multimedia sources. A technique is urgently required to provide a clear and accurate summary of the original video to highlight the most informative segments of the video content. However, the varying video resolution, multi-dimensional feature representation, and massive storage create difficulties in key frame extraction techniques. As a result, many unnecessary features of the videos must be dropped to count their unique qualities. Therefore, a novel deep learning-based approach is proposed where frames are first retrieved using 25 frames per second; then, objects are detected on extracted frames using YOLOv5, and the frames with the target object only are processed further to overcome time consumption and high-speed computing hardware limitations. Further features are obtained using VGG-16 and object of Interest (OoI) based ResNet-50, respectively, and comparisons are performed to find the best solution. The extracted features are compressed using Principal Component Analysis (PCA) based on unsupervised Learning, which may efficiently minimize information loss, and potentially reduce dimension. By performing a comprehensive evaluation to obtain the best value of K using the Silhouette score, Candidate frames are extracted with the maximum mean and standard deviation from each K-means algorithm-based cluster. Pearson Correlation Coefficient (PCC) is used as a post- processing step to remove the redundant frames from the candidate frames and final keyframes extraction. The experiment was performed on the benchmark office dataset from industrial surveillance, which outperforms the state-of-the-art models in terms of recall score.

Defining lipid goals solely on low-density lipoprotein-cholesterol (LDL-C) levels in Indian population may cause misclassification due to high prevalence of hypertriglyceridemia and small dense LDL-C particles. International guidelines now recommend Apoliporotein-B (Apo-B) and non-high-density lipoprotein-cholesterol (non-HDL-C) levels as alternative targets. In this study, we used a cross-sectional representative population database to determine Apo-B and non-HDL-C cut-offs corresponding to identified LDL-C targets and compared them to international guidelines.

Consumers constitute one of the most important elements of marketing environment. Understanding their sentiments towards marketing practices is a sine qua non for the development of effective marketing strategies, thus garnering their patronage, and attaining improved business performance. Based on Gaski and Etzel’s (1986) framework, the present study attempts to investigate consumer sentiments towards marketing in India and compare the results with those of consumers in select other countries. Analysis of the data collected through a survey of consumers located in Delhi and the NCR region reveals consumers to be holding moderately favourable opinion that business firms pursue customer-oriented business philosophy. As regards marketing mix elements, retailing/selling emerges as the element towards which consumers have expressed the most positive sentiments. Practices in relation to advertising and product come next in order of favourability in consumer perceptions. Pricing is the variable for which consumers appear holding the least favourable opinion. In overall terms, though the consumer sentiments towards marketing in India are found only moderately favourable, these turn out to be relatively much more favourable when compared with those of consumers in other countries.

The accelerated cyberattacks presents a severe challenge to the companies, as they seem unprepared to confront the threat of cyberattacks, they will suffer enormous losses and have their performance suffer as a result. To better serve its population and communities, Kuwait will have improved and updated its national infrastructure by 2035. This study examines how the governmental top management support, cyber security readiness, and technology readiness affect employee's organizational security adoption intentions in Kuwait governmental organizations and realization of its benefits. The quantitative method was employed in this work. The study found that top management support influencing organizational security performance mediating by cyber security readiness and technology, which affects the tangible and intangible benefits. This study can help policy makers in governmental organizations to improve cyber security adoption. The findings of this study may be utilized for enhancing the sustainability of cyber security in governmental organizations in Kuwait.

Artificial intelligence (AI) and emotional intelligence (EI) are primary game changers in Industry 4.0. To ensure growth, organisations look to technological advances for support but should remain focused on developing people and resources that power organisations and drive it forward. This study attempts to combine these two concepts. This research investigates the impact of EI and AI on employee performance with focus on the Indian service industry. The data was collected from different service industry employees. Employee performance has been observed through internal and external services provided to customers and co-workers, respectively. Descriptive statistics and PROCESS macro were used to test the mediation (Model 4) and moderation model (Model 1). Both EI and AI significantly impact employee performance. All the bivariate correlations were significant at the 0.01 level. Correlations between the dimensions of EI and dimensions of employee performance were higher as compared to dimensions of AI and employee performance.To conclude, EI has a major impact on employee performance, while AI moderates the relationship between EI and employee performance.

