Miracle-dz/UniFAR-FTUs huggingface dataset:
Adverse Effects of Angiotensin-Converting Enzyme Inhibitors in Humans: A Systematic Review and Meta-Analysis of 378 Randomized Controlled Trials

Background: Although angiotensin-converting enzyme (ACE) inhibitors are among the most-prescribed medications in the world, the extent to which they increase the risk of adverse effects remains uncertain. This study aimed to systematically determine the adverse effects of ACE inhibitors versus placebo across a wide range of therapeutic settings. Methods: Systematic searches were conducted on PubMed, Web of Science, and Cochrane Library databases. Randomized controlled trials (RCTs) comparing an ACE inhibitor to a placebo were retrieved. The relative risk (RR) and its 95% confidence interval (95% CI) were utilized as a summary effect measure. A random-effects model was used to calculate pooled-effect estimates. Results: A total of 378 RCTs fulfilled the eligibility criteria, with 257 RCTs included in the meta-analysis. Compared with a placebo, ACE inhibitors were associated with an significantly increased risk of dry cough (RR = 2.66, 95% CI = 2.20 to 3.20, p < 0.001), hypotension (RR = 1.98, 95% CI = 1.66 to 2.35, p < 0.001), dizziness (RR = 1.46, 95% CI = 1.26 to 1.70, p < 0.001), and hyperkalemia (RR = 1.24, 95% CI = 1.01 to 1.52, p = 0.037). The risk difference was quantified to be 0.037, 0.030, 0.017, and 0.009, respectively. Conclusions: We quantified the relative risk of numerous adverse events associated with the use of ACE inhibitors in a variety of demographics. This information can help healthcare providers be fully informed about any potential adverse consequences and make appropriate suggestions for their patients requiring ACE inhibitor therapy.
Network Assignment and Equilibrium for Disaggregate Models

A gravure roll and associated coating methods are disclosed that produces a uniform coating gradient on a substrate. The gravure roll comprising a series of cells arranged in circumferential line density sections in which the line densities progressively increase from section to section, and in which the cell volumes progressively decrease within each section, and in which the cell volumes at the end of one section are substantially the same as the cell volumes at the beginning of the next adjacent section.
Approximation, Proof Systems, and Correlations in a Quantum World

This thesis studies three topics in quantum computation and information: The approximability of quantum problems, quantum proof systems, and non-classical correlations in quantum systems. In the first area, we demonstrate a polynomial-time (classical) approximation algorithm for dense instances of the canonical QMA-complete quantum constraint satisfaction problem, the local Hamiltonian problem. In the opposite direction, we next introduce a quantum generalization of the polynomial-time hierarchy, and define problems which we prove are not only complete for the second level of this hierarchy, but are in fact hard to approximate. In the second area, we study variants of the interesting and stubbornly open question of whether a quantum proof system with multiple unentangled quantum provers is equal in expressive power to a proof system with a single quantum prover. Our results concern classes such as BellQMA(poly), and include a novel proof of perfect parallel repetition for SepQMA(m) based on cone programming duality. In the third area, we study non-classical quantum correlations beyond entanglement, often dubbed "non-classicality". Among our results are two novel schemes for quantifying non-classicality: The first proposes the new paradigm of exploiting local unitary operations to study non-classical correlations, and the second introduces a protocol through which non-classical correlations in a starting system can be "activated" into distillable entanglement with an ancilla system. An introduction to all required linear algebra and quantum mechanics is included.
Recent developments and perspectives of advanced high-strength medium Mn steel: from material design to failure mechanisms

Advanced high-strength steels are key structural materials for the development of next-generation energy-efficient and environmentally friendly vehicles. Medium Mn steel, as one of the latest generation advanced high-strength steels, has attracted tremendous attentions over the past decade due to its excellent mechanical properties. Here, the state-of-the-art developments of medium Mn steel are systematically reviewed with focus on the following crucial aspects: (a) the alloy design strategies; (b) the thermomechanical processing routes for the optimizations of microstructure and mechanical properties; (c) the fracture mechanisms and toughening strategies; (d) the hydrogen embrittlement mechanisms and improvement strategies.
FAST MAGNETIC TWISTER AND PLASMA PERTURBATIONS IN A THREE-DIMENSIONAL CORONAL ARCADE

We present results of three-dimensional (3D) numerical simulations of a fast magnetic twister excited above a foot-point of the potential solar coronal arcade that is embedded in the solar atmosphere with the initial VAL-IIIC temperature profile, which is smoothly extended into the solar corona. With the use of the FLASH code, we solve 3D ideal magnetohydrodynamic equations by specifying a twist in the azimuthal component of magnetic field in the solar chromosphere. The imposed perturbation generates torsional Alfvén waves as well as plasma swirls that reach the other foot-point of the arcade and partially reflect back from the transition region. The two vortex channels are evident in the generated twisted flux-tube with a fragmentation near its apex which results from the initial twist as well as from the morphology of the tube. The numerical results are compared to observational data of plasma motions in a solar prominence. The comparison shows that the numerical results and the data qualitatively agree even though the observed plasma motions occur over comparatively large spatio-temporal scales in the prominence.
Bio-Inspired Hierarchical Micro-/Nanostructures for Anti-Icing Solely Fabricated by Metal-Assisted Chemical Etching

