Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of a Second Dose of Measles Vaccine at 18 Months of Age on Nonaccidental Deaths and Hospital Admissions in Guinea-Bissau: Interim Analysis of a Randomized Controlled Trial

Abstract Background The world is set on the eradication of measles. Continuation of the measles vaccine (MV) after eradication could still reduce morbidity because the MV has so-called beneficial nonspecific effects. We evaluated the effect of a “booster” dose of the MV on overall severe morbidity. Methods We conducted a randomized controlled trial among children aged 17.5 to 48 months in Guinea-Bissau, where the MV is recommended only at 9 months of age. At the time of this interim analysis, 3164 children had been allocated 1:1 to a second dose of measles vaccine (MV2) at 18 months of age or to no vaccine. Severe morbidity (a composite outcome of nonaccidental deaths and hospital admissions) rate ratios (SMRRs) were calculated by Cox regression analysis censored for national oral polio vaccine (OPV) campaigns. Results There were no measles cases during the trial period. There were 43 nonaccidental deaths or hospital admissions during follow-up. Severe morbidity was 2.6 per 100 person-years in the MV2 group and 3.6 per 100 person-years among controls; hence, the estimated effect of MV2 on severe morbidity was 28% (SMRR, 0.72; 95% confidence interval [CI], .38–1.38). At 12 months of follow-up, the number needed to treat to prevent 1 severe morbidity event was 137 children. After OPV campaigns, the estimated effect of MV2 was reduced to 9% (SMRR, 0.91; 95% CI, .46–1.81). Conclusions MV2 may reduce nonmeasles severe morbidity by 28% (−38% to 62%), although this did not achieve statistical significance in this study. If significant in higher powered studies, this has major implications for child health, even after measles eradication. Clinical Trials Registration NCT02943681.
Towards successful inculcation of generic skills: issues and strategies

Engineering education faces significant challenges from globalisation, rapid technological evolution, and problems related with funding for higher education. The rapidly emerging global economy has a strong influence on career prospects and the employment patterns of engineering graduates. This year we are focussed on addressing the current challenges facing engineering education in producing world class human capital. The development of the human capital is essential to produce sufficient knowledge workers that are needed by most nations to achieve a knowledge-based economy.
Longitudinal analysis of UK Biobank participants suggests age and APOE-dependent alterations of energy metabolism in development of dementia

Experimental models shows that bioenergetic homeostasis changes with increasing age based on apolipoprotein E (APOE) gene. However, such link with dementia remains unclear in population. We used H1-NMR metabolome in blood from 118,021 random-selected participants in UK Biobank (n=118,021 individuals), and identified 56 metabolites associated with the risk of dementia. In the participants without developing dementia during follow-up, 82% (46/56) metabolites are also associated with reaction time, and dementia shares metabolite signatures with total brain volume. We found that incident Alzheimer,s disease (AD) is associated with energy metabolism-related metabolites, i.e, {beta}-hydroxybutyrate, acetone, and valine, whose concentrations in blood are influenced by age and APOE genotype. Valine shows a declined trajectories after a plateau at age around 60 years which is in parallel with BMI. Moreover, we found associations of AD with valine and {beta}-hydroxybutyrate in brain tissue are different as their associations in the periphery, which implies the key role of transports in regulating the energy metabolism of AD. Our study provides strong evidence that the onset of AD in APOE4 carriers is regulated by the impaired energy balance in the brain.
Assessment of quality predictions achieved with machine learning using established measurement process capability procedures in manufacturing

Abstract The increasing amount of available process data from machining and other manufacturing processes together with machine learning methods provide new possibilities for quality control and condition monitoring. A prediction of the workpiece quality in an early machining stage can be used to alter current quality control strategies and could lead to savings in terms of time, cost and resources. However, most methods are tested under controlled lab conditions and few implementations in real manufacturing processes have been reported yet. The main reason for this slow uptake of this promising technology is the need to prove the capability of a machine learning method for quality prediction before it can be applied in serial production and supplement current quality control methods. This article introduces and compares approaches from the fields of machine learning and quality management in order to assess predictions. The comparison and adaption of the two approaches is carried out for an industrial use case at Bosch Rexroth AG where the diameter and the roundness of bores are predicted with machine learning based on process data.
Photons and π 0 discrimination in the electromagnetic calorimeter ( EMCal ) of the ALICE experiment

