Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-channel queues in heavy traffic

In this paper, convergence theorems for heavy traffic queues are extended to multi-channel systems under general assumptions. Whitt (1968), and Iglehart and Whitt (1970) have proved weak convergence of the queue length process and the wait process for one-channel queues. Extensions to multi-server queues were established for the queue length process but only partly for the wait process (ρ = 1 only). We give here convergence theorems for the wait process when ρ > 1. Our approach uses weak convergence theory, but is different from previous ones in that we use the virtual delay as an intermediate result. The class of queues considered is more general than GI/G/m.
Comparison of Magnetic Resonance Imaging and High-Resolution Ultrasound for the Diagnosis of Pathologies of the Peripheral Nerve

The study was done to evaluate high-resolution ultrasound with Magnetic Resonance Imaging (MRI) for the diagnosis of peripheral nerve pathologies. Ultrasound examination and MRI were performed for selected cases, and their sensitivity and specificity were compared. Ultrasound was found to detect peripheral pathologies with greater accuracy as compared to MRI. Also, ultrasound was found to have higher sensitivity and specificity than MRI. It was concluded that ultrasound is a better adjunct tool for the diagnosis of peripheral nerve lesions when compared with MRI.
Downlink Covariance Estimation in URA FDD Massive MIMO Systems

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations.
Dry matter accumulation and macronutrient uptake in sugarcane varieties

Abstract The increase of the area cultivated with sugarcane has demanded information about the culture, mainly in relation to its nutritional needs. There are few studies that report the nutrient uptake in sugarcane, in addition, there are no reports of studies focused on the different periods of sugarcane growth and development. In view of the above, the objective of this research was to determine the macronutrient uptake and dry matter production in the shoots of two sugarcane varieties. The experiment was carried out in Vicentina, Mato Grosso do Sul, Brazil, during the 2017/2018 harvest, using two varieties of sugarcane (RB92579 and SP832847). The experimental design was in randomized blocks with two sugarcane varieties, nine collecting times (90, 120, 150, 180, 210, 240, 270, 300 and 330 days after planting (DAP) and 4 replications. Samples were collected from the shoots of 10 plants, which were divided into leaves, sheaths and stalks, then dried and weighed, and finally the samples were submitted to laboratory analysis to determine the nutrient concentrations in each organ. The accumulation of macronutrients in the shoots of both varieties followed the order: K > N > Ca > S>Mg > P. The export by the stalk followed the order K > N > Ca > S > P > Mg for RB92579 and K > N > Ca = P > S > Mg for SP832847. Maximum accumulation of nutrients and the maximum dry matter production occurred in the interval between 240 and 300 DAP. The variety SP832847 accumulated higher amounts of nutrients and dry matter in relation to RB92579, as well as the time required for the maximum accumulation was shorter.
Improving the efficiency of mode automation using synchrophasor measurements to identify stability disturbance

The paper presents new approaches and principles for identifying the conditions of stability disturbance based on detecting dangerous disturbances in the early stages using information about changes in regime parameters and their Rate of Change. As a mode parameter, the mutual voltage angle between the controlled 500 kV substation and its Rate of Change was selected in the study. It is suggested to take the values of the mentioned parameters from the Wide Area Measurements System (WAMS). The relevance of the research is due to the need to improve the efficiency and eliminate the drawbacks of existing revealing devices of regime automatics, which will reduce the number of accidents due to disturbances of the power system stability. The proposed principle of predicting stability violation is based on using the provision of Lyapunov’s stability theory, according to which the assessment of stability is carried out by the total system energy consisting of kinetic and potential. In contrast to the existing principles of detecting stability violation, where the exit from the stability area is determined by the main parameter (potential energy), the prediction principle allows evaluating stability by its rate of change (kinetic energy), which provides the early detection of stability disturbance. Calculations were performed on modeling power surges in the North-South interconnection of the Kazakhstan Unified Energy System in the «DigSILENT Power Factory» software on the model, which was verified by real perturbations in the power system according to the WAMS data. The calculations confirmed the effectiveness of the proposed principles and the possibility of using WAMS data for detecting emergency power surges on transit power networks in the initial stage
Introduction to Biostatistics