We report a cost-effective and scalable methodology for producing a hierarchical micro-/nanostructured silicon surface solely by metal-assisted chemical etching. It involves two major processing steps of fabricating micropillars and nanowires separately. The process of producing micro-scale structures by masked metal-assisted chemical etching was optimized. Silicon nanowires were created on the micropillar’s surface via maskless metal-assisted chemical etching. The hierarchical micro-/nanostructured surface exhibits superhydrophobic properties with a high contact angle of ~156° and a low sliding angle of <2.5° for deionized water. Furthermore, due to the existence of microscale and nanoscale air trapped at the liquid/solid interface, it exhibits a long ice delay time of 2876 s at −5 °C, more than 5 times longer than that of smooth surfaces. Compared to conventional dry etching methods, the metal-assisted chemical etching approach excludes vacuum environments and high-temperature processes and can be applied for applications requiring hierarchical micro-/nanostructured surfaces or structures.
Proposed Site-Specific Response Spectra for Surabaya-Madura Bridge

This paper presents a site-specific seismic hazard study to determine the recommended seismic design criteria for Suramadu Bridge. The study is performed using probabilistic seismic hazard approach to determine maximum acceleration and response spectra at bedrock and followed by local site effect analysis to determine maximum acceleration and response spectra at ground surface. The probabilistic seismic hazard analysis (PSHA) is carried out using 3-dimension (3-D) seismic source models (fault source model). Two hazard levels are analysed to represent 150 and 3,300 years return period of ground motion around site location. The local site effect analysis is performed using 1-dimension (1-D) shear wave propagation theory to obtain peak ground acceleration and response spectra at ground surface. Finally, the site-specific surface response spectra with 5 percent damping are developed based on the mean plus one standard deviation concept from the result of local site effect analysis.
Reliability Analysis and Maintenance Optimization of Projection Spot Welding Machine in the Automotive Industry

This study analyzed the operational characteristics of the reliability and maintainability of the Projection Spot Welding (PSW) machine in the automotive industry. The components of the machine were grouped into three sub-systems: electrical, pneumatic, and hydraulic, and they were ordered into a hierarchical structural model for calculating reliability characteristics. The obtained maintenance lists and failure and repair data were studied and classified. Trend and serial correlation tests were carried out to select a proper modeling technique for each sub-system. Then, the reliability-maintainability model was constructed to estimate the failure behavior of the sub-systems, identify the best-fit distribution model, and calculate reliability characteristics. Finally, based on the calculated reliability characteristics, the preventive maintenance intervals for a different reliability level were calculated and requirements for increasing reliability were suggested. The analysis of the main structural sub-systems of the RSW machine showed that the hydraulic sub-system significantly affects the overall reliability level of the machine. However, the mean time to repair (MTTR) of the hydraulic sub-system is the lowest among other sub-systems. The analysis also found that the reliability of the electric and pneumatic sub-systems decreases by up to 50% after approximately 80h and 40h, respectively. The results of the study concluded that the reliability and maintainability analysis can improve the accuracy of preventive maintenance intervals and assess the reliability of each component of the RSW machine, which in turn can help reduce operating costs and extend the life of machine components.
Coping with Access Difficulties and Absenteeism through Data Visualization: A Case Study from a Rural Vocational School in Northern Greece

Absenteeism and early school leaving (ESL) constitute two main problems in education with a significant impact on adolescents’ life. Early leavers from education and training may face considerable difficulties in the labor market later as adults. Motivated by the necessity of minimizing this phenomenon, we developed a novel application that uses big data, generated from a student attendance management system in a vocational senior high school in Greece. This application first automatically conducts data preprocessing and data transformation and saves the processed data on a cloud data warehouse. Then, an online analytical processing (OLAP) analysis is performed, resulting in a real-time visualization that provides a variety of different graphs. In this study, we demonstrate the need for real-time visualization of the analyzed data. Such a type of presentation provides information on the spot regarding potential early leavers’ behavior, helping school administrators gain time for prompt and effective actions. Through the data processing and analysis, the application provides instructors with constant information, in addition to those acquired by the formal student attendance management systems. Our research provides indicative evidence in favor of the use of such applications, as by adequately reacting to the observed patterns in real-time, we observed a significant decrease in students’ unnecessary absences and a reduction of the ELS phenomenon in a three-year school period.
Segmentation and morphological analysis of wear track/particles images using machine learning

