Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparisons of Multi Resolution Based AI Training Data and Algorithms Using Remote Sensing Focus on Landcover

The purpose of this study was to construct artificial intelligence (AI) training datasets based on multi-resolution remote sensing and analyze the results through learning algorithms in an attempt to apply machine learning efficiently to (quasi) real-time changing landcover data. Multi-resolution datasets of landcover at 0.51- and 10-m resolution were constructed from aerial and satellite images obtained from the Sentinel-2 mission. Aerial image data (a total of 49,700 data sets) and satellite image data (300 data sets) were constructed to achieve 50,000 multi-resolution datasets. In addition, raw data were compiled as metadata in JavaScript Objection Notation format for use as reference material. To minimize data errors, a two-step verification process was performed consisting of data refinement and data annotation to improve the quality of the machine learning datasets. SegNet, U-Net, and DeeplabV3+ algorithms were applied to the datasets; the results showed accuracy levels of 71.5%, 77.8%, and 76.3% for aerial image datasets and 88.4%, 91.4%, and 85.8% for satellite image datasets, respectively. Of the landcover categories, the forest category had the highest accuracy. The landcover datasets for AI training constructed in this study provide a helpful reference in the field of landcover classification and change detection using AI. Specifically, the datasets for AI training are applicable to large-scale landcover studies, including those targeting the entirety of Korea.
Endoscopic Bariatric and Metabolic Therapies and Their Effects on Metabolic Syndrome and Non-alcoholic Fatty Liver Disease - A Systematic Review and Meta-Analysis

Background Endoscopic bariatric and metabolic therapies (EBMTs) are procedures that utilize instruments that require flexible endoscopy or placement of devices for inducing weight loss. We perform a systematic review and meta-analysis to evaluate four modalities – intragastric balloon (IGB), endoscopic sleeve gastroplasty (ESG), duodeno-jejunal bypass liner (DJBL), and duodenal mucosa resurfacing (DMR), for their efficacy and safety on weight loss, non-alcoholic fatty liver disease, and metabolic syndrome. Methods Databases MEDLINE via PubMed, and EMBASE are searched and relevant publications up to January 26, 2022 are assessed. Studies are included if they involved human participants diagnosed with obesity and obesity-related comorbid conditions who are treated with any of the 4 EBMTs. IGB and DJBL were chosen as the interventions for the meta-analysis with weight loss (percentage total body weight loss or body mass index) and glycemic control (fasting plasma glucose or HbA1c) as the two main outcomes analyzed. Results Six hundred and forty-eight records are reviewed, of which 15 studies are found to be duplicates. Of the 633 records screened, 442 studies are excluded. One hundred and ninety-one articles are assessed for eligibility, for which 171 are excluded. A total of 21 publications are included. Twelve studies are on IGB, two studies on ESG, five studies on DJBL, and two studies on DMR. In these studies with appropriate control, IGB, ESG, and DJBL showed promising benefits on weight loss reduction compared to standard medical therapy (SMT), while DMR appeared to have the least weight reduction benefit. However, the impact on glycemic control featured more prominently in DMR as compared to the rest of the modalities. Different EBMTs have different adverse effect profiles, although device-related adverse events are featured more prominently in DJBL. In the IGB group, there was a significant reduction in 6-month %TBWL [weighted mean difference (WMD) 5.45 (3.88, 7.05)] and FPG WMD −4.89 mg/dL (−7.74, −2.04) compared to the SMT group. There was no significant reduction in BMI between the DJBL and SMT group WMD −2.73 (−5.52, 0.07) kg/m2. Conclusion EBMTs have demonstrated a significant impact on weight loss and metabolic comorbidities, and reasonable safety profiles in the studies reviewed. Some data is available to demonstrate reduction of hepatic steatosis, but there is no high-quality data supporting benefits on hepatic lobular inflammation or fibrosis.
Changes of Serum Homocysteine and Vitamin B12, but Not Folate Are Correlated With Obsessive-Compulsive Disorder: A Systematic Review and Meta-Analysis of Case-Control Studies

