Response Model Research Articles

Center of mass states render multi-joint torques throughout standing balance recovery.

Reactive balance control requires coordination of neurally-mediated feedforward and feedback pathways to generate stabilizing joint torques at the hip, knee, and ankle. Using a sensorimotor response model, we decomposed reactive joint torques into feedforward and feedback contributions based on delays relative to center of mass kinematics. Responses across joints were driven by the same signals, but contributions from feedforward versus feedback pathways differed, likely due to differences in musculotendon properties between proximal and distal muscles.

Study on Mechanical Response and Constitutive Model of Rubber Concrete under Impact Load and Water Content Coupling

Study on Mechanical Response and Constitutive Model of Rubber Concrete under Impact Load and Water Content Coupling

Receiver operating characteristic analysis for paired comparison data

Abstract Paired comparison models are used for analysing data that involves pairwise comparisons among a set of objects. When the outcomes of the pairwise comparisons have no ties, the paired comparison models can be generalized as a class of binary response models. Receiver operating characteristic (ROC) curves and their corresponding areas under the curves are commonly used as performance metrics to evaluate the discriminating ability of binary response models. Despite their individual wide range of usage and their close connection to binary response models, ROC analysis to our knowledge has never been extended to paired comparison models since the problem of using different objects as the reference in paired comparison models prevents traditional ROC approach from generating unambiguous and interpretable curves. We focus on addressing this problem by proposing two novel methods to construct ROC curves for paired comparison data which provide interpretable statistics and maintain desired asymptotic properties. The methods are then applied and analysed on head-to-head professional sports competition data.

Parallel explorations in LA-NSCLC: Chemoradiation dose-response optimisation considering immunotherapy and cardiac toxicity sparing

Background and purposeChemoradiotherapy (CRT) for locally-advanced non-small cell lung cancer (LA-NSCLC) has undergone advances, including increased overall survival (OS) when combined with immune checkpoint blockade (ICB), and using cardiac-sparing techniques to reduce the radiotoxicity. This research investigated 1) how radiotherapy schedules can be optimised with CRT-ICB schemes, and 2) how cardiac-sparing might change the OS for concurrent CRT (cCRT). Methods and materialsSurvival data and dosimetric indices were sourced from published studies, with 2-year OS standardised and the hazard ratio of mean heart dose (MHD) against radiotoxicity tabulated in purpose. A published CRT dose–response model was selected, then modified with ICB and cardiac-sparing hypotheses. Models were maximum likelihood fitted, then visualised the prediction outcomes after bootstrapping. ResultsThe modelled 2-year OS rate of cCRT-ICB reached 71 % (95 % confidence intervals, CI 62 %, 84 %) and 66 % (95 % CI: 53 %, 81 %) for stage IIIA and IIIB/C, respectively, given 60 Gy in 2 Gy-per-fraction. 60 Gy in 30 fractions remained the best schedule for cCRT-ICB, whereas modest dose de-escalation to 55 Gy only reduced the OS in 2 %. Sequential CRT (sCRT)-ICB provided 6 % OS increases versus the best OS rate achieved by sCRT alone. Photon MHD-sparing achieved a 5–10 % increase in modelled 2-year OS, with protons providing a further roughly 5–10 % increase. ConclusionNeither dose-escalation nor de-escalation relative to 60 Gy in 30 fractions influenced the survival with cCRT-ICB, while 5 Gy dose de-escalation might benefit patients with heavily irradiated organs at risk. Cardiac-sparing improved OS, and protons provided advantages for tumours anatomically overlapped or lay below the heart.

Optimized scheduling study of integrated energy system considering user response characteristics

ABSTRACT Integrated Energy Systems (IES) can effectively promote the utilization of renewable energy. Reducing the impact of uncertainties and achieving energy supply-demand balance has always been a key issue in energy scheduling. However, existing research often focuses only on energy prices when designing demand response strategies, neglecting user response characteristics, particularly the influence of environmental temperature and user consumption levels. Therefore, this paper proposes an IES optimization scheduling model that considers user load response characteristics, analyzing changes in user response loads under different environmental and consumption levels. Additionally, consumption levels and environmental parameters are introduced to build an interactive response model. Different incentive measures are designed based on load types to guide users in adjusting their electricity usage behavior, optimizing the supply-demand curve. Furthermore, to address source-load uncertainties, a two-stage robust optimization model is constructed and solved iteratively using the column-and-constraint generation (C&CG) algorithm. The results indicate that considering user response characteristics reduces the peak-to-valley differences in electrical, thermal, and cooling loads by 18.6%, 7.0%, and 13.3% respectively, and reduces total costs by 8.9%. This model can ensure reliable operation while improving the economic efficiency of the system.

