Miracle-dz/UniFAR-FTUs huggingface dataset:
Injection-induced Sciatic Nerve Injury Among Children Managed in a Nigerian Physiotherapy Clinic : A five-year review

Injection-induced sciatic nerve injury is a well-known complication of intra-muscular gluteus muscle injections. Affected individuals usually present with foot drop and this results in varying degrees of motor disability depending on the timing, quality and duration of the remedial measures instituted. This study was carried out to evaluate the pattern of cases of injection-induced sciatic nerve injury among children referred to the Outpatient Physiotherapy Clinic of the Federal Medical Centre, Ido-Ekiti, Nigeria. A retrospective review of the case notes of all paediatric patients referred to the physiotherapy clinic on account of injection-induced sciatic nerve injury from November 2005 to October 2010 was done. Information obtained included age, gender, side affected, personnel/place where injection was administered, the mode of physiotherapy intervention, number of sessions, impairment variables at presentation and underlying indication for injection administration. A total of 33 (19.64%) cases of sciatic nerve palsy out of 168 paediatric patients managed at the physiotherapy clinic within this 5-year period were reviewed. The ages of the participants ranged from 0-14 years. Sixteen (48.5%) of the patients were male while 17 (51.6%) were female. The side affected in 17 (51.6%) of the patients was the right while the left was affected in 16 (48.5%) patients. The right side was more affected in the females (12) while the left side was more affected in the males (11). Most incidences of injection-induced sciatic nerve palsy occurred in private hospitals (45.5%). A majority of the patients (69.7%), as reported by their carers and relatives, had the injections administered by nurses. The results revealed that a lot of effort should be made by the relevant authorities to ensure that medical and health professionals who administer injections adhere strictly to safe ethical practices and that unqualified personnel are not allowed to operate.
Job shop planning and scheduling for manufacturers with manual operations

Job shop scheduling systems are widely employed to optimise the efficiency of machine utilisation in the manufacturing industry, by searching the most cost‐effective permutation of job operations based on the cost of each operation on each compatible machine and the relations between job operations. Such systems are paralysed when the cost of operations are not predictable led by the involvement of complex manual operations. This paper proposes a new genetic algorithm‐based job shop scheduling system by integrating a fuzzy learning and inference subsystem in an effort to address this limitation. In particular, the fuzzy subsystem adaptively estimates the completion time and thus cost of each manual task under different conditions based on a knowledge base that is initialised by domain experts and then constantly updated based on its built‐in learning ability and adaptability. The manufacturer of Point of Sale and Point of Purchase products has been utilised in this paper as an example case for both theoretical discussion and experimental study. The experimental results demonstrate the promising of the proposed system in improving the efficiency of manual manufacturing operations.
Government Operation Excellence (GOE): Case of Sultanate of Oman

In the current century, e-government transformation is considered as one of the biggest challenges among and within the IT-related sector from the scale and complexity perspective. Having the main objective as of adapting an existing e-government system and methodology in order to obtain new computing requirements based on the concept of citizens" new service. Thus, increase the features of service level, quality and maintaining a high policies and rules consistency. Moreover, mission criticality and time of services, information sharing and interoperability, efficiency and productivity along with the reduction of operation expenses are all considered as priorities in this transformation project (Raymond, 2007). Such a transformation is considered crucial for the service concept as it is changing rapidly; the roadmap coherence in e-government transformation is critical (Raymond, 2007). However, there is a big aspect regarding the e-Government progress by citizens due to many aspects and quality. Hence, there is a need to further study in order to be able to obtain better understanding of the complete picture and to have a balanced view and concepts all under one e-Government umbrella. This paper will review and analyze through a quantitative approach in Sultanate of Oman case study. This study targeted citizens in the country to check their intention to use e-Government in Oman (e-Oman) through checking GOE by SERQUAL. After reviewing and discussing questionnaire outputs using SPSS program, the writer will listed some recommendations and conclusion of the study and the nature of them they will be categorized upon to their point of view.
Small‐angle X‐ray investigation of the equilibria between copper(II) and glycyl‐l‐histidylglycine in water solution. A method for analysing polydispersed systems

Water solutions of copper(II) and glycyl-l-histidylglycine contain a large particle of molecular weight of approximately 9000 and also low-molecular-weight particles with an average molecular weight of 430. This is shown in a small-angle X-ray scattering study at 21° and neutral pH using a Kratky camera. The concentration of copper(II) was 34–72 mM and the molar ratio of peptide to copper was 1.39 to 1. This study further indicates that the particles contain about ten weight per cent water, and their shapes are an oblate ellipsoid of revolution with an axial ratio of 0.2. The scattering data were analysed with a method similar to that of Mittelbach & Porod [Kolloid Z. Z. Polym. (1965). 202, 40–49]. It was assumed that the particles in solution follow a discontinuous size distribution function dependent on two parameters: one associated with the complex formation constants, and the other with the relative weights of the units building up the complexes. That a very large particle exists is in qualitative agreement with a previous electromotive-force study which indicated that a series of large complexes is formed. The presence of such a large particle in the solution is also in agreement with a single-crystal study on the same system. The crystal structure consists of a three-dimensional network which contains 40% disordered water located in channels limited by rings of six dimers of copper and peptide.
Understanding Continuance Intention to Use Social Media in China: The Roles of Personality Drivers, Hedonic Value, and Utilitarian Value

