Goal Research Articles

Credit risk and Tunisian bank stability in the Covid-19 wave

<p style="text-align:justify"><span style="font-size:16px"><span style="font-family:Times New Roman,Times,serif"><span dir="ltr" lang="EN-US"><span style="color:black">Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.</span></span></span></span></p>

EV Battery Management using Adaptive Kalman Filter and ECC

This initiative addresses the critical concerns of enhancing battery management and easing the calculation of battery capacity in electric vehicles. By using Coulomb counting method, the State of Charge (SoC) of Electric Vehicle (EV) batteries is estimated by analysing real-time battery data through simulations and interpolation techniques. The primary objective is to offer precise SoC estimation to mitigate the range anxiety. Furthermore, this research proposes methods of estimating the SoC of Lithium-Ion batteries and an enhanced coulomb counting model with Adaptive Kalman Filter to estimate the state of charge with higher accuracy. This comprehensive approach seeks to address critical challenges in EV battery management contributing significantly to the electric vehicle technology for the society.

Enhanced human activity recognition through deep multi-layer perceptron on the UCI-HAR dataset

Using the UCI-HAR dataset, this paper examines human activity recognition (HAR) from the perspectives of data science and artificial intelligence. The primary objective is to present and evaluate the effectiveness of a multi-layer perceptron (MLP) model, concentrating on six different activity categories. We train and assess the MLP model using the UCI-HAR dataset, contrasting its results with those of convolutional neural networks (CNN). The MLP model shows competitive results, attaining an amazing 97% validation and testing accuracy, highlighting its efficiency for smaller datasets. An extensive study is carried out to assess the model's adaptation to a larger Motion Sense dataset using confusion matrices and cross-entropy, the model shows robustness with an accuracy of 89%. The MLP model performs admirably, demonstrating its capacity to pick up complex patterns. Results from comparative analysis with CNN are competitive, especially when dealing with smaller datasets. The suggested MLP model shows up as a practical and efficient way to advance HAR techniques. Its remarkable performance and versatility not only show its usefulness in real-world scenarios but also point to interesting directions for further study in the area of HAR.

Assessment of immunosuppression induction with basiliximab compared to antithymocyte-globulin in adult heart transplant patients.

Patients undergoing heart transplants are at risk of rejection which can have significant morbidity and mortality. Induction immunosuppression at the time of transplant reduces the early risk and has additional benefits. The induction agent of choice within our program was changed from rabbit antithymocyte-globulin (rATG) to basiliximab, so it was necessary to evaluate whether this had any impact on patient outcomes. Our primary objective was to describe rejection, infection, and other outcomes in adult heart transplant patients at the University of Alberta Hospital in Edmonton, Canada. This study was a nonrandomized, retrospective cohort study. Sixty-three patients were included with median ages 50 years versus 54 years. More female patients received rATG (20%vs. 42.4%). The most common indication for transplant in both cohorts was ICM (63.3%vs. 57.6%). Patients who received rATG had significantly higher PRA (0%vs. 43%, p<.001). Acute rejection episodes were similar between basiliximab and rATG at 3 months (16.7%vs. 15.1%; p=1.0) and 6-months (30.0%vs. 18.1%; p=.376). Infections were not statistically different with basiliximab compared to rATG at 3-months, 43.3%vs. 63.6% and at 6-months 60.0%vs. 66.7%). There were no fatalities in either group. Our study did not demonstrate differences in rejection with basiliximab compared to rATG. Mortality did not differ, but basiliximab-treated patients had fewer infections and infection-related hospitalizations than those treated with rATG. Larger studies with longer durations are needed to more completely describe the differences in rejection and infectious outcomes.

