Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences of Fly‐In, Fly‐Out and Drive‐In, Drive‐Out Rural and Remote Psychologists

Objective Fly‐in, fly‐out (FIFO) and drive‐in, drive out (DIDO) work practices have been central to the resource sector in Australia for many years. While research considering the impacts of this lifestyle on mining workers is emerging, comparatively little is known about the experiences of FIFO/DIDO health professionals. The lack of information on FIFO/DIDO psychologists may be detrimental to both the communities serviced by them and the profession in terms of developing appropriate workforce planning and training for psychological service delivery in rural and remote areas. This qualitative study therefore aimed to explore the experiences of FIFO/DIDO psychologists. Method Semi‐structured qualitative interviews were conducted with 10 psychologists living in metropolitan South Australia and working in rural and remote areas. Interviews were conducted by telephone, face‐to‐face, or in a group. Data were analysed using thematic analysis. Results While participants reported experiencing similar challenges as resident rural psychologists, including diverse and complex cases and a lack of referral options, they also reported unique challenges, advantages, and support needs as FIFO/DIDO psychologists. These were encompassed by the themes of “Living away from home” and “Working away from home,” which included two sub‐themes: “Limited time” (referring to the limited time spent in the rural setting) and “Professional isolation” (factors associated with working away from professional supports). Conclusions This study may assist in recruitment and retention of FIFO/DIDO psychologists by providing insight into what is required in the role and may inform training and models of rural and remote psychological service delivery.
Risk Factors for Late Preterm Births: A Case-Control Study

Aim: This study examines the influence of selected sociodemographic and pregnancy specific factors on the risk of Late Preterm Births (LPB) analyzing separately induced and spontaneous. Methods: We conducted a case-control study of LPB observed in 5 Italian Centers. Cases are 305 women who delivered at 34, 35 and 36 weeks gestation. The controls were 269 women who gave birth at term (>37 weeks of gestation). Results: Considering the total series, the risk of LPB was above unity in hypertensive women, but the OR was 6.70 (95% CI 3.25-13.82) in women with induced LPB and 0.90 (95% CI 0.35-2.32) in women with spontaneous LPB. Positive culture of vaginal smear was associated with an increased risk of LPB. Conclusions: According to our results hypertension increases the risk of medically indicated LPB while a history of preterm birth or positive vaginal smear are associated with an increased risk of spontaneous and induces LPB.
Performance analysis of security framework for software defined network architectures

Software defined data centers (SDDC) and software defined networking (SDN) are two emerging areas in the field of cloud data centers. SDN based centrally controlled services takes a global view of the entire cloud infrastructure between SDDC and SDN, whereas Network Function Virtualization (NFV) is widely used for providing virtual networking between host and Internet Service Providers (ISP’s). Some Application as a Service used in NFV data centers have a wide range in building security services like Virtual firewalls, Intrusion Detection System (IDS), load balancing, bandwidth allocation and management. In this paper, a novel security framework is proposed to combat SDDC and SDN based on NFV security features. The proposed framework consists of a Virtual firewall and an efficient bandwidth manager to handle multiple heterogeneous application requests from different ISPs. Real time data were taken from an experiment for a week and A new simulation based proof of concept is admitted in this paper for validation of the proposed framework which was deployed in real time SDNs using Mininet and POX controller.
Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Sparse matrix–vector multiplication (SpMV) kernel dominates the computing cost in numerous applications. Most of the existing studies dedicated to improving this kernel have been targeting just one type of processing units, mainly multicore CPUs or graphics processing units (GPUs), and have not explored the potential of the recent, rapidly emerging, CPU-GPU heterogeneous platforms. To take full advantage of these heterogeneous systems, the input sparse matrix has to be partitioned on different available processing units. The partitioning problem is more challenging with the existence of many sparse formats whose performances depend both on the sparsity of the input matrix and the used hardware. Thus, the best performance does not only depend on how to partition the input sparse matrix but also on which sparse format to use for each partition. To address this challenge, we propose in this article a new CPU-GPU heterogeneous method for computing the SpMV kernel that combines between different sparse formats to achieve better performance and better utilization of CPU-GPU heterogeneous platforms. The proposed solution horizontally partitions the input matrix into multiple block-rows and predicts their best sparse formats using machine learning-based performance models. A mapping algorithm is then used to assign the block-rows to the CPU and GPU(s) available in the system. Our experimental results using real-world large unstructured sparse matrices on two different machines show a noticeable performance improvement.
Deep Analytics based Patterns Evaluation from Nano-Particle Datasets

