Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative risk predictors of temporomandibular joint osteoarthritis progression

In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.
Multistep finite difference schemes for the variable coefficient delay parabolic equations

In this paper, we study the high efficient numerical methods to solve a class of variable coefficient delay parabolic differential equations. We propose two types of multistep finite difference schemes while prove the solvability, convergence and stability of both schemes. The convergence orders are and , respectively, in the sense of -norm. By some new analytical transformations, we extend our schemes to solve a more general variable coefficient delay convection–diffusion–reaction equations and also apply them to the higher dimensional cases. Furthermore, multistep finite difference method and compact multistep finite difference method for two dimensional variable coefficient diffusion–reaction equations are proposed and the suitable alternate direction implicit (ADI) technique is constructed for the multistep finite difference scheme. At last, several numerical experiments are carried out to illustrate the effectiveness of both schemes.
Electromechanical Analysis for the Integration of a Nb3Sn and a Bi-2212 CCT Dipole Magnet for a Hybrid Magnet Test

Within the U.S. Magnet Development Program, LBNL is planning to fabricate and test hybrid magnets combining Nb3Sn and Bi-2212 canted-cosine theta (CCT) magnets. The first planned hybrid magnet test integrates a 1 m long, 90 mm bore Nb3Sn dipole magnet (CCT5), and a 39 cm long, 30.8 mm bore Bi-2212 dipole magnet (BIN5c), which are already fabricated and were tested individually. The second planned hybrid magnet test integrates a 1.5 m long, 120 mm bore Nb3Sn dipole magnet (CCT6), still under design, and a 1 m long, 40 mm bore Bi-2212 dipole magnet (BiCCT1), which is under fabrication. This work gives an update of the status of the Bi-2212 CCT magnet program, and focuses on the first hybrid magnet (CCT5/BIN5c). The electromagnetic and mechanical analysis of the CCT5/BIN5c hybrid magnet is presented, as well as the proposed mechanical assembly process. The quench protection strategy associated with the hybrid magnet test is also presented.
Enhancement Mechanism of Pt/Pd-Based Catalysts for Oxygen Reduction Reaction

The oxygen reduction reaction (ORR) is one of the key catalytic reactions for hydrogen fuel cells, biofuel cells and metal–air cells. However, due to the complex four-electron catalytic process, the kinetics of the oxygen reduction reaction are sluggish. Platinum group metal (PGM) catalysts represented by platinum and palladium are considered to be the most active ORR catalysts. However, the price and reserves of Pt/Pd are major concerns and issues for their commercial application. Improving the catalytic performance of PGM catalysts can effectively reduce their loading and material cost in a catalytic system, and they will be more economical and practical. In this review, we introduce the kinetics and mechanisms of Pt/Pd-based catalysts for the ORR, summarize the main factors affecting the catalytic performance of PGMs, and discuss the recent progress of Pt/Pd-based catalysts. In addition, the remaining challenges and future prospects in the design and improvement of Pt/Pd-based catalysts of the ORR are also discussed.
A Soil Moisture Retrieval Method for Reducing Topographic Effect: A Case Study on the Qinghai–Tibetan Plateau With SMOS Data

The topography can be very important for passive microwave remote sensing of soil moisture due to its complex influence on the emitted brightness temperature observed by a satellite microwave radiometer. In this study, a methodology of using the first brightness Stokes parameter (i.e., the sum of vertical and horizontal polarization brightness temperature) observed by the soil moisture and ocean salinity (SMOS) was proposed to improve the soil moisture retrieval under complex topographic conditions. The applicability of the proposed method is validated using in-situ soil moisture measurements collected at four networks (Pali, Naqu, Maqu, and Wudaoliang) on the Qinghai–Tibetan Plateau. The results over Pali, which is a typical mountainous area, showed that soil moisture retrievals using the first brightness Stokes parameter are in better agreement with the in-situ measurements (the correlation coefficient R > 0.75 and unbiased root mean square error <0.04 m3/m3) compared with that using the single-polarization brightness temperature. At the other three networks with relatively flatter terrains, soil moisture retrievals using the first brightness Stokes parameter are found to be comparable to the single-polarization retrievals. On the contrary, the maximum bias of the retrieved soil moisture caused by topographic effects exceeds 0.1 m3/m3 when using vertical or horizontal polarization alone, which is far beyond the expected accuracy (0.04 m3/m3) of SMOS satellite. In the regions on the Qinghai–Tibetan Plateau where the vegetation effect can be ignored, soil moisture retrieved using horizontal polarization brightness temperature is generally underestimated, overestimated when using vertical polarization brightness temperature. It is reasonable due to the polarization rotation effect (depolarization) caused by the topographic effects. It is concluded that the proposed method for soil moisture retrieval using the first brightness Stokes parameter has a great potential in reducing the influence of topographic effects.
Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition

Simple Summary In addition to the historical indicator of mammary gland health, milk somatic cell count (SCC), the differential SCC (DSCC) has been introduced to improve the accuracy of mastitis detection. No studies have yet explored DSCC variability in local breeds used for milk production such as Burlina and Alpine Grey. Although local cattle breeds show greater rusticity and resistance to disease compared to cosmopolitan specialized dairy breeds, udder health status needs to be monitored for reasons related to profitability, management improvement, and animal welfare. In the present study, we aimed at investigating the factors affecting SCC and DSCC in Italian local breeds. Finally, by combining both SCC and DSCC, we attempted to estimate the effect of the udder health status on milk yield and composition traits. Abstract Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.
Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.
MM-RNN: A Multimodal RNN for Precipitation Nowcasting

Precipitation nowcasting, the high-resolution forecasting of precipitation in a short term, is essential in various applications in the real world. Previous deep learning methods use huge samples to learn potential laws, and the learning process lacks regularity, making it difficult to model the complex nonlinear precipitation phenomenon. Inspired by traditional numerical weather prediction models, we propose the multimodal recurrent neural network (MM-RNN), which introduces knowledge of elements to guide precipitation prediction. This constraint forces the movement of precipitation to follow the underlying atmospheric motion laws. MM-RNN not only can provide accurate precipitation nowcasting but other meteorological elements’ predictions. Besides, it has high flexibility and is compatible with multiple RNN models, such as ConvLSTM, PredRNN, MIM, and MotionRNN. We conduct experiments on two multimodal datasets (MeteoNet and RAIN-F), and the results indicate that MM-RNN is superior to common RNN [multiscale RNN (MS-RNN)] using a single radar modality. For the MeteoNet, compared to MS-MotionRNN, the critical success index (CSI) ( $R\geqslant 10$ ) of MM-MotionRNN increases by 23.4%, and the mean square error (mse) of MM-MotionRNN decreases by 6.7%. For the RAIN-F, compared to MS-MIM, the HSS ( $R\geqslant 5$ ) of MM-MIM increases by 209.4%, and the balanced mse (B-MSE) of MM-MIM decreases by 4.6%.
Development of optimized operators based on spherical-harmonic expansions for 3D FDTD schemes

The subject of this paper pertains to the construction of finite-difference expressions with minimized, as well as controllable discretization errors, suitable for 3D FDTD simulations in large-scale setups. The proposed spatial approximations are designed to mimic the behavior of the exact operators, when applied to plane-wave trial functions. To eliminate undesirable directional dependencies, the expansion of proper error formulae in terms of spherical harmonic functions is performed, which facilitates accuracy improvement and produces optimized operator coefficients in closed-form expressions. The combination of the new operators with low- as well as high-order time integrators yields efficient space-time discrete models, whose reliability renders them desirable substitutes for other traditional solutions.
A New NOMA-Based Two-Way Relaying Scheme

Two-Way Relaying (TWR) has been widely investigated in the literature as an efficient relaying scheme for dual-hop cooperative transmissions. In TWR, two nodes exchange data through an intermediate relay, where both nodes simultaneously send signals towards the relay in the first time slot. The relay, in turn, combines both received signals into one signal and sends it back to both nodes on the second time slot. In this paper, the spectral efficiency is further enhanced in TWR scheme by adopting Non-Orthogonal Multiple Access (NOMA) technique. Specifically, the transmitted signals from the nodes in the first time slot are composed based on NOMA transmission and intended to multiple intermediate relay-nodes. Consequently, each relay will extract its own symbols, combine them and send them in the second time slot. The improvement of the spectral efficiency in our system, as compared to the Conventional TWR, is linearly proportional to the number of relay-nodes that can be installed. The performance of the proposed scheme is mathematically analyzed in terms of the spectral efficiency and error performance, shown significant gain when compared to the conventional TWR scheme.
Ultragreen Relay Transmission With Wireless Power Transfer for Advanced IoT: Session-Specific Analysis and Optimization

