Miracle-dz/UniFAR-FTUs huggingface dataset:
TELKOMNIKA Telecommunication Computing Electronics and Control

Received Mar 24, 2021 Revised Apr 06, 2022 Accepted Apr 14, 2022 Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields’ dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.
Bilateral video-assisted thoracoscopic surgery in tracheotomised patients: E-Z blocker, a promising alternative

A 78-year-old, post-coronavirus disease 2019 (COVID-19), tracheotomised patient was admitted to the intensive care unit (ICU) for 7 months. He was also a known case of CKD (chronic kidney disease), DM (diabetic mellitus) and IHD (ischaemic heart disease) with an ejection fraction of 20%. He was on mechanical ventilation with high oxygen and noradrenaline support. During his ICU course, he developed an episode of ventilator-associated pneumonia (VAP) and acute kidney injury for which he underwent dialysis twice. He had two episodes of cardiac arrest during the ICU course. High-resolution computed tomography (HRCT) of the chest revealed empyema and collapse on both sides of the lung. The patient was posted for bilateral VATS for lung decortication. After the application of routine American Society of Anesthesiologists (ASA) monitoring, the patient was induced with injections of fentanyl 2 μg/kg, etomidate 0.2 mg/kg and rocuronium 0.8 mg/kg. After induction, a multiport adaptor was connected to the tracheostomy tube. With the help of a 3.7 mm internal diameter (ID) fibre-optic bronchoscope (FOB), the EZ-blocker was anchored at the carina and its extensions into the right and left main bronchus [Figure 1]. Sequential OLV was achieved by inflating the left and right cuff. The total time taken for EZ-blocker insertion was 3 min. The condition of lung collapse was satisfactory during the procedure. The surgery took 210 min. At the end of the surgical procedure, the EZ-blocker was removed and the patient was shifted to the ICU.
Investigating the Effects of Pedestrian-to-Vehicle Human–Machine Interface Design Using Driving Simulator Experiment

Pedestrian-to-vehicle (P2V) warning technology is expected to reduce pedestrian crashes and improve roadway safety. Previous studies have demonstrated the effectiveness of P2V; however, compared with a general P2V human–machine interface (HMI) design adopted in these studies, the necessity of applying different HMI designs specific to driving scenarios remains uncertain. To resolve the issue, this study conducted a driving simulator experiment to test the performance of various P2V HMI designs considering scenario heterogeneity. Two aspects of the HMI design, that is, the warning urgency level and warning content, were tested in five pedestrian pre-crash scenarios. The warning urgency level is categorized into two types, a “gradually changed” warning and an “emergency” warning, and the warning content focuses on either providing scenario-based distance information as a supplement or not. Data from 36 participants were collected in the study. The results show that using a “gradually changed” warning design can help a driver make gradual driving adjustments to the upcoming conflict, which improves the driving performance; in addition, providing scenario-based distance information can increase the safety buffer. Additionally, insights about driver features’ effects on P2V HMI design were also proposed. Drivers who had been in a not-at-fault crash before and their experience related to the advanced driver assistance system would interact with the P2V influence. This study’s findings have practical implications for both automobile manufacturers and researchers.
Event-based imaging polarimeter

Abstract. The design and initial experimental results from an event-based, rotating-polarizer, imaging polarimeter are presented. The speed of division-of-time imaging systems is traditionally constrained by the frame rate of the focal plane sensor and the need to sample several polarization angles during rotation. The asynchronous event reporting of event-based cameras (EBCs) frees these constraints but introduces challenges. The Stokes vectors used to describe polarization are based on irradiance, but EBCs are inherently change-detection systems that struggle to estimate irradiance. Two methods of estimating the polarization state without first recovering irradiance images are presented and compared. The DAVIS 346 sensor enables simultaneous recording of events with conventional frame-based images, enabling direct comparison of event-based polarization estimates to traditional techniques.
THE DETECTION OF DISTURBANCE EFFECTS OF NORWAY SPRUCE (<i>PICEA ABIES</i> (L.) KARST.) IN UAV MULTISPECTRAL IMAGERY

