Load Components Research Articles

To Hear or Not to Hear: Cognitive Load Theory and Learning

ABSTRACT Background: Cognitive load is the amount of information one’s working memory can process at any given time. Key components of cognitive load include germane load, intrinsic load, and extraneous load. Learning is hindered if overload occurs, thus reducing cognitive performance. This study addresses the impact of auditory stimulation on cognitive load through repeated recognition tasks. Methods: Twenty-eight Queen’s University students were recruited and randomly allocated to control and experimental groups who completed the experiment with noise-cancelling headphones and with ambient noise, respectively. The experiment consisted of learning and testing phases. The learning phase involved memorizing a series of words. Subsequently, the testing phase required participants to recall words from the list. Each trial consisted of one learning and one testing phase, with three trials per participant. Cardiovascular measurements were taken prior to the experiment, at the beginning of each phase, and after each trial was completed. Results: Primary outcomes demonstrated that the experimental group experienced greater percent changes in heart rate and rate pressure product, higher perceived mental effort, and greater within-group variation for heart rate compared to controls. Scores decreased as trials progressed. Secondary outcomes showed that the top 33% of participants were less susceptible to changes in heart rate and blood pressure. Overall, four heart rate or blood pressure responders were identified. Conclusions: Exposure to 70 decibels of ambient noise contributes to cognitive load as noted by elevated cardiovascular measure and perceived mental effort. Results can inform educational platforms and hospital logistics.

Reliability analysis of load sharing systems under unequal load-sharing rule with applications

We are examining a setup consisting of several parallel-connected k-out-of-m load sharing systems. The load sharing is modeled using proportional conditional failure rates for component failures and load sharing under the unequal load-sharing rule. We have derived the system reliability for the entire setup. To illustrate this, we examine two cases: one with n systems connected in parallel, each following a 1-out-of-2 configuration, and the other with n systems connected in parallel, each following a 2-out-of-4 configuration. The system components are assumed to be identical with an exponential as well as Weibull baseline distributions. We propose two methods to estimate the baseline parameter and load-sharing parameters. Additionally, we present a confidence interval using bootstrapping techniques, specifically the percentile bootstrap (boot-p). We assess the performance of these parameter estimation and interval estimation methods through simulation studies. Furthermore, we evaluate the practical applicability of the proposed techniques by analyzing two real datasets. The same approach can be applied to a system that includes multiple k-out-of-m load sharing systems, where these systems are connected in either a series or a p-out-of-q configuration.

How reliable is the Real Adsorbed Solution Theory (RAST) for estimating ternary mixture equilibrium in microporous host materials?

Microporous crystalline adsorbents such as zeolites, and metal-organic frameworks (MOFs) have potential use in a wide variety of separations applications. In applications such as CO2 capture, the Ideal Adsorbed Solution Theory (IAST) often fails to provide a quantitative description of mixture adsorption equilibrium especially in cation-exchanged zeolites. The failure of the IAST is ascribable to non-compliance with one or more tenets mandated by the IAST such as (a) homogeneous distribution of adsorbates within the pore landscape, (b) no preferential location of guest species, and (c) absence of molecular clustering due to say hydrogen bonding. The focus of this article is on the reliability of the Real Adsorbed Solution Theory (RAST) models for quantitative estimation of adsorption equilibrium. Configurational-Bias Monte Carlo (CBMC) simulations are undertaken to determine the adsorption equilibrium for ternary CO2/CH4/N2, CO2/CH4/C3H8, CO2/CH4/H2, and water/methanol/ethanol mixtures in NaX, LTA-4A, CHA, DDR, and MFI zeolites. Additionally, CBMC simulations of the constituent binary pairs are used to determine the Wilson or NRTL parameters, taking due account of the dependence of the activity coefficients on the spreading pressure. Use of the binary pair Wilson or NRTL parameters allows the estimation of ternary mixture adsorption equilibrium, that is tested against the CBMC data on component loadings. In all investigated guest/host combinations, the RAST provides a good estimation of ternary mixture adsorption equilibrium.

Co-Delivery of VEGF siRNA and THPP via Metal-Organic Framework Reverses Cisplatin-Resistant Non-Small Cell Lung Cancer and Inhibits Metastasis through a MUC4 Regulating Mechanism.

