Search

Reproductive traits hold considerable economic importance in pig breeding and production. However, candidate genes underpinning the reproductive traits are still poorly identified. In the present study, we executed a genome-wide association study (GWAS) and runs of homozygosity (ROH) analysis using the PorcineSNP50 BeadChip array for 585 Yorkshire pigs. Results from the GWAS identified two genome-wide significant and eighteen suggestive significant single nucleotide polymorphisms (SNPs) associated with seven reproductive traits. Furthermore, we identified candidate genes, including ELMO1, AOAH, INSIG2, NUP205, LYPLAL1, RPL34, LIPH, RNF7, GRK7, ETV5, FYN, and SLC30A5, which were chosen due to adjoining significant SNPs and their functions in immunity, fertilization, embryonic development, and sperm quality. Several genes were found in ROH islands associated with spermatozoa, development of the fetus, mature eggs, and litter size, including INSL6, TAF4B, E2F7, RTL1, CDKN1C, and GDF9. This study will provide insight into the genetic basis for pig reproductive traits, facilitating reproduction improvement using the marker-based selection methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Whole-genome SNP data from 613 Chinese indigenous pigs were collected to comprehensively estimate the genetic diversity, genetic relationship, and population genetic structure of the indigenous pig meta-populations. These Chinese meta-populations were characterized by genetic distance, genetic difference index, and runs of homozygosity (ROH). The contribution of genetic diversity of each meta-population was scaled based on genetic and allelic diversity, which provides a basis for improving the conservation project. Further, a selective sweep analysis revealed that genes related to fat deposition and heat stress may contribute to the adaptability of the indigenous pigs to cold and heat. These findings provide a theoretical basis for the improvement of conservation strategies and insights into the environmental adaptability of Chinese indigenous pigs. The diversification of indigenous pig breeds in China has resulted from multiple climate, topographic, and human cultural influences. The numerous indigenous pig breeds can be geographically divided into six meta-populations; however, their genetic relationships, contributions to genetic diversity, and genetic signatures remain unclear. Whole-genome SNP data for 613 indigenous pigs from the six Chinese meta-populations were obtained and analyzed. Population genetic analyses confirmed significant genetic differentiation and a moderate mixture among the Chinese indigenous pig meta-populations. The North China (NC) meta-population had the largest contribution to genetic and allelic diversity. Evidence from selective sweep signatures revealed that genes related to fat deposition and heat stress response (EPAS1, NFE2L2, VPS13A, SPRY1, PLA2G4A, and UBE3D) were potentially involved in adaptations to cold and heat. These findings from population genetic analyses provide a better understanding of indigenous pig characteristics in different environments and a theoretical basis for future work on the conservation and breeding of Chinese indigenous pigs. [ABSTRACT FROM AUTHOR]

The organic flash cycle (OFC) is a novel power cycle with small exergy loss in the endothermic process. However, the low-pressure throttle valve in the cycle has a large throttling loss. Aiming to reduce the cycle exergy loss and improve the system performance, this study constructs a new configuration named the organic flash Rankine cycle (OFRC). Using the R600/R245fa mixture as the circulating working fluid and 200 °C geothermal water as the heat source, the effects of the change in working fluid composition on the thermal properties of the OFRC were studied based on the first and second laws of thermodynamics. Then, the economic performance of the proposed OFRC was investigated and then compared with that of the conventional OFC. The results show that the OFRC system has a significant improvement in thermal performance and economy compared with the OFC system. When the mole composition of the R600/R245fa mixture is 0.5/0.5, the net output work, thermal efficiency, and exergy efficiency of the OFRC system can reach a maximum at 146.39 kW, 21.51%, and 80.94%, respectively, which are 98.2 kW, 14.43%, and 54.3% higher than those of the OFC system. The dual heaters in the OFRC system can effectively reduce loss in the endothermic process. When the R600 mole composition is 0.5 in the OFRC system, the exergy loss of the heater is only 7.42%, and the power generation cost (0.3267 $·kW−1·h−1 only) is lower than that in the OFC system. [ABSTRACT FROM AUTHOR]

