Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirically-based path loss model for wireless channels in suburban environments

We present a statistical path loss model derived from 1.9 GHz experimental data collected across the United States in 95 existing macrocells. The model is for suburban areas, and it distinguishes between different terrain categories. Moreover, it applies to distances and base antenna heights not well-covered by existing models. The characterization used is a linear curve fitting the dB path loss to the dB-distance, with a Gaussian random variation about that curve due to shadow fading. The slope of the linear curve (corresponding to the path loss exponent, /spl gamma/) is shown to be a random variate from one macrocell to another, as is the standard deviation, /spl sigma/ of the shadow fading. These two parameters are statistically modeled, with the dependencies on base antenna height and terrain category made explicit. The resulting path loss model applies to base antenna heights from 10 to 80 meters; base-to-terminal distances from 0.1 to 8 km; and three distinct terrain categories.
Random Forest Classification of Remote Sensing Data

Land cover classifications based on ensemble classifiers have been performed successfully during the recent years and seem particularly interesting for multisource and high-dimensional data sets. Contrary to standard classifiers, which are based on one single classifier to obtain a decision, ensemble methods to train several classifiers and combine their results through a voting process. Many ensemble classifiers have been proposed [1,2] and two strategies exist to construct an ensemble: (1) a combination of different classifier algorithms and (2) a combination of variants of the same classifier. Benediktsson and Kanellopoulos [3], for example, used a neural network and a statistical classifier for combining synthetic aperture radar (SAR) and multispectral data. The two data sets undergo a separate classification and the individual outputs are combined by decision fusion, which can be defined as a strategy for combining information from different data sources. Waske and Benediktsson [4] use a decision fusion strategy that is based on support vector machines (SVMs) for the classification of multitemporal SAR data and multispectral imagery. However, most applications combine a set of independent variants of the same classifier, the so-called base classifier. Often a simple decision tree (DT) is used, because of the fast training time and simple handling. Nevertheless, the use of more sophisticated methods for ensembles was CONTENTS
Integrative analysis reveals novel associations between DNA methylation and the serum metabolome of adolescents with type 2 diabetes: A cross-sectional study

Objective Rates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D. Methods We performed a cross sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through Research (iCARE) cohort study, to explore the molecular changes in adolescents with T2D. Results Our analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others. Conclusions We identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.
Replacing Animal Testing: How and When?

An important discussion in today’s society is whether we should make animals suffer for the sake of science and product development. In this article, I present four examples of animal tests that were introduced in the past to protect patients and consumers, and I discuss attempts to replace those animal tests with other methods. When we started using small animals such as mice and rats for testing more than 100 years ago, there were not many alternatives. Today, we have more knowledge and a greater number of options. Scientists can now create tiny functioning organs in the laboratory, and even combine multiple mini-organs, to help us understand how the human body works when it is healthy or sick. This increased understanding will allow scientists to move beyond the use of animals in many cases, which will improve both the accuracy of the scientific tests and the welfare of animals.
Chitosan degradation products promote healing of burn wounds of rat skin

Burns can impair the barrier function of the skin, and small burns can also cause high mortality. The WHO has described that over 180,000 people die of burns worldwide each year. Thus, the treatment of burn wounds is a major clinical challenge. Chitooligosaccharides (COS) are alkaline amino oligosaccharides with small molecular weights obtained by enzyme or chemical degradation of chitosan. With the characteristics of biocompatibility, water solubility and degradability, it has attracted increasing attention in the fields of biomedicine. In the present study, we used COS to treat deep second-degree burn wounds of rat skin and found that COS was able to promote wound healing. We also revealed that COS could promote fibroblast proliferation. Transcriptome sequencing analysis was performed on COS-treated fibroblasts to identify the underlying mechanisms. The results showed that COS was able to promote wound healing through regulation of the mitogen-activated protein kinase (MAPK) pathway and growth factor Hepatocyte Growth Factor (HGF). Our results provide a potential drug for burn wound therapy and the related molecular mechanism.
Acute effects of combined isometric and plyometric conditioning activities on sports performance and tendon stiffness in female volleyball players