[Display omitted]

This study addresses two critical aspects of gesture classification: data acquisition and feature extraction. It introduces an efficient surface electromyogram (sEMG) acquisition band with seven active electrodes and conducts experiments with eight healthy subjects for six gestures. A novel feature extraction method, the Hjorth secant line (HSL), is also introduced. The self-generated dataset (DB1) and two publicly available datasets (DB2 and DB3), containing data from both healthy subjects and amputees, are analyzed to assess the proposed feature set’s performance. The study evaluates the sEMG acquisition system’s effectiveness by measuring the signal-to-noise ratio (SNR), and the performance of the proposed feature set was evaluated in terms of gesture classification accuracy. The feature set’s performance is compared to other existing feature sets in the literature. In addition, a time complexity analysis is performed for the proposed feature set. The SNR for the sEMG acquisition system is 49.36 ± 5.50 dB, demonstrating its efficiency. When using the proposed feature set with a random forest (RF) classifier, the study achieves the classification accuracy of 97.94% ± 0.47%, 98.94% ± 0.46%, and 82.16% ± 2.07% for DB1–DB3, respectively. Paired ${t}$ -tests indicate that the proposed feature set significantly improves gesture classification accuracy across all three datasets ( ${p}$ -value < 0.05). The time required to calculate the proposed features was 0.207 ms, which is shorter than the computational time for other feature extraction methods reported in the literature. This study highlights the enhanced SNR from the proposed acquisition system and the feature set’s potential to improve gesture classification. These findings have substantial implications for advancing intelligent prosthetics.

Plant disease diagnosis is one of the latest critical research areas of sustainable agriculture. The evolution of computer vision-based systems in order to identify, classify and localize diseases has automated the process of plant disease identification. CNNs are the pre-eminent deep learning-based algorithms used to automate plant disease recognition that has proven decisive on various benchmarks. However, a substantial part of the research lacks adequate attention to specific issues like the unavailability of datasets, high annotation costs and non-conformity of the models. Therefore, there is a pressing need to exploit the latest trends and technologies in this area to solve the above-mentioned problems. As a step ahead in this direction, a new framework has been proposed using semi-supervised & ensemble learning. The proposed framework is validated through a series of experiments on benchmark datasets. The results reported a significant performance improvement in classifying plant diseases, outperforming existing works with an improvement of 18.03% and 15% regarding the accuracy and F1 score, respectively. The mean average precision for detection is improved by 13.35%. Findings from this research will be beneficial for farmers, plant pathologists and researchers, which in turn will strengthen the sustainable facet of agriculture.

Vivipary is an unusual reproductive process in which seed germination starts inside the fruit while it is still attached to the plant. Initiation of seed germination and heat unit accumulation or growing degree days (GDD) in mango fruit have been attributed as major causes for jelly seed formation, which is a significant constraint in the quality fruit production of mango. We studied the incidence of vivipary and heat unit accumulation or GDD in the fruits of medium and late maturing mango cv. ‘Dashehari’ and ‘Amrapali’, respectively, and their effects on the occurrence of jelly seed in mango. Jelly seed occurrence, heat unit accumulation or GDD, Total Soluble Solid (TSS), carotenoids, antioxidants, calcium, potassium, zinc, copper and phenolic compound contents in viviparous non-jelly pulp were investigated. To the best of our knowledge, this is the first report of occurrence of vivipary in mango cultivars ‘Dashehari’ and ‘Amrapali’ under subtropical climatic condition. Our findings show that excessive heat unit accumulation or GDD and vivipary/seed germination are not the cause/factors that induce jelly seed disorder. Vivipary and jelly seed formation are independent biological phenomenon. TSS, phenolic content, carotenoid content and antioxidants were significantly higher in viviparous fruit. Jelly seed incidence is higher in tree-ripe fruits than in non-tree-ripe fruits of early and late mango varieties, and it seems to be genetic and environmental factors and their interaction that govern the induction and level of incidence of jelly seed formation. A better understanding of the problem will help in the development of a package of practices for production of consistently superior quality mango fruits by reducing adverse effects of jelly seed malady.