Abstract. This study analyses spectral separability of Norway spruce (Picea abies (L.) Karst. ) trees several months after induction of mechanical damage (a simulated wind damage), and one year after the damage – at the beginning of vegetation season and then following a period of drought. Experiment includes sample group of trees (Bent trees) over three plots that underwent static pulling test, therefore simulating survival after storm event. They were compared to a group of trees of the same dimensions that did not undergo static pulling test (Control trees). Spectral reflectance data was collected using unmanned aerial vehicle (UAV) and multispectral sensor. Reflectance of four bands were extracted, converted into indices, and analysed with statistical tests. There were differences in plot response to experiment, as one of the plots failed to show significant difference between groups. Overall, multiple indices proved great results for spectral separation between Control and Bent trees. MACI (Modified Anthocyanin Content Index) was the most consistent in differentiating between groups. Other indices that represent chlorophyll content and photosynthetic activity were also relatively sensitive for stress detection. However, on the contrary to what was expected, period of drought did not seem to affect spectral reflectance of canopies. There was a distinct difference of chlorophyll and anthocyanin content, and photosynthetic activity in sample trees, but these deviations did not manifest in larger scale post drought. Moreover, MACI and GNDVI (Green Normalized Difference Vegetation Index) even reduced mean value gap after the period of low precipitation.
Modulations in Chlorophyll a Fluorescence Based on Intensity and Spectral Variations of Light

Photosynthetic efficiency is significantly affected by both qualitative and quantitative changes during light exposure. The properties of light have a profound effect on electron transport and energy absorption in photochemical reactions. In addition, fluctuations in light intensity and variations in the spectrum can lead to a decrease in photosystem II efficiency. These features necessitate the use of a simple and suitable tool called chlorophyll a fluorescence to study photosynthetic reactions as a function of the aforementioned variables. This research implies that chlorophyll a fluorescence data can be used to determine precise light conditions that help photoautotrophic organisms optimally function.
Joint Waterpipe and Cigarette Smoking as Key Correlate of History of Depression in Iranian Population: A Cross-Sectional Study

Research on the association between waterpipe smoking and depression is limited. This study aimed to explore the prevalence and adjusted associated correlates of depression among Iranian adult. We analyzed data from 974 participants of a population-based cross-sectional study enrolling 18-50-year-old residents of Tehran, Iran in 2015. Data on lifetime self-reported history of depression, smoking behaviors, socioeconomic status, self-rated health, physical activity, stressful life events as well as a number of relevant confounders was obtained. Logistic regression models were employed for estimating adjusted odds ratios (ORs) and their 95% confidence intervals (CI). The mean (SD) age of the study sample was 32.55 (8.58) years. Of 974 recruited adults, 52.36% were female. The lifetime prevalence of depression in the general population was 17.0%. In general, 21.77% and 24.79% of participant reported lifetime history of cigarette and waterpipe smoking, respectively. While only cigarette smoking (OR = 1.94, 95% CI: (1.04-3.61) and only waterpipe smoking (OR = 1.65, 95% CI: (.95-2.86) were significantly associated with depression, joint cigarette and waterpipe smoking (OR= 3.76, 95% CI: (1.99-6.08) was the strongest correlate of depression followed by female gender (OR = 3.28, 95% CI: (2.08-5.15) and poor self-rated health (OR = 2.47, 95% CI: (1.73-3.53). The prevalence of self-reported depression in general population of Tehran is considerably higher than its global mean. We reported joint cigarette and waterpipe smoking as a significant correlate with depression in the general population. Future health promotion interventions should highlight the disadvantages of joint cigarette and waterpipe smoking targeting adults and especially females.
Deep Learning-Based Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
A Comparison of Solvent-Based Extraction Methods to Assess the Central Carbon Metabolites in Mouse Bone and Muscle

The identification of endogenous metabolites has great potential for understanding the underlying tissue processes occurring in either a homeostatic or a diseased state. The application of gas chromatography-mass spectrometry (GC-MS)-based metabolomics on musculoskeletal tissue samples has gained traction. However, limited comparison studies exist evaluating the sensitivity, reproducibility, and robustness of the various existing extraction protocols for musculoskeletal tissues. Here, we evaluated polar metabolite extraction from bone and muscle of mouse origin. The extraction methods compared were (1) modified Bligh–Dyer (mBD), (2) low chloroform (CHCl3)-modified Bligh–Dyer (mBD-low), and (3) modified Matyash (mMat). In particular, the central carbon metabolites (CCM) appear to be relevant for musculoskeletal regeneration, given their role in energy metabolism. However, the sensitivity, reproducibility, and robustness of these methods for detecting targeted polar CCM remains unknown. Overall, the extraction of metabolites using the mBD, mBD-low, and mMat methods appears sufficiently robust and reproducible for bone, with the mBD method slightly bettering the mBD-low and mMat methods. Furthermore, mBD, mBD-low, and mMat were sufficiently sensitive in detecting polar metabolites extracted from mouse muscle; however, they lacked repeatability. This study highlights the need for a re-thinking, towards a tissue-specific optimization of methods for metabolite extractions, ensuring sufficient sensitivity, repeatability, and robustness.
Discovery of novel coumarin-indole derivatives as tubulin polymerization inhibitors with potent anti-gastric cancer activities.

