Miracle-dz/UniFAR-FTUs huggingface dataset:
Bovine FRAS1: mRNA Expression Profile, Genetic Variations, and Significant Correlations with Ovarian Morphological Traits, Mature Follicle, and Corpus Luteum

Simple Summary Herein, the mRNA expression profile and genetic variations of bovine FRAS1 detected by GWAS, as well as their association with fertility-related characteristics in unilateral ovaries from 2111 cows at the same age phase, were explored. Two deletion mutations, P3-D20-bp and P4-D15-bp, were significantly correlated with ovarian morphological traits, mature follicles, or corpora lutea in dioestrum or metaestrus. Moreover, the mRNA expression of cows with a different genotype of P3-D20-bp was also significantly different. According to binding prediction by online databases, the deletion of P3-D20-bp could disturb the binding efficiency of WT1-I and Sox2 with FRAS1 sequence, indicating that the mutation may affect gene expression levels and traits by influencing the binding of transcription factors. So, P3-D20-bp and P4-D15-bp of FRAS1 gene could be candidates for the application of MAS in optimizing female fertility in bovine breeding. Abstract The amelioration of bovine fertility caused by a multi-factorial problem has always been a hot topic, among which the detection of available target genes is the most crucial. It was hypothesized that the Fraser extracellular matrix complex subunit 1 (FRAS1) gene detected by GWAS is involved in physiological activities such as ovarian development. Herein, unilateral ovaries from 2111 cows were used to examine the mRNA expression profile and polymorphisms of bovine FRAS1 and their associations with fertility-related characteristics. Firstly, it was confirmed that FRAS1 gene transcripts are expressed in various bovine tissues. Then, among five potential insertion–deletion (indel) loci, the 20 bp (named P3-D20-bp) and 15 bp (P4-D15-bp) deletion mutations were confirmed to be polymorphic with linkage equilibrium. Secondly, the P3-D20-bp polymorphism was significantly associated with ovarian weight and corpus luteum diameter in the metaestrus phase and ovarian length in the dioestrum stage. Additionally, both ovarian length and mature follicle diameter in metaestrus are significantly correlated with different genotypes of P4-D15-bp. Thirdly, the transcriptional expression of the FRAS1 gene in groups with a minimum value of ovarian weight or volume was significantly higher than the expression in groups with a maximum value. Instead of that, the more corpus luteum and mature follicles there are, the higher the transcription expression of the FRAS1 gene is. Furthermore, FRAS1 expression in cows with a heterozygous genotype (ID) of P3-D20-bp was significantly higher than others. Eventually, P3-D20-bp deletion could disturb the binding efficiency of WT1-I and Sox2 to FRAS1 sequence according to binding prediction, indicating that mutation may affect gene expression and traits by influencing the binding of transcription factors. Overall, the polymorphisms of P3-D20-bp and P4-D15-bp of the bovine FRAS1 gene significantly correlated to follicle or ovarian traits that could be applied in optimizing female fertility in cow MAS breeding programs.
Transcriptomic Analyses Reveal Insights into the Shared Regulatory Network of Phenolic Compounds and Steviol Glycosides in Stevia rebaudiana

