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In the current knowledge-based economy, the abilities of the national research system are a key driver of the country's competitiveness and socio-economic development. This paper compares the scientific standing of the OECD countries and eight other relevant economies. We use a bibliometric indicator of research performance, applied first at the individual level. This approach avoids the distortions of the aggregate-level analyses extant in literature and practice, which overlook the different publication intensities across research fields. We find a strong correlation between research performance and the economic competitiveness of nations and a moderate but significant correlation between research performance and the propensity to spend on research. [ABSTRACT FROM AUTHOR]

The increasingly prominent issue of unfair competition on Internet platforms (IPUC) severely restricts the healthy and sustainable development of the platform economy. Based on the IPUC "multi-agent co-governance" scenario, this paper introduces stochastic disturbances and continuous strategy set to improve the classical binary deterministic evolutionary game system. The results show that after considering stochastic disturbances, the positive state corresponding to the equilibrium point (1,1) is no longer stable, and the required parameter conditions are more stringent. The IPUC "multi-agent co-governance" system under stochastic disturbances exhibits specific vulnerability. In the continuous strategy set evolutionary game system, government departments and Internet platforms can flexibly make optimal decisions based on maximizing expected returns, and strategy selection has better elasticity. Regardless of the evolutionary game scenario, maintaining the participation level of NGOs and the public above a certain threshold while increasing the penalty intensity is conducive to the evolution of the game system toward the positive state. The analysis process and conclusions provide insights and guidance for the governments to design the IPUC regulatory system and frameworks. [ABSTRACT FROM AUTHOR]

The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity. [ABSTRACT FROM AUTHOR]

Background: Depression is a very common psychiatric disorder in worldwide. Globally, Human Immunodeficiency Virus (HIV) is highly prevalent among women, and are disproportionately affected by depression. Antiretroviral Therapy (ART) adherence which could highly be affected by depression is yet to be explored effectively. Depression affects overall poor HIV clinical outcomes, socioeconomic and social interactions. However, it is not well understood specifically how depression affects ART adherence in women living with HIV (WLWHIV). Investigating the effects of depression on ART adherence is critical in order to develop nuanced new evidence to address non-adherence in WLWHIV. Objective: To conduct a meta-analysis on the correlation between depression and adherence to antiretroviral therapy among women living with HIV in the globe. Method: Using population, exposed and outcome approach, we searched Scopus, PubMed, EMBASE, Cochrane Library, Psych info, Web of science and google scholar for cohort and cross-sectional studies globally. The search strategy was structured comprising terms associated with antiretroviral therapy and adherence, women living with HIV and depression. We evaluated the paper quality, using the Newcastle-Ottawa Scales (NOS). The fixed effect model was used to analysis the effect of depression on ART adherence. Result: A total of 8 articles comprise 6474 participants were included in this study. There were controversial findings related to the effect of depression to ART adherence. Among three cross-sectional study, one article demonstrating, depression was associated with ART adherence. Of the five cohort studies, four cohort studies reported association. The overall pooled estimated effect of depression on ART adherence was 1.02 [RR = 1.015 with 95% CI (1.004, 1.026)] with a p-value of 0.005. Conclusion and recommendation: Depression was the risk factor for ART adherence among women living with HIV. It is therefore, necessary for clinician to note this and perform screening for ART adherence. Trial registration: The review protocol was developed with prospero registration:CRD42023415935. [ABSTRACT FROM AUTHOR]

This paper proposes a retinal prosthesis edge detection (RPED) algorithm that can achieve high visual acuity and low power. Retinal prostheses have been used to stimulate retinal tissue by injecting charge via an electrode array, thereby artificially restoring the vision of visually impaired patients. The retinal prosthetic chip, which generates biphasic current pulses, should be located in the foveal area measuring 5 mm × 5 mm. When a high-density stimulation pixel array is realized in a limited area, the distance between the stimulation pixels narrows, resulting in current dispersion and high-power dissipation related to heat generation. Various edge detection methods have been proposed over the past decade to reduce these deleterious effects and achieve high-resolution pixels. However, conventional methods have the disadvantages of high-power consumption and long data processing times because many pixels are activated to detect edges. In this study, we propose a novel RPED algorithm that has a higher visual acuity and less power consumption despite using fewer active pixels than existing techniques. To verify the performance of the devised RPED algorithm, the peak signal-to-noise ratio and structural similarity index map, which evaluates the quantitative numerical value of the image are employed and compared with the Sobel, Canny, and past edge detection algorithms in MATLAB. Finally, we demonstrate the effectiveness of the proposed RPED algorithm using a 1600-pixel retinal stimulation chip fabricated using a 0.35 μm complementary metal-oxide-semiconductor process. [ABSTRACT FROM AUTHOR]

Distance-based topological indices are numerical parameters that are derived from the distances between atoms in a molecular structure, and they provide a quantitative measure of the topology and geometry of a molecule. The distance-based topological indices uses to predict various properties of molecules, including their boiling points, melting points, and solubility. It also predicts the biological activity of molecules, including their pharmacological and toxicological properties. Pentagonal chain molecules are organic compounds that consist of a linear chain of five-membered (pentagons) connected by carbon and bonds. These molecules have unique structural and electronic properties that make them useful in a variety of applications. Motivated by the pentagonal chain molecules, we have considered a pentagonal chain graph and it is denoted by Pn. We have computed some distance based topological indices for Pn. The paper focuses on a pentagonal chain molecules denoted by G, and derives several distance-based topological indices. These indices compromise insights into physicochemical properties, aid in identifying structural characterizations, and enhance understanding of molecular properties. [ABSTRACT FROM AUTHOR]