Cisplatin resistance significantly impacts the antitumor efficacy of cisplatin chemotherapy and contributes to poor prognosis, including metastasis. In this study, we present the utilization of metal-organic framework (MOF) nanoparticles as the therapeutic component and drug loading scaffold for implementing a ternary combination therapeutic strategy to combat cisplatin-resistant lung cancer and metastasis. Specifically, by engineering MOFs (Cis@MOF-siVEGF) through the self-assembly of THPP as photosensitizer for photodynamic therapy (PDT), along with the incorporation of cisplatin (DDP) and VEGF siRNA (siVEGF), we propose the leverage of photodynamic-induced oxidative damage and gene silencing of the angiogenic factor to reverse cisplatin resistance and sensitize therapeutic potency. Our findings demonstrated that the chemo/photodynamic/antiangiogenic triple combination therapy via Cis@MOF-siVEGF under irradiation effectively inhibits cisplatin-resistant tumor growth and induces abscopal effects. Importantly, molecular mechanistic exploration suggested that MUC4 exerted regulatory effects on governing cancer metastasis, thus representing a potential immunotherapeutic target for cancer intervention. Overall, our study creates a MOFs-based multicomponent delivery platform for complementary therapeutic modules with synergistically enhanced antitumor efficacy and sheds light on potential regulatory mechanisms on cisplatin-resistance cancers.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Small Molecule Hydrogels Loading Small Molecule Drugs from Chinese Medicine for the Enhanced Treatment of Traumatic Brain Injury.

Self-assembly of hydrogels for mechanical support and drug delivery has been extensively researched in traumatic brain injury (TBI), where treatment options are limited. The chief challenge is that most self-assembled hydrogels rely on high molecular carriers or the incorporation of exogenous inactive substances as mediators. It is difficult for these drug delivery systems to achieve clinical translation due to concerns regarding biological safety. Here we report a small molecule hydrogel (GBR-gel) loading small molecule drugs (glycyrrhizic acid, berberine, and rhein) that originated from popular Chinese medicines without additional drug loading or inactive components under physiological conditions. In the long run, GBR-gel possesses several advantages, including ease of preparation, cost-effectiveness, and high biocompatibility. As a proof-of-concept, GBR-gel allows for prompt administration at the site of brain injury to exert potent pharmacodynamic effects. Further single-cell RNA sequencing and experimental validation indicated that GBR-gel can effectively rescue the suppressed glutamatergic synapse pathway after TBI, thereby attenuating inflammatory responses and neural impairments. Our work provides an alternative strategy for timely intervention of TBI.

Mapping dominant tree species of miombo woodlands in Western Tanzania using PlanetScope imagery

Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Key performance indicators and reference values for turn performance in elite youth, junior and adult swimmers

ABSTRACT This study aimed to determine kinematic and kinetic key performance indicators (KPI) of swimming turn performance using principal component analysis (PCA) and multiple linear regression analysis and provide reference values using percentiles. Touch and tumble turn performances of male (n = 68) and female (n = 48) Swiss national team members from three age categories—adult (20.2 ± 2.7 yrs, 790 ± 57 points), junior (16.2 ± 0.8 yrs, 729 ± 53 points) and youth swimmers (14.4 ± 1.0 years of age, 667 ± 53 World Aquatics swimming points, respectively)—were assessed with a motion analysis system equipped with a force plate on the pool wall, one over- and four underwater cameras sampling forces at 500 Hz and footages at 100 Hz. The PCA reduced the 27 original variables by up to 15% depending on turn type and age category using Varimax component loading of >0.6 and explained up to 91% of the total variance. The highest Varimax component loadings for each principal component were used to determine KPI for each turn type and age category using multiple-regression analysis with total turn time as dependent variable. These KPI should be used to interpret turn performances and identify individual swimmers’ strengths, weaknesses and future potentials with the help of the percentiles as reference values.

