bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sustainable Bacterial Cellulose-Based Composite Aerogels with Excellent Flame Retardant and Heat Insulation
Authors: Wang Zhixin , E YuYu , Li Jie , Du Tingting , Wang Kun , Yao Xi , Jiang Jianxin , Wang Meng 
Published: September 2022

Abstract
Bacterial cellulose (BC) aerogels with ultralight, low density, and low thermal conductivity are hopeful candidates for environmentally friendly heat insulating materials. However, the application of BC in packaging and building as a heat nonconductor is seriously limited by its flammable characteristics. Hence, we report a moderate approach to fabricating a flame retardant and heat-insulating composite aerogel by introducing zinc borate (ZB) particles into BC via an ultrasound-assistant deposition process. The in situ synthesis of ZB particles resulted in the separation of single fibers from fiber bundles in BC instead of damaging its homogeneous porous structure, improving its thermal stability by weakening the convective intensity of heat rays. The heat release capacity of the prepared composite aerogel was only 8 J·g− 1 k− 1, exhibiting excellent flame retardancy. The probable mechanism proposed to be that the dehydration of ZB particles lowered the surface temperature by releasing the bound water, and simultaneously produced metallic oxides (ZnO and B2O3) for retarding the spread of heat and isolating the flammable fibrils within the combustion area. Because of the natural substrate and simple process, the present approach has potential applications for industrial-scale production of green and flame retardant material.
Performance Evaluation of LIBSVM and MSVM in Human Age Estimation and Gender Identification from Digital Images of Femur bone
Authors: C Santosh K , Pradeep N , Chakrabarti Tulika , Chakrabarti Prasun , Elngar Ahmed A. , Nami Mohammad , Ravi Vinayakumar , Rajchakit Grienggrai , Akbar M Ali 
Published: September 2022

Abstract
In forensic science, identification of age and gender from the skeletal remains is a time-consuming and challenging task. It is known that bones in the human skeleton develop from several separate centers of ossification. It is challenging to determine the gender and the age of fatalities in mass disasters, aviation crashes, wildfire victims, exhumations, and bomb explosions. Therefore, the use of computer-assisted techniques is necessary and important in medical image analysis since they enable forensic science to produce results quickly. Manual assessment techniques based on a visual inspection of the bones reveal their incapacity for precise classification. Long bone plays an important role in medical and legal investigations since they resist environmental impacts. Hence, digital x-ray images of the femur bone are considered as input for the experiment. In this paper, the major contributing features of the femur are identified which will facilitate determining the age and gender of a person. We have developed a classification system using SVM Classifiers. For age estimation, the Multiclass SVM (MSVM) classifier is used, and gender determination uses the LIBSVM classifier. As per the medical literature, the average neck-shaft angle is 132° for men and 121° for women. The average head diameter is 46 mm for men and 39 mm for women. Males' femur neck angles can range from 122° to 149°, while females can range from 113° to 141°. Males' head diameters can range from 33.6 mm to 51.9 mm, while female head diameters can range from 31.4 mm to 46.7 mm. From the experimental results, the age estimation and gender identification are above 90% using Multiclass SVM (MSVM) and LIBSVM respectively. The findings of the current study reveal that there are differences in statistical significance between male and female femur radiograph measurements, with male feature values marginally predominating over female feature values in most of the samples.
A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detection
Authors: Fki Zeineb , Ammar Boudour , Fourati Rahma , Fendri Hela , Daoued Emna , Mnif Zeineb , Hussain Amir , Ayed Mounir Ben 
Published: September 2022

