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The ride comfort of a high-speed railway vehicle is a significant factor in assessing its performance. Despite having sound suspension systems and efficient control algorithms, the suspension system performance deteriorates with time. Hence, in this paper, a robust active vibration control system used in railway vehicles has been proposed. For analysis, a 27-degree of freedom (DOF) model consisting of structured and unstructured uncertainties has been considered. Two different robust control techniques, H∞and μ-synthesis integrated with the Kalman estimator, have been adopted. The robust stability and the robust performance of the proposed control strategies have been evaluated using the structured singular values. The time and frequency domains of the closed loop perturbed responses to random track disturbances are shown. The root mean square (RMS) values of the accelerations of the car bodies are used to compare the suggested control schemes to the passive system. The primary focus of this study is to examine the level of ride comfort provided by the railway vehicle. To achieve this, simulated findings are compared and verified against experimental data collected and reported by the Research Designs and Standards Organization (RDSO). The observational data demonstrates that the results obtained from the suggested control methods are in close agreement with the experimental findings, with a minimal disparity ranging from 2.36% to 8.81% for lateral motion and 2.84% to 6.30% for vertical motion. Also, the % improvement of RMS values with theμ-synthesis controller confirms the enhancement of the ride comfort as compared to other techniques.

Renewable biomass is a potent, CO2neutral substitute to fossil coal and coke in iron making. However, there has been relatively little work on the utilization of biomass for the production of directly reduced iron (DRI). The aim of the present work is to characterize Acacia nilotica(W1), Albizia lebbeck(W2), and Leucaena leucocephala(W3) biomass and to reduce iron ore pellets primarily with the volatile matter from the wood at high temperature, yielding charcoal simultaneously as a co-product. The chemical, physical, and mechanical properties of the three biomass species were investigated. Effect of different biomasses and reduction parameters like C/O ratio, reduction temperature, and time on %Reduction (%R) was studied. DRI was characterized using %Metallization (%M), XRD, and SEM. Up to 89% reduction was achieved in the simultaneous carbonization of biomass and the reduction of iron ore pellets at a temperature of 1000 °C. The pellets cooled in the furnace after reduction resulted in lower %R(e.g., W1:57%, W2:54%, and W3:63%) than the respective air-cooled pellets (e.g., W1:88%, W2:83%, and W3:86%). Oxidation was the cause for lower %Rin furnace cooled (slow cooling) reduction experiments than in the air cooled. Co-produced wood charcoal after the reduction of iron ore contained approximately 76–81% carbon which competes well with a typical value for coal of 70%.

Breast cancer is a significant global health concern, with invasive ductal carcinoma (IDC) being a significant subtype. Detecting IDC is a challenging task that can be hindered by the oversight of important contextual cues within whole slide images (WSIs). To address this issue, we present the Sequential-LSTM Invasive Ductal Carcinoma Detection with EfficientNet (SLIDE-Net) framework. SLIDE-Net synchronizes tissue image patch locations within WSIs, allowing for comprehensive and subtle identification of IDC. The inherent issue of data imbalance in IDC datasets, particularly the variable density of positive IDC patches concerning WSI size, is effectively tackled through the introduction of adaptive fuzzy C-Mean as a data balancing policy. This novel approach enhances model efficacy and robustness, resulting in superior performance across key metrics, such as accuracy (86%), balanced accuracy (BAC) (86%), sensitivity (87%), specificity (86%), and GMean (86%). Our findings were substantiated by a comprehensive analysis revealing the significant cross-patch dependencies observed among patches of similar classes. When tested on the balanced PatchCAM dataset, SLIDE-Net once again showcased superiority with accuracy (89%), BAC (89%), F1-Score (87%), sensitivity (87%), and specificity (90%). This underscores the efficient utilization of shared contextual information within WSIs. These results firmly establish SLIDE-Net as a robust and reliable solution for accurate IDC detection. Our work not only advances the precision of IDC detection, but also contributes valuable insights into the intricate dynamics of histopathology image analysis, paving the way for enhanced diagnostic accuracy in the ongoing battle against breast cancer.

AbstractAcinetobacter baumanniiis an ESKAPE pathogen that causes endocarditis, pneumonia, blood infections, urinary tract infections, and several other illnesses. In addition, it is mainly responsible for nosocomial infection-related mortality. Gram-negative A. baumanniibacterium (AYE Strain) has high MDR and XDR levels. Due to its function in synthesizing purines and amino acids, folic acid is a significant molecule necessary for the growth of bacteria. The metabolic pathway of folate production is therefore a potential therapeutic target to inhibit bacterial growth. In the current study, the three-dimensional model of 6-Hydroxy-methyl dihydropterinpyrophosphokinase (HPPK) was predicted and subsequently processed through a virtual high throughput screening (vHTS) against compounds from Enamine HTSC library, that could bind to its active site. Three lead candidates (Z73322064, Z354558542, and Z906123504) and a control molecule (7,8 dihydro-7,7-dimethyl-6-hydroxymethlypterin; Accession Number: DB02278) were identified using several screening criteria namely estimated binding affinity, estimated inhibition constant, drug-like properties, ADME properties, mode of binding, and interaction patterns of the screened compounds. The physiological behavior of ligand binding on the HPPK enzyme was then studied using molecular dynamics simulations of apo and ligand bound complexes. This study proposed the following three molecules: Z73322064, Z354338542, and Z906123504 as promising lead candidates against the substrate-binding site of the HPPK enzyme from A. baumanniiusing global, essential dynamics studies along with MM/PBSA based binding free energy analysis.Communicated by Ramaswamy H. Sarma

