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This paper aims to present the design and implementation of VirSense, a novel six-DOF haptic interface system, with an emphasis on its gravity compensation and fixed-base motors.

In this paper, the design and manufacture of the VirSense robot and its comparison with the existing haptic devices are presented. The kinematic analysis of the robot, design of the components, and manufacturing of the robot are explained as well.

The proposed system is employed to generate a Virtual Sense (VirSense) with fixed-base motors and a spring compensation system for counterbalancing the torques generated by the weight of the links. The fixed bases of the motors reduce the system's effective mass and inertia, which is an important factor in haptic interface systems. A novel cabling system is used to transmit the motor torques to the end-effector. The spring-based gravity compensation system causes more reduction in the effective mass and inertia.

This paper provides the details of the VirSense haptic device, its gravity compensation system, and a novel cabling power transmission.

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

This study aims to examine the reuse of plastic and fly ash (FA) to improve the soil and achieve sustainable development goals.

Sand from the Anzali port was reinforced with Geopet (GP) and stabilized with FA plus 3% sodium hydroxide. The GP was placed in FA-stabilized soil and the California bearing ratio (CBR), and unconfined compressive strength (UCS) tests were performed on samples at the optimum moisture content.

The results showed that the improvement in the optimum CBR was 174.9%. The UCS increased 15.25% and 48.65% in soil reinforced with three layers of GP plus 15% FA over those containing 10% and 5% FA, respectively. Additionally, the current analysis used response surface methodology (RSM) to investigate the impact of FA percentage, GP layers and their interaction on CBR. The results highlight the efficacy of the used RSM model, as evidenced by the significantly low p-value (<0.0001).

This demonstrates the suitability and effectiveness of RSM for evaluating CBR in this scientific study.

This study aims to use the thermal non-equilibrium approach to inquire the entropy production and conjugate natural heat exchange in a porous medium. Entropy generation is studied separately for the solid matrix and the hybrid nanoliquid.

The characteristic equations are unraveled by applying the finite element method. Mathematical relations are used to calculate the generated entropy for the hybrid nanoliquid and matrix structure.

Based on the results, the produced entropy and the viscous friction term associated with the hybrid nanoliquid phase are not affected by increasing the thermal conductivity ratio of the rigid wall to nanoliquid. Moreover, a higher amount of entropy is generated by the thermal gradients in the hybrid nanoliquid phase compared to the solid matrix.

No investigation in the literature has been reported in this context.

The aim is to examine, critique, and synthesize commonly used methodological approaches that capture middle-aged children’s experiences of their physical environments.

The systematic review identified 174 empirical studies from peer-reviewed journals published in English between 2014 and 2023. Fifty-two studies met the inclusion criteria. A thematic analysis (1) identified study characteristics and common methodological approaches, (2) synthesized the literature to reveal major themes and trends, and (3) pointed out significant research gaps.

Qualitative methods, combining traditional and participatory approaches, are most effective in capturing children’s spatial experiences. Participatory methods offer more authentic insights and reduce power imbalances compared to traditional methods. Place-based methods, such as child-led walks and participant observations, are particularly valuable for capturing the multidimensional and sensory aspects of children’s interactions with their environments.

The choice of keywords, selected databases, and the English-language criterion restricted the number of captured reviewed articles that might contribute to the topic.

This systematic review contributes to a deeper understanding of the methodological approaches used in researching middle-aged children’s experiences of their physical environments. It highlights common strategies used with children to communicate their experience of place, identifying the strengths and limitations of each method. Additionally, the review discusses the various aspects of space revealed by different methods.