China's "the Belt and Road Initiative" (BRI) is a top-level cooperation initiative among countries proposed by China, which has promoted China's cooperation with relevant countries in various aspects and fields. Research reports from think tanks and experts on the evaluation, analysis, and research conclusions of the BRI can reflect the stance, opinions, and demands of various countries abroad regarding the initiative. This paper takes the BRI reports of important think tanks in the " Global Go To Think Tank Index Report 2020" as the subject of its research, and analyzes the key points and development trends of foreign think tank research on the BRI by using text mining, topic evolution, and social network analysis. It provides reasonable suggestions and ideas for promoting the construction of the BRI and deepening related cooperation in China. Research shows that the thematic distribution of research reports on the BRI by think tanks is mainly focused on the fields of politics, economy, and military. The research areas are relatively stable, and there is not a strong trend of thematic evolution. The evolution paths are also mainly distributed in the fields of politics, economy, and military. There are not many expansions in the thematic evolution directions over the years, and there is a strong inheritance of themes. The connection between research themes and the main purpose of the BRI is somewhat inadequate, indicating a certain limitation in the understanding of the BRI. [ABSTRACT FROM AUTHOR]

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of 854 abstracts of peer-reviewed literature for the most common usages of nanoparticle targeting of tumor associated macrophages (TAMs) in solid tumors. The data spans 20 years of literature, providing a broad perspective of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of "birds-eye-view" analysis provides a useful assessment tool for exploring new and emerging themes within a large field. [ABSTRACT FROM AUTHOR]

Borophene nanosheets appear in various sizes and shapes, ranging from simple planar structures to complicated polyhedral formations. Due to their unique chemical, optical, and electrical properties, Borophene nanosheets are theoretically and practically attractive and because of their high thermal conductivity, boron nanosheets are suitable for efficient heat transmission applications. In this paper, temperature indices of borophene nanosheets are computed and these indices are employed in QSPR analysis of attributes like Young's modulus, Shear modulus, and Poisson's ratio of borophene nanosheets and borophene β12 sheets. The regression model for the F-Temperature index is discovered to be the best fit for shear modulus, the reciprocal product connectivity temperature index is discovered to be fit for Poisson's ratio and the second hyper temperature index is discovered to be fit for Young's modulus based on the correlation coefficient. [ABSTRACT FROM AUTHOR]

Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL. [ABSTRACT FROM AUTHOR]

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet. [ABSTRACT FROM AUTHOR]

Based on a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV), this paper investigates the dynamic effects of geopolitical risk on mutual fund risk taking in China across three-time horizons and at three selected time points. Overall, the impulse responses are time-varying and we find a negative effect of geopolitical risk on mutual fund risk taking until 2015, with the short-term effect being the most pronounced, suggesting that when professional investors such as mutual fund managers are faced with the stock valuation uncertainty due to a geopolitical shock, they choose to reduce market risk exposures. After 2015, the short-term effect begins to diminish and gradually turns positive, which could be explained by the fact that with the increasing abundance and diversification of investment instruments, fund managers have more effective investment tools and more sophisticated trading strategies to hedge against geopolitical risk, rather than reducing market risk exposure. Further, we explore the heterogeneous effects of eight types of geopolitical risk and three types of mutual fund. The results indicate that the effect of geopolitical actions is stronger than that of geopolitical threats, while the effect of narrow geopolitical risk is stronger than that of broad geopolitical risk. Moreover, we find that the response of the risk taking of growth funds to the geopolitical risk is weaker than that of balanced and income funds. [ABSTRACT FROM AUTHOR]

Monitoring in highway projects is significant for the safety, efficiency and quality of construction. This paper proposes a detailed method for monitoring soft-soil roadbed and high-slope, and the layered settlement gauge and total station are employed to carry out experimental monitoring. The law and stability of soft-soil roadbed settlement and deformation under high-slope are further analyzed. The results show that the cumulative values of roadbed settlement and slope platform deformation in general both increase with the increase of monitoring time. However, near 180 days, an abnormal settlement phenomenon was monitored on both sides of the highway with a maximum value of 9.44 mm. This phenomenon was captured at gauge #1, #3, and #4 on observation stake 3, and exact oppositely, it was also observed at gauge #2 on observation stake 4. Moreover, unusual deformations of the high-slope platforms occurred over a period of 10 to 30 days, and these unusual settlements and deformations are indicative of the highway's instability. Therefore, the monitoring on soft-soil roadbed settlement and high-slope deformation can provide reference for highway construction. [ABSTRACT FROM AUTHOR]

This paper demonstrates how artificial-intelligence language analysis can inform understanding of human–nature relationships and other social phenomena. We demonstrate three techniques by investigating relationships within the popular word2vec word embedding, which is trained on a sample from over 50,000 worldwide news sources. Our first technique investigates what theory-generated analogies are most similar to nature:people. The resource:user analogy is most similar, followed by the playground:child and gift:receiver analogies. Our second technique explores whether nature-related words are affiliated with words that denote race, class, or gender. Nature words tend slightly toward associations with femininity and wealth. Our third technique demonstrates how the relationship between nature and wellbeing compares to other concepts' relationships to wellbeing—e.g., spirituality–wellbeing, social relations–wellbeing. Nature is more semantically connected to wellbeing than money, social relations, and multiple other wellbeing correlates. Findings are consistent with previous social science and humanities research on human-nature relationships, but do not duplicate them exactly; our results thus offer insight into dominant trends and prevalence of associations. Our analysis also offers a model for using word embeddings to investigate a wide variety of topics. [ABSTRACT FROM AUTHOR]

