Miracle-dz/UniFAR-FTUs huggingface dataset:
Nursery production and morphophysiological response of Pinus leiophylla in the field

The low survival of forest plants in reforestations makes it necessary to adopt nursery techniques according to the characteristics of the plantation site. An experiment was established to determine the effects of two nursery management factors on morphophysiological variables of Pinus leiophylla during initial field development. The factors tested from the nursery were established at two levels: factor 1 substrate: peat moss, raw pine sawdust; factor 2 nutrient addition rate: constant, exponential. It is concluded that the use of raw sawdust as a substrate produces small plants with high nutritional concentrations of N, P and K due to a concentration effect caused by the exponential fertilization rate, which presents adequate survival and initial growth in the field, in shallow soils of low fertility, exhibiting greater increases in height and needle biomass. Exponential fertilization increases nutrient reserves, improving initial growth in the field with both substrates, peat moss and sawdust. All transplanted individuals had a survival rate of 80 % within a degraded site, a Figure that exceeds that reported in Mexico during the last decade.
A FRAMEWORK TO MONITOR A FAILURE PREDICTIVE MACHINE LEARNING MODELS PERFORMANCE WITH NON-FAILURE DATA

We built a new framework to evaluate a production failure predictive model performance using a set of healthy non failure data. This framework gives us the capability to quickly access the model performance as soon as we deploy it into production. In the past, in order to access a failure predictive model performance, ones need to have some number of failures to happened in the field and then looking back in time if prediction was ever made for those failures. If the true positive windows is 60 days then you have to wait more than 60 days to be able to access a field model performance. We can shorten that waiting period by leveraging healthy data that we collected from the field.
Ensemble based Approach for Fake News Detection

Fake news detection is a stimulating topic for computer scientists and science. Online users are creating and sharing information more than ever before. There are many fake articles from disparate sources among various users around the world. Most of these articles are misleading, false or have no relevance to reality. It is challenging to automatically classify text articles as rumor or fake. Furthermore, analyzing the text and categorizing the news headlines or false articles for reporting purposes is also daunting. The truthfulness of the text depends on several factors. The work will consist of using an ensemble approach for machine-driven categorization of news articles. Textual properties will be identified using NLTK to summarize the contents of large articles to derive knowledge. The developed ensemble methods based approach will be evaluated with real world data and will provide considerable accuracy and also help to increase prediction power of the individual models.
An Exploratory Investigation of Learned Optimism

In the early 1960‘s, psychologist thought that they had answers to most of the important questions about human behavior. Why do human beings act the way they do? Why they become depressed, anxious or hostile? Why do they do poorly at work or in other endeavors? Present study tries to find the answer of these questions. For the study Automobile and Pharmaceutical Industries took into the consideration Keywords-Optimism, positive attitude, pessimism
CROCODILE CONSERVATION OF SONE

The Sone Gharial Sanctuary has been a safe adobe for Gharial and mugger for past three decades. No extensive study has been done so far to establish proper population density and trends. The present study was carried out to find out the status of Gharial. Approximately 122 km. stretch in the Sanctuary was surveyed and data related to population of Gharial and Mugger, in relation to their habitat, profile of river, human interference and threats were collected. During the present study, a total of 24 Gharials comprising 08 adult females, 07 sub-adult, 09 juveniles were observed. Major stretch of the river was found sub-optimal for sustenance of viable population of Gharial since biotic pressure was observed to be too high in terms of sand mining, fishing, grazing, cattle wading, agriculture and other anthropogenic activities. Thus these animals were found confined in deep pools at scattered places. Recommendations suggested on the basis of the observations during the present survey includes need of extensive survey of the entire river stretch for habitat management, reduction in human interference and flow management.
The Boltzmann Equation of Phonon Thermal Transport Solved In the Relaxation Time Approximation – I – Theory

