Miracle-dz/UniFAR-FTUs huggingface dataset:
Seroprevalence of bovine tuberculosis and brucellosis in Agropastoralist livestock herds and wildlife in Yankari game reserve: Public health implications

Publication History: Received: 12-092017 Accepted: 05-03-2017 Abstract Wildlife, livestock, people and particularly communities surrounding Yankari Game Reserve of Bauchi State, Nigeria compete for spaces and available resources . This study was therefore designed to determine the seroprevalence of bovine tuberculosis (bTB) and brucellosis which are zoonotic. Serum samples were collected from 300 cattle located at the fringes of the game parks and also from 47 zebra, 12 waterbuck, 12 wildebeest, 24 eland, 1 kudu and 1 hartebeest during the study. The samples were tested for the presence of immunoglobulin antibodies against Mycobacterium bovis, Brucella abortus and B. melitensis using immunochromatography rapid test kits (Bionote Incorporated, South Korea). The percentage of positive bTB reactors were 30(10%) in cattle 5(10.6%) zebra, 1(8.3%) waterbuck, 4(33.3%) wildebeest and 3(12.5%) Eland. The sex distribution of bTB among cattle showed that of the 104 males sampled 12(11.5%) were positive and of the 196 females sampled 18(9.2%) were positive. The sex distribution of bTB among the diversity of wildlife species sampled indicated higher prevalence in female zebra 18.8%, waterbuck 14.3% and eland 16.7% compared to male zebra 6.5%, waterbuck 0%, and eland 8.3%. Positive reactors to B. abortus were seen in cattle 27(9%), hartebeest 1(100%), eland 4(16.7%), waterbuck 3(25%) and eland 4(16.7%). Sex distribution of B. abortus and B. melitensis among cattle showed that of the 104 males sampled, 10(9.6%) cattle were positive for B. abortus and the 196 females sampled 17(8.7%) were positive for B. abortus. The high prevalence of B. abortus in male Eland (25%) and waterbuck (40%) portends danger to the herds. The presence of antibodies of bTB and B.abortus in both cattle and wildlife species and B. melitensis in wildlife species showed that the diseases are likely to pose significant threat to public health. There is the need for enlightenment of the herders and communities of the dangers of these important diseases.
Cornerstones of 'fair' drug coverage: appropriate cost sharing and utilization management policies for pharmaceuticals.

At the heart of all health insurance programs lies ethical tension between maximizing the freedom of patients and clinicians to tailor care for the individual and the need to make healthcare affordable. Nowhere is this tension more fiercely debated than in benefit design and coverage policy for pharmaceuticals. This paper focuses on three areas over which there is the most controversy about how to judge whether drug coverage is appropriate: cost-sharing provisions, clinical eligibility criteria, and economic-step therapy and required switching. In each of these domains we present 'ethical goals for access' followed by a series of 'fair design criteria' that can be used by stakeholders to drive more transparent and accountable drug coverage.
Spectral Analysis of Line Sources With Complex Longitudinal Wavenumbers in Planarly Layered Media

Spectral properties of line sources carrying electric or magnetic current placed in a planarly layered medium are analyzed. It is shown that when the current along the line varies in the longitudinal direction by an exponential function with a complex exponent, classical Sommerfeld radiation condition that ensures outgoing and decaying fields simultaneously cannot always be used. Instead, depending on the exponent and the material properties of the layered medium, outgoing and exponentially increasing or incoming and exponentially decreasing waves are generated. This property is not observed when the exponent is real where the classical Sommerfeld radiation condition is fulfilled. In this article, an in-depth analysis of layered media is first performed in spectral domain as a function of the longitudinal dependence of the line sources. Then, the fields generated by the line sources are obtained by Sommerfeld integrations using two different radiation conditions separately. The effect of the radiation condition on the fields are discussed both in the spectral and spatial domains. In addition, field patterns are compared with the results obtained by finite element method (FEM) combined with perfectly matched layers (PMLs) and several guidelines for increasing the efficiency of simulations by FEM are introduced.
Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average.
A comparative study of pattern recognition methods for predicting glaucoma diagnosis