Background Obsessive–compulsive disorder (OCD) a complex neuropsychiatric disorder, is characterized by irresistible obsessive thinking and compulsive behavior. Folate is a member of water-soluble vitamins in the human body and sustains many normal daily activities (e.g., exercise, sleep, and memory). Homocysteine, a sulfur-containing non-essential amino acid, has been investigated in numerous psychiatric disorders (e.g., OCD). Vitamin B12 is a type of complex organic compound with cobalt contained. Moreover, vitamin B12 and folate deficiency and high levels of homocysteine were found to have an effect on brain functions and also lead to non-specific psychiatric symptoms. Objectives This study aimed to confirm the epidemiological evidence of OCD and investigate whether vitamin B12, folate, and homocysteine have an effect on the etiology of OCD. Methods A systematic search was conducted on eight databases (i.e., PubMed, Embase, Web of Science, the Cochrane Library, China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database), and the retrieval time was up to March 2021. The available articles involving patients with OCD with/without abnormal serum levels of vitamin B12, folate, and homocysteine were comprehensively reviewed and analyzed. Results A total of 5 studies involving 309 patients were included in this meta-analysis, including 172 cases in the experimental group and 137 in the control group. The content of folate in the OCD group was not significantly different from that in the control group (SMD = −0.089, 95%CI −0.755 to 0.577, p = 0.794). And serum homocysteine was significantly higher in the patients with OCD (SMD = 1.132, 95%CI 0.486 to 1.778, p = 0.001). Vitamin B12 was significantly lower in patients with OCD (SMD = −0.583, 95%CI −0.938 to −0.229, p = 0.001). Conclusions This meta-analysis shows serum high levels of homocysteine, low levels of vitamin B12, and normal folate level are closely correlated with OCD. However, high-quality case-control studies should be further conducted to explore the correlation between serum levels of vitamin B12, folate, homocysteine, and OCD. Systematic Review Registration https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42021262161; PROSPERO (Number CRD#42021262161).
Gathering Contextual Data with Power Information Using Smartphones in Internet of Everything

The advent of smart devices, interacting with each other as well as remote services, has paved the way for the Internet of Everything (IoE). IoE is a direct successor of Internet of Things (IoT), composed of smart devices interacting with remote services. The devices in an IoE environment are power constrained. At the core of an IoE environment, there is a context-aware system that gathers the context and classifies it. Various datasets have been published by authors for context-aware systems. This paper presents a mechanism that gathers a dataset of contextual information along with power information using smartphones. An Android application “PowerIpsum” is developed for gathering contextual information, power information, and user input activity labels. The dataset includes the sensor data as the contextual data, timestamps, average current, and average voltage as well as user activity labels. Time elapsed and power consumption is forecasted using Monte Carlo method. The results provide useful insights and demonstrate the advantages of power information within a context-aware system.
Designing a policy-making model in skill based education using a Grounded Theory approach

The purpose of this study was to present a model for policy making in technical and skills education. Ruling paradigm over this study was a constructivist interpretation and a qualitatively methodology. This research was conducted using Grounded Theory (Strauss and Corbin design). In order to design a policy-making model, the experts in the subject of skill training policy were interviewed and the open, axial and selective coding procedure. The study population was 41 of University of Applied Sciences and Technology' administrators of which 19 were selected by purposive sampling method. According to the aim of the study, in-depth and semi-structured interview method was used to collect data. Findings showed that identifying educational needs of the work market and necessity of the dynamic and flexible policy-making is for those axial components that is being affected by casual factors including political, economic and managerial factors. Internal and external factors such as intervening conditions and internal organizational environment, external organizational environment and social factors are the underlying conditions that influence policy strategies. Internal and external factors such as intervening conditions and internal organizational environment, external organizational environment and social factors are the underlying conditions that influence policy strategies. The policymaking model with managerial and educational strategies leads to results in three dimensions: social-individual, managerial-governance and scientific.
An Android Malware Detection Leveraging Machine Learning

Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.
Dysregulation of E-cadherin in pulmonary cell damage related with COPD contributes to emphysema

Air pollution, especially at chronic exposure to high concentrations, is a respiratory risk factor for the development of chronic obstructive pulmonary disease (COPD). E-cadherin, a cell–cell adhesion protein, is involved in the integrity of the alveolar epithelium. Causes of E-cadherin decreases in emphysematous areas with pulmonary cell damage related to COPD are not well understood. We aimed to determine the molecules causing the decrease of E-cadherin and interactions between these molecules. In emphysematous and non-emphysematous areas of lungs from COPD patients (n = 35), levels of E-cadherin, HDACs, Snail, Zeb1, active-β-catenin, p120ctn, and Kaiso were determined by using Western Blot. The interactions of HDAC1, HDAC2, and p120ctn with transcription co-activators and Kaiso were examined by co-immunoprecipitation experiments. The methylation status of the CDH1 promoter was investigated. E-cadherin, Zeb1, Kaiso, and active-β-catenin were decreased in emphysema, while HDAC1, HDAC2, and p120ctn2 were increased. Snail, Zeb1, Twist, active-β-catenin, Kaiso, and p120ctn co-precipitated with HDAC1 and HDAC2. E-cadherin, Kaiso, and active-β-catenin co-precipitated with p120ctn. HDAC1–Snail and HDAC2–Kaiso interactions were increased in emphysema, but p120ctn-E-cadherin interaction was decreased. The results show that HDAC1–Snail and HDAC2–Kaiso interactions are capable of decreasing the E-cadherin in emphysema. The decreased interaction of p120ctn/E-cadherin leads to E-cadherin destruction. The decreased E-cadherin and its induced degradation in pneumocytes cause impaired repair and disintegrity of the epithelium. Approaches to suppress HDAC1–Snail and HDAC2–Kaiso interactions may help the protection of alveolar epithelial integrity by increasing the E-cadherin stability in pneumocytes.
Potential Therapeutic Effects of Mi-Jian-Chang-Pu Decoction on Neurochemical and Metabolic Changes of Cerebral Ischemia-Reperfusion Injury in Rats

As a traditional Chinese medicine formula, Mi-Jian-Chang-Pu decoction (MJCPD) has been successfully used in patients with language dysfunction and hemiplegia after ischemic stroke (IS). Given the excellent protective effects of MJCPD against nerve damage caused by IS in clinical settings, the present investigation mainly focused on its underlying mechanism on ischemia-reperfusion (IR) injury. Firstly, by applying the MCAO-induced cerebral IR injury rats, the efficacy of MJCPD on IS was estimated using the neurological deficit score, TTC, HE, and IHC staining, and neurochemical measurements. Secondly, an UHPLC-QTOF-MS/MS-based nontargeted metabolomics was developed to elucidate the characteristic metabolites. MJCPD groups showed significant improvements in the neurological score, infarction volume, and histomorphology, and the changes of GSH, GSSG, GSH-PX, GSSG/GSH, LDH, L-LA, IL-6, TNF-α, and VEGF-c were also reversed to normal levels after the intervention compared to the MCAO model group. Metabolomics profiling identified 21 different metabolites in the model group vs. the sham group, 10 of which were significantly recovered after treatment of MJCPD, and those 10 metabolites were all related to the oxidative stress process including glucose, fatty acid, amino acid, glutamine, and phospholipid metabolisms. Therefore, MJCPD might protect against IS by inhibiting oxidative stress during IR.
Investigation of Circularly Polarized CPW Fed Antenna as a 2.45 GHz RFID Reader