This study aimed to compare the effects of bilateral and unilateral conditioning activities (CA; combined isometric and plyometric) on countermovement jump performance, modified t-agility test, Achilles tendon stiffness and skin surface temperature. Thirteen female semi-professional volleyball players performed two CAs in random order: 1) bilateral isometric half back squats followed by bilateral drop jumps (BI-CA); and 2) unilateral isometric half back squats followed by unilateral drop jumps (UNI-CA). To assess the effects of CAs, countermovement jump, modified t-agility test, Achilles tendon stiffness and skin surface temperature measurements were performed 5 min before and 6 min after the CA. Both CAs significantly increased thigh skin surface temperature from pre- to post-CA (BI-CA, p < 0.001; effect size [ES] = 1.41 and UNI-CA, p = 0.001; ES = 1.39) but none of them influenced modified t-agility test time (interaction: p = 0.338, main effect of time: p = 0.121 and condition: p = 0.819). The countermovement jump height and modified reactive strength index significantly increased from pre-to post-CA during the BI-CA condition (p = 0.003, ES = 0.45, and p = 0.008, ES = 0.48) but not for UNI-CA (p = 0.061, ES = 0.18 and p = 0.065, ES = 0.26). No significant impact has been found for countermovement depth (interaction: p = 0.054, main effect of time: 0.097, and condition: p = 0.41) as well as for contraction time (interaction: p = 0.536, main effect of time: p = 0.224, and condition: p = 0.807). Moreover, stronger and weaker limb CMJ relative peak force significantly decreased from pre-to post-CA (p = 0.014, ES = −0.31, and p = 0.027, ES = −0.26; respectively) during UNI-CA condition but not for BI-CA (p = 0.096, ES = 0.23, and p = 1.41, ES = 0.18). The stronger and weaker limb Achilles tendon stiffness significantly increased from pre-to post-CA during the UNI-CA condition (p = 0.013, ES = 0.60 and p < 0.001, ES = 0.79; respectively) but not for BI-CA (p = 0.66; ES = 0.15 and p = 0.265; ES = 0.42). Furthermore, the post-CA stronger limb Achilles tendon stiffness during the UNI-CA was significantly higher than that noted during the BI-CA (p = 0.006, ES = 0.7). The present study showed that combined isometric and plyometric bilateral CA effectively improved the countermovement jump but did not enhance the t-agility test performance. These findings indicate that exercise combinations could effectively produce a post-activation performance enhancement effect but should replicate the following explosive task as much as possible.
Fundamentals of Inflammation

Harrison’s Principles of Internal Medicine is one of the excellent, but enormous, “bibles” of medicine. In the 17th edition of this work, the sections on hematology and cancer have been combined to create a single book entitled Harrison’s Hematology and Oncology. This volume, edited by Dan Longo, is written by specialists in the field and cites recent references, which makes it both trustworthy and up-to-date. The book serves as a condensed source of information intended for medical doctors, students, and, secondarily, scientists looking for a general overview of the biology, symptoms, and treatments of cancer and hematological diseases. Approximately one-third of Harrison’s Hematology and Oncology is devoted to hematology, while the remaining two-thirds focus on cancer. Each broad topic is subdivided into six sections, which are in turn subdivided into several chapters. The first sections consist of general introductions for the cellular basis of hematopoiesis and the biology of cancer. These introductions are good for someone of the intended audience because they are short and precise, thereby reminding the reader of complex biological processes while updating the reader as necessary. The subsequent chapters are organized by organs, systems, or disease subtypes. The reader is given an overview of the epidemiology, incidence, clinical symptoms, and treatments for the diseases of interest. Tables and pictures are used efficiently to ensure that the chapters are both exhaustive and short. Such features make the book generally informative, yet also very easy to use when attempting to answer a specific question. The current, extensive information available in Harrison’s Hematology and Oncology makes it a good reference book for those interested in hematology or oncology. The organizational clarity of the manuscript also makes it easy to quickly find answers to a multitude of medical queries. For these reasons, it is anticipated that this work will serve as a valuable reference for the general medical community.
Learning Methods for Improving News Retrieval Systems

Content providers require an eﬃcient and accurate way of retrieving relevant content with minimal human aid. News retrieval, for instance, often requires human intervention to recognize which text documents are news articles and which are not. The diﬀerences between a factual news article and an opinionated blog piece may be subtle, yet are critical for providing informative and relevant content to users. This thesis explores the problem of format classiﬁcation: the task of classifying text documents based on the format in which they are written, such as a news article, blog entry or forum text. More explicitly, the goal of the thesis is to examine how well state-of-the-art supervised text classiﬁcation techniques work for format classiﬁcation. We select a number of classiﬁers that have been shown to perform well in other text classiﬁcation tasks and evaluate their performance in this unexplored task. Experimental evaluation, performed on a novel dataset created from multiple existing datasets, explores both binary and multi-class classiﬁcation in a bag-of-words feature space. Based on our experimental results, we have found that state-of-the-art supervised text classiﬁcation techniques perform acceptably well at format classiﬁcation. Furthermore, we propose a Gradient Boost model as a candidate classiﬁer for the task of format classiﬁcation, and provide a discussion of future work.
Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.
Making Thin Data Thick: User Behavior Analysis with Minimum Information

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior’s “big-data. ” This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a “big-data paradox”, where this bigdata is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick. In this dissertation, how human behavior’s thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information? In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that
Does central environmental inspection promote the industrial structure upgrading in China? An attention-based view

While central environmental inspection (CEI), a sort of campaign-style enforcement, has been adopted in China to tackle environmental issues, it is unclear if the CEI has promoted industrial structure upgrading. Based on a sample of 279 cities from 2011 to 2018 in China, this study investigates the impact of CEI on industrial structure upgrading and its intrinsic mechanism using the difference-in-differences approach (DID). The results reveal that 1) CEI significantly promoted industrial structure upgrading. 2) Local government environmental attention played a mediating role between CEI and industrial structure upgrading. 3) Heterogeneity analysis revealed that the effect of CEI on industrial structure upgrading differed significantly with regional discrepancies and the officials’ promotion motivation. Specifically, the CEI had greater effects on the eastern region and those cities whose officials have a strong promotion motivation. This study indicates that the central government should continue to promote routine inspection, special inspection, and look-back mechanism construction, thus enhancing the sustainability of the industrial structure upgrading effect. Overall, this study contributes to industrial structure upgrading theoretical research and offers useful insights into the environmental governance of emerging countries.
INFLUENCE OF STRATEGY FORMULATION DRIVERS ON STRATEGIC PERFORMANCE

