Miracle-dz/UniFAR-FTUs huggingface dataset:
An effective web page reorganization through heap tree and farthest first clustering approach

Web usage mining is used to extract interesting and useful information from a web server log file. The log file is automatically stored in web server. It contains details about the web user activity on the website. Web users' interest changes over time, which is not stable. Hence, the static website will get outdated soon. So Website needs to be modified with minimal changes to meet user requirements. The objective of this research is to retrieve required web pages with minimal search cost in a website. Preprocessing is done to get the required data. An algorithm Max Heap with Farthest First Clustering Approach (HFCA) is proposed in which Farthest first clustering algorithm is used to cluster frequently accessed web pages and then the web pages are reorganized using the max heap tree. Reorganization is based on the frequently accessed web pages. Experimental results shows that max heap tree along with the farthest first clustering gives better performance and minimal search cost between the web pages. It is suitable for the dynamic website that needs a change periodically.
Long‐term immunosuppression for CNS mouse xenotransplantation: Effects on nigrostriatal neurodegeneration and neuroprotective carotid body cell therapy

The use of long‐term immunosuppressive treatments on neural transplantation has been controversial during the last decades. Although nowadays there is a consensus about the necessity of maintaining a permanent state of immunosuppression to preserve the survival of cerebral grafts, little is known about the effects that chronic immunosuppression produces both on the neurodegenerative process and on transplants function.
Dose-interval study of a dual probiotic in preterm infants

Objective The objective of this study was to investigate the appropriate dosing interval of a probiotic (Infloran) given daily, biweekly and weekly in preterm infants <32 weeks’ gestation. Methods There were 8 infants in the daily group, 8 infants in the biweekly group and 10 infants in the weekly group, all born between 25 and 32 weeks’ gestation. The control group consisted of 12 preterm infants who did not receive the probiotic. Infloran (250 mg/capsule), containing Bifidobacterium bifidum (1×109 colony-forming unit (CFU)) and Lactobacillus acidophilus (1×109 CFU), was administered in 2.5 mL of breast milk per kilogram weight of the infant (2×109 CFU of bacteria in total), until 34 weeks postmenstrual age (PMA). Stool samples were collected at 31, 34, 41 and 44 weeks PMA and frozen at −20°C. Results After administration of the probiotic at 31 weeks PMA, Bifidobacterium were significantly higher in the daily group (45%) in comparison with the biweekly (17%) and weekly (9%) groups. At 34 weeks PMA, Bifidobacterium were significantly higher again in the daily (60%) group in comparison with the biweekly (21%), weekly (23%) and control (15%) groups. At 41 weeks PMA a decrease in the relative abundances of Streptococcaceae and Enterococcaceae was found in all three probiotic groups, and by 44 weeks PMA significantly higher levels of Lactobacillus were found in the biweekly group (16.5%) in comparison with the weekly group (2.1%). Conclusion Our results indicate that a daily dose of Infloran is a suitable dosage for preterm infants in the neonatal intensive care unit, with significantly higher levels of Bifidobacterium found in the daily probiotic group up to 44 weeks PMA.
Comparison of accuracy values of biomedical data with different applications decision tree method

Data mining is used in many different areas. But the most important of these is the use of biomedical data in data mining. The results obtained from these data contribute to the pre-detection, prevalence and treatment of the diseases. Decision trees are one of the important tools that enable accurate assessment of these biomedical data. There are many applications that use decision trees such as Weka, R, Rapid Miner, Knime and Orange. Whether these practices correctly apply decision trees is of particular importance in terms of biomedical data. In this study, biomedical datasets taken from UCI Machine Learning Repository were classified by C4.5 algorithm. Data mining applications, which are widely used in the market for classification, have been selected. The accuracy parameter is used for the analysis. This study aims to reach the conclusion which application will give more accurate results about not analyzed biomedical datasets.
Predicting Mortality in Patients with Tuberculous Destroyed Lung Receiving Mechanical Ventilation