Parametric Investigation of Die-Sinking EDM of Ti6Al4V Using the Hybrid Taguchi-RAMS-RATMI Method

Ti6Al4V is a widely used alloy due to its excellent mechanical qualities and resistance to corrosion, which make it fit for automotive, aerospace, defense, and biomedical sectors. Due to its high strength and limited heat conductivity, it is difficult to machine. Both the workpiece’s and the electrode’s conductivity are important factors that impact the electro-discharge machining (EDM) process. In this case, the machining efficiency is also influenced by the electrode selection. As a result, choosing the right electrode and machining parameters is essential to improving EDM performance on the Ti6Al4V alloy. Research on EDM for Ti6Al4V is limited, with little focus on electrode material selection and shape. The impact of EDM settings on MRR, TWR, and surface roughness is complex, and a comprehensive optimization strategy is needed. Copper electrodes are widely used, but further investigation is needed on EDM’s effects on Ti6Al4V’s surface properties and surface integrity. Addressing these research gaps will improve the understanding and application of EDM for Ti6Al4V, focusing on parameter optimization, surface integrity, and thermal and mechanical effects. By employing copper tools to optimize four crucial EDM process parameters—peak current, duty cycle, discharge current, and pulse-on time—this research aims to increase surface integrity and machining performance. A comprehensive Taguchi experimental design is used to systematically alter the EDM settings. By optimizing parameters using tolerance intervals and response modelling, the recently developed RAMS-RATMI approach improves the dependability of the EDM process and increases machining efficiency. With the optimized EDM settings, there were notable gains in depth of cut enhancement, surface roughness minimization, tool wear rate (TWR) reduction, and material removal rate (MRR). The results of the surface integrity examination showed fewer heat-affected zones, fewer microcracks, and a thinner recast layer. Analysis of variance was used to verify the impact and resilience of the optimized parameters. Superior machining performance, higher surface quality, and increased operational dependability were attained with the Ti6Al4V-optimized EDM process, providing industry practitioners with insightful information and useful recommendations.

School-Home Crisis Communication During the First Wave of COVID-19

ABSTRACT In early 2020, UNESCO acknowledged that the scale of educational disruption associated with COVID-19 was unparalleled. Schools were forced to react swiftly. However, while remote learning has received considerable attention, less is understood of how educational leaders communicated during the COVID crisis. The study of school leaders’ initial crisis communications in response to government-mandated school closures is thus warranted. Accordingly, this study analyses a cross-section of seventeen Australian schools for the period late February through early April 2020 (i.e. term 1), with a focus on the emergency stage of schools’ crisis response, rather than the later remote learning continuity planning phase. A mixed-method research approach was adopted. Phase one involved quantitative content analysis of e-mail frequency, words per-sentence, readability (as measured by Flesch), and grade-level communication pitched at (as measured by Flesch-Kinkaid). Surprisingly, parents required university education to effectively comprehend the e-mails. Phase two confirmed that schools performed well in the first four of Smith & Riley’s (2012) five-step school crisis response model (getting the facts, implementing contingency plans, being decisive, showing concern), but were less effective at facilitating two-way communication. An additional (sixth) insight emerged in the form of school community.

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China.

China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.

Thermodynamic analysis and equilibration response time prediction of recuperator in the SCO2 Brayton cycle

The recuperator of supercritical carbon dioxide (SCO2) Brayton cycle significantly affects the system's flexible control due to its thermal inertia. In this study, the structural parameters and operating parameters of recuperator are considered to quantitatively analyze the law of their influence on equilibration response time. Chebyshev spectrum method has been innovatively applied to the one-dimensional dynamic response model of recuperator. The dynamic response time of recuperator under the condition of temperature variation on the hot side is analyzed. The results show that reducing the thermal inertia of solid wall (by increasing the specific surface area of recuperator) can effectively decrease the equilibrium time. Additionally, the rate of inlet temperature variation should not be too large to reduce the thermal inertia of fluid. The zigzag channel demonstrates better dynamic response characteristic than straight channel. A novel dimensionless formula, based on structure and operating parameters, is proposed to quantitatively characterize and predict the equilibrium time for both straight and zigzag channel, the error is within ±20 %. Moreover, a high-precision neural network is trained to predict the equilibrium time directly. The results provide significant theoretical and application support for alleviating the thermal inertia of PCHE and improving the flexibility of SCO2 system.

