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Effective and timely diagnosis and treatment of ocular diseases is essential for swift recovery of the patients. Among ocular diseases, cataract and glaucoma are the most prevalent globally and need adequate attention. The present paper aims to develop an optimised deep learning based convolutional neural network (CNN) for the multi-classification of ocular diseases (normal, glaucoma and cataract). Three pre-trained CNNs (SqueezeNet, Darknet-53, EfficientNet-b0) were optimised concerning batch size (6/8/10) & optimiser type (SGDM, RMSProp, Adam) for obtaining maximum possible accuracy in the detection of multiple ocular diseases (cataract & glaucoma). Darknet-53 (batch size-6, optimiser type-Adam) gave the highest accuracy of 99.4% for a test sample of 1000 images. The performance metrics of Darknet-53 have been computed using a confusion matrix. Confusion matrix is also applied to calculate accuracy, sensitivity, specificity, f1 score and receiver operating curve (ROC). Through comparative performance analysis of the three CNNs, SqueezeNet, Darknet-53 and EfficientNet-b0 achieved the highest accuracy of 95%, 99.4% and 90%, respectively. The results indicate the importance of batch size and optimiser type on the performance of CNN models.

It is imperative to understand the significance of the early diagnosis of pneumonia using a convolutional neural network (CNN) to reduce the processing time and increase the quality of treatment that is delivered to the patient. We have implemented transfer learning for processing of available datasets and constructed an ensemble of 3-CNNs: SqueezeNet, ResNet-50 and EfficientNet-b0. In this work, a multiclassification model has been built that can help in early pneumonia diagnosis. The chest X-rays of patients have been classified in 2-stages. In the first stage, the EfficientNet-b0 convolutional neural network with 99% accuracy is employed to diagnose whether the patient’s chest X-ray is Normal/Abnormal. If the output of the first stage is found abnormal then the chest X-ray is processed to the second stage wherein ResNet-50 with 97% accuracy is employed to diagnose the pneumonia type, pneumonia bacteria/pneumonia virus. Further, performance metrics have been computed from the confusion matrix for both stages of X-ray. Also, it is imperative to mention that a pneumonia diagnosis app has been developed in Matlab-2023 for the ease of patients who can self-evaluate the scan report and understand the course of clinical treatment.

In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix.

Breast cancer is one of the most prevalent malignancies and the primary origin of cancer-related deaths among females worldwide. Ultrasound image segmentation plays a crucial role in identifying breast tumours by precisely delineating the boundaries of the tumour within the images. Deep learning segmentation networks such as DeepLabV3+ have been used in the literature for this purpose. The coupling of DeepLabV3+ with base convolutional neural networks (CNN) can play a key role in its accuracy in tumour detection. The present study investigates the segmentation performance of four combinations of DeepLabV3+ segmentation network by coupling with the four base decoder CNN networks: DarkNet53, SqueezeNet, EfficientNet-b0 and DarkNet19. The accuracy of segmentation is confirmed using standard segmentation error metrics such as global and mean accuracy, mean and weighted Intersection over union (IoU), Boundary F1 (BF) Score and Dice Score. DeepLabV3+ coupled with DarkNet53 and EfficientNet-b0 as the decoder CNNs performed better than the other two combinations with a global accuracy of 96.50% and 96.18%. Precise tumour delineation can assist in tumour growth monitoring and treatment planning by providing detailed information on tumour size, shape, and location; thereby aiding in patient management and follow-up care.

Plasma-surface modification method (PSMM) is an efficient and inexpensive surface processing method for various materials and has generated great interest in the field of biomedical engineering. This paper focuses on the numerous conventional plasma methods and experimental approaches applied to materials research for suitable biomedical applications, including plasma deposition, laser plasma deposition, plasma sputtering and etching, plasma polymerization, plasma spraying, plasma implantation, and so on. The distinctive benefit of plasma modification is its biocompatibility and surface properties can be enhanced on a selective basis while the bulk characteristics of the materials stay unaltered. Existing materials can hence be used and the requirement for new materials may be circumvented thereby reducing the time for the development of novel and efficient biomedical devices.