Novel coumarin-indole derivatives were designed, synthesized and evaluated as tubulin polymerization inhibitors targeting the colchicine binding site. Among these compounds, compound MY-413 displayed the most potent inhibitory activities against gastric cancer cell line MGC-803 with an IC50 value of 0.011 μM. Furthermore, the IC50 values of compound MY-413 was less than 0.1 μM for other 17 cancer cell lines and less than 0.05 μM for other 8 cancer cell lines. Compound MY-413 effectively inhibited the tubulin polymerization (IC50 = 2.46 μM) by binding to the colchicine site. Screening for the inhibitory effects of compound MY-413 on 61 kinases, it was found that compound MY-413 could inhibit MAPK pathways-related kinases. Because of the inhibitory effects of compound MY-413 on tubulin polymerization and MAPK signaling pathway, compound MY-413 induced cell apoptosis, arrested the cell cycle in the G2/M phase, induced the inhibition of cell proliferation and migration in gastric cancer cells MGC-803 and HGC-27. In addition, compound MY-413 could significantly inhibit tumor growth in MGC-803 xenograft tumor models with tumor growth inhibition (TGI) rates of 70% (15 mg/kg) and 80% (30 mg/kg) without obvious toxicity. Consistent with the in vitro results, compound MY-413 also inhibited MAPK signaling pathway, and induced apoptosis and proliferation inhibition in vivo. In conclusion, this work indicated that compound MY-413 was a promising lead compound for the further investigation as a potential anti-gastric cancer agent.
Cryo-EM structure of acylpeptide hydrolase reveals substrate selection by multimerization and a multi-state serine-protease triad

The first structure of tetrameric mammalian acylaminoacyl peptidase, an enzyme that functions as an upstream regulator of the proteasome through the removal of terminal N-acetylated residues from its protein substrates, was determined by cryo-EM and further elucidated by MD simulations. Self-association results in a toroid-shaped quaternary structure, guided by an amyloidogenic β-edge and unique inserts. With a Pro introduced into its central β-sheet, sufficient conformational freedom is awarded to the segment containing the catalytic Ser587 that the serine protease catalytic triad alternates between active and latent states. Active site flexibility suggests that the dual function of catalysis and substrate selection are fulfilled by a novel mechanism: substrate entrance is regulated by flexible loops creating a double-gated channel system, while binding of the substrate to the active site is required for stabilization of the catalytic apparatus – as a second filter before hydrolysis. The structure not only underlines that within the family of S9 proteases homo-multimerization acts as a crucial tool for substrate selection, but it will also allow drug design targeting of the ubiquitin-proteasome system.
A Silicon Carbide Linear Voltage Regulator for High Temperature Applications

Current market demands have pushed the capabilities of silicon to the edge. High temperature and high power applications require a semiconductor device to operate reliably in very harsh environments. This situation has awakened interests in other types of semiconductors, usually with a higher bandgap than silicon’s, as the next venue for the fabrication of integrated circuits (IC) and power devices. Silicon Carbide (SiC) has so far proven to be one of the best options in the power devices field. This dissertation presents the first attempt to fabricate a SiC linear voltage regulator. This circuit would provide a power management option for developing SiC processes due to its relatively simple implementation and yet, a performance acceptable to today’s systems applications. This document details the challenges faced and methods needed to design and fabricate the circuit as well as measured data corroborating design simulation results. This dissertation is approved for recommendation to the Graduate Council. Dissertation Director: _________________________________________ Dr. H. Alan Mantooth Dissertation Committee: _________________________________________ Dr. Randy Brown _________________________________________ Dr. Juan C. Balda _________________________________________ Dr. Scott Smith _________________________________________ Dr. Jia Di Dissertation Duplication Release I hereby authorize the University of Arkansas Libraries to duplicate this dissertation when needed for research and/or scholarship. Agreed ______________________________________ Javier Antonio Valle Mayorga Refused ______________________________________ Javier Antonio Valle Mayorga
SPATIAL ANALYSIS OF VEGETATION DENSITY CLASSIFICATION IN DETERMINING ENVIRONMENTAL IMPACTS USING UAV IMAGERY

