Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Radial Extracorporeal Shock Wave Therapy for Chronic Pelvic Pain Syndrome: A Nonrandomized Controlled Trial

This study aims to determine the effect of radial extracorporeal shock wave therapy (rESWT) versus drug when treating chronic pelvic pain syndrome (CPPS; type III B chronic prostatitis). The study included 45 participants with CPPS, divided into two groups: Group I comprised 25 participants, who were treated with rESWT (3,000 pulses each; pressure: 1.8–2.0 bar; frequency: 10 Hz) once a week; Group II consisted of 20 participants who received a combination of an α-blocker and an anti-inflammatory agent. Participants were treated for 8 weeks. The assessments were done before treatment, after the fourth and eighth rESWT, and 3 months after the end of treatment by Visual Analogue Scale (VAS) for pain, National Institutes of Health-developed Chronic Prostatitis Symptom Index (NIH-CPSI), International Prostate Symptom Score (IPSS), quality of life (QoL), and International Index of Erectile Function-5 (IIEF-5). Both groups of participants showed statistically significant improvement in all the assessments (p < .001) after the treatment, with significantly better results in Group I in NIH-CPSI (p < .001). The recurrence rate of symptoms in Group I at 3 months after end of treatment was much lower than that in Group II (4% vs. 50%, p < .001). This prospectively nonrandomized, control study revealed perineal rESWT as a new therapy option for CPPS with statistically significant effects in comparison to drugs at least for 3 months after cessation of treatment.
Remittance or persistence of attention deficit–hyperactivity disorder (ADHD) and its impact on recidivism in risky driving behaviors

Abstract Objective: Recent data regarding the persistence or remittance of attention deficit–hyperactivity disorder (ADHD) diagnosis into adulthood raise the question of its possible role in crucial public health issues, including road safety, especially when neurocognitive capacities are challenged. Methods: The study included 611 participants with serious traffic violations. The Spanish version of the Mini International Neuropsychiatric Interview (M.I.N.I.) was used to assess psychopathology. They were grouped into 3 diagnostic conditions: non-ADHD, persistent ADHD (ADHD-P), and remittent ADHD (ADHD-R). Several risky driving behaviors were analyzed. Results: Although participants with ADHD have more driving violations relative to non-ADHD, ADHD-R, and ADHD-P drivers have similar profiles. ADHD-R and ADHD-P drivers are more prone to perform risky and recidivistic behaviors relative to non-ADHD counterparts (P = .044 and P = .047, respectively); ADHD-R and ADHD-P participants are statistically comparable in this proneness (P = .772). Conclusion: These results suggest that the underlying core deficits of ADHD—attention and other executive disabilities—persist despite the fact that some people no longer reach the threshold for clinical diagnosis.
Knowledge, use and sensory evaluation of red palm oil ogi by caregivers in Makurdi, Nigeria

Vitamin A deficiency is one of the major public health challenges in many developing countries affecting mainly children under 5 years of age and contributes to severity of life-threatening infections such as diarrhoea and measles. Over the years, several effective strategies have been adopted to combat vitamin A deficiency with the attendant challenge of sustainability. Food-based approaches based on vitamin A-rich, locally available food sources are considered sustainable and could help in filling this gap. The use of red palm oil among other locally available pro-vitamin A foodstuffs is recommended for incorporation into complementary foods given to children under 5 years of age by health workers in Nigeria. This study was conducted to ascertain the level of awareness, use and sensory evaluation of red palm oil ogi (a cereal-based porridge) by caregivers of under-fives within Makurdi, Nigeria. Data were obtained through a cross-sectional survey and two focus group discussions. The survey involved 100 respondents that were purposively sampled from three well patronized health facilities rendering post-natal services in Makurdi. The results of the survey were analyzed using descriptive statistics. The focus group discussions involved 17 members in two separate groups and responses were sorted and summarized. The results show that though caregivers consider red palm oil as a taboo-free cooking ingredient, knowledge on its use in ogi was low (11%). However, some caregivers were willing to adopt red palm oil ogi provided the method of preparation is demonstrated and presents no adverse effects on the child. Ogi prepared with 5 and 10% red palm oil (w/v) scored 6.8 and 6.6 for maize, 5.9 and 6.0 for millet, respectively for overall acceptability on a 9-point Hedonic scale while ogi without red palm oil was the most acceptable. This study has identified low awareness in the use of red palm oil ogi among caregivers of under-fives in Makurdi; ignorance about red palm oil’s nutritional benefits and appropriate preparation methods as well as actual concentration of red palm oil to be employed as some of the critical gaps in the use of red palm oil ogi in alleviating the burden of vitamin A deficiency among children under 5 years of age in Makurdi, Nigeria.
A real-time face recognition system using multiple mean faces and dual mode Fisherfaces