Glaucoma is the second leading cause of blindness globally, it is characterized by degeneration of the optic nerve with particular patterns of corresponding defects in the visual ﬁeld. Aiding doctors in early diagnosis and detection of progression is crucial, as glaucoma is asymptomatic in nature. Furthermore there is good therapeutic results in early cases before irreversible visual loss occurs. Thus it is of great importance to ﬁnd automated methods to discriminate glaucomatous diseases giving insight to doctors. In order to develop a Computer Aided Diagnosis system (CAD), we realised an extensive competitive study of pattern recognition methods should be undertaken. A range of methods have been evaluated including the use of Deep Neural Networks (DNN), Support Vector Machines (SVM), Decision Trees (DT) and K-Nearest Neighbours (KNN) for diagnosing glaucoma. Using a range of classiﬁ-cation techniques, this paper aims to diagnose glaucomatous diseases. Results have been produced with data comprising of Visual Field and OCT Disc readings from anonymous patients with and without glaucoma. Multiple systems are proposed that can predict diagnosis for ocular hypertension, primary open angle glaucoma, normal tension glaucoma and healthy patients with a reasonable conﬁdence. Best performance has been obtained from voting classier comprised of SVM and KNN at 0 . 87 (AUC) and DNN at 0 . 87 (AUC) which possibly could be used as an automatic diagnosis aid in order to streamline the diagnosis of glaucoma for complex cases or ﬂagging of urgent cases.
Localization With Magnetic Field Distortions and Simultaneous Magnetometer Calibration

Magnetic field localization utilizes position dependent and time persistent distortions of the earth magnetic field. These distortions are introduced by stationary ferromagnetic material in the environment and can be stored in a map to enable localization. Estimating the position of a magnetometer with these distortions requires a calibration of the sensor to enable the matching of the measurements to the map. Typically, the calibration is performed in a prior step and requires specific maneuvers like sensor rotations in a homogenous field. The goal of the maneuvers is to render the calibration parameters observable. For heavy platforms, e.g., cars, trains and driverless transport systems in factories, performing special maneuvers is cumbersome or even impossible. In addition they operate in an environment with an inhomogeneous magnetic field. To address this issue, this article proposes a novel method that exploits the magnetic field distortions to render the calibration parameters observable. To simplify the calibration process, the calibration parameters are estimated simultaneously with the position of the platform. The method employs a Rao-Blackwellized particle filter that reduces the computational complexity and enables real time processing. The feasibility of the method is shown in an evaluation with measurements of a magnetometer mounted on a model train. The results show a high accuracy of the position and calibration parameter estimation.
Multi-Task Travel Route Planning With a Flexible Deep Learning Framework

Travel route planning aims to map out a feasible sightseeing itinerary for a traveler covering famous attractions and meeting the tourist’s desire. It is very useful for tourists to plan their travel routes when they want to travel at unfamiliar scenic cities. Existing methods for travel route planning mainly concentrate on a single planning problem for a special task, but is not capable of being applied to other tasks. For example, previous must-visit planning methods cannot be applied to the next-point recommendation, despite these two tasks are closely related to each other in travel route planning. Besides, most of the existing work do not consider the important auxiliary information such as Point of Interests (POI) attributes, user preference, and historical route data in their approaches. In this paper, we propose a flexible Multi-task Deep Travel Route Planning framework named MDTRP to integrate rich auxiliary information for more effective planning. Specifically, we first construct a heterogeneous network through the relations between users and POIs and employ a heterogeneous network embedding method to learn the features of users and POIs. Then we present an attention-based deep model to integrate the auxiliary information and focus on important visited points for the prediction of next POIs. Finally, a beam search algorithm is introduced to flexibly generate multiple feasible route candidates for three types of planning tasks (next-point recommendation, general route planning, and must-visit planning). We introduce six public datasets to conduct extensive experiments, of which the results demonstrate the flexibility and superiority of the proposed approach in travel route planning.
La gestion des ressources humaines dans les PME : une analyse systématique de la littérature

Alors que le capital humain demeure l’actif strategique le plus important pour assurer la competitivite des entreprises et leur perennite, que savons-nous de la gestion des ressources humaines dans les PME ? Ce texte presente une revue de la litterature scientifique concernant la gestion des ressources humaines (GRH) dans les PME. Les 167 articles empiriques repertories a partir des bases de donnees bibliographiques ABI/Inform et Repere sont classes en fonction de la nature des connaissances produites. Ces articles sont ainsi divises en cinq categories : (1) les pratiques RH utilisees dans les PME, (2) leurs caracteristiques distinctives par rapport aux grandes entreprises ; (3) les determinants de l’adoption des pratiques RH ; (4) leurs consequences sur la performance des PME ; (5) les processus a travers lesquels les pratiques RH sont modifiees. Les resultats montrent que les etudes ont surtout consiste a verifier si le modele de GRH des grandes entreprises s’applique aux PME. La formalisation des pratiques RH a egalement beaucoup retenu l’attention. De facon generale, peu d’etudes ont ete realisees sur chacun des divers aspects de la GRH et leurs resultats sont discordants. Des pistes de recherche sont proposees en vue de favoriser le developpement des connaissances.
A kirigami approach for controlling mechanical and sensing properties of films

