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Development of a novel konjac glucomannan/soy protein isolate/fatty acid composite film: Insights into the structure, properties and interaction mechanism
The preparation, structural characterization and properties analysis of novel konjac glucomannan (KGM)/soy protein isolate (SPI)/fatty acid (KS-FA) films were carried out in this paper. KS-FA films exhibited higher crystallinities than KS film. Moreover, incorporating fatty acids significantly affected the surface roughness and morphologies of KS film. KS-FA films had significantly (p < 0.05) higher water contact angle values than KS film. The rheological properties of the film-forming solution, thermal stabilities, mechanical, water vapor barrier and water resistance properties of KS film were also improved after incorporating appropriate concentrations of fatty acids. There were a 21.3-percent increase of glass transition temperature value, a 17-percent increase of water contact angle value, a 14.5-percent decrease of water solubility value, a 43-percent decrease of water vapor permeability value, a 1.08-fold increase of tensile strength value, and a 1.52-fold increase of elongation at break value in KS film with 0.7% lauric acid when compared with those of KS film. Furthermore, molecular docking and fourier transform infrared spectroscopy analyses suggested that KS-FA films were formed through hydrogen bonds and hydrophobic interactions. These findings highlight fatty acid-modified KGM/SPI films as promising biodegradable food packaging materials.
Synthesis and assessment of ionic liquid derived from benzalkonium chloride as corrosion inhibitor for carbon steel
Abstract Corrosion poses a significant challenge in the materials industry, especially when carbon steel (C-steel) is subjected to highly corrosive environments such as 1 M HCl. In this study, four ionic liquids (ILs)-1-benzyl-1-methylpiperidin-1-ium chloride (IL I ), 1-benzyl-2-methylpyridin-1-ium chloride (IL II ), 1-benzyl-3-methylpyridin-1-ium chloride (IL III ), and 1-benzyl-4-methylpyridin-1-ium chloride (IL IV ) were synthesized and characterized using spectroscopic techniques, including FT-IR and 1 HNMR. Electrochemical analyses such as potentiodynamic polarization (PP), electrochemical impedance spectroscopy (EIS), and electrochemical frequency modulation (EFM) demonstrated excellent protective performance for C-steel in 1 M HCl, reaching a maximum inhibition of 97% at an optimum concentration of 100 ppm. Insights from polarization and impedance analyses confirmed that these compounds act as mixed-type inhibitors by adhering to the steel surface. The adsorption behavior followed the Langmuir isotherm model, with ΔG 0 ads values suggesting a combination of physisorption and chemisorption. Quantum chemical assessments validated the experimental results, indicating that IL II –IL IV , due to their two aromatic rings, exhibited superior surface interaction and inhibition efficiency because of their reduced HOMO-LUMO energy gaps. These findings affirm the strong inhibitory potential of the tested ILs in acidic media.
The effect of a familiarization critical speed testing session on critical speed determination during treadmill running
Background The effect of familiarization with the critical speed (CS) testing process on the outcome of CS tests has yet to be determined. Objectives The main aims of the present study were to determine whether a familiarization session prior to CS testing sessions affects time to task-failure (TTF) on subsequent tests and CS estimations, and whether individual characteristics such as sex and fitness status influence any familiarization effect. Methods 27 healthy adults (10 females, 25 ± 4 yrs) performed the following treadmill protocol: i) a maximal incremental test to identify peak speed; ii) a familiarization constant-intensity trial (CT fam ) at the intensity of the first constant-intensity trial used for CS determination (CT CS ), and iii) four randomly ordered maximal constant-intensity trials at ~ 85%, 92%, 98%, and 105% of peak speed. CS was estimated using 2-parameter hyperbolic, linear, and 1/time models including the CT fam (CS not familiarized) or the CT CS (CS familiarized). Familiarized and not-familiarized TTFs and CSs were compared using Wilcoxon tests. Sex effect was analyzed using Mann-Whitney test on the difference in the TTF between CT CS and CT fam . The correlation between peak speed and difference in the TTF between CT CS and CT fam was assessed using Spearman’s rank correlation. α was set at 0.05. Results CT fam TTF (399 [299] s, median [interquartile range]) was lower (p = 0.009) than CT CS TTF (495[316] s), whereas CS familiarized and not-familiarized were not different in any model (p > 0.05). Sex did not affect the differences between familiarized and not-familiarized TTFs and CS. However, differences in the TTF between CT CS and CT fam were negatively correlated with peak speed (ρ = −0.381, p = 0.050). Conclusions Familiarization with constant-intensity trials affected the TTF but not CS. Importantly, the familiarization effect was larger in less fit individuals, showing a negative correlation between the TTF differences (i.e., CT CS minus CT fam ) and peak speed.
