Search Results (9,951)

Pseudo-random number generators (PRNGs) are important cornerstones of many fields, such as statistical analysis and cryptography, and the need for PRNGs for information security (in fields such as blockchain, big data, and artificial intelligence) is becoming increasingly prominent, resulting in a steadily growing demand for high-speed, high-quality random number generators. To meet this demand, the multiple deep-dynamic transformation (MDDT) algorithm is innovatively developed. This algorithm is incorporated into the skewed tent map, endowing it with more complex dynamical properties. The improved one-dimensional discrete chaotic mapping method is effectively realized on a field-programmable gate array (FPGA), specifically the Xilinx xc7k325tffg900-2 model. The proposed pseudo-random number generator (PRNG) successfully passes all evaluations of the National Institute of Standards and Technology (NIST) SP800-22, diehard, and TestU01 test suites. Additional experimental results show that the PRNG, possessing high novelty performance, operates efficiently at a clock frequency of 150 MHz, achieving a maximum throughput of 14.4 Gbps. This performance not only surpasses that of most related studies but also makes it exceptionally suitable for embedded applications. Full article

Background: Inflammation plays a critical role in myocardial infarction as a critical process in the development of heart failure, involving the development of cardiac fibrosis. Colchicine is a well-established anti-inflammatory drug, but its scientific application in controlling post-acute myocardial infarction (AMI) inflammatory processes has not been established. IL-10 is a key cytokine in modulating inflammatory responses, underscoring its potential as a crucial therapeutic target of colchicine. The objective was to explore the protective role of IL-10 modulated by colchicine in myocardial healing and repair following AMI, particularly cardiac fibrosis. Methods: The predicted protein of colchicine was assessed using WAY2DRUG PASS as probability active value. Proteins associated with colchicine, cardiac fibrosis, and acute myocardial infarction were analyzed with DisGeNET and Open Target databases. Analysis and visualization of protein–protein interactions were conducted using STRING and Cytoscape. A 3T3 cell line treated with CoCl₂ was used to mimic hypoxic. HIF-1α and IL-10 expression were measured by flow cytometry and analyzed using a one-way ANOVA test. This observational clinical trial examined acute myocardial infarction patients undergoing immediate and delayed primary percutaneous coronary interventions. Subjects were randomized into control groups receiving placebo and intervention groups treated with colchicine. Assessments occurred at 24 h and five days after the intervention. IL-10 expression in the clinical trial was measured by ELISA and analyzed using a T-test. Results: Colchicine demonstrates promising bioactivity in treating acute myocardial infarction, with notably activity values highlighting its probable role as a tubulin antagonist (0.744), beta-tubulin antagonist (0.673), and NOS2 inhibitor (0.529). Its primary action targets IL-10, with the protein–protein interactions analysis indicating interactions between IL-10 and key inflammatory mediators—IL-1β, IFN-γ, CCL2, TNF, and TGF-β1—during acute myocardial infarction and cardiac fibrosis. Hypoxic conditions in the CoCl₂-induced 3T3 cell model show significantly elevated HIF-1α compared to controls (p < 0.0001). Colchicine use significantly increased IL-10 expression in CoCl₂-treated cells (p < 0.0001) and in AMI patients within five days (p < 0.05). Conclusions: Colchicine may bolster the anti-inflammatory response post-myocardial infarction by activating IL-10 pathways in fibroblasts and in clinical settings, potentially reducing inflammation after AMI. Further investigation into broader aspects of this pathway, particularly in cardiac fibroblasts, is required. Full article