We welcome the reader who wishes to learn biostatistics. In this chapter we introduce you to the subject. We define statistics and biostatistics. Then examples are given where biostatistical techniques are useful. These examples show that biostatistics is an important tool in advancing our biological knowledge; biostatistics helps evaluate many life-and-death issues in medicine. We urge you to read the examples carefully. Ask yourself, “what can be inferred from the information presented? ” How would you design a study or experiment to investigate the problem at hand? What would you do with the data after they are collected? We want you to realize that biostatistics is a tool that can be used to benefit you and society. The chapter closes with a description of what you may accomplish through use of this book. To paraphrase Pythagoras, there is no royal road to biostatistics. You need to be involved. You need to work hard. You need to think. You need to analyze actual data. The end result will be a tool that has immediate practical uses. As you thoughtfully consider the material presented here, you will develop thought patterns that are useful in evaluating information in all areas of your life.
White Dwarf Remnants of Binary Star Evolution

Abstract White dwarfs grow as the cores of red giants and, in particular, carbon-oxygen white dwarfs grow in asymptotic giant branch (AGB) stars. The evolution of an AGB star is a competition between growth of the core and loss of the stellar envelope, typically in a wind. It is complicated by thermal pulses driven periodically by unstable helium shell burning. Dredge up following such pulses delays core growth. The compression at the center of a cold carbon-oxygen core means that carbon ignites when it reaches a mass of 1.38 M⊙. This begins the familiar thermonuclear runaway of the Type Ia supernova (SN Ia). At higher temperatures carbon can ignite more gently and burn mostly to neon to leave a core rich in oxygen, neon and magnesium. Such cores can go on to collapse to neutron stars with a release of only neutrinos. Accepted mass-loss prescriptions for giants mean that the range of masses of single stars that leave carbon-oxygen white dwarfs is somewhere from around 1 to 8 M⊙. We investigate how unusual mass loss, perhaps brought about by interaction with a binary companion, can radically alter the single star picture. Though population syntheses treat some possibilities with various prescriptions, there is sufficient doubt over the physics, the observations, and the implementation of mass loss and binary interaction that there is scope for several more unusual progenitors of carbon-oxygen white dwarfs and hence SNe Ia.
CAN2V: Can-Bus Data-Based Seq2seq Model for Vehicle Velocity Prediction

Vehicle velocity prediction is an important task in the automotive industry because it can improve a car’s fuel economy and reduce emissions. Velocity prediction task has been studied for many years, and recently deep learning-based techniques have received a lot of attention. To accurately predict vehicle velocity, it is essential to analyze vehicle characteristics, driving patterns, and road conditions. Previously reported methods have not been able to consider driving patterns, which is the most crucial factor in predicting velocity. In this paper, we propose a model named "CAN2V," which effectively analyzes the vehicle characteristics and driving patterns in the encoder through multi-task learning. This model is an interpretable model of what input variables were used for velocity prediction through a variable selection network. Experimental results on a real driving dataset show that our proposed method outperforms the previous methods on a mean absolute error (MAE) and root-mean-square error (RMSE).
Performance And Prediction Of Centrifugal Pumps With Steady And Unsteady CFD-methods