Time stability of Intensive Care Infection Score (ICIS) as a cellular hematology biomarker for infection in critically ill patients
Enhanced extractive text summarization framework for low-resourced Urdu language
This era has witnessed an enormous increase in textual corpus available in digital form. Therefore, an intelligent mechanism is required to extract the essential information. This task is performed using an automatic text summarization that converts the text into a shorter form while the semantics are preserved. The popular languages of the world such as English, Chinese etc. have well-developed text summarization models. However, for low-resourced languages such as Urdu, well-established methods are missing. This research work proposes improved supervised and unsupervised extractive text summarization models. A large-scale dataset containing text documents and their human-annotated extractive summaries has been created. In the supervised approach, fifteen features are extracted against each sentence in the text. Further, to reduce the computational complexity, feature reduction is performed. Multiple machine learning and deep learning models are employed, including the proposed model. In an unsupervised approach, four different models are utilized. Further, the high results-producing model is incorporated with the top three features that significantly contributed to the supervised approach. The results are evaluated using ROUGE scores. Experimental results demonstrate that the proposed supervised and unsupervised approaches outperform existing Urdu text summarization methods, achieving approximately 7% and 12% improvements in ROUGE scores, respectively.
Investigation of 50 temperature-based models for estimating potential evapotranspiration (PET) in a semi-arid region
Usual gait speed is inversely associated with depression in middle-aged and older adults: A cross-sectional study in Korea
Background Depression is a serious mental disorder and leading cause of suicide. This study investigated the association between usual gait speed (UGS) and risk of depression. Methods Data from 2,419 participants from a community-based Korean cohort were analyzed. Participants were categorized into sex-specific UGS tertiles (low, mid, or high). Depression was defined based on a previous physician diagnosis, current use of antidepressants, or a score of ≥6 on the Korean version of the Geriatric Depression Scale-Short Form (SGDS-K). Multiple linear and logistic regression models were used to assess the association between UGS and SGDS-K scores and estimate the odds ratios (ORs) with 95% confidence intervals (CIs) for the risk of depression, respectively. Results Prevalence rates of depression were 13.33% and 26.29% among men and women, respectively. Compared with participants with low UGS, men with high UGS had a 50% (OR=0.50; 95% CI [0.29, 0.86]; p < 0.05) lower risk of depression, and women with mid and high UGS had a 43% (OR=0.57; 95% CI [0.41, 0.79]; p < 0.001) and 44% (OR=0.56; 95% CI [0.38, 0.82]; p < 0.01) lower risk, respectively. The SGDS-K scores were lowered by 0.14 (95% CI [–0.23, –0.04]; p < 0.01) and 0.33 points (95% CI [–0.45, –0.21]; p < 0.0001) in men and women, respectively, with each 0.1 m/s increase in UGS. Conclusions Hence, faster UGS was significantly associated with a reduced risk of depression in both sexes. Thus, maintaining a fast UGS may have protective benefits against the risk of depression.
Bending behavior of concrete-filled FRP wound tubular arches with internal FRP bars
A hybrid framework for notebook market analysis: Integrating social media sentiment mining with expert knowledge for feature prioritization
The increasing complexity of consumer preferences in the notebook market requires advanced methodologies to effectively analyze user sentiments and prioritize product features for strategic decision-making. Traditional market research methods often fail to capture real-time, spontaneous consumer feedback while lacking integration with expert knowledge of actual purchasing behavior. This study introduces a novel hybrid framework that systematically combines aspect-based sentiment analysis (ABSA) of social media data with expert evaluations to bridge the gap between expressed consumer preferences and actual purchase drivers. The methodology analyzes 329,091 Twitter posts from January 2023 to June 2024, covering seven major notebook brands. Using the PyABSA framework, consumer sentiments toward 16 key notebook attributes are extracted and analyzed. Expert evaluations, conducted through fuzzy logic defuzzification, assign importance weights based on observed purchasing patterns, with features subsequently ranked using the TOPSIS multi-criteria decision-making method. Findings reveal that price, display quality, CPU performance, RAM capacity, and design constitute the most influential factors in notebook purchasing decisions. Negative sentiments concentrate on cooling systems, battery chargers, and warranty services, indicating critical improvement areas. Brand-specific analysis demonstrates that display quality ranks highest for Lenovo, while price dominates concerns for Dell, HP, and Microsoft, validating distinct market positioning strategies. By integrating machine learning-based sentiment analysis with structured expert knowledge, this research provides manufacturers with a quantifiable, actionable framework for optimizing product development and marketing strategies. The methodology enables companies to prioritize genuinely purchase-influencing features while addressing critical pain points, enhancing competitive positioning in dynamic markets. Future research should expand to cross-cultural consumer behavior analysis and real-time sentiment tracking systems.
