Miracle-dz/UniFAR-FTUs huggingface dataset:
Lessons learnt from the past and preparedness for the future: how a developing country copes with major incidents

Objective Research shows that previous experience of major incidents has positive impacts on awareness and preparedness of organisations. This manuscript aims to examine the preparedness indicators of health organisations against major incidents in two different types of organisation: those with direct involvement in response to the Bam earthquake and other organisations. Method A descriptive study was conducted, and 230 questionnaires were distributed among the health managers in both types of organisations to compare the questionnaire results between them. Result A total of 114 questionnaires were returned. 85% of the respondents were male, and 15% were female. Considering the organisational factor, a significant difference between the organisations was observed. Considering the communication factor, only differences in relationship with outside stakeholders and knowing the responsibility of other organisations were significant. Rehearsal and multiagency plans for warning were considerably different in the organisations. In planning, coordination, training, business continuity and cultural factors, there were no important differences. For resource variables, only the statement of sufficient resources and tools for the employees was significantly different, and, for the policy factor, there were significant differences in two variables: incorporation of major incident issues into the service design and management of voluntary efforts in a major incident. Conclusion The findings have implications for health organisations in developing countries, especially in the Middle East. A major incident-management system re-engineering is required to bring the lessons learnt from previous major incidents into preparedness efforts and to shift individual-based activities toward systematic approaches in major incident management.
Grasp-Shell vs gesture-speech: A comparison of direct and indirect natural interaction techniques in augmented reality

In order for natural interaction in Augmented Reality (AR) to become widely adopted, the techniques used need to be shown to support precise interaction, and the gestures used proven to be easy to understand and perform. Recent research has explored free-hand gesture interaction with AR interfaces, but there have been few formal evaluations conducted with such systems. In this paper we introduce and evaluate two natural interaction techniques: the free-hand gesture based Grasp-Shell, which provides direct physical manipulation of virtual content; and the multi-modal Gesture-Speech, which combines speech and gesture for indirect natural interaction. These techniques support object selection, 6 degree of freedom movement, uniform scaling, as well as physics-based interaction such as pushing and flinging. We conducted a study evaluating and comparing Grasp-Shell and Gesture-Speech for fundamental manipulation tasks. The results show that Grasp-Shell outperforms Gesture-Speech in both efficiency and user preference for translation and rotation tasks, while Gesture-Speech is better for uniform scaling. They could be good complementary interaction methods in a physics-enabled AR environment, as this combination potentially provides both control and interactivity in one interface. We conclude by discussing implications and future directions of this research.
Bimanual human robot cooperation with adaptive stiffness control

We propose a human-robot cooperation scheme for bimanual robots. After the initial task demonstration, the human co-worker can modify both the spatial course of motion as well as the speed of execution in an intuitive way. To achieve this goal, speed-scaled dynamic motion primitives are applied for the underlying task representation. The proposed adaptation scheme adjusts the robot's stiffness in path operational space, i. e. along the trajectory. It allows a human co-worker to be less precise in the parts of the task that require high precision, as the precision aspect can be provided by the robot. The required dynamic capabilities of the robot were obtained by decoupling the bimanual robot dynamics in operational space, which is attached to the desired trajectory. The proposed scheme was validated in a task where two Kuka LWR-4 robot arms cooperate with a human to carry an object.
Data Mining of Family, School, and Society Environments Influences to Student Performance

It is a common fact that every parents wants their children to get achievements, especially academic achievements. Demands by demands then addressed to their children to get higher and higher score in every subjects. However, parents and teachers are often forgot about one thing, besides of just demanding the children to get high scores, they also responsible in providing the best conditions to boost the children’s potential and performance. To help solving this issue, the author used data mining model in binary classification approach. Data mining methods that author chose was decision tree due to its high accuracy comparing to the neural network and support vector machine that have lower accuracy than decision tree. Despite of previous result that is the main factor that affects student performance, in this research, we also found several factors of environments that may affects the student performance
1 Uncertain Penalties and Compliance

We present the results of a series of laboratory economic experiments designed to study compliance behavior of polluting firms when information on the penalty is uncertain. The experiments consist of a regulatory environment in which university students face emission standards and an enforcement mechanism composed of audit probabilities and penalties (conditional on detection of a violation). We examine how uncertainty on the penalty affects the compliance decision and the extent of violation under two enforcement levels: one in which the regulator induces perfect compliance and another one in which it does not. Our results suggest that in the first case, uncertain penalties increase the extent of the violations of those firms with higher marginal benefits. When enforcement is not sufficient to induce compliance, the uncertain penalties do not have any statistically significant effect on compliance behavior. Overall, the results suggest that a cost-effective design of emission standards should consider including public and complete information on the penalties for violations.
Mitigating voltage sags due to DOL starting of three phase asynchronous motors using dynamic voltage restorer (DVR)