Abstract. Tribology is the science and engineering of interacting surfaces in relative motion. In this context, dry friction between two bodies generates wear particles known as third body particles. We propose to characterize these particles using image acquisition and analysis. The images of wear particles are observed by scanning electron microscopy and further segmented using machine learning at the pixel level. Thereafter, the most relevant geometrical and textural descriptors are selected by a sensitivity study and correlated to tribological characteristics. The proposed tools give first quantitative results to better understand, for industrial purposes, the mechanisms involved in the wear phenomenon, and the morphology of ejected third body particles.
MePRiSIA: risk prevention methodology for academic information systems

Information of academic systems can be stolen, modified or erased by attackers, causing losses to institutions. Applying a risk prevention methodology at educational institutions would help to avoid academic information misuse by users or attackers. MePRiSIA was designed as a risk prevention methodology to be simple and easy to understand while including the human factor in each step. This methodology has four steps to be considered in the process: setting the context, risk identification, risk analysis, and risk prevention. After being applied to the academic information system of Universidad de Pamplona (Colombia) called ACADEMUSOFT, MePRiSIA was evaluated by experts. In conclusion, after applying MePRiSIA to ACADEMUSOFT, the human factor was part of its most important assets and involved in the very high-level risks identified. According to the experts, implementation of MePRiSIA is hard when institution directors do not provide staff and financial resources for this purpose.
POTENCY OF ANTIOXIDANT, ANTICHOLESTEROL AND PLATELET ANTIAGGREGATION OF BLACK TEA (Camelia sinensis )

Epidemiological studies have shown an in-verse correlation betweendiets rich in po-lyphenols, flavonoids and reduced risk of cardiovasculardisease. These associationswere mainly ascribed to the antioxidant, antiaggregation and anticholesterol capa-city of polyphenols. This association has been explained that atherogenesis is ini-tiated by hypercholesterol, lipid peroxida-tion and hyper aggregation platelet. The research was carried out to evaluate anti-oxidant, anticholesterol and antiaggrega-tion activities of methanol extract and frac-tions of black tea ( Camelia sinensis ). To evaluate antioxidant activity of methanol extract and fractions were compared with (-)-Epigallocatechine 3-gallate (EGCG), an-ticholesterol activity were compared with simvastatin and antiaggregation activity were compared with aspirin. Antioxidant activity using the 1,1-diphenyl-2-picryl-hy-drazyl (DPPH) free radical scavenging acti-vity at two concentrations 100 µg/mL, 50 µg/mL and anticholesterol was cholesterol oxidase activity at two concentrations 0.005 µg/mL, 0.0025 µg/mL, the antiag-gregation activity used epinephrine (EPN) agonist at two concentrations 300 μM, 75 μM and one concentration 37.1 µg/mL of methanol extract and fractions of black tea. To know the difference of treatment, the data were analysed using Analysis of Variance (ANOVA) and be continued Dun-can’s new multiple range test. The results showed that DPPH scavenging activity were high (88.59-93.556%), the anticho-lesterol showed high activity (93.663-97.434%) and the antiaggregation sho-wed low up to high activity (79.967-4.31%). Using Duncan’s new multiple ra-nge test showed that anti-oxidant activity were not different among extract and fractions of black tea,the highest anti-cholesterol activity was black tea extract 0.005 µg/mL (97.434%) and the highest antiaggregation activity was ethyl acetate fraction on 75 μM EPN agonist (4.310 %), water fraction 37.1 µg/mL with 300 μM EPN agonist (21.833%).
Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Land use and land cover (LULC) change is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image in 2001, 2014, and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the support vector machine algorithm and the Markov chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318, and KlocationStrata = 0.9320, showing good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001–2021, the significant increase of agricultural land (14317 ha) and barren area (9063 ha), and the sharp decline of grassland (26215 ha), and forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that forest cover will continue to decrease from 29.46 % (50720.2667 ha) in 2021 to 25.67 % of area (44207.78694 ha) in 2040, as well as, unceasing expansion of barren area, agricultural land, and built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.
Bayesian group sequential designs for cluster‐randomized trials

Flexible approaches have been proposed for individually randomized trials to save time or reduce the sample size. However, flexible designs for cluster‐randomized trials in which groups of participants rather than individuals are randomized to treatment arms are less common. Motivated by a cluster‐randomized trial designed to assess the effectiveness of a machine‐learning based clinical decision support system for physicians treating patients with depression, two Bayesian group sequential designs for cluster‐randomized trials are proposed to allow for early stopping for efficacy at pre‐planned interim analyses. The difference between the two designs lies in the way that participants are sequentially recruited. Given a maximum number of clusters as well as the maximum cluster size allowed in the trial, one design sequentially recruits clusters with the given maximum cluster size, while the other recruits all clusters at the beginning of the trial but sequentially enrolls individual participants until the trial is stopped early for efficacy or the final analysis has been reached. The design operating characteristics are explored via simulations for a variety of scenarios and two outcome types for the two designs. We make recommendations for Bayesian group sequential designs of cluster‐randomized trials based on the simulation results.
Channel Estimation Methods by Using Prebeamforming Technique in Massive MIMO

