Search Results (22)

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

Background: Scientific research on fly-in/fly-out (FIFO) workers has identified a gap in understanding the dynamics of job stress parameters among forest workers throughout the shift cycle. Methods: This study investigated the relationship between psychological and psychophysiological parameters of job stress and work capacity among loggers. The research was conducted during two simultaneous scientific expeditions in July 2024, involving 47 loggers from two teams with differing socio-psychological characteristics. Data were collected daily (morning and evening) using a battery of psychophysiological and psychological tests. Teams’ socio-psychological characteristics were assessed five times during the 15-day fly-in period. Results: The adaptation (beginning) and fatigue (end) phases of the shift were significantly more stressful than the middle period. During these critical phases, assessments of functional state showed greater consistency but were less favorable. Key findings indicate a psychological mobilization effect at the period’s start, where high subjective comfort coexisted with physiological strain. By the end, functional capabilities were maintained despite high fatigue. Furthermore, loggers in teams with a positive socio-psychological climate exhibited a more favorable functional state throughout the shift. Conclusions: The study’s novelty lies in its comprehensive mapping of the dynamic interplay between job stress and work capacity across the FIFO cycle, using both instrumental and questionnaire-based methods. The results underscore the critical influence of the team’s socio-psychological climate on worker well-being and highlight specific high-stress phases that warrant targeted interventions. Full article

Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment, and it is assessed through subjective evaluation, according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers. While research has traditionally emphasised physical factors, growing evidence highlights the role of the state of mind in shaping thermal perception. In a prior Monte Carlo sensitivity analysis, six mood subscales—Anger, Confusion, Vigour, Tension, Depression, and Fatigue—were examined for how they affect the absolute difference between actual and predicted thermal sensation. Depression and vigour were found to be the most influential, while confusion appeared least impactful. However, to accurately assess the role of confusion, it is necessary to consider its potential interactions with other mood subscales. To this end, a mediation analysis was conducted using Hayes’ PROCESS tool. The mediation analyses revealed that confusion partially mediated depression’s effect in males and vigour’s effect in females. These results suggest that, despite a weak direct impact, confusion critically influences thermal perception by altering the effects of key mood states. Accounting for the indirect effects of mood states may lead to more accurate predictions of human sensory experiences and improve the design of occupant-centred environments. Full article

The mental fatigue of crane operators can pose a serious threat to construction safety. To enhance the safety of crane operations on construction sites, this study proposes a rotary switch semi-dry electrode for detecting the mental fatigue of crane operators. This rotary switch semi-dry electrode overcomes the problems of the large impedance value of traditional dry electrodes, the cumbersome wet electrode operation, and the uncontrollable outflow of conductive liquid from traditional semi-dry electrodes. By designing a rotary switch structure inside the electrode, it allows the electrode to be turned on and used in motion, which greatly improves the efficiency of using the conductive fluid and prolongs the electrode’s use time. A conductive sponge was used at the electrode’s contact end with the skin, improving comfort and making it suitable for long-term wear. In addition, in this study, the multifractal detrend fluctuation analysis (MF-DFA) method was used to detect the mental fatigue state of crane operators. The results indicate that the MF-DFA is more responsive to the tiredness traits of individuals than conventional fatigue detection methods. The proposed rotary switch semi-dry electrode can quickly and accurately detect the mental fatigue of crane operators, provide support for timely warning or intervention, and effectively reduce the risk of accidents at construction sites, enhancing construction safety and efficiency. Full article

Introduction: WELLfed is a community-based adult education programme focused on improving food literacy, with a stated aim to “nourish communities through food and connections”, in a low-income community in Aotearoa New Zealand. Adult learners are taught food preparation and cooking skills in weekly interactive sessions. Here, we describe two pilot phases of a three-phase evaluation. Methods: Our overall aim is to evaluate the effectiveness of the WELLfed programme. Phase 1, in keeping with the co-design approach of WELLfed, co-designed outcome measures through five focus groups (n = 20) involving a wide range of stakeholders. Phase 2 developed and refined a survey based on these co-designed measures. The survey was administered to WELLfed learners at baseline (n = 15) and again towards the end of their programme (follow-up n = 12). Wilcoxon rank sum tests of significance were performed, restricted to participants with both baseline and follow-up data. Results: Five domains of importance identified in Phase 1 were as follows: (i) engagement support, (ii) food knowledge and skills, (iii) personal development, (iv) relationship transformation, and (v) community flourishing. Phase 2 findings include increased comfort level at engagement (p = 0.063). Food knowledge and preparation skills improved on a range of factors. All metrics of self-confidence and self-efficacy increased, with the greatest change being the ability to find support in times of crisis. Self-reported excellent or very good health increased (p = 0.050). Fewer people reported food running out due to a lack of money (p = 0.016) or eating less because of a lack of money (p = 0.008). Conclusions: The pilot study shows the effectiveness of the WELLfed programme for improving food knowledge and skills, reducing food insecurity, and improving health outcomes. Further research with a larger sample size is required to confirm these pilot results. Full article