This study presents a methodology that combines convolution features with shallow classifiers for classifying the walking pattern on different surfaces. At first, convolution features are extracted from six different inertial measurement units (IMU) sensors mounted on the human body. The shallow classifiers namely quadratic SVM, wide neural network, fine KNN, and linear discriminant analysis are trained using convolution features that successfully pass through the global pooling layer of the CNN model. The proposed methodology is also evaluated on the features extracted from both individual IMU sensor and combined IMU sensors. It is observed that proposed methodology performs better for convolution features extracted from all IMU sensors combined together. The proposed methodology is also compared to the CNN model to demonstrate its efficacy. The obtained results show that the shallow classifier (quadratic SVM) achieves the maximum accuracy of 98.2%, whereas the CNN model achieves the accuracy of 90.73% only. Thus, the proposed methodology can be successfully utilised for classifying different walking surfaces based on the gait cycle data. The proposed approach can benefit in automatic gait adjustment of prosthetic foot based on the walking surfaces.

Near‐infrared spectroscopy is an established tool for the estimation of different nutrients in diverse sample matrices. Of these, near‐infrared transmittance (NIT) has very wide usage in whole grain analysis for oil, protein, and other macronutrients. NIT spectra obtained from samples are regressed with actual laboratory values for developing prediction models. However, the spectra obtained are sloppy, slightly noisy, and show baseline drifts. To increase the resolution and signal‐to‐noise ratio, derivatives are common preprocessing tools, typically implemented along with smoothing. A systematic study on different derivatives (1, 2, and 3) and gaps (2–90) was performed. The germplasm set with high variability for protein content (8.63%–19.56%) was used, and regression models were developed using the modified partial least squares method. Among all, the second‐order derivative gave best‐fit models; hence, the results of the gap with second‐order derivatives are studied in detail. The plot of R2for external validation set with different gaps at second‐order gave three peaks, namely, at 47, 60, (69, 70, 71) where the highest R2(0.985) was obtained for the third peak having three consecutive gap segments. Hence, math treatment (2, 70, 2, 1) was finalized considering stability where a high residual prediction deviation of 7.149 and a low bias of (0.021) was obtained. A paired ttest and reliability test between predicted and laboratory values confirmed nonsignificant differences between them. Thus, the developed model is robust and precise and can be utilized in high throughput screening of wheat germplasm. Better performance at second derivative and higher gap can be used for developing robust models with low bias by avoiding multi‐collinearity, which is usually a limitation in multi‐variate analysis.

Monitoring of the introduction of unapproved genetically modified (GM) events is required because the approval status of a GM event may differ from country to country. The on-site methods such as loop-mediated isothermal amplification (LAMP) offer a technological answer for the rapid GM detection beyond the laboratories. Real-time LAMP assays detecting one GM target were reported earlier. To increase the efficiency of the assay, a multiplex real-time LAMP simultaneously targeting Figwort Mosaic Viruspromoter (P-FMV) that constructs region between the Cauliflower Mosaic Virus35S promoter and cry1Acgene (p35S-cry1Ac) and neomycin phosphotransferase II (nptII) marker gene was developed. The assay could detect as low as 0.1% for each GM target within 45 min. To the best of our knowledge, multiplexing in real-time LAMP using the Genie II system with applicability in GM detection has been reported herein for the first time. The developed method provides rapid, on-site, and real-time GM detection in seeds and food products.

This study provides a comprehensive analysis of capital structure stability over long horizons for firms listed in India. It covers a period of 28 years from 1992 to 2019, leading to 20,371 firm-year observations to determine the relative significance of cross-firm leverage variation and time-series leverage variation. It also identifies the circumstances and industries when each type of variation emerges stronger. The study employs simple econometric tools along with financial modelling techniques in SPSS and Microsoft Excel. It was found that corporate leverages are sticky and predictable in the short run. Also, with an increase in a firm’s age, leverage stability increases. This study contributes to the existing literature by empirically establishing that leverages can also be persistent at high levels and identifying industries that are persistent at low and high leverages. The results show that persistence at low leverage is a common phenomenon; however, a few industries such as textile, construction, and metals and mining are persistent in their capital structures even at higher levels of leverage.

In this article, gain augmentation of a microstrip antenna (MSA) operating at 28 GHz has been enunciated in a cost-effective manner using a phase-graded index lens. Different metamaterial unit cells with transmissive characteristics have been designed and placed in an array to create a radial phase graded metalens with locally varying refractive index and spatially varying surface impedance. Transformation of spherical wavefronts into planar wavefronts results in beam collimation which helps in obtaining a directive beam in the 5G frequency band. The ratio of focal length to the diameter (f/d) of the lens is 0.38. The gain of MSA is enhanced by 4.4 dB with radiation efficiency above 95% due to the focusing effect of the lens placed vertically above the antenna. The proposed planar lens is compact in size and easy to fabricate when compared with the conventional 3-D lens reported in existing works. The microstrip patch integrated lens prototype exhibits a gain of 12.4 dBi operating at 28 GHz. The performance of the lens has been analyzed theoretically and evaluated further by conducting experiments on the fabricated prototype. Measured results validate the full–wave simulation results carried out using finite element method.

