Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental and numerical study of basalt fibre cloth strengthened structural insulated panel under windborne debris impact

Strong wind causes damages and losses around the world. The windborne debris carried by strong wind might impact on building and create openings on the building envelop, which might threaten the occupants and cause further damages to the building. To address this issue, some wind loading codes including the Australian Wind Loading Code (AS/NZS 1170:2:2011) give design requirements. The resistance capacity of oriented strand board skins structural insulated panel was investigated and proved having low resistance to the projectile impact, and could not meet the impact resistance requirement for application in cyclonic region C and D defined in Australian Wind Loading Code. In this study, basalt fibre cloth is used to strengthen oriented strand board structural insulated panel to increase its capacity to resist windborne debris impact. This paper presents experimental and numerical study of structural insulated panel with or without basalt fibre cloth strengthening under windborne debris impact. Five specimens with different configurations were tested. The dynamic responses were quantitatively compared in terms of residual speed of debris after impact. The results indicate that basalt fibre cloth enhanced the resistance capacity of oriented strand board structural insulated panel. A numerical model is developed in LS-DYNA to simulate the debris impact. The testing results are used to verify the accuracy of the numerical model, which can be used in subsequent parametric studies.
Intelligent Modeling Method for a Combined Radiation-Convection Grain Dryer: A Support Vector Regression Algorithm Based on an Improved Particle Swarm Optimization Algorithm

This paper aims to investigate an intelligent model for a combined infrared radiation-convection (IRC) dryer by using a support vector regression (SVR) algorithm based on an improved particle swarm optimization algorithm (IPSO). The IPSO algorithm was designed to optimize the parameters of the SVR algorithm, which has improved the optimization ability of the IPSO [linear decreasing inertia weight (LDIW)] algorithm and the standard PSO algorithm by introducing a relative fitness deviation (FD) to the LDIW equation and by combining a concept of mutation. Based on the data collected from a practical drying experiment, the best IPSO-SVR (LDIW-FD) model for the IRC dryer was successfully constructed, and the prediction performance comparisons of different modeling methods were also made. The resulting IPSO-SVR (LDIW-FD) model has achieved a remarkable predictive accuracy compared with the other models, demonstrating the effectiveness of the proposed model. In addition, a model of concurrent-counter flow drying was also successfully established by using the same method, depicting the proposed method can be readily used to precisely predict different drying processes. This modeling method can give relatively good predictive output information of the nonlinear system, and it may provide an accurate model for the prediction control of grain drying.
Gradual Complex Numbers and Their Application for Performance Evaluation Classifiers

Usually, the evaluation of the classifiers performance is not an easy task to be performed, mainly when we analyze different criteria (output parameters). In this evaluation process, we can use quantitative measures (accuracy, specificity, among others), however, when the output values are very close and we have several criteria, the results are difficult to be interpreted by users. This paper aims to propose a new <italic>linguistic model</italic> to evaluate the performance of several classifiers. It is based on the notion of <italic>gradual complex numbers</italic> (GCN), proposed in <xref ref-type="bibr" rid="ref18">[18]</xref>. In this paper, we present the theoretical basis of GCNs for classifier evaluator and we assess the performance of the proposed model (GCN) through an empirical study. In addition, the performance of GCN is compared with that of <italic>fuzzy complex numbers</italic> <xref ref-type="bibr" rid="ref6">[6]</xref>, and it reveals gains.
Obesity and Dental Caries in Young Children in Plymouth, United Kingdom: A Spatial Analysis.

