Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Cognitive–Achievement Relations Vary by General Ability Level?

Cognitive–achievement relations research has been instrumental in understanding the development of academic skills and learning difficulties. Most cognitive–achievement relations research has been conducted with large samples and represent average relations across the ability spectrum. A notable gap in the literature is whether these relations vary by cognitive ability levels (IQ). This study examined cognitive–achievement relations across different general ability levels (Low, Average, and High) to fill this gap. Based on Spearman’s Law of Diminishing Returns, it would be expected that general intelligence would be a stronger predictor of academic skills at lower levels of IQ, and more specific abilities would be stronger predictors of academic skills at higher levels of IQ. To test this, multi-group path analysis and structural equation modeling were used to examine whether integrated models of cognitive–reading relations are differentiated by IQ levels in the Woodcock–Johnson III and Woodcock–Johnson IV standardization samples. Global and broad cognitive abilities were used as predictors of basic reading skills and reading comprehension for elementary and secondary school students. The magnitude of prediction differed across ability groups in some cases, but not all. Importantly, the variance explained in basic reading skills and reading comprehension tended to be larger for the Low group compared to the Average and High groups. When variance accounted for by general intelligence was removed from the broad abilities, the effects of the broad abilities were similar across ability groups, but the indirect effects of g were higher for the Low group. Additionally, g had stronger relative effects on reading in the Low group, and broad abilities had stronger relative effects on reading in the Average and High groups. The implications and limitations of this study are discussed.
Allopurinol for Secondary Prevention in Patients with Cardiovascular Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: The effects of allopurinol in patients with cardiovascular disease are not well defined; therefore, the latest evidence is summarized in this study. Methods: PubMed, Embase, Cochrane Library, and ClinicalTrials.gov databases were searched for randomized controlled trials (RCTs) of allopurinol in patients with cardiovascular disease published up to 11 February 2023. The primary outcome was cardiovascular death. Results: We combined the results of 21 RCTs that included 22,806 patients. Compared to placebo/usual care, allopurinol treatment was not associated with a significant reduction in cardiovascular death (RR 0.60; 95% CI 0.33–1.11) or all-cause death (RR 0.90; 95% CI 0.72–1.12). However, evidence from earlier trials and studies with small sample sizes indicated that allopurinol might confer a protective effect in decreasing cardiovascular death (RR 0.34; 95% CI 0.15–0.76) across patients undergoing coronary artery bypass grafting (CABG) or having acute coronary syndrome (ACS). In comparisons between allopurinol and febuxostat, we observed no difference in cardiovascular death (RR 0.92; 95% CI 0.69–1.24) or all-cause death (RR 1.02; 95% CI 0.75–1.38). Conclusion: Allopurinol could not reduce cardiovascular (CV) death or major adverse CV outcomes significantly in patients with existing cardiovascular diseases. Given the limitations of the original studies, the potential advantages of allopurinol observed in patients undergoing CABG or presenting with ACS necessitate further confirmation through subsequent RCTs. In the comparisons between allopurinol and febuxostat, our analysis failed to uncover any marked superiority of allopurinol in reducing the risk of adverse cardiovascular incidents.
Enhancing Near-Field Wireless Localization With LiDAR-Assisted RIS in Multipath Environments

In next-generation wireless networks that adopt millimeter-waves and large reconfigurable intelligent surfaces (RISs), the user is expected to be in the near-field region, where the widely adopted far-field algorithms based on far-field can yield low positioning accuracy. Also, the localization of user equipment (UE) becomes more challenging in multipath environments. In this letter, we propose a localization algorithm for a UE in the near-field of a RIS in multipath environments. The proposed scheme utilizes a light detection and ranging (LiDAR) to assist the UE positioning by providing geometric information about some of the scatterers in the environment. This information is fed to a sparse recovery algorithm to improve the localization accuracy of the UE by reducing the number of variables (i.e., angle of arrivals and distances) to be estimated. The numerical results show that the proposed scheme can improve the localization accuracy by 65% compared to the standard compressed sensing (CS) scheme.
Cerebrovascular Accident Prognosis using Supervised Machine Learning Algorithms