The thermal transport of a dielectric solid can be determined by means of the Boltzmann equation regarding its distribution of phonons subjected to a thermal gradient. After solving the equation, the thermal conductivity is obtained. A largely used approach for the solution is that of considering a relaxation time approximation, where the collisions of phonons are represented by relaxation times. This approximation can be questionable, but its use is able of providing reliable information on thermal conductivity and on the role of impurities and lattice defects in the thermal transport. Here we start a discussion on the thermal conductivity in dielectric solids. The discussion is divided in two parts. In the first part, which is proposed in this paper, we analyse the Boltzmann equation and its solution in the relaxation time approximation. In a second part, which will be the subject of a next paper, we will show comparison of calculated and measured thermal conductivities
Long Time Spans Stock Trend Prediction based on LSTM

The inherent volatility of stock markets around the world makes the task of prediction challenging. Therefore, forecasting and diffusion modeling underling many difficulties in the field of stock trend forecasting. To minimize the error rate of the prediction is to minimize the risk of the investment. In the current work, this problem was used as a directional prediction exercise with predicted direction as profit and loss. A model based on the comparison of classifying stock closing price up and down N days earlier was realized. LSTM(Long Short-Term Memory), an algorithm suitable for processing and predicting data with relatively long intervals and delays in time series. Based on LSTM, a new model was developed, and according to experiment results, this model was improved in each data set by comparing with other existing models. Compared with other traditional models, the innovation point of this model lies in simple extraction of characteristics and attention to a up and down prediction relative to the past in the future. Due to high prediction accuracy, this model is of great practical value.
In Vivo and in Vitro Evaluation of the Anti- in ammatory Effect of Thymoquinone Toward Ischemic Brain Injury Alleviation via Nrf2/HO-1 Pathway

Nashwa Amin zhejiang university,School of medicine Xiaoxue Du Zhejiang Provincial First Hospital: Zhejiang University School of Medicine First A liated Hospital Shijia Chen zhejiang university,school of medicine Qiannan Ren Zhejiang Medical University: Zhejiang University School of Medicine Azhar Badry zhejiang university school of medicine Benson O.A. Botchway Zhejiang University School of Medicine Zhiying Hu Zhejiang University of Traditional Chinese Medicine A liated Integrated Chinese and Western Medicine Hospital: Hangzhou Red Cross Hospital Marong Fang (  fangmaro@zju.edu.cn ) Institute of Neuroscience, School of Medicine, Zhejiang University, Hangzhou, China. https://orcid.org/0000-0002-6636-4347
Real Time Human Activity Recognition: Smartphone Sensor based-using Deep Learning

Human Activity Recognition is an emerging field of study with a lot of innovations and applications. With digitalization, mobile development and advancement in technology taking over mankind, Smartphones have become an integral part of our life. We’ve been so dependent on Science and its innovations, that living without mobile phones is nearly impossible. With advancement in technology, comes the responsibility of providing mankind with efficient, conventional and sustainable resources. Our project aims to implement the idea of “Technology at your fingertips”. The number of elderly people is predicted to elevate over the years, “aging in place” (living at home regardless of age factors and other aspects) is becoming an important topic in the area of ambient assisted living (AAL). Therefore, we have proposed a human activity recognition system based on data collected from smartphone motion sensors for daily physical activity monitoring. The proposed approach implies developing a prediction model using mainly two sensors available on a smartphone: accelerometer and gyroscope. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional wearable or mountable devices or sensors that might impede their activities. For our proposal, we target six basic human daily activities walking, jogging, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (one using only accelerometer data and the other using only gyroscope data) with great effect. We've also implemented predictive models using Deep Learning approaches LSTM (Long ShortTerm Memory) and CNN (Convolution Neural Networks). The predicted results show decent accuracy for recognizing all six activities, with especially good and accurate results obtained for walking, running, sitting, and standing. The proposed system is fully implemented on a smartphone device as an Android application, which proves to be efficient, sustainable, and accurate. This study revolves around Deep Learning methodologies and techniques, precisely an approach based on semantic analysis mimicking the human ability to perform various activities.
α-Lipoic acid Prevents Angiotensin II-induced Endothelial Dysfunction via Antioxidant Effect and PI3K/Akt/eNOS Signaling Pathway

