ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The Relationship Between Vertical Facial Type and Maxillary Anterior Alveolar Angle in Adults Using Cone-Beam Computed Tomography

Background Cone-beam computed tomography (CBCT) imaging provides detailed and thorough information about the dentofacial complex. However, not all aspects have been yet explored among different types of malocclusion. The maxillary anterior alveolus is one of the components of the maxillary bone which affects the upper lip position and the esthetics of the smile. The inclination of this alveolus may vary between the different vertical growth patterns of patients who may seek orthodontic treatment. The objective of this study was to investigate possible differences in maxillary anterior alveolar angle (MAAA) among orthodontically untreated adults with different vertical facial types in a Syrian sample. Methods CBCT images of 84 orthodontically untreated adult patients were included. Three groups of vertical facial type (n=28 for each group; 14 males, 14 females) were created using disproportionate multi-stratified random sampling. CBCT-derived lateral cephalograms were used to categorize the patients into three groups. Measurements were made at three regions (region 1 (R1), region 2 (R2), and region 3 (R3)), located in the maxillary anterior alveolar bone using OnDemand3D™ software (Cypermed Inc., Seoul, South Korea). Results No significant differences in the mean MAAA were detected between females and males for the three measured regions in all groups. Analysis of variance showed significant inter-group differences in the MAAA (p<0.05) for all measured regions. The hyperdivergent facial type group had the greatest MAAA mean value of 68.72° (± 6.01), 67.30° (± 4.15), and 68.01° (± 5.12) at R1 in the female, male, and the entire sample of both sexes respectively. Whereas the hypodivergent facial type group had the least mean MAAA values of 58.47° (± 5.34) at R3, 59.83° (± 6.23) at R2, and 59.23° (± 5.75) at R3 in the female, male, and the entire sample of both sexes respectively. Conclusions The maxillary anterior alveolar bone was more buccally inclined in the hypodivergent facial type. The MAA bone inclination did not differ between females and males in the same vertical facial type group.
Polyethylene Glycol 3350 Crystal Nephropathy in Association With Glomerular Mesangial Immunoglobin A Deposition

Polyethylene glycol (PEG) 3350, an active ingredient of over-the-counter MiraLAX, is a commonly used laxative in children and is produced by polymerization of ethylene glycol (EG). Masked EG toxicity secondary to contamination of PEG 3350 could occur. We present a 7-year-old child with developmental delay who presented with altered mental status and acute kidney injury (AKI) following intake of generic PEG 3350 for few days prior to presentation. There was high anion gap metabolic acidosis, hypernatremia, elevated osmolar gap, lactic acidosis, and AKI. Urinalysis showed tubular proteinuria, microscopic hematuria, and calcium oxalate crystals. Prior urinalyses were normal without hematuria or proteinuria. Renal biopsy revealed evidence of mesangial dominant immunoglobulin A (IgA) and complement 3 (C3) deposits along with dense tubular deposition of calcium oxalate crystals. He subsequently developed worsening oliguric AKI and required hemodialysis (HD) for several sessions. The AKI resolved within 2 weeks and further HD was not required. Mental status improved in few days. Follow-up urinalyses showed resolution of microscopic hematuria and crystalluria. We hypothesized that the generic PEG 3350 most likely was contaminated with EG leading to the presentation. A high index of suspicion of contamination of PEG 3350 with EG is required in patients presenting with unexplained high anion gap metabolic acidosis, elevated osmolar gap, lactic acidosis, AKI, calcium oxalate crystalluria, and oxalate crystals on renal biopsy. Further studies are needed to determine whether there is an association between transient glomerular mesangial IgA deposition and crystal nephropathy.
State of the art in thermal insulation materials and aims for future developments

Insulation materials are the key tool in designing and constructing a energy thrifty buildings. This is demonstrated by the increasing thicknesses used in buildings, which also reflects in the growing sales of the branch. The European market of insulation materials is characterised by the domination of two groups of products inorganic fibrous materials and organic foamy materials. They all feature similar performance in terms of insulating capabilities, but otherwise present significant differences. These are discussed in detail in the following paper. Despite the fact that the thermal properties of the materials has not improved significantly of the last decade, a series of other features, like reaction to fire and moisture or mechanical properties have improved, sometimes even at the cost of insulation abilities. Furthermore, environmental and public health aspects play an increasing role, both in the search for 'optimum' materials for given applications, and in the aims set by the industry for future developments. These aims, examined within the legislative and market framework, are discussed in this paper, both as criteria for evaluating state of the art materials and as goals for future research developments. (C) 2004 Elsevier B.V. All rights reserved.
hTetro-Infi: A Reconfigurable Floor Cleaning Robot With Infinite Morphologies