ABSTRACT Despite the enormous increase in active users of WeChat, research examining users’ continuance intention based on personality habits and value perceptions is very scarce. To fill this gap, the present study aims to offer a research model which examines the role of four personality drivers, namely, (1) personal innovativeness, (2) face consciousness, (3) need for uniqueness, and (4) ambiguity intolerance in WeChat users’ hedonic and utilitarian value perceptions. Further, our model examines how hedonic and utilitarian value perceptions influence continuance intention. Data were collected from 262 active users of WeChat. Structural equation modeling was employed to test study hypotheses. The findings reveal that personal innovativeness had a positive effect on users’ hedonic value. Face consciousness and need for uniqueness were found to be a strong predictor of both hedonic and utilitarian value. A negative association was found between ambiguity intolerance and utilitarian value. The hedonic and utilitarian value positively affects continuance intention. The authors offer theoretical and practical implications for Information System scholars and practitioners, and close by acknowledging the limitations and future research direction.
Big-Data Analytics for Cloud, IoT and Cognitive Computing

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley. com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.
Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines

The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values of finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been proposed as an elegant solution to overcome these challenges, with the ultimate goal to achieve greater profits. However, previous studies on recent spot pricing schemes reveal artificial pricing policies that do not comply with the dynamic nature of these phenomena. Motivated by these facts, this paper investigates dynamic pricing of stagnant resources in order to maximize cloud revenue. Specifically, our proposed approach manages multiple classes of virtual machines in order to achieve the maximum expected revenue within a finite discrete time horizon. For this sake, the proposed approach leverages the Markov decision processes with a number of properties under optimum controlling conditions that characterize a model’s behaviour. Further, this approach applies approximate stochastic dynamic programming using linear programming to create a practical model. Experimental results confirm that this approach of dynamic pricing can scale up or down the price efficiently and effectively, according to the stagnant resources and the load thresholds. These results provide significant insights to maximizing the IaaS cloud revenue.
Odds and Trends: Recent Developments in the Chemistry of Odorants

Fragrance chemistry is, together with the closely related area of flavor chemistry, one of the few domains, if not the only one, in which chemists can immediately experience structure–activity relationships. This review presents structure–odor correlations and olfactophore models for the main odor notes of perfumery: “fruity”, “marine”, “green”, “floral”, “spicy”, “woody”, “amber”, and “musky”. New trendsetters and so-called captive odorants of these notes are introduced, and recent activities and highlights in fragrance chemistry are summarized. The design of odorants, their chemical synthesis, and their use in modern perfumery is discussed. Our selection is guided and illustrated by creative fragrances, and features new odorants which encompassed current trends in perfumery. New odorants for grapefruit and blackcurrant, for galbanum, and leafy top notes are presented. Compounds with fashionable marine, ozonic, and aquatic facets are treated, as well as new odorants for classical lily-of-the-valley, rose, and jasmine accords. Compounds with sweet and spicy tonalities are also discussed, as are the most recent developments for woody notes such as sandalwood and vetiver. We conclude with musky and ambery odorants possessing uncommon or unusual structural features. Some odor trends and effects are illustrated by microencapsulated fragrance samples, and areas where there is need for the development of new synthetic materials and methodologies are pointed out. Thus, chemists are invited to explore fragrance chemistry and participate in the design and synthesis of new odorants. This review gives the latest state of the art of the subject.
Improving the process of analysis and comparison of results in dependability benchmarks for computer systems

