Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling Solid Electrolyte Interphase Formation at the Molecular Level: Computational Insights into Bare Li-Metal Anode and Li6PS5–xSexCl Argyrodite Solid Electrolyte

The development of high-energy-dense, sustainable all-solid-state batteries faces a major challenge in achieving compatibility between the anode and electrolyte. A promising solution lies in the use of highly ion-conductive solid electrolytes, such as those from the argyrodite family. Previous studies have shown that the ionic conductivity of the argyrodite Li6PS5Cl can be significantly enhanced by partially substituting S with Se. However, there remains a lack of fundamental knowledge regarding the effect of doping on the interfacial stability. In this study, we employ long-scale ab initio molecular dynamics simulations, which allowed us to gain unprecedented insights into the process of solid electrolyte interface (SEI) formation. The study focuses on the stage of nucleation of crystalline products, enabling us to investigate in silico the SEI formation process of Se-substituted Li6PS5Cl. Our results demonstrate that kinetic factors play a crucial role in this process. Importantly, we discovered that selective anionic substitution can accelerate the formation of a stable interface, thus potentially resolving anode–electrolyte compatibility issues.
A Study on the Effects of Global Warming in Bangladesh

Global warming turns out to be the biggest challenge to the mankind in the near future. It is not a problem to write only theories and do research on; it is a problem which humankind must face unless proactive measures are taken immediately. Global warming causes the climate to change including the rise of sea-level. Recently, the IPCC (Intergovernmental Panel on Climate Change), in its Fifth Assessment Report, has suggested a 1.5 mm sea-level rise per year for Bangladesh. As a developing country, where around half of the total population is below the poverty line, Bangladesh is going to be one of the worst sufferers of this due to its disaster prone and low elevation geography. This paper will explore the impacts of global warming on Bangladesh. Preparation of Bangladesh to face the adverse effects of global warming will also be presented. At the end of the paper, few recommendations for reducing the carbon emission have been suggested.
Orchard Soil Health Protocols: Evaluation And Economic Impacts

We are developing a protocol for evaluating soil health in apple (Malus X domestica Borkh.) orchards of New York State (NYS). Soil health is defined functionally as agricultural productivity, environmental awareness, and resource conservation, but soil health research has focused primarily on annual cropping systems. We evaluated 52 biological, chemical, and physical soil properties in three orchards under long-term management regimes – a groundcover management systems (GMS) study, an integrated and organic fruit production (IFP-OFP) comparison, and an apple replant disease (ARD) study. In order to understand how treatments influenced orchard soil health, we compared soil properties from these systems to upper and lower 25 percentile ranges developed from 15 NYS commercial orchard soils. We then determined which soil properties best separated the treatments and could be considered in a parsimonious set of orchard soil health indicators. Using discriminant analysis, we found total carbon (C) (P < 0.0001), vesicular arbuscular mycorrhizae (VAM) spore count (P = 0.0006), soil respiration (P = 0.0021), available calcium (Ca) (P < 0.0001), and available phosphorus (P) (P < 0.0001) to be important indicators. Biomass amendments – such as bark mulch groundcover or pre-plant compost – and avoiding tree-row cultivation, improved soil health by enhancing the aforementioned soil properties.
Differences in Milk Proteomic Profiles between Estrous and Non-Estrous Dairy Cows