This research features an automatic face detection and recognition system. The purpose of the system is for access control to a building or an office. The main feature of the system is face detection and face recognition robust to illumination changes. A novel template matching technique using multiple mean faces (MMF) of various sizes, luminance, and rotations is employed to achieve robust face detection. The system is also capable of operating in two different modes for face recognition: under normal illumination condition and under severe illumination changes.
An Experiment on Graph Analysis Methodologies for Scenarios

Visual graph representations are increasingly used to represent, display, and explore scenarios and the structure of organizations. The graph representations of scenarios are readily understood, and commercial software is available to create and manage these representations. The purpose of the research presented in this paper is to explore whether these graph representations support quantitative assessments of the underlying scenarios. The underlying structure of the scenarios is the information that is being targeted in the experiment and the extent to which the scenarios are similar in content. An experiment was designed that incorporated both the contents of the scenarios and analysts’ graph representations of the scenarios. The scenarios’ content was represented graphically by analysts, and both the structure and the semantics of the graph representation were attempted to be used to understand the content. The structure information was not found to be discriminating for the content of the scenarios in this experiment; but, the semantic information was discriminating.
Deep incremental learning for efficient high-fidelity face tracking

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors. Our Geo-Tex VAE model extends the convolutional variational autoencoder for face tracking, and jointly learns and represents deformations and variations in geometry and texture from tracked meshes and texture maps. To accurately model variations in facial geometry and texture, we introduce the decomposition layer in the Geo-Tex VAE architecture which decomposes the facial deformation into global and local components. We train the global deformation with a fully-connected network and the local deformations with convolutional layers. Despite running this model on each frame independently - thereby enabling a high amount of parallelization - we validate that our framework achieves sub-millimeter accuracy on synthetic data and outperforms existing methods. We also qualitatively demonstrate high-fidelity, long-duration facial performance tracking on several actors.
TCP over Wireless Networks Using Multiple Acknowledgements

TCP protocol has been designed and tuned to perform well on wired network where the packet loss is due mainly to congestion. On heterogeneous networks where some links can be wireless this assumption is no more valid. In this paper, we propose a protocol which makes the TCP protocol layer aware of high bit error rates on wireless links while it tries to minimize the traffic load on the wired network. This is done by avoiding unnecessary retransmissions from TCP. The idea is to acknowledge {\em partially} a packet which reaches a base station, if it experiences difficulty (errors or congestion) on the wireless link. The base station is responsible for retransmissions on the wireless link, while it delays timeout at the sender by sending a {\em partial acknowledgement}.
A user´s manual for the Occupational Circumstances Assessment Interview and Rating Scale

The OCAIRS provides a structure for gathering, analyzing, and reporting data on the extent and nature of an individual's occupational participation. It can be used with a wide range of clients, and would be appropriate for any adolescent or adult client who has the cognitive and emotional ability to participate in an interview. The OCAIRS provides a structured and theoretically based means of developing interview skills in evaluation and treatment.
Tool support for change impact analysis in requirement models : exploiting semantics of requirement relations as traceabilitt relations

In the process of software engineering, eliciting requirements yields the first software artifacts; software requirements specifications. Software requirements are used to describe what a software system should do and are used to validate if the implementation satisfies the needs determined for it. From the moment that the first artifacts are produced, they are subject to change. Software artifacts are related to each other through traceability information. Using the traceability information, change impact analysis is performed and it is determined which related software artifacts require changes as well. However, without additional semantics of traceability information an explosion of impacts occurs. This means that change impact analysis yields all related artifacts and causes many false positives. Existing literature states that additional semantics should be employed to counter the explosion of impacts. Recent research yielded a metamodeling approach for requirements models that describes semantics for requirements relations. Tool support has been developed following this approach, that allows for inferencing and consistency checking of requirements models using these relations. However, the tool provides no support for performing change impact analysis. In this work, the semantics of requirements relations are exploited for the use of change impact analysis. This is done by identifying a classification of change for the formalized requirements model. Consequently different rationales of change are identified. It is determined that domain changes drive the change impact analysis. By combining the formalization for the rationale of change and the determined change classification, the semantics of requirements relations allow for a more precise propagation of change. It also enables semi-automatic support for relation validation, as well as identifying inconsistencies of multiple changes. Existing tool support for the metamodeling approach is extended to support change impact analysis using the semantics of requirements relations. The approach is then evaluated by performing an example case study. The example case study yields that using semantics of requirements relation for change impact analysis requires additional effort. The investment of additional effort yields a more precise change impact analysis and reduces the problem of the explosion of impacts
Feature selection for RBF networks