Face recognition system have gained significant attention from last few years due to its applications in several domains such as security, authentication, and surveillance. The performance of face recognition is affected by analysing facial quality with a single property. However, there are still some attributes, such as occlusion and entropy, that are not well studied but play a significant role in face recognition. Measuring the quality of images is likely to filter out poor-quality images, which can improve the performance of downstream tasks. This paper presents a comparative study of various face image quality assessment (FIQA) techniques to select the best image for better recognition. In this study, the author’s measure some image quality factors to estimate the technique in the context of a face recognition system, which is followed by proposing an efficient fusion technique to combine all these factors to get a single face image quality index. The proposed technique has been tested statistically to obtain the confidence level between the FIQA technique and human observers, and it has demonstrated better performance in face recognition techniques.

Background: In leprosy, peripheral nerve involvement is well-documented, cranial nerve impairment in leprosy is less frequently reported, often through isolated case reports. This review aims to elucidate the pattern and spectrum of cranial nerve involvement in leprosy patients, enhancing understanding about pathogenesis and management. Methods: Adhering to PRISMA guidelines, we conducted a systematic review of case reports and series documenting cranial nerve involvement in leprosy. Searches were performed across PubMed, Scopus, Embase, and Google Scholar up to February 2, 2024, without language restrictions. Results: We identified 40 documents reporting on 49 patients, with a mean age of 41.3 years and a predominance of male patients (87.6%). Cranial nerve involvement included the trigeminal nerve (28.6%), facial nerve (38.8%), and instances of multiple cranial nerve palsies (10.2%). Magnetic resonance imaging findings indicated nerve T2/FLAIR hyperintensity/enhancements. Neuroimaging abnormalities extended up to brain stem. Approximately 30% of patients experienced lepra reactions, with 51% showing improvement following treatment. Following mutidrug therapy (MDT), neuroimaging abnormalities were vanished. Conclusion: Cranial nerve involvement in leprosy primarily affects the trigeminal and facial nerves, with multiple cranial nerves also being implicated. Exaggerated inflammation during lepra reaction involve nerve trunks and/or brainstem nuclei.

Video summarization (VS) is crucial process for compacting video content into a concise and informative representation, enhancing accessibility and the user experience. This work introduces a new approach based on spatiotemporal features derived from long short-term memory and pretrained convolutional neural network (CNN) models for static VS. It utilizes dual-CNN to identify keyframes by extracting features from benchmark datasets that contain user-generated summaries as the ground truth. Additionally, the incorporation of self-organizing map clustering into the dual-CNN model is investigated for superior performance compared to alternative clustering strategies. This spatiotemporal-based VS method effectively selects the most representative frames from the extracted spatiotemporal features. Unlike traditional methods, it does not require training on specific VS datasets, eliminating the need for extensive labeled data. Compared to existing state-of-the-art techniques in the literature, the proposed approach demonstrates promising results, consistently generating high-quality video summaries across various content categories. It achieved average F-scores of 84.7%, 86.4%, 61.9%, and 53.6% on four benchmark Open Video, YouTube, TVSum, and SumMe datasets, respectively, showing its effectiveness in producing informative video summaries.

This study explores the factors that affect the accumulation of employee emotional capital and the subsequent returns generated in an organisation. It uses qualitative research methods based on interpretative phenomenological analysis (IPA) to collect data which enables an in-depth investigation of the lived experience of the participants. This study identifies how factors like investment, leadership, culture, and systems impact the accumulation of employee emotional capital in an organisation. It also identifies which specific returns emotional capital can generate for individual employees and the organisation. This study also augments the Socio-technical systems theory (Trist and Bamforth, 1951) and makes it more relevant for the current business scenarios. The study provides a comprehensive understanding of factors which can strengthen the integration of technology and human components of an organisation and generate a higher order performance.