Simple Summary Discovering estrus biomarkers is of utmost importance to develop an easy, reliable, and accurate estrus detection method in dairy cows. The proteomics approach is an attractive and powerful tool and recently has been used to explore proteomic alterations in a variety of bodily fluids to identify the relative biomarkers. Milk produced by dairy cows is a complex biofluid consisting of hundreds of different components that may be a good source for biomarker discovery. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus, which may be helpful for further development of an easy and reliable estrus detection method in dairy cows. Abstract Efficient reproductive management of dairy cows depends primarily upon accurate estrus identification. However, the currently available estrus detection methods, such as visual observation, are poor. Hence, there is an urgent need to discover novel biomarkers in non-invasive bodily fluids such as milk to reliably detect estrus status. Proteomics is an emerging and promising tool to identify biomarkers. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus. Dairy cows were synchronized and timed for artificial insemination, and the cows with insemination leading to conception were considered to be in estrus at the day of insemination (day 0). Milk samples of day 0 (estrus group) and day −3 (non-estrus group) from dairy cows confirming to be pregnant were collected for proteomic analysis using the tandem mass tags (TMT) proteomics approach. A total of 89 differentially expressed proteins were identified, of which 33 were upregulated and 56 were downregulated in the estrus milk compared with the non-estrus milk. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that acetyl coenzyme A carboxylase α (ACACA), apolipoprotein B (APOB), NAD(P)H steroid dehydrogenase-like (NSDHL), perilipin 2 (PLIN2), and paraoxonase 1 (PON1) participated in lipid binding, lipid storage, lipid localization, and lipid metabolic process, as well as fatty acid binding, fatty acid biosynthesis, and fatty acid metabolism, and these processes are well documented to be related to estrus regulation. These milk proteins are proposed as possible biomarkers of estrus in dairy cows. Further validation studies are required in a large population to determine their potential as estrus biomarkers.
Corporate Social Responsibilities and Firm Values: Evidence from Indonesia

This study examines the effect of corporate social responsibilities (CSR) on the financial sector firms' value in Indonesia. Furthermore, this research also tries to prove the differences in CSR expenditures between firms that are politically connected and those that are not connected. There are five sub-sectors in the Financial sector: banking, insurance, financing, others, and securities. The final sample size is 437 firm annual reports from 2014 to 2021. The results show that CSR influences firm value positively and significantly. Another interesting finding is the significant difference in CSR expenditure between politically and non-politically connected firms.
Experimental Studies on the Fire Flame Behavior of Reinforced Concrete Beams with Construction Joints

This study was experimentally investigated experimentally, investigating the effect of such mechanical properties and deflection behavior on the performance of reinforced concrete beams with construction joints exposed to fire flame. These beams were built using a slanted connection at a 45° angle in the middle of the beams. Four beams were for a wide temperature range (25–800°C) for the fire flames used on the concrete beams. Two temperature levels (600°C and 800°C) were chosen, with (1 and 2) hrs. period time, respectively. One beam wasn't burnt (Reference specimen). When put through a fire, flexural strength drastically decreased. The experimental program's results suggested the worst effect on construction joint beam for temperature 800°C with 2 hrs. period time because that heating reduces the bonds between two surfaces in the joint and makes like slip and disconnect between the joint after exposure to load. After 1 and 2 hours in a fire at 600°C, the residual flexural strength was 85% and 72%, respectively, whereas, after 1 and 2 hours in a fire at 800°C, it was 41% and 28%, this is showed that the stiffness of beam its decrease when exposed to fire flam with raising temperature and time and increase the deformation by cracks that appear on the beams. It was noticed that the load-deflection relation to beams exposed to fire flame is flat, representing softer load-deflection behavior than that of the control beam.
Hepatitis B Virus Epsilon (ε) RNA Element: Dynamic Regulator of Viral Replication and Attractive Therapeutic Target

Hepatitis B virus (HBV) chronically infects millions of people worldwide, which underscores the importance of discovering and designing novel anti-HBV therapeutics to complement current treatment strategies. An underexploited but attractive therapeutic target is ε, a cis-acting regulatory stem-loop RNA situated within the HBV pregenomic RNA (pgRNA). The binding of ε to the viral polymerase protein (P) is pivotal, as it triggers the packaging of pgRNA and P, as well as the reverse transcription of the viral genome. Consequently, small molecules capable of disrupting this interaction hold the potential to inhibit the early stages of HBV replication. The rational design of such ligands necessitates high-resolution structural information for the ε–P complex or its individual components. While these data are currently unavailable for P, our recent structural elucidation of ε through solution nuclear magnetic resonance spectroscopy marks a significant advancement in this area. In this review, we provide a brief overview of HBV replication and some of the therapeutic strategies to combat chronic HBV infection. These descriptions are intended to contextualize our recent experimental efforts to characterize ε and identify ε-targeting ligands, with the ultimate goal of developing novel anti-HBV therapeutics.
Polyethylenimine-based iron oxide nanoparticles enhance cisplatin toxicity in ovarian cancer cells in the presence of a static magnetic field