Stevia rebaudiana (Bertoni) is a highly valuable crop for the steviol glycoside content in its leaves, which are no-calorie sweeteners hundreds of times more potent than sucrose. The presence of health-promoting phenolic compounds, particularly flavonoids, in the leaf of S. rebaudiana adds further nutritional value to this crop. Although all these secondary metabolites are highly desirable in S. rebaudiana leaves, the genes regulating the biosynthesis of phenolic compounds and the shared gene network between the regulation of biosynthesis of steviol glycosides and phenolic compounds still need to be investigated in this species. To identify putative candidate genes involved in the synergistic regulation of steviol glycosides and phenolic compounds, four genotypes with different contents of these compounds were selected for a pairwise comparison RNA-seq analysis, yielding 1136 differentially expressed genes. Genes that highly correlate with both steviol glycosides and phenolic compound accumulation in the four genotypes of S. rebaudiana were identified using the weighted gene co-expression network analysis. The presence of UDP-glycosyltransferases 76G1, 76H1, 85C1, and 91A1, and several genes associated with the phenylpropanoid pathway, including peroxidase, caffeoyl-CoA O-methyltransferase, and malonyl-coenzyme A:anthocyanin 3-O-glucoside-6″-O-malonyltransferase, along with 21 transcription factors like SCL3, WRK11, and MYB111, implied an extensive and synergistic regulatory network involved in enhancing the production of such compounds in S. rebaudiana leaves. In conclusion, this work identified a variety of putative candidate genes involved in the biosynthesis and regulation of particular steviol glycosides and phenolic compounds that will be useful in gene editing strategies for increasing and steering the production of such compounds in S. rebaudiana as well as in other species.
Research of Microwave Radiation Absorption of FDM Printing Materials

This paper presents the results of microwave radiation absorption research for such FDM 3D printing materials as ABS, PLA, PETG. Samples for measurements were made using the FDM 3D-printer “FlyingBear Ghost 4S”. Anechoic chamber AMS-8500 was used for performing the measurements in. A “П 6-140-3” microwave antenna and a high frequency generator were used to generate microwave radiation. Radiation absorption was measured on frequencies ranging from 18 to 24 GHz. Also the influence of infill percentage on a microwave radiation absorption was studied during the experiments. Data gained during the research will be used in design and production of radio transparent enclosures for electronic devices via FDM 3D-printing.
Joint AP Selection and Power Allocation for Interference Management in Visible Light Communication Networks

Visible light communication (VLC) is considered as an important green and energy-efficient indoor wireless accessing technique. However, the potential intercell interference of VLC network will degrade the signal quality and restrict the overall system performance. In this paper, the user-centric philosophy was employed for interference mitigation by determining a joint optimization of access point selection and sub-carrier power allocation, which was formulated as an achievable data rate maximization of wideband multi-user dense VLC system. We divided this complex combinatorial and non-convex problem into two subproblems and then transformed them to be binary programming and convex optimization problems, respectively. Moreover, we proposed a time-efficient algorithm to obtain the local optimal solution in an alternating iterative way. Simulation results demonstrate that the proposed scheme can delicately alleviate the adjacent interference among multi-users, which is facilitating for improving the achievable data rate of dense VLC system as compared with conventional schemes.
Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques

The nutritional status of rubber trees (Hevea brasiliensis) is inseparable from the production of natural rubber. Nitrogen (N) and potassium (K) levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree. Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly. However, high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model. A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech. Therefore, a less intensive and streamlined method, remining information from hyperspectral image data, was assessed. From this new perspective, a semisupervised learning (SSL) method and resampling techniques were employed for generating pseudo-labeling data and class rebalancing. Subsequently, a 5-classification spectral model of the N and K statuses of rubber leaves was established. The SSL model based on random forest classifiers and mean sampling techniques yielded optimal classification results both on imbalance/balance dataset (weighted average precision 67.8/78.6%, macro averaged precision 61.2/74.4%, and weighted recall 65.7/78.5% for the N status). All data and code could be viewed on the:Github https://github.com/WeehowTang/SSL-rebalancingtest. Ultimately, we proposed an efficient way to rapidly and accurately monitor the N and K levels in rubber leaves, especially in the scenario of small annotation and imbalance categories ratios.
Characteristics and controls of extremely large wildfires in the western Mediterranean Basin