Treating burnout as an independent variable while performance is the dependent variable, earlier studies revealed that job burnout experienced by academics adversely affects how well they perform. Whether performance may contribute to the emergence of burnout is yet to be analyzed—it is an issue investigated in this paper. Readjusting the nature of the variables, this quantitative study adopted group regression and it discovered that the performance of academics instead regulates their burnout without making performance a consequence of burnout—a new dynamic that challenges the earlier assumption. Following this earlier belief, counselling strategy to boost the employees' psyche was deemed to be the main post-measurement tool to deal with the burnout crisis. With respect to both tenets (current and earlier), psychological counselling was treated as a moderating variable to check whether it is important enough in removing the burnout felt by employees so that they subsequently could function better. It is further discovered that although psychological counselling removes employees' burnout to some extent, it failed to transform them into better-functioning people. This study suggests a pre-measurement counselling strategy will ensure academics are competently engaged since ensuring competency is a fundamental aspect of eliminating a job burnout crisis. The sustained competency of employees will eventually prevent burnout and may halt the transmission of a burnout crisis at large—it adds to this study's theoretical contribution to the topic. [ABSTRACT FROM AUTHOR]

The one-sample log-rank test is the preferred method for analysing the outcome of single-arm survival trials. It compares the survival distribution of patients with a prefixed reference survival curve that usually represents the expected outcome under standard of care. However, classical one-sample log-rank tests assume that the reference curve is known, ignoring that it is frequently estimated from historical data and therefore susceptible to sampling error. Neglecting the variability of the reference curve can lead to an inflated type I error rate, as shown in a previous paper. Here, we propose a new survival test that allows to account for the sampling error of the reference curve without knowledge of the full underlying historical survival time data. Our new test allows to perform a valid historical comparison of patient survival times when only a historical survival curve rather than the full historic data is available. It thus applies in settings where the two-sample log-rank test is not applicable as method of choice due to non-availability of historic individual patient survival time data. We develop sample size calculation formulas, give an example application and study the performance of the new test in a simulation study. [ABSTRACT FROM AUTHOR]

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neural network training, which limits their practical application in LDCT imaging. In this paper, we propose an unsupervised and training data-free learning reconstruction method for LDCT imaging that avoids the requirement for training data. The proposed method is a post-processing technique that aims to enhance the initial low-quality reconstruction results, and it reconstructs the high-quality images by neural work training that minimizes the ℓ1-norm distance between the CT measurements and their corresponding simulated sinogram data, as well as the total variation (TV) value of the reconstructed image. Moreover, the proposed method does not require to set the weights for both the data fidelity term and the plenty term. Experimental results on the AAPM challenge data and LoDoPab-CT data demonstrate that the proposed method is able to effectively suppress the noise and preserve the tiny structures. Also, these results demonstrate the rapid convergence and low computational cost of the proposed method. The source code is available at https://github.com/linfengyu77/IRLDCT. [ABSTRACT FROM AUTHOR]

Background: Stroke survivors with aphasia want to improve their everyday talking (discourse). In current UK practice, 90% of speech and language therapists believe discourse assessment and treatment is part of their role but are hampered by barriers in resources, time and expertise. There is a clinical need for well-articulated discourse assessment and treatments. LUNA is a multi-level treatment targeting words, sentences and discourse macrostructure in personal stories that addresses this clinical need. Objectives: This study aimed to assess the feasibility and acceptability of LUNA trial procedures in a randomised waitlist-controlled trial; and to evaluate preliminary efficacy. Methods: This paper reports a phase II, waitlist-controlled, proof-of-concept feasibility trial. Participants with chronic aphasia (n = 28) were recruited from the community and randomised to an Immediate (n = 14) or Delayed (n = 14) group. LUNA treatment was delivered twice weekly for 10 weeks via the videoconferencing technology, Zoom. Feasibility was assessed in terms of participant recruitment and retention, adherence, missing data, and treatment fidelity. Preliminary treatment efficacy was assessed in terms of between group differences in outcome measures relating to discourse, language, and psychosocial state. Results: The remote LUNA trial was feasible: 85% of those eligible consented to the trial; trial retention was 86%; 87% of treatment sessions were delivered as scheduled, and 79% of participants completed 80%+ of the treatment programme; data was missing only for participants who withdrew; treatment fidelity was high at 92% adherence; and only one clinical outcome measure demonstrated ceiling effects. ANCOVA analysis of the clinical outcome measures revealed group differences with medium and large effect sizes, indicating, improvements in the production of words, sentences, discourse macrostructure, overall language functioning (WAB-R), and psychosocial state (VAMS) following LUNA treatment. For most outcomes measured, similar treatment benefits were suggested in a secondary, non-parametric analysis. Conclusions: Large-scale evaluation of the clinical efficacy and cost-effectiveness of LUNA is warranted and supported by these findings. Trial registration: Clinical trials registration: NCT05847023 (clinical trials.gov). [ABSTRACT FROM AUTHOR]

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance. [ABSTRACT FROM AUTHOR]

Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder that predominantly affects movement and currently has no cure. Alongside medication, non-invasive brain stimulation (NIBS) may be used as an adjunct therapy to attenuate the motor symptoms experienced by people with PD. However, there is considerable heterogeneity in the evidence exploring the effects of NIBS for improving aspects of physical function in people with PD. Therefore, this protocol paper will outline the objectives, structure and procedure of a proposed umbrella review which will comprehensively summarise and map the current body evidence on the effectiveness of NIBS for improving physical function in people with PD. Methods and analysis: This study will adhere to the Joanna Briggs Institute (JBI) reviewer's manual and the PRISMA guidelines for conducting an umbrella review. The protocol is registered in PROSPERO (CRD42022380544). The population, intervention, comparison, and outcomes (PICO) method will be used to guide the search strategies and inclusion/exclusion criteria. Systematic reviews, with or without meta-analyses, based on quantitative or mixed-methods studies, will be searched for, and then critically evaluated by two authors using the Assessment of Multiple Systematic Reviews 2 (AMSTAR2) tool. If the data allows, we will run a random effects pooled meta-analysis using standardized mean differences (SMDs), with heterogeneity and publication bias reported using the I2 statistic. We will determine the level of evidence using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) tool. Overlap in studies across reviews will be assessed using citation matrices and corrected covered areas (CCAs). Lastly, visual bubble plots will display the effects and strength of evidence from each review. Discussion: This umbrella review will be the first to examine the collective evidence on the effectiveness of NIBS in improving physical outcomes for individuals with PD. It aims to provide an overall understanding of the relationship between NIBS and motor function changes, discuss underlying physiological mechanisms, and identify future therapeutic strategies. Trial registration: PROSPERO registration number:CRD42022380544. [ABSTRACT FROM AUTHOR]