Unlike traditional encryption methods, Generative Adversarial Network (GAN)-based methods possess a high level of security for digital images. Many existing simple encryption methods may be less secure than expected and have high storage costs. This paper proposes a GAN-based encryption method to secure digital images, solving these problems. First, a random sequence generator using a GAN with cross-coupled logistics and a Henon map is generated to encrypt an image. Next, the encrypted image is downsampled into one-fourth of the original size and sent to the receiver. Finally, image reconstruction uses a Customized Super Resolution Network (CSRNet) rather than decompressing the image at the receiver side. Our extensive experimental results demonstrate that the proposed method achieves NPCR, UACI, entropy, PSNR and SSIM up to 0.99604, 0.33460, 7.9993, 37.0462 dB and 0.94561, respectively. Further, our encryption method achieves up to 75% faster than the recent methods when evaluated on two standard datasets. Therefore, the proposed GAN-Based solution can possess a high level of security and save sufficient storage space for any practical application.

Several deep learning (DL) based data hiding techniques have been proposed in recent years. Generally, these techniques embed and extract hidden marks using deep learning networks. Although these methods are imperceptible and robust, many algorithms are being demonstrated to embed a single mark, which may incur the risk of transmission insecurity. To solve these problems, this paper proposes a robust and secure data-hiding method using deep learning for embedding dual marks in the form of images within a carrier image file. This has significant advantages in data tracing, data usage monitoring and multiple property management. Further, the proposed framework uses a chaotic system over the marked file to achieve a high level of security. Extensive experimental results demonstrate that our proposed method using deep learning is superior to the state-of-the-art techniques with significant improvement of 42.33% and 41.62% in terms of visual quality and robustness, respectively.

Recently, digital images are an essential source of data obtained from consumer devices, thus playing a crucial role in various important scenarios such as consumer apps, businesses, e-commerce, education, entertainment and healthcare. However, security and privacy are primarily concerned with the transmission and storage of these images. Therefore, there is an urgent demand to protect their copyright and prevent leakage. To address these concerns, this paper presents a novel multimodal biometric, encryption and watermarking-based method for digital image security. First, multimodal biometric features are extracted and fused using the customized deep learning model. Second, different secret keys are used to encrypt the fused features of the biometric images. Here, the fused features, considered as watermarks, are divided into sub-features before encrypting them with different keys. Third, to improve security while maintaining imperceptibility, the encoded sub-features are embedded into multiple cover media-based convolutional neural network (ConvNet). The experimental results demonstrate that the proposed system is highly secure and that it can recover complete marks under different attacks. Further, experiments illustrate the superior performance of our proposed system in terms of both imperceptibility and robustness compared with the competing schemes. It indicates a considerable improvement in imperceptibility and robustness of 62.83% and 25.16%, respectively over existing schemes.

Communicable diseases are transmitted through water, food, contaminated surfaces, bodily fluids, air. In such a situation, staying in home isolation for fewer chronic health problems and monitoring health status frequently through Medical Sensors (MSs) is recommended. The use of Artificial Intelligence (AI) in smart consumer electronics and sustainable healthcare has recently demonstrated remarkable results. However, the healthcare domain requires high levels of accountability and transparency for communicable disease prediction and sustainable life in edge networks. This paper aims to present an intelligent healthcare prototype that can identify risk factors according to monitoring parameters by analyzing the Explainable XGBoost (XXGB) model. Using edge networks for sustainable living, we explore the intersection between healthcare and consumer electronics. Initially, the prototype has been trained using the XXGB model over one publicly available dataset related to communicable diseases. Next, the prototype identifies patient risk factors by analyzing real-time monitoring parameters. Simulation results illustrate the efficiency of the proposed XXGB model up to 84.2% accuracy, which is higher than existing models.

Federated Learning (FL) collaboratively trains a model while preserving privacy and providing intelligence. This makes it ideal for Consumer Electronics (CE) applications, involving continuous streaming of data. However, the existence of a single central server for orchestrating the entire process and the participation of an abundant number of clients introduce major bottlenecks like a single-point failure of the server and communication inefficiency. Additionally, in most CE applications, local data is used by the clients as a single dataset, which fails to retrieve deep insights from data features for better decision-making. In this light, we propose a multi-view ensemble FL framework for CE applications in fog networks, namely EnsembleFed. The proposed approach derives multiple views from raw data to gain deeper insights from it and trains a model for each view. Later, the output of these models is ensembled for robust predictions. The distributed fog nodes are incorporated into the FL framework to combat single-point failure risk and improve communication efficiency. Empirical results demonstrate that the proposed approach outperforms state-of-the-art techniques on a real-time dataset in terms of increased accuracy by 4.02%, 4.32%, and 5.27%, compared to FedYogi, FedAdam, and FedAvg, respectively.