The nano-particles are not limited to the domain of physics rather it is now days used for the applications in information technology and bio-informatics whereby the assorted streams can be integrated. In this research paper, the analysis of the nano-particles related to the brain waves are taken so that the predictions on the datasets can be done for effective discovery and identification of the brain related diseases. This manuscript is having focus on the usage of machine learning based approach for the predictive analysis of the brain related diseases and found that the need to integrate the information technology to biological domains are quite mandatory for the medical sciences.
Solenoid actuated Five-fingered robotic hand design: evaluating the capability of solenoid actuators in generating basic finger motions

There are numerous robotic hand designs but the five-fingered robotic hand design is the most dexterous robotic hand design due to its similar appearance and motions with the human hands. The fingers' motions are commonly driven by geared motors or other types of emerging technologies. However, the motions are yet to be driven directly by solenoid actuators due to its limited stroke length and also general perceptions of its applications as an actuator. Nevertheless, solenoid actuators are known for their fast reaction time and strong holding force which are useful to generate fast motions and strong grasping actions. To realise this concept, previous research and developments had been done by the authors but the outcome was not convincing enough due to the absence of a working prototype. Therefore, this paper introduced a new solenoid actuated robotic hand design and its prototype. The prototype was then tested and evaluated in accordance to the operational concept where the findings showed that the solenoid actuators were capable of generating basic finger motions.
Ar home deco: virtual object manipulation technique using hand gesture in augmented reality

—Augmented Reality (AR) has the ability to blend the virtual and real objects. It enables users to interact with virtual objects or images with a real object as a medium of interaction. Hence, in order to provide natural and intuitive user interaction in AR environment, real hands are widely explored as an input in effortless way. However, there are remaining issues have been learnt which is the occlusion problems between the real hands and virtual objects and to find the suitable hand gesture for 3D object manipulation. Therefore, this work aims to produce a virtual object manipulation technique using real hand-gesture in AR environment. In order to actualize this aim, several phases have been carried out. The first phase is to conduct a preliminary study on the previous works and real hand gesture interaction. Second phase carries out to select the appropriate 3D object manipulation method for gesture interaction. Subsequently, the Leap Motion is used to capture user’s hands and the gesture inputs have been executed in an AR environment called AR Home Deco. Finally, the evaluation phase is conducted with the AR Home Deco to validate the user satisfaction. Based on the results, it achieves its aim to produce intuitive virtual object manipulation technique with using gestures for AR. The gesture interaction has significantly enhanced user experience in the AR interior design application and Leap Motion which capable to track the real hands, has produced a robust hand gesture tracking in AR.
Requirements Alternative Flow Detection Method Using Use Case Patterns at Use Case Scenario

This paper proposes a method for detecting requirements alternative flow using use case scenario and word similarity. Alternative flow influences in a software system as a condition of satisfying the requirements of completeness. Clear criteria for the alternative flow should be established before writing alternative flow in requirements specification but existing research doesn’t deal with clear criteria for the alternative flow. Use case scenario is general form of a requirements specification in software system project. In addition, use case scenario is written by acts performed by subjects such as user and system. Thus, if it can identify the subject and the act of using the word similarity, it can be provided recommended alternative flow criteria. Therefore, this paper discusses a method for recommending alternative flow based use case scenario patterns. The proposed methodology has been applied to three different systems.
Searching for approximate description of decision classes

We discuss a searching method for synthesis of approximate description of decision classes in large data tables (decision tables). The method consists of the following stages: (i) searching for basic templates which are next used as elementary building blocks for decision classes description; (ii) performing templates grouping as a pre-processing for generalisation and contraction; (iii) generalisation and contraction operations performed on the results of grouping. The main goal of the method is to synthesize the approximate description of decision classes. The control in the searching method is focused on attempts to reduce uncertainty in approximate description of decision classes. On the other hand uncertainty in temporary synthesised descriptions of decision classes is the main "driving force" for the searching method. In the paper we concentrate on the different methods for template generation and on the discussion of performed computer experiments. We also present a general searching scheme for approximate description of decision classes as well as we point out the relationship of our approach to the rough mereological approach to the synthesis of complex objects. The presented method can be adopted for synthesis of adaptive decision algorithms what is the goal of our ongoing research project.
Surface Fairing towards Regular Principal Curvature Line Networks