Real-Time estimation of internal and solar heat gains in buildings using deep learning

This paper presents real-time monitoring of dynamic internal and solar heat gains using programmable low-cost cameras and deep learning techniques. For monitoring changes in occupancy, equipment, lighting, and window status in real time, a convolutional neural network (CNN)-based multi-head classification model was developed and trained with High Dynamic Range (HDR) images, collected using a low-cost fisheye camera in offices. A Python-based region of interest (ROI) generation program was developed to predefine the target areas for monitoring. The classification heads of the model were trained from scratch using a small office image dataset first; then, they were fine-tuned using another HDR image dataset collected in a larger open-plan office with more complex scenes, to evaluate the transferability of the model. The weighted mean classification precision and recall results showed that the developed model could classify the detailed status of each target heat gain (occupants, equipment, lighting, windows) in the predefined areas of the scene with great performance. Finally, to evaluate the impact of real-time monitoring of heat gains on energy demand, the open plan office space used for the experimental dataset collection was modeled using EnergyPlus software using (i) commonly assumed fixed schedules for occupancy, equipment and lighting and (ii) real-time monitored dynamic schedules for internal and solar gain components under the same weather conditions. The results showed that recommended fixed schedules may lead to significant errors in estimated internal and solar gains. The largest discrepancy was noted for occupancy and equipment usage, but other categories also showed both underestimation and overestimation of thermal load components. Implementing reliable and continuous monitoring of dynamic internal and solar heat gains is important for efficient demand-driven HVAC control.

Metal oxide particle electrodes for degradation of high concentration phenol wastewater via electrocatalytic advanced oxidation

High-concentration phenol wastewater is pollutant of concern that pose significant risks to human health and the environment. Three-dimensional electrocatalytic oxidation is one of the most promising wastewater treatment technologies because of its high treatment efficiency, low energy consumption and low secondary pollution. Lower-cost and higher-performance particles still faces great challenges. In this work, metal oxide particle electrodes were prepared using granular activated carbon (GAC) as a substrate to study the degradation of phenol by three-dimensional electrocatalytic oxidation. GAC particle electrodes loaded with different monometallic oxides (Mn, Fe, Co, Ce) and bimetallic oxides (Fe and Ce) were prepared by the impregnation method. The effectiveness of the particle electrodes in degrading phenol was greatly improved after active components loading. Among all monometallic oxide particle electrodes, the concentration degradation efficiency was in the order of Ce/GAC > Co/GAC >Mn/GAC > Fe/GAC, and the COD degradation efficiency was Ce/GAC > Fe/GAC > Co/GAC >Mn/GAC. After optimizing the loading metal type and loading amount, it was found that the 1.1% Fe-2.7% Ce/GAC particle electrode perform the best, with a phenol degradation efficiency of 95.48%, a COD degradation rate of 94.35%, an energy consumption of 0.75 kWh/kg COD. This lower-cost and higher-performance particle highlights a reliable route for solving the problem of particle electrode materials limiting the efficient treatment of phenol-containing wastewater.

Exploring body morphometry and weight prediction in Ganjam goats in India through principal component analysis.

The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue.

Effect of Residual Stresses on the Fatigue Stress Range of a Pre-Deformed Stainless Steel AISI 316L Exposed to Combined Loading

AISI 316L austenitic stainless steel is utilized in various processing industries, due to its abrasion resistance, corrosion resistance, and excellent properties over a wide temperature range. The physical and mechanical properties of a material change during the manufacturing process and plastic deformation, e.g., bending. During the combined tensile and bending loading of a structural component, the stress state changes due to the residual stresses and the loading range. To characterize the component’s stress state, the billet was bent to induce residual stress, but a phase transformation to martensite also occurred. The bent billet was subjected to combined tensile–bending and fatigue loading. The experimentally measured the load vs. displacement of the bent billet was compared with the numerical simulations. The results showed that during fatigue loading of the bent billet, both the initial stress state at the critical point and the stress state during the dynamic loading itself must be considered. Analysis was demonstrated only for one single critical point on the surface of the bent billet. The residual stresses due to the phase transformation of austenite to martensite affected the range and ratio of stress. The model for the stress–strain behaviour of the material was established by comparing the experimentally and numerically obtained load vs. displacement curves. Based on the description of the stress–strain behaviour of the pre-deformed material, guidelines have been provided for reducing residual tensile stresses in pre-deformed structural components.

Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices

This study introduces an innovative optimization process for calibrating probabilistic load and resistance factors (LRFs) in limit state designs, effectively accommodating multiple target reliability indices. Given the impracticality of direct Monte Carlo simulations (MCS) for this task, a response surface method (RSM) is proposed to approximate load and resistance components separately rather than fitting conventional safety factors. This approach eliminates the need for additional implicit evaluations, thereby improving the efficiency of LRF calibration across multiple targets. The process is further enhanced by an adaptive boundary algorithm that updates search domains in real-time, streamlining the optimization. Validation through three examples—including one explicit and two implicit performance functions (a structural and a geotechnical example)—demonstrates that the method achieves accurate results with fewer iterations by dynamically narrowing search domains. Specifically, the accuracy of the proposed method is confirmed by comparing results with those from the literature for the explicit example and with basic MCS results applied to the initial implicit problems. Performance on the illustrative examples shows that the structural example achieves calibration for three targets within ten iterations. Additionally, this method eliminates the need for approximately ten thousand implicit evaluations when calculating limit state points for the geotechnical example.

