Search

The energy crisis and related environmental issues due to the increasing power demand are the primary concerns in today's evolving globe. Modern energy conversion systems need dependability and scalability to overcome obstacles. This manuscript systematically examines recent research on high-performance thermoelectric materials, including novel alloys, nanostructured composites, and flexible materials designed to enhance energy conversion efficiency and adaptability across diverse environments. The literature selection spans peer-reviewed articles, patents, and emerging studies, emphasizing innovations in material properties, manufacturing methods, and design adaptations for transportation-specific challenges. In addition, this paper is organized to provide a dual perspective on both material innovation and practical integration in transportation systems, highlighting recent advancements in manufacturing processes and design. The unique structure of this review illuminates current limitations and prospective research pathways, offering insights for enhancing TEG performance and fostering sustainable, energy-efficient transportation technologies.

Abstract Background Aboriginal and Torres Strait Islander peoples are disproportionately impacted by type 2 diabetes. Continuous glucose monitoring (CGM) technology (such as Abbott Freestyle Libre 2, previously referred to as Flash Glucose Monitoring) offers real-time glucose monitoring that is convenient and easy to use compared to self-monitoring of blood glucose (SMBG). However, this technology’s use is neither widespread nor subsidised for Aboriginal and Torres Strait Islander peoples with type 2 diabetes. Building on existing collaborations with a national network of Aboriginal and Torres Strait Islander communities, this randomised controlled trial aims to assess the effect of CGM compared to SMBG on (i) haemoglobin A1c (HbA1c), (ii) achieving blood glucose targets, (iii) reducing hypoglycaemic episodes and (iv) cost-effective healthcare in an Aboriginal and Torres Strait Islander people health setting. Methods This is a non-masked, parallel-group, two-arm, individually randomised, controlled trial (ACTRN12621000753853). Aboriginal and Torres Strait Islander adults with type 2 diabetes on injectable therapy and HbA1c ≥ 7.5% (n = 350) will be randomised (1:1) to CGM or SMBG for 6 months. The primary outcome is change in HbA1c level from baseline to 6 months. Secondary outcomes include (i) CGM-derived metrics, (ii) frequency of hypoglycaemic episodes, (iii) health-related quality of life and (iv) incremental cost per quality-adjusted life year gained associated with the CGM compared to SMBG. Clinical trial sites include Aboriginal Community Controlled Organisations, Aboriginal Medical Services, primary care centres and tertiary hospitals across urban, rural, regional and remote Australia. Discussion The trial will assess the effect of CGM compared to SMBG on HbA1c for Aboriginal and Torres Strait Islander people with type 2 diabetes in Australia. This trial could have long-term benefits in improving diabetes management and providing evidence for funding of CGM in this population. Trial registration Australian and New Zealand Clinical Trials Registry ACTRN12621000753853. Registered on 15th June 2021.

Interactive teaching methods have been receiving attention in application to innovate teaching methods in education and training, especially in training human resources in technical fields. With the participation of computers and supporting software, along with other means, the interaction process between teacher - learner - environment increasingly plays an important role in forming knowledge and skills for learners. For the University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen University, interactive teaching methods are applied in training Information Technology students flexibly in each subject to improve education quality. Through survey methods and other research methods, the article analyses the actual conditions for applying interactive teaching methods in Programming Engineering course for students of the Faculty of Information Technology, University of Information and Communication Technology, Thai Nguyen University, from which the limitations will be evaluated. On that basis, the article offers some measures to improve the effectiveness of applying interactive teaching methods in Programming Engineering for students.

Stromal sarcoma of the prostate is extremely rare. In this article, we report the case of a 43-year-old male admitted to the local hospital due to dysuria. Although the pathological findings from transurethral prostatic resection showed low-grade stromal sarcoma, the surgical specimen after radical prostatectomy revealed high-grade sarcoma with hypercellularity, marked atypical spindle cells, and high mitotic activity. This case study and literature analysis aim to emphasize its rarity and raise awareness about clinical and pathological diagnosis.

Abstract In this work, we present the results of the ortho-positronium (o-Ps) annihilation lifetimes and nitrogen adsorption measurements for different porous materials and an approach for describing the annihilation of o-Ps in a pore, which results in a surface-volume formula (SVF) for calculating the pore-related o-Ps lifetime. This proposed formula gives the relationship between the o-Ps annihilation rate and the effective pore radius, bulk composition, and pore structure, including pore geometry and topology. The pore-related o-Ps lifetimes of different materials calculated by the SVF are consistent with experimental results for both micro- and mesopores (and macropores) with different geometries and topologies. The SVF is convenient for calculations of pore dimensions for many cases of metal organic frameworks and zeolites. This approach enables us to fully explain the temperature dependence of the o-Ps annihilation lifetime over a wide temperature range, 20–700 K.