Background: Oxidative stress is associated with endothelial dysfunction, the initial step in the pathogenesis of various cardiovascular disorders. Methods: HUVECs were divided into 3 groups including control group, model group (cells treated with 10-6 M Ang II for 24 hr) and treatment groups (cells treated with 0.1 mM, 0.25 mM, 0.5 mM and 1 mM ALA for 30 minutes and further incubated with 10-6 M Ang II for 24 hrs). The cytoprotective effect of ALA against Ang II was tested by MTT assay, ROS generation was evaluated using Di-chloroflourescein (DCFH) and Mitosox Red, NO, ET-1 and antioxidant enzymes (SOD, GPx, CAT) and IDH) activity were analyzed by their respective kits. Furthermore, Western blot was used to detect the protein expression of Akt, p-Akt, eNOS, p-eNOS, Nrf2, PGC1-α, Sirt3 and Nox4. RT-PCR was used to detect the gene expression of Nox4 and eNOS. Results: The results indicated that ALA in a dose-dependent manner lowered Ang II-induced loss in endothelial cell viability, reactive oxygen species and ET-1 production. Furthermore, ALA pre-treatment increased NO Level and antioxidant enzyme activity which is crucial for ROS elimination. The western blot results showed that Ang II markedly decreased Akt, eNOS, Nrf2, PGC1-α, Sirt3 protein expression which was enhanced with the pre-treatment of ALA that was further inhibited by a Wortmannin and L-NAME. Conclusion: Our findings demonstrated that ALA possesses antioxidant activity against Ang II-induced oxidative stress partly by antagonizing AT1 receptor, suppressing Ang II-induced NADPH oxidase, increasing antioxidant enzyme activity and up-regulating PI3K/Akt/eNOS/NO dependent signaling pathway.
Extracting R2D2 Malware from Memory Using Volatility

There is still a growing interest in memory forensics technology today. There are several Cyber Forensics techniques and tools available to help combat Cyber Crime. Among them, memory forensics refer to the analysis of volatile data in a computer’s memory dump. Volatile data is the data stored in RAM on a computer while it is running. Memory forensics can also be triggered the running process of the system such as open connections, recent executed commands in a computer. This paper analyses the memory to detect malware by using volatility tools. This tool is popular volatile memory software analyzer. It can help us to recover useful information stored on the memory of the computer. This extracting scheme focuses on applied operations of memory forensics in Kali Linux machine to detect Trojan malware R2D2 attacks. This paper provides a generalized framework and step by step analysis to retrieve useful information from memory. Keywords—Cyber Forensics, Cyber Crime, RAM, Memory Forensics, Trojan Malware, Kali Linux, Volatile data, Volatility tools
Probability Analaysis of Tourists Revisit Intention based on Data Mining Techniques

This paper analyzes application architecture of data mining systems, creates new mining theoretic models, and designs a series of new algorithms based on such theories. The main contributions are as follows: On the basis of the definition of ecological tourism as well as combine the plan behavioral theory and customer satisfaction theory, the paper construct a complete empirical research model of ecological tourist visitors revisit intention. Introduce ecological tourism cognitive as antecedent, proving cognition of ecological tourism has a positive influence on the attitude of ecological tourism. Examine the differences of internal formation mechanism of revisit intention between initial swimmers and heavy swimmers.There are a large number of high liquid limit soil distributed in southern China.
Anterior Interhemispheric Transcallosal Transchoroidal Fissure Approach for Third Ventricle Hydatid Cyst: An Interesting Approach to a Rare Entity