OBJECTIVE To examine the spatial clustering of obesity and dental caries in young children in Plymouth, United Kingdom, to evaluate the association between these conditions and deprivation, and explore the impact of neighbourhood-level characteristics on their distribution. BASIC RESEARCH DESIGN Cross-sectional study analysing data from the National Child Measurement Programme (N=2427) and the Local Dental Health Survey (N=1425). The association of deprivation with weight status and caries was determined at individual and area level, using ANOVA and Poisson models. The overall spatial clustering was assessed using a modified version of the Global Moran's I, while clusters were located through Local Indicators of Spatial Association. Spatial autocorrelation was assessed using the variograms of the raw values. Log-linear Poisson models were fitted to assess the significance of neighbourhood characteristics on overweight/obesity and caries distribution. RESULTS At an individual level, deprivation was not associated with BMI z-scores but was a significant predictor of caries (p⟨0.05). However, at area level, deprivation related to the rates of both conditions. A significant positive autocorrelation was observed across neighbourhoods for caries. The variograms suggested spatial autocorrelations up to 2.5 km and 3 km for overweight/obesity and caries, respectively. Among several neighbourhood characteristics, the proportion of people on benefits was found to be a significant predictor of caries rates. CONCLUSIONS Our results underline the importance of considering geographic location and characteristics of the broader environment when developing strategies to target obesity and caries.
Urea Amendment Decreases Microbial Diversity and Selects for Specific Nitrifying Strains in Eight Contrasting Agricultural Soils

Application of nitrogen (N) fertilizers, predominantly as urea, is a major source of reactive N in the environment, with wide ranging effects including increased greenhouse gas accumulation in the atmosphere and aquatic eutrophication. The soil microbial community is the principal driver of soil N cycling; thus, improved understanding of microbial community responses to urea addition has widespread implications. We used next-generation amplicon sequencing of the 16S rRNA gene to characterize bacterial and archaeal communities in eight contrasting agricultural soil types amended with 0, 100, or 500 μg N g-1 of urea and incubated for 21 days. We hypothesized that urea amendment would have common, direct effects on the abundance and diversity of members of the microbial community associated with nitrification, across all soils, and would further affect the broader heterotrophic community resulting in decreased diversity and variation in abundances of specific taxa. Significant (P < 0.001) differences in bacterial community diversity and composition were observed by site, but amendment with only the greatest urea concentration significantly decreased Shannon indices. Expansion in the abundances of members of the families Microbacteriaceae, Chitinophagaceae, Comamonadaceae, Xanthomonadaceae, and Nitrosomonadaceae were also consistently observed among all soils (linear discriminant analysis score ≥ 3.0). Analysis of nitrifier genera revealed diverse, soil-specific distributions of oligotypes (strains), but few were correlated with nitrification gene abundances that were reported in a previous study. Our results suggest that the majority of the bacterial and archaeal community are likely unassociated with N cycling, but are significantly negatively impacted by urea application. Furthermore, these results reveal that amendment with high concentrations of urea may reduce nitrifier diversity, favoring specific strains, specifically those within the nitrifying genera Nitrobacter, Nitrospira, and Nitrosospira, that may play significant roles related to N cycling in soils receiving intensive urea inputs.
Phosphatidylserine Lateral Organization Influences the Interaction of Influenza Virus Matrix Protein 1 with Lipid Membranes

ABSTRACT Influenza A virus matrix protein 1 (M1) is an essential component involved in the structural stability of the virus and in the budding of new virions from infected cells. A deeper understanding of the molecular basis of virion formation and the budding process is required in order to devise new therapeutic approaches. We performed a detailed investigation of the interaction between M1 and phosphatidylserine (PS) (i.e., its main binding target at the plasma membrane [PM]), as well as the distribution of PS itself, both in model membranes and in living cells. To this end, we used a combination of techniques, including Förster resonance energy transfer (FRET), confocal microscopy imaging, raster image correlation spectroscopy, and number and brightness (N&B) analysis. Our results show that PS can cluster in segregated regions in the plane of the lipid bilayer, both in model bilayers constituted of PS and phosphatidylcholine and in living cells. The viral protein M1 interacts specifically with PS-enriched domains, and such interaction in turn affects its oligomerization process. Furthermore, M1 can stabilize PS domains, as observed in model membranes. For living cells, the presence of PS clusters is suggested by N&B experiments monitoring the clustering of the PS sensor lactadherin. Also, colocalization between M1 and a fluorescent PS probe suggest that, in infected cells, the matrix protein can specifically bind to the regions of PM in which PS is clustered. Taken together, our observations provide novel evidence regarding the role of PS-rich domains in tuning M1-lipid and M1-M1 interactions at the PM of infected cells. IMPORTANCE Influenza virus particles assemble at the plasma membranes (PM) of infected cells. This process is orchestrated by the matrix protein M1, which interacts with membrane lipids while binding to the other proteins and genetic material of the virus. Despite its importance, the initial step in virus assembly (i.e., M1-lipid interaction) is still not well understood. In this work, we show that phosphatidylserine can form lipid domains in physical models of the inner leaflet of the PM. Furthermore, the spatial organization of PS in the plane of the bilayer modulates M1-M1 interactions. Finally, we show that PS domains appear to be present in the PM of living cells and that M1 seems to display a high affinity for them.
Design of a low noise amplifier with inductive peaking technique