Freeform surfaces whose principal curvature line network is regularly distributed, are essential to many real applications like CAD modeling, architecture design, and industrial fabrication. However, most designed surfaces do not hold this nice property because it is hard to enforce such constraints in the design process. In this paper, we present a novel method for surface fairing which takes a regular distribution of the principal curvature line network on a surface as an objective. Our method first removes the high‐frequency signals from the curvature tensor field of an input freeform surface by a novel rolling guidance tensor filter, which results in a more regular and smooth curvature tensor field, then deforms the input surface to match the smoothed field as much as possible. As an application, we solve the problem of approximating freeform surfaces with regular principal curvature line networks, discretized by quadrilateral meshes. By introducing the circular or conical conditions on the quadrilateral mesh to guarantee the existence of discrete principal curvature line networks, and minimizing the approximate error to the original surface and improving the fairness of the quad mesh, we obtain a regular discrete principal curvature line network that approximates the original surface. We evaluate the efficacy of our method on various freeform surfaces and demonstrate the superiority of the rolling guidance tensor filter over other tensor smoothing techniques. We also utilize our method to generate high‐quality circular/conical meshes for architecture design and cyclide spline surfaces for CAD modeling.
An Improved Focused Web Crawler based on Hybrid Similarity

Web crawler is an efficient strategy for downloading data automatically from the Internet. Focused web crawler is a special kind of web crawler that is responsible for getting certain information from webpages and making them available to users. The most important problem of focused web crawler is to confirm the similarity between the target webpages and the topics. Therefore, this paper proposes an improved focused web crawler algorithm, whose similarity calculating methods derive from three aspects: anchor text, content, and structure of the webpages. This improved algorithm is called hybrid similarity. If the anchor text similarity is bigger than the threshold, the target webpages are downloaded directly; otherwise, the target webpages' similarity is analyzed by using the TF-Gini feature weighting algorithm and the improved cosine similarity algorithm. The experimental results in this paper have proven that the hybrid similarity algorithm is more effective than the traditional algorithm. The precision increases by nearly 10% compared with the traditional algorithm.
Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

BACKGROUND In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain. METHODS The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images. RESULTS The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC. CONCLUSION The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.
Poly[(μ2-4,4′-bipyridine)bis(μ4-5-tert-butylisophthalato)bis(μ3-5-tert-butylisophthalato)di-μ3-hydroxido-pentazinc(II)]

The asymmetric unit of the title compound, [Zn5(C12H12O4)4(OH)2(C10H8N2)]n, consists of three ZnII ions (one of which is located on a twofold rotation axis), two 5-tert-butylisophthalate ligands, one 4,4′-bipyridine ligand and one hydroxide group. The five ZnII ions form a pentanuclear zinc cluster, which is further bridged by ten organic ligands, forming two-dimensional sheets. The central zinc ion of the cluster has site symmetry 2 and is octahedrally coordinated in a N2O4 donor set, whereas the other four zinc atoms are tetrahedrally coordinated by four O atoms. The coordination modes for the 5-tert-butylisophthalates are bis(bidentate) or bidentate-monodentate. Hydrogen bonds are formed between adjacent sheets through the hydroxide groups and the O atoms of the monodentate carboxylate groups. The two tert-butyl groups are disordered over two positions with ratios of 0.64 (2):0.36 (2) and 0.85 (3):0.15 (3).
Non-Cooperative and Cooperative Optimization of Scheduling With Vehicle-to-Grid Regulation Services

Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for the power grid, stimulates new ideas in current smart grid research. Since EVs are selfish individuals owned by different parties, how to motivate them to provide ancillary services becomes an issue. In this paper, game theoretic approaches using non-cooperative and cooperative game are proposed to motivate EVs to provide frequency regulation services for the power grid. In a non-cooperative V2G system, the interaction between the EV aggregator and EVs is formulated as a non-cooperative Stackelberg game. The EV aggregator as the leader decides the electricity trading price, and EVs as the followers determine their charging/discharging strategies. In a cooperative V2G system, a potential game is formulated to achieve the optimal social welfare of the V2G system. The existence and uniqueness of the Nash equilibrium of these two games are validated. Our simulation results show that the proposed game theoretic approaches can motivate EVs to smooth out the power fluctuations from the grid while EVs schedule their charging/discharging activities to maximize their utilities. This demonstrates the effectiveness of the use of the V2G game in providing regulation services to the grid. Through cooperation and extra information exchange, the social welfare of EVs and the EV aggregator can be improved to the global optimum and the V2G regulation services can also achieve near-optimal performance.
Deciding feasibility of a booking in the European gas market on a cycle is in P for the case of passive networks