Surface roughness measurement in the notch area for estimating dynamic component load bearing capacity

The surface of a part can have an essential influence on its dynamic load bearing capacity as grooves and scratches act as more or less significant imperfections or additional micro-notches, depending on the interpretation. In order to accurately predict the fatigue strength, detailed knowledge of the surface structure in critical areas is essential but cannot always be established due to geometric constraints. To overcome these restrictions, a method utilizing impressions and a laser scanning microscope to take surface topography measurements in critical notch areas is used in this work. It enables precise and reproducible measurements to be taken. The method is used to conduct surface roughness measurements on fatigue strength specimens made of four different materials. Several roughness influence factors are calculated based on the results and their influence on the result of a recalculation using a fatigue strength assessment is shown as example. The calculated fatigue limit is compared against experimental results. The comparison shows significant scatter in prediction accuracy and no optimal surface influence factor can be recommended so far. It has to be noted that only a small sample of specimens, which were not designed for researching surface roughness influence in particular, were available for this work. The effects of roughness in the notch may not be completely captured by the available roughness influence factors. Furthermore, issues arise in the correct implementation of stress concentration factor based roughness influence factors. Further research is required to provide a more comprehensive understanding of the effects of surface roughness on the fatigue limit.

Observation and modeling of potential sub-threshold damage growth mechanism for nitinol in ultra-high cycle fatigue

Fatigue fracture of small nitinol components commonly used in medical devices is currently dominated by the initiation and/or growth of small cracks at non-metallic inclusions preceding conventional fatigue crack growth. Therefore, an understanding of the threshold and growth of small cracks is critical to inform fatigue performance of devices. In this paper, we conduct rotary bend fatigue experiments of nitinol wire to 2 billion cycles, measure the inclusion from which the crack initiated, and calculate the stress intensity threshold. Inclusion size is compared to the size of a unique feature observable with a scanning electron microscope which appears as a smooth area surrounding the inclusion and only on specimens that fractured after > 10 million cycles. The hypothesis presented is that the smooth feature around the inclusion is the growth of a small crack which continues until it reaches a size large enough to cause conventional fatigue crack growth. Relating inclusion size to that of the smooth feature creates a damage curve that can be written as a function of cycles to fracture. This damage curve may be useful to estimate the critical size of the largest allowable defect based on the design life and applied loading of the nitinol component.

Salivary and Serum Analytes and Their Associations with Self-rated Health Among Healthy Young Adults.

Understanding the biological processes underlying poor self-rated health (SRH) can inform prevention efforts. The COVID-19 pandemic highlighted the importance of using self-reported measures and self-collected biospecimens, such as saliva, to understand physiological functioning and assist with health surveillance and promotion. However, the associations between salivary analytes and SRH remain understudied. The current study addresses this gap. In a laboratory-based study, 99 healthy adults (Mage = 23.8years, SD = 4.5, 55% men, 43% non-Hispanic White) reported their SRH and provided saliva and blood samples that were assayed for adiponectin, C-reactive protein (CRP), uric acid (UA), and cytokines(IL-1β, IL-6, IL-8, TNF-α). Principal component analyses assessed the component loadings and generated factor scores for saliva and serum analytes. Binary logistic regressions examined the associations between these components and poor SRH. Salivary analytes loaded onto two components (component 1: adiponectin and cytokines; component 2: CRP and UA) explaining 58% of the variance. Serum analytes grouped onto three components (component 1: IL-8 and TNF-α; component 2: CRP, IL-1β, and IL-6; component 3: adiponectin and UA) explaining 76% of the variance. Higher salivary component 1 scores predicted higher odds of reporting poor SRH (OR 1.53, 95%CI [1.10, 2.11]). Higher serum component 2 scores predicted higher odds of reporting poor SRH (OR 2.37, 95%CI [1.20, 4.67]). When examined in the same model, salivary component 1 (OR 1.79, 95%CI [1.17, 2.75]) and serum component 2 were associated with poorer SRH (OR 7.74, 95%CI [2.18, 27.40]). In our sample, whether measured in saliva or serum, indices of inflammatory processes were associated with SRH.