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

In society, there are numerous hurdles hindering the advancement of women. Additionally, violence against women remains a significant issue, exacerbating their challenges. Therefore, addressing these violence-related challenges in a mature manner is crucial to creating a comfortable environment for women to thrive. In this paper, we present a nonlinear mathematical model focusing on women’s issues and empowerment as key factors. The model exhibits two equilibrium points: a violence-free equilibrium point and a violence-present equilibrium point. We analyze the stability of these equilibrium points and explore the influence of parameters within the model. Furthermore, we extend the model to incorporate stochastic effects. Subsequently, we compare the results of the stochastic model with those of the deterministic model. Our comparison reveals that the stochastic and deterministic models exhibit similar behaviors, albeit with slight oscillations in stochastic populations compared to deterministic ones.

Substance abuse is considered to be predominantly a problem for all in society, and much substance abuse research is done with a male majority. Recent drug addiction studies, however, reveal that there are substantial gender variations in substance-related epidemiology. The epidemiology of women’s substance misuse raises issues distinct from those addressed by men’s substance abuse. Women with addictions are more likely than males to experience several impediments to accessing and entering substance misuse treatment. In this regard, we framed a mathematical model by considering the whole population as a women population. For this system, we found pair of equilibrium points namely the Addiction-free equilibrium point and the Non-trivial equilibrium point. Moreover, the addicts generation number (threshold parameter) RA has been found to investigate the spread of addiction. Also, the local and global stability analysis for both the equilibrium points have investigated. Further, to analyze the decrease and increase of the infected population and recovery population respectively, an optimum control analysis is done with two control parameters using Pontryagin’s maximum principle. The influence of addict generation numbers on significant parameters involved in RA has been shown. An optimum control study projected that social media awareness is substantially more efficient than the fixed control in optimizing drug addiction among women. Through numerical simulations we have exhibited, the effect of with and without control on each population. Finally, it produced positive efficacy by reducing the number of infected and increasing the rehabilitation population.

Early childhood caries has an intricate etiology and it requires a helpless tooth surface, fermentable starches, and cariogenic microorganisms throughout some undefined time frame to start the carious cycle. Tooth-hued materials gained popularity in recent years for reestablishing primary and youthful blended dentitions. Hence, the main aim of this study was to investigate the choice of restorative materials in Class III dental caries in primary maxillary lateral incisors in 3–6-year-old children. Data collected from the records of the children 3–6 years of age for the choice of restorative materials of primary maxillary lateral incisors between September 2020 and February 2021 were included in the study. Retrospective study data were collected through the software DIAS and data analysis was carried out using Chi-square tests. Variation in the percentage of children who underwent restoration utilizing strip crowns was the highest within 3–4 years (38.26%) when compared to light composite restorations (LCR) (14.9%), whereas the least preferred restoration was glass-ionomer cement (5.37%) which was noted statistically significant. Considering the age group of 5–6 years preferred form of restoration was LCR (19.80%) when compared to strip crown (17.79%), whereas 4.70% of the treatment cases were utilized for glass-ionomer cement restorations. Strip crowns are a more predominantly used choice of restorative material in Class III dental caries in primary maxillary lateral incisors in between 3- and 6-year-old children.

