Thermal Stability, Durability, and Service Life Estimation of Woven Flax-Carbon Hybrid Polyamide Biocomposites.

Woven flax-carbon hybrid polyamide biocomposites offer a blend of carbon fibers' mechanical strength and flax's environmental advantages, potentially developing material applications. This study investigated their thermal behavior, degradation kinetics, and durability to water uptake and relative humidity exposure and compared them with pure flax and carbon composites with the same matrix. The hybrid composite exhibited intermediate water/moisture absorption levels between pure flax and carbon composites, with 7.2% water absorption and 3.5% moisture absorption. It also displayed comparable thermal degradation resistance to the carbon composite, effectively maintaining its weight up to 300 °C. Further analysis revealed that the hybrid composite exhibited a decomposition energy of 268 kJ/mol, slightly lower than the carbon composite's value of 288.5 kJ/mol, indicating similar thermal stability. Isothermal lifetime estimation, employing the activation energy (Ed) and degree of conversion facilitated by the Model Free Kinetics method, indicated a 41% higher service life of the hybrid laminate at room temperature compared to the carbon laminate. These insights are crucial for understanding the industrial applications of these materials without compromising durability.

Decomposition Kinetics and Lifetime Estimation of Thermoplastic Composite Materials Reinforced with rCFRP.

Because of the high demand for carbon fiber reinforced polymer (CFRP) materials across all industries, the reuse and/or recycling of these materials (rCFRP) is necessary in order to meet the principles of the circular economy, including recycling and reuse. The objective of this study is to estimate the lifespan of thermoplastic matrix composite materials reinforced with waste materials (CFRP), which undergo only a mechanical cutting process. This estimation is carried out through the thermal decomposition of polymers, including polymer matrix composite materials, which is a complex process due to the numerous reactions involved. Some authors calculate these kinetic parameters using thermogravimetric analysis (TGA) as it is a quick method, and it allows the identification of gases released during decomposition, provided that the equipment is prepared for it. This study includes a comparison between polyamides 11 and 12, as well as between polyamide composite materials with carbon fiber (CF) and polyamides reinforced with CF/epoxy composite material. The latter is treated with plasma to improve adhesion with polyamides. The behavior of weight as a function of temperature was studied at speeds of 3, 6, 10, 13, 17, and 20 °C/min, finding stability of the polyamides up to a temperature of 400 °C, which was consistent with the analysis by mass spectroscopy, where gas evolution is evident after 400 °C. The estimation of the lifespan was carried out using two different methods including the Toop equation and the free kinetics model (MFK). The energy of the decomposition process was determined using the MFK model, which establishes the energy as a function of the degree of conversion. It is estimated that at 5% decomposition, mechanical properties are lost.

Wear Behavior of Epoxy Resin Reinforced with Ceramic Nano- and Microparticles.

Cavitation erosion poses a significant challenge in fluid systems like hydraulic turbines and ship propellers due to pulsed pressure from collapsing vapor bubbles. To combat this, various materials and surface engineering methods are employed. In this study, nano and micro scale particles of silicon carbide (SiC) or boron carbide (B4C) were incorporated as reinforcement at 6% and 12% ratios, owing to their exceptional resistance to abrasive wear and high hardness. Microparticles were incorporated to assess the damage incurred during the tests in comparison to nanoparticles. Wear tests were conducted on both bulk samples and coated aluminum sheets with a 1mm of composite. Additionally, cavitation tests were performed on coated aluminum tips until stability of mass loss was achieved. The results indicated a distinct wear behavior between the coatings and the bulk samples. Overall, wear tended to be higher for the coated samples with nanocomposites than bulk, except for the nano-composite material containing 12% SiC and pure resin. With the coatings, higher percentages of nanometric particles correlated with increased wear. The coefficient of friction remained within the range of 0.4 to 0.5 for the coatings. Regarding the accumulated erosion in the cavitation tests for 100 min, it was observed that for all nanocomposite materials, it was lower than in pure resin. Particularly, the composite with 6% B4C was slightly lower than the rest. In addition, the erosion rate was also lower for the composites.

Clinical Phenotype and Disease Course of Inflammatory Bowel Disease in Iran: Results of the Iranian Registry of Crohn's and Colitis (IRCC).