Development of a lactate metabolism signature for predicting homologous recombination repair status in breast cancer
Exploring heterogeneity in treatment effects: The impact and interaction of asset-based wealth and mass azithromycin distribution on child mortality
Objective To examine how child mortality among children aged 1–59 months varies by asset-based wealth status in rural Burkina Faso, and to assess the interaction between mass azithromycin (AZ) distribution and wealth status on child mortality at both the household and community levels. Methods We used data from a cluster-randomized trial and population census data on household characteristics and assets. A wealth index score for each household, used to classify the population by wealth, was generated using principal component analysis. We used the Relative Index of Inequality (RII), the Slope Index of Inequality (SII), and the concentration index to assess wealth-related inequalities in mortality, and the Gini Index to assess variability in child mortality across households and communities. Poisson regression models were used, with person-time at risk included as an offset, and robust standard error to estimate changes in mortality rates by wealth and treatment arm. Interaction was assessed on both the multiplicative and additive scales. Results Mortality declined with increasing wealth at both the household and community levels, with a significant gradient at the community level (RII = 1.17, 95% CI: 1.05–1.29; SII = 2.3 per 1,000 person-years, 95% CI: 0.2–4.4), reflecting higher mortality among the poorest. The effect of AZ did not vary significantly by wealth index, and changes in mortality rates across wealth levels were similar between the two treatment arms. There was no evidence of a statistically significant interaction between AZ and asset-based wealth on either the multiplicative or additive scale at the household or cluster level. Conclusion Our findings demonstrate a wealth gradient in child mortality, with the highest mortality rates observed among households and communities in the lowest wealth quintiles. These disparities were consistent across both AZ-treated and placebo groups, suggesting that the role of AZ in health disparities may be limited to addressing gaps in treatment access rather than broader wealth-related disparities. While the study may have been underpowered to detect modest interaction effects, AZ appeared to offer similar benefits across economically diverse communities, with no evidence suggesting enhanced benefits for disadvantaged communities or for prioritizing treatment based on wealth status. Further work is needed to address the wealth-related disparities in child mortality in these communities. Trial registration ClinicalTrials.gov NCT03676764
RAGMail: a cloud-based retrieval-augmented framework for reducing hallucinations in LLM text generation
Abstract Cold emailing is used to personalize, target emails for outreach without prior contact. Automating this personalized cold email generation process can significantly improve outreach efficiency for job seekers, particularly in competitive industries. It streamlines the process of composition, saves time and increases engagement, tailored to a specific industry or role. In today’s competitive market, where job application is made easy, such a tool scales communication and boosts the conversion rate. The cold email generator. RAGMail, is an intelligent cold email generator that is cloud-integrated and uses Retrieval-Augmented Generation (RAG) to reduce hallucinations. The cloud-native infrastructure on which the system is built makes use of services including managed Large Language Model (LLMs) APIs, scalable vector databases, and object storage. With real-time document retrieval and cloud-hosted, metadata-aware templates, RAGMail guarantees high personalization accuracy and factual foundation. This cloud-native architecture provides elastic scalability, low-latency inference, and real-time personalization at scale, all while protecting data and user privacy with role-based access control and encrypted storage. Beyond job applications, the approach can be applied to a wide range of outreach sectors, including sales, academia, and commercial relationships, where factual accuracy and context sensitivity are critical. The system ensures high availability and load balancing during peak demand periods by utilizing distributed cloud resources. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios beyond the trained data, and as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots. The proposed framework offers a scalable and reliable solution for generating contextually grounded, high-quality cold emails using Retrieval-Augmented Generation.
