Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambulance Drone Using Raspberry Pi 3 in Emergency Healthcare

In case of crucial medical emergency a huge number of individuals suffer majorly or even die on a daily basis due to the time lag taken by the Ambulance administration to reach the accident/emergency site. This occurs because of traffic jams, road blockage in the city, or unavailability of ambulance. However it is possible to reduce this healthcare problem situation. A model of an Unmanned Ariel Vehicle (UAV) or an ambulance drone, which can reach the deadly cases quicker than any ordinary rescue vehicle, which spares time and likewise measures the distinctive wellbeing parameters utilizing its estimating gadgets. At the point when a telephone call is given to the emergency number, the administrator tracks the area and sends the UAV with required immediate first aid to the location. The healthcare drone enters the emergency scene and continuous instructions are given by the administrator. The UAV comprises of a small framework which includes various sensors which can be helpfully joined to the casualty body and the essential parameters is estimated and it is instantly sent to the rescue vehicle and to the control room or nearby hospital utilizing worldwide situating framework and ZIGBEE. The specific outcome helps the paramedics and additionally the specialists to assess the circumstance easily with better symptomatic and remedial decisions. In this manner, the objective is to build up a generally useful therapeutic toolbox that can be flown to any emergency circumstance and it is utilized to give the real-time circumstance results to the rescue vehicle and the clinic group so they will be prepared to serve to the requirements of the patients. This model backs the standard ambulances instead of supplanting the ordinary emergency vehicle. Keywords—ambulance drone, raspberry pi 3, ECG sensor, Arduino Nano
Diversity and composition of zooplankton in rice fields during a crop cycle at Pathum Thani province, Thailand

A study of zooplankton communities in three rice fields (RF1, RF2 and RF3) at Pathum Thani province was carried out from August to November 2005. A total of 88 taxa were identified. Of these, 74 belonged to Rotifera, 11 were Cladocera and 3 were Copepoda. The highest number of zooplankton species was recorded in RF1 (75 species), followed by RF2 (55 species) and RF3 (51 species). The highest average H’ was registered in RF2 (1.83), followed by RF3 (1.59) and RF1 (1.52). The highest average zooplankton abundance was reported from RF3 (22,630.7 indL -1 ), followed by RF2 (1,756.8 indL -1 ) and RF1 (1,519.5 indL -1 ). The main zooplankton components in the three areas were nauplii, rotifers and Cladocera. Cladocera played a major role in structuring rotifer communities in rice fields.
Boosting Service Innovation: The Role of Consultancies

This paper seeks to make a particular contribution in what are the roles of consultancies as key innovation intermediaries in service industry. In line with previous literature, the research purposes to test the application of Pinto et al (2016) framework within service companies, triangulating the findings of Pinto et al (2017). Four in-depth case studies of service companies were developed, resulting on detailed descriptions of the phenomenon using constructs to order the data and relate to earlier literature. Key findings highlight changes in motivations for consultancy engagement, from an initial focus on knowledge (output) to a focus on methodology (process). The adequacy of the framework proposed by Pinto et al (2016) is confirmed, and a new function of intermediaries arises from this study, related with “evaluation of innovation outcomes”. The study highlights the contribution of consultancies in preparing companies to innovate.
Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.
Classification of Electronic Club-Music

Technical advances like broadband internet access and digital vinyl systems have made digital music distribution one of the major ways to obtain electronic club-music. Services like beatport or djtunes give Djs and other music lovers the opportunity to download hundreds of thousands of songs from the electronic club-music genre. Music discovery in databases of this size is challenging. Besides artist and title information, music providers therefore arrange their content into classes, e.g., subgenres. This way of expressing similarity according to musical attributes can be helpful to find new music. Generally, the class labels are assigned manually, which is a time consuming procedure that requires to be done by music experts. We present a system for the automatic content-based classification of electronic club-music into 15 different electronic club-music subgenres. Allthough not undisputed, genre taxonomies are one of the most common ways to describe music. Genre recognition is therefore a prominent task in music information retrieval, that is investigated by many groups (e.g. [1]). Usually, systems focus on a discrimination on main genre level. Another automatic content-based system specialized for the classification of electronic club-music is presented in [2], classifying into six genres (intelligent dance music, house, techno, drum and bass, trance, and downtempo).
1 Perfect C ombination of LBL with S olG el F ilm to 2 E nhance the A nticorrosion P erformance on Al A lloy 3 under S imulated and A ccelerated C orrosive 4 E nvironment 5

