Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Hybrid Polymeric Material Based on Polycaprolactone on the Environment

Polymers are of great interest in areas such as agriculture, medicine and pharmacy, the food and cosmetic industries, and the chemical and construction industries. However, many polymers are nonbiodegradable and are not environmentally friendly. They are highly resistant to degradation and therefore can lead to waste disposal problems. In recent years, the interest in the microbial degradation of polymeric materials has grown due to the desire for less waste pollution in the environment. In this study, the biodegradable polymer that was obtained by the ring-opening polymerization of ε-caprolactone (CL) using an aminopropyl-polydimethylsiloxane (APDMS) oligomer and the effects of the polymer towards the growth and development of tomato plants (Lypercosium esculentum) were investigated. The obtained product was characterized using FTIR spectroscopy, NMR spectroscopy, and energy dispersion spectroscopy (EDX) analysis, and the effects of this compound on the evolution of tomato plants (Lypercosium esculentum) were studied. We also studied the biological stability of the product by identifying some of the microorganisms that developed on the surface, given its susceptibility to biodegradation.
Performance evaluation of GRNN and ANFIS controlled DVR using machine learning in distribution network

A machine learning based generalized neural network estimator (GRNNE) and Takagi‐Sugeno (T‐S) fuzzy control system is implemented to accelerate the functional performance indices of dynamic voltage restorer (DVR). The GRNNE predictive model is recommended for the fast estimation of the reference load voltage under the distorted power supply. The fruit fly optimization learning strategy is employed to optimize the weights and smoothing parameters for the extraction of the reference voltage signals as well as unit vectors, resulting in the required sinusoidal load component. The dynamics in the DC‐link voltage are optimized by the metaheuristic‐based gray wolf optimization algorithm. The coefficients of the adaptive controller are updated automatically to achieve the best fitted adaptive neuro‐fuzzy inference system predictive model with the least tracking voltage deviation error even in the presence of voltage disturbances. In comparison to classical techniques, the recommended neural‐based approach offers a faster convergence speed with fewer parameters to tune, shorter training time, and lower risks of local entrapment. The performance metrics such as mean square error, root mean square error, mean absolute error, mean absolute percent error, coefficients of correlational and determination (R and R2) are used to evaluate the efficacy of the proposed controller and hence enhance the DVR performance. Finally, the simulation results of the hybrid approach confirm that the NN‐based DVR estimator has proven its ability to alleviate voltage sensible issues at critical loads and outperform others in reducing power quality issues.
Reliability of dynamic perfusion digital radiography as an alternative to pulmonary perfusion scintigraphy in predicting postoperative lung function and complications

Background Accurate perioperative risk assessment can enhance the perioperative management of patients undergoing radical surgery for lung cancer. In this study, we compared the accuracy of predicting perioperative complications by lung function values, estimated by blood flow ratios (BFRs), to determine whether dynamic perfusion digital radiography (DPDR) could substitute for pulmonary perfusion scintigraphy (PPS). Methods Patients scheduled for radical surgery for lung cancer who underwent simultaneous dynamic chest radiography (DCR) and lung perfusion scintigraphy were assessed. We confirmed the agreement between two methods in the assessment of the BFR and its predicted postoperative (ppo) value. Besides, the best spirometry thresholds for the risk of perioperative respiratory or cardiovascular complications were calculated from a receiver operating characteristic (ROC) analysis. The imaging methods were compared for sensitivity and specificity. Results Among the 44 cases enrolled, DPDR and PPS showed high correlations in BFR (r=0.868, P<0.01) and its postoperative value (r=0.975, P<0.01) and between the predicted and measured spirometry values. In both imaging modalities, the estimated postoperative diffusing capacity test for carbon monoxide (DLco) had the best prediction [area under the curve (AUC) >0.7] for respiratory complications within 1 month (with different cut-offs for same target cases). For predicting, respiratory complications within 1–3 months after surgery, these values were similar between two modalities. Furthermore, the ppoDLco values from both imaging methods were excellent indicators of the induction of postoperative long term oxygen therapy, with the AUC greater than 0.8. Conclusions This study showed that simple and less invasive DPDR can be a good alternative to PPS for predicting postoperative pulmonary function values and the risk of postoperative respiratory complications. This new imaging modality will offer new insights and possible functional analyses of pulmonary circulation.
Using Procedure Cloning for Performance Optimization of Compiled Dynamic Languages