This paper proposes a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on approximately flat ground using a Lidar–IMU–Odometer system. When TWIP is in motion, it is constrained by the ground and suffers from motion disturbances caused by rough terrain or motion shaking. Combining the motion characteristics of TWIP, this paper proposes a framework for localization consisting of a Lidar-IMU-Odometer system. This system formulates a factor graph with five types of factors, thereby coupling relative and absolute measurements from different sensors (including ground constraints) into the system. Moreover, we analyze the constraint dimension of each factor according to the motion characteristics of TWIP and propose a new nonholonomic constraint factor for the odometry pre-integration constraint and ground constraint factor in order to add them naturally to the factor graph with the robot state node on SE(3). Meanwhile, we calculate the uncertainty of each constraint. Utilizing such a nonholonomic constraint factor, a complete lidar–IMU–odometry-based motion estimation system for TWIP is developed via smoothing and mapping. Indoor and outdoor experiments show that our method has better accuracy for two-wheeled inverted pendulum robots. [ABSTRACT FROM AUTHOR]

Approximately 50% of the world population is infected with Helicobacter pylori. Antibiotics are widely used for H. pylori infection treatment but there are drawbacks, e.g., the emergence of antibiotic-resistant bacteria. Sialic acids are a family of acylated derivatives of a nine-carbon carboxylated monosaccharide. Because sialic acid of the host cells is vital to H. pylori pathogenesis, sialic acid-guided therapies have been proposed for the prevention and treatment of H. pylori infection, including anti-adhesive therapy and site-specific delivery. This review aims to shed light on the prospects of sialic acid-based strategies in the food industry for developing functional foods with potent anti-H. pylori activity. In this work, progress on the identification of sialic acid-containing components as anti-adhesive agents against H. pylori is reviewed. The current applications of sialic acid-based delivery systems in eradicating H. pylori are discussed, including microspheres, beads, hydrogels, and nanoparticles. The challenges and future perspectives of sialic acid-guided strategies and the possibility of their applications in food industry are highlighted. Antibiotic resistance is still a major challenge and the sialic acid-based technologies have tremendous potential to be utilized to develop functional foods that hold promise to be a future trend for preventing or treating H. pylori infection. [ABSTRACT FROM AUTHOR]

Accurately understanding the thermal conductivities of nanorefrigerants is crucial to the development of heat transfer enhancement technology in applications such as cooling, microelectronics, and refrigeration. In this work, three types of nanorefrigerants, Al 2 O 3 −R141b, TiO 2 −R141b, and SiO 2 −R141b, were prepared and characterized. The effective thermal conductivities of those nanorefrigerants at different nanoparticle concentrations (φ v : 0.05–0.6 vol%), temperature (T : 283–298 K), hydrodynamic size (d a : 48.56–115.92 nm) and nanoparticle thermal conductivities (k p : 1.2–36 W/m·K) was experimentally investigated. For the first time, five intelligent models—the radial basis function (RBF), multilayer perceptron (MLP), least-squares support-vector machine (LSSVM), dendrite morphological neural network (DMNN), and spiking neural network (SNN) models—were developed to predict the effective thermal conductivities of nanorefrigerants based on 288 experimental samples. Through statistical and graphical methods, the performances of these intelligent models and seven commonly used theoretical/empirical correlations were compared. In addition, trend and sensitivity analyses were conducted to evaluate the influence degree of each independent input parameter. The results indicated that the effective thermal conductivity increased with increasing nanoparticle concentration, temperature, and nanoparticle thermal conductivity, while it decreased with increasing hydrodynamic size. The RBF model outperformed the other intelligent and theoretical, and empirical models with the highest accuracy (R 2 = 0.985) and the lowest error (RMSE = 0.0178, MAPE = 1.26%) in the complete dataset. The RBF model accurately captured the physical trends of the effective thermal conductivity as the input parameters were varied. Meanwhile, the nanoparticle concentration was the most influential factor of the four input parameters. [Display omitted] • The maximum enhancement in thermal conductivity is achieved for Al 2 O 3 –R141b. • Concentration has the largest impact on thermal conductivity of nanorefrigerant. • The RBF model outperformed all the other intelligent and empirical models. • The RBF model accurate captured the physical trends as the input parameters varied. [ABSTRACT FROM AUTHOR]

