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Complicated Grief during COVID-19: An International Perspective
Cultures across the globe have evolved time-tested rituals to honor those who die and offer solace and support to survivors with the goal of helping them to accept the reality of the death, cope with the feelings of loss, adjust to life without the deceased, and find ways to maintain a connection to the memory of the deceased. The COVID-19 pandemic has disrupted these rituals and brought significant changes to the way we mourn. Specifically, public health responses to COVID-19 such as social distancing or isolation, delays or cancellations of traditional religious and cultural rituals, and shifts from in-person to online ceremonies have disrupted rituals and thus made it more difficult to access support and complete the psychological tasks typically associated with bereavement. This paper conceptualizes the common bereavement tasks including emotion-focused coping, maintaining a connection to the deceased, disengagement and reframing death and loss, and problemfocused coping. It provides examples of how the COVID-19 pandemic has altered mourning rituals across several cultures and religions and contributed to prolonged grief disorder as defined by the ICD-11 that includes depressive symptoms and post-traumatic stress. Early evidence suggested that the suddenness of loss, the social isolation, and the lack of social support often associated with COVID-19-related death are salient risk factors for complicated grief. As a consequence, psychological assessments, grief counseling, and mental health support are needed by families of patients who died from COVID-19. These services must be essential components of any comprehensive public health response to the pandemic. 2022 Hogrefe Publishing. -
On estimation of extropy for non-negative data with application on uniformity testing
{Poisson weights-based density estimator is used to estimate the extropy function to the non-negative data}. The traditional class of nonparametric extropy estimators, typically constructed using kernel density estimators with symmetric kernels, is not well suited for non-negative data. To address this limitation, we propose two Poisson-weights-based density estimators that are naturally adapted to the non-negative domain. The asymptotic properties of the proposed estimators are rigorously established, providing theoretical support for their use. A comprehensive simulation study demonstrates that both estimators outperform their conventional kernel-based counterparts in terms of bias and mean squared error. Furthermore, we introduce uniformity tests based on extropy and obtain their critical values through simulation. The practical utility of the proposed methods is illustrated through analyses of real data sets. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Big data analytics in tourism development and marketing: Theoretical perspectives on big data analytics in tourism marketing
The title of the suggested book chapter is " Theoretical Perspectives on Big Data Analytics in Tourism Marketing" and it is about the influence of big data analytics in the growth and promotion of tourism. It just shows how the AI and Metaverse can strategically use big data for better Market Segmentation and Customer behaviour analysis. This chapter looks at how metaverse technology allows tourists to participate in virtual experiences. Tourism companies can refine their marketing strategies, streamline operations, and provide value added experiences to their consumers by utilizing big data analytics. This Chapter underlines the power that big data has to change the tourism industry by enhancing decision making and spurring innovation in service provision. 2025 by IGI Global Scientific Publishing. All rights reserved. -
An empirical analysis of android permission system based on user activities
In today's world there has been an exponential growth among smart-phone users which has led to the unbridled growth of smart-phone apps available in Google play store, app store etc., In case of android application, there are many free applications for which the user need not shell out a penny to use the services. Here the magic word is "free" which entices millions of pliant people into installing those apps and giving unnecessary access to their data and device control. Current studies have shown that over 70% of the apps in market, request to gather data digressive to the most functions of apps that might cause seeping of personal data or inefficient use of mobile resources. Of late, couple of malignant applications gather unobtrusive information of the user through third-party applications by increasing their permissions to high-level on the Android Operating System. Android permission system provides, the user access to the third party apps and in return based on the permissions granted by the user, an app can access the related resource from the user's mobile. A user is bound to grant or deny permits during the installation of the application. For the most part, users don't focus on the asked permissions, or sometimes users do not understand the meaning of the permission and install the app on their device. They allow a way for attackers to perform the malicious task by demanding for more than expected set of permissions. These extra permissions permit the attacker to exploit the device and also retrieve sensitive information from it. In this research paper we describe how permission system security can create an awareness among the users that would assist them in deciding on permission grants. This improved and responsible user activities in Android OS can help the users in utilizing their device securely. 2018 Ankur Rameshbhai Khunt and P. Prabu. -