This paper describes the implementation and experimental studies of a low noise common source amplifier with various inductive peaking techniques. The effect of inductive series peaking and shunt-series peaking techniques on the bandwidth of the amplifier is experimentally investigated. A maximum bandwidth enhancement factor of 1.96 is achieved with inductive shunt-series peaking technique which results in a 3-dB bandwidth of 143 MHz. The input voltage noise density of the LNA is also measured which is found to be ∼ 2.5 nV/√Hz.
Lithium Ion Battery Anodes Produced from Densified, Silicon Coated, Carbon Nanotube Arrays.

TANG, CAN. Lithium Ion Battery Anodes Produced from Densified, Silicon Coated, Carbon Nanotube Arrays. (Under the direction of Dr. Philip Bradford.) The increasing demand in energy storage for portable devices and electric vehicles requires the further development of lithium ion batteries. In this study, lithium ion battery anodes were produced from densified, silicon coated, carbon nanotube arrays. One of the goals of this study was to uniformly coat silicon onto individual carbon nanotubes. Carbon nanotube arrays were used to compensate for the volume expansion of silicon while providing the anode with sufficient electrical conductivity. A chemical vapor deposition system was built to synthesize carbon nanotube arrays and deposit the silicon coating. Baseline anodes of carbon nanotube arrays and silicon coated carbon nanotube array anodes were assembled and tested in coin cells without binder or a current collector metal foil. SEM, TEM, XRD and Raman spectroscopy confirmed relatively uniform crystalline silicon nano-particles were coated throughout the carbon nanotube arrays. The results showed that carbon nanotube array anodes with silicon coating had improved capacity as well as coulombic efficiency. Silicon coated post-treated carbon nanotube array anodes showed improved cycling performance with about twice capacity retention compared to the composite anodes produce with as-grown, pristine carbon nanotube arrays. After composite structure optimization, composite carbon nanotube array (chlorine+30%Si+carbon) anodes exhibited a charge capacity of 1360mAh/g for the first cycle with about 73% capacity retention on the 20 cycle. Lithium Ion Battery Anodes Produced from Densified, Silicon Coated, Carbon Nanotube Arrays by Can Tang A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Master of Science Textile Engineering Raleigh, North Carolina 2012
Heterogeneous data based danger detection for public safety

In recent days, public safety service attracts attentions because of increasing crime rates. In this paper, heterogeneous data based danger detection (HDDD) mechanism is proposed for reducing crime rates. The HDDD mechanism is a mechanism for detecting dangerous situations (e.g., homicide and violence) based on heterogeneous data that are data gathered from multiple devices such as closed circuit television (CCTV) cameras, smartphones, and wearable devices. The HDDD enables immediate detection of dangerous situations and then reduces crime rates by responding to the dangerous situations.
Buccal patches of atenolol formulated using fenugreek (Trigonella foenum-graecum L.) seed mucilage.