BackgroundHealth risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia.MethodsWe used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status.ResultsAfter adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66–2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21–1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45–0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97–2.29, p < 0.001) after adjusting for all other health risk factors and covariates.ConclusionOur study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.

Abstract Objectives To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD). Methods We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models. Results The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6–9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. Conclusions This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.

Today, facial emotion recognition is widely adopted in many intelligent applications including the driver monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions can be well represented by facial landmarks which are hard to be detected from images, due to the high number of discrete landmarks, the variation of shapes and poses of the human face in real world. Over decades, many methods have been proposed for facial landmark detection including the shape fitting, the coordinate regression such as ASMNet and AnchorFace. However, their performance is still limited for real-time applications in terms of both accuracy and efficiency. In this paper, we propose a novel method called SHELF which is the first to combine the shape fitting and heatmap regression approaches for landmark detection in human face. The heatmap model aims to generate the landmarks that fit to the common shapes. The method has been evaluated on three datasets 300W-Challenging, WFLW, 300VW-E with 31557 images and achieved a normalized mean error (NME) of 6.67% , 7.34%, 12.55% correspondingly, which overcomes most existing methods. For the first two datasets, the method is also comparable to the state of the art AnchorFace with a NME of 6.19%, 4.62%, respectively.

In this paper, we calculate the diagonal contributions W(R, s = 0) of the imaginary part of the microsopic optical potential at negatives energies, where W(R, s) = Σlj(2j+1)/4π Im∆Σlj(r, r′, ω), with R = 1/2.(r + r′) corresponding to the radius and shape of Im∆Σ, and s = r - r′. To do it, the microscopic optical potential has been calculated by using the nuclear structure approach which is based on the Green function method. The coupling between the particle and collective phonon has been performed to calculate the dynamic part of the optical potential. It has been found that the imaginary part at negative energies is very small as expected. The calculated W(R, 0) is maximum on the surface and deacreases to zero in the interior.

Abstract Vietnam harnesses a rich diversity of rice landraces adapted to a range of conditions, which constitute a largely untapped source of diversity for the continuous improvement of cultivars. We previously identified a strong population structure in Vietnamese rice, which is captured in five Indica and four Japonica subpopulations, including an outlying Indica‐5 group. Here, we leveraged that strong differentiation and 672 native rice genomes to identify genomic regions and genes putatively selected during the breeding of rice in Vietnam. We identified significant distorted patterns in allele frequency (XP‐CLR) and population differentiation scores (FST) resulting from differential selective pressures between native subpopulations, and later annotated them with QTLs previously identified by GWAS in the same panel. We particularly focussed on the outlying Indica‐5 subpopulation because of its likely novelty and differential evolution, where we annotated 52 selected regions, which represented 8.1% of the rice genome. We annotated the 4576 genes in these regions and selected 65 candidate genes as promising breeding targets, several of which harboured alleles with nonsynonymous substitutions. Our results highlight genomic differences between traditional Vietnamese landraces, which are likely the product of adaption to multiple environmental conditions and regional culinary preferences in a very diverse country. We also verified the applicability of this genome scanning approach to identify potential regions harbouring novel loci and alleles to breed a new generation of sustainable and resilient rice.

Schizophrenia is a severe mental illness, characterized by psychotic symptoms including delusions, hallucinations, dystonia, adolescent behavior and language, and is accompanied by severe functional impairments. Antipsychotics, which are used mainly in the treatment of schizophrenia, have contributed to the development of metabolic syndrome. Studies on metabolic syndrome in schizophrenic patients have been still limited in Vietnam. The objective of this study was to investigate the prevalence of metabolic syndrome in Vietnamese inpatients with schizophrenia. The study population of this descriptive cross-sectional study was all patients with schizophrenia from September 2020 to June 2021 at Inpatient Department of Binh Dinh Provincial Psychiatric Hospital in Viet Nam. Three hundred and twelve patients who met the sampling requirements were enrolled in this study. The prevalence of schizophrenia in female patients was lower than in male patients (36.9% vs. 63.1%) in our study. The prevalence of metabolic syndrome was 30.4% (25.9% in men and 38.3% in women), more in patients ≥40 years old than in patients under 40 years old (the highest prevalence in patients ≥60 years). There was a significant relationship between age, body mass index (BMI), and duration of antipsychotic treatment with the frequency of metabolic syndrome. In conclusions, the prevalence of schizophrenia was higher in male inpatients than in female inpatients. However, the prevalence of metabolic syndrome was higher in female inpatients than male inpatients with schizophrenia, and increased with age, BMI, and duration of antipsychotic treatment.