: Compilation of dynamic programming languages into strongly typed platforms such as . NET and JVM has proven useful in enhancing the security and interoperability of the resulting programs, as well as in enabling their source-less distribution. In order to produce the best intermediate code possible, dynamic language compilers can employ advanced interprocedural type analysis and perform various optimizing transformations. However, these efforts are often limited due to the ambiguity of types in these languages and the need to preserve soundness. In this paper, we improve the performance of global functions by adapting the technique of procedure cloning, focusing on different parameter types those specialized clones can be called with. We devise several heuristics to identify the most promising specializations and call them from their respective call sites. Our technique was implemented into PeachPie, a compiler of PHP to . NET, and evaluated on standard benchmarks distributed with PHP. Benchmarks containing deep recursion show a speedup factor up to 3.65, while benchmarks of computation-intensive loops reveal a speedup factor up to 2.64.
ZmIAA5 regulates maize root growth and development by interacting with ZmARF5 under the specific binding of ZmTCP15/16/17

Background The auxin indole-3-acetic acid (IAA) is a type of endogenous plant hormone with a low concentration in plants, but it plays an important role in their growth and development. The AUX/IAA gene family was found to be an early sensitive auxin gene with a complicated way of regulating growth and development in plants. The regulation of root growth and development by AUX/IAA family genes has been reported in Arabidopsis, rice and maize. Results In this study, subcellular localization indicated that ZmIAA1-ZmIAA6 primarily played a role in the nucleus. A thermogram analysis showed that AUX/IAA genes were highly expressed in the roots, which was also confirmed by the maize tissue expression patterns. In maize overexpressing ZmIAA5, the length of the main root, the number of lateral roots, and the stalk height at the seedling stage were significantly increased compared with those of the wild type, while the EMS mutant zmiaa5 was significantly reduced. The total number of roots and the dry weight of maize overexpressing ZmIAA5 at the mature stage were also significantly increased compared with those of the wild type, while those of the mutant zmiaa5 was significantly reduced. Yeast one-hybrid experiments showed that ZmTCP15/16/17 could specifically bind to the ZmIAA5 promoter region. Bimolecular fluorescence complementation and yeast two-hybridization indicated an interaction between ZmIAA5 and ZmARF5. Conclusions Taken together, the results of this study indicate that ZmIAA5 regulates maize root growth and development by interacting with ZmARF5 under the specific binding of ZmTCP15/16/17.
Comprehensive Analysis of Voltage Step-Up Techniques for Isolated SEPIC

High step-up converters are often used in photovoltaic applications due to low voltage of photovoltaic modules. In this paper, a study of several voltage step-up cells added on primary and secondary sides of an isolated dc-dc SEPIC (single ended primary inductor converter) is made, in order to increase its static gain and synthesize a family of converters. To perform this study and select the most appropriate resulting converter, theoretical and comparative analyzes are accomplished. They include a brief description of each converter, tables and curves with relevant information, such as voltage gain, voltage stresses and component count, and the identification of the use of coupled inductor or transformer based on the analysis of dc magnetizing current. Moreover, theoretical analysis and experimental results on a 50 kHz and 200 W prototype of selected isolated SEPIC with voltage doubler cell are presented to validate the proposed topology and concept.
Heavy-Duty Vehicle Braking Stability Control and HIL Verification for Improving Traffic Safety