This paper presents the initial exploration of the free radical scavenging capabilities of peptides derived from protein hydrolysates (PPH) obtained from Zingiber cassumunar rhizomes (Phlai). To replicate the conditions of gastrointestinal digestion, a combination of pepsin and pancreatin proteolysis was employed to generate these hydrolysates. Subsequently, the hydrolysate underwent fractionation using molecular weight cut-off membranes at 10, 5, 3, and 0.65 kDa. The fraction with a molecular weight less than 0.65 kDa exhibited the highest levels ABTS, DPPH, FRAP, and NO radical scavenging activity. Following this, RP-HPLC was used to further separate the fraction with a molecular weight less than 0.65 kDa into three sub-fractions. Among these, the F5 sub-fraction displayed the most prominent radical-scavenging properties. De novo peptide sequencing via quadrupole-time-of-flight-electron spin induction-mass spectrometry identified a pair of novel peptides: Asp-Gly-Ile-Phe-Val-Leu-Asn-Tyr (DGIFVLNY or DY-8) and Ile-Pro-Thr-Asp-Glu-Lys (IPTDEK or IK-6). Database analysis confirmed various properties, including biological activity, toxicity, hydrophilicity, solubility, and potential allergy concerns. Furthermore, when tested on the human adenocarcinoma colon (Caco-2) cell line, two synthetic peptides demonstrated cellular antioxidant activity in a concentration-dependent manner. These peptides were also assessed using the FITC Annexin V apoptosis detection kit with PI, confirming the induction of apoptosis. Notably, the DY-8 peptide induced apoptosis, upregulated mRNA levels of caspase-3, -8, and -9, and downregulated Bcl-2, as confirmed by real-time quantitative polymerase chain reaction (RT-qPCR). Western blot analysis indicated increased pro-apoptotic Bax expression and decreased anti-apoptotic Bcl-2 expression in Caco-2 cells exposed to the DY-8 peptide. Molecular docking analysis revealed that the DY-8 peptide exhibited binding affinity with Bcl-2, Bcl-xL, and Mcl-1, suggesting potential utility in combating colon cancer as functional food ingredients. [ABSTRACT FROM AUTHOR]

The rapid evolution of the digital economy has significantly accelerated progress towards achieving green and sustainable processes, particularly in the field of green production. While existing research has delved into the effects of the digital economy on Green Innovation (GI) and the consequences of digital transformation on Corporate Value (CV), there remains a notable gap in the literature regarding the potential for synergistic enhancements in firms' GI&CV through the ongoing digital revolution. This study utilizes an evolutionary game model and employs system dynamics methods to simulate the dynamic evolution trajectory of the influence of the digital economy on the synergy between GI&CV. Subsequently, it empirically assesses the interconnected synergies between GI&CV using a dataset comprising information from Chinese listed firms spanning from 2011 to 2020, examining the impact of the digital economy on these synergies. Moreover, the study delves into the analysis of the transmission mechanism and conducts an extended investigation to further explore this phenomenon. The findings of this paper including: (1) The digital economy acts as a driving force behind the synergistic enhancement of firm GI&CV. Moreover, this effect is further augmented by governmental environmental regulation and green subsidy policies. (2) Drawing upon the information asymmetry theory and the resource-based theory, the regional marketization level and firms' digital transformation play intermediary roles. (3) The heterogeneity test indicate that firms situated in eastern regions and those classified as non-heavily polluted benefitted to a greater extent. This study sheds light on the incentive implications of digital economy for the synergistic upgrading of GI&CV, thereby extending the breadth of study on the consequences of digital economy. Moreover, it offers actionable suggestions for enterprises to leverage digital economy development towards achieving a synergistic improvement of GI&CV. [ABSTRACT FROM AUTHOR]

Background: Oral antineoplastic agents have caused a paradigm shift in cancer treatment, however, they produce many unique challenges. Although oral antineoplastics can have complex administration regimes, low adherence rates and high possibilities of drug-drug interactions, they are administered unsupervised at home. Cancer services pharmacists have the required skillsets to improve patient outcomes associated with oral antineoplastic treatment by increasing patient health literacy, improving concordance and optimising administration protocols. Aim: To evaluate patients' perceptions, experiences and overall satisfaction with dedicated clinical pharmacist consultations in patients treated with oral antineoplastic agents at a major public hospital. Method: In this retrospective cross-sectional study at a quaternary hospital in Western Australia, data were collected by a paper questionnaire (mailed in March 2022) to a random sample of 191 patients initiated on oral antineoplastic drugs between January 2021 and February 2022. Demographics, prescribed antineoplastic drug/s, cancer type data were collected including using 5-point Likert scale questions assess patients' overall satisfaction with the clinical pharmacist consultations. Results: The questionnaire response rate was 27.7% (52/188) (mean age 63.2 years; 57.5% female). Most patients (42/52; 80.8%) were satisfied with pharmacist consultations, trusted the pharmacist's advice (45/52; 86.5%), considered that the pharmacist improved their understanding of how to manage side effects (43/52; 82.7%) and they provided an important service in outpatient care (45/52; 86.5%). Conclusion: Overall, patients reported positive perceptions, experiences, and satisfaction with the cancer services pharmacist counselling services during their oral antineoplastic treatment. [ABSTRACT FROM AUTHOR]

