Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion

In recent years, multimodal biometric systems are increasingly employed in many application field due to several advantages in terms of universality, recognition rate. and security. Among various acquisition technologies, Ultrasound shows important merits, because it allows obtaining volumetric images of the human body and hence a more accurate description of characteristics and to verify liveness. In this work, a multimodal ultrasound recognition system based on the fusion between 3D hand geometry and 3D palmprint features is proposed and experimentally evaluated. The system acquires a volumetric image of the whole hand and for both characteristics, several 2D images are extracted at different depth levels. From each image, 2D features are extracted and then properly combined to achieve a 3D template. Recognition performances are evaluated through verification and identification experiments by employing a homemade database. Experiments are carried out first for the two unimodal biometrics and successively, by fusing the two modalities at score level. Results have shown that fusion is able to dramatically improve the recognition performances of the single biometrics, achieving an Equal Error Rate of 0.08% and an identification rate of 100%.
Tutorial on Support Vector Machines

The aim of this tutorial is to help students grasp the theory and applicability of support vector machines (SVMs). The contribution is an intuitive style tutorial that helped students gain insights into SVM from a unique perspective. An internet search will reveal many videos and articles on SVM, but free peer-reviewed tutorials are generally not available or are incomplete. Instructional materials that provide simplified explanations of SVM leave gaps in the derivations that beginning students cannot fill. Most of the free tutorials also lack guidance on practical applications and considerations. The software wrappers in many modern programming libraries of Python and R currently hide the operational complexities. Such software tools often use default parameters that ignore domain knowledge or leave knowledge gaps about the important effects of SVM hyperparameters, resulting in misuse and subpar outcomes. The author uses this tutorial as a course reference for students studying artificial intelligence and machine learning. The tutorial derives the classic SVM classifier from first principles and then derives the practical form that a computer uses to train a classification model. An intuitive explanation about confusion matrices, F1 score, and the AUC metric extend insights into the inherent tradeoff between sensitivity and specificity. A discussion about cross-validation provides a basic understanding of how to select and tune the hyperparameters to maximize generalization by balancing underfitting and overfitting. Even seasoned self-learners with advanced statistical backgrounds have gained insights from this tutorial style of intuitive explanations, with all related considerations for tuning and performance evaluations in one place.
ROX Index as a Predictor of Intubation Risk and 28-Day Mortality in COVID-19 Patients with Acute Respiratory Distress Syndrome

Background: The main concern when treating COVID-19 acute respiratory distress syndrome (ARDS) during high flow nasal cannula (HFNC) is delayed intubation, thus increasing the risk of prior mortality. This study aims to analyze the prognostic ability of the Respiratory Rate Oxygenation (ROX) index as a predictor for intubation and 28-days mortality in COVID-19 patients. Methods: A retrospective analysis of COVID-19 patients admitted to Intensive Care Unit (ICU) Dr. Soetomo General Hospital in Surabaya from July to December 2020 with ARDS. The ROX indices were recorded at the 1st hour, 2nd, 4th, 6th, 12th, 18th, 24th, and 48th hours of treatment in ICU with HFNC. Identification of ROX association with HFNC failure led to intubation and 28-days mortality was through Cox proportional hazards regression. The most specific cut-off of the ROX index for predicting intubation and 28-days mortality was assessed. Result: Among 78 patients who met the inclusion criteria, 26 (33.3%) patients required intubation. Out of 26 patients, six patients were extubated and had survived. The 28-days mortality rate is 20 (25.6%) patients. The ROX index at 12th hours (ROX-12) ≤ 4.85 (AUC 0.857; p<0.001; HR 4.7) and the ROX index at 48th hours (ROX-48) ≤ 5.68 (AUC 0.858; p<0.001; HR 5.4) can accurately predict the need for intubation. ROX-12 ≤ 4.745 is a predictor of 28-days mortality (AUC 0.85, p<0.001; HR 10.2). Conclusion: ROX index predicts the risk of intubation and 28-days mortality especially ROX-12 and ROX-48. Utilization of ROX index for rapid assessment of the respiratory deterioration in COVID-19 is recommended.
Learning Deeply Enriched Representations of Longitudinal Imaging-Genetic Data to Predict Alzheimer’s Disease Progression

Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive declines, therefore early diagnosis is imperative to prevent the progression of AD. To this end, many biomarker analysis models have been presented for early AD detection. However, these models may not realize the full data potential due to their failure to integrate longitudinal (dynamic) phenotypic data with (static) genetic data. Sometimes, they may not fully utilize both labeled and unlabeled samples either. To overcome these limitations, we propose a semi-supervised enrichment learning method to learn a fixed-length vectorial representation for each participant, by which the static data record can be integrated with the dynamic data records. We have applied our new method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and achieved 75% accuracy on multiclass AD progression prediction by one year in advance.
Remote Dyslexia Screening for Bilingual Children

Ideally, language and reading skills in bilingual children are assessed in both languages spoken in order to avoid misdiagnoses of communication or learning disorders. Due to limited capacity of clinical and educational staff, computerized screenings that allow for automatic evaluation of the children’s performance on reading tasks (accuracy and speed) might pose a useful alternative in clinical and school settings. In this study, a novel web-based screening platform for language and reading assessment is presented. This tool has been preliminarily validated with monolingual Italian, Mandarin–Italian and English–Italian speaking primary school children living and schooled in Italy. Their performances in the screening tasks in Italian and—if bilingual—in their native language were compared to the results of standardized/conventional reading assessment tests as well as parental and teacher questionnaires. Correlations revealed the tasks that best contributed to the identification of risk for the presence of reading disorders and showed the general feasibility and usefulness of the computerized screening. In a further step, both screening administrators (Examiners) and child participants (Examinees) were invited to participate in usability studies, which revealed general satisfaction and provided suggestions for further improvement of the screening platform. Based on these findings, the potential of the novel web-based screening platform is discussed.
TAFS: A Truthful Auction for IoT Application Offloading in Fog Computing Networks

Emerging as an alternative to cloud computing, fog computing is expected to provide low-latency, high-throughput, reliable services for ever-growing Internet of Things (IoT) applications, especially real-time applications with strict responsiveness requirements. By offloading time-critical and computation-intensive applications to proximal fog nodes (FNs), both application response time and network congestion can be markedly reduced. However, the FNs commonly suffer from limited resources compared to cloud computing nodes and, hence, may not serve all application users with guaranteed performance. The dynamic and heterogeneous nature of FNs also brings difficulty and overhead to fog computing resource management. These issues are addressed in the present study with the design of a double auction mechanism, namely, truthful auction for the fog system (TAFS), which provides incentives for FNs to satisfy as many application demands as possible with guaranteed performance. TAFS takes into account the latency tolerance of application users during the FN assignment and resource allocation to satisfy real-time requirements. We theoretically prove that TAFS satisfies several desired economic properties, including truthfulness, individual rationality, and budget balance. The performance of TAFS is evaluated through simulation experiments.
Pooling it all together – the role of distractor pool size on stimulus-response binding

ABSTRACT Distractors and responses are integrated in an event file when they occur together. Further, when all or some features repeat, the whole event file is retrieved, affecting later action as observed in so-called binding effects. Previous research used varying distractor pool sizes (ranging from just two to well over 30) to choose distractors from, but it is unclear whether distractor pool size has an effect on the size of distractor-based binding effects. The present study investigates, if and how distractor pool size modulates binding effects. Using an adapted prime-probe design, participants were assigned to large (384 distractors) or small (2 distractors) distractor pool sizes, and distractor-response binding effects were measured. Binding effects were stronger for the large distractor pool condition compared to the small pool condition. We discuss these findings against the background of the negative priming literature and research on novelty.
An improved ant colony algorithm for routing assignment in optical networks

Design an algorithm which is suitable for automatically switched optical network based on ant colony algorithm.In pointing out the basic ant colony algorithm is easy to fall into local optimum prematurely,algorithm prone to stagnation and a series of problems at the same time,and targeted improvement strategies-the state transitions select,dynamic pheromone updating.Simulation results show that the algorithm proposed in this paper can effectively reduce the average blocking probability of the whole network,efficient distribution of various network resources,to promote uniform load of the whole network.
DESIGN OF PRIVATE CLOUD STORAGE  USING SECURITY METHODS IDS AND IPS

Cloud computing is a form of technological progress that has developed along with the times, this has spurred the increasing use of the internet. By usingtechnology internet that is able to implement server a virtual, which has the aim of building a cloud computing server at the District Communications and Information Office. Padang Pariaman uses the Operating System (OS) Proxmox VE (Virtual Environment) 6.4. Cloud computing is able to provide storage services that can be used simultaneously. The results of this study produce a cloud computing server that implements a security system with themethods ids (intrusion detection system) and ips (intrusion prevention system)that are able to process data(storagestorage), use software simultaneously in the network, and use infrastructure within the scope of this research.network cloud computing at the District Communications and Information Office. Padang Pariaman using aservice model private cloud
New Light on an Old Story: Breaking Kasha’s Rule in Phosphorescence Mechanism of Organic Boron Compounds and Molecule Design