The braking failure of heavy vehicles under long downhill or curved conditions may cause traffic crash and reduce road traffic efficiency. Therefore, to improve the traffic safety and braking stability of vehicles under special road conditions, a braking dynamic model and control system based on the interval uncertainty analysis are proposed, and the safety of the active control model is verified by experiments (HIL). Firstly, the interval uncertain dynamic model is established based on the Monte Carlo method, and the braking failure simulation analysis of the right front wheel of heavy vehicles is carried out in the set of three uncertain intervals. Secondly, the fuzzy PID and sliding mode controller based on yaw and centroid error are designed to find the optimal control strategy from the two kinds of control strategies for HIL experiments. Finally, the actual control effect and feasibility of these control algorithms for heavy vehicle braking under special road conditions are verified by HIL experiments. The experimental results show that under the action of the fuzzy PID control strategy, the running stability of the vehicle is significantly improved compared with no control, which effectively reduces the risk of vehicle braking failure and improves the active safety and stability of the vehicle.
Biosorption Potential of Microbial and Residual Biomass of Saccharomyces pastorianus Immobilized in Calcium Alginate Matrix for Pharmaceuticals Removal from Aqueous Solutions

Two types of biosorbents, based on Saccharomyces pastorianus immobilized in calcium alginate, were studied for the removal of pharmaceuticals from aqueous solutions. Synthetized biocomposite materials were characterized chemically and morphologically, both before and after simulated biosorption. Ethacridine lactate (EL) was chosen as a target molecule. The process performance was interpreted as a function of initial solution pH, biosorbent dose, and initial pharmaceutical concentration. The results exhibited that the removal efficiencies were superior to 90% for both biosorbents, at the initial pH value of 4.0 and biosorbent dose of 2 g/L for all EL initial concentrations tested. Freundlich, Temkin, Hill, Redlich-Peterson, Sips, and Toth isotherms were used to describe the experimental results. The kinetic data were analyzed using kinetic models, such as pseudo-first order, pseudo-second order, Elovich, and Avrami, to determine the kinetic parameters and describe the transport mechanisms of EL from aqueous solution onto biosorbents. Among the tested equations, the best fit is ensured by the pseudo-second-order kinetics model for both biosorbents, with the correlation coefficient having values higher than 0.996. The many potential advantages and good biosorptive capacity of Saccharomyces pastorianus biomass immobilized in calcium alginate recommend these types of biocomposite materials for the removal of pharmaceuticals from aqueous solutions.
Investigation of the Renal Protective Effect of Combined Dietary Polyphenols in Streptozotocin-Induced Diabetic Aged Rats

Natural polyphenols are widely reported to have a large range of pharmacological properties, especially antioxidant activities and free radical scavenging capacities. In this study, we investigate the effects of naringin, chlorogenic acid, and quercetin mixtures (NCQ) on renal fibrosis in streptozotocin (STZ)-induced diabetic aged rats and its underlying mechanisms for ten consecutive weeks. The oxidative defense system in the kidneys of treated rats was found to be improved. Several biomarkers were investigated including the blood urea nitrogen, creatinine, and uric acid. Moreover, antioxidant parameters were evaluated and we found that superoxide dismutase, catalase, glutathione peroxidase, Na+-K+-ATPase activities, the nitric oxide production, the protein carbonyl, the advanced oxidation protein products, lipid peroxidation, and reduced glutathione levels were all significantly balanced and close to control values. In addition, NCQ restored renal injuries and fibrosis as assessed by histological method and molecular biology investigation of the matrix metalloproteinase, the transforming growth factor-beta TGF-β, the tumor necrosis factor TNFα, and p53 expression. Our study proposes the NCQ combination as potential plant-derived bioactive compounds to prevent diabetic nephropathy.
Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness

: This paper deals with the group fairness issue that arises when classifying data, which contains socially in-duced biases for age and ethnicity. To tackle the unfair focus on certain age and ethnicity groups, we propose an adaptive boosting method that balances the fair treatment of all groups. The proposed approach builds upon the AdaBoost method but supplements it with the factor of fairness between the sensitive groups. The results show that the proposed method focuses more on the age and ethnicity groups, given less focus with traditional classiﬁcation techniques. Thus the resulting classiﬁcation model is more balanced, treating all of the sensitive groups more equally without sacriﬁcing the overall quality of the classiﬁcation.
Construction of Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Psychological education is beneficial in maintaining the psychological health of college students, resolving psychological issues, and creating a harmonious campus and society. Mental health education for college students is a development-oriented basic education activity that contributes significantly to educational quality. To improve the accuracy of college students' mental health assessments, a model for mental health education for college students based on fine-grained parallel computing programming is proposed. This study uses a deep learning algorithm to subdivide the classification of an emotion dictionary, which can be classified by adding negative word lists, polarity conversion dictionaries, and online dictionaries, among other things, based on the theory of ecological instantaneous evaluation. It can be used for both multiclass and detailed emotion analysis. The model is more accurate in assessing the mental health of college students, according to the results of the study. The current emotional state of users can be identified, as well as signs of psychological risk, using emotional analysis of Weibo data, which will become a valuable resource for users seeking clinical psychological consultation in the future.
Effect of Bariatric Surgery on Flow-Mediated Vasodilation as a Measure of Endothelial Function: A Systematic Review and Meta-Analysis

Objectives. Flow mediated vasodilation (FMD) is a marker of endothelial function and its decline is related to increased cardiovascular risk. This systematic review and meta-analysis evaluated the impact of bariatric surgery on FMD. Materials and methods. A systematic literature search in PubMed, Scopus, Embase, and Web of Science was performed to 1 May 2021. Meta-analysis was performed using Comprehensive Meta-Analysis (CMA) V2 software. All types of bariatric surgery were considered, with the inclusion that FMD had to have been tested before and after the surgical procedure. Meta-analysis was carried out using a random-effects model and the generic inverse variance approach. The leave-one-out approach was used for sensitivity analysis. To assess metabolic parameter confounders, a weighted random-effects meta-regression was used. Results. A meta-analysis and a systematic review of 23 studies (n = 891 individuals) demonstrated improvement in FMD following bariatric surgery (weighted mean difference (WMD): 5.867, 95% CI: 4.481, 7.252, p < 0.001; I2: 96.70). Iteratively removing each item in the meta-analysis did not result in a significant alteration in the pooled estimate of effect size. There was an improvement in FMD for up to 6 months following bariatric surgery in a meta-analysis from 7 trials that included 356 subjects (WMD: 5.248, 95% CI: 2.361, 8.135, p < 0.001; I2: 98.18). The meta-analysis from 9 trials (n = 414 subjects) showed an improvement in FMD 6 to 12 months after bariatric surgery (WMD: 5.451, 95% CI: 3.316, 7.587, p < 0.001; I2: 94.18). The meta-analysis from 10 trials (n = 414 subjects) demonstrated an improvement in FMD 12 months after bariatric surgery (WMD: 2.401, 95% CI: 0.944, 3.859, p = 0.001; I2: 88.35). Random-effects meta-regression did not show any association between the alteration in FMD and percent body mass index (BMI) change (slope: 0.0258; 95% CI: −0.323, 0.375; p = 0.884), or changes in blood pressure; however, there was an association between the changes in FMD and the duration of follow-up (slope: −0.106; 95% CI: −0.205, −0.008; p = 0.033) with greater changes in FMD after 12 months. Conclusions. Bariatric surgery significantly improved FMD that increased with time, and the resultant improvement in endothelial function was independent of weight loss or a reduction in blood pressure.
Beyond Battery State of Charge Estimation: Observer for Electrode-Level State and Cyclable Lithium With Electrolyte Dynamics