Radial basis function networks (RBFN) can be used for data classification. We present an a-posteriori feature selection method for trained RBFN that is not related to the specific learning procedure as long as every neuron belongs to exactly one class. Considering the center positions, the radii, the weights and the class labels of the neurons, we can easily calculate an index for feature selection that is based on one-dimensional projections. We present examples on different data sets by using Berthold and Diamond's RBFN.
Respiration and applied tension strategies to reduce vasovagal reactions to blood donation: A randomized controlled trial

Whether produced by breathing too fast or too deeply, hyperventilation is common in stressful situations and may contribute to blood donation–related vasovagal symptoms. The effects of some previously tested interventions for vasovagal symptoms, for example, applied tension (AT), may be related to reduction of hyperventilation. More targeted breathing techniques might be useful.
Anonymous and location-concealing routing for multihop ad hoc and sensor networks

Anonymity and location privacy are two important aspects of network security for users. This thesis is concerned with location privacy and anonymity in multihop wireless ad hoc networks. More specifically, it proposes techniques to protect these features from being violated as a by-product of ad hoc routing protocols. We have proposed a novel destination-centric routing approach called Destination Controlled Routing (DCR) in which the destination is the master and controls how packets are delivered to it so that it remains unknown. In addition, DCR is designed in a way that it also preserves the location privacy and anonymity of the source node as well as the communication anonymity. Unlike other anonymous routing protocols for ad hoc networks, DCR is based on the proactive as opposed to the reactive routing approach. We have instantiated DCR for various kinds of multihop ad hoc networks. For networks of type mesh and MANET, we have proposed three variations of our V-routing protocol based on this approach. We have also adapted V-routing to multicasting applications. Given a sufficient number of data flows in the network, V-routing can protect location privacy and communication anonymity against a global eavesdropper. It achieves this goal by using a transient destination node and hop-by-hop anonymous routing between that node and the real destination node in a way that the location of the real destination node does not need to be revealed. For multihop wireless sensor networks, we have developed different versions of our DCARPS (Destination Controlled Anonymous Routing Protocol for Sensornets) protocol for high and low volume traffic load situations. Unlike most routing protocols
A voltage disturbances prototype for testing electrical generators connected to microgrids

The search for new energy models arises as a necessity to have a sustainable power supply. The inclusion of distributed generation sources (DG) allows to reduce the cost of facilities, increase the security of the grid or alleviate problems of congestion through the redistribution of power flows. In remote microgrids it is needed in a particular way a safe and reliable supply, which can cover the demand for a low cost; due to this, distributed generation is an alternative that is being widely introduced in these grids. But the remote microgrids are especially weak grids because of their small size, low voltage level, reduced network mesh and distribution lines with a high ratio R/X. This ratio affects the coupling between grid voltages and phase shifts, and stability becomes an issue of greater importance than in interconnected systems. To ensure the appropriate behavior of generation sources inserted in remote microgrids -and, in general, any electrical equipment-, it is essential to have devices for testing and certification. These devices must, not only faithfully reproduce disturbances occurring in remote microgrids, but also to behave against the equipment under test (EUT) as a real weak grid. This also makes the device commercially competitive. To meet these objectives and based on the aforementioned, it has been designed, built and tested a voltage disturbances generator, in order to provide a simple, versatile, full and easily scalable device to manufacturers and laboratories in the sector.
Feature extraction using wavelet packets strategy

In this paper, improved wavelet packets (WPs) decomposition coefficients of the frame are applied in the feature extraction method. In the proposed speech recognition system, the static WPs coefficients+dynamic WPs coefficients of the frame were employed as a basic feature. The framework of linear discriminant analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric vector space for the speech recognition system. Using the continuous hidden Markov model (HMM) as the speech recognition model, the speech recognition system was successfully constructed. Experiments are performed on the speaker independent isolated-word speech recognition task. It is found that the improved WPs method achieves better recognition performance than the most popular Mel frequency cepstral coefficients (MFCC) feature extraction method in a noisy environment.
Binocular Vision and Stereopsis