Voltage sags caused by starting large asynchronous motors, especially in industrial distribution system have huge impacts on system power quality. The paper analyses and proposes an extended application of custom power devices using dynamic voltage restorer for mitigating voltage sags due to the direct-on-line (DOL) starting of asynchronous motors in industrial distribution system. In the paper, different control strategies used for custom power devices are analyzed and an effective method is proposed. A test system is simulated in Matlab/Simulink with different DVR configurations that proves the effectiveness and robustness of the proposed control strategy with dynamic voltage restorer for voltage sag mitigation. The results show that voltage sags are totally compensated.
Perceptions of success factors in rural K-12 public alternative education programs

Alternative education for students in the K-12 public school settings are designed to fit the needs of the District due to the nature of the student’s learning needs being accommodated through the program. This qualitative case study provides rural K-12 public alternative education programs with success factors identified by teachers and administrators working in or who have worked in Region X in Northwest Missouri. The researcher used archival data, interviews with teachers and administrators, and a focus group of administrators to find common themes which were then viewed through the conceptual framework of Continuous Quality Improvement.
TOPOLOGICAL CELL DECOMPOSITION AND DIMENSION THEORY IN P-MINIMAL FIELDS

Abstract This paper addresses some questions about dimension theory for P-minimal structures. We show that, for any definable set A, the dimension of $\bar A\backslash A$ is strictly smaller than the dimension of A itself, and that A has a decomposition into definable, pure-dimensional components. This is then used to show that the intersection of finitely many definable dense subsets of A is still dense in A. As an application, we obtain that any definable function $f:D \subseteq {K^m} \to {K^n}$ is continuous on a dense, relatively open subset of its domain D, thereby answering a question that was originally posed by Haskell and Macpherson. In order to obtain these results, we show that P-minimal structures admit a type of cell decomposition, using a topological notion of cells inspired by real algebraic geometry.
Improved Differential Evolution for Solving Optimal Reactive Power Flow

Differential evolution (DE) is a promising evolutionary algorithm for numerical optimization. However, with descending population diversity, the DE will also encounter premature convergence as other evolutionary algorithms (EAs). Based on analysis of premature convergence of DE and close observation on various improved schemes of EAs, two simple improved DE schemes are developed in the paper. Comparative simulation on the optimal reactive power flow problems shows that the scheme utilizing the variable population size and population reinitialization technique has the best performance. Parameter setting of the schemes has also been investigated in the paper.
Title Student-faculty Co-inquiry Into Student Reading : Recognising SoTL as Pedagogic Practice Type

This paper reports the evaluation of a student-faculty collaborative study investigating international students’ perceptions of the role of reading in higher education. The study examined the academic reading and source-use practices of ten undergraduate students in a range of disciplines in one UK university. In previous research on student literacy practices, students are often positioned as research “objects” rather than as active participants with an investment in enhancing the student experience through engagement in pedagogic research. In this paper we present a case study of student faculty collaboration in the scholarship of teaching and learning (SoTL). Drawing on the analysis of student and lecturer accounts of the collaborative research experience, we identify the benefits and challenges of student-faculty partnership approaches. We conclude by arguing that conceptualising SOTL as pedagogy may facilitate the engagement of students as co-researchers and expose to scrutiny a “hidden curriculum” of current approaches to SoTL.
The input efficiency of chord keyboards

This study focused on an innovative mobile input device model that integrated ergonomic design principles to create a chord keyboard for use in concert with pointing devices. We conducted usability tests to demonstrate the operating performances of such keyboards using keys with four characters per key. Twelve right-handed subjects between the ages of 20 and 30 years and with no wrist disability participated in the usability tests. We analyzed the resulting data to compare the differences among the means. The results indicated that keyboards with multiple characters per key created some confusion among users but that keys with multiple characters per key nonetheless resulted in better input performance.
Reliability analysis of large fault trees using the Vesely failure rate