Large fires account for a disproportionally high percentage of area burned with potentially severe environmental and socioeconomic impacts. This study characterizes extremely large fires (ELFs; 2500–24,843 ha) in Portugal (1998–2013) and the concomitant fuel and weather conditions, analyzing the response of ELF size to their variation. ELF burned less shrubland‐grassland (33% of the total ELF area) than forest (59% of total), the latter primarily composed by pine and pine‐eucalypt. High fuel hazard was the norm, as indicated by median values of 0.98 for fuel load as a fraction of potential (maximum) load and time since fire >14 years over 91% of the burned area. ELF occurred under anticyclonic circulation patterns, especially ridging, and 78% of them coincided with extreme fire danger days (corresponding to infrequent conditions) in conjunction with unstable atmosphere. Containment time, fire growth rate, and energy release metrics varied by 1 more order of magnitude than ELF size, hence indicating that size alone is insufficient to describe extreme fires. Distinct combinations between ambient weather conditions, atmospheric instability, and drought defined three categories of ELF as defined by size. Quantile regression indicated that increasingly larger fires showed gradually stronger responses to fire weather severity, highlighting the difficulty in restraining fire spread in flammable landscapes in the absence of extensive fuel treatments. Data limitations inherent to the methods used are discussed, and improvements to further advance the understanding of extreme fires are suggested.
Accurate Poultry Disease Identification and Risk Assessment through Object Detection

Poultry diseases, such as Coccidiosis, Salmonella, and Newcastle, have substantial economic and public health consequences for the poultry industry. This research endeavors to establish a comprehensive system for the precise identification of prevalent poultry diseases through object detection techniques. By analyzing uploaded fecal images, the system accurately detects diseases. Moreover, a customized algorithm evaluates the risk levels associated with these diseases, facilitating the formulation of appropriate treatment strategies. Field-testing and training with poultry farmers validate the system's accuracy and reliability. The proposed solution holds the potential to enhance farmers' economic well-being and bolster consumer health. Key components of this system encompass fecal image analysis, object detection technologies, and risk level assessment. This research contributes to the advancement of automated disease detection systems, ultimately supporting improved disease management and overall poultry health.
People with Autism Spectrum Disorder Could Interact More Easily with a Robot than with a Human: Reasons and Limits

Individuals with Autism Spectrum Disorder show deficits in communication and social interaction, as well as repetitive behaviors and restricted interests. Interacting with robots could bring benefits to this population, notably by fostering communication and social interaction. Studies even suggest that people with Autism Spectrum Disorder could interact more easily with a robot partner rather than a human partner. We will be looking at the benefits of robots and the reasons put forward to explain these results. The interest regarding robots would mainly be due to three of their characteristics: they can act as motivational tools, and they are simplified agents whose behavior is more predictable than that of a human. Nevertheless, there are still many challenges to be met in specifying the optimum conditions for using robots with individuals with Autism Spectrum Disorder.
From Research to Practice

Abstract Academic researchers dedicated to contributing to quality improvement in professional practice need to find more effective ways to bridge the gap between research and practice. Bridging the gap requires understanding what encourages the use of research findings by practitioners and what discourages it, as well as understanding how practitioners can incorporate research results into program practices. The purpose of this exploratory study was to examine how afterschool, camp, and community recreation practitioners make use of research results to inform their quality improvement processes. The study's findings are discussed in light of current research on knowledge utilization, and suggestions are provided for improving practice through more effective application of research results.
Building mmWave on the evolving C-V2X: MmWave NR-V2X

Since the evolving standardization of cellular V2X (C-V2X) technologies is continuously developed by 3GPP, research on new radio (NR-V2X) has been on schedule by academic and industries. Though millimeter wave (mmWave) frequency band is supposed to provide large transmission bandwidth, yet the development of mmWave band in NR-V2X is still in preliminary stage. In this article, a comprehensive discussion of mmWave NR-V2X is given covering trends, standardization landscape, and enabling technologies, aiming at tackling the challenge of channel modeling, directional channel access, beamforming and blockage management. The vision of mmWave NR-V2X is to fully support the development of automated driving, holographic control display and improved in-car infotainment for the future.
Artificial Intelligence Agent to Identify the Correct Human Resources

