Software Model Research Articles

Transient Thermal Simulation of Lithium‐Ion Batteries for Hybrid/Electric Vehicles

This paper focuses on the development of a plug‐in hybrid vehicle (PHEV) full‐vehicle transient thermal model in thermal modelling software to predict the battery surface temperature at various locations. The full‐vehicle thermal model consists of a full exhaust piping system, a high‐voltage lithium‐ion battery pack system, and a battery liquid coolant system. All modes of heat transfer including conduction, forced and natural convection, radiation from the exhaust system, battery cooling, and battery internal heat generation are considered in the model. The full‐vehicle model is simulated under various vehicle conditions to represent four standard customer drive cycles. The simulated battery surface temperature at specified points along the battery module surfaces is compared to experimental vehicle test‐cell data to provide model validation. Using the results from the transient thermal simulations, prediction of the battery thermal degradation is performed throughout the entire vehicle lifecycle. The thermal degradation is estimated using thermal goals and equivalent exposure times.

Application of the Rasch Model to the Evaluation of Biological Problems

Evaluation of student abilities in Biology subjects often faces challenges in ensuring that the instruments used actually measure what they are supposed to measure. The data analysis used is the Rasch model, which is part of item response theory, offering a more accurate approach in analyzing evaluation data. This model allows the identification of items that are inappropriate or too difficult, as well as providing more in-depth information about individual student abilities. Through analysis of instruments and question items from 30 multiple choice questions that emphasize critical thinking skills, the aim of this research is to assess the quality of Biology questions for class X SMA. Testing was carried out using WinStep software and Rasch modeling analysis on 166 students from five high schools/vocational schools. The research method used is quantitative. The results of the instrument investigation show that the issue has very good dependability and the unidimensionality of the instrument is indicated by unexplained changes in the first differentiation. These results show that the Rasch test can help educators in selecting questions that meet the evaluation criteria and contribute to improving the quality of national education. The implications of this research are By applying the Rasch Model, educators can develop more accurate and fair evaluation tools, which are able to identify individual student difficulties and strengths.

Personalized College English Learning Experience Assisted by Artificial Intelligence: An Algorithm-Driven Adaptive Learning Approach

Personalized College English Learning offers customized education based on individual necessities and aims. Students enhance their linguistic ability efficiently over engaging sessions, specific curricula, and professional teachers. This study aims to progress a smart Artificial Intelligence (AI)-based adaptive learning method for enhancing personalized college English learning experience. Our proposed model provides an intelligent detecting device-enabled setting for learning English over big data analysis and machine learning based on a DM approach. Intelligent sensing strategies detention significant statistics from college English students and big data analysis examines the data to produce beneficial insights that increase real-time personalizing of English learning experiences. We suggest an innovative Starling Murmuration fine-tuned Dynamic Weighted Random Forest (SM-DWRF) system for classifying the data. Our model influences perceptions from collective behavior in starling flocks to dynamically alter feature weights in DWRF arrangement. It integrates fine-tuning mechanisms to adaptively weight features, developing classification accurateness and strength in adapted college English learning settings. We implemented our suggested model in Python software. In the assessment stage, we accurately measure the effectiveness of our proposed SM-DWRF model in identifying diverse features of English learning across various parameters. Our experimental findings incontestably showcase the greater performance of our model associated with conventional approaches in classifying content from multimodal statistics. Significantly, we perceive prominent enhancements in reliability and robustness, when acclimating to dynamic learning settings.

PIMCoSim: Hardware/Software Co-Simulator for Exploring Processing-in-Memory Architectures

As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlenecks in AI systems, and processing-in-memory (PIM) architectures have been proposed to overcome this issue. PIM is an architecture that performs computations within memory, thereby reducing data movement between the CPU and memory. However, since PIM is difficult to optimize as a general-purpose architecture, it is essential to adopt an architecture suitable for the target application. While various simulators and emulators have been introduced for the design space exploration (DSE) of different PIM architectures, simulators are limited in debugging hardware operations, and emulators face challenges in flexibly modifying the system configuration, as emulators implement the entire architecture in hardware. Therefore, this paper introduces PIMCoSim, a comprehensive hardware–software co-simulator for the DSE of DRAM-PIM systems. This co-simulator partially emulates simplified hardware-implemented processing elements (PEs) and integrates software models for memory operations, facilitating the DSE of PIM systems. To validate PIMCoSim, we analyzed results for different computational workloads by varying PIM structures and operational policies, demonstrating the efficiency of DRAM-PIM systems. The co-simulation approach in PIMCoSim aims to contribute to analyzing DRAM-PIM configurations and adopting optimized structures.