The development of floor cleaning robots is an emerging area in robotics. Maximizing the area coverage is a foremost mission for a floor cleaning robot. Reconfigurable floor cleaning robots outperform floor cleaning robots with fixed morphology in the aspect of area coverage. A reconfigurable robot should be more flexible in changing its morphologies by considering the shapes of objects occupied in an environment to gain more coverage. Nevertheless, the state of the art methods of tiling robots considers only a limited number of morphologies for the reconfiguration, which is not sufficient to match the shape of an object. Therefore, this paper proposes a novel method to synthesize an appropriate morphology for a reconfigurable robot in accordance with the shape of an object. The proposed concept is named hTetro-Infi since it is not limited to a finite number of morphologies. The major novelty of the proposed concept overt the state of the art is the consideration of an infinite number of morphologies for the reconfiguration without sticking into a limited number of morphologies. Feedforward Neural Network (FNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for determining the hinge angle required for synthesizing a given morphology. Different configurations of FNNs and ANFISs were trained and evaluated to find the most suitable configurations. The area coverage performance of the proposed hTetro-Infi was compared against that of the state of the art methods of an existing class of tiling robots, which considers only a limited number of morphologies, through simulations. According to the statistical conclusions, the proposed hTetro-Infi is capable of significantly improving area coverage compared to an existing tiling-theory based floor cleaning robot. Furthermore, the area coverage improvement of hTetro-Infi is noteworthy. Therefore, the proposed concept is beneficial in improving the abilities of a reconfigurable cleaning robot. Real-world experiments with the hardware platform of the robot for evaluating the performance is expected to be conducted in the next phase of the work. Furthermore, consideration of hTetro-Infi for navigation through confined areas is proposed for future work.
Epistemic community in transboundary river regime: a case study in the Mekong River Commission regarding mainstream hydropower development

Despite the importance of transboundary water management, cooperation mechanisms are limited, especially in the case of Mekong River basin where environmental and social aspects are threatened by recent anthropogenic pressures like hydropower development. Existing transboundary mechanism such as the Mekong River Commission (MRC) is challenged to facilitate the cooperation between riparian states. An epistemic community (EC) is considered to effectively influence international governance and is studied as part of transboundary river regimes. The existence of an MRC EC is part of that regime but understanding about its characteristics is yet limited. This research aims to fill in the gap by unraveling the main features of the EC in relation to hydropower development. We analyze shared causal beliefs and policy goals that developed in the EC framework of Haas applying literature review and semi-structured interviews of experts. Results show that the community experts share causal beliefs and policy goals only to a limited extent while disagreeing on many aspects. It resembles a "disciplined" or "professional" group rather than an EC. This suggests that the knowledge factor has not gained proper influence and attention in the region, resulting in incoherent policy advice leading to policymakers developing policies based on incomplete and fragmented knowledge. The role of the MRC in the decision-making process could become more relevant if it would facilitate the development of an EC. Bringing key stakeholders including policymakers and experts into a platform where policy goals and causal beliefs are facilitated to reach possible consensus is recommended. Narrowing the science-policy gap while acknowledging differences in interests and policy objectives is crucial to reach a sustainable transboundary management of the Mekong River given its rapid development, especially on hydropower.
Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

Nowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode.
CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.
Emotion Based Automated Priority Prediction for Bug Reports

Issue tracking systems allow users to report bugs. Bug reports often contain product name, product component, description, and severity. Based on such information, triagers often manually prioritize the bug reports for investigation. However, manual prioritization is time consuming and cumbersome. DRONE is an automated state-of-the-art approach that recommends the priority level information of the bug reports. However, its performance for all levels of priorities is not uniform and may be improved. To this end, in this paper, we propose an emotion-based automatic approach to predict the priority for a report. First, we exploit natural language processing techniques to preprocess the bug report. Second, we identify the emotion-words that are involved in the description of the bug report and assign it an emotion value. Third, we create a feature vector for the bug report and predict its priority with a machine learning classifier that is trained with history data collected from the Internet. We evaluate the proposed approach on Eclipse open-source projects and the results of the cross-project evaluation suggest that the proposed approach outperforms the state-of-the-art. On average, it improves the F1 score by more than 6%.
Accurate and Robust Time Reconstruction for Deployed Sensor Networks