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the most challenging research topics in this field. Due to the small size of these targets, limited information and less surrounding contextual information, it increases the difficulty of target detection and recognition. To address these issues, this paper proposes YOLO-ISTD, an improved method for infrared small target detection based on the YOLOv5-S framework. Firstly, we propose a feature extraction module called SACSP, which incorporates the Shuffle Attention mechanism and makes certain adjustments to the CSP structure, enhancing the feature extraction capability and improving the performance of the detector. Secondly, we introduce a feature fusion module called NL-SPPF. By introducing an NL-Block, the network is able to capture richer long-range features, better capturing the correlation between background information and targets, thereby enhancing the detection capability for small targets. Lastly, we propose a modified K-means clustering algorithm based on Distance-IoU (DIoU), called K-means_DIOU, to improve the accuracy of clustering and generate anchors suitable for the task. Additionally, modifications are made to the detection heads in YOLOv5-S. The original 8, 16, and 32 times downsampling detection heads are replaced with 4, 8, and 16 times downsampling detection heads, capturing more informative coarse-grained features. This enables better understanding of the overall characteristics and structure of the targets, resulting in improved representation and localization of small targets. Experimental results demonstrate significant achievements of YOLO-ISTD on the NUST-SIRST dataset, with an improvement of 8.568% in mAP@0.5 and 8.618% in mAP@0.95. Compared to the comparative models, the proposed approach effectively addresses issues of missed detections and false alarms in the detection results, leading to substantial improvements in precision, recall, and model convergence speed. [ABSTRACT FROM AUTHOR]

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy. [ABSTRACT FROM AUTHOR]

On October 18, 2019, Chile experienced the most important social upheaval since the country regained democracy in the late 1980s. The "Social Outbreak" surprised economic and political elites and seemed paradoxical to the international community who had often praised Chile as a model of successful development. In this paper, we used structural-demographic theory to analyze the interaction between the overproduction of elites and the stagnation in the relative income of the population as the underlying structural cause of Chilean political instability. This theory was able to predict the three most significant instances of political tension in the recent history of Chile: the crisis of the late 1960s that culminated in the coup d'état of 1973, popular mobilizations during the 1980s, and the recent student mobilizations and social upheaval. Our results suggest that, at least during the period 1938–2019, Chilean sociopolitical dynamics is determined by the same structural drivers. [ABSTRACT FROM AUTHOR]

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment. [ABSTRACT FROM AUTHOR]

The primary focus of all sample surveys is on providing point estimates for the parameters of primary interest, and also estimating the variance associated with those point estimates to quantify the uncertainty. Larger samples and important measurement tools can help to reduce the point estimates' uncertainty. Numerous effective stratification criteria may be used in survey to reduce variance within stratum. In fine stratification design, the population is divided into numerous small strata, each containing a relatively small number of sampling units as one or two. This is done to ensure that certain characteristics or subgroups of the population are well-represented in the sample. But with many strata, the sample size within each stratum can become small, potentially resulting in higher errors and less stable estimates. The variance estimation process becomes difficult when we only have one unit per stratum. In that case, the collapsed stratum technique is the classical methods for estimating variance. This method, however, is biased and results in an overestimation of the variance. This paper proposes a bootstrap-based variance estimator for the total population under fine stratification, which overcomes the drawbacks of the previously explored estimation approach. Also, the estimator's properties were investigated. A simulation study and practical application on survey of mental health organizations data were done to investigate properties of the proposed estimators. The results show that the proposed estimator performs well. [ABSTRACT FROM AUTHOR]

Oral health is high on the global agenda following the adoption of the 2022 global strategy on oral health at the 75th World Health Assembly. Given the global burden of oral disease, workforce development to achieve universal health coverage [UHC] is crucial to respond to population needs within the non-communicable disease agenda. The aim of this paper is to present an overview of the oral health workforce [OHWF] globally in relation to key contextual factors. Data from the National Health Workforce Accounts and a survey of World Health Organization [WHO] member states were integrated for analysis, together with country-level data on population and income status. Data are presented using the WHO categorisation of global regions and income status categories established by the World Bank. Workforce densities for key OHWF categories were examined. Multiple regression was used to model workforce density and contextual influences. Challenges and possible solutions were examined by country income status. There are approximately 3.30 dentists per 10,000 population globally, and a combined OHWF [dentists, dental assistants/therapists and dental prosthetic technicians] of 5.31 per 10,000. Marked regional inequalities are evident, most notably between WHO European and African regions; yet both make greater use of skill mix than other regions. When adjusted by region, 'country income status' and 'population urbanization' are strong predictors of the workforce density of dentists and even more so for the combined OHWF. Maldistribution of the workforce [urban/rural] was considered a particular workforce challenge globally and especially for lower-income countries. Strengthening oral health policy was considered most important for the future. The global distribution of dentists, and the OHWF generally, is inequitable, with variable and limited use of skill mix. Creative workforce development is required to achieve the global oral health agenda and work towards equity using innovative models of care, supported by effective governance and integrated policies. [ABSTRACT FROM AUTHOR]

