Miracle-dz/UniFAR-FTUs huggingface dataset:
Disaster Feature Classification on Aerial Photography to Explain Typhoon Damaged Region using Grad-CAM

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the south Chiba prefecture in Japan, whose damages included with electric and water provision stop and house roof break because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took eighteen days for recovery longer than past ones. Initial responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to red or yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. This paper demonstrates case studies applied to aerial photographs recorded at the south Chiba prefecture in Japan after typhoon disaster.
Effect of Educational Workshops on Disaster Risk Perception in Nurses

INTRODUCTION: The enhancement of nurses’ risk perception plays a significant role in their preparedness during disasters and emergencies. Therefore, this study aimed to investigate the effect of educational workshops on disaster risk perception in nurses METHODS: This randomized controlled field trial study included 62 nurses working at Razi Hospital, Birjand, Iran. The participants were divided into intervention and control groups. The intervention group was then requested to participate in a one-day intensive educational workshop based on the current national standards. The workshop content included a combination of lecturing methods, round-table exercises, and film display. The data were collected using the demographic characteristic form and researcher-made questionnaires measuring the nurses’ risk perception during disasters and emergencies. Subsequently, the data were analyzed using independent t-test, repeated measures analysis, and Bonferroni post hoc tests. FINDINGS: The mean total score of risk perception were significantly higher in the intervention group before, immediately, and two months after the workshop session, compared to those in the control group (P˂0.001). CONCLUSION: The implementation of nurses’ national preparation program during disasters and emergencies can result in an increase in the nurses’ risk perception during these events. Therefore, regarding the importance of nurses’ preparedness in confrontation with disasters and emergencies, it seems necessary to integrate the National Preparedness Program into the educational programs immediately after recruitment and in the form of in-service courses.
Relative Analysis of Blockchain, Bitcoin and Consensus Algorithm

: The decentralized ledger in which the transactions of cryptocurrency are recorded and securely exchanged is commonly known as Blockchain. Blockchain (commonly known chain of blocks) is the latest trend that follows decentralization concept. The collections of previously executed Bitcoin transactions are linearly arranged in a sequence, commonly known as Blockchain ledger. The addition of new completed block in Blockchain is done by the help of consensus protocols. Blocks on Blockchain consist of some information and timestamp value and they are interconnected by a link in such a way that every block points to its previous block. Blockchain is nearly impossible to tamper because of Hashing function used in it. Blockchain relies on consensus algorithm to reach on an agreement among nodes participating in it. There are numerous consensus algorithms among which PoW-Proof of work algorithm and PoS-Proof of Stake algorithm are the most widely used consensus algorithms. This paper covers the basic detailed overview and review of Blockchain along with its architecture and characteristics, Bitcoin and comparison of various consensus algorithms
VOH.CoLAB at TREC 2020 Precision Medicine Track

This paper describes our participation in the Scientific Ab-stracts task of the TREC 2020 Precision Medicine Track. We present our approach and the methods implemented, including both submitted runs and several post-mortem experiments using different methods. We performed experiments with Drugbank-based synonym expansion, Rocchio-based pseudo-relevance feedback, and neural re-ranking using the BioBERT biomedical pre-trained language models. In our evaluation, the Rocchio-based pseudo-relevance feedback method was the best performing method. Finally, we found that metadata and other textual fields in the document (e.g., journal name), are useful for retrieval and, when indexed, can improve recall-oriented metrics considerably leading to improvements in retrieval performance across the board.
A Replicate Study: Adoption of a STEM Outreach Program in Kuwait

Saﬁa Malallah is a web developer, artist, and Ph.D. candidate at Kansas State University. She obtained her master’s degree in computer science from Montana State University in 2017. Her research is centered around metamorphic relations ranking for reducing testing costs in scientiﬁc software. Saﬁa’s research interests expanded to include computer science education after observing the inﬂuence computer science has on her children. Her current research project is examining methods of teaching young children computational thinking by modeling playground environments. She seeks to expand her experience by volunteering and helping to facilitate STEM workshops.
CDM method transformation: updating and transforming CDM methods for use in an Article 6 context