Abstract The lipid storage droplet-2 (LSD-2) protein of Drosophila is a homolog of mammalian perilipin 2, which is essential for promoting lipid accumulation and lipid droplet formation. The function of LSD-2 as a regulator of lipolysis has also been demonstrated. However, other LSD-2 functions remain unclear. To investigate the role of LSD-2, we performed tissue-specific depletion in the salivary glands of Drosophila using a combination of the Gal4-upstream activating sequence system and RNA interference. LSD-2 depletion inhibited the entry of salivary gland cells into the endoreplication cycle and delayed this process by enhancing CycE expression, disrupting the development of this organ. The deficiency of LSD-2 expression enhanced reactive oxygen species production in the salivary gland and promoted JNK-dependent apoptosis by suppressing dMyc expression. This phenomenon did not result from lipolysis. Therefore, LSD-2 is vital for endoreplication cell cycle and cell death programs.

Central Vietnam is characterized by severe flooding associated with heavy rainfall events caused by interactions between multiscale atmospheric circulations and the complex local terrain. Previous studies believed rainfall in central Vietnam is closely related to the cold surge; however, it fails to explain the cause of the early rainfall occurrence in August in the subregion. For the first time, this study investigates the detailed atmospheric mechanisms associated with rainfall variations in central Vietnam using the empirical orthogonal function (EOF) applied to the recently developed high-resolution Vietnam gridded precipitation (VnGP) dataset. Reanalysis data NCEP/NCAR is used to associate the rainfall changes with respective atmospheric mechanisms. EOF analysis detected two dominant rainfall modes. The primary mode explains the rainfall variation from October to November over the central and is directly related to the interaction of cold surges and tropical disturbances. The second mode accounts for rainfall occurring in north central from September to mid-October, which is attributed to the westerly summer monsoon activities. Also, we revealed that, while the first mode exhibits a significant correlation with El Niño-southern oscillation, the second depends highly on the contrast of sea surface temperature in the northern and southern Hemispheres. This different oceanic forcing and the local topological effect of Truong Son mountain range reasonably explain the opposite rainfall pattern in central Vietnam in early fall.

Abstract Background Vietnam possesses a vast diversity of rice landraces due to its geographical situation, latitudinal range, and a variety of ecosystems. This genetic diversity constitutes a highly valuable resource at a time when the highest rice production areas in the low-lying Mekong and Red River Deltas are enduring increasing threats from climate changes, particularly in rainfall and temperature patterns. Results We analysed 672 Vietnamese rice genomes, 616 newly sequenced, that encompass the range of rice varieties grown in the diverse ecosystems found throughout Vietnam. We described four Japonica and five Indica subpopulations within Vietnam likely adapted to the region of origin. We compared the population structure and genetic diversity of these Vietnamese rice genomes to the 3000 genomes of Asian cultivated rice. The named Indica-5 (I5) subpopulation was expanded in Vietnam and contained lowland Indica accessions, which had very low shared ancestry with accessions from any other subpopulation and were previously overlooked as admixtures. We scored phenotypic measurements for nineteen traits and identified 453 unique genotype-phenotype significant associations comprising twenty-one QTLs (quantitative trait loci). The strongest associations were observed for grain size traits, while weaker associations were observed for a range of characteristics, including panicle length, heading date and leaf width. Conclusions We showed how the rice diversity within Vietnam relates to the wider Asian rice diversity by using a number of approaches to provide a clear picture of the novel diversity present within Vietnam, mainly around the Indica-5 subpopulation. Our results highlight differences in genome composition and trait associations among traditional Vietnamese rice accessions, which are likely the product of adaption to multiple environmental conditions and regional preferences in a very diverse country. Our results highlighted traits and their associated genomic regions that are a potential source of novel loci and alleles to breed a new generation of low input sustainable and climate resilient rice.