This system presents an innovative approach to streamline traditional recruitment processes and assist human resources in selecting the ideal candidates who align with the company's objectives. By addressing individual job requirements, the system ensures a good match between candidates' personal traits and company goals, creating a favorable environment for interviews. The Smart Recruiting System consists of four stages: Resume Parsing and NER(Named Entity Recognition) Tagging, Skill Set Classification using Big Data Analysis, Communication with New Recruiters through a Chatbot, and Improving Productivity with a Human Resource Management Portal and Skill Matrix. The process involves using advanced NLP models to extract information from resumes, including Named Entity Recognition and custom entity recognition. Big Data Analysis is used to predict job-specific skill sets and identify appropriate candidates. An automated interview system is created, analyzing candidates' behavior based on the Big 5 model. The system utilizes ASR, NLP, and deep-learning models to improve objectivity and efficiency. The Human Resource Management Portal integrates behavior analysis and candidate selection data, evaluating performance, training, and learning activities. The system identifies gaps in skills and connections, promoting cross-training and targeted development. This innovative approach combines advanced NLP, AI, and Big Data analytics to revolutionize recruitment practices. The demonstrated accuracy, efficiency, and objectivity show great potential for improving candidate selection, aligning individual skills with organizational goals, and ultimately enhancing the recruitment process.
Lithospheric velocity structure of the Anatolian plateau-Caucasus-Caspian region

[1] The Anatolian plateau-Caucasus-Caspian region is an area of complex lithospheric structure accompanied by large variations in seismic wave velocities. Despite the complexity of the region, little is known about the detailed lithospheric structure. Using data from 31 new, permanent broadband seismic stations along with results from a previous 29 temporary seismic stations and 3 existing global seismic stations in the region, a 3-D velocity model is developed using joint inversion of teleseismic receiver functions and surface waves. Both group and phase dispersion curves (Love and Rayleigh) were derived from regional and teleseismic events. Additional Rayleigh wave group dispersion curves were determined using ambient noise correlation. Receiver functions were calculated using P arrivals from 789 teleseismic (30°–90°) earthquakes. The stacked receiver functions and surface wave dispersion curves were jointly inverted to yield the absolute shear wave velocity to a depth of 100 km at each station. The depths of major discontinuities (sediment-basement, crust-mantle, and lithosphere-asthenosphere) were inferred from the velocity-depth profiles at the location of each station. Distinct spatial variations in crustal and upper mantle shear velocities were observed. The Kura basin showed slow (∼2.7–2.9 km/s) upper crustal (0–11 km) velocities but elevated (∼3.8–3.9 km/s) velocities in the lower crust. The Anatolian plateau varied from ∼3.1–3.2 in the upper crust to ∼3.5–3.7 in the lower crust, while velocities in the Arabian plate (south of the Bitlis suture) were slightly faster (upper crust between 3.3 and 3.4 km/s and lower crust between 3.8 and 3.9 km/s). The depth of the Moho, which was estimated from the shear velocity profiles, was 35 km in the Arabian plate and increased northward to 54 km at the southern edge of the Greater Caucasus. Moho depths in the Kura and at the edge of the Caspian showed more spatial variability but ranged between 35 and 45 km. Upper mantle velocities were slow under the Anatolian plateau but increased to the south under the Arabian plate and to the east (4.3–4.4 km/s) under the Kura basin and Greater Caucasus. The areas of slow mantle coincided with the locations of Holocene volcanoes. Differences between Rayleigh and Love dispersions at long wavelengths reveal a pronounced variation in anisotropy between the Anatolian plateau and the Kura basin.
A Hybrid Precoding Design Based on Alternate Minimization