Calibration of discrete element parameters for cohesive soils at different moisture contents
To obtain discrete element method simulation parameters for cohesive soil during the scrapping process of loaders, this paper calibrates parameters of cohesive soil with different moisture contents based on the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) Cohesion contact model in Experts in Discrete Element Modeling (EDEM). First, five cohesive soil samples with different moisture contents were prepared. By combining vibration sieving, the inclined plane method, and angle of repose experiments, the measured data ranges of particle size distribution, soil-steel friction coefficient, and angle of repose were obtained. Secondly, the JKR V2 adhesion model was constructed in EDEM. Significant parameters were screened using the Plackett-Burman test. Finally, the response surface analysis range was determined by integrating climbing experiments, and a quadratic regression model was established through Box-Behnken design to optimize parameter combinations. Furthermore, a Particle Swarm Optimization (PSO) algorithm was introduced for single-objective optimization of the angle of repose. The experimental results show that with the increase of moisture content, the angle of repose increases from 30.83° to 37.13°, and the significant parameters are JKR surface energy, soil-soil restitution coefficient, and static friction coefficient. The simulation error of the PSO algorithm is reduced from the maximum 3.38% in the response surface method to within 2.2%. This study provides a high-precision parameterization method for DEM modeling of cohesive soil, offering references for establishing DEM simulations of loaders scraping cohesive soil.
Age-dependent efficiency of magnetic drug targeting in young and old patient-specific aortic models
Genetic analysis of drought and heat tolerance combined with Striga hermonthica resistance in tropical maize (Zea mays)
The occurrence of combined biotic and abiotic stresses with more damaging effect is worsening in maize production fields in SSA due to climate change. The development of multiple stress tolerant maize hybrids is thus critical to assure food security. This study was conducted to (i) examine the mode of inheritance of combined tolerance to drought and heat stress (CDHS) with resistance to Striga in tropical maize and (ii) assess the feasibility of selecting hybrids with combined tolerance to drought and heat stress and resistance to Striga infection. Single cross hybrids formed from Striga resistant lines with contrasting resistance reactions to tassel blasting were then evaluated under combined drought and heat stress as well as under Striga infested (STIN) and non-infested (STNO) conditions. The observed high GCA/SCA ratio and narrow sense heritability estimates indicated that additive gene action had a major effect on the inheritance of most traits under all testing conditions. Seven parental lines had positive GCA effects for grain yield under CDHS and STIN conditions when they were used as both male and female parents. We also found single crosses with positive specific combining ability (SCA) for grain yield. Grain yield under STIN had positive and significant genotypic and phenotypic correlations with yield recorded under CDHS and STNO conditions. It thus appears that grain yield may be regulated by common alleles across the three growing conditions. Selected parental lines are potential parents for developing source populations of new inbred lines and superior hybrids with combined CDHS tolerance and Striga resistance. Promising single crosses could be used as female parents to develop multiple stress tolerant 3-way cross hybrids.
Hydrothermal transformation of kerogen and oil in Low-permeability rocks of the domanic deposits in carbon dioxide media
Validation of an agent-based model for cell interactions in a microfluidic chip
Objectives: Microfluidic cell Co-Culture, Tissue Co-Culture and Organ-on-Chip (OoC) technologies enable modeling of tissues and organs in vitro, facilitating cell-environment interaction studies and early therapeutic evaluation. The combination of physiology-based models, agent-based models (ABMs), cellular automata, and in-vitro modelling of complex processes provides a powerful tool to formalize, quantify, and predict observed phenomena. Methods: Estimating parameters for these hybrid computational models using observational data is challenging. Approximate Bayesian computation (ABC) is particularly well suited for this task due to the intractability of the likelihood function. This work extends a hybrid ABM for a cell co-culture experiment on a chip. Cell tracking data is used to estimate model parameters via a Sequential Monte Carlo ABC (ABC-SMC) approach. Results: The resulting model accurately reproduces observed cellular behavior and distinguishes between different experimental conditions. Conclusion: The combination of cell co-culture and microfluidic technology with hybrid computational models and ABC-SMC provides a robust framework for modeling and predicting cellular behavior in vitro, enhancing the potential for early therapeutic evaluation and understanding of cell-environment interactions.
Graphene oxide/metal–organic framework composite as an effective catalyst for esterification reactions
Abstract Herein, we report the synthesis and characterization of graphene oxide (GO) impregnated into a Zr-based metal–organic framework (MOF-801), denoted as MOF-801@GO, as a catalyst for esterification reactions. The synthesized catalyst was thoroughly characterized using various techniques, including Fourier-transform infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET) surface area measurements, and scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and inductively coupled plasma (ICP) spectroscopy. The surface acid strength was evaluated by ammonia temperature-programmed desorption (NH₃-TPD). The catalytic performance of MOF-801@GO was assessed in the esterification of carboxylic acids with alcohols using no solvent. Under optimized reaction conditions, the catalyst achieved a high esterification yield of up to 95%. Notably, the catalyst was readily recovered and reused over multiple cycles with minimal loss of catalytic activity or structural integrity, highlighting its promise for practical esterification applications.