A quantum leaky integrate-and-fire spiking neuron and network

Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

Tsunami debris motion and loads in a scaled port setting: Comparative analysis of three state-of-the-art numerical methods against experiments

We present an international comparative analysis of simulated 3D tsunami debris hazards, applying three state-of-the-art numerical methods: the Material Point Method (MPM, ClaymoreUW, multi-GPU), Smoothed Particle Hydrodynamics (SPH, DualSPHysics, GPU), and Eulerian grid-based computational fluid dynamics (Simcenter STAR-CCM+, multi-CPU/GPU). Three teams, two from the United States and one from Germany, apply their unique expertise to shed light on the state of advanced tsunami debris modeling in both open source and professional software. A mutually accepted and meaningful benchmark is set as 1:40 Froude scale model experiments of shipping containers mobilized into and amidst a port setting with simplified and generic structures, closely related to the seminal Tohoku 2011 tsunami case histories which majorly affected seaports. A sophisticated wave flume at Waseda University in Tokyo, Japan, hosted the experiments as reported by Goseberget al. (2016b). Across dozens of trials, an elongated vacuum-chamber wave surges and spills over a generic harbor apron, mobilizing 3–6 hollow debris -modeling sea containers-, in 1–2 vertical layers against friction. One to two rows of 5 square obstacles are placed upstream or downstream of the debris, with widths and gaps of 0.66x and 2.2x of debris length, respectively. The work reports and compares results on the long wave generation from a vacuum-controlled tsunami wave maker, longitudinal displacement of debris forward and back, lateral spreading angle of debris, interactions of stacked debris, and impact forces measured with debris accelerometers and/or obstacle load-cells. Each team writes a foreword on their digital twin model, which are all open-sourced. Then, preliminary statistical analysis contrasts simulations originating off different numerical methods, and simulations with experiments. Afterward, team’s give value propositions for their numerical tool. Finally, a transparent cross-interrogation of results highlights the strengths of each respective method.

Two Decades of Project-Based Learning in Engineering Education: A 21-Year Meta-Analysis

The implementation of project-based learning (PjBL) has contributed to improving students’ learning outcomes. However, strong evidence specifically linking PjBL to the improvement of students' higher-order thinking skills (HOTS) is still limited, especially in engineering education. Hence, this study aims to uncover the impact of PjBL on students' HOTS through a meta-analysis. A total of 16 studies were sourced from ACM, Dimensions, IEEE, Science Direct, Scopus, and WOB metadata, with a search time ranging from 2003 to 2023. Data sourced from relevant literature were analyzed using a random-effects hedge model in JASP software. The results showed that the application of PjBL had a significant impact, rRE (0.315), or 31.5% on students' HOTS in the context of engineering education. Furthermore, there was no publication bias in the analysis of the studies. Meanwhile, the review of moderator variables showed that PjBL is most frequently implemented in Eastern cultures, particularly in computer science subjects, and the most common method for measuring skills is through test scores. Generally, the integration of PjBL into the learning process could effectively improve students’ HOTS.

Seismic Behavior Improvement of Rigid Steel Frame Braced with Cable and Optimal Rotational Friction Damper