Background Patients with acute respiratory failure secondary to tuberculous destroyed lung (TDL) have a poor prognosis. The aim of the present retrospective study was to develop a mortality prediction model for TDL patients who require mechanical ventilation. Methods Data from consecutive TDL patients who had received mechanical ventilation at a single university-affiliated tertiary care hospital in Korea were reviewed. Binary logistic regression was used to identify factors predicting intensive care unit (ICU) mortality. A TDL on mechanical Ventilation (TDL-Vent) score was calculated by assigning points to variables according to β coefficient values. Results Data from 125 patients were reviewed. A total of 36 patients (29%) died during ICU admission. On the basis of multivariate analysis, the following factors were included in the TDL-Vent score: age ≥65 years, vasopressor use, and arterial partial pressure of oxygen/fraction of inspired oxygen ratio <180. In a second regression model, a modified score was then calculated by adding brain natriuretic peptide. For TDL-Vent scores 0 to 3, the 60-day mortality rates were 11%, 27%, 30%, and 77%, respectively (p<0.001). For modified TDL-Vent scores 0 to ≥3, the 60-day mortality rates were 0%, 21%, 33%, and 57%, respectively (p=0.001). For both the TDL-Vent score and the modified TDL-Vent score, the areas under the receiver operating characteristic curve were larger than that of other illness severity scores. Conclusion The TDL-Vent model identifies TDL patients on mechanical ventilation with a high risk of mortality. Prospective validation studies in larger cohorts are now warranted.
Major Chronic Respiratory Diseases in Chiang Mai: Prevalence, Clinical Characteristics, and Their Correlations.

Objective To identify the prevalence, clinical characteristics, disease severity, and correlations of major chronic respiratory diseases (CRDs) among the adult population living in Chiang Mai. Material and Method A cross-sectional study was conducted with adults living in municipal areas of Chiang Mai. All clinical relevant data collected by face-to-face interview was confirmed by pulmonologists. The chest radiographic findings and post-bronchodilator spirometry were done in all subjects. The aeroallergen skin test and rhinoscopy were performed in all chronic rhinitis and asthma subjects. Results Five hundred seventy four subjects with mean age 52.9±10.0 years, 59.6% female, and 37.5% smokers were recruited. The prevalence of overall CRDs was 59.2%. Chronic rhinitis was the most prevalent chronic respiratory disease (n = 239, 41.6%), followed by asthma (n = 58, 10.1%), and chronic obstructive pulmonary disease (COPD) (n = 21, 3.7%). The most common abnormal pulmonary function test was restrictive lung disorders (n = 53, 9.6%). Asthma subjects were determined to be more allergic than chronic rhinitis subjects (58.1% vs. 39.9%, p-value = 0.033). Regarding the disease severity, 14.9% of chronic rhinitis and 10.3% of asthma subjects were classified as moderate to severe degree, whereas 81% of chronic obstructive pulmonary disease subjects were classified as moderate to very severe degrees. In asthma patients, there were positive association with chronic rhinitis (OR 3.9, 95% CI 2.1-7.0, p-value <0.001). Conclusion The prevalence of major CRDs in adults among Chiang Mai population was significantly high with overlapped respiratory symptoms and varying disease severity. Additionally, chronic rhinitis had correlation with asthma but not with COPD.
Use of linear interpolation for automated drip irrigation system in agriculture using wireless sensor network

India is better known for its agriculture and cultivation land. Indian economy is dependent on farming and farm product Business. It is one of the most adorable countries for farming/cultivating various farm products. As Indian economy depends on Agriculture product and its development, lots of experiments needs to be done in this sector to increase the productivity and the profit margin. The biggest concern in land cultivation is water utilization. In India Rainfall is very uncertain in various parts of country. And because of this agriculture yield production is unreliable. Especially utilization of available water for agriculture is major issue now days. In this paper, proposed system provides a mechanism for available water distribution among various crops cultivated in the farms and its effect on farm yield production. The Wireless Sensor Network provides a very optimal solution for water distribution using Interpolation methods. WSN describes the methods controlling various parameter like Temperature, Soil moisture and humidity to water management process.
An Efficient and Cost-Effective Hybrid MPPT Method for a Photovoltaic Flyback Microinverter