The ALICE experiment at the LHC identifies photons, electrons and neutral mesons with the electromagnetic calorimeters PHOS and EMCal. At high transverse momentum the π decays into two photons with a small aperture angle in the lab system, what makes difficult their identification as separated photons in the calorimeters. One of the proposed methods to discriminate between photons and high momentum πs is to study the shape of the shower produced in the calorimeters. In this note we present our results for shower shape MC studies using EMCAL. The photons can be identified with an efficiency higher than the 80% with energies in the range 10-25 GeV.
The Learner Data Institute - Conceptualization: A Progress Report

This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem.
Critical Analysis of Diagnosis for Angioplasty and Stent Patient Using Enhanced Decision Tree Technique (EDTA)

: Information mining is a procedure of extraction of helpful data and examples from immense information. It is likewise called as information disclosure process, information mining from information, information extraction or information design investigation. We present an improved way to deal with help closest neighbor questions from portable hosts by utilizing the sharing abilities of remote specially appointed systems. We outline how past inquiry results reserved in the nearby stockpiling of neighboring portable friends can be utilized to either completely or in part figure or confirm spatial questions at a neighborhood have. The practicality and intrigue of our method is outlined through broad recreation results that show a significant decrease of the inquiry load on the remote database.
Smart Antennas: Technological Advancement for Wireless Communication Networks

: In the Era of digital (wireless &mobile) communication the requirements of efficient antennas and antenna systems in high-speed and high-capacity wireless communication systems are well understood. Conventionally, Antenna is a unit of wireless communication system electrically 9transmits signals to transmitter and receiver . An Antenna Array or Adaptive Array is known as Smart Antenna with multiple transmitter and receiver system which performs spatial signal processing with multiple antennas by latest advanced antenna techniques. This paper provides a general explanation of the requirements of antennas and antenna systems in recent high-speed and high-capacity wireless communication systems and the key technologies for satisfying these requirements, In communication systems, The adoption of multiple antenna tech-niques in future wireless systems is expected to have a significant impact on the efficient use of the spectrum, the minimization of the cost of establishing new wireless networks, the enhancement of the quality of service, we have to increase the reliability of the communication operation between transmitter and receiver while maintaining a high spectral efficiency. The ultimate solution relies in the use of spatial Signal processing algorithms and diversity techniques, which can be viewed as a form of redundancy. Multiple-input multiple-output systems include a variety of techniques capable of not only increasing the reliability of the communication but also impressively boost the channel capacity. In addition, smart antenna system can increase the link quality and lead to appreciable interference reduction. Smart antenna technology is one of the most vital developments in mobile communication. The signal that is been transmitted by a smart antenna cannot be tracked or received by any other antenna thus ensuring a very high security of the data transmitted.
PRBM_A_352060 459..470

Purpose: To explore the relationship between self-control, rejection sensitivity, loneliness, and problematic internet use in college students. Patients and Methods: A total of 725 college students were investigated using Self-control Scale, Rejection Sensitivity Questionnaire, Loneliness Scale, and Internet Addiction Scale. Results: 1 Correlation analysis showed that self-control was negatively related to rejection sensitivity, loneliness, and problematic internet use; rejection sensitivity was positively related to loneliness and problematic internet use; loneliness was positively related to problematic internet use. 2 Chain mediating effect analysis showed that self-control can not only affect problematic internet use in college students, but also through three indirect paths, as follows: the mediating role of rejection sensitivity and loneliness, the chain mediating roles of rejection sensitivity and loneliness, and the mediating effect size, accounting for 9.76%, 20.73%, and 4.88% of the total effect, respectively. Conclusion: Rejection sensitivity and loneliness played a chain mediating role in the relationship between self-control and problematic internet use in college students.
Determination of the radon concentration in homes depending on the insulation used for the floor

A current environmental problem is the presence of radon inside the house. Radon (222Rn) is a noble, colourless and odourless gas that comes from radioactive elements naturally present in rocks and soil. Being gas, it is released from the ground with a tendency to concentrate in closed spaces such as caves, mines, cellars but also in any rooms in the basement, ground floor or with poor ventilation. Due to the differences between the temperature inside and the soil, the radon in the soil will move naturally to the interior of the houses, concentrating in closed rooms. The accumulation of radon inside buildings is a consequence of technological progress. Insulation work, tightly closed windows, poor ventilation of rooms lead to unobservable increase in radon concentration inside. In this paper we aim to present the values of the concentration of radon accumulated inside a house depending on the way of insulating the floor.
Systematic review and meta-analysis of diagnostic test accuracy (DTA) studies: the role of cerebral perfusion imaging in prognosis evaluation of mild cognitive impairment.