Tuning the layout of elasticity in materials opens new opportunities to add various functionalities into a system, ranging from load-enduring capacity and shape-morphing capability in aeronautics to self-foldability and controlled diffusion rates in drug delivery applications. Recently, the Japanese art of paper cutting technique called kirigami has positioned itself as a simple yet powerful strategy to program unique functionalities into intrinsically inextensible, stiff materials without adjusting chemical compositions, including elastic softening, creation of complex 3D structures, and extreme stretchability. Thus, various applications have been realized by utilizing the kirigami principle. These applications include wearable electronics, sensors, stretchable lithium batteries, solar trackers, and reconfigurable structures. However, coupling the primary geometric deformation modes (i.e., bending and rotation) in kirigami films to control mechanical response as well as electronic properties (e.g., shift in resonant frequency) have been limited. In this thesis, we present a strategy where the inclusion of carefully designed cuts allows for fine tuning of mechanical and electronic properties of materials. Starting from fundamentals of kirigami mechanics, we show that stiffness tunability and deformability of kirigami structures are significantly influenced by the addition of minor cuts adjacent to major cuts. The dimension and position of minor cuts relative to major cuts determines geometric deformation modes between bending of beams and hinge rotations, which results in high tunability of mechanical properties. The experimental results are validated by beam mechanics with different boundary conditions (Chapter 2). The sensors for human activity monitoring and soft robotic systems require considerable extents of deformation. Furthermore, reducing or eliminating wiring components allows for more compliant and less complex systems by excluding semirigid wiring or connection points. We create a kirigami-inspired passive resonant sensor where the deformation normal to the planar surface changes the capacitance, inductance, and resonant frequency. This
Design and Performance Analysis of Segmented Three-Phase IPMSM for EVs Integrated Battery Charger

A segmented three-phase interior permanent magnet synchronous motor (IPMSM) can bring a significant advantage in the development of electric vehicles (EVs) since it can be used as a simple inductor filter for an integrated battery charger without torque production during charging/vehicle-to-gird (V2G) mode. However, unlike an induction machine, majority of IPMSMs have low value of stator inductance, possibly below the minimum limit of the pulsewidth modulation rectifier requirement; therefore, the integrated battery chargers based on these types of IPMSMs may have difficulty dealing with grid current harmonics during charging/V2G mode. This article presents the method to design a suitable segmented three-phase IPMSM with nine windings (S3-IPMSM-9W) for integrated battery chargers. The optimization of stator winding inductance is proposed in order to get a large stator inductance capable of achieving acceptable ripple during charging and does not degrade the traction mode performance. Therefore, an optimized inductance, which is eight times greater than the nonoptimized stator inductance value was obtained. 96% of the efficiency of the optimized machine was achieved during the motoring mode. Moreover, with the help of optimized leakage inductance acting as a grid-side filter, the total harmonic distortion below 5% is reached during charging. Simulation and experimental results are provided to validate the theoretical analysis.
Completeness by Modal Definitions. Application to the Epistemic Logic With Hypotheses

We investigate the variant of epistemic logic S5 for reasoning about knowledge under hypotheses. The logic is equipped with a modal operator of necessity that can be parameterized with a hypothesis representing background assumptions. The modal operator can be described as relative necessity and the resulting logic turns out to be a variant of Chellas’ Conditional Logic. We present an axiomatization of the logic and its extension with the common knowledge operator and distributed knowledge operator. We show that the logics are decidable, complete w.r. t. Kripke as well as topological structures. The topological completeness results are obtained by utilizing the Alexandroﬀ connection between preorders and Alexandroﬀ spaces.
Factors effecting Knowledge Management Process inHigher Educational Institutions

Knowledge management has enabled many Higher Educational Institutions to excel in performance. Educational institutions create a lot of knowledge but fail to harness the knowledge through proper Knowledge Management strategy. This research aims at finding the effect of work experience, position and familiarity of Knowledge management (KM) on KM process and also tries to find the key influencing factors within knowledge management process prevalent in select private and public Higher Educational Institutions in Bangalore. The sample size is 300 Academic Staff from select private and public NAAC accredited institutions. The Key findings of the study was that work experience and position did affect the KM process and being familiar about KM did not influence the KM process. Also, the institutions are willing to hire new staff members to create new knowledge. Involvement of experts, who provided an explicit way to accomplish the tasks in the academic institutions, enrich the process of knowledge deposition. Knowledge sharing is best when staff are assigned new roles based on their expertise. Institutions are willing to discuss problems and issues, thereby assess the existing process and systems. Staff are encouraged to promote their research results to other academic and business organisation. Knowledge management empowers HEIs to excel in all their goals and gain a competitive edge over their competitors.
Energy-Efficient Heterogeneous Networking for Electric Vehicles Networks in Smart Future Cities