The research focused on enhancing the seismic performance of steel moment frames using cable braces and a central friction damper. By optimizing the design and pretensioning force of the cable braces, this study aimed to improve the energy absorption and overall behavior of the frames under cyclic earthquake loads. A quasi-cyclic loading test was developed through FE simulations using ABAQUS software, version 2023. To verify the modeling, an experimental test was compared with the numerical modeling, and the numerical results confirmed the accuracy of the experimental data. Results made by modeling in ABAQUS software (version 2023) include the impact of pretensioning force on stiffness and energy absorption, the relationship between pretensioning force and force required to move the target, the increase in absorbed energy with pretension force up to 25%, and the superior seismic performance of frames with rotational friction dampers. This study also highlighted the benefits of using cable braces with a friction damper regarding the symmetry of hysteresis diagrams, cyclic performance, and energy absorption capacity. The amount of pretensioning of the cables affects the energy dissipation capacity. As the pretensioning of the cables increases, the energy dissipation capacity initially increases. However, further increases in pretensioning lead to decreased energy dissipation capacity beyond a certain point. When the percentage of cable brace pretension increases from 2% to 25%, the energy dissipation capacity is enhanced by 2%, and when in the 25–30% range, it stabilizes at around 35%. Energy dissipation capacity decreases for pretensions of more than 30%.

Comparison of Chicken Genotypes for Growth, Egg Production and Adaptability Traits Under Semi-Scavenging Condition

The study was conducted to evaluate the growth, egg production and adaptability performances of different chicken genotypes under semi-scavenging condition. The performance of Improved Horro (H), Cosmopolitan (C), Indigenous (L), and Koekoek (KK) chicken genotypes was evaluated in semi-scavenging condition over a 44-week period. The study utilized a completely randomized design (CRD), and the data were analyzed using the General Linear Model (GLM) in SAS software. A total of 360 chickens from the four genotypes were provided. For each genotype, around 30 chickens (with a 1:10 male-to-female ratio) were allocated to each lowland site, with each genotype placed in three replicate sites. The data for each genotype from all sites were then pooled. KK exhibited the highest body weight at hatch, followed by C and H, while L had the lowest. At eight weeks of age, KK also had the highest body weight and average daily weight gain, with C and H following, and L recording the lowest. KK had the highest body weights and weight gains, followed by C and H, while L showed the lowest body weights and weight gains at both 16 and 24 weeks of age. L and H showed significantly higher survival rates compared to KK and C. L reached the highest age at first egg lay, followed by C, KK, and H. KK had the greatest body weight at first egg lay and at the end of the experiment compared to C and H, while L had the lowest body weights at both first egg lay and 44 weeks. KK had the highest egg weight at first egg lay and throughout the experiment when compared to C and H, while L recorded the lowest egg weights at both first egg lay and 44 weeks. KK produced the highest number of eggs, followed by H and C, while L had the lowest egg count. In conclusion: The genotype differences of chickens substantially influenced growth, egg production and adaptability performances. It could also serve as reference for future growth, egg production and adaptability studies of defferent chicken genotypes.

Effect of Triple Glass Layers on the Thermal Performance of Windows

This study introduces the application of software simulations to evaluate the performance of triple-pane glass windows to meet the requirements for energy-efficient thermal insulation. Using Therm 7.8.55 and Window 7.8.55 software, structural and physical heat transfer models for various single-frame plastic windows were developed. Numerical simulation methods were employed to calculate and analyse the impact of the number of glass layers on the windows' thermal performance, including the heat transfer coefficient and solar heat gain coefficient, to elucidate the energy-saving mechanisms. These simulations examined the physical properties of single-frame triple-glazed plastic windows, such as wind pressure resistance, airtightness, watertightness, and potential condensation on the inner surface. The results indicate that, under identical conditions, the heat transfer coefficient of single-frame triple-glazed plastic windows is reduced by 7.08%, and the solar heat gain coefficient is decreased by 4.18% compared to single-frame double-glazed plastic windows. Additionally, these windows' physical and economic performance meets the necessary standards. Furthermore, incorporating new materials such as Low-E glass can further enhance the thermal performance of the windows, significantly contributing to the reduction of overall building energy consumption.