This paper presents a grid-connected photovoltaic (PV) flyback inverter operating in discontinuous conduction mode (DCM) and controlled through an efficient, reliable, and cost-effective hybrid maximum power point tracking (MPPT) method. Although the proposed method can be applied to any grid-connected inverter topology, in this paper, it is applied on a modified flyback inverter circuit. Moreover, the proposed MPPT method is a two-stage scheme and hence, a hybrid solution, as it consists of a combination of fractional short-circuit current and hill climbing perturb and observe (P$\&$O) method reformulated. The change in weather condition is accurately detected using the current limit technique such that the difference in short-circuit current and photovoltaic current is monitored continuously. It is demonstrated that the use of current limit enables the system to swiftly track the maximum power point (MPP) in abruptly changing environmental conditions. The proposed MPPT method needs only two sensors for current and voltage measurements and is, therefore, cost effective and reliable. The system is tested through simulations and real-time setup using dSPACE DS1104. The results prove the effectiveness of the proposed hybrid MPPT compared with the P$\&$ O method under uniform and dynamic weather conditions.
8-F2-isoprostane, thioredoxin and thioredoxin reductase levels in children with obsessive–compulsive disorder

Abstract Purpose: Accumulating data demonstrate that oxidative stress may play a crucial role in obsessive–compulsive disorder (OCD). This study aimed to investigate the role of 8-F2-isoprostane, thioredoxin (Trx), and thioredoxin reductase (TrxR) in children with OCD. Materials and methods: Thirty-three drug-free children with OCD and 35 healthy controls were included in this study. The severity of OCD symptoms was assessed via the Children’s Yale Brown Obsessive-Compulsive Scale. The severity of anxiety levels was determined through the Screen for Child Anxiety-Related Emotional Disorders. Plasma levels of 8-F2-isoprostane, Trx, and TrxR were measured using commercial enzyme-linked immunosorbent assay kits. Results: Plasma 8-F2-isoprostane, Trx, and TrxR levels did not show any significant differences between patient and control groups. There were no significant correlations between plasma levels of these antioxidants and severity of OCD. Conclusions: Findings of this study did not support the involvement of oxidative stress in the etiology of childhood OCD.
A Social Ecological Modeled Explanation of the Resilience Processes of a Sample of Black Sesotho-Speaking Adolescents

The primary aim of the study that this article reports was to model and test a social ecological explanation of resilience as explained by Ungar. Its secondary aim was to investigate resilience-promoting supports in school-going Black South African adolescents. School attendance was specified as a culturally appropriate, functional outcome of resilience. The Pathways to Resilience Research Project gathered data through the Pathways to Resilience Youth Measure. Seven hundred and thirty school-going adolescents (age 12–19 years, 388 female, 341 male, one unspecified) from Thabo Mofutsanyana District, in South Africa’s Free State province, participated in this cross-sectional study. Latent variable modeling was used to test measurement models of adolescents’ self-reported perceptions of social ecological contributions (resources and risks) to their resilience. A complex model based on a social ecological explanation of resilience fitted the data best. The structural model showed that the resilience process predicted 32% of the variance in school attendance. Social skills, cultural, and spiritual resources were most supportive of adolescents’ resilience. The results confirmed that the complex model explained resilience in Black South African adolescents as a person-context relational process and prompt principals, parents, teachers, and governmental departments to encourage school attendance.
Time portability evaluation of RCNN technique of OD object detection — machine learning (artificial intelligence)

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.
DMS‐seq for In Vivo Genome‐Wide Mapping of Protein‐DNA Interactions and Nucleosome Centers