Abstract Incidence of various dreadful microbial infections and the development of antibiotic resistance by infection causative microbes are the main reasons for reducing aquaculture productivity. Hence, there is an immense need for the discovery of alternative and efficient treatment for quick recovery of diseased fishes. In the present study, Suaeda maritima leaf extracts (hexane, diethyl ether, ethanol, and water) were screened for in vitro and in vivo antibacterial and antioxidant activities. Out of all the four extracts, ethanolic extract showed highest antibacterial activity against S. aureus (4.9 ± 1.3 mm), B. subtilis (1.6 ± 0.3 mm), K. pneumoniae (4.2 ± 1.8 mm), and P. aeruginosa (4.1 ± 1.2 mm). Similarly, antioxidant activity was also higher for ethanolic extract (500 µg/mL) based on DPPH radical scavenging ability (71.6 ± 1.4%) and reducing potential (149 μg/mL) assays. Further, ethanolic extract was purified consecutively via column chromatography and preparative TLC where an active fraction was selected based on highest antibacterial (10.1 ± 1.4 mm) and antioxidant properties (82.3 ± 2.8%). Active fraction was loaded onto mass spectroscopy and identified the presence of four active constituents such as 1,2,9,10-tetramethoxy-6-methyl-5,6,6a,7-tetrahydro-4H-dibenzo[de,g]quinolin-3-yl) methanol; 3',7-Dimethoxy-3-hydroxyflavone; Saponin and (19R)9acetyl19hydroxy10,14dimethyl20oxopentacyclo[11.8.0.0 .0 .0 ]henicos-17-yl-acetate. Besides, in vivo studies were conducted on Catla catla fingerlings infected with P. aeruginosa under laboratory conditions. The fingerlings were segregated into 5 groups, among which group 4 and 5 were treated with crude and purified extracts. Both the extracts were efficient in treating infected fingerlings and recorded 100% survival rate which is even better than group-3 treated with a synthetic antibiotic (77%). Hence, S. maritima leaf extract can be considered as a possible alternative medicine in aquaculture.

Effective and timely diagnosis and treatment of ocular diseases is essential for swift recovery of the patients. Among ocular diseases, cataract and glaucoma are the most prevalent globally and need adequate attention. The present paper aims to develop an optimised deep learning based convolutional neural network (CNN) for the multi-classification of ocular diseases (normal, glaucoma and cataract). Three pre-trained CNNs (SqueezeNet, Darknet-53, EfficientNet-b0) were optimised concerning batch size (6/8/10) & optimiser type (SGDM, RMSProp, Adam) for obtaining maximum possible accuracy in the detection of multiple ocular diseases (cataract & glaucoma). Darknet-53 (batch size-6, optimiser type-Adam) gave the highest accuracy of 99.4% for a test sample of 1000 images. The performance metrics of Darknet-53 have been computed using a confusion matrix. Confusion matrix is also applied to calculate accuracy, sensitivity, specificity, f1 score and receiver operating curve (ROC). Through comparative performance analysis of the three CNNs, SqueezeNet, Darknet-53 and EfficientNet-b0 achieved the highest accuracy of 95%, 99.4% and 90%, respectively. The results indicate the importance of batch size and optimiser type on the performance of CNN models. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent malignancies and the primary origin of cancer-related deaths among females worldwide. Ultrasound image segmentation plays a crucial role in identifying breast tumours by precisely delineating the boundaries of the tumour within the images. Deep learning segmentation networks such as DeepLabV3+ have been used in the literature for this purpose. The coupling of DeepLabV3+ with base convolutional neural networks (CNN) can play a key role in its accuracy in tumour detection. The present study investigates the segmentation performance of four combinations of DeepLabV3+ segmentation network by coupling with the four base decoder CNN networks: DarkNet53, SqueezeNet, EfficientNet-b0 and DarkNet19. The accuracy of segmentation is confirmed using standard segmentation error metrics such as global and mean accuracy, mean and weighted Intersection over union (IoU), Boundary F1 (BF) Score and Dice Score. DeepLabV3+ coupled with DarkNet53 and EfficientNet-b0 as the decoder CNNs performed better than the other two combinations with a global accuracy of 96.50% and 96.18%. Precise tumour delineation can assist in tumour growth monitoring and treatment planning by providing detailed information on tumour size, shape, and location; thereby aiding in patient management and follow-up care. [ABSTRACT FROM AUTHOR]

It is imperative to understand the significance of the early diagnosis of pneumonia using a convolutional neural network (CNN) to reduce the processing time and increase the quality of treatment that is delivered to the patient. We have implemented transfer learning for processing of available datasets and constructed an ensemble of 3-CNNs: SqueezeNet, ResNet-50 and EfficientNet-b0. In this work, a multiclassification model has been built that can help in early pneumonia diagnosis. The chest X-rays of patients have been classified in 2-stages. In the first stage, the EfficientNet-b0 convolutional neural network with 99% accuracy is employed to diagnose whether the patient's chest X-ray is Normal/Abnormal. If the output of the first stage is found abnormal then the chest X-ray is processed to the second stage wherein ResNet-50 with 97% accuracy is employed to diagnose the pneumonia type, pneumonia bacteria/pneumonia virus. Further, performance metrics have been computed from the confusion matrix for both stages of X-ray. Also, it is imperative to mention that a pneumonia diagnosis app has been developed in Matlab-2023 for the ease of patients who can self-evaluate the scan report and understand the course of clinical treatment. [ABSTRACT FROM AUTHOR]