Inhibiting extracellular cathepsin d reduces hepatic steatosis in spraguedawley rats y
Dietary and lifestyle changes are leading to an increased occurrence of non-alcoholic fatty liver disease (NAFLD). Using a hyperlipidemic murine model for non-alcoholic steatohepatitis (NASH), we have previously demonstrated that the lysosomal protease cathepsin D (CTSD) is involved with lipid dysregulation and inflammation. However, despite identifying CTSD as a major player in NAFLD pathogenesis, the specific role of extracellular CTSD in NAFLD has not yet been investigated. Given that inhibition of intracellular CTSD is highly unfavorable due to its fundamental physiological function, we here investigated the impact of a highly specific and potent small-molecule inhibitor of extracellular CTSD (CTD-002) in the context of NAFLD. Treatment of bone marrow-derived macrophages with CTD-002, and incubation of hepatic HepG2 cells with a conditioned medium derived from CTD-002-treated macrophages, resulted in reduced levels of inflammation and improved cholesterol metabolism. Treatment with CTD-002 improved hepatic steatosis in high fat diet-fed rats. Additionally, plasma levels of insulin and hepatic transaminases were significantly reduced upon CTD-002 administration. Collectively, our findings demonstrate for the first time that modulation of extracellular CTSD can serve as a novel therapeutic modality for NAFLD. 2019 by the authors. -
Self-Control and Decision-Making Skills as Predictors of College Enrollment: Role of Parental Influences
Self-control and planful decision-making can play a critical role in promoting academic outcomes. Nonetheless, little is known about how parental influences impact these noncognitive skills in promoting college enrollment. Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examined adolescent self-control and decision-making skills (at wave 1) as predictors of college enrollment (at wave 3). Further, we assessed if the effect of parental influences (i.e., maternal academic involvement, maternal academic expectations, parental control/limit-setting, and parental education) on college enrollment was indirect and operated through the associations of parenting variables with adolescent self-control and planful decision making. Both self-control and decision-making skills significantly predicted college enrollment, controlling for age, gender, family income, and cognitive ability. Parental control/limit-setting and educational level had significant direct effects on college enrollment and were not significantly related to adolescent self-control or planful decision making skills. The effect of maternal academic involvement on college enrollment was indirect and operated through its associations with adolescent self-control and decision-making skills. The effect of maternal academic expectations on college enrollment was both direct and indirect, through its association with adolescent decision-making skills. Our findings suggest that individual and family-based interventions that target critical noncognitive skills, such as self-control and planful decision making, hold promise in promoting college enrollment. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Associations Between Early Life Adversity, Moral Development, and Psychopathology in Children and Adolescents: A Cross-Sectional Study
Introduction: Moral psychological development is shaped by socio-cultural and neurobiological factors, with the formation of conscience central to this process. Early Adverse Childhood Experiences (ACEs) have been linked to delays in moral development and increased risk of psychiatric disorders. This study examined how adversity affects conscience functioning, specifically the association between Psychopathological Interference (PI) and delays in Conscience Stages (CS) compared to youth raised in relative advantage. Methods: We analyzed 125 conscience-sensitive psychiatric interviews with youth admitted to a Psychiatric Residential Treatment Facility (PRTF). CS scores were compared with expected stages from community youth, using the Conscience Development Quotient (CDQ = CS attained CS expected 100). PI was rated on a Likert scale, incorporating full psychiatric evaluations, behavioral ratings, and DSM diagnoses. Multiple regression models examined the associations between CDQ, PI, and Clinical Global Assessment of Functioning (CGAF) scores, controlling for six