The Recommendation System (RS) plays a crucial role in various platforms as an information filtering agent however, our literature survey found that there is an unexplored area where the user feedback is in ordinal value and the movies are from the Indian regional language-based. Here, we have introduced a Multi-head cross-attention-based recommendation system for the Indian language-based multi-modal Hindi movie dataset where user feedback is considered from the three different classes, i) Dislike, ii) Like, and iii) Neutral/Not watched. Here, we have used the Audio-Video information of Hindi movie trailers of the Flickscore dataset. Besides that, we have also investigated the performance of a classification-based model in two factors, (i) GenreLike-score GL-score: (which we have formulated to match the user’s preferred genres with the genre of the movie), and (ii) Different Audio/Video embeddings. The performance of different combinations of these factors is tested on different modalities of the dataset and proved that GL-score is supportive in preference prediction. The performance of different keyframes extraction techniques has been investigated, and modality-wise different embedding processes have also been introduced here.

The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.

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The current trend leans towards employing an in vitro-based regenerative approach to tackle the various challenges associated with conventional propagation techniques. Musa acuminata(AAA) cv. Amrit Sagar, native to North-east India, holds substantial nutritional and biological significance. However, its commercial cultivation faces obstacles such as poor sucker quality and biotic pressures. In this study, multiple shoots were obtained using shoot tip explants of M. acuminatacv. Amrit Sagar. These explants were cultured on Murashige and Skoog (MS) solid medium supplemented with varying concentrations of 6-Benzylaminopurine (BAP), adenine sulfate (AdS), and α-Naphthaleneacetic acid (NAA). To facilitate in vitro propagation, surface sterilization was optimized using 4.0% sodium hypochlorite (NaOCl) for 20 min, followed by 0.1% mercuric chloride (HgCl2) for 20 min, resulting in a contamination rate as low as 13.33%. Shoot regeneration achieved an establishment rate of 87.33% using shoot tips in MS medium with 2.5 mg L-1BAP, 40 mg L-1AdS, and 0.5 mg L-1α-Naphthalene acetic acid (NAA). Optimal multiple shoot formation was observed in MS medium containing 10 mg L-1BAP, 40 mg L-1AdS, and 0.5 mg L-1NAA. Regarding root development, 92.90% efficiency was noted in half-strength MS medium supplemented with 2.5 mg L-1Indole-3-butyric acid (IBA).

Graphical Abstract:

Cancer is one of the most complicated and prevalent diseases in the world, and its incidence is growing worldwide. Natural products containing pharmacological activity are widely used in the pharmaceutical industry, especially in anticancer drugs, due to their diverse structures and distinctive functional groups that inspire new drug results by means of synthetic chemistry. Terrestrial medicinal plants have traditionally been the primary source for developing natural products (NPs). However, over the past thirty years, marine organisms such as invertebrates, plants, algae, and bacteria have revealed many new pharmaceutical compounds known as marine NPs. This field constantly evolves as a discipline in molecular targeted drug discovery, incorporating advanced screening tools that have revolutionised and become integral to modern antitumor research. This review discusses recent studies on new natural anticancer alkaloids obtained from marine organisms. The paper illustrates the structure and origin of marine alkaloids and demonstrates the cytotoxic action of new alkaloids from several structural families and their synthetic analogs. The most recent findings about the potential or development of some of them as novel medications, together with the status of our understanding of their current mechanisms of action, are also compiled.

Recently, significant attention has been focused on the copyright protection of medical images, causing watermarking to become a prevalent research topic. In comparison to embedding a single watermark directly into cover images, inserting multiple marks through a deep neural network enables image authentication and verification of ownership, which is more suitable for health-care applications. Most of the existing methods have proposed watermarking through the insertion of a single mark in a deep learning environment, which does not consider an excellent relationship between robustness, imperceptibility, and capacity and, therefore, limits watermarking performance. In this article, we propose a new watermarking method based on a convolutional neural network (ConvNet) called ConvNet-HIDE as well as principal component analysis (PCA). First, the customized UNet3+ model is applied to segment the region of interest and background information of the image. Then, the ConvNet model is used with Mobi-ConvNet, a downsampler, to imperceptibly hide a mark in both segmented parts of the image. Finally, the ConvNet model, which is followed by PCA, is used to recover both marks robustly. Extensive experimental results demonstrate the effectiveness of our proposed ConvNet-based method compared to state-of-the-art schemes. The method is able to achieve a peak signal-to-noise ratio and structural similarity index of 39.59 dB and 0.9812, respectively, with capacity of 2 bit per pixel. Further, the average execution time for our method is found to be 0.22 s, demonstrating its efficiency compared to other similar techniques that require more time.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work, which utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely $A_{cc}$, $S_{n}$, $P_{r}$, $F_{s}$, $S_{p}$, $B_{a}$, and $M{cc}$ obtained by proposed framework for test data is $\approx$ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Dermatological disease states have psychological impacts that affect a patient's life. In the management of such disorders, topical delivery has an important role. However, the conventional topical delivery systems suffer from various limitations, like skin irritation, a minute quantity of drugs reaching disease sites, and over and under medication, which leads to an adverse reaction and therapeutic failure, respectively. Therefore, researchers continuously search for an alternate delivery system for treating skin disease. In recent years, nanostructured lipid carriers (NLC) have emanated as promising carrier systems for topical delivery. The current review provides an in-depth insight into topical administration for treating a variety of dermatological issues using NLCs as a carrier. This review highlights the suitability of NLCs as carriers for topical delivery, their method of preparation, and their characterization. In the present review, the main emphasis has been given to the management of various dermatological problems by using NLCs as a carrier; a plethora of literature investigating NLC as the carrier for topical delivery has been included in this review. In this paper, an attempt has been made to provide a summary of the research carried out in this field that will encourage further research in this arena.