In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. [ABSTRACT FROM AUTHOR]

This paper presents an innovative Hybrid Electric Vehicle (HEV) configuration utilizing a fuel cell as the primary energy source and an onboard Photovoltaic (PV) array as a supplementary source. The system features an advanced Model Predictive Control (MPC) enhanced by a Recurrent Neural Network (RNN) to manage the induction motor efficiently. Key components include a PV array, a fuel cell, and an electrolyzer. The PV array supplements the fuel cell during optimal sunlight conditions, while excess energy during idle periods is converted to hydrogen via the electrolyzer and stored in a hydrogen tank for future use. A quadratic bidirectional buck-boost converter (QBBC) regulates voltage, ensuring compatibility between energy sources and the motor. The system's performance is evaluated under various sunlight and speed conditions, with the RNN-based MPC compared to an Artificial Neural Network-based MPC (ANN-MPC) and a traditional Proportional-Integral (PI) controller. An incremental conductance algorithm is implemented for Maximum Power Point Tracking (MPPT) to optimize PV power extraction. The RNN model predicts motor speed, enhancing control precision. Simulations in MATLAB/SIMULINK reveal that the RNN-based MPC outperforms ANN-MPC and PI controllers, demonstrating improved efficiency and speed control. This work contributes to advancing intelligent and energy-efficient HEV technologies. [ABSTRACT FROM AUTHOR]

Tapping into digital footprints on social media, this research focuses on providing new insights into detecting depression through textual analysis. Initially, emotional raw data found in social media posts, aimed particularly at the expressions of anger, fear, joy, and sadness, were collected and analyzed. These emotions, each scored by their intensity, offer a quantifiable view into the users' mental state, serving as possible depression markers. Central to the methodological framework adopted is the binary classification system, which classifies texts into depressive or non-depressive states, well founded by the patterns unearthed from the data. The proposed model rigorously trains Artificial Intelligence/Machine Learing (AI/ML) models to traverse through the complexities of natural language, concentrating on noticing delicate indications that signal depression. The introduced models are tested and measured with accuracy, precision, recall, and F1-score. RoBERTa, DistilBERT, and Electra are the transformer-based models emphasized in this research. Their performance is critically evaluated, with the results denoting particular capabilities in understanding and contextualizing language, which is the key advantage in the early identification of mental health issues. This research stands at the intersection of technology and mental health, revolutionizing mental health monitoring and intervention. [ABSTRACT FROM AUTHOR]

Plasma medicine is branch that employs Cold Atmospheric Plasma (CAP) as a potent tool for biomedical applications. CAP produces high-level reactivity (free radicals, electrons) and can be generated by noble gases. CAP is rich in Reactive Nitrogen Species (RNS) and Reactive Oxygen Species (ROS). These ROS and RNS which include Ozone (O3), Nitrous Oxide (NO), Hydroxyl Radicals (OH), and Nitrogen Dioxide (NO2), gas particles, charged ions, neutral reactive oxygen which are primarily responsible for decontamination of microbes in various living tissues. Furthermore, CAP only have high excitation energies of electrons as compared to neutrals and ions which makes CAP an excellent tool for application on cells and tissues without any thermal damage. Cold plasma has also been successfully implemented for virus disinfection as its regarded as eco-friendly, efficient and novel technique for decontamination of virus. CAP treatment has also enabled inactivation of virus strain in both plant and animal species without inducing any physiological damage to them. Plasma chemistry essential for inactivation of pathogens is dependent on fine-tuning of various parameters which include plasma supply frequency, gas composition, input energy duration, pulse form, and modulation which has led to development and research of numerous portable plasma devices for different treatment methods in plasma medicine. CAP generated is extensively applied for wide range of biomedical applications including dentistry, microbial disinfection (bacteria, viruses, fungi), treatment of skin diseases, wound treatment, and biofilm treatment.