In this case report, we describe the first case of transcallosal removal of a rare third ventricle hydatid cyst, which can be considered in the differential diagnosis of suprasellar arachnoid cysts. Caution must be taken in determining the best surgical approach to prevent unexpected complications. A primary intraventricular hydatid cyst is a rare phenomenon, which is most commonly in the lateral ventricle. After thorough research, only two cases of hydatid cyst have been discovered in the third ventricle. Therefore, we present an interesting case of a three-year-old girl with nausea, vomiting, and progressive drowsiness that upon neuroradiological examination was found to have a large, spherical, well-defined cystic lesion within the third ventricle. The patient underwent surgery via anterior interhemispheric transcallosal approach, and the cyst was successfully removed using Dowling’s technique. Seyed Ali Mousavinejad1, Hamid Borghei-Razavi2, Ahmad Jabbari1, Mohammad Samadian3, Kristen Almagro2*, PAParviz Karimi4 and Omidvar Rezaei5 1Skull Base Research Center, Loghman Hakim Medical Center, Shahid Beheshti University of Medical Sciences, Iran 2Department of Neurosurgery, Pauline Braathen Neurological Center, Cleveland Clinic Florida, USA 3Department of Neurosurgery, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Iran 4Department Pediatric Neurology, Ilam University of Medical Science, Iran 5Department of Neurosurgery, Skull Base Research Center, Loghman Hakim medical center, Shahid Beheshti University of Medical Sciences, Iran
Recommendation System Using Hybrid Filtering

1Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai (M.S.), India 2Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai (M.S.), India 3Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai (M.S.), India 4Associate Professor, Shah & Anchor Kutchhi Engineering College, Mumbai (M.S.), India 5Assistant Professor, Shah & Anchor Kutchhi Engineering College, Mumbai (M.S.), India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract The purpose of recommendation system is to apply multiple data processing techniques and prediction algorithms aimed to predict users’ interest on services, information and products. Today, they have become an unavoidable service in our day-to-day lives. The constant increase in the number of users of the system possesses few challenges like managing and handling a large amount of user data and producing accurate recommendations. Therefore, the new recommendation system aims at implementing technology able to deliver fast and accurate recommendations even for large information sets. This paper aims at developing a recommender system using hybrid of content-based and collaborative filtering which yields highly accurate recommendations.
Silver nanoparticles and their applications-A comprehensive review

The nanotechnology is a rapidly flourishing field with broad spectrum applications in almost every field. It has established that smaller size nanoparticles are more toxic compared to larger ones. Use of silver nanoparticles (AgNPs) has been recognized since the last couple of years. Though many commercial methods are available for their synthesis but biogenic synthesis has proven more efficient and environmentally friendly. AgNPs have shown quite interesting, useable and promising characteristics in various fields of human life. The biomedical applications of AgNPs have gained a huge interest of scientific community, and have significantly improved personalized healthcare practice (various types of surgeries, antibiotics, drug delivery formulations). It has been said that AgNPs have novel antimicrobial applications as anti-bacterial, anti-biofilm, anti-fungal and anti-viral agents. Strong antibacterial activity of AgNPs makes these ideal in fighting against multi drug resistant bacteria (MDR). Similarly, AgNPs also have proven their potential in agriculture field, environment and industrial sector. This review aims to provide an insight on biogenic synthesis and characterization of AgNPs alongwith applications in everyday life from economical, commercial and industrial perspective. Due to easy synthesis and wider availability of AgNPs, they are being considered as important constituent in the preparation of different commercially and bio-medically useful products alongwith enormous use in food packaging system for longer shelf life and prevention of spilage.
Implementation of Power Electronics in Hybrid Electric and Plug-in Hybrid Electric Vehicles

