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Congenital hypothyroidism (CH), characterized by insufficient thyroid hormone production at birth, is frequently associated with fatigue, particularly in cases with delayed diagnosis. This study employed liquid chromatography–tandem mass spectrometry (LC-MS/MS) to profile carnitine and acylcarnitines in late-diagnosed congenital hypothyroid patients receiving levothyroxine (LT4) therapy, with the aim of identifying metabolic alterations that may be associated with fatigue symptoms. A total of 56 late-diagnosed congenital hypothyroid patients and 107 age-, sex-, and BMI-matched healthy controls were enrolled. Blood samples were collected in EDTA tubes and as dried blood spots (DBS) on Whatman® 903 filter paper. LC-MS/MS was used to quantify free carnitine and 28 acylcarnitines, and plasma triglyceride (TG) levels were measured using a biochemical analyzer. Compared to healthy controls, congenital hypothyroid patients showed higher mean (±SD) concentrations of free carnitine (45.38 ± 12.61 vs. 41.54 ± 9.85 µmol/L; P = 0.049), total carnitines (67.33 ± 18.27 vs. 62.51 ± 14.13 µmol/L), and total acylcarnitines (21.95 ± 7.66 vs. 20.96 ± 5.61 µmol/L), although only free carnitine levels were statistically significant. Long-chain acylcarnitines were significantly lower in congenital hypothyroid patients (2.67 ± 0.87 µmol/L) compared to controls (3.15 ± 0.93 µmol/L; P = 0.0014). The β-oxidation ratio C0/(C16 + C18), a proxy for Carnitine Palmitoyltransferase I (CPT-I) activity, was significantly elevated in patients compared to healthy controls (34.55 ± 14.88 vs. 25.73 ± 6.87; P < 0.0001). Plasma TG levels were also significantly higher in patients (88.92 ± 59.54 mg/dL) than in controls (58.33 ± 15.79 mg/dL; P = 0.02). Metabolic profiling in congenital hypothyroid patients revealed impaired long-chain fatty acid oxidation and elevated triglyceride levels. These metabolic changes may contribute to fatigue symptoms and are potentially associated with reduced CPT-I activity, which is essential for mitochondrial β-oxidation. Additionally, mutations in the TPO and TSHR genes identified within this cohort may be linked to the observed metabolic alterations. Collectively, these findings suggest a possible interplay between genetic variants, disrupted lipid metabolism, and clinical features of congenital hypothyroidism.

Background Intimate partner violence (IPV) remains a significant public health concern in Bangladesh, with societal norms heavily influencing women’s attitudes toward its justification. Education is often linked to reduced IPV justification, as it can empower women to challenge harmful gender norms. However, this relationship is likely moderated by community norms, where entrenched beliefs about IPV may limit the protective effects of education. This study examines the interplay between education and community norms in shaping the attitudes toward IPV among ever-married women aged 15 to 49 in Bangladesh. Methods This study utilized data from the 2022 Bangladesh Demographic and Health Survey (BDHS). A sample of 19,022 ever-married women was analyzed. A composite measure of attitudes toward IPV was developed, assessing agreement with various justifications for IPV. Statistical analyses, including chi-square tests, rank-biserial correlation test, and generalized linear models with binomial link function, were utilized to evaluate associations between IPV justification and sociodemographic factors. The parametric g-formula was employed to estimate potential outcome probabilities of IPV justification under hypothetical scenarios, with bootstrapping used for standard error calculations. Results 13.9% of the ever-married women in Bangladesh aged 15–49 years justified IPV. Women with no formal education had 78% higher odds of justifying IPV compared to higher educated women (AOR 1.78 with 95% CI 1.11 to 2.85). Women from communities with a high prevalence of IPV justification had 3.21 times higher odds of perceiving IPV as justifiable compared to those from low-prevalence community (AOR 3.21 with 95% CI 2.06 to 5.01). Additionally, poor older women with limited household decision-making autonomy, having uneducated husband, and belonged to certain divisions were more prone to justify IPV. Within a fixed level of community norms, the prevalence of IPV justification among women with no formal education is not significantly different from the women with primary or secondary education (all p  values > 0.40). On the other hand, if all women from the study population had no formal education and belonged to a community with low IPV justification, they would have been 6% less likely to perceive IPV as justifiable compared to if all of them were to belong to a highly educated community high IPV justification (95% CI: -11% to -2%). Conclusion The findings highlight that education alone is insufficient to combat the norm regarding IPV justification, particularly in communities where such beliefs are entrenched. The results underscore the necessity of targeted, community-level interventions aimed at shifting societal attitudes toward non-violence and gender equality, creating sustainable change, and reducing the normalization of IPV in Bangladesh.