Feedback control of heart rate during robotics-assisted tilt table exercise in patients after stroke: a clinical feasibility study

BackgroundPatients with neurological disorders including stroke use rehabilitation to improve cognitive abilities, to regain motor function and to reduce the risk of further complications. Robotics-assisted tilt table technology has been developed to provide early mobilisation and to automate therapy involving the lower limbs. The aim of this study was to evaluate the feasibility of employing a feedback control system for heart rate (HR) during robotics-assisted tilt table exercise in patients after a stroke.MethodsThis feasibility study was designed as a case series with 12 patients (n=12\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 12$$\\end{document}) with no restriction on the time post-stroke or on the degree of post-stroke impairment severity. A robotics-assisted tilt table was augmented with force sensors, a work rate estimation algorithm, and a biofeedback screen that facilitated volitional control of a target work rate. Dynamic models of HR response to changes in target work rate were estimated in system identification tests; nominal models were used to calculate the parameters of feedback controllers designed to give a specified closed-loop bandwidth; and the accuracy of HR control was assessed quantitatively in feedback control tests.ResultsFeedback control tests were successfully conducted in all 12 patients. Dynamic models of heart rate response to imposed work rate were estimated with a mean root-mean-square (RMS) model error of 2.16 beats per minute (bpm), while highly accurate feedback control of heart rate was achieved with a mean RMS tracking error (RMSE) of 2.00 bpm. Control accuracy, i.e. RMSE, was found to be strongly correlated with the magnitude of heart rate variability (HRV): patients with a low magnitude of HRV had low RMSE, i.e. more accurate HR control performance, and vice versa.ConclusionsFeedback control of heart rate during robotics-assisted tilt table exercise was found to be feasible. Future work should investigate robustness aspects of the feedback control system. Modifications to the exercise modality, or alternative modalities, should be explored that allow higher levels of work rate and heart rate intensity to be achieved.

Centrifuge modelling of the dynamic response of twin tunnels under train-induced vibration load

The impact of train-induced vibrations originating from underground tunnels has emerged as a significant environmental issue within urban areas. A multitude of studies, encompassing both numerical analyses and experimental investigations, have been undertaken to explore and address this concern. In the experimental tests, the dynamic characteristics of tunnels and soil has generally been studied using physical models with a single tunnel under a 1 g (gravity) environment. However, twin tunnels are commonly constructed in the field, and the stress of the soil is unrealistically simulated in 1 g models. In this paper, the dynamic characteristics of twin tunnels were studied by centrifuge testing. In this paper, results are presented from scaled model tunnel tests conducted at 50 g for four different conditions: a single tunnel, vertical twin tunnels with 60 mm spacing, horizontal twin tunnels with 60 mm and 120 mm spacing. Train load and sweep load were applied by a parallel pre-stressed actuator at the tunnel invert. Accelerometers were employed to gauge the dynamic response of both the tunnels and the surrounding soil. The experimental results illustrate that the interaction between twin tunnels has a clear amplification effect on the dynamic response of the loading tunnel, with the maximum peak particle acceleration (PPA) increasing by up to 43 %. This amplification phenomenon is reduced as the space between the twin tunnels is increased. The experimental results also indicate that the interaction between twin tunnels has a comparatively lower impact on the dynamic response of the surrounding soil when contrasted with the dynamic response of the tunnels themselves.

Mathematical Modeling of Impacts of Patient Differences on Renin-Angiotensin System and Applications to COVID-19 Lung Fibrosis Outcomes.

Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating the effects of these factors. Recent evidence suggests patient-specific alteration of RAS homeostasis concentrations with premorbidity and the expression level of angiotensin converting enzyme 2 (ACE2) during COVID-19. However, conflicting evidence suggests decreases, increases, or no changes in RAS after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced RAS alteration are still unknown. Here, a multiscale model is developed to quantify the systemic contribution of heterogeneous factors of RAS during COVID-19. Three submodels are connected-an ABM COVID-19 in-host lung tissue model, a RAS model, and a fibrosis model to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicate cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2. In contrast, there are no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. Reduction of ACE and ACE2 downregulates the homeostasis concentration of RAS, including angiotensin II (ANGII), in the lung tissue. At the same time, the decrease in ACE2 increases systemic ANGII and results in severe lung injury and fibrosis. The model explains possible mechanisms for conflicting evidence of RAS alterations in previously published studies, and simulated results are consistent with reported RAS peptide values for SARS-CoV-2-negative and SARS-CoV-2-positive patients. We observed decreased RAS peptides for all virtual patient groups with aging in both sexes. In contrast, large variations in the magnitude of reduction were observed between male and female virtual patients in the older and middle-aged groups. We also predicted that feedback of ANGII·AT1R to renin could restore ANGI homeostasis concentration but fails to restore homeostasis values of RAS peptides downstream of ANGI. In addition, the results show that ACE2 variations with age and sex significantly alter the concentrations of RAS peptides and lead to collagen deposition with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Assessment of Effectiveness and Suitability of Soil and Water Conservation Measures on Hillslopes of the Black Soil Region in Northeast China

There are four sizable black soil regions throughout the world, all of which are valuable natural resources. The black soil region in Northeast China is a major foundation for grain production. Serious risks of soil erosion do exist, and they have an immediate impact on both the country’s food security and future ecological security. Many soil and water conservation measures have been put in place to control soil erosion. However, how effective and suitable are these measures? Currently, systematic analyses and assessments are lacking. The objective of this study was to assess the effectiveness and suitability of soil and water conservation measures on hillslopes using the comprehensive index method and the Pressure–State–Response model. The categorization of effectiveness and suitability of these measures were similar for both methods: that is, very effective and suitable measures included no-tillage + straw mulch and ridge belt or contour ridge. The two methods validated one another. Thus, this categorization standard is useful for choosing the best soil and water conservation measures for different soil erosion regions.

Numerical modelling of aerodynamic response to gusts and gust effect mitigation

Unmanned aerial vehicle (UAV) flights in urban environments are challenging due to the complex flow structures and elevated turbulence around buildings. Consequently, research has shifted towards investigating the impact of gusts on the aerodynamic stability and control of UAVs. This study focuses on enhancing gust numerical modelling capabilities to understand the aerodynamic response, specifically exploring gust mitigation strategies for UAVs operating in turbulent urban environments. The split-velocity method, originally designed for two-dimensional compressible inviscid flows, where the velocity components were decomposed into a prescribed gust velocity and the remaining velocity components, is extended to three-dimensional incompressible viscous flows. To facilitate effective gust mitigation techniques, a radial basis function is applied to the modified split-velocity method to numerically model wings in pitching motions under gust encounters. A novel strategy is proposed to correct the discretized gust velocities and ensure gust flux conservation, showing effective improvement to the numerical predictions. The computed results agreed well with the experimental data available in the public domain, confirming that wing pitch motion can effectively mitigate the effects of gusts.

Correction: The impact of measurement noninvariance across time and group in longitudinal item response modeling

Correction: The impact of measurement noninvariance across time and group in longitudinal item response modeling

In-store augmented reality experiences and its effect on consumer perceptions and behaviour

PurposeThis study aims to explore the impact of augmented reality (AR) technologies on consumer information processing and value perceptions in physical stores. Specifically, it investigates how the vividness, novelty and interactivity of AR shape utilitarian and hedonic value perceptions, and influence consumer purchase and continuance behaviour.Design/methodology/approachThe study used the store intercept survey method at 15 retail outlets across four metro cities in India, representing nine prominent retail brands deploying AR technologies. The data collected (n = 650) were subjected to exploratory and confirmatory factor analysis.FindingsMajor findings confirm a significant effect of vividness, novelty and interactivity on utilitarian and hedonic value perceptions of in-store AR experiences of customers in physical stores. Hedonic value was found to affect continuance intention but not purchase intention, while utilitarian value was found to affect purchase intention but not continuance.Research limitations/implicationsThis study extends the stimulus–organism–response model’s application to AR in physical stores by integrating the impact of vividness, novelty and interaction on both utilitarian and hedonic values and revealing their significance in influencing purchase intentions and continuance.Practical implicationsMajor findings advise retailers to increase AR experience adoption in stores and illustrate the process through which purchase and continuance intentions may be influenced.Originality/valueThis is one of the few studies that explore the impact of AR on consumer attitudes and intentions in physical stores. In addition, the study explores the effect of AR tools as a process that passes through value perceptions and then affects the consumer.

Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.