This work intends to enhance the standard of rehabilitative care provided to patients by optimizing the medical automation monitoring system enabled by wearable computing by edge cloud and Internet of Things technology. First, the recent research literature on edge cloud and wearable computing devices is analyzed. Recent studies in virtual reality and automated medical rehabilitation are used to analyze and contrast various data fusion techniques in wearable sensors. Subsequently, an edge cloud model is constructed to enable real-time tracking of patients’ vital signs, allowing for timely assessment of their health status and rehabilitation progress. Then, a wearable device information monitoring rehabilitation system is established to provide effective rehabilitation treatment for stroke patients. The monitoring module of the rehabilitation system incorporates an edge computing terminal device, which models the virtual circuit for enhanced functionality. Based on the findings, when the number of output codes from the network structure is set to 1000, the average conversion energy of the dynamic conversion system is 140, the first conversion energy of the proposed model is 160, and the second conversion energy is 220. Regarding the rehabilitation treatment effectiveness, the system developed here demonstrates superior operational efficiency and delivers improved outcomes in rehabilitation treatment. This work serves as a practical reference for advancing the intelligent transformation of the medical service system. Note to Practitioners—The goal of this work is to examine the viability of an automated virtual rehabilitation system for use in medical rehabilitation training for stroke, auto accident, and post-operative stroke rehabilitation. Combining the current research status of edge cloud and wearable devices reveals that the majority of existing rehabilitation training approaches are primarily reflected at the theoretical level, with only a few instances of practical applications highlighted. Therefore, this work presents a novel method for designing a wearable medical information monitoring system that operates on the edge cloud with the wearable device computing automation service as its foundation. It can track the patient’s vitals in real time to gauge the rehabilitation’s success. When compared to other cutting-edge rehabilitation options, the validated established edge cloud model clearly stands out as superior and can aid patients in achieving better outcomes from their rehabilitation treatment. The result demonstrates that the method is effective. In the future, it is planned to apply this rehabilitation system to more treatment cases and improve the overall design of the system.

In this paper, a coumarin-based Schiff base chemosensor has been synthesized and developed to detect Cu2+and Zn2+ions in nanomolar concentrations. The probe selectively distinguishes Cu2+and Zn2+from among several metal ions in DMF : H2O (7 : 3, v/v, pH 7.4) HEPES buffer. The structure of the probe and its sensing behavior were investigated by FT-IR, UV-vis, fluorescence, HRMS, and NMR analyses, along with X-ray crystallography and computational studies. CIHdetects Zn2+and Cu2+using different strategies: CHEF-induced fluorescence enhancement and paramagnetic fluorescence quenching, respectively. Job's plots show a 1 : 1 binding interaction between CIHand Cu2+or Zn2+ions. The binding constant values for Cu2+(1.237 × 105M−1) and Zn2+(1.24 × 104M−1) suggest a better ability for Cu2+to interact with CIHthan Zn2+. An extremely high sensitivity of the probe was highlighted by its very low detection limits (LOD) of 5.36 nM for Cu2+and 3.49 nM for Zn2+. The regeneration of the probe with the addition of EDTA in its complexes allows the formation of molecular logic gates. CIHhas been successfully employed in mitotracking and intracellular detection of Zn2+and Cu2+in SiHa cells.

The stock market is the reflection of the trading pattern of various investors. The present study attempts to examine the trading behaviour followed by foreign institutional investors, domestic institutional investors and mutual funds during 2010–2020 in the Indian stock market. The whole time period is divided into two subperiods using the chow breakpoint test. Two vector autoregression framework models accompanied with impulse response function and variance decomposition analysis are employed in both the subperiods, namely calm period and volatile period. It is found that the institutional investors do not pay heed to the market returns in the calm period, while the interdependency among the institutional investors increases in the volatile period. The structural break enhances the forecasting accuracy of the model significantly. This study will help the government to understand the impact of calm period and volatile period on the trading behaviours of the institutional investors and thereby their sentiments in the Indian stock market.JEL Codes:G1, G23

This letter proposes a compact, self-isolated extremely low correlated, 1×4 folded-SIW-based slot antenna configuration for a 5G multiple-input–multiple-output (MIMO) communication system operating at 38 GHz band. To minimize the near-field coupling between the feed lines and ensure impedance matching at the microstrip (MS) to T-type folded substrate integrated waveguide feeding (TFSIW) section, the capacitive and longitudinal currents at the input section of the TFSIW are precisely tuned by adjusting the position of lateral walls and optimizing the dimension of the central wall and septum. The compactness of this antenna is attained through the utilization of a TFSIW, which possesses a high compression ratio resulting in a total MIMO size of 9.8 × 38.64 × 0.508 mm3. The self-isolated MIMO antenna offers an impedance bandwidth of 7.17% (36.5–39.28 GHz), a maximum gain of 10 dBi, minimum isolation of 30 dB. The envelope correlation coefficient (ECC) remains below 3 × 10−5, whereas the diversity gain (DG) exceeds 9.99993 dB. The radiation efficiency remains over 90%, the minimum cross-pol difference (XPD) is 20 dB, maximum side lobe level (SLL) remains around −15 dB in the XZ-plane. The measured results closely match the simulated one. Furthermore, the proposed design demonstrates enhanced performance and higher compactness when compared to the recent and limited state-of-the-art works.