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits. Full article

Background: Format readability, including font and spacing, impacts reading metrics in adults, but will the research generalize to children? We examined how eight fonts (four serif and four sans serif) and three-character spacing variations influenced children’s reading comprehension and reading speed. Methods: Fifty-one students in third–fifth grade read 11 narrative text passages on a computer and answered comprehension questions. Passages were randomized in terms of order. First, the font in which the text of each passage was presented was manipulated. Then passages were presented in three spacing manipulations (narrow, normal, and wide). Results: A linear mixed effects model demonstrated that, on a group-level, passages presented in Roboto and Arial font were read significantly faster (words-per-minute) than other fonts. On the individual level, students experienced significant boosts in reading speed (words-per-minute) between their best and worst fitting font, and spacing. A chi-square test showed no one font or spacing setting that was most likely to be a students’ font fit or clash. For reading comprehension, no speed–comprehension tradeoff was observed. Conclusions: Changes to text format at the group and individual level may yield boosts in reading speed for students, without negatively impacting reading comprehension. Full article

Several bacterial taxa enriched in inflammatory bowel diseases and colorectal cancer (CRC) are found in the oral cavity. We conducted a pilot study nested within a six-week aspirin intervention in a randomized placebo-controlled trial to test their response to aspirin intervention. Fifty healthy subjects, 50–75 years old, were randomized to receive 325 mg aspirin (n = 30) or placebo (n = 20) orally once daily for six weeks. Oral tongue swabs were collected at baseline and week six. We estimated the association between aspirin use and the temporal changes in the relative abundance of pre-specified genus level taxa from pre- to post-treatment. The temporal change in relative abundance differed for eight genus level taxa between the aspirin and placebo groups. In the aspirin group, there were significant increases in the relative abundances of Neisseria, Streptococcus, Actinomyces, and Rothia and significant decreases in Prevotella, Veillonella, Fusobacterium, and Porphyromonas relative to placebo. The log ratio of Neisseria to Fusobacterium declined more in the aspirin group than placebo, signaling a potential marker associated with aspirin intervention. These preliminary findings should be validated using metagenomic sequencing and may guide future studies on the role of aspirin on taxa in various oral ecological niches. Full article

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively. Full article

Parenteral nutrition (PN) is a life-sustaining method to provide adequate nutrients to patients unable to receive oral or enteral nutrition. PN typically contains a mixture of macro- and micro-nutrients, although the lipid composition has been identified as a concern for liver disease. Therefore, the study of the intravenous lipid emulsion (ILE) prescribing practices in home-based PN (HPN) patients and whether differing lipid PN alters liver function tests (LFTs) is needed. Methods: A retrospective study of monthly LFTs from a random sample of 105 adult HPN patients in the U.S. over a 6-month period was conducted. Patients were receiving olive oil/soy oil (n = 53, Clinolipid), mixed ILE (n = 39, SMOF Lipid), soy oil (SO; n = 4, Intralipid), or none (n = 7). LFTs monitored were alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST), and total bilirubin (T Bili). Results: No differences were observed in baseline LFTs across groups (all, p > 0.25, η² < 0.04), nor were there differences in age, body mass index, days of PN, or mean PN volume (all, p > 0.36, η² < 0.05). There were no significant interactions between ILE type and time (all p > 0.64, η_p² < 0.03), no effect of ILE type (all p > 0.60, η_p² < 0.03), and no effect of time (all p > 0.69, η_p² < 0.01) in terms of LFTs. Average LFTs over six months were also not different between ILE types (all p > 0.30, η² < 0.04). Conclusion: These findings suggested that patients were mostly prescribed mixed or ILE PN containing more than one lipid source and that differing ILEs in long-term HPN patients did not alter LFTs over a six-month period. Full article

Proppant is one of the key materials for hydraulic fracturing. For special situations, such as middle-deep reservoirs and closure pressures ranging from 40 MPa to 60 MPa, using a single proppant cannot solve the contradiction between performance, which means crushing rate and fracture conductivity, and cost. However, using combined proppants is an economically effective method for hydraulic fracturing of such special reservoirs. Firstly, for different types, particle sizes, and proportions of combined proppants, various contact relationships between proppant particles are considered. The random phenomenon of proppant particle arrangement is described using the Monte Carlo method, and the deterministic phenomenon of proppant particles is processed using an optimization model, achieving computer simulation of the microscopic arrangement of proppant particles. Secondly, a mathematical model for the force analysis of combined proppant particles is established, and an improved singular value decomposition method is used for numerical solution. A computational model for the crushing rate and fracture conductivity of combined proppants is proposed. Thirdly, the numerical calculation results are compared and discussed with the test values, verifying the accuracy of the computational model. Finally, the application of combined proppants is discussed, and a model for optimizing the proportion of combined proppants is proposed. The onsite construction technology is introduced, and the cost and economic benefits of combined proppants are compared with those of all ceramic particles and excessive all-quartz sand. It is proved that combined proppants can balance performance and price, and are an economically effective method for hydraulic fracturing of special reservoirs. The research results can select the optimal proppant material and optimize the combination of different proppant types, which can help achieve cost reduction and efficiency increase in oil and gas development. Full article