covariates. Results: Participants (mean age, 14.2 years; 59% male, 41% female) exhibited significantly greater distress signals across conscience domains compared to community youth. No differences emerged by age at the onset of ACE. However, lower CDQ was associated with higher PI, earlier ACE onset, DSM Axis II disorders, and lower CGAF. Legal history and ACE count were not significant predictors. The model explained 22.7% of the variance in CDQ (p = 0.00018). Discussion: Findings highlight CDQ as a sensitive measure of developmental impact, beyond simply identifying red flags, consistent with prior ACE research. Retrospective design may limit sensitivity to ACE characteristics. Conclusion: Systematic conscience-sensitive interviewing, attuned to cultural and developmental contexts, may enhance clinical assessment of moral functioning. 2025, Bentham Science Publishers -
Synthesis of carbon nanomaterials from vegetables
This chapter looks into new horizons of sustainable nanotechnology developed through innovative carbocentrism that focuses on the development of carbon based nanomaterials from different categories of vegetables. This chapter is centered on the green synthesis of vegetable-derived sweet potato, garlic, lemon, and radish into carbon dots (CDs), graphene sheets, and carbon quantum dots through hydrothermal and aqueous extraction methods. To surpass traditional methods of nanomaterial synthesis, researchers are developing vegetable-derived nanomaterials that possess unique properties such as fluorescence and ranging surface functionalities. Such practices are recommended for reducing environmentally hazardous substances while upholding important eco-friendly principles and sustainable accountable nanotechnology. These methodologies address the misuse of dangerous substances and provides effective eco friendly approaches which emphasizenew direction towards sustainable nanotechnology. The versatility of these vegetable-derived carbon nanomaterials is evident in their applications, spanning from biomedical fields, such as drug delivery and bioimaging to environmental monitoring, particularly in the selective detection of metal ions. The advancements of medical technology are much needed in society today that is being more particular about green approaches and innovations. This willthe help low toxic and biocompatible nanomaterials live up to their full potential for eco-friendly biomedical technologies. This chapter serves as a comprehensive exploration of the synthesis, applications, and broader implications of carbon nanomaterials from vegetables, providing valuable insights into the evolving landscape of green nanotechnology. 2025 Elsevier Inc. All rights reserved. -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Powerlessness in the moral self: a social cognitive perspective on drug users
Powerlessness resides in devalued self-images of drug users. This study, drawing on social and moral psychology, examined the moral functioning of drug users compared to non-drug users. Self-reported data concerning moral identity and moral judgment on drug use were assessed and compared between groups. Drug users appeared to have significantly weaker moral identity centrality and pro-drug moral judgment than non-drug users. They also showed dissociation in the relationship between moral identity and moral judgment. As a result, the study proposed a moral identity model of drug use to better approach social cognitive powerlessness in drug users moral self. 2021 Taylor & Francis Group, LLC. -
High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble
A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 2022 Tech Science Press. All rights reserved. -
Portrait segmentation using ensemble of heterogeneous deep-learning models
Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmenta-tion, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Selfie Segmentation in Video Using N-Frames Ensemble
Many camera apps and online video conference solutions support instant selfie segmentation or virtual background function for entertainment, aesthetic, privacy, and security reasons. A good number of studies show that Deep-Learning based segmentation model (DSM) is a reasonable choice for selfie segmentation, and the ensemble of multiple DSMs can improve the precision of the segmentation result. However, it is not fit well when we apply these approaches directly to the image segmentation in a video. This paper proposes an N-Frames (NF) ensemble approach for a selfie segmentation in a video using an ensemble of multiple DSMs to achieve a high-performance automatic segmentation. Unlike the N-Models (NM) ensemble which executes multiple DSMs at once for every single video frame, the proposed NF ensemble executes only one DSM upon a current video frame and combines segmentation results of previous frames to produce the final result. For the experiment, we use four state-of-the-art image segmentation models to make an ensemble. We evaluated the proposed approach using 81 videos dataset with a single-person view collected from publicly available websites. To measure the performance of segmentation models, Intersection over Union (IoU), IoU standard deviation, false prediction rate, Memory Efficiency Rate and Computing power Efficiency Rate parameters were considered. The average IoU values of the Two-Models NM ensemble, Two-Frames NF ensemble, Three-Models NM ensemble and Three-Frames NF ensemble were 95.1868%, 95.1253%, 95.3667% and 95.1734% each, whereas the average IoU value of single models was 92.9653%. The result shows that the proposed NF ensemble approach improves the accuracy of selfie segmentation by more than 2% on average. The result of cost efficiency measurement shows that the proposed method consumes less computing power like single models. 2021 IEEE. -