The genome exerts its functions through interactions with proteins. Hence, comprehensive identification of protein‐occupied sites by genomic footprinting is critical to an in‐depth understanding of genome functions. This unit describes the protocol of dimethyl sulfate‐sequencing (DMS‐seq). DMS is an alkylating reagent that methylates guanine and adenine in double‐stranded DNA. DMS added to the culture medium readily enters the cell and methylates its DNA throughout the genome except for the regions bound by proteins, thereby obviating the need for nuclear isolation in genomic footprinting. Polyamine/AP‐endonuclease treatment of DNA isolated from DMS‐treated cells induces cleavages at the methylated sites. Deep sequencing of these fragments identifies protein‐bound sites as peaks of protected fragments or troughs of cleavage sites. Furthermore, DMS displays an unexpected preference to nucleosome centers, enabling their direct detection without genetic manipulation. Therefore, DMS‐seq provides a unique method for non‐targeted profiling of in vivo protein‐DNA interactions. © 2018 by John Wiley & Sons, Inc.
Can job insecurity be managed? Evaluating an organizational-level intervention addressing the negative effects of restructuring

ABSTRACT Although downsizing and reorganisation are recognised as serious threats to the psychological well-being of employees, intervention strategies for addressing these events are limited. This study evaluated the effects of a participatory organisational-level intervention in which employees and managers chose to address the psychosocial consequences, specifically job insecurity, of restructuring. The intervention was conducted among postal service letter carriers in Denmark and was evaluated based on quantitative and qualitative data. Using interviews (N = 24) and observations, the programme theory of the intervention and to what extent the intervention had been implemented were assessed. Using survey data (N = 238), repeated measures ANOVAs were conducted to test for differences in the development of job insecurity between the intervention group and a comparison group. The results indicate that the intervention group had a significantly smaller increase in one dimension of job insecurity as compared to the comparison group. Therefore, we conclude that employees’ experiencing of job insecurity, which typically follows in the wake of restructuring, can be addressed by planned efforts at the workplace level.
Case study: Integrating IoT, streaming analytics and machine learning to improve intelligent diabetes management system

Today petabytes of data are stored in healthcare sector such as clinical and pharmaceutical and academic research archives. Trillions of structured and unstructured data are continuously streaming from wearable sensors such as activity trackers, continuous glucose monitoring devices, and implantable defibrillators. Present day machine learning diabetes management applications are helping numerous patients to live healthier lives by having easy access to advice and information from health specialists. The advent of digital devices and sophisticated analytics has attracted traditional companies into the digital revolution. The paper studies how Zion China technical solution E-Followup which was based mostly on traditional Business Intelligence with data sourced from on-premises and various devices or cloud storage. In this engagement, they wanted a smart, fast, and cost-effective way to continuously feed data from devices to the cloud and had other technical goals. The result provide an insight how they handled massive data volumes efficiently and improved analysis of data.
Proteoglycan Isolation and Analysis

Proteoglycans can be difficult molecules to isolate and analyze due to large mass, charge, and tendency to aggregate or form macromolecular complexes. This unit describes detailed methods for purification of matrix, cell surface, and cytoskeleton‐linked proteoglycans. Methods for analysis of glycoaminoglycan size and type and of core protein species are described. © 2018 by John Wiley & Sons, Inc.
Fundamental frequency switching strategy of 120-unit cascaded half-bridge multilevel converter for battery management system

This paper presents a fundamental frequency switching strategy for single phase modular multilevel converter that consists of 120 cascaded battery-based half-bridge units, aiming to generate voltage with low harmonics. The strategy is based on the calculation of the Delay-Time for each unit, after which the half-bridge switches to change the state (connected or disconnected) of the corresponding battery. In this manner, the number of battery cells connected in the circuit is varied and a staircase shape voltage is synthesized. Moreover, the internal resistance of battery can cause the terminal voltage deviation and lead to inaccurate calculation of Delay-Time, which will introduce more harmonics. An improved method is developed to compensate the battery voltage drop. State-space representation is used to describe the circuit and battery model. Simulation is conducted to verify the modulation technique.
Combat Exposure, Wartime Performance, and Long-Term Adjustment Among Combatants