Background: The disease of the posterior segment of the eye is a major concern worldwide, and it affects more than 300 million people and leads to serious visual deterioration. The current treatment available is invasive and leads to serious eye complications. These shortcomings and patient discomfort lead to poor patient compliance. In the last decade, Nanostructured lipid carriers (NLC) have established a remarkable milestone in the delivery of drug substances to the posterior segment of the eye. Additionally, NLC can reduce the clearance due to adhesive properties which are imparted due to nano-metric size. This attribute might reduce the adverse effects associated with intravitreal therapy and thus enhance therapeutic efficacy, eventually raising patient adherence to therapy. The current review provides an inclusive account of NLC as a carrier to target diseases of the posterior segment of the eye.Objective: The review focuses on the various barrier encountered in the delivery of drugs to the posterior segment of the eye and the detail about the physicochemical property of drug substances that are considered to be suitable candidates for encapsulation to lipid carriers. Therefore, a plethora of literature has been included in this review. The review is an attempt to describe methods adopted for assessing the in-vivobehavior that strengthens the potential of NLC to treat the disease of the posterior segment of the eye.Conclusion: These NLC-based systems have proven to be a promising alternative in place of invasive intravitreal injections with improved patient compliance.

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods.

This paper presents a de-noising method for speckle noise removal called de-speckling, retinal optical coherence tomography image by combining the features of wavelet transform, statistical modeling, Bayesian estimators and Gaussianization procedure. De-speckling methods based on statistical modeling of wavelet coefficients depend on the correct estimation of probability density function (PDF). The density estimation problem has been solved by using the Gaussianization procedure. The dynamic range capability of Gaussian PDF has been used for sharp estimation of density. A Cauchy PDF is used for modeling wavelet coefficients and finding the cumulative distribution function (CDF) of wavelet coefficients. This CDF is used as the input to the Gaussianization procedure, where it equalizes with the Gaussian CDF to estimate the density function of wavelet coefficients. Finally, the wavelet coefficients are recovered using Bayesian minimum mean square error estimator. Both visual and quantitative comparisons are performed for demonstrating the prominence of the proposed method. From simulation result it is seen that the proposed method outperforms the state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity, correlation coefficient (CoC) and edge preservation index (EPI). The proposed method has been achieved improvement of 5.13% in PSNR, 2.44% in SSIM, 2.11% in CoC and 3.45% in EPI over the well accepted existing method.

This paper proposes a 17-degree-of-freedom dynamic railway vehicle model integrated with wheel-rail forces and active suspensions. The passive response of the model is validated with the experimental results. Afterward, a decentralised control structure having fractional order proportional integral derivative (FOPID) controller has been formulated to mitigate the track disturbances. A novel metaheuristic optimisation technique named hybrid PSO-GWO has been proposed to provide the optimum force. The performance of FOPID controllers was evaluated in the time and frequency domain under the periodic and random track disturbances, and the results were compared against the passive system and classical tuning method. From the results, it is seen that hybrid PSO-GWO is more capable of reducing the vehicle accelerations as compared to other tuning methods as it can be able to reduce the lateral, roll, and yaw acceleration up to 77.81%, 75.29% and 63.74% under periodic and, 57.22%, 77.88%, and 69.58%, under random excitations.

The wild edible fruits are an important source of nutritional supplementation for the tribal communities in the northeastern mountainous region of India. In this study, the nutrient and antinutrient composition of 44 wild edible fruit species from the northeastern India were investigated. Among the studied plants, Elaeagnus umbellatashowed high content of soluble solids (24.50 ± 0.41ºBrix), free fatty acids (67.79 ± 1.75 mg KOH g–1) and vitamin A (136.22 ± 1.44 mg 100 g–1). Terminalia chebulawas shown to be a good source of reducing sugars (8.38 ± 0.07%) and total flavonoids (445.20 ± 1.18 mg 100 g–1) and Spondias pinnatawith high total carbohydrate (12.51 ± 0.22%) and vitamin C (74.16 ± 5.33 mg 100 g–1) content. Castanopsis hystrixhad the highest starch (1764.84 ± 8.85 mg 100 g–1) and cellulose (711.62 ± 7.68 mg 100 g–1) content and Machilus eduliswith high fat content (36.44 ± 1.23%). Chlorophyll, carotenoid and anthocyanin contents were highest in Phoebe cooperiana, Rhus semialata, and Prunus nepalensis,respectively. Artocarpus lakoocha(84.58 ± 2.38%) and Spondias pinnata(84.09 ± 0.62%) were found to be good sources of antioxidants. Regarding antinutritive factors, Canarium strictumshowed the highest phenols (902.00 ± 5.72 mg 100 g–1), highest tannins in Terminalia chebula(1059.33 ± 17.46 mg 100 g–1), alkaloids in Streblus asper(274.27 ± 6.31 mg 100 g–1), cyanogen in Castanopsis hystrix(31.04 ± 1.28 mg 100 g–1) and phytic acid in Viburnum foetidum(10.64 ± 0.30 mg 100 g–1). The results indicate that all studied fruit species possess nutritional and antinutritional properties and are considered promising for nutraceutical and pharmaceutical industries.