Background Intimate partner violence (IPV) is a severe issue in many low-and middle-income countries and remains a persistent public health problem. Research suggests that controlling attitudes by husbands can increase the risk of women experiencing IPV. To delve deeper into this issue, this study aims to investigate the relationship between women’s autonomy and their approval of IPV against women. Methods This study utilized the Bangladesh Demographic and Health Survey (BDHS) 2017-18, a cross-sectional nationally representative survey consisting of 20,127 ever-married women aged 15–49. Both unadjusted and adjusted associations between acceptance of IPV against women and four dimensions of autonomy— economic decision-making, self-health-related decision-making, freedom of movement, and non-threatening sexual agreement within partnerships—were investigated. Chi-square tests were used to assess bivariate associations, followed by Cramer’s V to measure the strength of these associations. A multivariable binary logistic regression model was then applied to estimate adjusted associations. Results One in every five women approved of IPV. Among the four dimensions of women’s autonomy, women having autonomy in self-health care had the lowest prevalence in accepting IPV (6.52%). Except for self-economic decision autonomy, the other three dimensions of women’s autonomy showed a significant unadjusted or preliminary association with the perception of approving IPV (p-value < 0.01). Women who shared control over their earnings with their husbands, rather than exercising solo control, demonstrated higher odds of approving IPV (AOR 1.16, p-value 0.04). Additionally, women lacking the final say on important household purchases and visits to close relatives exhibited increased odds of approving IPV compared to those with sole decision-making power (AOR 1.35, p-value < 0.001; AOR 1.37, p-value < 0.001, respectively). Conversely, women who lacked the final say on their own health care were less likely to approve of IPV compared to those with sole decision-making authority (AOR 0.60, p-value < 0.001). Conclusion This study highlighted that a significant number of women in Bangladesh approve of violence by their husbands, which could be a major obstacle to the reduction of violence in society. Further, the results indicate a significant correlation between women’s autonomy—in terms of decision-making, mobility, freedom from threatening sexual relations with their husbands, and access to and control over economic resources— and their approval of IPV. Strengthening women’s autonomy emerges as a vital strategy for decreasing the acceptance of IPV, advancing gender equality, and enhancing the overall well-being of women.

Due to the dramatic increase of spectrum demand, efficient usage of the limited spectrum resources has become a crucial issue for the next-generation wireless networks. Auction-based spectrum trading, utilization and pricing have many promising features and have proven to be a fair and consistent way of secondary spectrum trading and management. In this paper, we present a mathematical approach to the future spectrum market where multiple buyers (secondary network operator) compete to gain spectrum resources through a number of auctions from multiple sellers (primary network operator, PNO). Through static and dynamic auctions, the secondary network operators borrow underutilized licensed spectrum resources from primary operators either through predefined contracts or through instantaneous contracts. Our main focus is on the optimal choice of the secondary operator, contiguous spectrum resource to maintain the quality and utilization history based fair allocation of the spectrum resources through auctions controlled by the third party spectrum regulators (SR), which has not been addressed previously. We first develop a matching problem to identify the most suitable auctions for secondary operators. A price-based optimal number of auctions and a utility-based ranking of the optimal auctions to be bid by the secondary operators are proposed, where the secondary operator maximizes the net utility surplus (NUS). The win or lose, pricing and allocation of spectrum resources are determined by a proposed Vickery-type mechanism. Finally, we provide simulation results to evaluate the performance of the proposed auction mechanism.

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. In this study, the effect of covariates has been analyzed for three stock indices: S & P 500, Morgan Stanley Capital International (MSCI) world stock index, and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Results show that parametric approaches can be good forecasting models for the S & P 500 index and the VIX index. On the other hand, a random walk model can be adopted to forecast the MSCI index. Moreover, among the three random walk forecasting methods for the MSCI index, the naïve method provides the best forecasting model.