When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the literature. In this paper, we propose a new method of show-through removal based on the following principle. The proposed method uses two scanned images with the front side and with the back side as input images. The proposed method is based on Ahmed's Blind Image Deconvolution method discovered in 2013, which succeeded in formulating Blind Image Deconvolution as a nuclear norm minimization. Since the structure of show-through removal resembles that of Blind Image Deconvolution, we discovered that the show-through removal can be reformulated into a nuclear norm minimization in the space of outer product matrix constructed from an image vector and a point spread function vector of blurring. Using this key idea, we constructed the proposed method as follows. First, our cost function consists of the following three terms. The first term is the data term and the second term is the nuclear norm derived from the above reformulation. The third term is a regularization term to overcome the underdetermined nature of show-through removal problem and the existence of noise in the measured images. The regularization term consists of Total Variation imposed on the images. The resulting nuclear norm minimization problem is solved by using Accelerated Proximal Gradient method and Singular Value Projection with some problem-specific modifications, which converges fast and requires a simple implementation. We show results of simulation studies as well as results of real image experiments to demonstrate the performances of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of low-carbon economy and the dominant position of retailers, through the establishment and comparison of three cooperative advertising models: model of supplier's independent advertising, model of supplier's independent advertising and model of retailer's and supplier's cooperative advertising, this paper studies the cooperative advertising decision-making of fresh agricultural products supply chain with two channels, and the demand of both channels is affected by the level of advertising investment, the proportion of advertising cost sharing and the efficiency of carbon emission reduction. The results show that when supplier and retailer adopt the two-way cooperative advertising mode, the demand and profit level of the two channels reach the optimal level. Numerical analysis shows that carbon emission reduction efficiency has an important impact on profits and market demand, which is closely related to cooperative advertising strategy. [ABSTRACT FROM AUTHOR]

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive needs analysis of English for Specific Purposes (ESP) courses in colleges of art and design in China. By examining the perspectives of undergraduate students, graduate students, art teachers, and English teachers, the research identifies consensus and divergence in ESP needs and implementation challenges. The analysis reveals a strong demand among students for specialized English learning, with a particular emphasis on receptive and interactive skills. However, there is a widespread acknowledgement of students' difficulties across ESP skills, indicating a gap between student competencies and the professional demands, thus highlighting the need for targeted educational interventions to effectively address the difficulties. Notably, reading and writing abilities emerge as the most lacking skills. While there is unanimous recognition of the positive impact of ESP on students' professional abilities and international competitiveness, a significant disparity exists in the enthusiasm for implementing ESP courses, paticularly with resistence from English teachers. The study highlights the critical challenges in ESP teaching, with teacher qualifications identified as the most prominent issue. Interestingly, English teachers attribute difficulties primarily to teaching materials, indicating a potential lack of readiness for ESP course delivery. A gap is also observed between teachers' evaluations of student abilities and students' self-assessment, especially among undergraduate freshmen, suggesting an overestimation of their English proficiency in professional contexts. The paper concludes with the implication that for effective ESP course implementation in colleges of art and design in China, targeted faculty development programs and a collaborative approach involving English teachers, art teachers, and professionals are essential. The collaboration should aim to develop materials that integrate specific professional knowledge with linguistic expertise. Additionally, a balanced approach combining general English proficiency and specialized English training is recommended to address both foundational and specialized language skills. Overall, the study underscores the need for targeted ESP courses in art and design education, tailored to bridge the gap between current proficiency levels and professional language requirements. [ABSTRACT FROM AUTHOR]

Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model's robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods. [ABSTRACT FROM AUTHOR]

Background: Since the early 2000s, there have been marked trends in adolescent health and wellbeing indicators across Europe, North America and Australia. In particular, there have been substantial declines in youth drinking. We know little about how these trends are underpinned by co-occurring indicators within individuals. This paper aims to analyse change over time in how indicators cluster within individuals and differences in these patterns between five countries with different trends in youth drinking. Methods: We analysed four waves of repeat cross-sectional survey data from 15-year-olds in England (n = 5942), Italy (n = 5234), the Netherlands (n = 5408), Hungary (n = 5274), and Finland (n = 7446), which were included in the Health Behaviours in School-aged Children (HBSC) study between 2001/02 and 2013/14. We defined clusters of individuals using multigroup latent class analyses which accounts for change over time. The class indicators included health behaviours, attitudes, wellbeing and relationships. We modelled associations between class membership, sex, and family affluence over time. Results: We identified four classes in all countries: Overall unhealthy, Overall healthy, Moderately healthy and Substance abstainers with behaviour risk indicators. The proportion of adolescents in the Overall unhealthy class declined between 2001/02 and 2013/14 by between 22.8 percentage points (pp) in England and 3.2pp in Italy. The extent to which indicators of health and wellbeing changed as linked clusters differed across countries, but changes in alcohol consumption, smoking, drug use and sexual activity were typically concurrent. Adolescents with low family affluence were more likely to be in the Overall unhealthy class in all years. Conclusions: Improvements in indicators of adolescent health and well-being are due mainly to concurrent declines in drinking, smoking, sexual activity, and cannabis use, but these declines are not consistently associated with improvements in other domains. They have also not led to reductions in inequalities in indicators of health and well-being. [ABSTRACT FROM AUTHOR]

Post-partum haemorrhage is among the main causes of (preventable) mortality for women in low-resource settings (LRSs), where, in 2017, the mortality ratio was 462 out of every 100 000 live births, over 10 times higher than for high-resource settings. There are different treatments available for post-partum haemorrhage. The intrauterine balloon tamponade is a medical device that proved to be a simple and cost-effective approach. Currently, there are several balloon tamponades available, with different design and working principles. However, all these devices were designed for high-resource settings, presenting several aspects that could be inappropriate for many lower-income countries. This paper presents the results of a preclinical study aiming at informing the design, prototyping and validation of a 3D-printed intrauterine balloon tamponade concept, contributing towards the United Nation's Sustainable Development Goal 3: Good health and Well-being. Frugal engineering concepts and contextualised design techniques were applied throughout, to define the design requirements and specifications. The performance of the final prototype was validated against the requirements of the UK National Health System (NHS) technical guidelines and relevant literature, measuring the water leak and pressure drop over time, both open air and in a approximate uterus model. The resulting prototype is made up of six components, some of which are easy to retrieve, namely a water bottle, a silicone tube and an ordinary condom, while others can be manufactured locally using 3D printers, namely a modified bottle cap, a flow stopper and a valve for holding the condom in place. Validation testing bore promising results with no water or pressure leak open air, and minimal leaks in the approximate uterus model. This demonstrates that the 3D printed condom-based intrauterine balloon tamponade is performing well against the requirements and, when compared to the state of the art, it could be a more appropriate and more resilient solution to low-resource settings, as it bypasses the challenges in the supply of consumables and presents a greener option based on circular economy. [ABSTRACT FROM AUTHOR]