A Study on the Effect of Canny Edge Detection on Downscaled Images
Abstract: Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling. 2020, Pleiades Publishing, Ltd. -
Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models
Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models 2020 IEEE. -
Analyzing the Performance of Canny Edge Detection on Interpolated Images
Surveillance cameras are extensively used nowadays in many commercial and domestic places to monitor theft, intrusion and other illegal activities. Typically, the cameras are placed at a high position to monitor a large area. Therefore, the captured images include background area in addition to the target objects. Under such situation, the image can be zoomed to focus on the target objects using various interpolation techniques. For further processing of the image, many techniques like edge detection, image sampling and image thresholding etc. are available. Considering edge detection to be a basic step for many application such as Object detection, Object recognition etc, in this work, we analyze the performance of the Canny Edge Detection algorithm on images interpolated using Nearest Neighbour, Bilinear and Bicubic interpolation methods. Canny Edge Detection is applied to the input image and the resultant image is saved for later comparison. The same image is upscaled using interpolation and the Canny Edge Detection algorithm is used on this upscaled image. This image is then resized to the original image size. Both the images are compared to check for their similarity using the Structural Similarity Index Method (SSIM). 2019 IEEE. -
In vitro storage under slow growth, plant regeneration, and ex vitro acclimatization of Ligusticum officinale (Makino) Kitag
Ligusticum officinale is an important medicinal plant belonging to Apiaceae. It does not set seeds and is propagated by rhizome division. However, its sensitivity to high summer temperatures makes field cultivation and genetic resource conservation challenging. To conserve L. officinale germplasm, we employed an in vitro slow-growth storage (SGS) method. Shoot cultures of L. officinale were established on Murashige and Skoog medium supplemented with 1.0 mg/L benzyl adenine, 30 g/L sucrose, and 2.4 g/L gelrite. Cultures were kept for one, three, five, and seven months. The effects of storage temperatures of 25 C (control) versus 15 C, medium supplementation with or without mannitol (3%), and abscisic acid (ABA), 0.5 mg/L, were examined. At the conclusion of the conservation period, survival was measured right away. Four weeks later, the shoot proliferation medium was used to measure the regrowth rate and recovery features. Subsequently, the regenerated shoots were transferred to MS medium supplemented with 1.0 mg/L indole-3-butyric acid for rooting of shoots for 4 weeks. The findings showed that even after seven months, shoot cultures kept at 15 C with medium supplemented with 3% mannitol and 0.5 mg/L ABA maintained a good survival rate of 83.3%. When compared to the control, most growth indices, including shoot length, fresh weight, number of shoots, and number of leaves, were significantly suppressed by mannitol and ABA combined treatment. A regrowth rate of 71% was achieved after transfer to proliferation medium. All the shoots that were cultured on rooting medium involved in rooting and plantlets were successfully acclimatized in controlled conditions. 2026 SAAB. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
An in vitro slow-growth callus conservation strategy for several medicinal plants using response surface methodology and machine learning
Background: In vitro culture of callus is an effective method for conserving the genetic resources of economically important crops. However, continuous subculturing is a costly and labor-intensive process. Therefore, establishing an efficient in vitro long-term conservation system applicable to various plant species is required. In this study, calli derived from five medicinal plant species, Camellia japonica (Cj), Centella asiatica (Ca), Ligusticum afficinale (Lo), Panax ginseng (Pg), and Sageratia thea (St) were used to optimize storage conditions and establish a suitable in vitro conservation strategy. Calli cultures were maintained on the appropriate culture medium at 5C for 120 days. Cell viability and regrowth rate were assessed during the storage period, and