In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focusing on learning-based methods with safety considerations. We start by reviewing various notions of safety and liveness properties, and modeling frameworks used for problem formulation of MAS. Then we provide a comprehensive review of learning-based methods for safe control design for multi-robot systems. We start with various shielding-based methods, such as safety certificates, predictive filters, and reachability tools. Then, we review the current state of control barrier certificate learning in both a centralized and distributed manner, followed by a comprehensive review of multi-agent reinforcement learning with a particular focus on safety. Next, we discuss the state-of-the-art verification tools for the correctness of learning-based methods. Based on the capabilities and the limitations of the state-of-the-art methods in learning and verification for MAS, we identify various broad themes for open challenges: how to design methods that can achieve good performance along with safety guarantees; how to decompose single-agent-based centralized methods for MAS; how to account for communication-related practical issues; and how to assess transfer of theoretical guarantees to practice. [ABSTRACT FROM AUTHOR]

Accurately predicting the thermal conductivity of nanofluids under various thermodynamic conditions is of great importance to promote the industrial application of nanofluids. Unfortunately, the accuracy and applicability of the current theoretical or empirical models cannot meet the demand, due to the inherent complex of nanofluids. In this study, an intelligent model, named radial basis function artificial neural network (RBF–ANNs), is developed to predict the thermal conductivity of nanofluids under various conditions. Five parameters including nanoparticle volume concentration, temperature, nanoparticle diameter, thermal conductivity of nanoparticle and thermal conductivity of base fluid are selected as the input variables. A total of 1444 experimental data samples are collected to optimize the structure of model. The RBF model is compared with six theoretical models and three intelligent models through statistical and graphical analyses. Also, trend analysis and sensitivity analysis are conducted to evaluate the influencing mechanism of nanoparticle concentration, temperature, nanoparticle size, thermal conductivities of base fluids and nanoparticle on the thermal conductivity of nanofluids. Meanwhile, the quality of the experimental data is evaluated by means of leverage algorithm. Results indicate the superiority of the RBF, especially when the data size is large. The overall correlation coefficient (R2), average absolute relative deviation (AARD%) and root-mean-squared error of the developed model are 0.9931, 2.715 and 0.0316, respectively. Among the five input parameters, the volume fraction of nanoparticles has the greatest impact on the thermal conductivity of nanofluid. The results of outlier detection demonstrate that the proposed RBF model and data samples are statistically valid. [ABSTRACT FROM AUTHOR]

A new theoretical model for thermal conductivity of nanofluids is developed incorporating effective medium theory, interfacial layer, particle aggregation and Brownian motion-induced convection from multiple nanoparticles/aggregates. The predicated result using aggregate size, which represents the particle size in the actual condition of nanofluids, fits well with the experimental data for water-, R113- and ethylene glycol (EG)-based nanofluids. The present model also gives much better predictions compared to the existing models. A parametric analysis, particularly particle aggregation, is conducted to investigate the dependence of effective thermal conductivity of nanofluids on the properties of nanoparticles and fluid. Aggregation is the main factor responsible for thermal conductivity enhancement. The dynamic contribution of Brownian motion on thermal conductivity enhancement is surpassed by that of static mechanisms, particularly at high volume fraction. Predication also indicated that the viscosity increases faster than the thermal conductivity, causing the highly aggregated nanofluids to become unfavourable, especially fordf = 1.8. [ABSTRACT FROM PUBLISHER]