In the context of crediting mechanisms for emission reduction projects, methodologies define how to set the crediting baseline, to test additionality, and to monitor and quantify emission reductions. They are therefore crucial for ensuring the environmental integrity of the carbon credits issued. The over 250 methodologies approved under the Clean Development Mechanism (CDM) of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) constitute the most important body of knowledge in this regard. Therefore, these methodologies are often used as a starting point in pilot activities for the use of market mechanisms under Article 6 of the Paris Agreement (PA). Given the absence of agreed rules on Article 6, it is unclear to what extent CDM methodologies will be formally transitioned into the Article 6.4 mechanisms. Under Article 6.2, countries can choose freely what methodologies to apply. Given that the CDM methodologies were developed prior to the adoption of the PA, they must be adapted or combined with new approaches to ensure that the underlying activity promotes an increase of mitigation ambition and does not jeopardise the achievement of the host country’s Nationally Determined Contribution (NDC). Moreover, given the lack of mandatory rules under the CDM to consider Agenda 2030 and its Sustainable Development Goals (SDGs), reporting and monitoring requirements for sustainable development (SD) contributions are generally absent. Under Article 6, cooperating Parties may wish to see stronger consideration of SD in the activity design and monitoring, reporting and verification (MRV) of SD impacts.
Facile synthesis and characterisation of nanosized ZnO modified with Ag2S for visible-light-induced phenol degradation

This study investigates the synthesis, characterisation, and application of a visible-light activated binary nanocomposite catalyst for the degradation of phenol in water. Zinc oxide with a wide direct band gap (3.4 eV) is efficient under ultraviolet irradiation but shows less activity under visible light. Therefore, a sol-gel method was applied to modify it using silver sulphide whose band gap is much lower (1.1 eV) to shift the photosensitivity of the composite catalyst towards the visible light spectrum. The synthesised nanoparticles were characterised using scanning electron microscopy, transmission electron microscopy, and X-ray diffraction which confirmed the purity of the synthesised material. At a catalyst loading of 1 gL-1, results showed that under visible light, the binary composite Ag2S/ZnO exhibited the highest phenol degradation efficiency of 50 % which was greater than ZnO that recorded a 16 % removal after 210 minutes of irradiation. These results support further development and application of visible-light-induced catalysts for photocatalytic degradation.
Some Clinical and Experimental Applications of Xeroradiography

AbstractThe contrast pattern obtainable in Xerox recording can be chosen to be very different from that of a film emulsion. One can strongly emphasise edges and suppress contrast between large uniform areas. The application of this method to soft tissue radiographs reveals detail in the vasculature and can give valuable information complementary to that in a film. The edge contrast is particularly effective in stereoscopic views of whole animals or of tumour specimens and some stereomammograms have been taken.
Truth or Troll: An Automated Framework for Identifying Authoritarian Regime Trolls in Twitter

In the age of information, social media plays a vital role facilitating information flow. Unfortunately players with political agenda intervene and affect the information flow by spreading disinformation, that in turn affects our day to day life considerably. One recent such incident is the use of trolls players who are paid to spread disinformation by agencies with intention to meddle with political events that has affected the information flow significantly. This thesis focuses on a crucial problem of identifying the trolls, especially on twitter, from regular accounts by analyzing the user behavior. To the end, we also propose a pipeline to identify tweets that may potentially belong to a troll account by classifying how likely the tweet is a misinformation. Index words: Troll account identification, Twitter data analysis, Latent Dirichlet Allocation, Text Classification, Word2vec Truth or Troll: An Automated Framework for Identifying Authoritarian Regime Trolls in Twitter
A chemical language based approach for protein - ligand interaction prediction

Identification of high affinity drug-target interactions (DTI) is a major research question in drug discovery. In this study, we propose a novel methodology to predict drug-target binding affinity using only ligand SMILES information. We represent proteins using the word-embeddings of the SMILES representations of their strong binding ligands. Each SMILES is represented in the form of a set of chemical words and a protein is described by the set of chemical words with the highest Term Frequency- Inverse Document Frequency (TF-IDF) value. We then utilize the Support Vector Regression (SVR) algorithm to predict protein - drug binding affinities in the Davis and KIBA Kinase datasets. We also compared the performance of SMILES representation with the recently proposed DeepSMILES representation and found that using DeepSMILES yields better performance in the prediction task. Using only SMILESVec, which is a strictly string based representation of the proteins based on their interacting ligands, we were able to predict drug-target binding affinity as well as or better than the KronRLS or SimBoost models that utilize protein sequence.
Task parallel assembly language for uncompromising parallelism

