Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Precoding and Combining for Quantized Full-Duplex MU-MIMO Systems

We consider a full-duplex (FD) multi-user multiple-input multiple-output (MU-MIMO) system with low-resolution quantizers at an access point (AP). In the considered FD system, there are main bottlenecks: self-interference (SI), co-channel interference (CCI), and quantization errors. In this paper, we propose a novel precoding and combining method to maximize the sum spectral efficiency (SE) by incorporating the effect of the quantization errors as well as the SI and CCI. Since the beamformers are intertwined with the quantization errors, SI, and CCI, it is highly challenging to solve the sum SE maximization problem. To address the challenges, we convert the problem into the Rayleigh quotient form. Then, we derive the first-order optimality condition with interpreting it as a generalized eigenvalue problem by leveraging the principle of the Rayleigh quotient problem. Accordingly, we adopt a power iteration method for identifying the leading eigenvector: the best local optimal precoding solution. Consequently, we propose an alternating algorithm to jointly optimize the precoder and combiner. Simulations validate the proposed algorithm.
Importance of Silicon in the Growth of Jatropha curcas L. Plants Irrigated With Salina Water

The use of silicon in plant cultivation is one of the strategies to mitigate the negative effects of salinity. This study aimed to evaluate the effect of silicate fertilization on the morphophysiological, biochemical and nutritional characteristics of Jatropha curcas L. plants under saline stress. The work was carried out in a greenhouse at the State University of Goiás. The experiment was set up in a completely randomized design in a 5 × 2 factorial arrangement (plants irrigated with salt water with sodium chloride (NaCl) and electrical conductivities equal to 0 dS m-1; 2 dS m-1; 4 dS m-1; 6 dS m-1 and 8 dS m-1 applied at 80 days after emergence (DAE) and absence or presence of silica fertilization of 1 g L-1 with Si applied at 80 and 95 DAE by volume of 30 mL of the solution with the aid of a spray manual), five replicates and experimental plot of one plant per pot. The evaluations were carried out at 130 DAEs. The absence of differences in the concentrations of photosynthetic pigments and visible damages is indicative of the absence of severe toxic effects caused by salinity. The tolerance of Jatropha curcas L. plants to salinity is independent of silicon. The Jatropha curcas L. plant tolerates salinity by minimizing transpiration and remaining hydrated through the water stored in the succulent stem. In addition, the plants control sodium uptake and eliminate toxic compounds through increases in Calcium concentration and antioxidative metabolism respectively.
Prevalence and Risk Factors of Hyperuricemia in the Urban Health Checkup Population in Xinjiang, China: A Cross-Sectional Study

Purpose In this study, the aim was to investigate the prevalence and risk factors of hyperuricemia (HUA) in the urban health checkup population in Urumqi, Xinjiang, and thus provide clues for the prevention of HUA. Methods People who attended medical examinations from May 2021 to June 2022 at a hospital in Urumqi, Xinjiang, were selected for evaluation based on their general information, physical examination results, and laboratory test results. The chi-square test was used to determine whether there was a difference in the prevalence of HUA among participants with different characteristics. Using logistic regression analyses, risk factors for HUA were identified. Results There were 8722 participants diagnosed with HUA, with an overall prevalence of 26.96%. The prevalence in men was 37.72%, significantly higher than in women (13.29%). Participants were characterized by a multiethnic composition, with Han (28.61%), Hui (27.88%) and Manchu (38.46%) being the three ethnicities with the highest prevalence. According to logistic regression analyses, HUA was associated with age, ethnicity, residence, marital status, body mass index (BMI), diastolic blood pressure (DBP), fasting blood glucose (FPG), triglyceride glucose (TyG) index, abdominal obesity, and dyslipidemia differently in males and females. Conclusion The prevalence of HUA was high in the urban health checkup population in Urumqi, Xinjiang, particularly among men and youth. The early intervention for HUA should be enhanced to reduce the risk of cardiovascular disease and other related conditions.
Biofertilizer science and practice for agriculture and forestry: A review