Restarting pre-exposure prophylaxis (PrEP) for HIV: a systematic review and meta-analysis

High coverage of pre-exposure prophylaxis (PrEP) will reduce HIV transmission and help end the HIV/AIDS pandemic. However, PrEP users face challenges, including long-term adherence. The study aimed to document the proportions of individuals who restart HIV PrEP after they stop and the reasons for restarting PrEP. This study is a systematic review and meta-analysis. We systematically searched CINAHL, Embase, Emcare, Global Health, Medline, Scopus, and PsychINFO for peer-reviewed with no date restrictions. A grey literature search was conducted through Google search, a search of abstract books of AIDS conferences and the websites of WHO and UNAIDS. The data search was conducted in April 2023 and updated in February 2024. Two authors extracted data on the proportion of people who stopped and then restarted PrEP, reasons for restarting, and strategies to support people restarting PrEP. Two authors appraised the data using the Joanna Briggs Institute Appraisal Tools. We used a random-effects meta-analysis to pool estimates of restarting. We conducted meta-regression to determine potential sources of heterogeneity. This study is registered with PROSPERO, CRD42023416777. However, we deviated from our original plan as we did not identify enough studies for strategies to support restarting PrEP (primary objective). Subsequently, we revised our plan to strengthen our secondary objective to quantify the proportion of people who stopped and restarted PrEP, and explore possible reasons for its heterogeneity. Of 988 studies, 30 unique studieswere included: 27 reported the proportion restarting PrEP, and of these, 7 also reported reasons for restarting PrEP, and 3 studies reported only on the reasons for restarting PrEP. No study evaluated interventions for restarting PrEP. For the meta-analysis, we included 27 studies. Most studies were from high-income countries (17/27, 63%) or the USA (15/27, 56%). Overall, 23.8% (95% CI: 15.9-32.7, I2=99.8%, N=85,683) of people who stopped PrEP restarted PrEP. There was a lower proportion of restarting in studies from middle-income countries compared to high-income countries (adjusted odds ratio (aOR) 0.6, 95% CI: 0.50-0.73, p<0.001). There was higher restarting in studies from Africa compared to the USA (aOR 1.55, 95% CI: 1.30-1.86), heterosexual populations compared to men who have sex with men or transgender women (aOR 1.50, 95% CI: 1.25-1.81, p<0.001) and in studies defining restarting as those who had stopped PrEP for >1 month compared to those who stopped <1 month (aOR 1.20, 95% CI: 1.06-1.36, p<0.001). Reasons for restarting PrEP included perceived higher risk for HIV acquisition and removal of barriers to access PrEP. In terms of quality assessment, overall, both randomised controlled trials had a low risk of bias, while the observational studies used in the meta-analysis had some potential risk of bias related to not explicitly addressing potential confounders (15/25, 60%) or not describing strategies to address incomplete follow-up (24/25, 96%). About a quarter of people who stopped PrEP would restart, with substantial variation across countries and populations. It is important to understand the motivations and contextual factors influencing restarting PrEP and the support systems to enable restarting PrEP for those at ongoing risk. Australian National Health and Medical Research Council.

Review on Diabetes Mellitus: An Insight into the Current Scenarios, the Challenges of Therapy, and Application of Traditional Drugs

Abstract: Diabetes mellitus, an epidemic of the 21st century, is listed as one of the top ten causes of death around the world by the World Health Organization and is a complicated disease to deal with. Medically, a metabolic disorder characterized by elevated blood glucose levels and altered metabolism of biological macromolecules, mainly carbohydrates. Diabetes mellitus has a worldwide prevalence, and populations of all high, middle, as well as low-income countries, are affected by it. More than 400 million persons are currently living with diabetes. Many risk factors, such as lack of exercise, irregular diet, drinking, and smoking habits, as well as genetic factors, also contribute to the increased incidence of diabetes mellitus in the modern era. The conventional therapies for diabetes are based on insulin, insulin analogues, peptides, and oral hypoglycemics. The primary objective is to lower the elevated blood glucose levels and manage the distress to enhance the quality of life of the patient. The management of diabetes is very crucial to avoid complications having serious consequences such as the extra economic burden due to these complications. Conventional drug therapy has its own adverse effects, and its timely availability and cost are still an issue in many low and middle-income countries. Diabetes mellitus has been reported in various ancient kinds of literature along with various natural remedies (plants/herbals) useful in it. These plant-based remedies offer the benefits of lesser or no adverse effects, easy availability due to natural abundance, and low cost. Therefore, extensive research work is carried out in the past few decades to identify and understand the underlying principles of plant-based/herbal medicines to develop new potential lead molecules and a relatively safer, easily available and more economical alternative treatment therapy for diabetes mellitus.