Achieving parallel performance and scalability involves making compromises between parallel and sequential computation. If not contained, the overheads of parallelism can easily outweigh its benefits, sometimes by orders of magnitude. Today, we expect programmers to implement this compromise by optimizing their code manually. This process is labor intensive, requires deep expertise, and reduces code quality. Recent work on heartbeat scheduling shows a promising approach that manifests the potentially vast amounts of available, latent parallelism, at a regular rate, based on even beats in time. The idea is to amortize the overheads of parallelism over the useful work performed between the beats. Heartbeat scheduling is promising in theory, but the reality is complicated: it has no known practical implementation. In this paper, we propose a practical approach to heartbeat scheduling that involves equipping the assembly language with a small set of primitives. These primitives leverage existing kernel and hardware support for interrupts to allow parallelism to remain latent, until a heartbeat, when it can be manifested with low cost. Our Task Parallel Assembly Language (TPAL) is a compact, RISC-like assembly language. We specify TPAL through an abstract machine and implement the abstract machine as compiler transformations for C/C++ code and a specialized run-time system. We present an evaluation on both the Linux and the Nautilus kernels, considering a range of heartbeat interrupt mechanisms. The evaluation shows that TPAL can dramatically reduce the overheads of parallelism without compromising scalability.
FAST CAT

Descriptive and empirical sciences, such as History, are the sciences that collect, observe and describe phenomena to explain them and draw interpretative conclusions about influences, driving forces and impacts under given circumstances. Spreadsheet software and relational database management systems are still the dominant tools for quantitative analysis and overall data management in these these sciences, allowing researchers to directly analyse the gathered data and perform scholarly interpretation. However, this current practice has a set of limitations, including the high dependency of the collected data on the initial research hypothesis, usually useless for other research, the lack of representation of the details from which the registered relations are inferred, and the difficulty to revisit the original data sources for verification, corrections or improvements. To cope with these problems, in this article we present FAST CAT, a collaborative system for assistive data entry and curation in Digital Humanities and similar forms of empirical research. We describe the related challenges, the overall methodology we follow for supporting semantic interoperability, and discuss the use of FAST CAT in the context of a European (ERC) project of Maritime History, called SeaLiT, which examines economic, social and demographic impacts of the introduction of steamboats in the Mediterranean area between the 1850s and the 1920s.
The effect of circuit training on physical fitness and archery accuracy in novice athletes

Introduction: Physical condition is one of the most important factors in supporting archery. the better the physical condition, the better the concentration to support accuracy in archery. Concentration in archery is the ability to target targets accurately. Purpose: The purpose of this study was to determine the effect of circuit training on improving physical fitness and archery accuracy in novice athletes. Materials and Methods: Participants were 20 novice athletes, aged 10 to 12 years. Quasi-experimental research design, using two group pretest-posttest design. The method of collecting data on physical fitness uses the Indonesian physical fitness test (IPFT) aged 10-12 years and the accuracy test is by shooting arrows at the target. Data analysis used paired sample t-test, to determine differences in variable groups of men and women. The analysis results showed no difference if the significant value was less than 0.05 (p<0.05). Before analyzing the data, a prerequisite test was carried out which included the Shapiro Wilk normality test and the variate homogeneity test using the Levene's test. Results: The results showed that there was a significant increase in physical fitness in the male and female groups. Therefore, there was a significant improvement in the accuracy of archery shots in the male and female groups. The study concluded that circuit training is effective for improving physical fitness and archery accuracy in novice athletes.
Body Weight Estimation using 2D Body Image

Two dimensional images of a person implicitly contain several useful biometric information such as gender, iris colour, weight, etc. Among them, body weight is a useful metric for a number of usecases such as forensics, fitness and health analysis, airport dynamic luggage allowance, etc. Most current solutions for body weight estimation from images make use of additional apparatus like depth sensors and thermal cameras along with predefined features such as gender and height which generally make them more computationally intensive. Motivated by the need to provide a time and cost efficient solution, a novel computer-vision based method for body weight estimation using only 2D images of people is proposed. Considering the anthropometric features from the two most common types of images, facial and full body, facial landmark measurements and body joint measurements are used in deep learning and XG boost regression models to estimate the person’s body weight. The results obtained, though comparable to previous approaches, perform much faster due to the reduced complexities of the proposed models, with facial models performing better than full body models. Keywords—Body weight estimation; deep learning; xgboost regressor; anthropometric features; computer vision
Stable Phase Equilibrium in the Aqueous Quaternary System Rb+, Mg2+//Cl–, borate–H2O at 323 K