A novel PCM-based foam concrete for heat transfer in buildings -Experimental developments and simulation modelling

Foam concrete is renowned for its lightweight and versatile properties, such as good thermal insulation, airborne sound absorption and adequate compressive strength. Phase change materials are recognized for their heat storage characteristics. The current study investigates the enhancement of building envelope thermal performance by integrating phase change materials in foam concrete composites. The phase change material (PCM) developed using exfoliated vermiculite impregnated with capric acid and absolute ethanol demonstrated optimal thermal stability. Among the various foam concrete mix composites, PCM in addition to the nano silica and coconut fibre (PNC) mix showed improved mechanical and thermal properties. Test results showed that optimum mix (PNC-5 %) possess a compressive strength of 7.96 MPa, with a thermal conductivity of 0.15 W/mK. The nano silica added in the PNC mix reacted with CH rapidly for the formation of C-S-H and filled the pores, which improved the bonding and reduced the brittleness of the samples, which is also evident from the SEM images. The PNC-5 % sample mixture exhibited a melting and freezing point of 31.42 °C and 21.08 °C respectively, with a latent thermal storage 9.72 J/g for freezing and 17.55 J/g melting. Scanning electron microscopy and thermogravimetric analysis confirmed the compatibility and high thermal resistance of PNC-5 %. TGA results confirmed that the mass loss corresponding to CH content was slightly lesser for the PNC-5 % mixture than for the PCM mixture, due to the inclusion of nano silica in the case of PNC-5 %. COMSOL Multiphysics ® software model analysis is implemented for validation of thermal characteristics of the developed PCM-impregnated foam concrete wall panel for insulation in buildings, emphasizing the potential for enhanced thermal comfort compared to that of noninsulated structures. This research proves the efficacy of PNC insulated foam concrete composites for sustainable and energy-efficient building solutions, paving the way for advancements in building applications.

A porohyperelastic scheme targeted at High-Performance Computing frameworks for the simulation of the intervertebral disc

Background and Objective:The finite element method is widely used for studying the intervertebral disc at the organ level due to its ability to model complex geometries. An indispensable requirement for proper modelling of the intervertebral disc is a reliable porohyperelastic framework that captures the elaborate underlying mechanics. The increased complexity of such models requires significant computational power that is available within high-performance computing systems. The objective of this study is to present such a framework, validated both against literature and experiments, aiming to enable intervertebral disc research to benefit from state-of-the-art computational resources. Methods:In the context of this work, we implement a biphasic model that captures the mechanical response of the intricate, tissue-dependent models of the solid phase along with the hydrostatic pressure effects of the fluid phase. The tissue-dependent models involve the hyperelastic ground substance, fibrillar reinforcement, and osmotic swelling. The derived porohyperelastic, staggered scheme is implemented in Alya, a finite element code targeted at high-performance computing applications. The formulation is subsequently verified and validated by comparing the results of consolidation simulations with literature data for simulations and experiments using either generic or patient-specific geometries. Additionally, in-house experiments are replicated, evaluating the model’s ability to simulate alternating loading. Finally, the implementation’s circadian response is compared to previous implementation of similar material models in commercial software. Results:Results align well with experimental and literature findings in terms of disc height reduction (4% error), intradiscal pressure (14% error) and disc bulging. Validating the patient-specific geometry results in 4% and 7% deviation in measuring height loss. Simulations show excellent agreement with in-house experimental results, with less than 1% error regarding height reduction. Finally, the comparison to similar, published, earlier implementation in commercial software unveils excellent agreement of less than 1% error for the water content during circadian simulations. Simulation times are reported at 4 min per circadian cycle in the supercomputer Marenostrum V. Conclusions:This work presents a clear and validated formulation for simulating porohyperelastic materials based on assumptions that comply with the non-linear elasticity theory. The implementation in Alya enables intervertebral disc research to benefit from high-performance computing systems.

The effects of garlic as a feed additive on ruminal fermentability and ruminant performance: A meta-analysis

Garlic exhibits antimicrobial activity and it may serve as a feed additive for ruminants, yet quantitative summaries of its effects are lacking. Therefore, the current study evaluated the addition of various garlic products as feed additives on ruminal fermentability and ruminant performance using a meta-analysis method. The database was compiled from 37 articles with 157 comparison studies in Scopus and PubMed. The data sets were analysed using a random-effects model in OpenMEE software and the bias publication was evaluated by using Jeffreys's Amazing Statistics Program (JASP) software. The results revealed that garlic significantly increased the percentage of propionate, butyrate, valerate and iso-butyrate, as well as the total number of rumen bacteria, dry matter intake, crude protein intake and ruminant average daily gain (p < 0.05). In contradiction, garlic addition in ruminant feeds led to decreases in methane production, percentage of acetate, the ratio of acetate to propionate, in vitro neutral detergent fibre digestibility and the total number of protozoa (p < 0.05). The subgroup analysis indicated that garlic oil, garlic powder and garlic bulb decreased methane emissions, while garlic powder and garlic bulb raised the percentage of propionate. In conclusion, garlic products altered the percentage of total volatile fatty acids and total number of rumen microflora, while increasing the feed intake and body weight gain. Thus, garlic products could be used as a dietary additive to support sustainable ruminant animal production.