To optimize scarce resources firms, need to formulate strategies that will enable them to achieve and sustain competitive advantage. The objective of the study was to propose recommendations to managers of MNCs operating in the mobile telecommunication sector in Nigeria on how to enhance and sustain strategic performance base on the evaluation of strategy formulation drivers. The focus on company vision, mission and long-term objectives helps to bring about new insights on the impact of strategy formulation drivers on strategic performance. The study was analysed using theoretical insights from strategic management literature to better understand how strategy formulation drivers enhance strategic performance of mobile telecommunication companies in Nigeria. The authors focused on a single industry using quantitative methods and a survey to obtain information from managers of mobile telecommunication companies operating in Nigeria. One hundred and twenty managers were randomly selected from finance/audit, sales, marketing, customer service and engineering departments of the head offices and eight regional offices of four mobile telecommunication companies in Nigeria. The authors used descriptive statistics and multiple regression techniques to analyse the resultant data. Statistical package for social sciences (SPSS 25) software was employed in the different analyses conducted. The study found that strategy formulation drivers jointly influence strategic performance of mobile telecommunication firms in Nigeria. Attention to company vision and long-term objectives has significant and positive impact on strategic performance. Furthermore, attention to company mission had a negative and significant impact on strategic performance. This study contributes to the existing literature by providing experience on strategy formulation drivers and their impact on strategic performance in mobile telecommunication companies in an emerging market (Nigeria).
Cinnamon essential oil and its emulsion as efficient antibiofilm agents to combat Acinetobacter baumannii

Acinetobacter baumannii is an emerging nosocomial pathogen resistant to a wide spectrum of antibiotics, with great potential to form a biofilm, which further aggravates treatment of infections caused by it. Therefore, searching for new potent agents that are efficient against A. baumannii seems to be a necessity. One of them, which has already been proven to possess a wide spectrum of biological activities, including antimicrobial effect, is cinnamon essential oil. Still, further increase of antibacterial efficacy and improvement of bioavailability of cinnamon oil is possible by emulsification process. The aim of this study was comparative analysis of cinnamon essential oil and its emulsion against biofilm forming A. baumannii clinical isolates. Furthermore, the investigation of toxicological aspects of possible applications of essential oil and emulsion was done as well. Gas chromatography–mass spectrometry of essential oil indicated trans-cinnamaldehyde as the most abundant component. The cinnamon emulsion was synthesized from cinnamon essential oil by combining modified low- and high- energy methods. Synthesized emulsion was characterized with Fourier-transform infrared spectroscopy and photon correlation spectroscopy. Both substances exhibited significant antibacterial (minimal inhibitory concentrations in the range 0.125–0.5 mg/ml) and antibiofilm effects (inhibitions of formation and reduction of pre-formed biofilm were 47–81 and 30–62%, respectively). Compared to essential oil, the efficacy of emulsion was even stronger considering the small share of pure oil (20%) in the emulsion. The result of biofilm eradication assay was confirmed by scanning electron microscopy. Even though the cytotoxicity was high especially for the emulsion, genotoxicity was not determined. In conclusion, strong antibacterial/antibiofilm effect against A. baumannii of the cinnamon essential oil and the fact that emulsification even potentiated the activity, seems to be of great significance. Observed cytotoxicity implicated that further analysis is needed in order to clearly determine active principles being responsible for obtained antibacterial/antibiofilm and cytotoxic properties.
Effectiveness of peer-mediated learning for English language learners: A meta-analysis

This manuscript reports the findings from a series of inter-related meta-analyses of the effectiveness of peer-mediated learning for English language learners (ELLs). Peer-mediated learning is a broad term that as operationalized in this study includes cooperative learning, collaborative learning, and peer tutoring. Drawing from research on interaction in second language acquisition, as well as from work informed by Vygotskian perspectives on socially-mediated second language learning, these meta-analyses synthesize the results of experimental and quasi-experimental studies. Included studies were conducted with language learners between the ages of 3 and 18 in order to facilitate comparisons to US students in K-12 educational settings. All participants were identified as ELLs, though learners in both English as a Second Language (ESL) and English as a Foreign Language (EFL) settings were included. Similarly, learners from a variety of language backgrounds were included in order to facilitate generalizations to the linguistic diversity present in US schools, and abroad. Main effects analyses indicate that peer-mediated learning is effective at improving a number of outcome types, including: language outcomes, academic outcomes, and social outcomes. Funnel plots and Egger’s regression analyses were conducted to examine the probability of publication bias, and it appears unlikely in most analyses. Moderator analyses were also conducted, where sample sizes were sufficient, to examine what measured variables were capable of explaining heterogeneity in effect sizes between studies.