Due to the shortage in fossil fuels and with the necessities for lessening outflows and improving fuel economy, car companies are creating electric, hybrid electric and plug-in hybrid electric vehicles. Power electronics is an empowering innovation for the improvement of these environmentally friendlier vehicles and executing the progressed electrical models to meet the request for expanded electric loads. Hence, it is basic that the automotive electrical control system will experience a drastic change within the next 5-15 years. Within the case of future HEVs, power electronics converters and related motor drive, which controls the stream of electrical energy inside the HEV control framework, guarantee to be the keys to making HEVs more fuel effective and emit lower hurtful pollutants. Power electronics play an important in the development of electric, hybrid electric, plug-in hybrid electric vehicles, and fuel cell vehicles. The article discusses the basic structure and circuit setup of EVs, PHEVs, HEVs, FEVs, and the power electronics system, power electronics solutions for HEVs, future trends in vehicular power electronics were also investigated.
Economic Impacts of Ocean Acidification: A Meta-Analysis

This paper presents a comprehensive review and synthesis of studies that forecast economic impacts of ocean acidification. The changes in seawater chemistry resulting from increased carbon dioxide emissions, known as ocean acidification, will have detrimental impacts to marine ecosystem services. Those services include wild capture fisheries, aquaculture, recreation, shoreline protection, and others. The current literature valuing expected impacts to those services is rather thin and tends to focus on mollusk harvesting and aquaculture. Despite the paucity of studies, we divide all relevant estimates into five additively separable economic sectors to provide an aggregate estimate of economic damage from ocean acidification at the end of this century. We perform nonparametric bootstrap simulations to characterize the distribution of estimates within each sector and the aggregation across sectors. We also perform meta-regressions to explore whether estimates provided by these studies are generally consistent with expectations based on ocean chemistry and economic theory.
Consumer-Oriented Evaluation of the Clinical Service Provided in Diabetic Health Education: A Prospective Observational Study

Background: Lack of knowledge and understanding about diabetes are important barriers to cure. Treatment of diabetes alone without education is not sufficient. Patient education could meet us today on our cell phones. Aim: Our aim is to establish an educational model for diabetic patients and evaluate its effectiveness in the clinical patient outcome. The theme of this study is diabetes education. And to know how far the prompt well-structured education could affect the clinical status and the quality of life of a poorly controlled diabetic patients. Methods: 702 patients with type 1 diabetes were followed for 2 years (2019, 2020), by condensed friendly education and training sessions. A conventional model groups (498 patients) were enrolled as comparative group. Our activities were mainly through direct individual approach and social media, structured in the form of visits. The study was done in a specialized clinics of diabetes (SDC) in a tertiary university hospital. We evaluated the clinical status, psychological, sociocultural and satisfaction of the patients, pre and post education, through patient satisfaction survey. We assessed our patients at the beginning of the study, throughout, and at the time of discharge. Quantitative and qualitative data were compared statistically by paired t test and chi-square test at 0.05 level of significance. Results: Patients were evaluated 2 years after the interventional program. 96.7% of our patients were completely satisfied by what they have achieved through this project. The average HbA1C become less than eight in 54% of patients. Mean HbA1C drop was 2.7%. Patients with HbA1C 8-9%, BP <140/80, and LDL <3.36 mmol/l, were 46%, 51% and 79% respectively. Normal mean blood pressure of 56% of participants were 132/77. The mean LDL level of 71% of participants was 2.25. Of the participants, 27% had mean discharge of HbA1c of 7.3. Net promotor score of SDC was 85%, were promotors, passives and detractors were 87.8%, 9.5% and 2.8% respectively, P-value <0.05. Conclusion: Training and selfmanagement assistance services are cost-effective and preferable to routine treatment. Education programs were more effective in lowering HbA1c. The attendees would find it useful and pleasant.
Investment Allocation and Performance in Venture Capital

We study venture capital investment decisions within and across funds of VC firms. We propose an investment allocation model in which VCs, with overlapping funds, are judged primarily on success of their newest funds. This induces VCs to allocate their best investment opportunities to newly-raised funds. Empirical evidence is supportive: investments in a VC’s newly-raised funds are more successful than concurrent investments in older funds. Consequently, investment allocation leads to funds’ early investments being more successful than its later investments. Finally, VC performance persistence across successive funds is driven almost entirely by the success of early investments in these funds.