BACKGROUND The use of mucoadhesive natural polymers in designing mucoadhesive patch systems has received much attention. OBJECTIVES The study involved the development and evaluation of buccal patches of atenolol using fenugreek (Trigonella foenum-graecum L.) seed mucilage with hydroxylpropyl methyl cellulose (HPMC K4M) and a backing membrane (ethyl cellulose 5% w/v). MATERIAL AND METHODS These atenolol-releasing buccal patches were prepared using a solvent casting technique. The buccal patches prepared were evaluated for average weight, thickness, drug content, folding endurance and moisture content. Ex vivo mucoadhesive strength, force of adhesion and bonding strength were determined using porcine buccal mucosa. The mucosal permeation of atenolol through the porcine buccal mucosa was carried out using a Franz diffusion cell in phosphate buffer saline, pH 6.8. These buccal patches were also characterized by SEM and FTIR spectroscopy. RESULTS The average weight, thickness, drug content, folding endurance and moisture content of these atenolol-releasing buccal patches were found satisfactory for all the patches. Amongst all, the F-4 buccal patch showed maximum mucoadhesive strength (31.12 ±1.86 g), force of adhesion (30.53 × 10-2 N) and bond strength (1748.89 N/m2). Ex vivo atenolol permeation from the buccal patches showed drug permeation across the excised porcine buccal mucosa over 12 h. The F-4 buccal patch showed maximum permeation flux (29.12 μg/cm2/h). CONCLUSIONS The developed atenolol-releasing buccal patches can be beneficial over the conventional drug delivery systems to decrease the dosing frequency and enhance patient compliance.
Learning data augmentation for consumer devices and services

Transferring the success of deep learning models to consumer electronic devices requires the construction of deep learning models that are small enough to fit on resource constrained hardware. Since embedded and mobile devices lack the resources in terms of power consumption requirements, processing speed, and available memory of the latest GPU technology, it is desirable to create neural networks that are significantly smaller without sacrificing accuracy. A new technique for data augmentation called “Smart Augmentation” has recently been introduced that has been experimentally shown to be effective at this task. In this paper, we show how Smart Augmentation can be used to train models that are significantly smaller than their equivalently performant counterparts, and thus more viable for deployment on consumer devices.
A Fragment Library Screening Approach to Identify Selective Inhibitors against an Essential Fungal Enzyme

Pathogenic fungi represent a growing threat to human health, with an increase in the frequency of drug-resistant fungal infections. Identifying targets from among the selected metabolic pathways that are unique to microbial species presents an opportunity to develop new antifungal agents against new and untested targets to combat this growth threat. Aspartate semialdehyde dehydrogenase (ASADH) catalyzes a key step in a uniquely microbial amino acid biosynthetic pathway and is essential for microbial viability. This enzyme, purified from four pathogenic fungal organisms (Candida albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Blastomyces dermatitidis), has been screened against fragment libraries to identify initial enzyme inhibitors. The binding of structural analogs of the most promising lead compounds was measured against these fungal ASADHs to establish important structure–activity relationships among these different inhibitor classes. The most potent of these inhibitors have been docked into structures of this fungal enzyme target to identify important structural elements that serve as critical binding determinants. Several inhibitors with low micromolar inhibition constants have been identified that showed selectivity against these related enzymes from different fungal species. Subsequent screening against a library of drugs and drug candidates identified some additional inhibitors containing a consistent set of functional groups required for fungal ASADH inhibition. Additional elaboration of these core structures will likely lead to more potent and selective inhibitors.
Investigation of glucose diffusion using an optofluidic silicon chips

We present a new method to study the diffusion of glucose with an opto-fluidic chip. A silicon-on-insulator chip comprising a series of micro-ring resonators is combined with micro-fluidics in poly(dimethylsiloxane) (PDMS). The ring resonators exhibit a shift in resonance wavelength depending on the applied glucose concentration. Based on this effect, the micro-rings are employed as glucose sensors to monitor how the glucose concentration in the microfluidic channels changes due to diffusion. The obtained concentration profiles are compared with diffusion simulations. The good agreement indicates that the opto-fluidic chip is a valuable platform to study glucose diffusion in a variety of settings.
Guanylate-Binding Protein 1 Inhibits Nuclear Delivery of Kaposi's Sarcoma-Associated Herpesvirus Virions by Disrupting Formation of Actin Filament