Considering the present state of the global environment, it is imperative for academic institutions to prioritise the promotion of sustainability. The potential challenges faced by rapidly developing economies such as India can be addressed through the implementation of innovative and science-driven approaches such as the creation of green technologies. The present investigation aimed to analyse the prevailing patterns of environmentally sustainable technologies. To evaluate the efficiency of the leading 50 higher educational institutions according to NIRF 2022, this study employed data envelopment analysis. The results indicate that 16 universities have made noteworthy advancements in the creation of technologies that foster environmental sustainability. Furthermore, leading academic institutions collectively filed 1217 patents. [ABSTRACT FROM AUTHOR]

In this study, we performed milling machining on carbon-epoxy polymer composites and jute-epoxy composites using a CNC vertical machining center. We focused on spindle speed, feed rate, depth of cut, and flute number and analyzed the cutting force and surface roughness. The optimal parameter combination to reduce cutting force in both composites was as follows: S = 600 rpm, FR = 100 mm/min, DOC = 0.25 mm, and FN = 6. The jute-epoxy composites required less cutting force (11.85 N/m2) compared to the carbon-epoxy composites (18.77 N/m2). The average surface roughness of the carbon-epoxy composites (6.685 µm) is higher than that of the jute-epoxy composites (3.08 µm). The type of reinforced material used greatly affects the cutting force and surface roughness during milling. [ABSTRACT FROM AUTHOR]

Breast cancer analysis is critical for clinical diagnosis and treatment because of its association with abnormal cell growth and tumor formation, which pose a significant health concern among women. Researchers have extensively explored traditional and individual deep learning algorithms, such as convolutional neural networks (CNNs) and artificial neural networks (ANNs), for this purpose. However, reliance on mono-based models often results in suboptimal classification accuracy in medical diagnosis. To get around this problem, we suggest a new method that combines generative adversarial networks (GANs) and residual neural networks (ResNets) in a way that works well together to accurately find breast cancer in histopathological images. Our approach leverages an enriched dataset to ensure better generalization during training. We employ GANs to augment the training dataset by generating synthetic images, which enhances feature recognition and improves the robustness of the model. We then use this augmented data to fine-tune ResNet, a robust deep learning architecture, for classification tasks. The GAN-ResNet framework uses discriminatory features taken from the generated images by the GAN discriminator. This combines the power of GANs for discrimination with the power of ResNet for classification. We specifically fine-tuned the final model layer for binary classification, enabling accurate differentiation between malignant and benign breast tissue. Additionally, we adapted the loss function to address imbalances in the medical dataset, ensuring a more robust and accurate model. Our proposed model demonstrates a remarkable 98% accuracy in analyzing histopathological images, validating its efficacy for early breast cancer detection. This innovative approach underscores its potential for significant advancements in clinical diagnostics. This version highlights the novelty of combining GANs with ResNet and emphasizes the unique aspects of your approach, such as the use of synthetic images for data augmentation and the adaptation of the loss function to handle dataset imbalances. [ABSTRACT FROM AUTHOR]

Plasma-surface modification method (PSMM) is an efficient and inexpensive surface processing method for various materials and has generated great interest in the field of biomedical engineering. This paper focuses on the numerous conventional plasma methods and experimental approaches applied to materials research for suitable biomedical applications, including plasma deposition, laser plasma deposition, plasma sputtering and etching, plasma polymerization, plasma spraying, plasma implantation, and so on. The distinctive benefit of plasma modification is its biocompatibility and surface properties can be enhanced on a selective basis while the bulk characteristics of the materials stay unaltered. Existing materials can hence be used and the requirement for new materials may be circumvented thereby reducing the time for the development of novel and efficient biomedical devices. [ABSTRACT FROM AUTHOR]

The long-term viability of reimplanted teeth is closely linked to the preservation of periodontal ligament (PDL) cells. Dental tissues possess a unique regenerative capacity, setting them apart from most other bodily tissues. Understanding the intricate processes governing repair and regeneration in oral tissues has proven to be a considerable challenge. Numerous authors have introduced various storage media designed to support the viability of PDL cells. This paper aims to present a comprehensive overview of the diverse range of storage media currently at our disposal. [ABSTRACT FROM AUTHOR]