Abstract
Background: The impact of coronavirus (COVID-19) pandemic on health care is universal. The risks resulting from emerging contagious viruses and the efficacy of vaccines are persisting due to the presence of different variants. Learning of deeper and more interpretable models from COVID-19 data are conducive to understand this disease and to study the virus spread, individual diagnosis and may be other engrossing relating issues. However, some difficulties and intricacies are arising from the scarcity of precisely labelled data. Previous works have exploited existing Deep Neural Network (DNN) models that are pre-trained on large datasets like ImageNet.
Method: In this paper, a new framework is proposed in order to monitor and predict COVID-19 cases and other diseases, pursuing medical data. The currently proposed framework essentially relies on (1) an Internet of Things (IoT) processing model to collect data and operate on them later, (2) a DNN model for data processing, known as REGATT. This proposed model is based on a pre-trained REGNet model finely tuned by spatial, channel ATTention and convolutional layers, boosting feature representation and discrimination.
Results: Comparative experimental results on four different benchmark datasets show that the proposed model leads to a promising solution for diagnosing COVID-19.
Conclusion: It is concluded that an IoT and DNN-based solution are a viable way for the diagnosis of not only COVID-19 but also other diseases. It is advisable that future works explore the development of interpretable models.
AMPK attenuates SHH subgroup medulloblastoma growth and metastasis by inhibiting NF-κB activation
Authors: Yue Shen , Cai Jing , Wang Yue , Wang Xin fa , Li Tian yuan , Ai Zi he , Pu Xiao hong , Yang Xin , Yao Yi xing , He Jun ping , Cheng Steven Y , Yu Ting ting , Liu Chen 
Published: September 2022

Abstract
Background: Medulloblastoma (MB) is one of the most common malignant pediatric brain tumors. Metastasis and relapse are the leading causes of death in MB patients. The initiation of the SHH subgroup of MB (SHH-MB) is due to the aberrant activation of Sonic Hedgehog (Shh) signaling. However, the mechanisms for its metastasis are still unknown. Results: AMP-dependent protein kinase (AMPK) restrains the activation of Shh signaling pathway, thereby impeding the proliferation of SHH-MB cells. More importantly, AMPK also hinders the growth and metastasis of SHH-MB cells by regulating NF-κB signaling pathway. Furthermore, Vismodegib and TPCA-1, which block the Shh and NF-κB pathways, respectively, synergistically restrained the growth, migration, and invasion of SHH-MB cells. Conclusions: This work demonstrates that AMPK functions through two signaling pathways, SHH-GLI1 and NF-κB. AMPK-NF-κB axis is a potential target for molecular therapy of SHH-MB, and the combinational blockade of NF-κB and Shh pathways confers synergy for SHH-MB therapy.
Gut microbiota-derived short-chain fatty acids promote prostate cancer progression through inducing cancer cell autophagy and M2 macrophage polarization
Authors: Liu Yufei , Zhou Quan , Ye Fangdie , Yang Chen , Jiang Haowen 
Published: September 2022

Abstract
Objective: Emerging research have reported the regulative role of gut microbiota-derived short-chain fatty acids (SCFAs) within tumor microenvironment. In previous study we have demonstrated abnormal gut microbial composition in castration-resistant prostate cancer (CRPC) patients, here we sought to reveal the mechanism of SCFAs as a mediator linking microbiota dysbiosis and prostate cancer (PCa) progression.
Methods:By using transgenic TRAMP mouse model, PCa patient samples, in vitro PCa cell transwell assay, and macrophage recruitment assay, we examined the effects of fecal microbiota transplantation (FMT) and SCFAs on PCa progression.
Results: FMT using CRPC patients’ fecal suspension increased the abundance of SCFAs-producing gut microbiotas in TRAMP mice including Ruminococcus, Alistipes, Phascolarctobaterium, and correspondingly raised mice’s gut acetate and butyrate levels. CRPC FMT or SCFAs supplementation accelerated TRAMP mice’s cancer progression. In vitro, SCFAs enhanced PCa cells migration and invasion by inducing TLR3-triggered autophagy that further activated NF-κB and MAPK signalings. Also, PCa cell-derived CCL20 activated by SCFAs reprogrammed the tumor microenvironment by recruiting more macrophage infiltration and simultaneously inducing M2 macrophage polarization, which in turn further strengthened PCa cells invasiveness. Finally in a large cohort of 362 PCa patients from our department, we demonstrated that CCL20 expression in prostate was positively correlated with Gleason grade, pre-operative PSA, neural invasion, seminal vesical invasion, and was negatively correlated with post-operative biochemical recurrence-free survival. Gut microbiota dysbiosis-related CCL20 could be a biomarker for predicting prognosis in PCa patients.
Conclusion: Collectively, gut microbiota dysbiosis-derived SCFAs promoted PCa progression through inducing cancer cell autophagy and M2 macrophage polarization. Intervention of SCFAs-producing microbiotas may be a useful strategy in the manipulation of CRPC.
Electrical modeling of high voltage windings of power transformers for online partial discharge localization
Authors: Hamidi Abdolsamad , Hamidi Abdolsattar , setayeshmehr Alireza 
Published: September 2022

