Miracle-dz/UniFAR-FTUs huggingface dataset:
Physicochemical characterization of biochars from Eucalyptus maiden Entandrophragma cylindricum (Liboyo), Milicia excelsa (Muvula.) and Ocotea michelsonie (licheche) used in Goma city, DR Congo for water treatment potentials

In recent years, research on biochar as an eco-friendly material and cost-effective means for water treatment, soil amendment and carbon sequestration has gained more attention due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. Given that pyrolysis temperature and resident time amongst other factors have significant effect on biochar’s pollutant removal efficacy, this study focused on the evaluation of some properties of biochars produced by pyrolysis (500 - 600 °C) from Entandrophragma cylindricum (ECB), Eucalyptus maiden (EMB), Milicia excelsa (MEB) and Ocotea michelsonie (OMB) sawdusts for their water treatment potentials by using standardized methods. Biomass generated from wood mill in the city of Goma (DR Congo) is valorised for the first time as potential water purifier. The highest yield was obtained from OMB (36.6%) which was found to be significantly greater that those of ECB and EMB (p<0.05). All the biochars had neutral to weakly alkaline pH (7.10-7.90), very high porosity (92-94%, with EMB having the highest value) and ash content between 9.40 and 18.40%, with the highest value attributed to OMB). Most physical and chemical characteristics of biochars varied significantly due to different wood species. Potential toxic elements were far belowenvironmental threshold values and exchangeable cations were equally detected in the biochars. The obtained biochars are therefore seen as good media for water treatment.
Formalization of Conceptual Classification Models of Knowledge

The paper explores the information (conceptual) systems in terms of the hierarchies of conceptual and material systems. Such systems are an object-oriented from the system-object approach side. The conceptual systems of which are external systems (systems-classes) that determine the properties of specific objects and objects are material or internal systems (facts systems) that implement real interactions. The paper also considers the results of a formal description of the hierarchy of conceptual systems. It takes into account their systemic relationships using a description logic for formalizing certain states of the system-object approach. The syntax and semantics of the ALCOIQ descriptive logic and its extension to the SHOIQ logic are described. Introduced and formally described the concepts of the volume and content of a conceptual system. It expands the system theory based on a system-object approach.
Proactive Defense for Internet-of-things: Moving Target Defense With Cyberdeception

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a software-defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved.
A Question Classiﬁcation in Closed Domain Question-Answer Systems

A Question-Answer System (QAS) is a Natural Language Processing (NLP) application whose purpose is to answer questions from users by consulting one or more available knowledge bases. Classifying the question posed by a user in a class of a predetermined set has risks and advantages. On the one hand, the correct classiﬁcation reduces the scope of the search for the answer, generally resulting in more correct answers and greater processing efﬁciency of the QAS; on the other hand, an error in the classiﬁcation reduces the chances of the system recovering from this error in the later stages of processing, thus resulting, almost always, in unsatisfactory responses. This work develops and evaluates a question classiﬁcation scheme for Closed Domain QAS. The experiments showed that the approaches described for deﬁning class and question classiﬁcation can be used successfully in QAS based on closed collections of documents.
Orchid: Building Dynamic Test Oracles with Training Bias for Improving Deep Neural Network Models †

The accuracy of deep neural network models is always a top priority in developing these models. One problem to affect it is to what extent such a model can resolve training samples conflicting with one another while learning from them. Such a model may easily learn the whole training dataset with high recall, leaving the problem only to appear when evaluating the model, and yet samples in the evaluation should not be used in training the model. This paper proposes Orchid, the first work to alleviate the problem of class pairs with training samples conflicting with one another. Orchid firstly rewinds the learning effect of a deep neural network model under fix on different subsets of the training dataset, producing a set of resultant rewound models. It then constructs a novel kind of reference oracle by dynamic selection and integration of these rewound models, each predicting the same sample in question correctly. It finally adapts the model under fix by retraining it on training samples with the reference oracle. The experiment on MobileNet and ShuffletV2 with the Cifar-100 and Tiny-ImageNet datasets shows that their test accuracies are improved over the baselines by 2.47% and 2.62%, respectively. Orchid also produces noticeable reductions in misclassification between classes of the adapted models on the test datasets.
Effect of the dosage of ferroferric oxide on batch anaerobic treatment of high strength synthetic wastewater

aKey Laboratory of Microorganism Application and Risk Control (MARC) of Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China, Tel. +86-755-26036390; Fax: +86-755-26036511; emails: wu.guangxue@sz.tsinghua. edu.cn (G. Wu), yqd14@mails.tsinghua. edu.cn (Q. Yin), 84622486@qq.com (Y. Sun), 137666925@qq.com (B. Li) bSchool of Civil Engineering, Hefei University of Technology, Hefei 230009, China, email: zhhu@hfut.edu.cn
The Yale Undergraduate Research Journal The Yale Undergraduate Research Journal