Feeding the world with good agricultural and forestry practices is becoming a challenge and researchers are working on different approaches to find the most ecofriendly solution. Chemical fertilizers are increasingly being used, despite the fact that their intensive use has proven to have a harmful effect on soil, water, the environment, and, ultimately, plants and humans’ health. Biofertilizer has been considered a better option than other conventional fertilizers since it contains a typical population of helpful microorganisms that provide nutrients to the soil required by the plant without destroying the precious environment. However, in terms of understanding their proper mechanisms and importance for sustainable farming, efficient knowledge of microorganism-based biofertilizers remains in the shadows. This study aims to explore the different challenges faced regarding microorganism-based biofertilizer use in agriculture and forestry. It will support the overcoming of identified challenges and increase farmers’ acceptance as the main option to provide the required nutrition for the soil to obtain better quality and a higher quantity of agricultural products. Overall, priority should be given to developing advanced technology and proper storage mechanisms, distribution mechanisms, and subsidies in order to gain popularity and reliability. This article is timely because biofertilizers are currently undergoing manufacture and application; now is the time to identify challenges and solutions for research and development.
Effects of Neural Network Architecture on Topography Estimation From Satellite Imagery for Multi-Terrain Autonomous Vehicle Path Planning and Control

Global warming is one of the world’s most pressing issues. The study of its effects on the polar ice caps and other arctic environments, however, can be hindered by the often dangerous and difficult to navigate terrain found there. Multi-terrain autonomous vehicles can assist researchers by providing a mobile platform on which to collect data in these harsh environments while avoiding any risk to human life and speeding up the research process. The mechanical design and ultimate efficacy of these autonomous robotic vehicles depends largely on the specific missions they are deployed for, but terrain conditions can vary wildly geographically as well as seasonally, making mission planning for these unmanned vehicles more difficult. This paper proposes the use of various UNet-based neural network architectures to generate digital elevation maps from satellite images, and explores and compares their efficacy on a single set of training and validation datasets generated from satellite imagery. These digital elevation maps generated by the model could be used by researchers not only to track the change in arctic topography over time, but to quickly provide autonomous exploratory research rovers with the topographical information necessary to decide on optimal paths during the mission. This paper analyzes different model architectures and training schemes: a traditional UNet, a traditional UNet with data augmentation, a UNet with a single active skip-layer vision transformer (ViT), and a UNet with multiple active skip-layer ViT. Each model was trained on a dataset of satellite images and corresponding digital elevation maps of Ellesmere Island, Canada. Utilizing ViTs did not demonstrate a significant improvement in UNet performance, though this could change with longer training. This paper proposes opportunities to improve performance for these neural networks, as well as next steps for further research, including improving the diversity of images in the dataset, generating a testing dataset from a completely different geographic location, and allowing the models more time to train.
Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine

Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff.
Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients’ quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan–Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.
Recent Observing System Simulation Experiments at the NASA DAO

Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order t o evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S (Observing System Experiments) and OSSE's (Observing System Simulation Experiments). The OSE's were conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. Such experiments have been performed for selected types of conventional data and for various satellite data sets as they became avail- able. (See for example the 1989 ECMWF/EUMETSAT workshop proceedings on "The use of satellite data in operational numerical weather prediction" and the references contained therein.) The OSSE's were con- ducted to evaluate the potential for future observing systems to improve Numerical Weather Prediction (NWP) and to plan for the Global Weather Experiment and more recently for EOS. In addition, OSSE's have been run t o evaluate trade-offs in the design of observing systems and observing net-works, and to test new methodology for data assimilation.
Relationship between objective and subjective indicators in urban soundscape analysis. The case of Cordoba - Argentina