Author(s): SEDIGHI, SADJAD | Advisor(s): Ayanoglu, Ender | Abstract: The number of connected wireless devices is anticipated to increase heavily in the next few years. Thus, there is a need for a new system which is able to handle billions of wireless devices. The category of massive multiple input multiple output (MIMO) systems is a great candidate for this purpose. Because of the large number of antennas in massive MIMO there is a need to reduce the dimension of the MIMO channel effectively to decrease the complexity. This could be achieved by using a particular prebeamforming technique that is introduced recently. An important aspect of wireless communication systems is the channel state information (CSI). In order to send and receive data through the channel, the transmitter and the receiver must know the CSI or at least have an estimation for it. In this thesis, different algorithms for estimating the channel vector coefficient and their performance are studied. Different approaches are used in order to find the best algorithm based on the performance of the estimating channel and the complexity of the algorithm. Also, algorithms are used to estimate the channel vector coefficient for different channel models.
Ethanol Leaf Extract of Synsepalum dulcificum Schumach. & Thonn. (Sapotaceae) Induces Distortions in Renal, Hepatic and Reproductive Indices in Sprague-Dawley Rats

Background and Purpose: Synsepalum dulcificum (Sapotaceae) is used in traditional medicine for treatment of diabetes, cough and cancer. This study was designed to investigate the in vivo toxicological endpoints of the ethanol leaf extract of S. dulcificum in male and female rats. Methods: After estimating the LD50 in mice, forty-eight rats were randomly assigned to four groups of twelve rats each in a sex ratio of 1:1 and were orally administered 10 mL/kg distilled water (control) and 118, 237, 475 mg/kg/day of the extract respectively for forty-five days. The animals were thereafter euthanized and blood samples collected for haematological, biochemical and antioxidant assays. Sperm parameters were evaluated and testis, liver, heart and ovary were harvested for histopathology. Constituents of the extract were determined by GC-MS. Result: The LD50 (oral) was estimated to be 4750 mg/kg in mice. There was a significant decrease in haemoglobin but lymphocytes and monocytes were significantly increased compared to control. Aspartate transaminase and alanine transaminase were significantly increased compared to control. Creatinine increased, high density lipoprotein (HDL) and albumin decreased significantly in treated rats. There was a significant increase in enzymatic and non-enzymatic antioxidants and a significant reduction in malondialdehyde (MDA). There were significant increases in reproductive markers of sperm count, motility and abnormality. Renovascular congestion and preserved cytoarchitecture in other organs were observed. Twenty-one compounds including n-hexadecanoic acid, oleic acid, 2,3-dihydro-3,5-dihydroxyl-6- methyl-4H-pyran-4-one (DDMP) and phytol were identified. Conclusion: Prolonged use of the ethanol leaf extract of S. dulcificum could result in hepatic and renal dysfunctions and may increase the risk for male infertility and cardiovascular diseases.
Uncertainty analysis of parameters in non‐point source pollution simulation: case study of the application of the Soil and Water Assessment Tool model to Yitong River watershed in northeast China

Uncertainty analysis of the model parameters in non‐point source pollution (NPSP) simulation is important because of its great effects on predictions and decision‐making. Understanding the main parameters that effect the uncertainty of NPSP is necessary to provide the basis for formulating control measures. In this study, two methods were applied to conduct parameter uncertainty analysis for Soil and Water Assessment Tool (SWAT). Sobol’ method was used to screen out the model parameters with great effects on the runoff, sediment, total nitrogen (TN) and total phosphorus (TP). The results obtained by sensitivity analysis were used subsequent model calibration and further uncertainty analysis. Monte Carlo (MC) method was employed to analyse the effects of parameter uncertainty on the model outputs. However, such problems are time‐consuming because the MC method required to invoke simulation model thousands of times. To address this challenge, a kriging surrogate model was developed to improve the overall calculation efficiency. The results obtained by sensitivity analysis showed that curve number value (CN2), soil evaporation compensation factor (ESCO), universal soil loss equation support practice factor (USLE_P) and initial organic nitrogen concentration in soil layer (SOL_ORGN) had significant effects on the SWAT outputs. The uncertainty analysis results showed that the uncertainty of runoff is the lowest, followed by TP and TN, and the uncertainty of sediment was the greatest. The kriging surrogate model has the ability to solve this time‐consuming problem rapidly with a high degree of accuracy, and thus it is very robust.