Cerebral vascular problems, such as stroke, have become a major concern in healthcare due to their impact on neurological health. Early detection of these illnesses can considerably enhance patient outcomes and lead proper treatment choices. Recently, supervised machine learning algorithms have emerged as potential techniques for anticipating cerebrovascular diseases utilizing numerous clinical indicators. This study compares the predictive performance of three supervised learning algorithms, Random Forest, Decision Tree, and XGBoost, in the context of cerebrovascular disease prediction. The dataset utilized for training and evaluation comprises a variety of clinical parameters such as age, blood pressure, cholesterol levels, smoking status, and medical history. Random Forest has the highest prediction accuracy of the three algorithms, at 99.93% for cerebrovascular diseases. This result demonstrates how effective and reliable Random Forest’s pattern detection algorithms are in this dataset. With a near accuracy of 97.94%, XGBoost exhibits a good capacity for prediction. Decision Tree nevertheless obtains an excellent accuracy of 97.53% despite somewhat inferior performance compared to XGBoost, making it a competitive choice for the prediction of cerebrovascular disease. These results outperform the most recent state-of-the-art results and show a considerable increase in accuracy when compared to existing methods. The study emphasizes how well supervised learning systems, in particular Random Forest, capture the complex relationships between clinical parameters for accurate predictions of cerebrovascular diseases. The outstanding accuracy of these models highlights their potential for accurate cerebrovascular prediction in clinical practice.
Microbiome and virome on indoor surfaces of an Antarctic research ship

BACKGROUND Few studies have focused on microbial diversity in indoor environments of ships, as well as the role of the microbiome and its ecological interconnections. In this study, we investigated the microbiome and virome present on the internal surfaces of a polar ship in different stages (beginning, during, and at the end) of the Brazilian Antarctic expedition in order to evaluate abundance of microorganisms in different periods. OBJECTIVES AND METHODS We used shotgun metagenomic analysis on pooled samples from sampling surfaces in the ship’s interior to track the microbial diversity. FINDINGS Considering the total fraction of the microbiome, the relative abundance of bacteria, eukaryotes, viruses, and archaea was 83.7%, 16.2%, 0.04%, and 0.002%, respectively. Proteobacteria was the most abundant bacterial phyla, followed by Firmicutes, Actinobacteria, and Bacteroidetes. Concerning the virome, the greatest richness of viral species was identified during the middle of the trip, including ten viral families after de novo assembly: Autographiviridae, Chrysoviridae, Genomoviridae, Herelleviridae, Myoviridae, Partitiviridae, Podoviridae, Potyviridae, Siphoviridae, and Virgaviridae. MAIN CONCLUSIONS This study contributed to the knowledge of microbial diversity in naval transportation facilities, and variations in the abundance of microorganisms probably occurred due to factors such as the number of passengers and activities on the ship.
Role of Gremlin-1 in Cancer

Gremlin-1 (GREM1) has been defined as an antagonist f bone morphogenetic proteins (BMPs), particularl y during embryonic development and tissue differentiation. H owever, recent studies have shown that GREM1 has BM Ps-dependent or -independent functions in diverse human diseases . GREM1 plays a key role in the process of organ fi brosis, including lungs, kidneys, and so on. The GREM1-induced fibros s typically promotes the development of other dise ases, such as pulmonary hypertension, renal inflammation, and dia betic nephropathy. More recently, considerable evid ence has been reported showing that GREM1 is involved in the prom otion and/or progression of tumors in vitro and in vivo. It also performs an oncogenic role in the maintenance of ca ncer stem cells. Although GREM1 is known to functio n in a variety of diseases, here we focus on the role of GREM1 in cancer, and suggest GREM1 as a potential therapeuti c target in certain types of cancer.
Giant pandas : biology and conservation