BACKGROUND The application value of cerebral perfusion imaging, such as fluorodeoxyglucose-positron-emission computed tomography (FDG-PET), single-photon emission computed tomography (SPECT), and structural magnetic resonance imaging (MRI), in the prognostic assessment of mild cognitive impairment (MCI) remains unclear. Thus, it was the focus of this meta-analysis, which aimed to provide a theoretical basis for early diagnosis of neurological diseases. METHODS The Boolean logic retrieval method was used to retrieve related literature, with "cognitive disorder", "cerebral perfusion imaging", "cerebral imaging", "mild cognitive impairment", and "prognostic assessment" as search terms. The PubMed, Medline, CNKI (China National Knowledge Infrastructure), and other databases were searched from the date of establishment of the database to December 30, 2020 for literature on the prognostic assessment of MCI using FDG-PET, SPECT, and MRI imaging. Meta-analysis was performed using Review Manager software. RESULTS A total of 16 references were identified, most of which had a low risk of bias (i.e. medium and high-quality). The meta-analysis results showed that the sensitivity and specificity of FDG-PET imaging was significantly higher than those of SPECT and MRI imaging, and the differences were statistically significant (P<0.05). The summary receiver operating characteristic (sROC) curves for the sensitivity and specificity of FDG-PET, SPECT, and MRI imaging did not present a "shoulder arm" distribution, and there was no threshold effect. DISCUSSION Cerebral perfusion imaging has good prognostic value for patients with MCI, and FDG-PET imaging has better predictive ability of the prognosis for patients with MCI.
A Case of Pulmonary Pneumatocoele with Splenic Laceration Caused by Vehicular Accident

Traumatic pneumatocoele is an uncommon complication, resulting from air trapping in an area of lung laceration, and may appear a few hours or even immediately after injury. Young adults and children are most commonly affected. Its clinical relevance lies in its rarity, which might mislead emergency physicians to perform unnecessary surgical intervention. We report a 10-year-old boy suffering from traumatic pneumatocoele with spleen laceration caused by blunt trauma. It is important for emergency physicians to be aware of this condition and of its benign course so as to avoid unnecessary operative procedures due to incorrect diagnosis.
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To explore the complex relationship between samples and latent representations, a neighborhood constraint and a view-existence constraint are proposed, for the first time, to construct a heterogeneous graph. Finally, to avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model over existing state-of-the-art approaches. Our code is available at: https://github.com/yxjdarren/LHGN_TMM_2022.
Comparative study of expert predictive models based on adaptive neuro fuzzy inference system, nonlinear autoregressive exogenous and Hammerstein–Wiener approaches for electrical discharge machining performance: Material removal rate and surface roughness

In this study, material removal rate (MRR) and surface roughness (Ra) in electrical discharge machining process have been modeled to make the process more efficient and reliable. First, adaptive neuro fuzzy inference system as one of the most used methods has been applied for prediction of material removal rate and Ra. Also a proposed method, that is, nonlinear modeling by system identification, has been applied to predict material removal rate and Ra. A group of electrical discharge machining experiments considering four input variables was conducted to collect dataset for training the predictive models. At the end, the comparison of predicted results from both approaches with experimental data shows that the new method has a much better performance than the adaptive neuro fuzzy inference system approach.
A SPORTS INJURY CASE STUDY MODEL : CAPITALIZING ON VIRTUAL REALITY TECHNOLOGY