The CFD-code FLUENT, version 5.4, has been used for the flow analysis of a test pump of end-suction volute type. FLUENT provides three calculation methods for analysis of turbomachinery flows: the Multiple Reference Frame method (MRF), the Mixing Plane method (MP) and the Sliding Mesh method (SM). In all three methods, the flow in the rotor is calculated in a rotating reference frame, while the flow in the stator is calculated in an absolute reference frame. In the MRF and MP methods steady flow equations are solved, while in the SM method, unsteady flow equations are solved. The SM method does not introduce physical approximations. The steady methods approximate the unsteady interaction between rotor and stator. The cost of the unsteady method is however typically 30 to 50 times higher than the cost of the steady methods. It is found that the MRF and MP methods lead to completely erroneous flow field predictions for flows far away from the best efficiency point. This makes the steady methods useless for general performance prediction.
Gridless Target Localization for FDA-Mimo Radar with Sparse Arrays

Most studies of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar are based on uniform linear array (ULA), and thus the extended aperture characteristic of sparse arrays cannot be utilized. To solve this problem, a gridless angle and range estimation method for monostatic FDA-MIMO radar with sparse linear receiving array is presented. First, a monostatic FDA-MIMO radar model is established with angle and range decoupled, and then an optimization problem is proposed based on low-rank matrix reconstruction, and solved by alternating projections. Simulation results show that our method not only achieves high-resolution angle and range estimation performance, but also reduces computational complexity.
Influence of Competitive Priorities Strategy on Organizational Performance of Cement Manufacturing Firms in Kenya

Purpose: The study sought to assess the influence of competitive priorities strategy on organizational performance of cement manufacturing firms in Kenya. Methodology: The study adopted the positivism research philosophy and the concurrent triangulation research design. The target population consisted of eight cement production companies located in the counties of Kisumu, Nairobi, Mombasa, Machakos, Nakuru, and Kajiado were the subject of the study. The study purposely excluded Savannah Cement Limited from the main study since it had been “temporarily” shut down at the time of data collection due to financial and ownership wrangles. Census sampling method and proportional stratified random sampling were used to select 365 employees which included 37 managers, 322 non-managers and 6 CEOs. Both primary and secondary sources were used to gather data for this study, and both quantitative and qualitative data gathering techniques were employed. The qualitative data was analyzed using content analysis technique (Hsieh & Shannon, 2015), while the quantitative data was analyzed using MS Excel 2016 and IBM's SPSS version 28. Findings: the study results indicated that competitive priorities strategies is an operational strategy that is often utilized by the cement manufacturing firms to improve their organizational performance. This was supported by the high level of agreement in all statement by most of the respondents. The study also concluded that competitive priorities strategies had a positive and significant relationship with organizational performance of cement manufacturing firms in Kenya. The regression results led to the rejection of the null hypothesis, thus adopting the alternative hypothesis; competitive priorities strategy has a significant influence on organizational performance of cement manufacturing firms in Kenya. Unique Contribution to Theory, Practice and Policy: The theory used in this study advocated for the need of cement manufacturing firms in Kenya, to focus on identifying strategic resources that are critical for success and improved performance of the industry. Hence, the theory will also be applicable to other studies with such related research topics. The study recommended that the management of cement manufacturing firms in Kenya should invest more in automated systems especially in production and delivery so as to increase their production, operational and supply-chain efficiency. They should also invest in better training programs so as to equip their employees with the necessary skills needed to improve their work efficiency. Moreover, policymakers should also formulate better policies to enhance the competitiveness between manufacturing firms in Kenya.
Overall influence of clothing and pinnae on shoulder reflection and HRTF

The overall influence of clothing and pinnae on shoulder reflection and head-related transfer function(HRTF) is investigated using HRTFs obtained on KEMAR with torso and a human subject.Analyses reveal that shoulder reflection is irrelevant to clothing below 3.0 kHz,while without pinnae clothing has some influence on should reflection and HRTF above 5.0 kHz.However,at high frequencies pinnae can conceal the shoulder reflection so that the shoulder reflection below 3.0 kHz is available as a stable localization information.The pinnae can weaken,or even eliminate,the influence of clothing on the shoulder reflection and localization.This is another physiological function of pinnae.
Effects of Dietary Nano-Zinc Oxide Supplementation on Meat Quality, Antioxidant Capacity and Cecal Microbiota of Intrauterine Growth Retardation Finishing Pigs