Background: Diabetes mellitus, a chronic metabolic disorder associated with elevated blood sugar levels, is a significant cause of morbidity, mortality, and disability globally. The rampant rise in the prevalence of diabetes presents a public health burden and a challenge to the primary care setting. Diabetes self-management coaching is an emergent, client-centered, solution-focused approach to enhance self-efficacy and self-care behavior, control glycemia, and prevent acute and chronic complications. Currently, there is no diabetes self-management support strategy in the primary care setting in Ethiopia. Therefore, this study assessed the feasibility, acceptability, and fidelity of implementing the Diabetes Self-Management Coaching Program in primary care in Ethiopia. Method: A single-center, single-blinded, parallel group mixed-methods feasibility randomized control design was applied to assess the feasibility, acceptability, and fidelity of the Diabetes Self-Management Coaching Program in primary care. Adult patients with type 2 diabetes with HbA1c ≥ 7%, taking diabetic medication, and living in Gondar town were included in the study. A block randomization technique with a block size of four was used to allocate participants into the treatment and control groups. The treatment group attended a 12-week Diabetes Self-Management Coaching Program in addition to undergoing usual care, while the control group received the usual care for the same period. Data were collected at baseline, at the end of the intervention, and after the follow-up period. Descriptive statistics such as the frequency, mean, median, and standard deviations were computed. Based on the normality assessment, the baseline group difference was examined using the independent sample Student’s t-test, the Mann–Whitney U test, and the chi-square test. Result: This study’s eligibility, recruitment, retention, and adherence rates were 23%, 70%, 90%, and 85%, respectively. Both the qualitative and quantitative findings show that the program was feasible to implement in primary care and acceptable to the participants. The fidelity assessment of the Diabetes Self-Management Coaching Program indicates an appropriate intervention implementation. Conclusions: This study demonstrated remarkable recruitment, retention, and adherence rates. The Diabetes Self-Management Coaching Program was feasible, acceptable, and implementable in primary care in Ethiopia. As a result, we recommend that a large-scale multi-center cluster randomized controlled trial with an adequate sample can be designed to evaluate the effect of the DSM Coaching Program on clinical and behavioral outcomes. Full article

Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis. Full article

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU: 27.80 in validation, 1.33 in test) outperforms LSTM (3.62 in validation, 0.00 in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling (“no strategy”) help improve the accuracy (BLEU 38.22, +10.42 on validation, 2.07, +0.74 on test). Specifically, back-translation with top-k sampling is less effective (k=10, BLEU 29.43, +1.83 on validation, 1.36, +0.03 on test), while sampling with a proper value of T, T=0.5 makes the model achieve a higher score (T=0.5, BLEU 35.02, +7.22 on validation, 2.35, +1.02 on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T. Full article

With the increasing depth of coal mining, the ground stress of coal seams rises, the gas content rises, and the permeability decreases, which is unfavorable to gas extraction and increases the possibility of gas accidents in coal mines. Blasting technology is often used to improve the permeability of coal seams and increase the effect of gas extraction, but it is difficult to control the blasting effect of ordinary flux coils, and the direction of the fissure and the degree of development of randomness, so it is important to study the effect of different flux coil structures on the blasting of coal seams to increase the permeability of the coal seam, in this paper, first of all, through the numerical simulation to analyze the effect of the blasting of the ordinary structure flux coils and the fissure change in the polygonal structure flux coils, and then make the experimental module. Then, we make the test module, build the test platform, and analyze the effect of blasting penetration of different drug coil structures by comparing the strain after blasting and the change in resistivity before and after blasting of two kinds of drug coil structures, and the results show that the polymerized drug coil can play the role of directional fracturing, and compared with the ordinary structure of the drug coil, the polymerized drug shows a better effect of fracturing and penetration increase. Full article