This study investigates the antimicrobial, antioxidant, and phytochemical properties of banana extracts from both ripe and unripe pulp and peel, using various solvents, including ethanol, methanol, hexane, and aqueous solutions. Antimicrobial test reveals the effectiveness of unripe pulp aqueous extract against Staphylococcus aureus,while the unripe pulp ethanol extract against Bacillus subtilisamong gram-positive bacteria. For gram negative bacteria, unripe pulp ethanol extract was most effective against Acinetobacter baumannii,and unripe pulp aqueous extract against Pseudomonas aeruginosa.In antifungal tests, ethanol extract of ripe banana pulp (1.8 ± 0.081) and aqueous extract of unripe pulp (2.5 ± 0.081) show effectiveness against Fusarium oxysporum.However, for Alternaria alternata, the aqueous extract of unripe pulp (1.8 ± 0.081) and methanolic extracts showed stronger inhibition compared to other samples. Phytochemical analysis detected secondary metabolites, including saponins, terpenoids, tannins and phytosterols. The highest polyphenol content was found in the ethanol extract of unripe peel (1.35 ± 0.01 mg GAE/g), and the highest flavonoid content in unripe peel ethanol extract (1.358 ± 0.03 mg QE/g). In contrast, carotenoid levels showed variation among extracts with no direct correlation to fruit maturity. Gas Chromatography–Mass Spectrometry analysis identified 14 bioactive compounds that were found to be common among all the solvent extracts of banana samples. Furthermore, the study reveals a decrease in free radical scavenging capacity with fruit maturity and solvent choice. The study suggests that both the banana fruit pulp and peel can result in a valuable source of natural antimicrobial and antioxidant agents for potential health and medicine applications.

Brain tumors present a significant challenge to healthcare professionals and can impact individuals of any age. Despite advancements in medicine, early detection and effective treatment remain challenging, often resulting in poor patient outcomes. To address this issue, a convolutional neural network (CNN) model is proposed for segmenting three-dimensional (3D) computed tomography (CT) images to predict the type of brain tumor present. This approach involves applying various pre-processing techniques to the widely used BraTS2020 and BraTS2021 datasets, which are essential in brain tumor research. The pre-processing methods aim to enhance the quality of the CT images, making them suitable for use in a 10-layer CNN architecture for brain tumor classification and prediction. By accurately identifying the type of brain tumor, the most effective treatment strategy can be determined. Extensive experiments on these datasets show that the proposed approach outperforms other contemporary 3D segmentation models, achieving superior recall, precision, Hausdorff, and F1-Score metrics, with Dice Similarity Coefficients of 89.63% and 88.96%, respectively. The proposed method demonstrates significant potential to improve the early detection and treatment of brain tumors, leading to better patient outcomes. Using a CNN model for image segmentation and classification allows healthcare professionals to identify and classify brain tumors accurately and efficiently, enabling the most effective treatment strategies for their patients.

The Wireless Power Transfer (WPT) rectenna systems suffer from low power conversion efficiency (PCE) due to lower receiver (Rx) gain, polarization mismatch, and angular misalignment that can occur between the transmitter (Tx) and receiver (Rx). This impedes the realization of self-sustaining freely rotating the Internet of Things (IoT) sensor nodes. To address this issue, an orthogonally polarized, multibeam, orientation-insensitive WPT system is proposed to achieve a nearly isotropic dc pattern. The system employs orthogonally polarized endfire rectennas (Rect-I and Rect-II) in each sector and a centrally placed cross-polarized patch rectenna (Rect-III) to attain 3-D dc coverage. Furthermore, the antenna and rectifier circuit are conjugately matched to achieve miniaturization. A fully integrated hybrid dc combining topology and a smart bypassing network is employed to minimize combining losses. The three rectenna elements offer a PCE of 51%, 44.9%, and 58.9%, with an open-circuit harvested dc voltage of 1600 mV. Thus, orthogonally polarized, nearly isotropic dc pattern and polarization insensitivity of the proposed multibeam rectenna system (MBRS) makes it suitable for powering small battery-less IoT sensor nodes.