Identification of heterozygous mutations of ABCC8 gene responsible for maturity-onset diabetes of the young with exome sequencing

Abstract Background Although the MODY12 subtype, caused by ABCC8 mutations, is rare, it is highly sensitive to sulfonylureas. The identification of ABCC8 mutations in patients clinically diagnosed with MODY has the ability to contribute to the precise management of diabetes. Methods Genetic analysis of two families with MODY were conducted using whole-exome sequencing (WES) and Sanger sequencing. The spatial structures of the mutant proteins were constructed using MODELLER and PyMOL software to provide further evidence of pathogenicity. Results The heterozygous missense mutations V357I and R1393H in ABCC8 were found in probands of two unrelated MODY pedigrees, which co-segregated with the hyperglycemic phenotypes in these two pedigrees. Detection of the V357I mutation enabled the proband of family A to successfully transfer from insulin to sulfonylurea (SU). After 3 months of follow-up for the SU trial, the HbA1c level of proband A improved from 12.4% at the initial diagnosis to 7.20%. Proband B was treated with insulin because of pregnancy and poor islet function. In silico analysis indicated that the R1393H mutation resulted in a longer hydrogen bond distance to L1389 and cleavage of carbon-hydrogen bonds to V1395, A1390, and L1389. Conclusions We have described two pathogenic missense mutations in ABCC8 in Chinese families with MODY. Our findings support the heterogeneity in the clinical features of MODY12 caused by ABCC8 mutations.

Numerical study of dimpled structures on heat transfer deterioration mitigation with supercritical CO2 heated in vertical tube

To suppress heat transfer deterioration (HTD) in supercritical CO2 Rankine cycle, the flow and heat transfer characteristics of supercritical CO2 in elliptical dimple tubes (ET) are numerically investigated by the Shear-Stress Transport k-ω model in Fluent software. The ET based on a smooth tube with length of 2000 mm and diameter of 9 mm. The operating conditions consist of pressure 8 MPa, mass flux 100–700 kg/(m2∙s), and heat flux 70–300 kW/m2. The results indicate that dimples can prevent sharp decrease in radial density near-wall where HTD originally occurs, which mitigates buoyancy effect and improves heat transfer performance. Compared to staggered dimple, inline elliptical dimple tubes exhibit a better heat transfer. Reducing space (p = 10–30 mm) and increasing rows (n = 3–6) of dimples can further improve the effectiveness. At mass flux 300 kg/(m2∙s) and heat flux 70 kW/m2, the performance evaluation criterion (PEC) increases by 42.7 % and 34.7 % respectively, while the maximum buoyancy value (Bumax) decreases by 52.2 % and 28.7 % respectively. Additionally, the Bumax is highly influenced by ac, which represents the product of dimple width and depth. The optimal ET has a PEC of 2.45 and Bumax of 6.42 × 10-5, begin to be affected by buoyancy effect at Bu ≥ 2 × 10-5 and recovers heat transfer at Bu ≥ 2 × 10-4. A new heat transfer correlation is developed and over 95 % of data falls in a 30 % accuracy.