In this work, the phosphorescence mechanism of (E)-3-(((4-nitrophenyl)imino)methyl)-2H-thiochroman-4-olate-BF2 compound (S-BF2) is investigated theoretically. The phosphorescence of S-BF2 has been reassigned to the second triplet state (T2) by the density matrix renormalization group (DMRG) method combined with the multi-configurational pair density functional theory (MCPDFT) to approach the limit of theoretical accuracy. The calculated radiative and non-radiative rate constants support the breakdown of Kasha’s rule further. Our conclusion contradicts previous reports that phosphorescence comes from the first triplet state (T1). Based on the revised phosphorescence mechanism, we have purposefully designed some novel compounds in theory to enhance the phosphorescence efficiency from T2 by replacing substitute groups in S-BF2. Overall, both S-BF2 and newly designed high-efficiency molecules exhibit anti-Kasha T2 phosphorescence instead of the conventional T1 emission. This work provides a useful guidance for future design of high-efficiency green-emitting phosphors.
Is D2 laparoscopic gastrectomy essential for elderly patients with advanced gastric cancer? A propensity score matched analysis.

Background The necessity of the standard D2 gastrectomy for elderly patients with advanced gastric cancer (GC) is controversial because only limited data are available to demonstrate its oncological benefit for them. Our aim was to compare the outcomes of D2 and Non-D2 and to evaluate the survival benefit of D2 laparoscopic gastrectomy (LG) in elderly patients. Methods We retrospectively identified 865 patients with GC who underwent radical LG at our hospital between 2011 and 2017. Patients aged ≥75 years who were diagnosed with clinical T1N+ or clinical T2-4 were eligible. The primary outcome was the 3-year overall survival (OS) rate. The confounding factors were minimized using propensity score matching. Results This study included 119 patients (63 D2 LG and 56 Non-D2 LG), and 52 patients (26 each for D2 LG and Non-D2 LG) were analyzed after matching. Although no significant difference was found in overall major complications (P=1.00), complications tended to occur in the D2 group (D2 vs. Non-D2 =3.9% vs. 0%). No differences in the 3-year OS were noted between the two groups (68.8% vs. 68.8%; HR 1.53, 95% CI: 0.56-3.19). Conclusions This study demonstrated the possible association between D2 LG and increased complication rate and no survival benefit of D2 LG in elderly patients.
Integral Sliding Mode Control for Electrohydraulic Servo System

A design method of integral sliding mode control is proposed for electrohydraulic servo system which easily affected by parameter variations and external disturbances.The sliding mode hyperplane was designed by using the method of pole assignment,and dynamic-state quality of the control system was guaranteed.The simulation results show that the integral sliding mode control has good dynamic-state performance and high robustness under parameter variations and external disturbances appeared within the electrohydraulic servo system.
Antennas Performance Comparison of Multi-Bands for Optimal Outdoor and Indoor Environments Wireless Coverage

Karrar Shakir Muttair, Ali Z. Ghazi Zahid, Oras A. Shareef Al-Ani, Ahmed Mohammed Q. ALAsadi, Mahmood F. Mosleh Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq. Department of Computer Engineering Techniques, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq. Al-Najaf Al-Ashraf International Airport, Najaf, Iraq.
Computational prediction of hydrodynamic coefficients for heave motion

In this study, the hydrodynamic coefficients associated with heave motion are obtained by using unsteady Reynolds-averaged Navier-Stokes (URANS) approach. The well-known Wigley hull is selected for the calculations of uncoupled added mass and damping coefficients (A33, B33) in deep water. Numerical simulations are performed for six different oscillation frequencies at the Froude number 0.3. First, the 3D ship model is forced in the heave direction with certain frequencies and then the hydrodynamic coefficients are computed with the help of Fourier series expansion. Numerical results are compared with those obtained by the experiments and strip theory. The verification and validation study for the damping term is also performed by implementing the Grid Convergence Index (GCI) method.
Characterization of tandem aerosol classifiers for selecting particles: implication for eliminating multiple charging effect