The Perceptual Crossing Device (PCD) introduced in this report is an updated tool designed to facilitate the exploration of brain activity during human interaction with seamless real time integration with EEG equipment. It incorporates haptic and auditory feedback mechanisms, enabling interactions between two users within a virtual environment. Through a unique circular motion interface that enables intuitive virtual interactions, users can experience the presence of their counterpart via tactile or auditory cues. This paper highlights the key characteristics of the PCD, aiming to validate its efficacy in augmenting the understanding of human interactions. Furthermore, by offering an accessible and intuitive interface, the PCD stands to foster greater community engagement in the realm of embodied cognitive science and human interaction studies. Through this device, we anticipate a deeper comprehension of the complex neural dynamics underlying human interaction, thereby contributing a valuable resource to both the scientific community and the broader public. [ABSTRACT FROM AUTHOR]

This paper reports the results of gamma irradiation experiments and whole genome sequencing (WGS) performed on vegetative cells of two radiation resistant bacterial strains, Metabacillus halosaccharovorans (VITHBRA001) and Bacillus paralicheniformis (VITHBRA024) (D10 values 2.32 kGy and 1.42 kGy, respectively), inhabiting the top-ranking high background radiation area (HBRA) of Chavara-Neendakara placer deposit (Kerala, India). The present investigation has been carried out in the context that information on strategies of bacteria having mid-range resistance for gamma radiation is inadequate. WGS, annotation, COG and KEGG analyses and manual curation of genes helped us address the possible pathways involved in the major domains of radiation resistance, involving recombination repair, base excision repair, nucleotide excision repair and mismatch repair, and the antioxidant genes, which the candidate could activate to survive under ionizing radiation. Additionally, with the help of these data, we could compare the candidate strains with that of the extremely radiation resistant model bacterium Deinococccus radiodurans, so as to find the commonalities existing in their strategies of resistance on the one hand, and also the rationale behind the difference in D10, on the other. Genomic analysis of VITHBRA001 and VITHBRA024 has further helped us ascertain the difference in capability of radiation resistance between the two strains. Significantly, the genes such as uvsE (NER), frnE (protein protection), ppk1 and ppx (non-enzymatic metabolite production) and those for carotenoid biosynthesis, are endogenous to VITHBRA001, but absent in VITHBRA024, which could explain the former's better radiation resistance. Further, this is the first-time study performed on any bacterial population inhabiting an HBRA. This study also brings forward the two species whose radiation resistance has not been reported thus far, and add to the knowledge on radiation resistant capabilities of the phylum Firmicutes which are abundantly observed in extreme environment. [ABSTRACT FROM AUTHOR]

The Butuo Black Sheep (BBS) is well-known for its ability to thrive at high altitudes, resist diseases, and produce premium-quality meat. Nonetheless, there is insufficient data regarding its genetic diversity and population-specific Single nucleotide polymorphisms (SNPs). This paper centers on the genetic diversity of (BBS). The investigation conducted a whole-genome resequencing of 33 BBS individuals to recognize distinct SNPs exclusive to BBS. The inquiry utilized bioinformatic analysis to identify and explain SNPs and pinpoint crucial mutation sites. The findings reveal that reproductive-related genes (GHR, FSHR, PGR, BMPR1B, FST, ESR1), lipid-related genes (PPARGC1A, STAT6, DGAT1, ACACA, LPL), and protein-related genes (CSN2, LALBA, CSN1S1, CSN1S2) were identified as hub genes. Functional enrichment analysis showed that genes associated with reproduction, immunity, inflammation, hypoxia, PI3K-Akt, and AMPK signaling pathways were present. This research suggests that the unique ability of BBS to adapt to low oxygen levels in the plateau environment may be owing to mutations in a variety of genes. This study provides valuable insights into the genetic makeup of BBS and its potential implications for breeding and conservation efforts. The genes and SPNs identified in this study could serve as molecular markers for BBS. [ABSTRACT FROM AUTHOR]

This paper introduces a refined approach for obtaining the analytical solution of the nonlinear shock wave model incorporating fractal derivatives. The Fractal Yang Variational Iteration Strategy (FYVIS) is utilized to obtain the approximate solution of a fractal model in the form of a series under Caputo fractional operator. The suggested method is the composition of the fractal Yang transform and the variational iteration approach. By using the two-scale fractal theory, we transform the fractal model into its traditional problem and then apply the yang transform to generate a recurrence relation. The variational iteration approach is now suitable to handle this recurrence relation without imposing any hypotheses or restrictions on variables. The derived results by the proposed scheme are shown in terms of series solution. Numerical calculations verify the accuracy and consistency of the suggested approach, demonstrating its excellent performance. The dynamic behavior of fractal components is explored by evaluating absolute errors and presenting two-dimensional diagrams across the fractal domain. This investigation underscores that the suggested technique offers an efficient and user-friendly solution for solving the nonlinear shock wave model involving fractal derivatives. [ABSTRACT FROM AUTHOR]