BACKGROUND: Data on the epidemiology of inflammatory bowel disease (IBD) in the Middle East are scarce. We aimed to describe the clinical phenotype, disease course, and medication usage of IBD cases from Iran in the Middle East. METHODS: We conducted a cross-sectional study of registered IBD patients in the Iranian Registry of Crohn's and Colitis (IRCC) from 2017 until 2022. We collected information on demographic characteristics, past medical history, family history, disease extent and location, extra-intestinal manifestations, IBD medications, and activity using the IBD-control-8 questionnaire and the Manitoba IBD index, admissions history, history of colon cancer, and IBD-related surgeries. RESULTS: In total, 9746 patients with ulcerative colitis (UC) (n=7793), and Crohn's disease (CD) (n=1953) were reported. The UC to CD ratio was 3.99. The median age at diagnosis was 29.2 (IQR: 22.6,37.6) and 27.6 (IQR: 20.6,37.6) for patients with UC and CD, respectively. The male-to-female ratio was 1.28 in CD patients. A positive family history was observed in 17.9% of UC patients. The majority of UC patients had pancolitis (47%). Ileocolonic involvement was the most common type of involvement in CD patients (43.7%), and the prevalence of stricturing behavior was 4.6%. A prevalence of 0.3% was observed for colorectal cancer among patients with UC. Moreover,15.2% of UC patients and 38.4% of CD patients had been treated with anti-tumor necrosis factor (anti-TNF). CONCLUSION: In this national registry-based study, there are significant differences in some clinical phenotypes such as the prevalence of extra-intestinal manifestations and treatment strategies such as biological use in different geographical locations.

Effect of mobile food environments on fast food visits.

Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings. In this work, we leverage population-scale mobility data in the U.S. to examine 62M people's visits to food outlets and evaluate how food choice is influenced by the food environments people are exposed to as they move through their daily routines. We find that a 10% increase in exposure to fast food outlets in mobile environments increases individuals' odds of visitation by 20%. Using our results, we simulate multiple policy strategies for intervening on food environments to reduce fast-food outlet visits. This analysis suggests that optimal interventions are informed by spatial, temporal, and behavioral features and could have 2x to 4x larger effect than traditional interventions focused on home food environments.

A Historical Overview of the Neurological Disorders Associated with Gastrointestinal Ailments from the Viewpoint of Avicenna

Reviewing historical medical manuscripts shows that neurological disorders have been previously described in the Islamic Golden Age. Ibn Sina, also known as Avicenna (980-1037 AD), was one of the most renowned scientists during this period. He widely practiced medicine, especially those disorders related to neurology, neurosurgery, and psychiatry in conventional medicine. In his extant book al-Qanun fi al-Tibb (the Canon of Medicine), he claimed that some types of brain diseases can be related to the "maraqq" and called them maraqq-related disorders. From Avicenna's viewpoint, "maraqq" is considered a membranous structure in the abdomen. Ibn Sina has illustrated the association between the "maraqq" and the brain through some direct and indirect pathways. As a result, some disturbances in the "maraqq" can influence the brain, which can contribute to the pathogenesis of a number of brain diseases. Accordingly, those patients who regularly had gastrointestinal discomforts experienced a higher prevalence of headache, melancholia, and epilepsy. This study aimed to explore the relationship between abdominal and brain diseases from Avicenna's viewpoint. Furthermore, the definition, clinical manifestation, and therapeutic strategies of maraqq-related disorders were described.

Hybridization Effect on Interlaminar Bond Strength, Flexural Properties, and Hardness of Carbon-Flax Fiber Thermoplastic Bio-Composites.

Hybridizing carbon-fiber-reinforced polymers with natural fibers could be a solution to prevent delamination and improve the out-of-plane properties of laminated composites. Delamination is one of the initial damage modes in composite laminates, attributed to relatively poor interlaminar mechanical properties, e.g., low interlaminar strength and fracture toughness. This study examined the interlaminar bond strength, flexural properties, and hardness of carbon/flax/polyamide hybrid bio-composites using peel adhesion, three-point bending, and macro-hardness tests, respectively. In this regard, interlayer hybrid laminates were produced with a sandwich fiber hybrid mode, using woven carbon fiber plies (C) as the outer layers and woven flax fiber plies (F) as the inner ones (CFFC) in combination with a bio-based thermoplastic polyamide 11 matrix. In addition, non-hybrid carbon and flax fiber composites with the same matrix were produced as reference laminates to investigate the hybridization effects. The results revealed the advantages of hybridization in terms of flexural properties, including a 212% higher modulus and a 265% higher strength compared to pure flax composites and a 34% higher failure strain compared to pure carbon composites. Additionally, the hybrid composites exhibited a positive hybridization effect in terms of peeling strength, demonstrating a 27% improvement compared to the pure carbon composites. These results provide valuable insights into the mechanical performance of woven carbon-flax hybrid bio-composites, suggesting potential applications in the automotive and construction industries.

Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.

Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations.

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.

Lavender (Lavandula angustifolia) syrup as an adjunct to standard care in patients with mild to moderate COVID-19: An open-label, randomized, controlled clinical trial.

Objective: ongoing COVID-19 pandemic has been associated with clinical signs characterized by fever, fatigue and cough. Our study aimed to assess the efficacy of a Persian medicine formulation, lavender syrup, as an add-on to standard care in patients with mild to moderate COVID-19. Materials and Methods: In this clinical trial which was conducted in Gorgan (Iran), 84 male and female COVID-19 outpatients were randomly allocated to either lavender syrup receiving 9 ml/twice/day for 21 days with standard conventional care or control groups. The primary objectives were to assess the improvement of clinical symptoms, while the secondary objectives were treatment satisfaction and anxiety levels which were evaluated once a week for 3 weeks. Results: Out of 84 participants, 81 were analyzed (41 in the add-on group). The comparison between groups for cough severity and anosmia showed a higher reduction in the lavender group. The effect size was 0.6 for cough relief. Other symptoms and the Hamilton total score decreased in both groups with no statistically significant differences between the groups. The lavender group showed greater patients' satisfaction score. Conclusion: Adjunctive therapy with lavender syrup could reduce cough and improve the quality of life in patients with COVID-19 patients.

Novel Sustainable Composites Incorporating a Biobased Thermoplastic Matrix and Recycled Aerospace Prepreg Waste: Development and Characterization.

Carbon fiber-reinforced polymer (CFRP) composite materials are widely used in engineering applications, but their production generates a significant amount of waste. This paper aims to explore the potential of incorporating mechanically recycled aerospace prepreg waste in thermoplastic composite materials to reduce the environmental impact of composite material production and promote the use of recycled materials. The composite material developed in this study incorporates a bio-based thermoplastic polymer, polyamide 11 (PA11), as the matrix material and recycled aerospace prepreg waste quasi-one-dimensionally arranged as reinforcement. Mechanical, thermal, and thermomechanical characterizations were performed through tensile, flexural, and impact tests, as well as differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Compared to previous studies that used a different recycled CFRP in the shape of rods, the results show that the recycled prepregs are a suitable reinforcement, enhancing the reinforcement-matrix adhesion and leading to higher mechanical properties. The tensile results were evaluated by SEM, and the impact tests were evaluated by CT scans. The results demonstrate the potential of incorporating recycled aerospace prepreg waste in thermoplastic composite materials to produce high-performance and sustainable components in the aerospace and automotive industries.

Effectiveness of "Centaurea behen" root on quality of life in patients with systolic heart failure: A randomized clinical trial.

Introduction: The effect of Centaurea behen (Cb) on patients with systolic heart failure is not known academically. This study was conducted to evaluate the effect of Cb on improving the quality of life (QoL) and echocardiographic and biochemical blood parameters in patients with systolic heart failure. Methods: This study was a parallel double-blind, placebo-controlled randomized trial of 60 patients with systolic heart failure, was conducted from May 2018 up to August 2019. Intervention group received 150 mg twice daily Cb capsules for two months + Guideline directed medical therapy (GDMT), and control group received GDMT + placebo capsules for two months. The main aim of the present study were to assess the QoL based on the 6-minute walk test (6MWT) and the Minnesota living with heart failure questionnaire (MLHFQ). Independent T-test, paired T-test, and ANOVA were used for the analysis. Results: At the beginning of the present study there were no significant differences between study groups in terms of QoL and clinical results. After treatment, the average values of QoL based on MLHFQ and 6MWT instruments were significantly improved 15.5 and 36.18, respectively (P<0.05). Conclusion: Based on the MLHFQ, and 6MWT tests, the consumption of Centaurea behen root extract was associated with significant improvement in the quality of life of patients with systolic heart failure.

Predicting merchant future performance using privacy-safe network-based features.

Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants' future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants' future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants' performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants' sensitive information is kept confidential.

The effect of a standardized capsule of Aloe vera gel on the quality of life in patients with systolic heart failure: A randomized double-blind placebo-controlled clinical trial.