Abstract. Along with remote sensing technology development, vegetation monitoring can be performed using satellite imagery or Unmanned Aerial Vehicle (UAV) data. UAV imagery with a high resolution, between 3–5 cm at an altitude <100 m, is able to present specific land conditions without being affected by the weather. Information related to vegetation density is one of the components in the Environmental Impact Analysis (EIA) study of a proposed project development due to vegetation removal. In this study, information from consumer-grade cameras of a low-cost UAV platform was explored to classify vegetation density using the potential of RGB imagery-based vegetation index (VI). The correlation coefficient (R2) between field observation data and the seven different values of VI demonstrated moderate to strong correlation. The highest linier correlation of 80.16% (R2 = 0.64) was performed by the Green Red Vegetation Index (GRVI). Classification of the vegetation density was established by applying the object-based image analysis method through the combination of supervised machine learning algorithm of Support Vector Machine (SVM) and the GRVI vegetation index. The vegetation density classification consists of very low, low, medium, high, and very high-density classes. The data can be utilized in determining vegetation management efforts from the presence of a proposed project in the EIA study. The use of UAV imagery is considered effective in identifying vegetation density.
An Iterative Regularization Method based on Tensor Subspace Representation for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But, how to precisely reconstruct the high spatial resolution hyperspectral image (HR-HSI) by fusion technology is a challenging issue. In this paper, we propose an iterative regularization method based on tensor subspace representation (IR-TenSR) for MSI-HSI fusion, thus HSI-SR. First, we propose a tensor subspace representation (TenSR)-based regularization model that integrates the global spectral-spatial low-rank and the nonlocal self-similarity priors of HR-HSI. These two priors have been proven effective, but previous HSI-SR works cannot simultaneously exploit them. Subsequently, we design an iterative regularization procedure to utilize the residual information of acquired low-resolution images, which are ignored in other works that produce suboptimal results. Finally, we develop an effective algorithm based on the proximal alternating minimization method to solve the TenSR-regularization model. With that, we obtain the iterative regularization algorithm. Experiments implemented on the simulated and real datasets illustrate the advantages of the proposed IR-TenSR compared with state-of-the-art fusion approaches. The code is available at https://github.com/liangjiandeng/IR-TenSR.
Influence of woven fabric construction parameters on electromagnetic shielding effectiveness: Part I – weave influence

The development of textiles with the ability to shield an electromagnetic field is still of great interest, and the influences of the construction parameters of woven fabric (weave, input fibrous material composition, three-dimensional (3D) construction) on the resulting electromagnetic shielding efficiency have still not been sufficiently investigated. The main goal of this study is therefore to analyze the weaves of special electrically conductive woven fabrics and evaluate their impact on electromagnetic shielding effectiveness (SE) using statistical methods. Here, the interlacing structure cells of woven fabric are newly used to describe the weave. The results confirmed that the proposed theory of the interlacing of woven fabric is satisfactory for evaluating and, especially, for prediction of the SE of electromagnetic shielding woven fabrics. For this study, the input yarn (made from a mixture of traditional and extremely thin discrete stainless steel fibers) is used for the production of 21 types of dobby woven fabrics with different weaves and its SE was analyzed using the coaxial transmission-line technique. This contribution can be used as theoretical guidance for the construction, design, and production of special woven fabrics (dobby and jacquard pattern) with controlled SE.
Parental Modeling and Normative Influence in Shaping Teenagers’ Phubbing: An Exploratory Study

Phubbing, the ignoring of one’s companions to pay attention to one’s phone, is ubiquitous. Yet far less is known about its interpersonal transmission. Based on the social learning theory and the theory of normative social behavior, this cross-sectional study tested the associations between perceived parental phubbing and teenagers’ phubbing. Two hundred ninety-nine Chinese teenagers (M = 14.03, SD = 0.87) answered questions about perceived parental phubbing, descriptive norms and injunctive norms of phubbing, and phubbing of themselves. The regression analyses showed that perceived parental phubbing was positively associated with teenagers’ phubbing and descriptive norms. The conditional process analyses showed that higher levels of perceived parental phubbing predicted higher levels of descriptive norms, which in turn predicted higher levels of teenagers’ phubbing. Moreover, the magnitude of the association between descriptive norms and teenagers’ phubbing was greater as injunctive norms became greater.