Fault trees provide a compact, graphical, intuitive method to analyze system reliability. However, combinatorial fault tree analysis methods, such as binary decision diagrams, cannot be used to find the reliability of systems with repairable components. In such cases, the analyst should use either Markov models explicitly or generate Markov models from fault trees using automatic conversion algorithms. This process is tedious and generates huge Markov models even for moderately sized fault trees. In this paper, the use of the Vesely failure rate as an approximation to the actual failure rate of the system to find the reliability-based measures of large fault trees is demonstrated. The main advantage of this method is that it calculates the reliability of a repairable system using combinatorial methods such as binary decision diagrams. The efficiency of this approximation is demonstrated by comparing it with several other approximations and provide various bounds for system reliability. The usefulness of this method in finding the other reliability measures such as MTBF, MTTR, MTTF, and MTTFF is shown. Finally, extending this method to analyze complex fault trees containing static and dynamic modules as well as events represented by other modeling tools.
The Impact of Emotional and Behavioural Problems on the Lives of Children Growing up in the Care System

Gerard McCarthy, Janet Janeway and Angus Geddes investigate the way in which the lives of looked after children and their carers are affected by the emotional and behavioural problems that these children and young people often experience. Starting from a sample of 115 children looked after by one English local authority, their study assessed the level of social impairment and distress experienced by looked after children with behavioural disorders. It also examined the duration of these difficulties and the impact of these problems on the carers. The carers, who completed the Strengths and Difficulties Questionnaire (n = 70), reported very high levels of social impairment in the areas of home life, peer relationships and learning. Fifty-nine per cent of looked after children had an overall impact score of 2 or more, thus indicating the presence of a child psychiatric disorder. Forty per cent had significant problems in three or more key areas of their lives: home, learning, peers and leisure. Where significant problems were identified by carers, 65 per cent reported that the problems had existed for over a year. Almost half the sample revealed that the children's problems were imposing a significant burden on families or other carers, and the level of conduct problems reported was found to be a significant predictor of whether a carer expressed feeling burdened. Some clinical and service implications are discussed.
Optimal sequential sampling from two populations.

Given two statistical populations with unknown means, we consider the problem of sampling chi 1, chi 2, . sequentially from these populations so as to achieve the greatest possible expected value of the sum Sn = chi 1 + . + chi n. In particular, for normal populations, we obtain the optimal rule and study its properties when the average of the two population means is assumed known, and exhibit an asymptotically optimal rule without assuming any prior knowledge about the population means.
Challenges of Foreign Direct Investment Strategy for a New Market: Case of Tullow Oil Company in Kenya

viii CHAPTER ONE: INTRODUCTION . 1 1.1 Background of the Study . 1 1.1.1 Foreign Direct Investment . 3 1.1.2 Direct Investment Strategies . 4 1.1.3 Tullow Oil Company in Kenya . 5 1.2 Research Problem . 6 1.3 Research Objective . 8 1.4 Value of the Study . 8 CHAPTER TWO: LITERATURE REVIEW . 10 2.
Fine-Tuned Residual Network-Based Features With Latent Variable Support Vector Machine-Based Optimal Scene Classification Model for Unmanned Aerial Vehicles

In recent days, unmanned aerial vehicles (UAVs) becomes more familiar because of its versatility, automation abilities, and low cost. Dynamic scene classification gained significant interest among the UAV-based surveillance systems, e.g., high-voltage power line and forest fire monitoring, which facilitate the object detection, tracking process and drastically enhances the outcome of visual surveillance. This paper proposes a new optimal deep learning-based scene classification model captured by UAVs. The proposed model involves a residual network-based features extraction (RNBFE) which extracts features from the diverse convolution layers of a deep residual network. In addition, the several parameters in RNBFE lead to many configuration errors due to manual parameter tuning. So, self-adaptive global best harmony search (SGHS) algorithm is employed for tuning the parameters of the RNBFE. The resultant feature vectors undergo classification by the use of latent variable support vector machine (LVSVM) model. The presented optimal RNBFE (ORNBFE) model has been tested using two open access datasets namely UC Merced (UCM) Land Use Dataset and WHU-RS Dataset. The presented technique attains maximum scene classification accuracy over the other recently proposed methods.
Visualizing and mapping the cerebellum with serial optical coherence scanner

Abstract. We present the visualization of the mouse cerebellum and adjacent brainstem using a serial optical coherence scanner, which integrates a vibratome slicer and polarization-sensitive optical coherence tomography for ex vivo imaging. The scanner provides intrinsic optical contrasts to distinguish the cerebellar cortical layers and white matter. Images from serial scans reveal the large-scale anatomy in detail and map the nerve fiber pathways in the cerebellum and brainstem. By incorporating a water-immersion microscope objective, we also present high-resolution tiled images that delineate fine structures in the cerebellum and brainstem.
The XYY Syndrome: Reality or Myth?

