Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family.

The growing use of additive manufacturing (AM) processes pushes research towards studying methods to reduce their environmental impact. The part build orientation is a significant process variable, which can be chosen through the Energy Performance Assessment (EPA), a straightforward method. The paper presents a method for identifying the best part build orientation considering energy consumption. The EPA has been adapted for this purpose, resulting in an approach based on four steps. The method was employed to determine the best printing direction for three parts and two AM technologies.

E-scooters are a cost-effective means of urban transport, however, there have been questions about their safety, performance, and energy efficiency. This paper investigates the rolling resistance of scooter tyres so that the performance of scooters can be more accurately determined. A rolling resistance trailer was manufactured to directly measure tractive force and closely approximate the rolling resistance force for nine commonly used scooter tyres at low speed on a smooth concrete surface. The results of this study will enable a better understanding of the energy losses of these devices.

This paper addresses the underexplored domain of hydraulic energy harvesters (HEH). Through a literature review, existing designs are identified, aiding in the categorisation of energy conversion technologies and fluid-mechanical interfaces. Recognizing a lack of standardized approaches to testing HEH, the paper proposes a re-configurable test platform. The platform, accommodating diverse configurations, operates at high pressures, aligns with existing hydraulic setups, and functions in static or dynamic modes. This tool aims to assist researchers further explore the implementation of HEHs.

Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing and implementing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbors, extreme gradient boosting, adaptive boosting, Random Forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches local, interpretable, model-agnostic explanation and SHapley Additive exPlanations as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations.

The literature investigates trade-environment relationship at the firm level, but does not focus on the environmental effect of trade policy uncertainty. In the context of de-globalization and Sino-US trade friction, trade policy uncertainty significantly increases. How does trade policy uncertainty affect firms’ pollution emissions? In this study, we incorporate energy, pollution, and trade policy uncertainty into Melitz’s (2003) framework and construct a theoretical model to reveal the relationship between trade policy uncertainty and pollution emissions. Then, we employ the event that the USA granted permanent normal trade relationship to China as a quasi natural experiment. We use difference-in-difference-in-difference model and the data of Chinese manufacturing firms for empirical analysis. Our results indicate that the decrease in trade policy uncertainty reduces emission intensity of exporting firms, but has no significant impact on emission levels. Given that these firms do not aggravate emission levels under the condition of expanding output scale, we conclude that the decrease in trade policy uncertainty can improve environmental performance. Mechanism analysis shows an interesting finding that the decrease in trade policy uncertainty reduces emission intensity mainly by improving energy efficiency rather than improving abatement technology and optimizing energy structure. In addition, pollution reductions mainly occur in pollution-intensive and capital-intensive industries as well as coastal regions. Altogether, this study contributes to the literature on trade-environment relationship and trade policy uncertainty.

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.

In order to solve the problems of low loading capacity and low driving efficiency for the powered exoskeleton, this paper presents a bionic multi-chamber pneumatic actuator based on muscle scale mechanism. Firstly, the bionic muscle scale mechanism and multi-chamber structure design for the novel pneumatic actuator are introduced. Afterward, the driving characteristics of the multi-chamber actuator are analyzed theoretically, including analysis of output force and analysis of energy efficiency. Then, the load matching control strategy for the novel actuator is optimized, and the load matching performance, displacement tracking accuracy, and energy efficiency are studied by simulation. Finally, the prototype of the multi-chamber actuator is developed, and the exoskeleton testing platform is built, experiment and discussion are conducted for the driving characteristics, which realized the high energy efficiency and the feasibility of load matching.

Electric vertical take-off and landing aircraft (eVTOLs) have been accessed on various configurations over the past decade. This literature review deals with the issue of determining the appropriate design for an Autonomous Passenger Drone (APD). APDs have been compared with VTOLs on their pros and cons. The authors analysed aerodynamics and propulsion systems of multiple APDs. Further, the comparative analysis aids in designing the best framework for the exterior form of APDs based on human capacity, flying technology, fuel type, travel distance, door type, size, material, safety, cost, etc.

Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.

The energetic advantages of series and parallel elastic actuators have been characterized in the literature considering different elastic systems and different tasks. These characterizations usually determine the energy consumption of a specific system during a specific task and generalize poorly. This paper proposes an energetic characterization of elastic actuators, following an analytical approach, rather than a data-driven one. In particular, this work analyzes the energy consumption of elastic actuators during resonant motion and introduces a novel efficiency index. This index characterizes energy consumption as a function of inherent actuator parameters only, generalizing over the specific tasks. The proposed analysis is validated using simulations and experiments, demonstrating its coherence with analytical results.

Agriculture is one of the high input energy using sectors which ultimately produces the output energy for the survival of human beings. Wheat is an important cereal in the agriculture production system. It is a major food crop and staple food for many countries in the world. Higher population growth has increased demand for wheat, and this demand has been met through the adoption of modern agricultural practices which are heavily dependent on energy. The current study was planned to examine the input energy use efficiency of rainfed wheat growers in Pakistan and Turkey (countries among the top 10 global wheat producers). A total of 119 wheat growers from the rainfed areas of both countries were randomly selected. The data envelopment analysis was executed to estimate the input energy use efficiency score of the growers. The results of the study revealed that almost a similar source of input energy is used in both countries in wheat cultivation. The largest input energy consumption in Turkey was nitrogen fertilizer (10,531.50 MJ ha−1), while in Pakistan was farmyard manure (12,837.32 MJ ha−1). The Turkish growers have higher energy use efficiency 2.42 as compared to Pakistani growers, whose energy use efficiency was 1.09. Results further revealed that there is a substantial potential for energy savings in both countries by optimizing energy use. The study concluded that the exchange of energy-efficient practices between both countries can significantly reduce energy use and improve the yield of wheat.