Reliable and energy-efficient wireless transmission is of critical importance to the success of future advanced Internet of Things (IoT). Due to the sporadic nature of IoT transmissions, the energy consumption of the individual IoT transmission session varies dramatically with the instantaneous operating environment as well as the Quality of Service (QoS) requirements. In this article, we analyze and design the energy-efficient relay transmission systems from an individual data transmission session perspective. Specifically, we consider a dual-hop transmission system with a decode-and-forward relay that is solely powered by wireless power transfer from source node. For both time switching and power splitting modes of simultaneous power and information transmission, we analyze and minimize the total energy consumption of the system when transmitting a fixed amount of data, under a piecewise linear energy harvesting (EH) model. Closed-form expressions for optimal transmission parameters are obtained with and without the consideration of latency constraint. Through selected numerical results, we illustrate various design tradeoffs between energy consumption and latency constraint. We show that with optimal transmission parameters, relay transmission with energy transfer can achieve considerable energy saving compared to the direct transmission when the direct link quality is poor and the latency constraint is not stringent. We also show that with optimized parameters, power splitting mode leads to lower energy consumption and smaller transmission duration than time switching mode, at the cost of higher implementation complexity.
Control of Impact Angle Using Biased Proportional Navigation

Two proportional navigation guidance laws based on the rotation of the relative velocity vector are presented. It is shown that the impact angle against a nonstationary target can be controlled through bias addition to guidance commands. The amount of bias to be applied is determined with the help of the expected collision triangle. The method may be implemented using either a discontinous or a continous biasing scheme, where the former has a twophased structure that does not need time-to-go whereas the latter, which leads to optimal control in a linear setting, utilizes time-to-go to provide robustness against disturbances including target maneuver. In addition to the line-of-sight rate, the knowledge of the target velocity vector is required, which is assumed to be readily available.
A comparative QoS survey of mobile ad hoc network routing protocols

Abstract A mobile ad hoc network (MANET) is a wireless, dynamic, infrastructure-less, self-organized, multi-hop, and decentralized network. Each node in MANET can act as a router as well as a work station. Many routing protocols have been developed to increase the efficiency of MANET. The primary objective of this paper is a detailed QoS comparison of reactive (AODV), proactive (DSDV), and hybrid (ZRP) routing protocols of MANET in order to find which routing protocol works best in a particular network scenario. The analysis was made for TCP-based traffic patterns. The performance differentials were analyzed on the basis of normalized routing overhead, packet loss, packet delivery ratio, control packets, end-to-end delay, packet received, and packet sent with a variation of nodes density and mobility. The results were obtained using the NS-2 simulator.
Sentiment classification from reviews for tourism analytics

User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain.
The unit group of some special semi-simple group algebras

Abstract Let G be a non-abelian group of order 2n which has a cyclic subgroup of index 2 and let U ( [G]) be the unit group of the group algebra [G] of G over the field containing q = pkelements. In this paper, recurrence relations are established to determine the structure of the unit group of [G] when p > 2.
Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

ABSTRACT The surging accumulation of trajectory data has yielded invaluable insights into urban systems, but it has also presented challenges for data storage and management systems. In response, specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches. However, these systems often utilize storage by point or storage by trajectory methods, both of which have drawbacks. In this study, we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries. We develop a prototype system that includes trajectory segmentation, serialization, and spatio-temporal indexing and apply it to taxi trajectory data in Beijing. Our findings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.
β Cell and Autophagy: What Do We Know?

Pancreatic β cells are central to glycemic regulation through insulin production. Studies show autophagy as an essential process in β cell function and fate. Autophagy is a catabolic cellular process that regulates cell homeostasis by recycling surplus or damaged cell components. Impaired autophagy results in β cell loss of function and apoptosis and, as a result, diabetes initiation and progress. It has been shown that in response to endoplasmic reticulum stress, inflammation, and high metabolic demands, autophagy affects β cell function, insulin synthesis, and secretion. This review highlights recent evidence regarding how autophagy can affect β cells’ fate in the pathogenesis of diabetes. Furthermore, we discuss the role of important intrinsic and extrinsic autophagy modulators, which can lead to β cell failure.
A Pipelining-Based Heterogeneous Scheduling and Energy-Throughput Optimization Scheme for CNNs Leveraging Apache TVM

Extracting information of interest from continuous video streams is a strongly demanded computer vision task. For the realization of this task at the edge using the current de-facto standard approach, i.e., deep learning, it is critical to optimize key performance metrics such as throughput and energy consumption according to prescribed application requirements. This allows achieving timely decision-making while extending the battery lifetime as much as possible. In this context, we propose a method to boost neural-network performance based on a co-execution strategy that exploits hardware heterogeneity on edge platforms. The enabling tool is Apache TVM, a highly efficient machine-learning compiler compatible with a diversity of hardware back-ends. The proposed approach solves the problem of network partitioning and distributes the workloads to make concurrent use of all the processors available on the board following a pipeline scheme. We conducted experiments on various popular CNNs compiled with TVM on the Jetson TX2 platform. The experimental results based on measurements show a significant improvement in throughput with respect to a single-processor execution, ranging from 14% to 150% over all tested networks. Power-efficient configurations were also identified, accomplishing energy reductions above 10%.