Virtual reality anatomical models were created to support an online case study approach to sports injury diagnosis and treatment in an undergraduate kinesiology program . A sample of college student was assigned a case study of an ankle injury . The online case included ®bject 2VR models to support the investigation. Mixed method research established that while the case study was designed to supplement classroom experiences, students preferred to engage the case with selective scaffolding from the instructor. This has led to a refined instructional strategy based on student feedback. The virtual reality models were found to be particularly effective for anytime – any place access to solving the case. (Journal of Sports Science and Technology, 2012 ; 12 (1) : 117 - 128 )
Transparent, Low‐Impedance Inkjet‐Printed PEDOT:PSS Microelectrodes for Multimodal Neuroscience

Transparent microelectrodes that facilitate simultaneous optical and electrophysiological interfacing are desirable tools for neuroscience. Electrodes made from transparent conductors such as graphene and indium tin oxide (ITO) show promise but are often limited by poor charge‐transfer properties. Herein, microelectrodes are demonstrated that take advantage of the transparency and volumetric capacitance of poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). Ring‐shaped microelectrodes are fabricated by inkjet‐printing PEDOT:PSS, encapsulating with Parylene C, and then exposing a contact site that is much smaller than the microelectrode outer diameter. This unique structure allows the encapsulated portion of the microelectrode volume surrounding the contact site to participate in signal transduction, which reduces impedance and enhances charge storage capacity. While using the same 100 μm diameter contact site, increasing the outer diameter of the encapsulated electrode from 300 to 550 μm reduces the impedance from 294 ± 21 to 98 ± 2 kΩ, respectively, at 1 Hz. Similarly, the charge storage capacity is enhanced from 6 to 21 mC cm−2. The PEDOT:PSS microelectrodes provide a low‐haze, high‐transmittance optical interface, demonstrating their suitability for optical neuroscience applications. They remain functional after a million 1 V stimulation cycles, up to 600 μA of stimulation current, and more than 1000 mechanical bending cycles.
Graph rotation systems for physical construction of large structures

In this dissertation, I present an approach for physical construction of large structures. The approach is based on the graph rotation system framework. I propose two kinds of physical structures to represent the shape of design models. I have developed techniques to generate developable panels from any input polygonal mesh, which can be easily assembled to get the shape of the input polygonal mesh. The first structure is called plain woven structures. I have developed the "projection method" to convert mathematical weaving cycles on any given polygonal mesh to developable strip panels. The width of weaving strips varies so that the surface of the input model can be covered almost completely. When these strip panels are assembled together, resulting shape resembles to a weaving in 3-space. The second structure is called band decomposition structures. I have developed a method to convert any given polygonal mesh into star-like developable elements, which we call vertex panels. Assembling vertex panels results in band decomposition structures. These band decomposition structures correspond to 2D-thickening of graphs embedded on surfaces. These band decompositions are contractible to their original graph. In a 2D-thickening, each vertex thickens to a polygon and each edge thickens to a band. Within the resulting band decomposition, each polygon corresponds to a vertex and each band corresponds to an edge that connects two vertex polygons. Since the approach is based on graph rotation system framework, the two structures do not have restrictions on design models. The input mesh can be of any genus. The faces in the input mesh can be triangle, quadrilateral, and any polygon. The advantages of this kind of large physical structure construction are low-cost material and prefabrication, easy assemble. Our techniques take the digital fabrication in a new direction and create complex and organic 3D forms. Along the theme of architecture this research has great implication for structure design and makes the more difficult task of construction techniques easier to understand for the fabricator. It has implications to the sculpture world as well as architecture.
Optimal Management of Partial Thickness Rotator Cuff Tears: Clinical Considerations and Practical Management

Abstract Partial thickness rotator cuff tears have been diagnosed with increased frequency due to heightened awareness and an improvement in diagnostic modalities. When >50% of the tendon thickness has ruptured, intra-tendinous strain of the residual tendon increases. Surgery is generally confined to patients who have failed non-operative measures and have persistent symptoms. The rationale for repairing partial thickness tears lies in their limited self-healing capacity, and propensity to enlarge over time and progress to a full thickness defect. Although tear debridement and acromioplasty can improve pain and function, tear progression can occur, in addition to worse results being noted in bursal-sided defects. Several surgical strategies have been recommended but there is a lack of evidence to advocate one form of treatment over another. The aim of this narrative review is to discuss the treatment options for partial thickness tears of the rotator cuff.