ABSTRACT Kaposi's sarcoma-associated herpesvirus (KSHV) is a typical gammaherpesvirus that establishes persistent lifelong infection in host cells. In order to establish successful infection, KSHV has evolved numerous immune evasion strategies to bypass or hijack the host immune system. However, host cells still produce immune cytokines abundantly during primary KSHV infection. Whether the immune effectors produced are able to inhibit viral infection and how KSHV successfully conquers these immune effectors remain largely unknown. The guanylate-binding protein 1 (GBP1) gene is an interferon-stimulated gene and exerts antiviral functions on several RNA viruses; however, its function in DNA virus infection is less well understood. In this study, we found that KSHV infection increases both the transcriptional and protein levels of GBP1 at the early stage of primary infection by activating the NF-κB pathway. The overexpression of GBP1 significantly inhibited KSHV infection, while the knockdown of GBP1 promoted KSHV infection. The GTPase activity and dimerization of GBP1 were demonstrated to be responsible for its anti-KSHV activity. Furthermore, we found that GBP1 inhibited the nuclear delivery of KSHV virions by disrupting the formation of actin filaments. Finally, we demonstrated that replication and transcription activator (RTA) promotes the degradation of GBP1 through a proteasome pathway. Taken together, these results provide a new understanding of the antiviral mechanism of GBP1, which possesses potent anti-KSHV activity, and suggest the critical role of RTA in the evasion of the innate immune response during primary infection by KSHV. IMPORTANCE GBP1 can be induced by various cytokines and exerts antiviral activities against several RNA viruses. Our study demonstrated that GBP1 can exert anti-KSHV function by inhibiting the nuclear delivery of KSHV virions via the disruption of actin filaments. Moreover, we found that KSHV RTA can promote the degradation of GBP1 through a proteasome-mediated pathway. Taken together, our results elucidate a novel mechanism of GBP1 anti-KSHV activity and emphasize the critical role of RTA in KSHV evasion of the host immune system during primary infection.
The Positive and Negative Aspects of Reactive Oxygen Species in Sports Performance

Physical exercise, especially moderate physical exercise, can benefit health in a wide range of ways [1-3]. Evidence from different age groups, genders and races has revealed that regular physical exercise is associated with high levels of physical fitness and a reduced risk of mortality, while sedentary habits are related to low levels of physical fitness and an increased threat of all-cause mortality [1-8]. However, the physiological mechanism of physical exerciseinduced physical fitness remains only partly understood.
Psychometric Properties of a Short Self-Reported Measure of Medication Adherence Among Patients With Hypertension Treated in a Busy Clinical Setting in Korea

Background We examined the psychometric properties of the Korean version of the 8-item Morisky Medication Adherence Scale (MMAS-8) among adults with hypertension. Methods A total of 373 adults with hypertension were given face-to-face interviews in 2 cardiology clinics at 2 large teaching hospitals in Seoul, South Korea. Blood pressure was measured twice, and medical records were reviewed. About one-third of the participants (n = 109) were randomly selected for a 2-week test-retest evaluation of reliability via telephone interview. Results Internal consistency reliability was moderate (Cronbach α = 0.56), and test-retest reliability was excellent (intraclass correlation = 0.91; P < 0.001), although a ceiling effect was detected. The correlation of MMAS-8 scores with scores for the original 4-item scale indicated that convergent validity was good (r = 0.92; P < 0.01). A low MMAS-8 score was significantly associated with poor blood pressure control (χ2 = 29.86; P < 0.001; adjusted odds ratio = 5.08; 95% CI, 2.56–10.08). Using a cut-off point of 6, sensitivity and specificity were 64.3% and 72.9%, respectively. Exploratory factor analysis identified 3 dimensions of the scale, with poor fit for the 1-dimensional construct using confirmatory factory analysis. Conclusions The MMAS-8 had satisfactory reliability and validity and thus might be suitable for assessment and counseling regarding medication adherence among adults with hypertension in a busy clinical setting in Korea.