Irradiance modeling of tubular ultraviolet light bulbs

Clean indoor air is crucial for human lives in residential and workplace settings, including food safety and supply chains. Since the COVID-19 outbreak, disinfecting air has become vital for the public as the SARS-CoV-2 virus and other infectious diseases transmit via inhalable aerosols. Ultraviolet (UV) light is known to be effective in disinfecting air. However, it is still challenging to properly design practical UV applications with common light design software. Visible light analysis software, AGi32, assists architectural applications. AGi32 utilizes digitized luminaire IES files to model light intensity on user-designed geometry in common far-field uses. IES files are based on far-field photometry to model light intensity at different distances and angles from the source. The common application of IES files is to model visible light intensities; however, IES files generated for UV light are still rarely available. Due to the increasing need for surface and air disinfection, modeling UV light intensity using IES files may be beneficial for designing and evaluating systems containing UV light bulbs in near-field applications. There is limited information regarding the accuracy of UV modeling in AGi32 software using IES files. Therefore, the research objectives were to (1) create IES files of a UV-C germicidal lamp (254 nm) and a visible fluorescent lamp with almost identical metrics; (2) compare the IES files output with physical UV measurements inside and outside of an air duct; (3) develop correlations between light intensities of visible and UV light bulbs. Linear correlations were observed when comparing UV irradiance and visible illuminance for both measured and modeled data. The results indicated high variability between the measured and modeled light data, signifying the importance of further investigation of potential error sources and improving the accuracy of modeling.

Modal analysis of natural dynamic frequency for a double deck cable-stayed steel bridge by using finite element method

Double-deck cable-stayed bridges are appropriate when there is a high traffic volume and little space for the structure. They combine road and rail transit to maximize the utilization of bridge structures and tackle traffic issues. This study investigates the fluctuations in the modal properties caused by several factors that impact the modal characteristics of bridges. The aim is to ascertain the system’s inherent frequencies, associated natural periods, and mode shapes. The modal analysis inquiry was conducted utilizing a software model grounded in the finite element method. The bridge deck slab, cable, pier, and pylon are discretized utilizing SHELL 181, CABLE 280, and BEAM 188 elements in this finite element model. The model’s dependability was validated by mesh convergence analysis and comparison with findings from prior research investigations. The cumulative mass participation factor analysis reveals that the bridge induces vibrations in 90% of the mass associated with the first five modes in the research of cumulative modal vibration. The application of prestressing force has significantly increased the natural frequencies of the bridges, resulting in a large rise. An increase in the damping ratio leads to a reduction in the natural frequency. Several factors, such as the existence or absence of prestressing force and different damping ratios in the bridge system, will affect free vibration results. Additionally, it can provide the foundation for subsequent fatigue analysis related to different dynamic loads.

A genome-wide association study of the racing performance traits in Yili horses based on Blink and FarmCPU models

Racing performance traits are the main indicators for evaluating the performance and value of sport horses. The aim of this study was to identify the key genes for racing performance traits in Yili horses by performing a genome-wide association study (GWAS). Breeding values for racing performance traits were calculated for Yili horses (n = 827) using an animal model. Genome-wide association analysis of racing performance traits in horses (n = 236) was carried out using the Blink, and FarmCPU models in GAPIT software, and genes within the significant regions were functionally annotated. The results of GWAS showed that a total of 24 significant SNP markers (P < 6.05 × 10− 9) and 22 suggestive SNP markers (P < 1.21 × 10− 7) were identified. Among them, the Blink associated 16 significant SNP loci and FarmCPU associated 12 significant SNP loci. A total of 127 candidate genes (50 significant) were annotated. Among these, CNTN6 (motor coordination), NIPA1 (neuronal development), and DCC (dopamine pathway maturation) may be the main candidate genes affecting speed traits. SHANK2 (neuronal synaptic regulation), ISCA1 (mitochondrial protein assembly), and KCNIP4 (neuronal excitability) may be the main candidate genes affecting ranking score traits. A common locus (ECA1: 22698579) was significantly associated with racing performance traits, and the function of the genes at this locus needs to be studied in depth. These findings will provide new insights into the detection and selection of genetic variants for racing performance and will help to accelerate the genetic improvement of Yili horses.

Impact of source (drain) doping profiles and channel doping level on self-heating effect in FinFET

In this work the impact of the doping profile in the source and drain areas on the self-heating effect in SOI FinFET is simulated at different doping levels in the channel. Constant profile as well as two types of analytical profiles are considered. The impact of the doping profile on the threshold voltage and on the ratio Ion/Ioff at different doping levels of the channel is also considered. To consider the self-heating effect the thermodynamic transport model in TCAD Sentaurus software is used for simulation. The results of simulation show that self-heating effect, threshold voltage, and Ion/Ioff ratio considerably depend on the doping profile in source and drain areas.