The much-loved giant panda, a secretive denizen of the dense bamboo forests of western China, has become an icon worldwide of progress in conservation and research. This volume, written by an international team of scientists and conservationists including Chinese researchers whose work has not been available in English, tells the promising story of how the giant panda returned from the brink of extinction. The most important sourcebook on giant pandas to date, it is the first book since 1985 to present current panda research and the first to place the species in its biological, ecological, and political contexts. More than a progress report on a highly endangered species, Giant Pandas: Biology and Conservation details the combination of scientific understanding, local commitment, and government involvement that has been brought into play and asks what more needs to be done to ensure the panda's survival. The book is divided into four parts - Evolutionary History of the Giant Panda, Studies of Giant Panda Biology, Pandas and Their Habitats, and Giant Panda Conservation. It combines the latest findings from the field and the laboratory together with panel and workshop summaries from a recent international conference. Taken together, the chapters highlight how international cooperation has led to better management in the wild and in captivity. The volume also shows how concepts such as buffer zones, links between forest fragments, multiple-use areas, and cooperation with local people who have a stake in the resources - highly relevant concepts for conservation problems around the world - have been key to the panda's survival.
Research progress of dihydromyricetin in the treatment of diabetes mellitus

Diabetic Mellitus (DM), a chronic metabolic disorder disease characterized by hyperglycemia, is mainly caused by the absolute or relative deficiency of insulin secretion or decreased insulin sensitivity in target tissue cells. Dihydromyricetin (DMY) is a flavonoid compound of dihydroflavonol that widely exists in Ampelopsis grossedentata. This review aims to summarize the research progress of DMY in the treatment of DM. A detailed summary of related signaling induced by DMY are discussed. Increasing evidence implicates that DMY display hypoglycemic effects in DM via improving glucose and lipid metabolism, attenuating inflammatory responses, and reducing oxidative stress, with the signal transduction pathways underlying the regulation of AMPK or mTOR/autophagy, and relevant downstream cascades, including PGC-1α/SIRT3, MEK/ERK, and PI3K/Akt signal pathways. Hence, the mechanisms underlying the therapeutic implications of DMY in DM are still obscure. In this review, following with a brief introduction of the absorption, metabolism, distribution, and excretion characteristics of DMY, we summarized the current pharmacological developments of DMY as well as possible molecular mechanisms in the treatment of DM, aiming to push the understanding about the protective role of DMY as well as its preclinical assessment of novel application.
Longitudinal patterns of lifestyle risk behaviours among UK adults with established cardiovascular disease: a latent transition analysis

Background People with cardiovascular disease (CVD) need to engage in healthy lifestyle behaviours. However, there is a gap in identifying longitudinal patterns of change in lifestyle behaviours among people with CVD. This study aimed to identify clustering of lifestyle risk behaviours and their 4 ± year changes among UK adults with CVD, and to determine the associated factors. Methods We used the UK Biobank data collected at two time points (2006–2010/baseline data = T0 and 2014+/third visit data = T4). Six key lifestyle risk behaviours were assessed: smoking, high alcohol intake, poor fruit and vegetable consumption, physical inactivity, poor sleep balance (<7 or >8 h/night) and prolonged sitting. A random intercept latent transition analysis was performed to identify patterns of lifestyle risk behaviours at T0 and their changes from T0 to T4. Results We included 5,304 participants with CVD whose data on lifestyle risk behaviours were collected at two-time points. Alcohol intake and current smoking were 75.7% and 5.4% at baseline, respectively, and 67.4% and 3.0% at follow-up. Three latent classes emerged: Latent class (LC) 1—“high alcohol intake, poor sleep balance and poor fruit and vegetable intake”, LC2—“high alcohol intake and poor fruit and vegetable intake”, and LC3—“high alcohol intake”. Most adults remained in the same LC over the 4 + years (range: 83.9%–100.0%). After 4 + years, 3.5% from LC3 and 10.4% from LC2 at baseline moved into LC1. The odds of transitioning to LC2 relative to staying in LC1 and LC3 were 2.22 and 4.13 times higher for males than for females, respectively. A single-year increase in participants' age was associated with a 1.16 times increase in the odds of moving to LC1 relative to staying in LC2. Conclusion People with CVD did not show improvement in lifestyle risk behaviours, and interventions targeting multiple lifestyle risk behaviours are needed to improve CVD.
Ruptured Ectopic Pregnancy: Epidemiological Profiles and Prognostic in a Reference Hospital in Antananarivo Madagascar