Background Drug resistance in cancer cells is a major concern in chemotherapy. Cisplatin (CIS) is one of the most effective chemotherapeutics for ovarian cancer. Here, we investigated an experimental approach to increase CIS cytotoxicity and overcome cell resistance using nanoparticle-based combination treatments. Methods Polyethylenimine (PEI)-based magnetic iron oxide nanocomplexes were used for drug delivery in genetically matched CIS-resistant (A2780/CP) and -sensitive (A2780) ovarian cancer cells in the presence of a 20 mT static magnetic field. Magnetic nanoparticles (MNPs) were synthesized and bonded to PEI cationic polymers to form binary complexes (PM). The binding of CIS to the PM binary complexes resulted in the formation of ternary complexes PM/C (PEI–MNP/CIS) and PMC (PEI–MNP–CIS). Results CIS cytotoxicity increased at different concentrations of CIS and PEI in all binary and ternary delivery systems over time. Additionally, CIS induced cell cycle arrest in the S and G2/M phases and reactive oxygen species production in both cell lines. Ternary complexes were more effective than binary complexes at promoting apoptosis in the treated cells. Conclusion PEI-based magnetic nanocomplexes can be considered novel carriers for increasing CIS cytotoxicity and likely overcoming drug resistance of ovarian cancer cells.
Additively Manufactured and Monolithically-Integrated Triple-Mode Post-Loaded Cavity-Resonator-Based Bandpass Filters

This manuscript reports on a new class of triple-mode post-loaded cavity-resonator based bandpass filters (BPFs) alongside their design methodology and a monolithic integration scheme using stereolithography apparatus (SLA) 3D printing. The proposed BPF concept exhibits the following unique features: i) miniaturized size accomplished by combining three modes within the volume of a single post-loaded cavity resonator, ii) bespoke coupling element configurations that facilitate the realization of transmission zeros (TZs) in the out-of-band response and iii) monolithic SLA integration that further reduces the size and weight of the entire filter as opposed to conventional split-block manufacturing schemes. For proof-of-concept validation purposes, two three-pole/two-TZ BPFs and a six-pole/four-TZ BPF operating at 2.9 GHz, 3.2 GHz and 3.2 GHz with a fractional bandwidth (FBW) of 16.3%, 15.2%, 14.2% respectively, were designed, manufactured, and tested. They exhibited passbands with in-band insertion loss < 0.37 dB, that corresponds to an effective quality factor (Qeffs) > 780.
Functional Properties of Acetylation-modified Starches of Three Purple Maize Cultivars from Côte d'Ivoire

Acetylation is one of the most used chemical modifications to improve the functional properties of starches. This allows the esterification of native starches by introducing acetyl groups into their structure. The objective of this study was to enhance the value of purple corn from Côte d'Ivoire. Some functional properties and enzymatic digestibility of acetylated and native starches of three cultivars of this maize have been determined. To this end, these maize were harvested in Katiola, a town on the Ivory Coast. The results showed that purple corn starch was modified using 10.2% acetic anhydride. Acetylated starches showed a degree of substitution and an acetyl content between 0.1 and 0.15% and 2.6 to 3.91% respectively. Acetylation increased clarity, swelling and solubility. This increase could contribute to the production of ice cream, frozen fruit, bakery products and sauces. This has also resulted in reductions in demotion trends compared to the corresponding native starches. Concerning the lowest gelling concentration, acetylated starches have recorded low values (1; 2 and 4%) which are very interesting in the food industries for infant foods. For enzymatic digestibility, the starch that has been digested much more quickly in the acetylated form is dark violet (ADP). Overall, these acetylated starches can be recommended in several food industries.
Remote Sensing Suspended Sediment Concentration in the Yellow River