AbstractPartial Discharge (PD) is one of the most important factors in the gradual destruction of transformer insulation, more importantly if the problem is not resolved can cause complete insulation failure. Physical localization of the fault inside the transformers in terms of saving repair time and cost is important. In this paper, by modeling a high voltage winding of a power transformer as a single-layer disk winding with air core the localization of PD is investigated. In this configuration, winding consists of six disks. Here, by applying impulse voltage as PD signal to the beginning of each disk, the voltages are measured and saved online at the bushing and neutral ends of the winding. By using the curve fitting method, the equations of PD signals at these two ends are extracted, then by computing transfer function in Laplace domain the magnitude of this transfer function are saved as a reference diagram. We have done this for all 6 disks. When PD occurred at winding by measuring voltages at bushing and neutral ends and drawing transfer function and comparison with reference diagrams, each diagram that has minimum difference with drawn diagram is the place of PD. Finally, the proposed method is validated with a laboratory winding with six disks.
Reciprocal Prospective Effects among Parental Psychological Distress, Family Cohesion, and Child Socioemotional Behavior in Three Dyads within Families
Authors: Yu Jeong Jin 
Published: September 2022

Abstract
The present study explores the dyadic longitudinal interplay among parents’ psychological distress and cohesion and children’s internalizing behaviors and peer-related social competence within an individual and across one’s dyadic partner, using data from three dyads and multiple informants. Data came from a nationally representative longitudinal cohort study of children born between April and July 2008 across South Korea. The current analyses were based on 1,779 families. Mothers, fathers, and preschool teachers responded to questionnaires. Children were four years old (48.6% girls) and the mean ages of mothers and fathers were 34.8 and 37.3 years, respectively at baseline. There were prospective and bidirectional associations between maternal psychological distress and children’s internalizing behaviors and between maternal cohesion and children’s internalizing behaviors. Paternal psychological distress was longitudinally related to children’s internalizing behaviors. Children’s peer social competence was predictive of their own internalizing behaviors and paternal psychological distress and cohesion. Maternal and paternal psychological distress and cohesion were prospectively and bidirectionally associated. The findings demonstrated prospective transactions within the three family dyads, while highlighting the importance of obtaining data on each family member and considering interactive effects of both parent and child gender in this line of enquiry.
Effects of impact energy on the crown formation and underwater cavity of free-falling thick disks
Authors: Janati Mahsa , Azimi Amir Hossein 
Published: September 2022