AbstractThe advent of multi drug resistance and extensive-drug resistance among various pathogens has caused a rise in nosocomial infections, which in turn has led to rising hospital-acquired infection-related mortality rates. Amongst them, carbapenem-resistant Acinetobacter baumanniiis one of the most notorious bacterial species, categorized as a Priority 1: Critical pathogen by the WHO. Therefore, the discovery and development of novel antibiotics, as well as the identification of potential inhibitors, have become the need of the hour. In this study, we have employed computational methods to explore and identify molecules capable of inhibiting enzymes essential in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. The high throughput virtual screening of small molecules (Enamine Advanced Collection (AC) library) against the highly conserved substrate-binding site of the DXS target protein provided us with a total of 1000 molecules. The top four potential candidate molecules, namely—Z3353989070, Z3353989049, Z2295848528, and Z1685501455, alongside fluoropyruvate (control), a known inhibitor of DXS, was chosen for a molecular dynamic simulation study. The molecular dynamic simulation trajectories suggested high structural and thermodynamical stability and strong binding affinity of all the DXS-ligand complexes. Moreover, the MM/PBSA-based binding free energy calculations also exhibited strong interactions of the selected ligand molecules with DXS. In conclusion, we have found that all four molecules displayed better results and stronger binding affinity than the control. In the end, based on all the above-mentioned criteria, we have proposed Z3353989049 to be the promising lead candidate against DXS from A. baumannii.Communicated by Ramaswamy H. Sarma

Purpose: Green nanotechnology as a field has emerged and gained popularity amongst biologists due to its cost-effective and environment-friendly advancements. The most preferred is the biological method which involves plants and their extracts. Methods: The silver nanoparticles were synthesized by a sunlight-driven aqueous extract (AE) of whole plant powder of Ascophyllum nodosum. Advanced techniques like high-resolution scanning electron microscopy (HRSEM), energy dispersive X-ray (EDX), high-resolution transmission electron microscopy (HRTEM), and particle size analysis were used to determine the nature of nanoparticles. Antioxidant, anti-fungal, and anti-leishmanial activities were evaluated. Result: The techniques confirmed the formation of spherical particles of the desired range of size. Silver nanoparticles exhibited a much greater DPPH (2,2-diphenyl-1-picryl-hydrazine-hydrate) radical scavenging activity which was almost six to seven folds more than that exhibited by the AE alone. The anti-leishmanial and cytotoxic activities were evaluated on Leishmania donovanipromastigote and amastigote. Conclusion: The synthesized AgNPs showed remarkable DPPH radical scavenging ability owing to their antioxidant properties. The anti-leishmanial activity was exceptionally viable in both AE and AgNPs. The findings all together support the tendency of Ascophyllum nodosumto efficiently synthesized AgNPs which could be utilized for its anti-leishmanial properties. Graphical Abstract:

The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.

The continuous development of Industry 5.0 technology has brought great convenience to people’s work and life. However, during digital data transmission and storage, the data may be accessed by unauthorized persons, resulting in privacy disclosure. Therefore, efficient data protection is always a high demand to solve this realistic problem. This work proposes a secure encryption algorithm, SIELNet, for colour images. First, we introduce a new three-dimensional chaotic map to encrypt colour images, obtaining the cipher images with a relationship to the plain images. We provide its excellent chaotic behaviour through standard randomness test. Secondly, we use a customized residual dense spatial network to perform the task of lossy image reconstruction from an encrypted, compressed image, which solves the constrained super-resolution task. Extensive experimental results on four public datasets demonstrate the superior performance of SIELNet against state-of-the-art techniques with excellent reconstruction quality. We believe the secure design of SIELNet can contribute to the favourable data integrity application of Industry 5.0.

There is a connection between bioavailability and absorption. In the field of pharmacology, this is referred to as a category of absorption and is defined as the proportion of a particular dosage of a drug that does not change and is absorbed into the systemic circulation. Both the overall absorption of medications and their specific bioavailability are important factors to consider in treatment. In this paper, we will go into great detail about the bioavailability of phytochemicals. We will also discuss the factors that influence bioavailability, the processes that improve bioavailability, and the phytochemicals that act as important bio enhancers, which are agents that improve the bioavailability of drugs.

Recent advances in Artificial Intelligence (AI) have revolutionized the area of biomedical imaging, providing unprecedented prospects for precision diagnoses. This special issue offers an overview of the integration of AI into biomedical imaging systems and its tremendous impact on improving diagnostic accuracy and efficiency. The combination of AI and biomedical imaging has resulted in intelligent systems capable of deciphering complex medical pictures with amazing precision. Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown exceptional capabilities in recognising patterns and extracting meaningful information from a variety of imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) [1].