This study aimed to develop an approach to use publicly accessible satellite imagery to quantify the suspended sediment concentration (SSC) in the Yellow River. The suspended sediment in the river affects to the hydrologic, geomorphologic, and ecologic functioning of river floodplains. Commonly used sampling methods are time consuming, labor intensive, and provide only point data. Current studies using remote sensing have focused mild waters (e.g. coastal, estuarine, lagoon, lakes and reservoirs) from where the method developed might not be appropriate for highly turbid inland waters. A laboratory spectrum experiment was conducted to investigate the reflective nature of sediment-laden water and the impact of sediment types on the reflectance. A spectral mixing algorithm based on a spectral linear mixture modeling approach was developed to estimate SSC from reflectance. We found that the models based on the spectral mixing algorithm were able to estimate SSC as high as 20 g/l. A field survey with on-site spectral and SSC measurements was conducted between the river channel and Sanmenxia reservoir on the Yellow River. The results confirmed an exponential relationship between SSC and reflectance. A single-band model was built to estimate SSC using band setting same as the band 4 of Landsat 7 image (760-900 nm)(R = 0.92, RMSE=0.241 g/l). The application of the spectral mixing approach to the on-site showed that the models based on the spectral mixing Liqin Qu – University of Connecticut, 2014 algorithm were performed as better as the single band exponential model (R = 0.86, RMSE=0.280 g/l) but the valid range for application was improved from 1.99 g/l to 347 g/l. The spectral mixing algorithm was also applied to the Landsat-7 ETM+ data for remote sense the SSC in the Yellow River. For calibration and validation purpose, the daily SSC measured by the Yellow River Hydrological Monitoring System were associated with Landsat-7 ETM+ imagery from 1999 to 2010. The results showed that the model based on the spectral mixing algorithmcould obtain a more promising relationship (R = 0.71) between SSC and reflectance than the conventional regression of a particular band (max R = 0.62). This study provides remote sensing method to estimate SSC with ready-to-use parameters for using Landsat-7 ETM+ imagery in the Yellow River. It could also provide spectral reference for remote sensing SSC in other highly turbid inland rivers. Remote Sensing Suspended Sediment Concentration in the Yellow River
Numerical modeling of the acoustically driven growth and collapse of a cavitation bubble near a wall

This paper describes the first high-order accurate, fully compressible, multiphase model to simulate the expansion and collapse of a near-wall cavitation bubble in a low-frequency ultrasound field. The model captures the compressibility of the fluids, subsequent shocks, and a physically correct representation of the acoustic input through an immersed moving boundary that represents the active face of the ultrasound transducer face. The model’s predictions of bubble dynamics are compared to existing models that are able to capture the collapse of a near wall bubble, (1) the Rayleigh growth and collapse model and (2) the Rayleigh-Plesset growth initialized collapse model, highlighting the limitations of the previously developed models.This paper describes the first high-order accurate, fully compressible, multiphase model to simulate the expansion and collapse of a near-wall cavitation bubble in a low-frequency ultrasound field. The model captures the compressibility of the fluids, subsequent shocks, and a physically correct representation of the acoustic input through an immersed moving boundary that represents the active face of the ultrasound transducer face. The model’s predictions of bubble dynamics are compared to existing models that are able to capture the collapse of a near wall bubble, (1) the Rayleigh growth and collapse model and (2) the Rayleigh-Plesset growth initialized collapse model, highlighting the limitations of the previously developed models.
ARTICLE; AGRICULTURE AND ENVIRONMENTAL BIOTECHNOLOGY Effect of 4-hydroxybenzoic acid on grape (Vitis vinifera L.) soil microbial community structure and functional diversity