We show that the feasibility of a booking in the European entry‐exit gas market can be decided in polynomial time on single‐cycle networks that are passive, i.e., do not contain controllable elements. The feasibility of a booking can be characterized by solving polynomially many nonlinear potential‐based flow models for computing so‐called potential‐difference maximizing load flow scenarios. We thus analyze the structure of these models and exploit both the cyclic graph structure as well as specific properties of potential‐based flows. This enables us to solve the decision variant of the nonlinear potential‐difference maximization by reducing it to a system of polynomials of constant dimension that is independent of the cycle's size. This system of fixed dimension can be handled with tools from real algebraic geometry to derive a polynomial‐time algorithm. The characterization in terms of potential‐difference maximizing load flow scenarios then leads to a polynomial‐time algorithm for deciding the feasibility of a booking. Our theoretical results extend the existing knowledge about the complexity of deciding the feasibility of bookings from trees to single‐cycle networks.
Urban organic aerosol composition in Eastern China differs from North to South: Molecular insight from a liquid chromatography-Orbitrap mass spectrometry study

Abstract. Particulate air pollution in China is influencing human health, ecosystem and climate. However, the chemical composition of particulate aerosol, especially of the organic fraction, is still not well understood. In this study, particulate aerosol samples with a diameter &leq; 2.5 μm (PM2.5) were collected in January 2014 in three cities located in Northeast, East and Southeast China, i.e., Changchun, Shanghai and Guangzhou, respectively. Organic aerosol (OA) in the PM2.5 samples was analyzed by ultrahigh performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry in both negative mode (ESI−) and positive mode electrospray ionization (ESI+). After a non-target screening including molecular formula assignments, compounds were classified into five groups based on their elemental composition, i.e., CHO, CHON, CHN, CHOS and CHONS. The CHO, CHON and CHN compounds present the dominant signal abundances of 81–99.7 % in the mass spectra and the majority of these compounds were assigned to mono- and polyaromatics, suggesting that anthropogenic emissions are a large source of urban OA in all three cities. However, the chemical characteristics of these compounds varied among different cities. The degree of aromaticity and the number of polyaromatic compounds were significantly higher in samples from Changchun, which could be attributed to the large emissions from residential heating (i.e., coal combustion) during winter time in Northeast China. Moreover, the ESI− analysis showed higher H / C and O / C ratios for organic compounds in Shanghai and Guangzhou compared to samples from Changchun, indicating that OA in lower latitude regions of China experiences more intense photochemical oxidation processes. The majority of sulfur-containing compounds (CHOS and CHONS) in all cities were assigned to aliphatic compounds with low degrees of unsaturation and aromaticity. Again, samples from Shanghai and Guangzhou exhibit a larger chemical similarity but largely differ from those from Changchun.
A Review of Static Malware Detection for Android Apps Permission Based on Deep Learning

– In recent years, Android has been the main mobile operating system. The proliferation of apps powered not only by Android magnetized app developers, but also by malware developers with criminal intent to design and distribute malicious apps that can influence the ordinary activity of Android phones and tablets, steal private information and credentials, or even worse, lock the phone and ask for ransom. This study was carried out with a view of bring out clearly the review of previous researches carried regarding static analysis and pinpoint out what to be done in future. A systematic literature review which involves studying 56 research papers published in regard to static analysis. This review elaborate permissions misuse, reverse engineering and concept of static analysis in general. The outcomes of the review revealed that static analysis is widely used since it is not performed at run-time hence malicious applications cannot access to the device during analysis unlike dynamic analysis. During the review no single work done to the satisfaction curbing the existing and future evolving malwares. This study will help academicians to gain insight concerning static analysis without extensively perusing several articles to understand static malware analysis based on deep learning.