Given of the outstanding versatile properties, the multilayered anti-corrosion coatings have drawn great interests in academic and engineering fields. However, the application of multilayered coatings is restricted by some limitations such as low interlayer compatibilities, harsh preparation process, etc. This work introduced a composite film fabricated on 2A12 aluminum alloy surface, including an anodic oxide film, a sol-gel film as well as a layer-by-layer (LBL) selfassembling film from bottom to top. The microstructure and elemental characterization indicated that the finish of the coating with the LBL film resulted in a closely connected multilayered coating with a smoother surface. The anticorrosion performance was systematically evaluated in the simulated corrosive medium and neutral salt-spray environment. The intergrated coating with the LBL film presented an excellent anticorrosion ability with the system impedance over 108 Ω•cm2 and the self-corrosion current density two orders of magnitude lower than that of the other coatings. After the acceleration test in salt-spray environment, the multilayered coatings could still show a good protective performance with almost no cracks and no penetration of chloride ions. It is believed that the as-constructed multilayered coating with high corrosive properties and fine surface state will have promising applications in the field of anticorrosion engineering.
Fractional Order Unknown Inputs Fuzzy Observer for Takagi–Sugeno Systems with Unmeasurable Premise Variables

This paper presents a new procedure for designing a fractional order unknown input observer (FOUIO) for nonlinear systems represented by a fractional-order Takagi–Sugeno (FOTS) model with unmeasurable premise variables (UPV). Most of the current research on fractional order systems considers models using measurable premise variables (MPV) and therefore cannot be utilized when premise variables are not measurable. The concept of the proposed is to model the FOTS with UPV into an uncertain FOTS model by presenting the estimated state in the model. First, the fractional-order extension of Lyapunov theory is used to investigate the convergence conditions of the FOUIO, and the linear matrix inequalities (LMIs) provide the stability condition. Secondly, performances of the proposed FOUIO are improved by the reduction of bounded external disturbances. Finally, an example is provided to clarify the proposed method. The obtained results show that a good convergence of the outputs and the state estimation errors were observed using the new proposed FOUIO.
Tumor Mutational Burden and Genomic Alterations in Chinese Small Cell Lung Cancer Measured by Whole-Exome Sequencing

Purpose Studies on genetic alterations of the heterogenous small cell lung cancer (SCLC) are rare. We carried out the present study to clarify the genomic alterations and TMB levels of Chinese SCLC patients by whole-exome sequencing. Materials and Methods Whole-exome sequencing by next-generation sequencing technique was implemented on twenty SCLC samples. Significant somatic mutations and copy number variations were screened, followed by comparison with the data extracted from COSMIC. Besides, altered signaling pathways were examined in order to figure out actionable targets. Results A total of 8,062 nonsynonymous mutations were defined. The number of mutations for each case ranged from 98 to 864. As for base substitutions, a total of 15,817 substitutions were detected with C > A conversion which was correlated to smoking occupying 25.57%. The TMB values ranged from 2.51/Mb to 22.1/Mb with a median value of 9.95/Mb. RB1 was the most frequently mutated gene altered in 18 (90%) cases, followed by TP53 altered in 17 (85%) cases. Other commonly changed genes were PTEN, and RBL1, with frequencies of 55% and 50%, respectively. SOX2 significantly amplified in 6 (30%) cases and MYCN amplified in 1 (5%) patient. Notch signaling pathway and PI3K/AKT/mTOR signaling pathway were universally and significantly changed. Major genomic alterations were in consistency with data from COSMIC, but frequencies of less common mutations were different. Conclusion TP53 and RB1 inactivations were universally detected in SCLC. The Notch and PI3K/AKT/mTOR signaling pathways were both significantly altered, implying potential actionable targets.
Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling

This study presents the classification of emotions on EEG signals using commercial BCI headsets known as wearable EEG. One of the key issues in this research is the lack of mental classification using VR as the medium to stimulate emotion. Moreover, we endeavor to present the first comprehensive and systematic analysis of intra-versus inter-subject variability in EEG-based emotion classification using VR and wearable EEG. The approach towards this research is by using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as the machine learning classifiers. Firstly, each of the participants will be required to wear the EEG headset to record their brain waves when they are immersed inside the VR environment. The data points are then marked if they showed any physical signs of emotion or by observing the brain wave pattern. Secondly, the data will then be tested and trained with KNN and SVM algorithms. We conduct subject-dependent as well as subject-independent classifications in order to compare intra-against inter-subject variability, respectively in VR EEG-based emotion modeling. The highest subject-dependent classification accuracy achieved was 97.9% while the highest subject-independent classification accuracy obtained was 91.4% throughout the brain wave spectrum (α, β, γ, δ, θ). These methods showed highly promising results and will be further enhanced using other machine learning approaches such as deep learning in VR stimulus.
Biomass Burning and the Production of Greenhouse Gases Page 1 of 15 9 BIOMASS BURNING AND THE PRODUCTION OF GREENHOUSE GASES

Biomass burning is a source of greenhouse gases, carbon dioxide, methane, and nitrous oxide. In addition, biomass burning is a source of chemically active gases, including carbon monoxide, nonmethane hydrocarbons, and nitric oxide. These gases, along with methane, lead to the chemical production of tropospheric ozone (another greenhouse gas) as well as control the concentration of the hydroxyl radical, which regulates the lifetime of almost every atmospheric gas. Following biomass burning, biogenic emissions of nitrous oxide, nitric oxide, and methane are significantly enhanced. It is hypothesized that enhanced postburn biogenic emissions of these gases are related to fireinduced changes in soil chemistry andor microbial ecoiogv. Biomass burning, once believed to be a tropical phenomenon, has been demonstrated by satellite imagery to also be a regular feature of the world's boreal forests. One example of biomass burning is the extensive 1987 fire that destroyed more than 12 million acres of boreal forest in the People's Republic of China and across its border in the Soviet Union. Recent estimates indicate that almost all biomass burning is human-initiated and that it
Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors

Sample-eﬃciency is crucial in reinforcement learning tasks, especially when a large number of similar yet distinct tasks have to be learned. For example, consider a smart wheelchair learning to exit many diﬀerently-furnished oﬃces on a building ﬂoor. Sequentially learning each of these tasks from scratch would be highly ineﬃcient. A step towards a satisfying solution is the use of transfer learning: exploiting the knowledge acquired in previous (or source ) tasks to tackle new (or target ) tasks. Existing work mainly focuses on exploiting only one source policy as an advisor for the fresh agent, even when there are several expert source policies available. However, using only one advisor requires arti-ﬁcial mechanisms to limit its inﬂuence in areas where the source task and the target task diﬀer, in order for the advisee not to be misled. In this paper, we present a novel approach to transfer learning in which all available source policies are exploited to help learn several related new tasks. Moreover, our approach is compatible with tasks that diﬀer by their transition functions , which is rarely considered in the transfer reinforcement learning literature. Our in-depth empirical evaluation demonstrates that our approach signiﬁcantly improves sample-eﬃciency.
Islamic values in elderly care in Finland

In recent decades, care-providing services in Finland have engaged care professionals with diverse cultural backgrounds, including Islamic cultures. Muslim women who adhere strictly to Islamic values in such work sometimes find it difficult to cope with the practices at care service institutions. In this article, we consider the experiences of Muslim women care professionals in such work, an environment that entails multiple interactions at different levels, among themselves and with care receivers.
Simple Vertical Human Climbing Control with End Effector State Machines