This paper studies the gender gap in academic achievement using a nationally representative survey of middle school students in China. Chinese girls outperform boys in Mathematics, Chinese, and English by 13.5%, 62.1%, and 61.5% of a standard deviation, respectively, which is one of the largest gender gaps in the world. Using the OLS regression with interactions between gender and family-, teacher-, and school-level variables, I show that girls benefit from having a same-sex teacher in Mathematics and Chinese. For boys, however, the effect of sex congruence with the subject teacher is insignificant in all three subjects. Girls’ resilience in the face of adverse family and school environments also contributes to the gap. In dysfunctional families and lower-ranking schools, the female advantage is even more pronounced.
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

In this demo, we introduce Exathlon – a new benchmarking platform for explainable anomaly detection over high-dimensional time series. We designed Exathlon to support data scientists and researchers in developing and evaluating learned models and algorithms for detecting anomalous patterns as well as discovering their explanations. This demo will showcase Exathlon’s curated anomaly dataset, novel benchmarking methodology, and end-to-end data science pipeline in action via example usage scenarios.
INTERIM PERFORMANCE REPORT

We determined the fatty acid signatures in milk, serum, and blubber samples collected from young free-ranging Steller sea lions (Eumetopias jubatus) and investigated the partitioning of fatty acids among these matrices. We assessed the relationship of fatty acids in each matrix with region, age, season, sex, and body condition to gain information needed to determine the most appropriate type of samples to collect and analyze to address future research questions. The variability of fatty acid composition was almost entirely explained by sample type, highlighting the importance of selecting a matrix consistent with the study objectives. Regional differences in fatty acid composition were found in all sample types and these differences varied among matrices. The proportion of fatty acids in milk and serum were influenced by season of capture whereas blubber samples were more affected by age of the pup. The influence of season on the fatty acid signatures in milk and serum highlights the use of these samples over blubber in studies investigating seasonal changes in diet. Further, our findings suggest that the use of milk samples collected from the stomachs of pups may be a viable alternative to directly collecting milk from adult females.
Identification Phytochemicals and Antioxidant Activities of Various Fractions of Methanol Extracts from Bark of Kulim Tree (Scorodocarpus borneensis Becc.)

Phytochemicals and antioxidant activity of methanolic extract the Kulim's bark were investigated. The extract of S. borneensis barks was partitioned by fractionation of gradient elution, sequentially, i.e., hexane, ethyl acetate, ethanol, methanol, and 70% methanol, then stirred for 24 h. The fractions were evaporated at T 40 oC to gain the concentrated samples. Phytochemical of Scorodocarpus borneensis bark fractions were determined qualitatively and quantitatively on the phenolic, flavonoid, and alkaloid compounds. The antioxidant activities of those fractions were investigated by DPPH free radical scavenging activity method. The result showed that all the crude fractions consisted of similar phytochemical substances, except n-hexane fraction, which alkaloid and flavonoid were absent. The fraction of ethyl acetate was identified the most significant flavonoid content and scavenging activity against free radical of DPPH (273.13 ± 2.25 mg QE/g extract and 57.88 ppm, respectively); while methanol fraction showed the highest total phenolic content and alkaloid content, were about 2332.64 ± 59.23 mg GAE/g and 19.67 ± 2.08 mg BE/g extract, respectively.
THE IMPACT OF ECONOMIC GROWTH, GLOBALIZATION, AND FINANCIAL DEVELOPMENT ON CO2 EMISSIONS IN ASEAN COUNTRIES