This trial was designed to evaluate the effect of a standardized capsule of Aloe vera gel (AVG) on the quality of life (QOL) in patients with systolic heart failure (HF). Forty-two patients were randomly divided into two groups to receive either AVG 150 mg or harmonized placebo capsules twice a day for 8 weeks. The patients were evaluated before and after the intervention using the Minnesota Living with Heart Failure Questionnaire (MLHFQ), New York Heart Association (NYHA) functional class, six-minute walk test (6MWT), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI) and STOP-BANG questionnaires. Post-intervention, AVG group indicated a significant reduction in the total score of MLHFQ (p < 0.001). The changes in MLHFQ and NYHA class were statistically significant after taking medication (p < 0.001 and p = 0.004, respectively). The change of 6MWT in the AVG group was more advanced; however, it was not statistically significant (p = 0.353). Moreover, in the AVG group, the severity of insomnia and obstructive sleep apnea decreased (p < 0.001, p = 0.01 respectively) and the sleep quality improved as well (p < 0.001). There were significantly fewer adverse events reported in the AVG group (p = 0.047). Therefore, AVG combined with standard medical therapy could provide more clinical benefits for patients with systolic HF.

Brain Network Analysis: A Review on Multivariate Analytical Methods.

Despite the explosive growth of neuroimaging studies aimed at analyzing the brain as a complex system, critical methodological gaps remain to be addressed. Most tools currently used for analyzing network data of the brain are univariate in nature and are based on assumptions borne out of previous techniques not directly related to the big and complex data of the brain. Although graph-based methods have shown great promise, the development of principled multivariate models to address inherent limitations of graph-based methods, such as their dependence on network size and degree distributions, and to allow assessing the effects of multiple phenotypes on the brain and simulating brain networks has largely lagged behind. Although some studies have been made in developing multivariate frameworks to fill this gap, in the absence of a "gold-standard" method or guidelines, choosing the most appropriate method for each study can be another critical challenge for investigators in this multidisciplinary field. Here, we briefly introduce important multivariate methods for brain network analyses in two main categories: data-driven and model-based methods. We discuss whether/how such methods are suited for examining connectivity (edge-level), topology (system-level), or both. This review will aid in choosing an appropriate multivariate method with respect to variables such as network type, number of subjects and brain regions included, and the interest in connectivity, topology, or both. This review is aimed to be accessible to investigators from different backgrounds, with a focus on applications in brain network studies, though the methods may be applicable in other areas too. Impact statement As the U.S. National Institute of Health notes, the rich biomedical data can greatly improve our knowledge of human health if new analytical tools are developed, and their applications are broadly disseminated. A major challenge in analyzing the brain as a complex system is about developing parsimonious multivariate methods, and particularly choosing the most appropriate one among the existing methods with respect to the study variables in this multidisciplinary field. This study provides a review on the most important multivariate methods to aid in helping the most appropriate ones with respect to the desired variables for each study.

Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempt to shed light on the fact that a response may be different depending on the health system capacity and each individuals' social status. For that, we analyze the correlation between different societal (i.e., education, income levels, population density, etc.) and healthcare capacity-related variables (i.e., hospital occupancy rates, percentage of essential workers, etc.) in relation to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a significant relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

Novel Thermoplastic Composites Strengthened with Carbon Fiber-Reinforced Epoxy Composite Waste Rods: Development and Characterization.

The increasing use of carbon fiber and epoxy resin composite materials yields an increase in the amount of waste. Therefore, we present a solution consisting of composites manufactured by hot pressing, employing polyamides (either PA11 or PA12) and a mechanically recycled carbon fiber-reinforced polymer (CFRP) as reinforcement. The main objectives are to study the manufacturing of those composites, to evaluate the fiber distribution, and to perform a mechanical, dynamical, and thermomechanical characterizations. The X-ray micro-computed tomography (µCT) shows that the fibers are well-distributed, maintaining a homogeneous fiber volume fraction across the material. The variability in the results is typical of discontinuous fiber composites in which the fibers, although oriented, are not as homogeneously distributed as in a continuous fiber composite. The mechanical and dynamic properties barely differ between the two sets of composites. A dynamic-mechanical analysis revealed that the glass transition temperature (Tg) increases slightly for both composites, compared to the polymers. These results illustrate the viability of the recycling and reuse route for preventing the deterioration of carbon fibers and promoting the subsequent reduction in the environmental impact by employing a thermoplastic matrix.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

A mixed-modeling framework for whole-brain dynamic network analysis.

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.