From Barriers to Boosters: Optimizing Open Government Data through Publishing Guidelines and Data Protection Strategies

Open Government Data (OGD) is a global endeavor, a collaborative effort between governments worldwide to share datasets that encapsulate a wide spectrum of government activities, from environmental issues like pollution and climate to social aspects like education and childcare, and urban concerns like traffic and congestion, and healthcare statistics. As governments, being among the largest producers and collectors of data, are making OGD available online in diverse formats, primarily Word, PDF, or Excel, they are contributing significantly to this global initiative. The OGD initiative holds immense potential to revolutionize the way we access and use government data. Its primary objective is to enhance the discoverability, accessibility, and availability of data in alternative and preferably machine-readable formats. This, in turn, empowers a diverse set of stakeholders to develop innovative data applications under licensing schemes that permit unrestricted reuse. Despite these promising aspects, challenges such as data heterogeneity, data protection, data quality, and data provenance issues persist. This study aims to analyze and categorize these challenges and obstacles that hinder the OGD initiative from realizing its full potential, with a particular emphasis on data protection and security concerns for data providers.

Original ArticleAssociation between GLIM-diagnosed malnutrition and quality of life in older patients with cancer

BackgroundOlder patients with cancer have a higher risk for malnutrition and impaired quality of life (QoL). The present study aimed to investigate the relationship between malnutrition diagnosed according to the Global Leadership Initiative Malnutrition (GLIM) criteria and QoL across various tumor types, and to evaluate the combined prognostic value of malnutrition and QoL in predicting survival among older patients with cancer. MethodsThis multicenter, observational cohort study included 5310 older patients with cancer and 2184 with malnutrition (moderate stage, n = 1023; severe stage, n = 1161). An empirical cumulative distribution curve was performed to illustrate the correlation between malnutrition and QoL. The primary objective was to investigate the association between malnutrition and QoL using logistic regression analysis. Survival analyses were performed to assess the combined prognostic value of malnutrition and QoL. ResultsThe median age of the patients (66.9% male, 33.1% female) was 70 years (interquartile range [IQR] 67–74 years) years. The median QoL score was highest in patients without malnutrition (91.88 [IQR 84.44–97.44]), followed by those with moderate (86.15 [IQR 76.18–93.85) and severe (82.31 [IQR 69.87–91.11]) malnutrition. Logistics regression revealed that the risk for developing impaired QoL increased 1.98 (95% confidence interval [CI] 1.64–2.38; P < 0.001) and 2.33 (95% CI 1.93–2.81; P < 0.001) times in patients with moderate and severe malnutrition, respectively. Kaplan–Meier curves showed that QoL in combination with GLIM criteria demonstrated a significant discriminative performance for survival and served as an independent prognostic factor among older patients with cancer, especially for lung and gastric cancers. ConclusionsMalnutrition diagnosed according to the GLIM criteria was a predictor of impaired QoL. Additionally, the combination of QoL and malnutrition demonstrated utility for predicting survival outcomes in older patients with cancer.

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.

Off-Label Reduced Dose Apixaban in Older Adults With Atrial Fibrillation and Associated Outcomes.