Effect of 4-hydroxybenzoic acid on grape (Vitis vinifera L.) soil microbial community structure and functional diversity Xiu-wu Guo, Bo Wang, Kun Li*, Zhen-dong Liu, Xiao Han, Shi-jie Xu, Yin-shan Guo and Hong-gang Xie Pomology Department, College of Horticulture, Shenyang Agricultural University, Shenyang, Liaoning Province, China; Crafts Department, Liaoning Economic Management Cadre College, Shenyang, Liaoning Province, China
ARTICLE; AGRICULTURE AND ENVIRONMENTAL BIOTECHNOLOGY Cloning and allelic variation of two novel catalase genes (SoCAT-1 and SsCAT-1) in Saccharum officinarum L. and Saccharum spontaneum L

Cloning and allelic variation of two novel catalase genes (SoCAT-1 and SsCAT-1) in Saccharum officinarum L. and Saccharum spontaneum L. Yang Liu*, Yanli Yao, Xiaowen Hu, Shulian Xing and Lei Xu Zhanjiang Experiment Station, Chinese Academy of Tropical Agriculture Science, Zhanjiang, Guangdong, P.R. China; Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Science, Haikou, Hainan, P.R. China
Body dysmorphic symptoms in youth with Obsessive-Compulsive disorder: Prevalence, clinical correlates

Little is known about the prevalence and clinical correlates of body dysmorphic disorder (BDD) symptoms in pediatric obsessive-compulsive disorder (OCD). This study estimates the prevalence of BDD symptoms in a sample of children and adolescents with OCD, investigating possible clinical correlates and whether BDD symptoms predict poorer treatment outcomes after cognitive behavioral therapy (CBT). The study included 269 children and adolescents with OCD (DSM-IV), aged 7–17 years, from Denmark, Sweden, and Norway, who were treated with 14 weekly sessions of manualized, exposure-based CBT. OCD symptom severity was assessed with the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS). BDD symptoms were assessed using the CY-BOCS symptom checklist. Group differences in treatment outcomes were analyzed using linear mixed effect modeling. Twenty-one patients (7.8%) had BDD symptoms. BDD symptoms in young people were associated with older age ( p = 0.003) and comorbid anxiety disorders were also more prevalent ( p = 0.025). In addition, young people with BDD symptoms endorsed a greater number of obsessive and compulsive symptoms than did those without BDD symptoms. Having symptoms of BDD did not affect the CBT outcome. The results of the study suggest that CBT for OCD is equally effective for those with and without BDD symptoms.
An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms

—Computers have become increasingly vulnerable to malicious attacks with an increase in popularity and the proliferation of open system architectures. There are numerous malware detection technologies available to protect the computer operating system from such attacks. This type of malware detector targets programs based on patterns detected in the properties of computer applications. As the amount of analytical data increases, the computer defense system is adversely affected. The performance of the detection mechanism has been hindered due to the presence of numerous irrelevant characteristics. The goal of this study is to provide a feature selection approach that will help malware detection systems be more accurate by detecting pertinent and significant traits. Furthermore, by selecting the most important features, it is possible to maintain an acceptable level of accuracy in the detection of malware while significantly lowering the computational cost. The proposed method displays the most important features (MIFs) obtained from each machine learning method, including data cleaning and feature selection. Furthermore, the method applies six machine learning classification techniques to the selected feature set. Several classifiers were evaluated based on several characteristics for malware detection, including Support Vector Machines (SVM), Logistic Regression (LR), K-nearest neighbor (K-NN), Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF). Our suggested model was tested on two malware datasets to determine its effectiveness. In terms of accuracy, precision, F1 scores, and recall, the experimental findings show that RF and DT classifiers beat other techniques.
The Pricing Kernel Equation

This paper provides an equation called the pricing kernel equation, which relates the subjective probability distribution on an arbitrary asset price to the risk-neutral probability distribution. It claims that the subjective probability distribution is priced by a static option portfolio, in which the weight of an option is determined by the level of the pricing kernel. As an application, we propose a new method for estimating empirical pricing kernels. Another application is to extract subjective probabilities and fundamental statistics from option prices. These examples show that the pricing kernel equation can be a versatile tool for various applications.