• Maximum increase in thermal conductivity is 16.87 % for SiO 2 -R141b nanorefrigerants in 0.1 vol. % concentration at 298 K. • Influence of temperature on thermal conductivity is significant at higher concentration. • Effect of hydrodynamic size on thermal conductivity depend on concentration. • The present model is superior to the widely used empirical models. • Dynamic mechanism is dominant for nanorefrigerants at low concentration. Nanorefrigerants with low particle concentration (≤0.1 vol. %) would bring more benefits in industrial application. In this article, R141b based nanorefrigerants containing Al 2 O 3 , TiO 2 and SiO 2 nanoparticles with concentrations of 0.02 − 0.1 vol. % were prepared and characterized. Thermal conductivities of these nanorefrigerants were measured in the temperature range of 278 − 298 K. The effect of concentration, temperature, and particle size on the thermal conductivity of nanorefrigerants was investigated. Meanwhile, a model for thermal conductivity of nanorefrigerants was proposed considering the mechanisms of interfacial layer, aggregation and Brownian motion. Results show that the thermal conductivity increased with increasing temperature, concentration, while decreased with increasing particle size. Thermal conductivity was increased by reducing particle size in all cases. Also, the deviation of the data obtained by present model from the experimental values was mostly < 3%. For nanorefrigerants with low concentration, the contribution of dynamic mechanism to thermal conductivity enhancement is dominant. [ABSTRACT FROM AUTHOR]

Owing to inherent flexibility, compliant actuator with physical elastic elements can implement compliant interactions between robot and environment. Nonlinear stiffness elastic actuators (NSEAs) use one motor to adjust position and stiffness, which improve compactness of variable stiffness actuators, and consider high torque/force resolution and high bandwidth. The primary challenge of designing a NSEA is designing a reliable compliant mechanism with a given torque-deflection profile. In this study, a general design method of torsional compliant mechanism through given discrete torque-deflection points was proposed, where a chain algorithm based on 2R pseudo-rigid-body model was used to describe the large deformation in elastic elements. Moreover, the theoretical model of stiffness of a compliant mechanism based on series flexible beams was developed. Based on the proposed model, the dimensional parameters of proposed compliant mechanism were designed satisfying the condition of the given torque-deflection points. Finally, simulation and physical experiment of the designed compliant mechanism through three given torque-deflection points are conducted to show the effectiveness of the proposed method. The designed compliant mechanism was tested and evaluated in the self-developed NSEA. [ABSTRACT FROM AUTHOR]

• New two-stage collector coupling flat plate and parabolic trough types is designed. • Four nanofluids are added to enhance heat transfer performance of solar collector. • Cost reductions of two-stage collector are evaluated in organic Rankine cycles. • Unit cost of two-stage collector is 6.8% lower than parabolic trough collector. • Cu-oil nanofluid has the best heat transfer performance. The flat plate collector (FPC) has a low economic cost, while the parabolic trough collector (PTC) has a high heat collection temperature. To reduce costs, improve efficiency, and promote the use of solar energy, this study proposes a new two-stage solar collector that couples FPC and PTC. The main objective of this paper is to verify the performance of this two-stage solar collector when containing nanofluids. This collector drives the organic Rankine cycle (ORC) with four nanoparticles (Cu, CuO, TiO 2 , and Al 2 O 3) in the oil heat transfer fluid. The FPC outlet temperature in the two-stage solar collector is optimized, the exergy of ORC is analyzed, and the economics of the new system using nanofluid are calculated and compared with those of the new system without nanofluid. Results show that Cu-oil outperforms the other nanofluids in terms of heat transfer. At a rated output power of 5 kW, the new two-stage solar collector with 5 % Cu-oil can reduce economic costs by 6.8 % compared with a single PTC. Meanwhile, the ORC with a two-stage solar collector using 5 % Cu-oil reduces the economic costs by 5.7 % compared with the ORC equipped with PTC without nanofluid. The new system has a minimum payback period of 17.3 years, and increasing the output power of the ORC is beneficial for reducing the investment payback period of the system. [ABSTRACT FROM AUTHOR]