We present a framework for physics based vertical climbing control for human like articulated figures. In our work motion of end effectors of the figure are categorized under four states: Holding, Swinging, Reached and Gave-Up. Each end effector owns a finite state machine for managing the transitions between these states. Different target poses are determined for the limbs for swinging and holding states and they are tracked with PD Controller and gravity compensation. At Reached and Gave-Up states the aim of the end effector is to touch the closest point on the wall. Physics based movement of end effectors for touching the wall is simulated by using Jacobian Transpose Control. Our framework also includes a strategy manager which enables building different climbing strategies. Keywords—physics based animation; climbing; character animation.
OTT_A_199136 3791..3804

*These authors contributed equally to this work Background: Preoperative treatments are considered for patients with worse outcome to improve overall survival and reduce tumor relapse. This study developed a prognostic risk estimation for patients with hepatitis B virus (HBV)-related solitary hepatocellular carcinoma after curative resection, including preoperative computed tomography (CT) signatures. Methods: Preoperative multiphasic CTs for 166 patients with operable HCC were performed in our hospital from 15 November 2013 through 15 May 2015. Follow-up information, until 5 June 2017, included: CT, pathological and clinical characteristics, and recurrence and metastases of HCC confirmed by pathological or radiological diagnosis. The parameters were analyzed by the Kaplan-Meier method and Cox proportional hazards regression analysis. Results: In multivariate analyses, overall survival was not significantly associated with any of the analyzed prognostic risk factors, but did show that the following were significant prognostic risk factors for disease-free survival: larger tumor size, positive radiogenomic venous invasion, non-smooth tumor margin, and histological microvascular invasion. These were all incorporated into the nomogram. The calibration curves for predicting the probability of disease-free survival between the nomogram and actual observation showed good conformity. Conclusion: In patients with HBV-related HCC, CT signatures were a noninvasive significant indicator of disease-free survival. Thus, consideration of CT signatures may optimize preoperative treatment strategies for the individual patient.
An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories

Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.
Four-dimensional variational assimilation without using the adjoint model

The sequential data assimilation method considers only observations up to and including the time of analysis. Moreover, no matter how many analyses for various time steps were performed during the assimilation processes, the precision of the final analysis has been restricted because of the growth of background error with integration of the forecast model. The restrictions that are not fit for a high quality analysis have rarely been referred in meteorological literatures (Cohn et al., 1994; Zhu et al., 2003). As a method of overcoming such a restriction, Horel and Colman (2005) emphasized recently the application of the retrospective analysis, by which observations after the time of analysis can be accounted for the past analysis. The 4D-Var is a well-known retrospective analysis (Klinker et al., 2000). The 4D-Var finds the most probable state to be true, for the given past, current, and future observations. The
Theoretical Modeling of Defects, Dopants, and Diffusion in the Mineral Ilmenite

The iron titanium oxide ilmenite (FeTiO3) is a technologically and economically important mineral in the industrial preparation of titanium-based pigments and spintronic devices. In this study, atomistic simulation techniques based on classical pair potentials are used to examine the energetics of the intrinsic and extrinsic defects and diffusion of Fe2+ ions in FeTiO3. It is calculated that the cation anti-site (Fe‒Ti) cluster is the most dominant defect, suggesting that a small amount of cations exchange their positions, forming a disordered structure. The formation of Fe Frenkel is highly endoergic and calculated to be the second most stable defect process. The Fe2+ ions migrate in the ab plane with the activation energy of 0.52 eV, inferring fast ion diffusion. Mn2+ and Ge4+ ions are found to be the prominent isovalent dopants at the Fe and Ti site, respectively. The formation of additional Fe2+ ions and O vacancies was considered by substituting trivalent dopants (Al3+, Mn3+, Ga3+, Sc3+, In3+, Yb3+, Y3+, Ga3+, and La3+) at the Ti site. Though Ga3+ is found to be the candidate dopant, its solution enthalpy is >3 eV, suggesting that the formation is not significant at operating temperatures.