This study examined the contribution of exposure to specific battlefield stressors (life-threatening situations, injuries and death, active fighting, and the fallibilities of one's own army) to combatants' battlefield functioning and long-term psychological adjustment. Participants consisted of 399 Israeli veterans of the 1973 Yom Kippur War, categorized by their wartime functioning into 3 groups: combat stress reaction (CSR) casualties, decorated war heroes, and controls (i.e., those who functioned adequately but without special distinction). Findings show that, even though the decorated war heroes reported the highest exposure to battlefield stressors, they functioned better than the other 2 groups during the war. Moreover, some 2 decades later, they showed lower rates of posttraumatic stress disorder and better general psychological health than the CSR casualties. Findings also show that battlefield functioning made a greater contribution to postwar pathology than battle stressors. These findings thus raise questions about how decisive the role of battle stress actually is in precipitating war-related pathology.
An improved domino logic

Power and area will remain the main constraints in the designing of VLSI circuits. Dynamic CMOS logic circuits are broadly designed for high performance circuits due to their high speed. Conversely, the demerit of dynamic logic style is its high noise sensitivity and during cascading several problems like charge sharing and charge degradation arises. The main cause of this is the sub-threshold leakage current flowing through the pull down network and with continuous technology scaling, this problem is getting more and more severe. Compared to static CMOS circuits which have larger capacitance and offers more delay, Dynamic circuits are used to improve those parameters of design. In this paper we have proposed a new circuit in order to improve the power consumption by applying a multi-threshold technique. Simulations are done in Cadence for 90nm with IBM technology for OR gates with supply voltage of 1v.
Marefa: Turning Publishers Catalogs' Data Into Linked Data

This article describes how recently, many new technologies have been introduced to the web; linked data is probably the most important. Individuals and organizations started emerging and publishing their data on the web adhering to a set of best practices. This data is published mostly in English; hence, only English agents can consume it. Meanwhile, although the number of Arabic users on the web is immense, few Arabic datasets are published. Publication catalogs are one of the primary sources of Arabic data that is not being exploited. Arabic catalogs provide a significant amount of meaningful data and metadata that are commonly stored in excel sheets. In this article, an effort has been made to help publishers easily and efficiently share their catalogs' data as linked data. Marefa is the first tool implemented that automatically extracts RDF triples from Arabic catalogs, aligns them to the BIBO ontology and links them with the Arabic chapter of DBpedia. An evaluation of the framework was conducted, and some statistical measures were generated during the different phases of the extraction process.
A NOVEL APPROACH TO CLASSIFY HUMAN-MOTION IN SMART PHONE USING 2D-PROJECTION METHOD

Classification of human motion is important for measuring the intensity of physical activities. Although the accelerometer in the smart phone has been used widely for classification, phone’s various orientations lead to erroneous wrong classification of human motions during physical activity. Hence, in this paper, we present a 2D-projection method to infer users’ horizontal (forward) and vertical (upward) acceleration from the phones’ roll angle and horizontal and vertical acceleration. To validate our method, we collected acceleration and orientation data for six common physical activities, such as sitting, standing, walking, running, climbing-up stairs and climbing-down stairs. We then used the 2D-projection to transform phones’ horizontal and vertical acceleration into users’ horizontal (forward) and vertical (upward) acceleration. In the results of decision tree classification, we find that using acceleration based on users improves performance over using acceleration based on phone, while the classification of climbing-up stairs and climbing-down stairs is improved somewhat but is still insufficient. Furthermore, the number of misclassification between two activities with similar horizontal (forward) acceleration, such as walking and climbing-up or climbing-down stairs was reduced by using 2D-projection.