Self-driving vehicles have attracted significant attention in the automotive industry that is heavily investing to reach the level of reliability needed from these safety critical systems. Security of in-vehicle communications is mandatory to achieve this goal. Most of the existing research to detect anomalies for in-vehicle communication does not take into account the low processing power of the in-vehicle Network and ECUs (Electronic Control Units). Also, these approaches do not consider system level isolation challenges such as side-channel vulnerabilities, that may arise due to adoption of new technologies in the automotive domain. This paper introduces and discusses the design of a framework to detect anomalies in in-vehicle communications, including side channel attacks. The proposed framework supports real time monitoring of data exchanges among the components of in-vehicle communication network and ensures the isolation of the components in in-vehicle network by deploying them in Trusted Execution Environments (TEEs). The framework is designed based on the AUTOSAR open standard for automotive software architecture and framework. The paper also discusses the implementation and evaluation of the proposed framework.

Mental health is one of the most neglected healthcare issues in developing countries. The situation is worse in the Global South due to stigma, superstitions, and many other social-cultural-financial constraints. Lack of mental health professionals and regular monitoring has deprived families raising children with Autism Spectrum Disorder (ASD) of the desired care and support. However, the overwhelming adoption of mobile phones in Bangladesh has created an unprecedented opportunity to overcome these various constraints. To leverage this opportunity, we designed, developed, and evaluated mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method), a mobile application that integrates the Experience Sampling Method (ESM) with the local healthcare practice by putting the caregivers in the loop. mCARE collected behavioral and developmental progress parameter values from the caregivers of 300 children with ASD periodically (daily/ weekly/ biweekly/ monthly) along with sociodemographic data of the family. This paper reports: (a) the context and challenge identification phases that validate the requirement of a mobile based tool; (b) evolution of mCARE following Value Sensitive Design; (c) short term and long-term impact analysis (qualitative and quantitative) of mCARE, and; (d) broader implications of these findings for the HCI scholarship along with the impact of mCARE during COVID-19.

A geometry-based three-dimensional (3D) novel stochastic channel model for air-to-ground (A2G) and ground-to-air (G2A) radio propagation environments is proposed. The vicinity of a ground station (GS) is modelled as surrounded by effective scattering points; whereas the elevated air station’s (AS) vicinity is modelled as a scattering-free region. Characterization of the Doppler spectrum, dispersion in the angular domain and second order fading statistics of the A2G/G2A radio communication channels is presented. Closed-form analytical expressions for joint and marginal probability density functions (PDFs) of Doppler shift, power and angle of arrival (AoA) are derived. Next, the paper presents a comprehensive analysis on the characteristics of angular spread on the basis of shape factors (SFs) for A2G/G2A radio propagation environments independently in both the azimuth and elevation planes. The analysis is further extended to second order statistics of the fading channel; where the behaviour of the level crossing rate (LCR), average fade duration (AFD), auto-covariance and coherence distance for the A2G/G2A radio propagation environment is studied. Finally, the impact of physical channel parameters, such as the mobility of AS, the height of AS, the height of GS and the delay of the longest propagation path, on the distribution characteristics of Doppler shift, angular spread and second order statistics is thoroughly studied.

Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M–MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers.

Objectives This work has developed a modified mental state assessment tool for impact analysis of therapeutic interventions for patients with cognitive impairment. This work includes a pilot study to validate the proposed tool and assess the impact of virtual reality-based interventions on patient well-being, which includes assessment of cognitive ability and mood. Methods The suggested tool’s robustness and reliability are assessed in care home facilities with elderly residents over the age of 55. Because of the repetitive nature of the pilot study, test-retest strategy for Cronbach’s alpha coefficient is employed to validate the internal consistency of the proposed tool over time. Qualitative and quantitative analyses are performed on the collected data to draw inferences on the impact of virtual reality-based interventions on patients with cognitive impairments. Results The Cronbach’s alpha coefficient value shows that the proposed tool’s resilience is comparable to that of its pre-intervention counterparts. The Cronbach’s alpha coefficient values are determined for Pre-virtual reality and Post-virtual reality interventions, which include 116 virtual reality sessions for 52-participant, and three cohorts of virtual reality sessions for 21 participants. These values for a majority of the interventions remained within the acceptable range of 0.6–0.8. Conclusions The proposed modified mental state assessment tool is observed to be a reliable tool for investigating the impact of virtual reality-based interventions on patients with cognitive impairments. One of the notable significance of the proposed tool is that this allows for resource allocation for such interventions to be tailored to the needs of the patient, leading to greater therapeutic efficacy and resource efficiency.