Background/Objectives: Vibration therapy approaches are an effective and safe treatment option for musculoskeletal disorders. This study examines the effects of vibration therapy using a percussion massage gun (PMG) on joint position sense, range of motion, pain, functionality, and kinesiophobia in individuals with cervical disc herniation (CDH). Methods: This single-blind randomized controlled trial involved 44 CDH patients divided into a Vibration Group (VG) and a Conventional Group (CG). The CG underwent a standard physiotherapy treatment heat application, Transcutaneous Electrical Nerve Stimulation (TENS), and exercises for range of motion and strengthening. VG received conventional therapy augmented with vibration therapy (VT) via a PMG. Joint position sense (JPS) using the Laser Pointer Assisted Angle Repetition Test; pain intensity with the Visual Analog Scale, kinesiophobia with the Tampa Scale for Kinesiophobia, and cervical dysfunction with the Neck Disability Index were assessed. Results: Both groups showed statistically significant improvements in pain, kinesiophobia, disability, and proprioception after treatment (p < 0.05). When comparing the difference values between groups, the VG was found to be more effective than the CG in the parameters of VAS activity (p = 0.013). The CG had more improvement in JPS neck left rotation than the VG (p = 0.000). Conclusions: VT, when combined with conventional physiotherapy, is effective in improving pain, proprioception, and functionality in individuals with CDH. These findings support the inclusion of VT as a beneficial adjunct therapy. Further research with larger sample sizes and longer follow-ups is recommended to validate these results and explore the long-term effects of VT on CDH. Full article

The determination of the distributions of the eigenvalues associated with matrix-variate gamma and beta random variables of either type proves to be a challenging problem. Several of the approaches utilized so far yield unwieldy representations that, for instance, are expressed in terms of multiple integrals, functions of skew symmetric matrices, ratios of determinants, solutions of differential equations, zonal polynomials, and products of incomplete gamma or beta functions. In the present paper, representations of the density functions of the smallest, largest and jth largest eigenvalues of matrix-variate gamma and each type of beta random variables are explicitly provided as finite sums when certain parameters are integers and, as explicit series, in the general situations. In each instance, both the real and complex cases are considered. The derivations initially involve an orthonormal or unitary transformation whereby the wedge products of the differential elements of the eigenvalues can be worked out from those of the original matrix-variate random variables. Some of these results also address the distribution of the eigenvalues of a central Wishart matrix as well as eigenvalue problems arising in connection with the analysis of variance procedure and certain tests of hypotheses in multivariate analysis. Additionally, three numerical examples are provided for illustration purposes. Full article

As the global demographic shifts toward an aging population, aging-related problems, particularly in older individuals with dynapenia, are increasingly gaining attention. However, interventional studies focusing on physical and cognitive function and sleep quality in such individuals are limited, indicating a need for further exploration. The present study investigated the effects of whole-body vibration (WBV) training on physical and cognitive function and sleep quality in older people with dynapenia residing in long-term care institutions. This study was a randomized controlled trial. The experimental group underwent WBV training three times a week for 3 months, whereas the control group continued with their regular daily care routine. Statistical analyses were performed using the Traditional Chinese version of SAS Statistics version 9.4. Paired t tests, a one-way analysis of variance, independent t tests, and generalized estimating equation analysis were performed. The results revealed that compared with the control group, the experimental group experienced significant improvements in grip strength, instrumental activities of daily living, cognitive function, and sleep quality in terms of latency and duration. These findings suggest that 3 months of WBV training can effectively enhance physical and cognitive function and sleep quality in older people with dynapenia residing in long-term care institutions. Full article