The quaternary water–salt system Rb+, Mg2+//Cl–, borate–H2O is an important subsystem of the composition of Pingluo underground brine, Sichuan province. An isothermal dissolution equilibrium method was used to study the phase equilibrium of the system, and the solubilities, densities, and refractive indices of the equilibrated solution of the system were determined. On the basis of the solubility data, the isothermal dissolution phase diagram, the water figure, the diagrams for the density-composition and refractive index-composition were obtained. Results suggest that the quaternary system is of a complex reciprocal system with the formation of double salt RbCl·MgCl2·6H2O. Three invariant points and seven univariant curves divide the phase diagram into five crystalline zones. The five crystalline regions correspond to four single salts, rubidium chloride (RbCl), bischofite (MgCl2·6H2O), rubidium pentaborate tetrahydrate (RbB5O6(OH)4·2H2O), and hungchaoite (MgB4O5(OH)4·7H2O), and one double salt rubidium-.
The geographic, environmental and phylogenetic evolution of the Alveolinoidea from the Cretaceous to the present day

The superfamily Alveolinoidea is a member of the Order Miliolida, and comprises three main families, the Alveolinidae, the Fabulariidae and the Rhapydioninidae. They are examples of Larger benthic foraminifera (LBF), which are single-celled organisms with specific characteristic endoskeletons. Alveolinoids are found globally from the Cretaceous to the present day, and are important biostratigraphic index fossils in shallow-marine carbonates. They are often associated with hydrocarbon reservoirs, and exhibit provincialism with characteristic genera often confined to one of the American, Tethyan or Indo-Pacific provinces. Previously, the systematic study of the global interrelationship between the various alveolinoid lineages has not been possible because of the absence of biostratigraphic correlation between the geographically scattered assemblages, and the scarcity of described material from the Indo-Pacific province. Here we use the literature and new material from the Americas, the French Alps, Iran, Tibet, India and South East Asia, coupled with the use of the planktonic foraminiferal zonal (PZ) correlation scheme to propose a comprehensive, global, systematic analysis of the biostratigraphic, phylogenetic and paleogeographic evolution of the alveolinoids. The alveolinoids originated in the Cretaceous in the Tethyan province. During a global sea-level low stand, a westward migration of some alveolinoids species to the Americas occurred, a behaviour previously reported in contemporaneous orbitolinid LBF. After the Cretaceous/Palaeogene (K–P) event, which saw the extinction of all Cretaceous alveolinoids, rare new forms of alveolinoids evolved again, first in the Americas and later independently in Tethys. As was found in previous studies of rotalid LBF, sea-level low stands in the Paleocene also allowed some alveolinoid forms to migrate, but this time in an eastward direction from the Americas to Tethys, and from Tethys on to the Indo-Pacific province. Alveolinoids still exist today (Borelis and Alveolinella), the former of which is cosmopolitan, while the latter is restricted to the Indo-Pacific province. Throughout their phylogenetic history, alveolinoids characteristically exhibit convergent evolution, with the repeated re-occurrence of certain morphological features. Understanding this propensity to homoplasy is essential in understanding and constructing the phylogenetic relationships within the alveolinoid superfamily.
Single phase seven level Z-source cascaded H-bridge inverter for photovoltaic systems

The Multilevel inverter (MLI) is identified as a viable power electronic unit for distributed energy conversion applications. In this paper discusses the performance of single phase seven level Z (impedance) source cascaded H-bridge inverter topology suited for solar photovoltaic systems. A modified sinusoidal pulse width modulation technique is used to control the switches of the H-bridge inverter. The simulation results are obtained interms of inverter output voltage and harmonic analysis confirm improvement in voltage boost and reduction in harmonic distortion of the suggested inverter topology. The experimental results matches with the simulation values, as verified through a scaled down prototype.
The correlation between self-efficacy and peer interaction towards students’ employability in vocational high school

Employability is an important factor that can determine the quality of prospective graduates, especially in vocational high schools. Therefore, vocational high schools, as one of the educational institutions that prepare workforce resources, need to pay attention to factors that can increase their students' employability. This study aims to empirically examine the role of self-efficacy and the peer interaction on student employability. The subjects of this study were 132 students at Vocational High School Muhammadiyah Prambanan. The data were collected through an employability scale, a self-efficacy scale, and a peer interaction scale. The data were then analyzed with multiple linear regression techniques. The results showed that: 1) Simultaneously, there was a very significant role that self-efficacy and peer interaction have on employability with p=0.000 (p<0.01); 2) Partially, there was also a very significant role that self-efficacy has on employability with p=0.000 (p<0.01); 3) Peer interaction also had a partially significant role on employability with p=0.001 (p<0.01). Both self-efficacy and peer interaction simultaneously contributed 40.8% to employability, but self-efficacy had a more dominant role (26.7%) than peer interactions (14.1%).