Introduction 1. Binocular correspondence and the horopter 2. Sensory coding 3. The physiology of binocular vision 4. The limits of stereoscopic vision 5. Matching corresponding images 6. Types of disparity 7. Binocular fusion and rivalry 8. Binocular masking and transfer 9. Vergence eye movements 10. Stereo constancy and depth cue interactions 11. Depth contrrast and cooperative processes 12. Spatiotemporal aspects of stereopsis 13. Vision in the cyclopean domain 14. Development and pathology of binocular vision 15. Binocular and stereoscopic vision in animals References Subject index
Overlay techniques in the underlay

Traditional routing algorithms as used today in the Internet rely on a hierarchical structure of the network to be able to scale reasonably. Similarly, routing algorithms in wireless mesh networks that do not rely on a location-imposed hierarchy do not scale well. Routing algorithms as used in overlay networks for Distributed Hash Tables (DHTs), however, have shown good scalability. This paper describes two novel approaches that take these overlay network routing algorithms and techniques and apply them to the routing in the underlying network. Namely Virtual Ring Routing [3], aimed at providing scalable mesh routing offering good performance, and Routing on Flat Labels [4], aimed at demonstrating that the use of routing schemes on the Internet, that do not rely on a hierarchical structure, could be feasible.
Action recognition in visual sensor networks: a data fusion perspective

Visual Sensor Networks have emerged as a new technology to bring computer vision algorithms to the real world. However, they impose restrictions in the computational resources and bandwidth available to solve target problems. This thesis is concerned with the definition of new efficient algorithms to perform Human Action Recognition with Visual Sensor Networks. Human Action Recognition systems apply sequence modelling methods to integrate the temporal sensor measurements available. Among sequence modelling methods, the Hidden Conditional Random Field has shown a great performance in sequence classification tasks, outperforming many other methods. However, a parameter estimation procedure has not been proposed with feature and model selection properties. This thesis fills this lack proposing a new objective function to optimize during training. The L2 regularizer employed in the standard objective function is replaced by an overlapping group-L1 regularizer that produces feature and model selection effects in the optima. A gradient-based search strategy is proposed to find the optimal parameters of the objective function. Experimental evidence shows that Hidden Conditional Random Fields with their parameters estimated employing the proposed method have a higher predictive accuracy than those estimated with the standard method, with an smaller inference cost. This thesis also deals with the problem of human action recognition from multiple cameras, with the focus on reducing the amount of network bandwidth required. A multiple view dimensionality reduction framework is developed to obtain similar low dimensional representation for the motion descriptors extracted from multiple cameras. An alternative is proposed predicting the action class locally at each camera with the motion descriptors extracted from each view and integrating the different action decisions to make a global decision on the action performed. The reported experiments show that the proposed framework has a predictive performance similar to 3D state of the art methods, but with a lower computational complexity and lower bandwidth requirements. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Appearance capture and modeling of human teeth

Recreating the appearance of humans in virtual environments for the purpose of movie, video game, or other types of production involves the acquisition of a geometric representation of the human body and its scattering parameters which express the interaction between the geometry and light propagated throughout the scene. Teeth appearance is defined not only by the light and surface interaction, but also by its internal geometry and the intra-oral environment, posing its own unique set of challenges. Therefore, we present a system specifically designed for capturing the optical properties of live human teeth such that they can be realistically re-rendered in computer graphics. We acquire our data in vivo in a conventional multiple camera and light source setup and use exact geometry segmented from intra-oral scans. To simulate the complex interaction of light in the oral cavity during inverse rendering we employ a novel pipeline based on derivative path tracing with respect to both optical properties and geometry of the inner dentin surface. The resulting estimates of the global derivatives are used to extract parameters in a joint numerical optimization. The final appearance faithfully recreates the acquired data and can be directly used in conventional path tracing frameworks for rendering virtual humans.
Person-centred Nursing: Theory, Models and methods

The concept of 'person-centredness' has become established in approaches to the delivery of healthcare, particularly with nursing, and is embedded in many international healthcare policy frameworks and strategic plans. This book explores person-centred nursing using a framework that has been derived from research and practice. Person-centred Nursing is a theoretically rigorous and practically applied text that aims to increase nurses' understanding of the principles and practices of person-centred nursing in a multiprofessional context. It advances new understandings of person-centred nursing concepts and theories through the presentation of an inductively derived and tested framework for person-centred nursing. In addition it explores a variety of strategies for developing person-centred nursing and presents case examples of the concept in action. This is a practical resource for all nurses who want to develop person-centred ways of working.