Adhesive and cohesive fracture of blood clots: Experiments and modeling

Blood clots represent living materials composed of a polymer network and an abundance of cells. They might fracture within the bulk material of the clot (cohesive fracture), at the interface between the clot and the surrounding tissue (adhesive fracture), or through a combination of both modes (hybrid fracture). The clot fracture within vascular systems and injury sites could lead to life-threatening conditions. Despite the significance, understanding and modeling the fracture behaviors of blood clots, including their dependence on mechanical loading and cellular components, remain in a nascent stage. In this study, we employ an integrated experimental-computational approach to comprehensively investigate the fracture behaviors of bovine blood clots. We explore various mechanical factors, substrates, and cellular components such as red blood cells (RBCs) and platelets. Our findings reveal that among various tissue substrates, blood clots exhibit the highest interfacial adhesion energy with muscle, and the lowest to the inner arterial lining, consistent with their biological function. Both interfacial adhesion energy and bulk fracture energy are rate-dependent, although they exhibit different dependencies. Also, RBCs and platelets have different effects on clot fracture. An increase in RBC content tends to toughen both adhesion and fracture of blood clots. However, an increase in platelet content enhances interfacial adhesion energy but lowers the bulk fracture energy. The platelet content also governs the shift from adhesive fracture to hybrid fracture. To model clot fracture, we developed two finite element models incorporating a coupled cohesive-zone and Mullins-effect approach to simulate pure shear fracture and peeling of blood clots. These models, validated through experimental data, elucidate the interplay between intrinsic fracture toughness, interfacial strength, and bulk energy dissipation during clot fracture. This study significantly advances our understanding of clot mechanics, providing valuable insights into the mechanics of similar living materials and the management of clot-related disorders such as hemorrhage and thrombosis.

An experimental study of a quasi-impulsive backwards wave force associated with the secondary load cycle on a vertical cylinder

Steep wave breaking on a vertical cylinder (a typical foundation supporting offshore wind turbines) will induce slam loads. Many questions on the important violent wave loading and the associated secondary load cycle remain unanswered. We use laboratory experiments with unidirectional waves to investigate the fluid loading on vertical cylinders. We use a novel three-phase decomposition approach that allows us to separate different types of nonlinearity. Our findings reveal the existence of an additional quasi-impulsive loading component that is associated with the secondary load cycle and occurs in the backwards direction against that of the incoming waves. This quasi-impulsive force occurs at the end of the secondary load cycle and close to the passage of the downward zero-crossing point of the undisturbed wave. Wavelet analysis showed that the impulsive force exhibits superficially similar behaviour to a typical wave-slamming event but in the reverse direction. To monitor the scattered wave field and extract run-up on the cylinder, we installed a four-camera synchronised video system and found a strong temporal correlation between the arrival time of the Type-II scattered wave onto the cylinder and the occurrence of this quasi-impulsive force. The temporal characteristics of this quasi-impulsive force can be approximated by the Goda wave impact model, taking the collision of the Type-II scattered waves at the rear stagnation point as the impact source.

Analysis of Wave Load Characteristics of Hovercraft Based on Model Test

The prediction of the wave load on a hovercraft is essential for the design of the hull structure and safety. However, theoretical methods for the prediction of wave loads are still not mature enough due to the unique and complex nature of the air cushion structure, and numerical modeling and simulation are challenging due to the complexity of the gas-solid-liquid three-phase coupling, so the study of wave loads on hovercrafts still relies on experimentation. In this study, we aim to analyze the wave load response characteristics of a four-chamber hovercraft by conducting a wave load model test under medium/low sea states. The load components and amplitude-frequency response characteristics were thoroughly analyzed based on the acquired data of the cushion pressure, acceleration, and bending moment. The main characteristics of the wave-induced response of the hovercraft were described in detail, and an analytical relationship between the cushion pressure and hull acceleration was derived. The reliability of the experimental results was confirmed through a comparison with the derived results. The relationship between the cushion pressure and cushion volume was investigated in terms of the observed geometric volume of the air chamber, and the relationship between the cushion pressure and flow rate was analyzed to validate the derivation of the theory of wave loads on hovercrafts.

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.