Introduction: Anxiety and depression are significant mental health concerns for individuals with congenital heart disease (CHD). As group therapy has proven to be a valuable and effective treatment option for managing anxiety and depression, the aim of this study was to determine its effects on patients with CHD and anxious–depressive symptoms. Methods: We used non-pharmacological psychological group intervention, of six weekly sessions of 90 min each, administered by trained personnel, in adult patients with CHD. Measurement tools included quality of life (Euro quality of life-5D questionnaire), self-esteem (Rosenberg Self-Esteem Scale), anxiety (State–Trait Anxiety Inventory), depression (Beck Depression Inventory-II), and satisfaction surveys. Results: A total of 18 out of 21 CHD patients (mean age 35.8 ± 9.0 years old and 13 (72%) females) completed the program. According to CHD complexity, five (28%) patients had mild, six (33%) moderate, and seven (38%) great defects. Patients with CHD scored significantly higher in the Euro quality of life visual analogue scale (7.83 ± 1.4 vs. 7.14 ± 1.6, p = 0.012) and lower in the Beck Depression Inventory-II (12.3 ± 10.9 vs. 18.1 ± 12.1, p = 0.003) post-program than pre-intervention. Meanwhile, the Rosenberg Self-Esteem Scale score was close to reaching statistical significance (27.4 ± 6.0 vs. 25.1 ± 5.4, p = 0.051), while the State–Trait Anxiety Inventory did not. Finally, participants scored high in the satisfaction questionnaire at the end of the sessions, on a scale from 0 to 3, especially in the questions related to feeling comfortable with others (2.5 ± 0.6), recommending the program (2.3 ± 0.6), or being willing to attend future sessions (2.6 ± 0.8). Conclusions: Group psychological therapy proved to be a useful tool to reduce depressive symptomatology after a 6-week program, providing a comfortable environment to patients with CHD. Full article

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMS_ad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMS_ad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle. Full article

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches. Full article

The assessment of infrastructure integrity is considered paramount to verify its structural health and to build its resilience. In this study, a monitoring strategy, consisting of a pre-developed microcontroller-based data acquisition system (DAQ) hardware and a software program for post processing built on LabVIEW platform, was conducted to assess the structural behavior of an arch-and-tie pedestrian bridge located in Haidari, Greece, following its construction phase. This endeavor aimed to delineate its systemic state and to verify the fulfillment of comfort criteria stated by EN1990, HIVOSS and SETRA guidelines. To this end, four trademark Bridge Diagnostic Inc. (BDI) triaxial accelerometers were meticulously deployed along the bridge expanse to scrutinize the structure’s response toward a spectrum of induced perturbations. The established framework effectively compiled the acquired acceleration time domain then employed a Butterworth bandpass filter to derive the bridge eigenfrequencies, eigenmodes, and damping ratios. The resultant findings conclusively indicate that the bridge response towards pedestrian crossing conforms to the established specifications and thus does not necessitate the installation of dampers. The bridge maintains comfortable structural integrity for pedestrian traversal up to an upper frequency limit of 3.67 Hz, substantiating its ability to absorb the dissipated energy generated by pedestrian movement. Full article