Apixaban is commonly used to prevent stroke in older adults with nonvalvular atrial fibrillation (AF). Although its package insert has specific dose reduction criteria, providers may dose reduce outside of these parameters based on clinical scenarios. The primary objective was to determine the incidence of apixaban off-label reduced dosing, while secondarily determining the safety and efficacy outcomes associated with such dosing. A retrospective analysis of patients aged 65 and older with orders for apixaban for AF was institutional review board (IRB)-approved and conducted across 3 academic medical centers. Patients receiving off-label reduced-dose apixaban (ie, "underdosed") were matched to a cohort of patients dosed according to the package insert at the standard dosing (5 mg twice daily) using stratified random sampling. Secondary outcomes included 1-year incidence of major bleeding, clinically relevant non-major bleeding (CRNMB), stroke or transient ischemic attack (TIA), and mortality. The Fisher exact tests were used to compare between-group differences. Of the 1172 patients meeting initial inclusion criteria, 201 (17%) were dosed off-label, with 175 (15%) "underdosed." The 147 "underdosed" patients with documented follow-up were matched with 139 patients receiving standard Food and Drug Administration (FDA)-labeled dosing. There were no significant differences in incidence of stroke (2.7% vs 2.2%), major bleeding (0% vs 0.7%), and CRNMB (2.7% vs 1.4%) in the off-label reduced dosing versus standard dosing groups. All-cause mortality was higher in the off-label reduced-dose group (16 [10.9%] vs 2 [1.4%], P < 0.05). Older adults with nonvalvular AF are commonly prescribed lower-than-recommended doses of apixaban. However, no significant association was found between empiric off-label reduced dosing and stroke or bleeding outcomes.

Performance of machine learning models to forecast PM10 levels

Machine learning techniques have garnered considerable attention in modern technologies due to their promising outcomes across various domains. This paper presents the comprehensive methodology of an optimized and efficient forecasting approach for Particulate Matter 10, specifically tailored to predefined locations. The execution of a comparative analysis involving eight models enables the identification of the most suitable model that aligns with the primary research objective. Notably, the test results underscore the superior performance of an ensemble model, which integrates state-of-the-art methodologies, surpassing the performance of the other seven state-of-the-art models. Adopting a case-specific methodology with machine learning techniques contributes to achieving a notably high regression coefficient (R²≈1) across all models. Furthermore, the study underscores the potential for future endeavors in predicting location-specific environmental factors.•This study focused on forecasting PM10 with machine learning models with the consideration of air quality factors and meteorological factors•Ensemble model was developed for the forecasting purposes with higher performance.

The population exposure risk of urban heat island effect: From the perspective of urban spatial expansion in China

Urban spatial expansion is a critical aspect of urbanization, but it must occur in a manner that ensures the sustainable development of the urban climate. The primary objective of this study was to assess the population exposure risk of urban heat island (UHI) effect from the perspective of urban spatial expansion. Additionally, the population weighted urban heat island intensity (UHII) index (Ep) was proposed to compare the population exposure risk of UHI effects in different types of urban spatial expansion in China during the summer of 2005–2020. It was found that, in China, urban land increased by an area of 74,894 km2 (1.01-fold) from 2005 to 2020. Leapfrogging emerged as the predominant pattern of urban spatial expansion and was primarily driven by the conversion of cropland to urban land. It witnessed a significant increase in the area covered by UHIs of 107,119 km2 (1.20-fold), accompanied by a rise in the UHII of 0.12°C in China. Notably, the UHII for infilling urban expansion was significantly higher at 2.03°C, in contrast to 1.66°C for edge expansion and 1.47°C for leapfrogging. Correspondingly, the Ep was markedly larger under the infilling urban expansion (2.10°C) in contrast to edge expansion (1.75°C) and leapfrogging (1.67°C). However, edge expansion dominated the newly developed urban land, contributing to 18.60% of the Ep, which was still far less than the contribution of the pre-existing urban land to Ep (61.24%). Importantly, the dominant driver of the increased Ep was the increasing UHII. This study established a connection between inevitable urban spatial expansion and the population exposure risk of UHI effect, providing a strong theoretical basis and practical guidance for the active adaptation and mitigation of urban environmental hazards in urban regions.