In this paper, an efficient perturbation algorithm combining the method of Multiple Scales and Modified Lindstedt–Poincare Techniques is proposed to solve the equation of Van der Pol oscillator with very strong nonlinearity. This algorithm combines the advantages of both methods. Solution of Van der Pol equation by the Multiple Scales Modified Lindstedt–Poincare (MSMLP) method is compared with the Multiple Scales method and numerical solution using MATLAB 7.8. The convergence criterion for the solution by Multiple Scales and MSMLP methods is discussed and shown that Multiple Scales method fails the convergence criterion for large values of small parameter, while MSMLP method satisfies the convergence criterion for both small and large values. Numerical simulation has been performed in MATLAB 7.8 for different values of small parameter to prove the efficiency and accuracy of the proposed method.

Nature-inspired algorithms (NIA) are proven to be the potential tool for solving intricate optimization problems and aid in the development of better computational techniques. In recent years, these algorithms have raised considerable interest to optimize feature selection problems. In literature, NIA is found to select relevant features among available features in the diagnosis of many chronic diseases. In this paper, a comprehensive review of existing nature-inspired feature selection techniques is presented. Along with this, the fundamental definitions of feature selection and the usage of NIA to optimize feature selection are shown. We have given a review showcasing the NIA application for selecting feature subsets from the available features in the domain of medical applications. The paper reviews and analyzes numerous relevant papers from 2008 to 2022 on feature selection through NIA on biomedical applications. Moreover, to find the best optimization algorithm for feature selection, we have conducted experiments among four well-known nature-inspired algorithms on ten benchmark datasets of the biomedical domain for classification. We have reported results on various state-of-the-art evaluation measures and presented the convergence graphs for analysis. Based on the average rank of fitness values, Particle Swarm Optimization is found to be better than Harris Hawk Optimization, Grey Wolf Optimization, and Whale Optimization. In this paper, we have also presented some open challenges of this research area to guide researchers as well as experts of computational intelligence for future work. The paper will help future researchers understand the use and implementation of nature-inspired algorithms for feature selection in the medical domain.

Objective: Pectoralis Minor Index (PMI) is a proposed parameter to evaluate the pectoralis minor length (PML), eliminating the effect of subject’s variability of height. Neither a PMI standard value nor any cutoff value to label a shortened pectoralis minor (PM) has been accepted yet, which can be applied to every individual. Moreover, the length of the PM has never been correlated to any fixed reference in the body. Hence, we estimated the PML in the Indian population and investigated its correlation to the individual’s hand length. Methodology: A cross-sectional study was conducted including 100 adult subjects without any shoulder pathology. Subjects with history of fracture/treatment involving upper limb/spine were excluded. Two assessors evaluated the height, PM length and hand length of subjects. PMI and hand correlation was evaluated using their mean values. Results: Mean PML and PMI for dominant and non-dominant shoulder were calculated to be 18.11/18.21 cm and 10.53/10.59, respectively. Mean hand length of dominant and non-dominant hand was found to be 18.27 cm and 18.31 cm, respectively. Pearson correlation coefficient between right/left PML with right/left hand length was 0.67 and 0.63, respectively, suggesting a good correlation (p?&lt;?0.01). Conclusions: PMI varies in different ethnic groups, which makes PMI a less reliable indicator for managing shoulder pain in ethnic groups where reference values are yet not available. Contralateral PMI can be used as a reference value in unilateral shoulder pathologies with short PML. Hand length can become an important parameter in evaluating painful shoulders even in bilateral pathologies. Hand length can be used as an easy and quick technique to compare the PML and effect of physiotherapy in patients with diagnosis of short PML, attending follow-up OPD. Though, a study comparing PML of normal subjects and patients with shoulder pain will be further required in different ethnic groups for further validation of this study.