As nano-zinc oxide (Nano-ZnO), a new type of nanomaterial, has antioxidant and intestinal protection effects, we hypothesized that dietary Nano-ZnO could modulate poor meat quality, oxidative stress and disturbed gut microbiota in the finishing pig model of naturally occurring intrauterine growth retardation (IUGR). A total of 6 normal-born weight (NBW) and 12 IUGR piglets were selected based on birth weight. The pigs in the NBW group received a basal diet, and IUGR pigs were randomly divided into two groups and treated with basal diet and 600 mg/kg Nano-ZnO-supplemented diet. Dietary Nano-ZnO ameliorated IUGR-associated declined meat quality by lowering the drip loss48h, cooking loss, shearing force and MyHc IIx mRNA expression, and raising the redness (a*), peak area ratio of immobilized water (P22), sarcomere length and MyHc Ia mRNA expression. Nano-ZnO activated the nuclear factor erythroid 2-related factor 2-glutamyl cysteine ligase (Nrf2-GCL) signaling pathway by promoting the nuclear translocation of Nrf2, increasing the GCL activities, and mRNA and protein expression of its catalytic/modify subunit (GCLC/GCLM), thereby attenuating the IUGR-associated muscle oxidative injury. Additionally, the composition of IUGR pigs’ cecal microbiota was altered by Nano-ZnO, as seen by changes in Shannon and Simpson indexes, the enhanced UCG-005, hoa5-07d05 gut group and Rikenellaceae RC9 gut group abundance. The UCG-005 and hoa5-07d05 gut group abundance were correlated with indicators that reflected the meat quality traits and antioxidant properties. In conclusion, Nano-ZnO improved the IUGR-impaired meat quality by altering water holding capacity, water distribution and the ultrastructure of muscle, activating the Nrf2-GCL signaling pathway to alleviate oxidative status and regulating the cecal microbial composition.
High-Order Interpolation Algorithms for Charge Conservation in Particle-in-Cell Simulations

High-order interpolation algorithms for charge conservation in Particle-in-Cell (PIC) simulations are presented. The methods are valid for the case that a particle trajectory is a zigzag line. The second-order and third-order algorithms which can be applied to any even-order and odd-order are discussed in this paper, respectively. Several test simulations are performed to demonstrate their validity in two-dimensional PIC code. Compared with the simulation results of one-order, high-order algorithms have advantages in computation precision and enlarging the grid scales which reduces the CPU time.
Selectivity and affinity of heavy metals and radiometals for organic biomass: Implications for water remediation

ABSTRACT Due to both natural and anthropogenic causes, waterways have a history of contamination with different heavy metals and radiometals. Among these, thorium (Th,) uranium (U,) arsenic (As,) and strontium (Sr) are noteworthy threats to humans due to chemical and radiological toxicity. Previous research has focused on inorganic materials to remove these metals. However, the use of recyclable and biodegradable waste materials to remove toxic metals has risen. This study seeks to use pistachio shells as a model for heavy metal selectivity and affinity for organic biomasses. The influence of kinetics and pH on selectivity/affinity of the shell for the metals was investigated. The individual metal affinity seen was Th > U > As > Sr. Selectivity for Th and U over As and Sr was seen at pH < 6, beginning to equalize at pH > 6. The maximum uptake for all metals tested occurs at pH 5 and t ≥2 h. Uptake of these metals follows pseudo-second order, intraparticle kinetics, and Freundlich isotherm. Finally, the selectivity of the shells for heavy metals was investigated using drinking water and seawater samples, with concurrent pronounced uptake of actinides and Sr being observed.
High-bandwidth graded-index polymer optical fiber with high-temperature stability