This paper presents the main results of a comparative evaluation of some acoustical parameters with the user´s perception of urban sounds. The study was carried out in three open spaces integrated with different environmental characteristics but similar objective conditions of urban noise. The subjective evaluation was done by means of a survey simultaneously with the objective measurements. The results of the crossed analysis confirmed that in environments with similar noise levels not always exists direct correlation between the objetive indicators and the acoustic comfort of the people. To predict the acustical quality of the soundscape it is necessary to consider aspects such as the background noise and the perception of natural or technological sounds as complements of the general sound level.
Extreme precipitation regimes in northern eurasia in the 20th century and their possible changes in the 21st century

This communication is devoted to the analysis and potential revision of variations in precipitation characteristics—its quantity, intensity, and the probability of days with precipitation and their extreme regimes—in some regions of northern Eurasia based on daily meteorological observations during the 20th century. The ability of climatic models to simulate these regimes and their variations is demonstrated by way of an assessment of probable changes in Siberian river basins, the Volga River basin, the Caspian Sea region, and the Caucasus in the 21st century.
On the Complexity of Minimizing Median Normal Forms of Monotone Boolean Functions and Lattice Polynomials

In this document, we consider a median-based calculus to represent monotone Boolean functions efficiently. We study an equa-tional specification of median forms and extend it from the domain of monotone Boolean functions to the domain of polynomial functions over distributive lattices. This specification is sound and complete. We illustrate its usefulness when simplifying median formulas algebraically. Furthermore, we propose a definition of median normal forms (MNF), that are thought of as minimal median formulas with respect to a structural ordering of expressions. We investigate related complexity issues and show that the problem of deciding whether a formula is in MNF, that is the problem of minimizing the median form of a monotone Boolean function, is in Σ P 2. Moreover, we show that it still holds for arbitrary Boolean functions, not necessarily monotone.
Real-World Implementation and Integration of an Automatic Scoring System for Workplace Safety Courses in Italian

Artificial Intelligence and Natural Language Processing techniques can have a very significant impact on the e-learning sector, with the introduction of chatbots, automatic correctors, or scoring systems. However, integrating such technologies into the business environment in an effective way is not a trivial operation, and it not only requires realising a model with good predictive performance, but also it requires the following: (i) a proper study of the task, (ii) a data collection process, (iii) a real-world evaluation of its utility. Moreover, it is also very important to build an entire IT infrastructure that connects the AI system with the company database, with the human employees, the users, etc. In this work, we present a real-world system, based on the state-of-the-art BERT model, which implements an automatic scoring system for open-ended questions written in Italian. More specifically, these questions pertain to the workplace safety courses which every worker must attend by law, often via e-learning platforms such as the one offered by Mega Italia Media. This article describes how our system has been designed, evaluated, and finally deployed for commercial use with complete integration with the other services provided by the company.
Expression Levels of the Tnni3k Gene in the Heart Are Highly Associated with Cardiac and Glucose Metabolism-Related Phenotypes and Functional Pathways

Background: Troponin-I interacting kinase encoded by the TNNI3K gene is expressed in nuclei and Z-discs of cardiomyocytes. Mutations in TNNI3K were identified in patients with cardiac conduction diseases, arrhythmias, and cardiomyopathy. Methods: We performed cardiac gene expression, whole genome sequencing (WGS), and cardiac function analysis in 40 strains of BXD recombinant inbred mice derived from C57BL/6J (B6) and DBA/2J (D2) strains. Expression quantitative trait loci (eQTLs) mapping and gene enrichment analysis was performed, followed by validation of candidate Tnni3k-regulatory genes. Results: WGS identified compound splicing and missense T659I Tnni3k variants in the D2 parent and some BXD strains (D allele) and these strains had significantly lower Tnni3k expression than those carrying wild-type Tnni3k (B allele). Expression levels of Tnni3k significantly correlated with multiple cardiac (heart rate, wall thickness, PR duration, and T amplitude) and metabolic (glucose levels and insulin resistance) phenotypes in BXDs. A significant cis-eQTL on chromosome 3 was identified for the regulation of Tnni3k expression. Furthermore, Tnni3k-correlated genes were primarily involved in cardiac and glucose metabolism-related functions and pathways. Genes Nodal, Gnas, Nfkb1, Bmpr2, Bmp7, Smad7, Acvr1b, Acvr2b, Chrd, Tgfb3, Irs1, and Ppp1cb were differentially expressed between the B and D alleles. Conclusions: Compound splicing and T659I Tnni3k variants reduce cardiac Tnni3k expression and Tnni3k levels are associated with cardiac and glucose metabolism-related phenotypes.
Disentangling the Contribution of Non-native Speech in Automated Pronunciation Assessment