Abstract
A series of laboratory experiments was conducted to investigate the effects of impact energy on the crown formation and cavity dynamics of free-falling thick disks in stagnant water. To simulate the effects of impact energy on the dynamics of free-falling thick disks, four different release heights, hr, and six different disk masses were considered. A threshold limit of hr = 4do, where do is the disk diameter, was obtained as different patterns of cavity dynamics and crown characteristics were observed during the impact and descending of solid disks. The evolution of crown characteristics such as crown shape, diameter, and height with time were measured, and the pinch-off time and location of pinch-off were extracted from image analysis. Experimental results revealed that the crown characteristics at the pinch-off varied linearly with the impact Froude number and they were larger than solid spheres. A threshold value of non-dimensional impact energy to create a full seal crown was obtained which occurred when impact energy was more than 36% of the initial energy. The variations of crown diameter at the pinch-off indicated relatively larger crown diameters for hr/do > 4 whereas the crown height was invariant with release height. The results indicated a relatively shorter pinch-off depth in gravity-driven disks in comparison with the force-driven disks and gravity driven sphere in stagnant water. The energy losses due to impact, crown formation, and pinch-off were calculated for all tests and an adverse correlation was found between normalized energy losses and impact Froude number. Experimental observations indicated that disks with smaller impact energy dissipated more energy due to the impact. The time variations of the frontal position and velocity of disks showed that the threshold release height highly affected the trajectory, falling speed, and the duration of the descending process.
An Algorithm to Create Sorted FP-Growth Tree for Extracting Association Rules
Authors: Barkhan M. , Ramazani R. , Kabani A. 
Published: September 2022

Abstract
Mining data in massive databases to find useful data, frequent patterns (FP) and knowledge discovery, has been studied popularly in data mining research fields minding governments, industries and sales companies. In data mining, frequent patterns as growth tree are an efficient method for discovering knowledge and compressing information in a tree structure. Previous studies presented various methods to achieve frequent patterns which even require complex process and costs, particularly if the patterns overnumbered. In this paper we provide a divide and conquer algorithm based on FP-Growth Tree to create an initially sorted tree structure of the nodes that the most frequent patterns would be available at every moment of the tree construction procedure. In addition, we can consider a parameter to avoid inserting less frequent branches, thus, the tree would be sorted from the beginning of its formation.
FPGA implementation of a real-time edge detection system based on an improved Canny algorithm
Authors: Guo Laigong , Wu Sitong 
Published: September 2022

Abstract
Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is complex, time-consuming process and has a high hardware cost. In addition, most of the existing implementations of the algorithm use the same fixed pair of high and low threshold values for all input images. This cannot automatically adapt to changes in the external detection environment and results in decreased performance. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware cost. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations,and the division operation is complex, has low efficiency and slow speed. We introduce a logarithmic unit to turn division into a subtraction operation that is easy to implement by hardware but which does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 x 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms have limited logical resources.
Kinetics of Thermal Degradation of Raw Lacquer Enhanced by Formaldehyde Urea Prepolymer
Authors: Xiao Qiang , Cao Yanjun , Zheng Wenyu , Hou Tianle , Gao Shuhang , Lyu Jianhua , Xiao Hui , Chen Yuzhu , chen ming 
Published: September 2022

Abstract
In this study, formaldehyde-urea prepolymer (FUP) were synthesized, which were used to modify the raw lacquer (RL), while the basic properties of the RL were tested. Thermal gravimetric analysis and scanning electron microscopy were used to analyze the degradative characteristics and the surface morphology of RL before and after modification. The result indicated that FUP can significantly improve the performance of RL. The drying time of the modifying RL is significantly shortened the gloss, the pencil hardness, and the impact performance are also significantly enhanced at the same time. TG analysis and thermal decomposition kinetics analysis illustrated that the thermal stability and the activation energy of LF2 were stronger than that of RL. In addition, scanning electron microscopy analysis illustrated that the surface smoothness of RL were also improved.
Parameter uncertainty of Land Use Change Impact Prediction on the Hydrology of the Upper Blue Nile Basin, Ethiopia
Authors: Anberber Tewodros 
Published: September 2022