Abstract: Cryogenic generation is one of the most important ways to utilize cold energy during LNG (liquefied natural gas) regasification. This paper fundamentally investigates LNG cryogenic generation with the Stirling cycle method based on previous studies. A basic process of LNG cryogenic generation with the Stirling cycle was presented initially with seawater and LNG as heat source and heat sink. And its thermodynamic analysis was performed to verify the theoretical feasibility of the Stirling cycle method. The generating capacity, the exergy efficiency and the cold energy utilization efficiency of the basic process were also calculated. Subsequently, the influences of evaporation pressure on net work, equipment performance and comprehensive efficiency of cold energy utilization were discussed and the effect of LNG mass flow as well as the ambient temperature was also studied. Finally an improved process of LNG cryogenic generation with Stirling cycle method combined with an air liquefaction process is proposed as feasibility in improvements of the basic process. [Copyright &y& Elsevier]

This paper addresses the challenging task of implementing dynamic jumping motions for biped wheel-legged robots, a motion mode that significantly enhances their mobility and adaptability to complex terrains. However, due to the under-actuation and dynamic instability nature of this type of robots, executing high-dynamic motions remains difficult. To tackle this issue, this paper proposes a novel Hierarchical Jumping Optimization (HJO) framework specifically designed for hydraulic biped wheel-legged robots. The framework involves an upper layer center of mass (CoM) trajectory optimization based on the dynamic properties of a dual-mass spring loaded inverted pendulum (DM-SLIP) model, along with the CoM dynamic coupling relationship introduced by the under-actuated nature of two-wheel self-balancing robots. This leads to a reduction in the dimensionality of optimization variables, facilitating effective CoM trajectory generation. Subsequently, the CoM motions of the robot in the Cartesian space are mapped into the joint space in real-time by using the middle layer hierarchical operational space control (HOSC). By incorporating centroid angular momentum as a task objective, the stability of the robot in highly dynamic motions can be enhanced. Finally, the proposed HJO framework is experimentally validated through jumping experiments on an actual robot. The experimental results demonstrate that the framework optimally utilizes the robot's under-actuated characteristics, significantly reducing computational costs associated with planning and tracking, while simultaneously preserving motion accuracy and robust. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify peptides derived from defatted wheat germ proteins with both emulsifying property and anti-adhesive activity against H. pylori. The emulsifying property was predicted by calculating the amphiphilic scores and secondary structures in silico. Six top-ranking peptides were synthesized for validation of their emulsifying and anti-adhesive activities. Three peptides (HLNLDFQLQEGGR, VNQAIYLLTTGAR, and ESLLNALTEHVK) showed high emulsifying activity by forming smaller oil droplet sizes of 1.396 ± 0.015 μm, 1.163 ± 0.010 μm, and 1.159 ± 0.257 μm, respectively. Compared to Tween 80, only VNQAIYLLTTGAR maintained good emulsifying stability at different pHs, ionic strengths, and heating. The anti-adhesive activity ranged from 36.3 ± 2.0% (VNQAIYLLTTGAR) to 5.5 ± 7.0% (AINDIRDQLER) at 10 mg/mL, which is attributable to binding of the peptides and H. pylori adhesins through hydrogen bonding and hydrophobic interactions. In conclusion, VNQAIYLLTTGAR showed both biological and techno-functional properties, thus making it a strong candidate for further development as a dual functional food ingredient. [Display omitted] • Emulsifying property of 267 identified wheat germ peptides was predicted in silico. • Six peptides were synthesized, and three peptides showed high emulsifying activity. • Only peptide VNQAIYLLTTGAR had good emulsifying stability under different conditions. • Anti-adhesive activity of peptides ranged from 36.3 ± 2.0% to 5.5 ± 7.0% at 10 mg/mL. • VNQAIYLLTTGAR has strong potential as a dual functional food ingredient. [ABSTRACT FROM AUTHOR]