In this study, both model analysis and electrical characterization of a dielectric barrier discharge based argon plasma jet have been carried at atmospheric pressure in a pin electrode configuration. The plasma and fluid dynamics modules of COMSOL multi-physics code have been used for the modeling of the plasma jet. The plasma parameters, such as, electron density, electron temperature and electrical potential have been analyzed with respect to the electrical parameters, i.e., supply voltage and supply frequency with and without the flow of gas. In all the experiments, gas flow rate has been kept constant at 1 liter per minute. This electrode configuration is subjected to a range of supply frequencies (10-25 kHz) and supply voltages (3.5-6.5 kV). The power consumed by the device has been estimated at different applied combinations (supply voltage & frequency) for optimum power consumption at maximum jet length. The maximum power consumed by the device in this configuration for maximum jet length of ∼26 mm is just ∼1 W.

Mining frequent patterns from voluminous datasets termed under 'Big data' and having inherent uncertainties poses a significant challenge. Minor changes carried out on the databases like; addition, deletion or modification of items should not lead to scanning the whole database. Besides, a number of algorithms proposed to handle these issues are effective, but their basis of mathematics and way of installation are complex. Keeping the above points in mind, we have proposed an approach, which innovatively combines the models Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) serially to improve the prediction accuracy. Here, the LightGBM brings its tree-based learning algorithms optimized for speed and performance, while LSTM contributes its advanced sequence modeling capabilities, effectively resolving the vanishing gradient dilemma that often plagues recurrent networks. Our approach is applied to the healthcare sector in general and particularly in the early detection of Breast Cancer from a dataset obtained from Kaggle, yielding outstanding results as are evident from the scores; precision rates of 0.92 for predicted negatives and 0.93 for predicted positives, recall rates of 0.96 for negatives and 0.88 for positives, alongside F1-scores of 0.94 and 0.90, respectively. With a comprehensive accuracy of 0.93 across 188 samples, our model demonstrates a remarkable potential for early medical diagnosis, outperforming existing single-model solutions. The robustness of our approach is further validated by the consistency of performance across various metrics, highlighting its suitability for deployment in high-stakes domains where predictive accuracy is paramount. [ABSTRACT FROM AUTHOR]

Introduction: Cavity preparations of posterior teeth have been frequently associated with decreased fracture strength of the teeth. Choosing the correct indirect restoration and the cavity design when restoring the posterior teeth i.e., premolars was difficult as it involves aesthetic, biomechanical and anatomical considerations. Aim: To evaluate the fracture resistance and failure pattern of three different cavity designs restored with monolithic zirconia. Materials and Methods: Human maxillary premolars atraumatically extracted for orthodontic reasons were chosen. A total of 40 teeth were selected and divided into four groups (n=10). Group I-Sound teeth (control with no preparation). Group II-MOD Inlay, Group III-Partial Onlay, Group IV-Complete Onlay. Restorations were fabricated with monolithic partially sintered zirconia CAD (SAGEMAX- NexxZr). All the 30 samples were cemented using Multilink Automix (Ivoclar) and subjected to fracture resistance testing using Universal Testing Machine (UTM) (Instron) with a steel ball of 3.5 mm diameter at crosshead speed of 0.5 mm/minute. Stereomicroscope was used to evaluate the modes of failure of the fractured specimen. Fracture resistance was tested using parametric one way ANOVA test, unpaired t-test and Tukey test. Fracture patterns were assessed using non-parametric Chi-square test. Results: Group IV (Complete Onlay) presented highest fracture resistance and showed statistical significant difference. Group II (MOD Inlay) and Group III (Partial Onlay) showed significantly lower values than the Group I (Sound teeth). However, Groups I, II and III presented no significant difference from each other. Coming to the modes of failure, Group II (MOD Inlay) and Group III (Partial Onlay) presented mixed type of failures; Group IV (Complete Onlay) demonstrated 70% Type I failures. Conclusion: Of the three cavity designs evaluated, Complete Onlay had shown a significant increase in the fracture resistance than the Sound teeth.