AbstractSortases are extracellular transpeptidases that play an essential role in the adhesion of secreted proteins to the peptidoglycan layer of the cell wall of Gram-negative bacteria. Sortases are an important drug target protein due to their involvement in synthesizing the peptidoglycan cell wall of Streptococcus pyogenes, and these are not found in Homo sapiens. In this study, initially, we have performed protein sequence analysis to understand the sequential properties of Sortase C. Next, a comparative protein modeling approach was used to predict the three-dimensional model of Sortase C based on the crystal structure of Sortase C from Streptococcus pneumoniae. Virtual screening with an in-houselibrary of phytochemicals from Syzygium aromaticumand molecular docking studies were performed to identify the promising lead molecules. These compounds were also analyzed for their drug-like and pharmacokinetic properties. Subsequently, the protein-ligand complexes of the selected ligands were subjected to molecular dynamics (MD) simulations to investigate their dynamic behavior in physiological conditions. The global and essential dynamics analyses result implied that the Sortase C complexes of the proposed three lead candidates exhibited adequate stability during the MD simulations. Additionally, the three proposed molecules showed favorable MM/PBSA binding free energy values ranging from −13.8 +/- 9.41 to −56.6 +/- 8.82 kcal/mol. After an extensive computational investigation, we have identified three promising lead candidates (CID:13888122, CID:3694932 and CID:102445430) against Sortase C from S. pyogenes. The result obtained from these computational studies can be used to screen and develop the inhibitors against Sortase C from S. pyogenes.Communicated by Ramaswamy H. Sarma

In a many-core chip, thermal flux and thermal correlation among the cores can be explored to create a thermal covert channel (TCC). In this paper, we provide an analytical model to quickly determine the key TCC performance metrics, in terms of bit error rate (BER), signal to noise ratio (SNR), and channel capacity, without going through lengthy computer simulation and/or physical experiments that are normally needed in current TCC performance studies. According to our model, the TCC’s BER is proportional to the square root of the transmission frequency, which can be explored quantitatively to boost the TCC’s transmission efficiency by letting the TCC’s thermal signal be transmitted at a higher frequency. In addition, our proposed model also links the jamming noise and application of Dynamic Voltage Frequency Scaling (DVFS) to TCC’s BER performance, a feature that can be explored to design/optimize the countermeasures against the TCC attacks. The TCC performance predicted by the proposed theoretical model is found in a good agreement with that obtained from computer simulations, with an average error lower than 7%.

Recently, artificial intelligence approaches are widely suggested to optimize numerous offloading task-scheduling purposes. However, they confront difficulties in maintaining data privacy regarding the context of the data offloading during the course of offloading in the different stages. To address this problem, in this article we propose C-fDRL, a framework to provide context-aware federated deep reinforcement learning (fDRL) to maintain the context-aware privacy of the task offloading. We perform this in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system. C-fDRL checks whether the privacy of high-context-aware data with the task being offloaded is maintained locally at the DeviceAI, and low-context-aware data distributedly at the EdgeAI. When there is an offloading task request or a user needs to offload the data, C-fDRL uses a context-aware data management approach to decouple the context-aware (privacy) data from the tasks. This separates the context-aware data from the task for local computation and allows a new scheduling technique called “context-aware multilevel scheduler.” This places high-context-aware data on local devices and low-context-aware data at the edge device for computation before the actual task execution. We performed experiments to evaluate the data privacy with the offloading tasks and the federated DRL. The results show that the proposed C-fDRL performs better than the existing framework.

The presented study proposes a novel approach to detect gait abnormalities in unilateral trans-tibial (TT) amputees using a wearable setup. The system uses force-sensitive resistors and potentiometers to collect data on the user’s gait patterns. A machine-learning algorithm based on Extreme Learning Machines is utilized to classify the gait patterns as normal or abnormal. The system is evaluated on a dataset of healthy and unilateral TT amputees, and the results reveal that the ELM-based classification technique achieved high accuracy, sensitivity, specificity, and F1 score. The proposed wearable gait setup is tested by conducting a standard 6-m walk test, and the collected data is segmented into stance and swing phases. The study also compares various gait parameters of healthy and amputated subjects, and the results show significant asymmetry in the amputated subjects. The proposed setup also detects asymmetry in force distribution under each foot. The study’s findings reveal that the proposed wearable gait setup is a reliable and effective tool for gait analysis in unilateral TT amputees, and the results are comparable with those obtained using a Vicon gait measurement system.

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.

In response to growing security challenges facing many-core systems imposed by thermal covert channel (TCC) attacks, a number of threshold-based detection methods have been proposed. In this paper, we show that these threshold-based detection methods are inadequate to detect TCCs that harness advanced signaling and specific modulation techniques. Since the frequency representation of a TCC signal is found to have multiple side lobes, this important feature shall be explored to enhance the TCC detection capability. To this end, we present a pattern-classification-based TCC detection method using an artificial neural network that is trained with a large volume of spectrum traces of TCC signals. After proper training, this classifier is applied at runtime to infer TCCs, should they exist. The proposed detection method is able to achieve a detection accuracy of 99%, even in the presence of the stealthiest TCCs ever discovered. Because of its low runtime overhead ($< 0.187\%$<0.187%) and low energy overhead ($< 0.072\%$<0.072%), this proposed detection method can be indispensable in fighting against TCC attacks in many-core systems. With such a high accuracy in detecting TCCs, powerful countermeasures, like the ones based on dynamic voltage and frequency scaling (DVFS), can be rightfully applied to neutralize any malicious core participating in a TCC attack.