Seven individuals having a 47, XYY chromosome were correctly distinguished from 28 individuals drawn from the general population by means of a double blind psychological evaluation (including TAT and Rorschach). The prevalence of XYY men, unscreened for height, was 1 per 1000, and for those screened for height, was 3.5 per 1000. Greater stature and certain psychological characteristics were found among the XYY group. However, there was no evidence of below‐average intelligence or criminality.
sutteForecastR: Forecasting Data using Alpha-Sutte Indicator

The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. https://cran.r-project.org/web/packages/sutteForecastR/index.html
Determination of Multi-Item Inventory Model with Limitations of Warehouse Capacity and Unit Discount in Leading Garment Industry in Indonesia

Inventory is important so that the company's produc tion process runs smoothly and efficiently. To main tain the supply of raw materials, it is necessary to have an appropriate inventory control system so that in ventory problems can be minimized. The raw material used is a type of multi-item to produce intimates of apparel is bra. The actual usage data of raw materials during January December 201 7 experienced a stock out of Nylon Tricot, N / S Ela st ne, FS Fiberfill, Mesh SM135 and Spacer. Management of raw material inventories has constraints that is the li mit of warehouse capacity and discount units. This study ai ms to determine the number of economical orders and the frequency of bookings for a year with constraints on warehouse capacity and discount units. The method u sed is EOQ probabilistic multi-item Lagrange Multiplier (LM) and dynamic programming method approaching the Wagne r-Within algorithm. From the two methods then compared to find the most optimal solution in determining the numbe r of orders that are economical. The Wagner-within method produc es a Total Annual Cost (TAC) of Rp. 137,453,491, while Q * was Rp. 187,339,290 and Q * lagrange multiplier of Rp . 135,982,935. The optimal results seen from the TAC of all the minimum raw materials are obtained by the Q * lagran ge multiplier method. With this method Q * lagrange multiplier is recommended for planning multi-item raw material con trol in the company.
Seasonal changes in avian song control circuits do not cause seasonal changes in song discrimination in song sparrows.

In seasonally breeding songbirds, brain nuclei of the song control system that act in song perception change in size between seasons. It has been hypothesized that seasonal regression of song nuclei may impair song discrimination. We tested this hypothesis in song sparrows (Melospiza melodia), a species in which males share song types with neighbors and must discriminate between similar songs in territorial interactions. We predicted that song sparrows with regressed song systems would have greater difficulty in discriminating between similar songs. Sparrows were housed either on short days (SD) and had regressed song circuits, or were exposed to long days and implanted with testosterone (LD+T) to induce full growth of the song circuits. We conducted two experiments using a GO/NO-GO operant conditioning paradigm to measure song discrimination ability of each group. Birds learned four (experiment 1) or three (experiment 2) pairs of song types sequentially, with each pair more similar in the number of shared song elements and thus more difficult to discriminate. Circulating T levels differed between the SD and LD+T groups. The telencephalic song nuclei HVc, RA, and area X were larger in the LD+T birds. The two groups of sparrows did not differ, however, in their ability to learn to discriminate between shared song types, regardless of the songs' similarity. These results suggest that seasonal changes in the song control system do not affect birds' ability to make difficult song discriminations.
Normalization of Immune Activation in Lymphoid Tissue Following Highly Active Antiretroviral Therapy

&NA;Although significant progress has been made in understanding immune reconstitution in peripheral blood following highly active antiretroviral therapy (HAART), less is known about immune changes in lymphoid tissue. Here, the expression of cytokine proteins (interferon gamma [IFN‐&ggr;], interleukin [IL]‐2, IL‐4, IL‐10, IL‐1&agr;, and IL‐1&bgr;) and surface antigens (CD4, CD8, CD1a, CD68) as well as cellular proviral HIV‐1 DNA were determined in sequential tonsil biopsies before and at 4, 12, and 48 to 56 weeks posttherapy by quantitative in situ image analysis and fluorescent in situ 5′‐nuclease assay (FISNA). Despite plasma virus suppression, a fraction of tonsil cells harbored pro‐viral DNA for up to 1 year. A fourfold to eightfold increase in CD8+ T cells in tissue compared with seronegative controls and an increased frequency of CD1a+ dendritic cells prior to HAART reached control levels at week 56. The frequency of IFN‐&ggr; expressing cells was 10‐ to 15‐fold higher than controls before therapy and was comparable with findings in seronegative controls by week 56. Elevated baseline expression of IL‐1&agr; and IL‐1&bgr; was reduced by week 4 but IL‐1&agr; levels remained elevated in 1 of 3 patients at week 56. These findings suggest that with effective viral suppression the immune system in tissue may return to a more resting state.