In this paper, a bi-directional coplanar-waveguide (CPW)-fed antenna with circular polarization (CP) is investigated as a radio frequency identification (RFID) reader antenna. The antenna geometry is square and printed on one side of the FR4 substrate only, which consumes an overall dimension of <inline-formula> <tex-math notation="LaTeX">$60\times 60$ </tex-math></inline-formula> mm2 with a thickness of 1.5 mm. The simulated and measured parameters of the antenna are in agreement with an impedance bandwidth (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{11} < -10$ </tex-math></inline-formula> dB) of 40.2% (2.18–3.28 GHz) and ARBW (<3 dB) of 28.3% (2.03-2.7 GHz), respectively. The peak gain of the proposed antenna is 3.17 dBi in both ± z directions. An Arduino UNO-based open-source microcontroller setup is used for studying this antenna as an RFID reader antenna, along with a wireless module NRF24L01 module at 2.45 GHz with an external antenna (tag). The path loss between the tag and reader antennas is found to increase in the desired band due to interference exists.
Implementation of Bird Species Detection Algorithm using Deep Learning

Automatically identifying what types of the bird is present in the sound recording using the monitor reading. To distinguishing automatic birds based on their sound patterns.This is useful in the field of ornithology for studying bird species and their behavior based on their sound. Proposed method will be used to distinguish birds automatically using different sound processing methods and mechanical learning methods based on their chirping patterns. We propose a sequential model for audio features within a short interval of time. The model will be used Mel Frequency Cepstral Coefficients to extract features from the audio files and presented it in the model. The proposed work classifies the data set containing three species of bird, and outperform support vector machines.
Phytochemical Screening, Antioxidant and Anti-Inflammatory Properties of Litsea floribunda Gamble, Leaf and Bark Extract

The Litsea floribunda Gamble was widely used for various medicinal purposes. The present study was aimed to screening the phytochemicals presentin theleaf and bark extract of L. floribunda and also assess their antioxidant and anti-inflammatory properties. The results revealed that the leaf and bark extract showed the extracts are rich in phytomoleculeslikephenolics and tannin. The leaf extract showed more antioxidant activitiessuch as total antioxidant, ABTS, superoxide radical scavenging, FRAP, and DPPH reducing power activities were done respectively. Better radical activities Albumin denaturation and Anti-proteinase were observed in leaf extractthan in the bark extract, also it showed significant anti-inflammatory activities. Finally, L. floribunda could be considered as the most valuablealternative medicinalsource for many diseases.
Learning-Based Resource Allocation in Heterogeneous Ultradense Network

Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This article proposes a novel learning-based paradigm to address resource allocation problems in heterogeneous ultradense networks (HUDNs). Our paradigm is a highly efficient realization for utilizing the inherence properties in HUDN, which comprise the local validity and correlation attenuation. Concretely, we formulate the HUDNs as a heterogeneous bipartite graph model and propose the corresponding heterogeneous bipartite graph neural network (HBGNN). The local sampling characteristic of HBGNN matches the inherence properties. Meanwhile, our paradigm combines data-driven and model-driven learnings and employs online and offline trainings. Hence, two of LRA’s obstacles: 1) the overreliance on the perfect data set and 2) the low calculation efficiency are mitigated, and the realizability of our paradigm is improved. Besides, entropy regularization is utilized to guarantee the effectiveness of exploration in the configuration space. We apply our approach to a representative resource allocation problem, the jointly user association (UA) and power allocation (JUAPA) problem. We formulate JUAPA as a combination classification and regression problem and adopt a dynamical hyperparameter output layer to address the discrete variable of UA. Simulation results demonstrate that the proposed method has better performance and higher computational efficiency than traditional optimization algorithms.
Recent progress in the production of lignin-based sunscreens: A Review