This study explores the impact of using non-native speech data in acoustic model training for pronunciation assessment systems. The goal is to determine how introducing non-native data in acoustic model training can influence alignment accuracy and assessment performance. Acoustic models are trained using different combinations of native and non-native speech data, and the Goodness of Pronunciation (GOP) metric is used to evaluate performance. Results show that models trained with manually labeled non-native data yield the highest assessment performance and alignment accuracy. Models trained with mixed non-native and native data perform best when considering the GOP distribution on both non-native and native speech. Additionally, models trained with native data are more robust to alignment variations. These findings highlight the importance of carefully selecting and incorporating non-native data in acoustic model training for pronunciation assessment systems.
Permanent Magnet-Based Superficial Flow Velometer With Ultralow Output Drift

Superficial flow velocity perception is inspired by aquatic organisms and has become increasingly important for underwater robots and vehicles. Existing marine electromagnetic velometers require very large alternating current (ac) coils with high power consumption for excitation, limiting their mobility and underwater applications. Permanent magnet excitation is preferable for miniature flow sensors because of low power consumption, small size, and high sensitivity. However, permanent magnet excitation has not been applicable due to the high output drift caused by electrode polarization voltage. This article proposes a permanent magnet-based flow velocity meter that measures the velocity based on the electrode output current instead of the output voltage. The relationship between the output current and the fluid velocity, fluid conductivity, permanent magnet parameters, and electrode position during permanent magnet excitation is derived, and a finite element simulation is conducted. A signal processing circuit is designed to obtain the current output. The experimental results show that the output drift ratio of the proposed sensor is 92% lower than that of the sensor measuring the voltage output, and the measurement $R^{2}$ is 0.998 in the test range of 0–0.875 m/s.
Technoeconomic assessment comparison of batch and continuous hydrothermal carbonization of waste corn stover into advanced biorefinery feedstock

The objective of this study was to investigate the economic feasibility of the hydrothermal carbonization (HTC) of waste corn stover. Two different cases of HTC of waste corn stover were considered for a 544 320 kg day−1 as‐received corn stover plant. Case I assumed a batch HTC process, and case II assumed a continuous HTC process. The results show that both cases were economically feasible; however, case I reached the break‐even point at 3 years whereas case II reached the break‐even point at 4 years. Case I also showed greater return on investment (5.02) than case II (3.98) at the end of the 20 year project life, including 26.6% lower total capital investment. About 73% of fixed capital cost came from investment in the reactor alone for case I, whereas 63% came from the reactor for case II. The break‐even selling price of the product was found to be $86.1/kg. A sensitivity analysis showed that the break‐even selling price of the product was the most sensitive to the variation of the cost of corn stover and the least sensitive to variation in the tipping income.
Knowledge Distillation on Joint Task End-to-End Speech Translation

An End-to-End Speech Translation (E2E-ST) model takes input audio in one language and directly produces output text in another language. The model requires to learn both speech-to-text modality conversion and translation tasks, which demands a large architecture for effective learning of this joint task. Yet, to the best of our knowledge, we are the first to optimize compression of E2E-ST models. In this work, we explore knowledge distillation for a cross-modality joint-task E2E-ST system from 3 dimensions: 1) student architecture and weight initialization scheme, 2) importance of loss terms associated with different tasks and data modalities, 3) knowledge distillation training scheme customized for the multi-task/module model. Comparing with the full size model, our compressed model’s encoder and decoder size are 50% smaller, while it retains 90% and > 95% performance on speech translation task and machine translation task respectively on MUST-C en → de testset.