The meaning of technological progress is to produce economic development and to increase the level of personal comfort. Sustainability can only be achieved if, at the microsystem level as well as at the macrosystem level, the secondary effects of the activities undertaken by people on the environment are in a state of neutrality compared to the impact they can produce on natural conditions. This neutrality can be intrinsic or can be achieved through coercive and compensatory measures. If we take into account the production of carbon dioxide that accompanies a product from the stages of conceptualisation, design, procurement of materials, execution, operation, maintenance, decommissioning and recycling the waste produced at the end of use, then nothing can be sustainable in pure form. Nevertheless, there are products whose production, both as a raw material and as a technological process, can be neutral in terms of carbon emissions. Moreover, they can even become carbon negative over time. This is also the case with eco-sustainable hemp concrete, whose capacity to absorb carbon dioxide starts from the growth phase of the plant from which the raw material is obtained and continues throughout the existence of the constructed buildings. Not only does it absorb carbon dioxide, but it also stores it for a period of at least 50 years as long as the construction is guaranteed, being at the same time completely recyclable. However, in order to obtain an optimal mixture from the point of view of raw material consumption, represented by industrial hemp wood chips and the binder based on lime and cement, multiple experiments are necessary. The study presented in this work is based on the use of an advanced method of experimental planning (design of experiments method), which makes possible the correlation between the values obtained experimentally and the algorithm that generated the matrix arrangement of the quantities of materials used in the recipes. This approach helps to create the necessary framework for parametric optimisation with a small number of trials. Thus, it is possible to obtain the mathematical law valid within the minimum and maximum limits of the studied domain that defines the characteristics of the material and allows the achievement of optimisation. The material is thus designed to satisfy the maximum thermal insulation requirements that it can achieve depending on a certain minimum admissible compressive strength. Full article

Driveline oscillation is a significant concern in the context of hybrid electric vehicles (HEVs), because it can adversely affect the vehicles’ sustainability. The reason for this is that the oscillation not only diminishes the longevity of components due to high mechanical contact stress but also results in poor driving comfort, which in turn reduces customer satisfaction. To address the issue of driveline oscillation effectively, two critical challenges, namely the time-varying torque load and driveline backlash, need to be tackled. To this end, this study constructs a control-oriented model of a second-order system plus a dead zone for the driveline backlash. An extended state observer is designed in order to estimate the unmeasurable load torque. As such, an extended-state-observer-based compensator is proposed to suppress driveline oscillations for HEVs. To evaluate the control and observation performance of the proposed extended-state-observer-based compensator, simulation and engine-in-loop experiments are conducted. Results obtained in the time and frequency domains reveal that the proposed control scheme substantially reduces driveline oscillation. Full article

Variable damping shock absorbers have received extensive attention for their efficient vibration reduction performance, and air springs have also been widely used in high-end commercial vehicles due to their nonlinear stiffness characteristics. This paper presents a novel semi-active cab suspension integrated with an air spring and a variable damping electromagnetic damper (A-EMD). The electromagnetic damper (EMD) prototype was designed, manufactured and tested. Then, due to the interference of nonlinear stiffness characteristics of the air spring with the controller in the subsequent design, the Takagi–Sugeno fuzzy method was adopted to segmentally linearize its nonlinearity, based on which an H∞ state feedback semi-active controller was designed to control the EMD to generate variable damping force. Furthermore, a Luenberger state observer was designed to provide immeasurable state parameters for the controller. Numerical simulations were carried out to validate the effectiveness of the proposed approaches, and the results show that the proposed control strategy can significantly improve the ride comfort of the A-EMD system. The vibration dose value (VDV) acceleration under the bump road and the frequency-weighted acceleration root mean square (FWA-RMS) under the random road decreased by 36.05% and 19.77%, respectively, compared with the passive suspension system. Full article

The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR. Full article

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air. Full article

In recent years, demand response (DR) has played an increasingly important role in maintaining the safety, stability and economic operation of power grid. Due to the continuous running state and extremely fast speed of response, the aggregated inverter air conditioning (IAC) load is considered as the latest and most ideal object for DR. However, it is easy to cause load rebound when the aggregated IAC load participates in DR. Existing methods for controlling air conditioners to participate in DR cannot meet the following three requirements at the same time: basic DR target, load rebound suppression, and users’ comfort. Therefore, this paper has proposed a genetic algorithm based temperature-queuing control method for aggregated IAC load control, which could suppress load rebound under the premise of ensuring the DR target and take users’ comfort into account. Firstly, the model of the aggregated IAC load is established by the Monte Carlo method. Then the start and end time of DR are selected as the main solution variables. A genetic algorithm is used as the solving tool. The simulation results show that the proposed strategy shows better performance in suppressing load rebound. In the specific application scenario of adjusting the frequency fluctuation of the microgrid, the results of the case show that this strategy can effectively control the frequency fluctuation of the microgrid. The effectiveness of the strategy is verified. Full article