The purpose of the paper is to investigate the impact of economic growth (EG), globalization (GL), and financial development (FD) on CO2 emissions in ASEAN countries. For the purpose of analysis, the data were collected form “World Development Indicator (WDI), KOF Swiss economic institute and World Bank” from 2004 to 2018. The panel “fixed effect model” has been used to run the regression analysis with “Discroll-Kraay Standard Error” to control the heteroscedasticity and serial correlation effects on the results. The findings discovered that EG, GL, and FD have positive relation with CO2 emissions in the ASEAN countries. This study recommended to the officials that they should develop policies regarding to control the CO2 emissions. Due to EG, GL, and FD, the energy consumption increases that need to develop the policies regarding efficient use of energy resources that it will not become the cause of CO2 emissions in the country.
Study on an Online Vibration Measurement System for Seismic Waves Caused by Blasting for Mining in Vietnam

Blasting has become a crucial work in mining operation. However, it produces high-intensity seismic waves which cause some serious troubles such as injure people, fly-rock, cracking, breaking and reducing the lifetime of adjacent buildings. In Vietnam, there have been many conflicts between residents and government about the compensation policy for these damages. The solution is proposed, in which a similar explosion is made and an instantaneous concussion meter is used to record the magnitude of the generated shock wave. The results received from this operation will be used to determine the effects of mining blast. In fact, that is an incorrect method because just by changing the type of explosives, the order, the explosives, etc., the shock wave will be significantly reduced. Nothing is ensured that another explosion causing a shock wave amplitude will not occur in the future. To solve this problem, this paper presents an online seismic wave monitoring system operating 24/24h, to transmit the recorded signal to an independent server located around the boundary of the mine. On the basis of the mechanism of generating explosive waves and the recording mechanism of shock waves, the authors have built a program to store records according to the permissible influence of Vietnam Standard and Circular 32/2019/TT- Vietnam Board of Directors.
Examining the Influence of Shared and Servant Leadership on Organizational Performance in Sport for Development and Peace

Aim Scholars have called for increased consideration of alternative leadership theories in sport, including collective or shared leadership (Jones, Wegner, Bunds, Edwards, & Bocarro, 2018). For instance, Welty Peachey and Burton (2017) conceptualized that the humanitarian and social-focused nature of organizations involved in Sport for Development and Peace (SDP) may require different leadership approaches compared to other types of sport organizations. To date, however, no studies have empirically examined the relationship between different leadership styles and organizational performance in SDP to help identify suitable leadership approach(es) that allow practitioners to better manage their programs for sustainable program outcomes. The purpose of this study, therefore, was to examine the influence of shared and servant leadership on organizational performance in SDP. The following hypotheses were tested: H1a: Shared leadership will be positively associated with organizational performance. H1b: Shared leadership will be a stronger predictor of organizational performance than servant leadership. H2a: Servant leadership will be positively related to shared leadership. H2b: Servant leadership will be positively associated with organizational performance. H3a: Human resources capacity will be positively related to shared leadership. H3b: Shared leadership will be a stronger predictor of organizational performance than human resources capacity.
DATA MINING PPV AN APPLIED DEEP LEANING NEUROMARKETING TOOL TO THE PERFORMANCE OF THE POINT OF SALE PROMOTION: A QUANTITATIVE STUDY

The Data Mining Promotion Point of Sale application algorithm is an online digital marketing application, user friendly, economical, interactive, and ecological, capable of transforming "data" in real-time into "knowledge" to allow marketeers to make agile decisions. To respond to the current challenges of the Point of Sale (PPV), Pereira, P. and Magalhães, M. (2019) developed a Deep Learning solution that incorporates Neuromarketing which allows the making of Promotion campaigns at the Points of Sale (PPV) more effective. Through the Data Mining PPV system, the manager of a point of sale will be able to answer the following questions: What is the profile and time of the Consumers who enter the Store? Who are those who see advertising campaigns? How many advertising campaigns were run and attracted Consumers? Can you quantify the time when “impulse sales” were made by consumer profiles resulting from the visualization of the “advertising campaigns” shown? As a methodology we use a Data Mining PPV application implemented in 20 convenience stores, in a company that produces and sells fuels, having its own network, with more than 200 service stations for fuel supply, where there is also, in each station, a convenience store. As a result of the test carried out during 234 calendar days, in 20 convenience stores, with the Data Mining PPV application, it allowed the marketeer to collect and process data in real-time. During the period in question, 3,276,000 visits were recorded and collected through a screen placed above the cashier's location. We saw an increase in “impulse sales” of around 30%, allowing us to display the right “ad”, at the exact “time”, for the right “persona”, allowing us to monitor in dashboards the effectiveness and efficiency of advertising campaigns carried out by a point in terms of volume and sales value. Regarding the limitations of the study, we can mention that this study focuses on a company that produces and sells fuels, with the sole purpose of observing, analyzing, and winning over its customers in its convenience stores. As for originality and value, we can record the use of a sample relevant to the study in question and an efficient and current method to collect and process data in real-time, increasing sales significantly.
A 2-Year-Old Boy Who Developed an Aortoesophageal Fistula After Swallowing a Button Battery, Managed Using a Novel Procedure with Vascular Plug Device as a Bridge to Definitive Surgical Repair