Dependability benchmarks are designed to assess, by quantifying through quantitative performance and dependability attributes, the behavior of systems in presence of faults. In this type of benchmarks, where systems are assessed in presence of perturbations, not being able to select the most suitable system may have serious implications (economical, reputation or even lost of lives). For that reason, dependability benchmarks are expected to meet certain properties, such as non-intrusiveness, representativeness, repeatability or reproducibility, that guarantee the robustness and accuracy of their process. However, despite the importance that comparing systems or components has, there is a problem present in the field of dependability benchmarking regarding the analysis and comparison of results. While the main focus in this field of research has been on developing and improving experimental procedures to obtain the required measures in presence of faults, the processes involving the analysis and comparison of results were mostly unattended. This has caused many works in this field to analyze and compare results of different systems in an ambiguous way, as the process followed in the analysis is based on argumentation, or not even present. Hence, under these circumstances, benchmark users will have it difficult to use these benchmarks and compare their results with those from others. Therefore extending the application of these dependability benchmarks and perform cross-exploitation of results among works is not likely to happen. This thesis has focused on developing a methodology to assist dependability benchmark performers to tackle the problems present in the analysis and comparison of results of dependability benchmarks. Designed to guarantee the fulfillment of dependability benchmark's properties, this methodology seamlessly integrates the process of analysis of results within the procedural flow of a dependability benchmark. Inspired on procedures taken from the field of operational research, this methodology provides evaluators with the means not only to make their process of analysis explicit to anyone, but also more representative for the context being. The results obtained from the application of this methodology to several case studies in different domains, will show the actual contributions of this work to improving the process of analysis and comparison of results in dependability benchmarking for computer systems.
New York State

change. Human resources management in New York state takes place within an exceptionally complex environment, both structurally and politically. That environment has made the enactment of major reform legislation impossible. Nonetheless, in spite of the fact that the system remains highly regulated, some reform has taken place quietly and incrementally within the existing structures and systems. This article begins with a brief description of the structural and political environment of human resources in New York state. It then looks at some of the current issues in
Cyber Data Anomaly Detection Using Autoencoder Neural Networks

The Department of Defense requires a secure presence in the cyber domain to successfully execute its stated mission of deterring war and protecting the security of the United States. With potentially millions of logged network events occurring on defended networks daily, a limited staff of cyber analysts require the capability to identify novel network actions for security adjudication. The detection methodology proposed uses an autoencoder neural network optimized via design of experiments for the identification of anomalous network events. Once trained, each logged network event is analyzed by the neural network and assigned an outlier score. The network events with the largest outlier scores are anomalous and worthy of further review by cyber analysts. This neural network approach can operate in conjunction with alternate tools for outlier detection, enhancing the overall anomaly detection capability of cyber analysts.
Subtyping of Parkinson’s Disease - Where Are We Up To?

Heterogenous clinical presentations of Parkinson’s disease have aroused several attempts in its subtyping for the purpose of strategic implementation of treatment in order to maximise therapeutic effects. Apart from a priori classifications based purely on motor features, cluster analysis studies have achieved little success in receiving widespread adoption. A priori classifications demonstrate that their chosen factors, whether it be age or certain motor symptoms, do have an influence on subtypes. However, the cluster analysis approach is able to integrate these factors and other clinical features to produce subtypes. Differences in inclusion criteria from datasets, in variable selection and in methodology between cluster analysis studies have made it difficult to compare the subtypes. This has impeded such subtypes from clinical applications. This review analysed existing subtypes of Parkinson’s disease, and suggested that future research should aim to discover subtypes that are robustly replicable across multiple datasets rather than focussing on one dataset at a time. Hopefully, through clinical applicable subtyping of Parkinson’s disease would lead to translation of these subtypes into research and clinical use.
Space–time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro

Dengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades. However, vector-control strategies have not led to a significant reduction in the disease burden and have not been sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015 and 2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly owing to competition between viruses for host resources, depletion of susceptible population, different introduction times and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Simultaneous clusters of the three diseases usually included neighbourhoods with high population density and low socioeconomic status, particularly in the North region of the city. The use of space–time cluster detection can guide intensive interventions to high-risk locations in a timely manner, to improve clinical diagnosis and management, and pinpoint vector-control measures.
Endodontic Microbiome of Fractured Non-vital Teeth in Dogs Determined by 16S rRNA Gene Sequencing

Dental fractures resulting in pulp exposure will lead to an endodontic infection with microbes from the oral cavity. However, data on the endodontic microbial composition in veterinary dentistry is lacking. The aim of this study was to examine the microbiome of naturally occurring primary endodontic infections in client-owned dogs. The endodontic microbiome of 10 non-vital teeth with exposed pulp cavities was assessed using a 16S rRNA gene sequencing approach. The results were compared to the microbiome of the subgingival plaque of the same teeth. Analysis revealed an abundant mixed microflora of a comparable richness and diversity and with mostly the same phyla obtained from sulcal and endodontic samples. However, further analysis revealed significant differences between sulcal and endodontic samples in the relative abundance of the most abundant phyla and genera, with the relative abundance of Bacteriodetes being significantly higher in endodontic samples. Although each sample presented a particular profile regarding the genera identified, Bacteroides was the most abundant genus in the endodontic samples. Snowella was also significantly more abundant in endodontic samples, while Porphyromonas and Fusobacterium were significantly more abundant in sulcal samples. We confirmed that the microbiome of the diseased endodontic system is comparably abundant with microorganisms to the healthy subgingival plaque indicating that previous culture-based studies of primary endodontic infections in dogs underestimated the richness and diversity of the endodontic microbiota.