This article presents a provably convergent battery estimation scheme based on a single particle model with electrolyte (SPMe) dynamics, by proposing a systematic methodology to estimate critical information such as electrode-level states, electrolyte dynamics, and cyclable lithium. Electrode-level state estimation suffers from weak observability originating from two standalone electrode dynamics, which is then aggravated by the addition of electrolyte dynamics. This lack of observability can be alleviated by exploiting lithium inventory conservation enabled by the Kalman decomposition, allowing one to separate out the unobservable subspace. Assuming the knowledge of cyclable lithium, a nonlinear state observer with provable convergence can be constructed for the SPMe model, using voltage and current measurements. To relax this strong assumption, a sensitivity-based parameter estimation scheme is also deployed to track cyclable lithium—a crucial physical variable for capacity fade. Ultimately, the estimation framework can perform finer monitoring and diagnosis of battery charge and health down to the level of individual electrode and the electrolyte. Experimental validation demonstrates < 1% estimation error for cyclable lithium inventory. Solid phase lithium concentration estimates, especially in the negative electrode, can be sensitive to disturbances in cyclable lithium.
Effect of Moringa oleifera Leaf Extract on Excision Wound Infections in Rats: Antioxidant, Antimicrobial, and Gene Expression Analysis

The present study investigated the wound healing activity of Moringa oleifera leaf extract on an infected excision wound model in rats. Infection was induced using methicillin-resistant Staphylococcus aureus (MRSA) or Pseudomonas aeruginosa. An investigation was also done to study the effect of Moringa extract on the vascular endothelial growth factor (VEGF) and transforming growth factor-beta 1 (TGF-β1) gene expression in vitro using human keratinocytes (HaCaT). The methanol extract of M. oleifera leaves was analyzed for the presence of phytochemicals by LCMS. The antimicrobial activity of the extract was also determined. Wound contraction, days for epithelization, antioxidant enzyme activities, epidermal height, angiogenesis, and collagen deposition were studied. M. oleifera showed an antimicrobial effect and significantly improved wound contraction, reduced epithelization period, increased antioxidant enzymes activity, and reduced capillary density. Effect of the extract was less in wounds infected with P. aeruginosa when compared to MRSA. The VEGF and TGF-β1 gene expression was increased by M. oleifera.
Does Influencer–Follower Relationship Matter? Exploring How Relationship Norms and Influencer–Product Congruence Affect Advertising Effectiveness across Product Categories

Abstract This study examines how influencer–product congruence and relationship norms (i.e., exchange versus communal) between an influencer and followers affect consumer responses to influencer-sponsored ads. In Study 1, in a low-involvement product condition (sunscreen), we found that relationship norms moderate the effect of influencer–product congruence on source credibility and brand attitudes. In particular, if the influencer–follower relationship is perceived as exchange oriented, participants in an influencer–product-congruent condition perceive higher credibility of the influencer and thus indicate more favorable brand attitudes toward the influencer-sponsored ad than those in a noncongruence condition. However, if the relationship is communal oriented, influencer–product congruence does not significantly affect participants’ responses. Source credibility mediates the interaction effect between congruence and relationship norms on brand attitudes. In Study 2, we further examined how product involvement interacts with influencer–product congruence, as well as relationship norms. We found a significant three-way interaction effect among the three constructs, such that the significant moderating effect of relationship norms on influencer–product congruence is evident in a low-product-involvement condition but not in a high-product-involvement condition.
MODELING DIPOLE ARRAYS THAT PRODUCE SYNTHESIZED PATTERNS USING NEC

|Pattern synthesis usually involves determining the strengths of the currents in a given array so that it radiates a speci(cid:12)ed pattern. Demonstrating that this pattern can be produced by an actual array of physical elements is the next step in the synthesis problem. This entails determining the excitations needed for a physical array when mutual interactions are accounted for. These excitations can be either source voltages or the synthesized currents themselves, with both options available in NEC 4 (the Numerical Electromagnetics Code). While this is done here using NEC, the same approach could use any wire-antenna computer code. When the current option is chosen, the code (cid:12)rst solves for the voltages that induce the required currents. This is a simple procedure that automatically includes the eﬀect of mutual coupling among the array elements. The purpose of this article is to examine how well a numerical model of a given array will match the speci(cid:12)ed pattern. Several examples of this approach are included here to demonstrate the process.