Successful Implementation of the AIUM Standardized 4-Year Residency Ultrasound Curriculum in Obstetrics and Gynecology: Lessons Learned and Way Forward.

Significant disparities in sonographic education exist in Obstetrics and Gynecology programs in the United States. To address the lack of standardization in ultrasound teaching, the American Institute of Ultrasound in Medicine (AIUM) assembled a multi-society task force in 2018 that resulted in the publication of a Consensus Report outlining a standardized ultrasound curriculum and competency assessment. The primary objective of the study was to implement the AUIM standardized curriculum within an Obstetrics and Gynecology residency program at a tertiary medical center, and report on the early implementation experience. The secondary objective was to assess current national practices in ultrasound training in Obstetrics and Gynecology residency programs. Study design was an effectiveness-implementation hybrid study of a structured ultrasound educational program based on the 2018 AIUM Consensus Report. The theoretical portion of the curriculum consisted of 26 hours of didactic lectures distributed over 4 years. For the practical component, residents received instruction from faculty and sonographers and independently acquired the required images as part of their regular clinical work. Simulator app access was also provided for additional opportunities to practice and acquire images. Deidentified images for the required competencies were uploaded to a WhatsApp group for review. Theoretical knowledge was assessed with a series of multiple-choice exams. Practical skills were assessed through grading of images submitted through WhatsApp as well as direct observations of resident scans. At the completion of 4 years, the assessment of both theoretical knowledge and practical skills was carried out using the ISUOG Basic Training Examination. National patterns in the area of ultrasound education were assessed with a four-question electronic survey of Obstetrics and Gynecology program directors in the United States. Analysis of both portions of the study was primarily descriptive. Four Obstetrics and Gynecology residents completed the curriculum spanning postgraduate years 1 through 4. The number of competencies completed ranged from 7 to 19 out of 41. All of the residents passed the theoretical and practical components of the ISUOG Basic Training Examination. For the residency program director survey portion of the study, response rate was 20% (59 of 290). Among the respondents, 55.9% reported having a structured curriculum and 34.4% reported implementing the AIUM curriculum. The two most commonly cited obstacles to implementation of structured ultrasound teaching were uncertainty with how to start the process (49.2%) and lack of faculty engagement (49.2%). Implementation of the AIUM Curriculum within the framework of an Obstetrics and Gynecology residency program at a tertiary medical center is feasible. In our experimental group, completion of the curriculum resulted in passing of an internationally recognized validated assessment. Obstacles to implementation in our program included a wide range of engagement and participation among residents, and limited opportunities to obtain some of the required competencies. Identification of obstacles to implementation from the perspective of residency program directors is critical for developing targeted approaches to ensure widespread implementation. Successful standardized validated sonographic training of graduating Obstetrics and Gynecology residents is crucial for this operator-dependent modality that has tremendous implications on patient safety and clinical care.

Effect of a low-carbohydrate diet on pain and quality of life in female patients with lipedema: a randomized controlled trial.

The primary objective of this study was to evaluate the effect of a low-carbohydrate diet (LCD) compared with a control diet on pain in female patients with lipedema. The secondary objectives were to compare the impact of the two diets on quality of life (QoL) and investigate potential associations of changes in pain with changes in body weight, body composition, and ketosis. Adult female patients with lipedema and obesity were randomized to either the LCD or control diet (energy prescription: 1200 kcal/day) for 8 weeks. Body weight and body composition, pain (Brief Pain Inventory measured pain), and QoL (RAND 36-Item Health Survey [RAND-36], Impact of Weight on Quality of Life [IWQOL]-Lite, and Lymphoedema Quality of Life [LYMQOL]) were measured at baseline and at postintervention. A total of 70 female patients (age, mean [SD], 47 [11] years; BMI 37 [5] kg/m2) were included. The LCD group had greater weight loss (-2.8 kg; 95% CI: -4.1 to -1.0; p < 0.001) and larger reduction in pain now (-1.1; 95% CI: -1.9 to -0.3; p = 0.009) compared with the control group. No association was found between changes in pain now and weight loss. Both groups experienced improvements in several QoL dimensions. Diet-induced weight loss in women with lipedema can improve QoL. An energy-restricted LCD seems to be superior to a standard control diet in reducing pain.