Background: Health is an inseparable part of life and central to all life supporting systems. The reproductive and child health shares a major portion of public health cases that is crucial for socio-economic development. Studies on reproductive and child health have traditionally been focused on demographic aspects using socio-economic parameters. Given the emphasis of Sustainable Development Goal (SDG)-3 on health and well-being, it is imperative to understand the geo-spatial dimension with the visible transition of key health indicators of fertility, maternal and infant/child health in the high burdened districts within these high focus Empowered Action Group (EAG) states of Rajasthan, Madhya Pradesh, Uttar Pradesh and Bihar that make up nearly 40% of India's population with relatively laggard health status. Methodology: This paper aims to understand the status and trend of key reproductive and child health indicators and vital statistics based on the recent representative demographic surveys. We intend to undertake a district level spatio-temporal analysis by developing District Composite Health Profile (DCHP) using Composite Index Method on selected 13 equally weighted key reproductive and child health indicators. The study has been carried out using data from National Family Health Survey-4 (2015–16) and National Family Health Survey-5 (2019–21) survey rounds. We employed geo-spatial techniques i.e. Moran's–I, and univariate LISA to comprehend the geographical clustering of high and low health burden districts and their heterogeneities at the district level. Results/Conclusions: The study highlights emerging inter-districts, and inter-state disparities over survey periods. With consistent improvement in the selected EAG states over time, the overall reproductive and child health status through DCHP along with each indicator was relatively better in the states of Rajasthan and worse in Bihar. Districts along the Terai belt in Uttar Pradesh and Bihar consistently performed sluggish during survey rounds. The geo-spatial clustering follows the political boundary of states, albeit with intra-state variations. Monitoring of key health indicators using composite index method provides a useful leverage for identifying priority districts/regions for universal health access that should also consider geographical space as an important policy dimension. [ABSTRACT FROM AUTHOR]

Mental health conditions tend to go unrecognised and untreated in adolescence, and therefore it is crucial to improve the health and social outcomes for these individuals through age and culturally appropriate interventions. This paper aims firstly to describe the development and implementation of the HYPE project platform (a research and resource platform co-designed and co-produced with young people). The second aim is to describe the characteristics of participants who engaged with the platform and an embedded pilot online survey. Participatory action research approach was used to address objectives of the HYPE project. Data were analysed to: (1) help improve access to health and social services, (2) guide provision of information of online resources and (3) deliver complementary community-based events/activities to promote mental health and to ultimately prevent mental health issues. Pilot and main phases of the HYPE project demonstrated the capacity and feasibility for such a platform to reach local, national, and international populations. Analyses demonstrated that the platform was particularly relevant for young females with pre-existing health difficulties. Some of the barriers to involving young people in research and help-seeking are discussed. [ABSTRACT FROM AUTHOR]

ATTR amyloidosis is caused by deposition of large, insoluble aggregates (amyloid fibrils) of cross-β-sheet TTR protein molecules on the intercellular surfaces of tissues. The process of amyloid formation from monomeric TTR protein molecules to amyloid deposits has not been fully characterized and is therefore modeled in this paper. Two models are considered: 1) TTR monomers in the blood spontaneously fold into a β-sheet conformation, aggregate into short proto-fibrils that then circulate in the blood until they find a complementary tissue where the proto-fibrils accumulate to form the large, insoluble amyloid fibrils found in affected tissues. 2) TTR monomers in the native or β-sheet conformation circulate in the blood until they find a tissue binding site and deposit in the tissue or tissues forming amyloid deposits in situ. These models only differ on where the selection for β-sheet complementarity occurs, in the blood where wt-wt, wt-v, and v-v interactions determine selectivity, or on the tissue surface where tissue-wt and tissure-v interactions also determine selectivity. Statistical modeling in both cases thus involves selectivity in fibril aggregation and tissue binding. Because binding of protein molecules into fibrils and binding of fibrils to tissues occurs through multiple weak non-covalent bonds, strong complementarity between β-sheet molecules and between fibrils and tissues is required to explain the insolubility and tissue selectivity of ATTR amyloidosis. Observation of differing tissue selectivity and thence disease phenotypes from either pure wildtype TTR protein or a mix of wildtype and variant molecules in amyloid fibrils evidences the requirement for fibril-tissue complementarity. Understanding the process that forms fibrils and binds fibrils to tissues may lead to new possibilities for interrupting the process and preventing or curing ATTR amyloidosis. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of air pollution on irrational behaviors in stock trading through behavioral experiments in laboratory, simulating air pollution by burning straw and mosquito coils. The results of this study show that air pollution significantly improves disposition effect and repurchase effect in an asymmetric way, which are thought as irrational behaviors in stock investments, making subjects prefer selling winning stocks (part of disposition effect) and repurchasing stocks that have fallen in price since the sale (part of repurchase effect). Furthermore, regret, a negative emotion, is the psychological mechanism by which air pollution influences the irrational behaviors. [ABSTRACT FROM AUTHOR]

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals. [ABSTRACT FROM AUTHOR]

International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students' mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the 'A Dataset of Students' Mental Health and Help-Seeking Behaviors in a Multicultural Environment,' focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm's performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students' mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression. [ABSTRACT FROM AUTHOR]

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making. [ABSTRACT FROM AUTHOR]

We propose a heuristic method of using network centralities for constructing small-weight Steiner trees in this paper. The Steiner tree problem in graphs is one of the practical NP-hard combinatorial optimization problems. Given a graph and a set of vertices called terminals in the graph, the objective of the Steiner tree problem in graphs is to find a minimum weight Steiner tree that is a tree containing all the terminals. Conventional construction methods make a Steiner tree based on the shortest paths between terminals. If these shortest paths are overlapped as much as possible, we can obtain a small-weight Steiner tree. Therefore, we proposed to use network centralities to distinguish which edges should be included to make a small-weight Steiner tree. Experimental results revealed that using the vertex or the edge betweenness centralities contributes to making small-weight Steiner trees. [ABSTRACT FROM AUTHOR]