correlations between growth and antioxidant traits were examined. Subsequently, pretreatment optimization using sucrose (39%), MeJA (0-200 M), and CTR (020mg/L) was performed using RSM, and the effects of pretreatment and storage temperature on callus conservation were evaluated. In addition, machine learning models such as GRNN, MLP, RF, SVR, and XGBoost were applied to the experimental data. Results: The findings demonstrated that, in comparison to Ca and St, Lo, Pg, and Cj exhibited noticeably higher antioxidant capacity while maintaining high cell viability and regrowth rates. Interestingly, Ca and St drastically decreased viability and regrowth after 60 days, whereas Lo, Pg, and Cj maintained viability and regeneration for up to 90 days. Both TPC and AC (measured by FRAP assay) showed a high positive correlation with cell viability and growth rate, according to correlation analysis. RSM predicted that the optimal pretreatment medium for enhancing antioxidant capacity was the species-specific proliferation medium supplemented with 3% sucrose, 135 M MeJA, and 20mg/L CTR, while the highest TSSC was achieved using the species-specific proliferation medium supplemented with 9% sucrose and 200 M MeJA. When the storage temperature was set to 15C following the antioxidant-enhancing pretreatment derived from the RSM optimization, all five species showed improved cell viability and regrowth rates, among the storage methods. Among the ML models tested, XGBoost demonstrated the most stable and accurate predictive performance for both viability and regrowth during in vitro conservation. SHAP-based analysis of the XGBoost model, focusing on regrowth rate, revealed that storage duration was the most influential factor affecting regrowth prediction, followed by storage temperature, while pretreatment conditions showed secondary but meaningful contributions. Conclusions: This study demonstrates that long-term callus conservation is closely associated with AC and TPC. Medium supplemented with sucrose 3%, 135 M MeJA, and 20mg/L CTR, followed by storage at 15C, significantly improved viability and regrowth, and calli could be maintained up to 120 days without subculturing. This approach provides an efficient and broadly applicable in vitro strategy for the conservation of diverse plant genetic resources. The Author(s) 2026. -
High-speed portrait video segmentation using the hybrid combination of deep-learning models and boundary movement adjustment
As global warming intensifies, the development of energy-efficient Artificial Intelligence (AI) technologies has become crucial. Additionally, the growing demand for on-device AI in smartphones, extended reality devices, and autonomous vehicles necessitates AI systems that can function effectively on low-performance hardware. To address these needs, this study proposes hybrid methods in the field of Portrait Video Segmentation (PVS). Our proposed hybrid models leverage Deep-learning based Segmentation Models (DSMs) and a novel Boundary Movement Adjustment (BMA) process to achieve speed and accuracy balance. The Hybrid Serial Model (HSM) not only accelerates PVS but also improves energy efficiency while maintaining a similar level of accuracy. On the other hand, the Hybrid Parallel Model (HPM) enables high-performance PVS even on low-performance devices, ensuring no video frames are lost during high-speed segmentation processing. Tests conducted on Jetson Nano, Raspberry Pi, and a desktop PC demonstrate the effectiveness of these models, showing improvements in PVS speed while maintaining accuracy close to that of traditional DSMs. HSM increased PVS speed from 15.2 Frames Per Second (FPS) to 25.1 FPS on a desktop PC with a 0.5 % accuracy loss, and from 6.3 FPS to 16.5 FPS on a Jetson Nano with a 1 % loss. HPM reached 30 FPS on a desktop PC with a 0.05 % loss, and 29.7 FPS on a Jetson Nano with a 1 % loss. On the Raspberry Pi, the HPM method improved speed from 2.9 FPS to 29.8 FPS, demonstrating its adaptability for low-performance devices. 2025 Elsevier Ltd -
Customers satisfaction towards online banking services of public sector banks
At present the banking industry around the world has been undergoing a rapid transformation. The deepening of information technology has facilitated better tracking and fulfillment of commitments, multiple delivery channels for online customers and faster resolution of issues. Customer satisfaction is important criteria for banks sustenance, now banks are offers online banking services according to the customer needs and requirements. This study analysed customers satisfactions towards online banking services of public sector banks in Tiruchirappalli district. It is understand from the present study that bank websites and technology platforms has to offer various knowledge features on financial services. To retain the existing customers, banks has to conduct regular surveys on the customer satisfaction. The results of the study shows that variables like prompt response, security and Website design and ease of use are top three factors affected customer satisfaction. IJSTR 2020.