3D printing technology is rapidly transforming paediatric dentistry by enabling personalized treatment approaches, reducing patient anxiety, and improving treatment outcomes. The versatility of 3D printing applications in this field continues to expand, promising a brighter future for young dental patients and practitioners alike. This abstract underscores the importance of staying abeam of these advancements and embracing the potential of 3D printing technology in paediatric dentistry. KEYWORDS: 3D printing, Additive manufacturing, Direct metal laser sintering, Bioprinting, Pediatric dentistry. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes is a chronic disease with life altering consequences. It not only forces one to question and alter one's life style but also thrusts the added responsibility of self-care upon them. Patients are required to maintain controlled levels of glycalated hemoglobin (HbA1c). But up to 50 percent fail to do so and land up with a wide array of complications. One among these many complications is the psychiatric comorbidity of depression. Materials and Methods: The present study is a cross sectional study conducted at Department of Psychiatry, Shimoga Institute of Medical Sciences, Shimoga, Karnataka from the period of February 2022 to October 2022. The study population was determined to be 120 in number. Participants who were diagnosed with diabetes mellitus under American Diabetic association criteria were randomly selected from outpatient department of General medicine and referred to Department of Psychiatry, Shimoga Institute of Medical Sciences, Shimoga. Results: 75% of the study population had history of diabetes mellitus for more than 5 years of duration. More than half of the study population did not have any substance use (56.7%), however use of nicotine (13%) or alcohol (11%) or both (2%) were found in 43.3% of the population in total. Systemic hypertension was found to be the most common medical co-morbidity accounting to 88.3% of the study participants. More than 60% of the study population did not have any family history of depression. 31.7% of the study population expressed worthlessness (21.7%), death wishes and suicidal thoughts (8.3%) or attempts (1.7%). Conclusion: Early screening and appropriate intervention may lead to improvement in both mental and physical wellness along with prevention of suicides in these patients. Prevalence of depressive disorders among diabetes mellitus patients is increasing in number and severity. We would like to stress upon the need for screening depression among all patients who have been diagnosed with diabetes mellitus to ensure early detection, diagnosis, management, and suicide prevention. [ABSTRACT FROM AUTHOR]

Background: The present study was carried out to compare macro-microscopy, powder microscopy and HPTLC analysis of six species such as Senna auriculata (L.) Roxb., Senna alata (L.) Roxb., Senna alexandrina Mill., Senna occidentalis (L.) Link., S. uniflora (Mill.) Irwin and Barneby, S. tora (L.) Roxb. of the genus Senna Mill. belongs to the family Caesalpiniaceae. Materials and Methods: Six selected Senna species were collected, shade dried and pharmacognostic study were performed used techniques such as microscopy and HPTLC analysis. Results: Seed macro-morphology of the selected six species shows significant variations in size, shape, colour, ornamentation and number of seeds per pod. The basic cellular structure of six Senna species was anatomically similar but variation was observed in number of layers. Microscopic characters of the selected species revealed key diagnostic features that can help to determine the relationship between the closely related species; macrosclerieds and osteosclerdies were observed in Senna auriculata and S. occidentalis. Powder microscopy of selected six Senna species varied in colour, texture and odour. Numerous rosette crystals in cotyledonary cells were found only in Senna tora which is a significant variation from other five Senna species. HPTLC finger print profiling was performed with ethanol extract at 254 nm, 366 nm and 575 nm derivatization using vannilin-sulphuric acid and the results were documented. In 254 nm 14 bands were separated but the major peak at Rf 0.31 (22.24%) appeared in S. alexandrina. Conclusion: A detailed morphological and pharmacognostical evaluations play an important role to avoid misidentification and delimitation of selected six Senna species and HPTLC profiling is an additional analytical tool for species identification. [ABSTRACT FROM AUTHOR]