Technical lignins are becoming an attractive natural, renewable, and non-toxic ingredient in sunscreens, having the capability for replacing synthetic compounds. Researchers have reported that lignin can increase the solar protection factor (SPF) of sunscreens and provide sun protection to body creams. However, to achieve the valorization of lignin in the fabrication of personal care products, it is necessary to overcome several challenges related to their molecular complexity and unattractive color. Fractionation, chemical modification, whitening, particle size reduction, and the synthesis of nanocomposites and copolymers are strategies reported to overcome the lignin challenges in the development of lignin-based sunscreens. This paper summarizes and analyzes previous research studies and outstanding findings (from 2016 to 2022) directed at the reduction of the problems that limit the extensive applications of lignin in skincare products such as sunscreens.
Structural damage identification based on time series analysis

A structural damage identification method based on time series analysis was proposed to solve a problem how to get structural damage indexes in structural responses.Firstly,an AR prediction reference model was established using undamaged structural response data after data pre-processing.Secondly,the residual error of damage condition was calculated with the AR prediction reference model,and then the varianceratio of the calculated residual error and the residual error of the AR model was taken as a damage index.At last,a simply supported beam was takes as a case study.Results showed that the damage index proposed can find if the damage exists,and the proposed method can be used to locate the damage.
The importance of context in engaging students large first year management classes: A case study

Engaging large first year classes in tertiary education poses a number of significant challenges, most of which have been discussed by others. One area that has not received the kind of attention that it warrants is the context within which the engagement activities take place. This paper examines both the processes used to engage a large first year management class in a major city university and how the context of the classes shaped activities and student responses to these activities. It was recognised that students had certain types of learning styles, but given the total number of students (in excess of 1200) it was realised that is would be impossible to cater to all possibilities. A key outcome of the exercise was the importance of context in shaping student behaviours.
Factors Affecting Intention to Use Smartphone Healthcare Applications

This was a descriptive survey to determine the intention of users to use smartphone healthcare applications (SHAs) and to clarify factors that may influence such intention. The data were collected during the month of April in 2015, using a structured self-report questionnaire that was distributed to 300 participants aged 20 to 70 years; 285 complete copies were used for the final analysis. The data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation coefficients, and hierarchical multiple regression. First, according to the results, the average intention to use SHAs was 3.28 out of 5, which varied according to age, final education, economy level, vacation, current disease, total period of smartphone use, and etc. Second, significant correlations were shown by exercise behavior, dietary management behavior, stress management, satisfaction with smartphone use, and satisfaction with using SHAs. Third, the explanatory power of the predictive model involving all general, health-related, smartphone use-related, and SHA use-related factors was 45.5%; and the economic level, interest, status, and awareness satisfaction of patients using SHA were identified to be the main influential factors. The results indicate that SHA developers need to put efforts into improving consumers' app recognition and to develop plans in provoking consumers' interests to increase the use of SHAs.
MAPPING BROWNFIELDS AS AN URBAN PLANNING ACTION

Brownfield sites have an important potential for a sustainable urban redevelopment, but in the same time they represent a challenge for the stakeholders. Finding solutions for brownfield redevelopment may be difficult for the authorities and owners alike. In this paper we classified the type of the Brownfield sites and we want to emphasize the importance of using these lands.
Modeling, analysis and optimization of integrated energy systems for multigeneration purposes

2 ACKNOWLEDGEMENTS 4 TABLE OF CONTENTS 5 LIST OF TABLES 7 LIST OF FIGURES 8 NOMENCLATURE 14 CHAPTER 1: INTRODUCTION 18 1.1 OVERVIEW 18 1.2 BENEFITS OF MULTIGENERATION ENERGY SYSTEMS 23 1.3 PRIME MOVERS 24 1.3. 1 Gas turbine prime mover 25 1.3. 2 Fuel cell prime mover 27 1.4 MULTIGENERATION ENERGY SYSTEMS 29 CHAPTER 2: MOTIVATION AND OBJECTIVES 32 2.1 MOTIVATION 32 2.2 OBJECTIVES 33 CHAPTER 3: LITERATURE REVIEW 36 3.