Patient: Male, 2-year-old Final Diagnosis: Aortoesophageal fistula Symptoms: Esophageal foreign body • gastrointestinal bleeding Medication: — Clinical Procedure: — Specialty: Gastroenterology and Hepatology • Pediatrics and Neonatology Objective: Rare disease Background: Button batteries (BBs) can be inhaled or swallowed accidentally, particularly by infants and children, who can present as a surgical emergency with a fatal outcome. This report is of a case of a 2-year-old boy who developed an aortoesophageal fistula (AEF) after swallowing a button battery and was successfully treated using a novel vascular plug device as a bridge to definitive surgical repair. AEF is diagnosed using computed tomography angiography (CTA), after laparotomy, and using aortography. Aortic endovascular stenting and vascular occluder placement is a minimally invasive emergency option until definitive treatment can be provided. The surgical options include repair the aortic defect primarily, or the diseased segment of the aorta is replaced with a graft. Case Report: A 2-year-old boy presented with hematemesis 9 days after ingestion of a button battery, which was removed endoscopically 16 hours after the incident. The patient was resuscitated aggressively and diagnosed to have AEF using computed tomography angiography (CTA). The aorta was successfully repaired using a vascular plug device, which kept the patient safe until the definitive surgical treatment was done 2 months later. The defect was repaired with direct anastomosis and completed with a patch of bovine pericardium, as well as closure of the fistula from the esophageal side with stitches. Conclusions: This report presents a rare but previously reported complication of swallowing a button battery, resulting in an aortoesophageal fistula. The aorta was successfully repaired using a vascular plug as a minimally invasive emergency option, which is considered as a lifesaving procedure and a bridge for definitive repair.
Demonstration of Apperception: A Database Management System for Geospatial Video Data

Many recent video applicationsÐincluding traffic monitoring, drone analytics, autonomous driving, and virtual realityÐrequire piecing together, combining, and operating over many related video streams. Despite the massive data volumes involved and the need to jointly reason (both spatially and temporally) about these videos, current techniques to store and manipulate such data are often limited to file systems and simple video processing frameworks that reason about a single video in isolation. We present Apperception, a new type of database management system optimized for geospatial video applications. Apperception comes with an easy to use data model to reason about multiple geospatial video data streams, and a programming interface for developers to collectively reason about the entities observed in those videos. Our demo will let users write queries over video using Apperception and retrieve (in real-time) both metadata and rendered video data. Users can also compare results and observe speedups achieved by using Apperception
Designing Strategies for Autonomous Stock Trading Agents using a Random Forest Approach

Machine learning-based autonomous agents are valuable for back-testing stock trading strategies, including algorithmic trading. Several studies in the financial literature have proposed artificial intelligence-based algorithms that support decision making for financial investment, but few studies have provided systematic processes for designing intelligent trading agents. This paper overviews the steps involved in designing agents that forecast stock prices in a trading strategy. These steps include data preprocessing, time-series segmentation, dimensionality reduction, clustering, and others. Our main contributions are: (i) a systematic process that guides the design and development of trading agents, and (ii) a random forest forecasting model. Keywords—Decision trees; financial forecasting; machine learning; random forest; trading agents; trading strategy
Implementation of Machine Learning Algorithms for Analysis and Prediction of Air Quality

—In this paper, the implementation of Machine Learning algorithms, Random forest (RF), Support vector machine (SVM), and, Artificial neural networks (ANN), have been discussed. Today air pollution is one of the biggest environmental issues in the world. It poses a major threat to health and climate. Air quality in cities is deteriorating day by day. Pollutants in the air also impact both water and land quality. This paper presents how machine learning algorithms can be used to analyze and predict the quality of air. Machine learning algorithms provide us the methods to forecast the air pollution levels so that preventive measures can be taken by the people in order to minimize air pollution.