Electric vehicles networks have become hot topics in research and industry and played an important role in smart future cities. However, high energy consumption is a significantly challenge for these applications. This article proposes a electric vehicles cloud computing framework to perform energy-efficient heterogeneous networking for electric vehicles network in smart future cities. The software-defined networking ideas is used to enable different devices including electric vehicles to access the cloud computing network for electric vehicles connected. The edge computing is exploited to run quick computing and communication for these application. Then an energy-efficient heterogeneous networking method is presented to overcome high energy consumption. The mixed integer linear programming optimization model and two heuristic models are proposed to perform energy-efficient networking. An networking algorithm is proposed to achieve highly energy-efficient networking for electric vehicles network. The detailed simulation experiments are conducted to validate our approach. Simulation results illustrate that the proposed method is efficient and feasible.
Innovative Management System for Environmental Sustainability Practices among Indian Auto-Component Manufacturers

The study was conducted in the practice of innovative management system (IMS) among the auto-component manufacturers in Ambattur Industrial Estate, Chennai, India. Today, there is urgency among manufacturer has needed to implement IMS and to produce the environmentally friendly product. Manufacturers alone contributed to 18% of world carbon's emission (OICA, 2017). This study was focused on how environmental friendly manufacturing practices help to drive into the harmless product by a practice of IMS among auto-component manufacturers. The partial least squares-structural equation modelling (PLS-SEM) was performed to extend the environmental responsibility of auto-component manufacturers, their analysis result shows that the sustainable procurement (30.4%) and eco-product design (30.1%) are determined by the practice of corporate governance. Further, it is noted that the practice of waste management system (WMS) 30.4% with innovating tactics by the method of 3R's are recycled, reuse and recovered, again it pushed the product into the eco-product design (16.1%) and WMS influence environmentally responsible manufacturing process by 29.8%.
Use of Social Networking Sites by Karnatakites for Finding Information Regarding COVID-19 - An Investigation

The focus of this paper is to study the usage of social networking sites by the Karnatakites for finding information regarding COVID-19 A web survey was distributed among the targeted participants to collect data for the study The study findings reported that cent percent of the participants were users of social networking sites in whichWhatsApp, YouTube, and Facebook were the most used SNSs for finding information regarding the COVID-19 The respondents used SNSs mainly to search for information regarding the status of COVID-19 in India as well as at the global level Most of the respondents (48 8%) agreed that the information available on SNSs regarding COVID-19was informative and up to date The study also reported that the majority of respondents found the use of social networking sites satisfiable in finding vital information regarding the COVID-19 pandemic The study is first of its kind to explore the use of SNSs among the Karnatakites for finding information regarding the COVID-19 outbreak The findings of the study, of course, give proper insights to the digital natives and netizens for using SNSs as information resources, especially during such critical pandemic disease outbreaks
Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees

Information security attacks on oil and gas (O&G) organizations have increased since the last decade. From 2015 to 2019, almost 70 percent of O&G organizations faced at least one significant security breach worldwide. Research has shown that 43 percent of security attacks on O&G organizations occur due to the non-compliant behavior of O&G employees towards information security policy. The existing literature provides multiple solutions for technical security controls of O&G organizations. However, there are very few studies available that address behavioral security controls, specifically for O&G organizations of developing countries. The purpose of this study is to provide a comprehensive framework for information security policy compliance (ISPC) for the O&G sector. A mixed-method approach is used to develop the research framework. Semi-structured interviews from O&G specialists refined the developed framework. Based on qualitative study a survey questionnaire was developed. To evaluate the research framework, structural equation modeling was applied to a sample of 254 managers/executives from 150 Malaysian O&G organizations. The obtained test results confirmed the proposed research model, according to which good social bonding among employees plays a critical role in improving ISPC. However, there was less support for the notion that all organizational governance factors significantly improve the social bonding of Malaysian O&G organizations employees. This paper contributes to the current information system (IS) literature by exploring the interrelationships among organizational governance, social bonding, and information security policy compliance (ISPC) in Malaysian O&G organizations.