AbstractLand use land cover (LULC) has changed significantly due to population growth and causing a huge environmental problem at global and regional level, especially in countries where their economy depends on agriculture. Previous studies have used hydrological models to assess the impact of LULC change on the hydrology of Upper Blue Nile (UBN) basin, but may not have addressed a wide range of uncertainties which arises due to parameter, error in input data, model structure uncertainty and level of understanding of the user. The aim of this research was to estimate the effect of parameter uncertainty on land use change impact prediction on UBN basin, Ethiopia. SWAT model was chosen and set up in the UBN basin and Sequential Uncertainty Fitting (SUFI2) technique was used for calibration, sensitivity and uncertainty analysis of the model. Parameter uncertainty range, prediction change uncertainty and the impact of land use change on the UBN basin hydrology was assessed using two hypothetical land use scenarios (Scenario 1: convert half of forested area in to agricultural land andScenario 2: completely change the forest cover into agriculture). The results of the simulation indicated that SWAT model simulate the flow for the UBN basin with p-factor of 0.94 and 0.85 for calibration and validation respectively, while the r-factor were 0.86 and 0.71 for calibration and validation, respectively. The goodness of fit results between the observed and simulated flow showed that NSE for calibration and validation period were 0.87 and 0.89, respectively, and PBIAS for calibration and validation period were − 2.7% and 1.4%, respectively. Average daily stream flow was increased by 5.5% and 11% for scenario 1 and scenario 2, respectively compared to the baseline. For both hypothetical scenarios, the parameter uncertainty shows an effect for the predicted flow and based on coefficient of variation CN, CANMX and ALPHA_BF have relatively shows higher uncertainty. Additionally, predicted change uncertainty for the minimum flow is higher than the average and maximum flow. The predicted effect of land use change on the basin is significant and there is also a chance of land use change to occur in the UBN basin, due to that soil and water management practices should be done in the basin to prevent environmental and socio-economic problems which arise due to land use changes. Uncertainties on model prediction should not be ignored in LULC change studies to get a better understanding for decision making and to avoid over and underestimation of possible pressures.
Exponentially fitted robust scheme for the solution of singularly perturbed delay parabolic differential equations with integral boundary condition
Authors: Gobena Wakjira Tolassa , Duressa Gemechis File 
Published: September 2022

Abstract
In this paper, an exponentially fitted finite difference method is developed to solve singularly perturbed delay parabolic partial differential equations having a large delay on the spatial variable with an integral boundary condition on the right side of the domain. The problem's solution exhibits an interior layer and a parabolic boundary layer on both ends of the spatial domain. Simpson's rule is applied to treat the integral boundary condition. Uniform convergence analysis has been carried out, and it is observed that the method is first-order convergent in the time direction and second-order in the spatial direction. Numerical examples and results are considered to validate the scheme's applicability, and it also improves the results of the methods existing in the literature.
MSC Classification: 65L11 , 65M06 , 65M12.
Bioprocess development for Extraction and purification of cellulases from Aspergillus niger 3ASZ using statistical experimental design techniques
Authors: Sorour Aman A. , Olama Zakia A. , El-Naggar Moustafa Y. , Ali Safaa M. 
Published: September 2022

AbstractBackground:The amount of cellulosic materials is large and may lead to environmental pollution, so it can be converted into useful materials for use in food or energy. Statistical design (Plackett–Burman and Box-Behnken) was the main topic of this study and was used to optimize the effect of environmental factors on cellulase production byAspergillus niger.Results:Cellulase production using Plackett–Burman was 6.86-fold higher than the production of cellulase using basal medium. Subsequently, the design of BOX-Benken was affected to find the superlative procedure environments. The expected cellulase activity was 79.4 U/ml/min, which was adjacent to 18 times the enzyme activity. Ammonium sulfate precipitation was applied to the crude enzyme, followed by sequential fractionation with an Amicon system. The Amicon was used to demonstrate the final volume, total enzyme activity, specific activity, purification fold, and yield of cellulase (partially purified enzyme).Conclusion:Numerous cellulolytic enzymes are abundant inAspergillusspecies. All of the data showed thatAspergillussp. might be a reliable source of industrially and economically useful cellulases. By statistically calculating the relevance of a large number of elements in one experiment using a multifactorial statistical design, time may be saved while still maintaining the validity of each component.