Introduction: Ectopic pregnancy (EP) is a serious condition and the leading cause of maternal death in the first trimester of pregnancy. The objectives of this study were to identify the epidemiological profiles, clinicals ectopic pregnancy ruptured to prevent complications and ensure early and appropriate treatment. Methods: We performed a prospective descriptive study over a period of two years from first January 2018 to December 2019. Results: Among the 14968 deliveries during the study period, 269 cases of ruptured EP were compiled, giving a frequency of 2.80%. The average age of onset was 28 years. The risk factors the most identified was the pelvic infection. The spontaneous abortion was predominant. Most of the EP was in ampullary position in 72.49 %. Radical treatment dominated the management in 79.55 % of cases. The totality of the patients was a satisfiable issue. Conclusion: The precocious detection of EP in all women who bleed in the first trimester of pregnancy is primordial. The decrease in frequency requires the sensibilization of the population on their health to avoid the many risk factors of ruptured EP and their complications.
Hot Electron-Induced Bremsstrahlung Emission in AlGaN/GaN Superlattice Castellated Field Effect Transistors

Hot electron effects have been studied in multichannel AlGaN/GaN-based Superlattice Castellated Field Effect Transistors (SLCFET). Current-Voltage (I-V) and simulations could identify channel position-dependent impact ionization within the device fins. Electroluminescence (EL) emission is observed simultaneously with the impact ionization, whose spectral analysis shows the signature of hot electron scattering (Bremsstrahlung radiation) and not electron-hole recombination.
Design and Analysis of Wide-bandwidth Actuator for Haptic Controller with Novel Magnetic Circuit

This study proposes a new actuator for a haptic controller that has a wider bandwidth than that of the prototype. The acceleration on the dummy jig of the prototype is analyzed using the electromagnetic-mechanical coupling method and verified using the experimental results. Several new designs have been proposed to provide higher accelerations on the dummy jig and a wider bandwidth. The new magnetic circuit, called the sandwich structure, includes one top plate, two permanent magnets, and two yokes. Response surface methodology is employed to perform the optimal analysis to find the maximum force factor while maintaining a magnet volume similar to that of the prototype. The predicted values (force factor and magnet volume) are confirmed by analysis results of the optimal type. Compared with the prototype, the optimal type has a higher force factor (77.74%) and 417.24% improvement in bandwidth with the same outer dimensions.
Relationship between water chemistry and sediment mineralogy in the Cerro Prieto geothermal field: a preliminary report

The chemical compositions of waters collected from the Cerro Prieto geothermal production wells and hydrothermal emanations are different. Compared to the Cerro Prieto well waters, the surficial waters generally contain significantly less potassium, slightly less calcium and chloride, and significantly more magnesium and sulfate. In comparison to the unaltered sediments, the changes in the mineralogy of the altered sediments appear to be controlled by the type of emanation (well, spring, mud pot, geyser, fumarole, or cold pool). However, an increase in quartz and potassium feldspar percentages seems to be characteristic of the majority of the sediments in contact with geothermal fluids. Preliminary attempts to model the chemical processes occurring in the Cerro Prieto geothermal field using chemical equilibrium calculations are reported. For this purpose the chemical compositions of thermal waters (well and surficial emanation) were used as input data to make calculations with SOLMNEQ and WATEQ2 computer programs. Then the theoretical mineral composition of altered sediments was predicted and compared to the mineralogy actually observed in the solid samples.
A Mathematical Study of the Impact on Salinity Intrusion of Deepening the Lower Mississippi River Navigation Channel.