An advanced approach for accurate pneumonia detection using combined deep convolutional neural networks

Pneumonia, a lung infection caused by viral or bacterial agents, poses a significant health risk by affecting one or both lungs in humans. Accurate diagnosis, particularly in pediatric cases, is crucial for timely intervention. chest X-rays (CXRs) are a common and non-invasive diagnostic tool to detect pneumonia-related abnormalities. Nonetheless, the minimal radiation exposure suitable for pediatric diagnosis poses a challenge in accurately detecting pneumonia in children. This work proposes a concatenation model that combines two pre-trained convolutional neural networks (CNNs) depending on the transfer learning (TL) technique and optimizes the training parameters to build a highly accurate model for detecting pediatric pneumonia from CXR images. The concatenated extracted features from the two pre-trained CNNs are passed through a convolutional layer to select more valuable semantic features to reduce the extracted features, which helps reduce the model parameters and execution time. Experimental results demonstrate that the feature concatenation technique, along with optimization of training parameters, surpasses the performance of individual CNNs and several state-of-the-art methods. The proposed method achieves a classification accuracy of 98.5%, precision of 99.5%, sensitivity of 98.4%, and F1 score of 99.1%. The primary objective of the proposed approach is to aid radiologists in achieving accurate pneumonia diagnosis in real-time.

Landslide susceptibility modelling in the Doda Kishtwar ramban (DKR) region of Jammu and Kashmir using remote sensing and geographic information system

The present study represents a significant understanding, employing cutting-edge Remote Sensing(RS) and Geographic Information System (GIS) technologies by conducting a comprehensive landslide susceptibility modelling in the Doda, Kishtwar, and Ramban (DKR) districts of the Union Territory of the Jammu and Kashmir in the NW Himalaya. The primary objective of this study is to determine the areas that are at high risk of landslides and accordingly classified these risk zones into five distinct categories, ranging from very high (VH) to very low (VL) susceptibility. To achieve this, a multifaceted approach, involving the utilization of various terrain thematic layers in the GIS environment was applied. The preferred framework employed in this study is the Weighted Overlay (WO) analysis. The final outcome of the study is the final landslide susceptibility map which is a significant and valuable resource for a wide range of stakeholders, including decision-makers, land managers, and local communities. It equips these stakeholders with the information and insights required to adopt proactive strategies in response to the identified landslide susceptibility zones.

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.

Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework

IntroductionIn low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs). ObjectivesThe primary objective was to explore IRs’ beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored. Materials and MethodsIn-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF. ResultsEleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs’ clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs’ understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited. ConclusionUsing TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

Machine learning-based lightweight block ciphers for resource-constrained internet of things networks: a review

The increasing number of internet of things (IoT) devices, wearable technologies, and embedded systems has experienced a significant increase in recent years. This surge has brought attention to the necessity for cryptographic algorithms that are lightweight and capable of providing security in resource-constrained environments. The primary objective of lightweight block ciphers is to provide encryption capabilities with minimal computational overhead and decreased power consumption. As a result, they are particularly well-suited for use on devices that have limited resources. At the same time, machine learning methodologies have evolved into powerful mechanisms for the purposes of prediction, categorization, and system optimization. This study introduces a challenges and issues involved in integrating machine learning techniques with the development of lightweight block ciphers.