The notion of global time is of great importance for many sensor network applications. Time reconstruction methods aim to reconstruct the global time with respect to a reference clock. To achieve microsecond accuracy, MAC-layer timestamping is required for recording packet transmission and reception times. The timestamps, however, can be invalid due to multiple reasons, such as imperfect system designs, wireless corruptions, or timing attacks, etc. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. ART is much more accurate and robust than threshold-based approach, especially in dynamic networks with inherently varying propagation delays. We evaluate our approach in both testbed and a real-world deployment. Results show that: 1) ART achieves a high detection accuracy with low false-positive rate and low false-negative rate; 2) ART achieves a high recovery accuracy of less than 2 ms on average, much more accurate than previously reported results.
Comparison of non-linear mixture models: Sub-pixel classification

Sub-pixel level classification is essential for the successful description of many land cover patterns with spatial resolution of less than similar to1 km and has been widely used in global or continental scale land cover mapping with remote sensing data. This paper presents a general comparison of four non-linear models for sub-pixel classification: ARTMAP. ART-MMAP. Regression Tree (RT) and Multilayer Perceptron (MLP) with Back-Propagation (BP) algorithm. The comparison is based oil four factors: accuracy. model complexity, interpolation ability and error distribution. Two data sets, one simulated and one real world MODIS satellite image. were used to demonstrate the characteristics of each model. Experimental results show the superior performance of MLP with the simulated data set and better performance of ART-MMAP with the MODIS data set. (C) 2004 Elsevier Inc. All rights reserved.
Evolution, Current Challenges, and Future Possibilities in ECG Biometrics

Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
Long-distance WPT unconventional arrays synthesis

Two innovative array concepts are introduced for the design of long-distance Wireless Power Transfer (WPT) radiating systems. The achievable tradeoffs between complexity/cost mitigation and power focusing capabilities of unconventional WPT architectures with respect to state-of-the-art optimal WPT solutions are investigated. To this end, clustered or sparseWPT arrangements are introduced by formulating their syntheses either as excitation or as pattern matching problems then solved by ad-hoc versions of the Contiguous Partition Method and Compressive Sensing algorithms. Selected numerical examples are presented to assess the features and the potentialities of unconventional WPT designs also in comparison with traditional state-of-the-art optimal methods.
Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternativemethod inmulti-modal brain imaging genetics.
Functional neuroanatomical correlates of contingency judgement

Contingency judgement is an ability to detect relationships between events and is crucial in the allocation of attentional resources for reasoning, categorization, and decision making to control behaviour in our environment. Research has suggested that the allocation of attention is sensitive to the frequency of contingency information whether it constitutes a negative, zero or positive relationship. The aim of the present study was to explore the functional neuroanatomical correlates of contingency judgement with different frequencies and whether these are distinct from each other or whether they rely on a common mechanism. Using three contingency tasks within a streaming paradigm (one each for negative, zero, and positive contingency frequencies), we assessed brain activity by means of functional magnetic resonance imaging (fMRI) in 20 participants. Contingency frequency was manipulated between blocks which allowed us to determine the neural correlates of each of the three contingency tasks as well as the common areas of activation. The conjunction of task activation showed activity in left parietal cortices (BA 23, 40) and superior temporal gyrus (BA42). Further, the interaction analysis revealed distinct areas that mainly involve lateral (BA 45) and medial (BA 9) prefrontal cortices in the judgment of negative contingencies compared with positive and zero contingencies. We interpret the finding as evidence that the shared regions may be involved in coding, integration, and updating of associative relations and distinct regions may be involved in the investment of attentional resources to varied degrees in the computation of contingencies to make a judgment.
Novel Treatment of Ventilator Dyssynchrony From Central Alveolar Hypoventilation Syndrome Utilizing Scheduled 5-Hydroxytryptamine-3 Receptor Antagonist

Traumatic brain injury (TBI) occurs in a large percentage of surgical trauma patients and is one of the leading causes of death amongst young teens and adults. Furthermore, individuals with TBIs often require mechanical ventilation and admission to the intensive care unit. As a result of their TBIs, these patients can develop central alveolar hypoventilation (CAH) secondary to disruptions in neuromodulatory respiratory brainstem control and neural signal initiation and integration. Prior studies have primarily focused their attention on treatment of congenital disorders of CAH, and limited research is available on intubated trauma patients who have signs of ventilator dyssynchrony. Current case reports and animal studies have suggested that noradrenergic and specific serotonergic medications are able to target specific neurologic pathways in the respiratory circuit and induce ventilator synchrony. This case series describes the clinical course of TBI patients treated for ventilator dyssynchrony secondary to CAH with a daily scheduled 5-hydroxytryptamine-3 (5-HT3) receptor antagonist. All patients were ultimately extubated and discharged from the hospital.