Abstract : The US Army Engineer District, New Orleans has been evaluating the feasibility of deeper draft access to the ports of New Orleans and Baton Rouge, Louisiana. One of the factors being considered in the evaluation is the impact of the deeper channel on salinity intrusion in the Lower Mississippi River. The importance of such an evaluation is because the city of New Orleans relies primarily upon the Mississippi River for its supply of fresh water. A major reason for conducting the numerical modeling study was not only to evaluate the impact of a deeper channel on salinity intrusion using historical riverflows but also to use the numerical model to determine the impact on the wedge intrusion of increasing the height of natural river crossings, i.e., creating a sill in the river. To address the question of the stability of such a sill composed of natural sediment, the HEC-6 computer program Scour and Deposition in Rivers and Reservoirs, HEC (1977), was applied.
A Quick Proof for the Volume of n-Balls

we have used the integral test and integration by parts. Also, we know that 21 sin wzl > e7rImzle-JImzl. These inequalities imply that iq(z)I < A + BlImzl for iRe zI < 1/2, for some positive constants A and B. Since q(z + 1) = q (z), we see that Iq (z)I < A + B IZ I for all z. But then, by (an easy extension of) Liouville's Theorem [1, p. 59], q is a linear polynomial. Because it is periodic (as well as even and nonzero), q must be constant. Specifically, q (z) -limz,o (7z/ sin wz) = 1.
Low-Cost Brain-Computer Interface Design Using Deep Learning for Internet of Brain Controlled Things Applications

Huge efforts have been made so far aiming to classify human thoughts. Controlling machines using the concept of Brain-Computer Interface (BCI) is a practical method that opens the way toward a fully synchronized method between human thoughts and controlled objects. Furthermore, a reliable design of a BCI-based Internet of Things (IoT) system is still in the early stages because it still has several challenges, such as the issue of accurately implementing the individual's intention. This paper presents a method for brain waves recognition using deep learning based on shapes and colors for use in merging concepts of the IoT and BCI, which we defined as the “Internet of Brain Controlled Things (IoBCT). ” We used a low-cost 8-channel Electroencephalography (EEG) headset, and the results showed an acceptable accuracy of 93% in the brain waves pattern recognition, which opens the way for designing a reliable IoBCT.
Filter Design for Grid Connected Three Phase Five Level Inverter with External Circuit for DC Link Capacitor Voltage Balancing

This paper presents the design of LCL (inductor-capacitor-inductor) filter for the grid connected 12 kW, 400 V, 3ɸ five level inverter. The need of harmonics mitigation as per IEEE 519 has been discussed for grid connection. The need of phase locked loop (PLL) as well as the problem of DC link capacitors voltage unbalancing in multilevel inverter has been discussed in this paper and methods have been explained to overcome the problem of dc link capacitor’s unbalance voltage. The designed parameters of LCL filter and the benefits of using DC link capacitor’s voltage balancing circuit have been verified with simulation results using MATLAB/Simulink.
Deep Learning Machine Vision Solutions for Monitoring Safety Structures to Supplement Building Information Models

Building Information Models (BIM), provide a static view on building structures on design and construction time. During construction time, the safety structures and their positioning among conditions are vital for the safety of the employees on site. The models usually lack the temporospatial information regarding non-static safety structures as maintaining of such information manually is laborious. Thus, automated and continuous safety structure monitoring is cost-efficient and vital keeping the model up-to-date while improving construction site safety and risk information sharing among the construction related personnel during the project. This paper researches of providing up-to-date and supplementary safety structure information to building models by means of various deep learning-based machine vision solutions. The machine vision tasks consist of deep learning safety related object detection in images, point-clouds, and further instance segmentation to enable safety structure fitness determination by more traditional machine vision means.