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• The take-over ability of drivers is assessed by visual characteristic indicators, physiological characteristic indicators, vehicle control indicators and take-over behavioral indicators. • Both text-based user manual and behavioral spectrum-based training methods are proposed to improve take-over ability. • The behavioral spectrum-based training group showed superior performance. • Drivers' take-over ability reached stability after undergoing 14 take-over operations. • These results could contribute to the safety of human–machine codriving vehicles and the design of future driver training systems. During human–machine codriving, drivers need to take over the vehicle when a take-over request (TOR) appears. If drivers have not received relevant training before driving, they may be unable to complete the take-over within the limited time, or the stability of subsequent vehicle control may be insufficient, which can lead to accidents. In this study, two types of take-over ability improvement methods are proposed. Participants were recruited and randomly divided into a control group (n = 15, no take-over training) and two experimental groups (n = 15, text-based training; n = 15, behavioral spectrum-based training). One-way ANOVA or the Kruskal–Wallis test and post hoc contrasts were used to analyze the differences in data indicators between the three groups of drivers after 20 take-over operations, and another method was proposed to validate the efficiency of the take-over operations on the stability of take-over ability. The results show that compared with the control group, both experimental groups demonstrated a significant improvement in take-over ability, with the behavioral spectrum-based training group exhibiting better take-over performance than the text-based training group. Moreover, after 14 take-over operations, drivers' take-over ability in the behavioral spectrum-based training group stabilized. The findings of this study can contribute to the safety of human–machine codriving vehicles and the design of future driver training systems. [ABSTRACT FROM AUTHOR]

This paper proposes an auto-decision making system, knowledge-based major choosing decision (KMCD), which helps remote students choose their majors. It is built within a first-order reasoning system with an uncertain reasoning function. The knowledge of both students who want to choose their majors and who have finished their studies are used in the decision making process of the system. KMCD provides personalized suggestions to those students who want to choose majors. Both the learning abilities of students and the career prospects of majors are carefully analyzed on the basis of the knowledge in database. As these analyzing results are well utilized in the decision making process of KMCD, it can be expected that the students who take the suggestions will have a better chance to get graduated smoothly and start their careers successfully after graduation. A detailed description of KMCD is given, which shows that the system is actually a reasonable decision making framework with a capability of expansion of other knowledge which is supposed to be useful for major choosing. An experimental study on data of real college students is devised to justify the effectiveness of KMCD. [ABSTRACT FROM PUBLISHER]

A novel thermal storage solar air heater (TSSAH) is proposed in this study. This TSSAH is composed of a vacuum glass tube, flat micro-heat pipe arrays (FMHPA), and a thermal storage material (i.e., paraffin) with a phase change temperature of 58 °C. The FMHPA acts as the core heat transfer component. Considering the low heat conductivity coefficient of paraffin, louver fins are utilized to enhance heat transfer. Thermal performance is analyzed and discussed. During thermal collection, natural convection evidently contributes to enhancing heat transfer. Increasing solar radiation intensity can improve collection efficiency. Thermal collection efficiency increases from 70.22% to 77.28% when solar radiation is increased from 675 W/m2 to 835 W/m2 under an ambient temperature range of 20.8 °C–23.3 °C. A high ambient temperature results in high collection efficiency that can reach 80.59%. During thermal discharge, enhancing the air volumetric flow rate can evidently increase the amount of useful energy and shorten discharge time. From 80 m3/h to 170 m3/h, the amount of useful energy is increased from 335 W to 550 W, and discharge time is shortened from 309 min to 195 min under inlet temperatures ranging from 24.1 °C to 24.2 °C. A high inlet temperature generates a high outlet temperature but decreases the amount of useful energy. From 19.7 °C to 32.0 °C, outlet temperature is increased from 30.4 °C to 36.6 °C, and the amount of useful energy is reduced from 692 W to 287 W at an air volumetric flow rate of 200 m3/h. During thermal discharge, the average amount of useful energy can reach 692 W at an inlet temperature of 19.7 °C and an air volumetric flow rate of 200 m3/h, indicating that the proposed TSSAH can release heat quickly. In the experiment scale, the discharged heat is 6150–6450 kJ at a phase change material temperature scale of 85 °C–35 °C. [ABSTRACT FROM AUTHOR]