This paper manages the secure searching, stockpiling and recovery of client information in the cloud framework. Different services of cloud, security issues and security necessities of cloud information being talked about. We have utilized fuzzy keyword searching scheme to seek and recover the encrypted data by utilizing wild card technique. We have devised a new approach of double encryption framework in light of authentication of the server to give more grounded security to the current fuzzy keyword searching techniques. We have coordinated symmetric and asymmetric encryption algorithms to improve information security. This work, for the most part, concentrates on authentication of the server to enhance the security framework and shield sensitive client’s information from unauthorized exposure.

The study aims to meta-analyse the role of brand commitment (BC) in internal branding practices (IB) and brand citizenship behaviours (BCB) relationships in service context. Hunter & Schmidt’s (2004) methodology is employed on 31 empirical studies in the service industry published during the period 2009 to 2023. The sub-group analysis is conducted to examine the role of potential moderators, using Meta Essentials. Based on empirically established results with varying degrees (significant and insignificant) for both direct and indirect relationships between IB and BCB, meta-analysis results confirmed a significant partial mediating role of BC in the IB-BCB relationship. Significant role of the type of country and service sector in the IB-BC-BCB is also confirmed.

Tax policy and tax administration are inextricably linked. The success of any tax policy depends to a large extent on efficient tax administration which would necessitate minimising arrears of assessment and collection, pendency of appeals, tax avoidance and tax evasion and improve taxpayers’ compliance. Despite increased collections, the prevailing system of direct taxes is beset with problem of increasing arrears of assessments and collections, pendency of appeals, delays in refunds and mounting tax evasion. Collection of every tax involves some money cost for the government and compliance cost (both in money and in real terms) for the taxpayers. It is desirable that these costs should be minimised. However, minimising the cost of collection does not mean that administrative staff should be kept at minimum. On the contrary, it is desirable that administrative strength of the Department should be considerably increased. Stress should be laid on effective and efficient administration.

Caesarean scar ectopic pregnancy (CSEP) is a uncommon presentation of pregnancy with incidence of nearly 1 in 2000 pregnancies. We present this case series of scar pregnancy with a objective to help obstetricians in early diagnosis and appropriate management to prevent its catastrophic complications.

In the modern era, the necessity of digitization is increasing in a rapid manner day-to-day. The healthcare industries are working towards operating in a paperless environment. Digitizing the medical lab records help the patients in hassle-free management of their medical data. It may also prove beneficial for insurance companies for designing various medical insurance policies which can be patient-centric rather than being generalized. Optical Character Recognition (OCR) technology is demonstrated its usefulness for such cases and thus, to know the best possible solution for digitizing the medical lab records, there is a need to perform an extensive comparative study on the different OCR techniques available for this purpose. It is observed that the current research is focused mainly on the pre-processing image techniques for OCR development, however, their effects on OCR performance specially for medical report digitization yet not been studied. Herein this work, three OCR Engines viz Tesseract, EasyOCR and DocTR, and six pre-processing techniques: image binarization, brightness transformations, gamma correction, sigmoid stretching, bilateral filtering and image sharpening are surveyed in detail. In addition, an extensive comparative study of the performance of the OCR Engines while applying the different combinations of the image pre-processing techniques, and their effect on the OCR accuracy is presented.

In the evolving paradigm of proteomics research, numerous novel proteins in living beings have been identified but understanding the existence of proteins alone does not provide much insight into their respective biological processes. To understand the related phenomenon in detail, a term called protein–protein interaction (PPI) came into light. Considering its significant involvement in numerous biological processes and associated signalling pathways, a thorough elucidation of protein–protein interactions is required. In the current review, we present a complete overview of how proteins do interact with each other beginning from historical perspective and exploring the recent high throughput technologies that are being utilized to detect various PPIs. Further, we have also mentioned about the defects linked with protein–protein interactions. This review thereby provides a comprehensive study on PPIs along with various detection techniques which can thereby turn out be a promising approach in the area of proteomics research.