Abstract. Accurate particle classification plays a vital role in aerosol studies. Differential mobility analyzer (DMA), centrifugal particle mass analyzer (CPMA) and aerodynamic aerosol classifier (AAC) are commonly used to select particles with a specific size or mass. However, multiple charging effect cannot be entirely avoided either using individual technique or using tandem system such as DMA-CPMA, especially when selecting soot particles with fractal structures. In this study, we demonstrate the transfer functions of DMA-CPMA and DMA-AAC systems, as well as the potential multiple charging effect. Our results show that the ability to remove multiply charged particles mainly depends on particles morphology and instruments setups of DMA-CPMA system. Using measurements from soot experiments and literature data, a general trend in the appearance of multiple charging effect with decreasing size when selecting aspherical particles was observed. Otherwise, our results indicated that the ability of DMA-AAC to resolve particles with multiple charges is mainly related to the resolutions of classifiers. In most cases, DMA-AAC can eliminate multiple charging effect regardless of the particle morphology, while particles with multiple charges can be selected when decreasing resolutions of DMA and AAC. We propose that the multiple charging effect should be reconsidered when using DMA-CPMA or DMA-AAC system in estimating size and mass resolved optical properties in the field and lab experiments.
The Existence of Solutions for Local Dirichlet (r(u),s(u))-Problems

In this paper, we consider local Dirichlet problems driven by the (r(u),s(u))-Laplacian operator in the principal part. We prove the existence of nontrivial weak solutions in the case where the variable exponents r,s are real continuous functions and we have dependence on the solution u. The main contributions of this article are obtained in respect of: (i) Carathéodory nonlinearity satisfying standard regularity and polynomial growth assumptions, where in this case, we use geometrical and compactness conditions to establish the existence of the solution to a regularized problem via variational methods and the critical point theory; and (ii) Sobolev nonlinearity, somehow related to the space structure. In this case, we use a priori estimates and asymptotic analysis of regularized auxiliary problems to establish the existence and uniqueness theorems via a fixed-point argument.
ANALISIS CART (CLASSIFICATION AND REGRESSION TREES) UNTUK PREDIKSI PENGGUNA SEPEDA BERDASARKAN CUACA

This study presents a rule-based classification model for user predictions based on weather. Cyclists are very popular because of the increased comfort and environment. The data used is public data from the Bike Sharing Dataset taken from Kaggle. The data has data on bicycle users every hour. With this data set, the authors managed to find the accuracy of the CART method which explains the accuracy of 96%. The results of this study show the estimated distribution under various bicycles with spatial variables, distribution of use and time including the most influential variables in the predictions of bicycle users
Function approximation concepts for multidisciplinary design optimization

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Clinical Features and Neurodevelopmental Outcomes for Infants with Perinatal Vertical Transmission of Zika Virus, Colombia

Transplacental transmission of Zika virus has been reported during all trimesters of pregnancy and might lead to central nervous system anomalies, including microcephaly. We report 3 cases of perinatal Zika infection identified during the epidemic in Colombia and provide detailed descriptions of clinical features, diagnosis, and neurodevelopmental outcome at 18 months of age (corrected).
Explaining the Variability in High‐Frequency Nitrate Export Patterns Using Long‐Term Hydrological Event Classification

Runoff events play an important role in nitrate export from catchments, but the variability of export patterns between events and catchments is high and the dominant drivers remain difficult to disentangle. Here, we rigorously asses if detailed knowledge on runoff event characteristics can help to explain this variability. To this end, we conducted a long‐term (1955–2018) event classification using hydro‐meteorological data, including rainfall characteristics, soil moisture and snowmelt, in six neighboring mesoscale catchments with contrasting land use. We related these event characteristics to nitrate export patterns from high‐frequency nitrate concentration monitoring (2013–2017) using concentration‐discharge (CQ) relationships. Our results show that low‐magnitude rainfall‐induced events with dry antecedent conditions exported lowest nitrate concentrations and loads but exhibited highly variable CQ relationships. We explain this by a low fraction of active flow paths, revealing the spatial heterogeneity of nitrate sources within the catchments and by an increased impact of biogeochemical retention processes. In contrast, high‐magnitude rainfall or snowmelt‐induced events exported highest nitrate concentrations and loads and converged to similar chemostatic export patterns across all catchments, without exhibiting source limitation. We explain these homogeneous export patterns by high catchment wetness that activated a high number of flow paths and by higher nitrate availability during high‐flow seasons. Long‐term hydro‐meteorological data indicated an increased number of events with dry antecedent conditions in summer and a decreased number of snow‐influenced events. These trends will likely continue and cause increased nitrate concentration variability during low‐flow seasons and changes in the timing of nitrate export peaks during high‐flow seasons.