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment. [ABSTRACT FROM AUTHOR]

• The harmonic and time sampling (HTS) algorithm is proposed. • The HTS can encode the fine structure cues of music. • Vocoder simulations are conducted to verify the effectiveness of HTS. • The HTS improves melody perception and increases the timbre of CI music. Music plays an essential role in the healthy life of humans. However, few sound processor algorithms effectively encode the fine structure cues of music, resulting in inferior music perception for cochlear implant (CI) users, especially in pitch and melody. In this study, an improved music vocoder algorithm is proposed based on the conjunction of harmonic and time sampling (HTS). The algorithm includes two branches: the first, the pitch (i.e., fundamental frequency, F0) and important harmonics of music signal are extracted. Second, on the existing CI channels, the music signal is split into multiple sub-bands and the relevant envelopes are respectively matched to the F0 and important lower harmonics, then modulated and aligned with appropriate intervals which are not less than the auditory nerve response absolute refractory period (ANR-ARP). The violin sounds were synthesized and experimented with the CI tone vocoder, and the HTS algorithm was compared and evaluated with the classical continuous interleaved sampling (CIS) algorithm. Twenty normal hearing (NH) subjects were recruited for audiometry experiments. The results showed that the pitch ranking scores of the HTS were obviously better than that of the CIS, and in quiet and noisy conditions the melody recognition rates of the HTS were 46.4% and 49% higher than that of the CIS, respectively. And further results showed that the HTS algorithm also increased the timbre perception of CI vocoder music. It is suggested that the HTS algorithm has the potential to enhance the music perception of CI users. [ABSTRACT FROM AUTHOR]

Abstract: Honeybee salivary glands (SGs) are important exocrine glands. However, the molecular basis of how SGs fulfill their biological duty is still elusive. Proteomics and phosphoproteomics of cephalic SG (HSG) and thoracic SG (TSG) were compared between normal and single-cohort honeybee colonies. Of 113 and 64 differentially regulated proteins and phosphoproteins, 86 and 33 were identified, respectively. The SGs require a wide spectrum of proteins to support their multifaceted functions and ensure normal social management of the colony. Changes of protein expression and phosphoproteins are key role players. The HSG triggers labor transition from in-hive work to foraging activities via the regulation of juvenile hormone and ethyl oleate levels. The stronger expression of proteins involved in carbohydrate and energy metabolism, protein folding, protein metabolism, cellular homeostasis and cytoskeleton in TSG, supports the gland to efficiently enhance honey processing by synthesis and secretion of saliva into nectar. The age structure of the colony is vital for increased production efficiency. This data reveals the molecular underpinning of SGs to accomplish their biological missions and provides new knowledge for the beekeeping industry for enhancing the management and production efficiency of the colony and honey quality through manipulation of potential target proteins. Biological significance: This study comprehensively analyses the characteristic of the proteome and phosphoproteome of honeybee salivary glands (SGs) between normal and single-cohort honeybee colonies. The SGs need a wide spectrum of proteins to support their multifaceted functions and ensure normal social management of the colony. The cephalic SG triggers labor transition from in-hive work to foraging behavior via the regulation of juvenile hormone and ethyl oleate titer. The thoracic SG stronger expressed of proteins related to carbohydrate and energy metabolism, protein folding, protein metabolism, cellular homeostasis and cytoskeleton to support the gland to efficiently enhance honey processing by synthesis and secretion of saliva into nectar. This data reveals the molecular underpinning of SGs to accomplish their biological missions and provides new knowledge for the beekeeping industry for enhancing the maintenance and production efficiency of the colony and honey quality through manipulation of potential target proteins. [Copyright &y& Elsevier]