Rosacea, atopic dermatitis and bullous disease are significant skin conditions with distinct characteristics and impacts, which affect millions of people globally. Early and quick diagnosis is crucial for these conditions to prevent complications, alleviate symptoms and improve quality of life for affected individuals. This research article presents an innovative approach to automated classification of common skin diseases using Convolutional Neural Network (CNN) models. The study focuses on diagnosing rosacea, atopic dermatitis and bullous disease, leveraging CNN technology. Four pre-trained CNN models – DarkNet-53, ResNet-18, SqueezeNet and EfficientNet-b0 – were investigated. Additionally, two improvised versions of DarkNet-53 and an improvised version of ResNet-18 were developed, integrating a specific number of fully connected neural network layers and adjusting other CNN layers such as batch normalisation and activation function layers accordingly. The performance of these improvised models surpassed that of the original architectures, demonstrating superior accuracy in identifying the targeted skin diseases. In terms of overall accuracy, improvised versions of DarkNet-53 and ResNet-18 achieved an accuracy of 98% and 98%, respectively, while the original models could achieve an accuracy between 75% and 80%. This research contributes to advancing automated diagnostic systems for dermatological conditions, potentially improving early detection and treatment outcomes.

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot. [ABSTRACT FROM AUTHOR]

In the title compound, C23H23NO8, the dihedral angle between the five- and six-membered rings of the indene-dione moiety is 3.09 (13)°. The mean plane of the five-membered ring (which has a flat envelope conformation with the spiro C atom as the flap) is inclined to the mean plane of the central five-membered ring of the pyrrolizine unit by 76.48 (12)°. This central ring has a twist conformation on the N—C(spiro) bond. The outer ring of the pyrrolizine unit has an envelope conformation with the N atom as the flap. The mean planes of these two fused rings are inclined to one another by 65.28 (15)°. The pyran ring has a screw-boat conformation and its mean plane makes a dihedral angle of 29.50 (11)° with the mean plane of the central five-membered ring of the pyrrolizine unit. In the crystal, molecules are linked via C—H...O hydrogen bonds, forming two-dimensional networks lying parallel to the ab plane.

In the title molecule, C19H20N2O2, the imidazole ring makes dihedral angles of 57.29 (5) and 31.54 (5)° with the attached dimethoxyphenyl residue and the phenyl ring, respectively. The dihedral angle between the dimethoxyphenyl and phenyl rings is 61.15 (5)°. In the crystal, pairs of C—H...N hydrogen bonds connect the molecules into inversion dimers.

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node's position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round. [ABSTRACT FROM AUTHOR]

Traditionally, the leaves of Clematis wightiana have been used in the treatment of rheumatism, indigestion, headaches, varicose veins, bone problems, nasal congestion and sinus. The present study was conducted to evaluate the phytochemical profile, quantification of secondary metabolites and free radical scavenging capacity of C. wightiana leaf. The total phenolic, tannin and flavanoid content of C. wightiana leaves were quantified and were found to be higher in the ethyl acetate extract. Subsequently, the extracts were subjected to appraise their antioxidant capacity by availing various in vitro antioxidant assays namely DPPH radical scavenging assay, ABTS assay, Phosphomolybedenum assay, Ferric Reducing assay, Superoxide Radical Scavenging assay and Reducing power assay. The results of the antioxidant assays revealed that the ethyl acetate extract of C. wightiana leaves possess better free radical scavenging activity than other solvent extracts. Thus, the finding of the study elucidates the perception on phytochemical and bioactivity of C. wightiana which could be used in development of phytotherapeutics to enchance human health. [ABSTRACT FROM AUTHOR]

In the present work, vibration control of a simply supported plate with line type and cross type piezoelectric (PZT) patches are investigated with and without actuation voltage. The plate is modeled under the assumption of Kirchhoff's Plate theory. The mass of PZT patches remain constant in all cases. In case of actuation, applied voltage considered are 1, 2 and 3 mV. The external excitation to the plate is in the form of harmonically varying point load of 1 mN. It is noticed that cross type PZT patch is more effective in deflection suppression of plate than that of line type PZT patch at 3 mV of actuation at patch thickness of 0.75 μm. Suppression of central deflection of plate for line type and cross type PZT patches are obtained in different frequency bands of (175–185 Hz) and (870–880 Hz) respectively. [ABSTRACT FROM AUTHOR]