A newly developed graded-index polymer optical fiber (GI-POF) with high-temperature and high-humidity stability was proposed. As it was found that the high numerical aperture and high glass transition temperature (T/sub g/) at the core center of the GI-POF were key issues to achieve both high-temperature and humidity stability, a partially fluorinated polymer material was adopted to obtain both characteristics in the GI-POF. The newly developed GI-POF had low-loss (140 dB/km at 650-nm wavelength), high-bandwidth (higher than 1 GHz for 100 m transmission), high-temperature and humidity stability at 70/spl deg/C, 80% relative humidity (R.H.) and low bending loss.
Speaker-Aware Hierarchical Transformer For Personality Recognition In Multiparty Dialogues

Personality recognition is one of the core technologies in human-machine interaction, which has received increasing attention. Previous works mainly focus on essays or monologues, while personality traits reveal more in the interactions with others. Due to the lack of appropriate datasets, a few approaches aim to recognize personality traits in conversations, and most of them ignore interdependence between speakers and connection between conversations. In this paper, we create a multiparty dialogue-based personality dataset derived from CPED containing 1,195 data samples. We center on one speaker and extract related dialogues to compose each data sample annotated with speaker’s Big-Five traits, which is conducive to fully describe a center speaker using diverse cues of personality in different dialogues. Along the same lines, we propose a Speaker-aware Hierarchical Transformer named SH-Transformer to address above concerns, in which Personalized Embeddings (PE) adopt special tokens to distinguish center speakers in complete conversations and hierarchical Transformer capture diverse cues in utterances and conversations. Experimental results show that our method outperforms the non-interactive baseline by 1.38%, which confirms the necessity of considering both interactive information and diverse cues among dialogues. Our code will be released at github.com/Chloehxxx/SH-Transformer.
A Large-Eddy Simulation Study of Atmospheric Boundary Layer Influence on Stratified Flows over Terrain

AbstractThe impact of atmospheric boundary layer (ABL) interactions with large-scale stably stratified flow over an isolated, two-dimensional hill is investigated using turbulence-resolving large-eddy simulations. The onset of internal gravity wave breaking and leeside flow response regimes of trapped lee waves and nonlinear breakdown (or hydraulic-jump-like state) as they depend on the classical inverse Froude number, Fr−1 = Nh/Ug, is explored in detail. Here, N is the Brunt–Vaisala frequency, h is the hill height, and Ug is the geostrophic wind. The results here demonstrate that the presence of a turbulent ABL influences mountain wave (MW) development in critical aspects, such as dissipation of trapped lee waves and amplified stagnation zone turbulence through Kelvin–Helmholtz instability. It is shown that the nature of interactions between the large-scale flow and the ABL is better characterized by a proposed inverse compensated Froude number, = N(h − zi)/Ug, where zi is the ABL height. In addition, it.
Making Waves: An Evaluation of the World Bank’s Support for the Blue Economy (2012–23) Approach Paper

1.1 Marine and coastal resources are critical for human survival. Oceans provide at least half of the oxygen on Earth. They also regulate the climate by transferring about 10 gigatons of carbon from the atmosphere deep into the oceans each year (Lindsey and Scott 2010). To put that into perspective, an estimated 36.3 gigatons of carbon dioxide were emitted globally in 2021 (IEA 2022). Fish and other aquatic foods account for 17 percent of the average per capita intake of animal protein globally. In many coastal least developed countries and small island developing states (including Bangladesh, Kiribati, Mozambique, and Sierra Leone), aquatic foods contribute no less than 50 percent of total animal protein intake (FAO 2022).
Deep Learning-Based Compressive Sampling Optimization in Massive MIMO Systems

In this paper, we develop a deep learning framework to optimize the compressive sampling matrix in a massive multiple-input multiple-output (MIMO) system. The optimized compressive sampling matrix is utilized to project high-dimensional data received at the massive MIMO system into a lower-dimensional space so that the directions of arrival and other signal parameters can be efficiently obtained with a reduced hardware complexity. The proposed deep learning approach for optimizing the compressive measurement matrix increases its robustness and generalizability.