In order to address the high power consumption issue caused by a large number of phase shifters in millimeter-wave Multiple Input Multiple Output (MIMO) systems, we propose a hybrid precoder design algorithm based on alternating minimization. This algorithm treats the non-convex design problem of the hybrid precoder as a matrix factorization problem and adopts two different approaches based on distinct matrix properties. For the optimization of the phase matrix in the analog precoder, it is transformed into a subproblem of the number of radio frequency (RF) chains. A novel algorithm based on Riemannian manifold optimization is proposed by temporarily relaxing the hardware constraints on phase shifters. Regarding the optimization of the switch matrix and digital precoder in the analog precoder, the unitary properties of the phase matrix and digital precoder are leveraged to transform the minimization of the Euclidean distance problem for hybrid precoder design into an upper-bound problem, significantly reducing the algorithm's complexity. Additionally, unlike existing fixed phase shifter architectures, the algorithm considers a variable phase shifter architecture, where the phase is variable and subject to hardware constraints, allowing optimization of the phase based on channel state information. Simulation results demonstrate that the algorithm outperforms existing typical precoding algorithms in terms of energy efficiency and spectral efficiency. Moreover, the algorithm exhibits lower complexity compared to algorithms with the same architecture and is more suitable for large-scale antenna systems.
Autonomous navigation simulation of an agricultural robot during soil fertilization in open fields

The involvement of robotic devices as drones, mobile robots and tractors can make agricultural operations more effective, faster, and more sustainable. This study reports the simulation in ROS environment of a four wheeled electrical robot useful for soil fertilization in open fields. The developed environment is represented by parallel rows of tomatoes and mobile obstacles. The robot manages to perform the task implementing the autonomous navigation using some waypoints saved in a warm-up lap.
A Verifiable Secure Multi-party Computation Scheme Based on Zero-Knowledge Proof for Energy Trading

Driven by the wide adoption of distributed renewable energy sources (RES), peer-to-peer (P2P) regional energy trading models have emerged as a major service offering, where prosumers can participate directly in local transactions. However, this distributed approach may result in privacy risks since the malicious users might manipulate the user data to compromise the integrity of the entire trading market. In this study, we introduce a verifiable secure multi-party computation scheme for P2P regional energy trading. This scheme facilitates the market pricing and clearing process in a distributed computing context without a third-party authority. Furthermore, we develop a multi-party zero-knowledge proof algorithm by leveraging the multi-party computation paradigm. This algorithm ensures the integrity of trading computations by guaranteeing the correctness of both data and computational process. We conduct experiments to evaluate the performance of our scheme in terms of proof generation and validation time by varying the number of participants. The results highlight the feasibility of our scheme for real-world applications, while preserving the privacy and integrity of energy trading computations.
An mmWave dual-band integrated substrate gap waveguide single cavity filter with frequency selectivity

A novel dual-band ISGW cavity filter with enhanced frequency selectivity is proposed in this paper by utilizing a multi-mode coupling topology. Its cavity is designed to control the number of modes, and then the ports are determined by analyzing the coupling relationship between these selected modes. By synthesizing the coupling matrix of the filter, a non-resonating node (NRN) structure is introduced to flexibly tune the frequency of modes, which gets a dual-band and quad-band filtering response from a tri-band filter no the NRN. Furthermore, a frequency selective surface (FSS) has been newly designed as the upper surface of the cavity, which significantly improves the bad out-of-band suppression and frequency selectivity that often exists in most traditional cavity filter designs and measurements. The results show that its two center frequencies are f01 = 27.50 GHz and f02 = 32.92 GHz, respectively. Compared with the dual-band filter that there is no the FSS metasurface, the out-of-band suppression level is improved from measured 5 dB to 18 dB, and its finite transmission zero (FTZ) numbers is increased from measured 1 to 4 between the two designed bands. Compared with the tri-band and quad-band filter, its passband bandwidth is expanded from measured 1.17 %, 1.14 %, and 1.13 % or 1.31 %, 1.50 %, 0.56 %, and 0.57 % to 1.71 % and 1.87 %. In addition, the filter has compact, small, and lightweight characteristics.