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Occupancy monitoring techniques raise questions regarding trade-offs between anonymity and accuracy. Building on a rich multi-dimensional dataset, this study explores the correlations between occupancy and energy consumption at different levels, from whole rooms to individual plugs. Data aggregation of individual plugs in zones, either according to their location or their specific functional use, has been evaluated. Confirming previous studies, room level presence proved to be strongly correlated with, in order, total plugs consumption, whole room power consumption, and lighting use. A binary presence classification using Random Forest on energy features was conducted also at room level, and showed good results. The same algorithm for a three-classes categorization of room occupancy showed acceptable results despite lower accuracy. Overall, the analysis proved that a level of granularity preserving anonymity might be sufficient and should be considered depending on the needs for occupancy information in order to reduce energy consumption.

Around-view multi-camera 3D object detection in BEV (Bird’s-Eye-View) space has been a research focus over the past few years. As a typical supervised training task, many researchers promote this area with different task-specific key designs, such as exploiting temporal information and correspondence of perspective image plane and BEV space. Most of these works follow the DETR detection framework, yet the nature of learnable queries in DETR, the encodings of objects’ center and bounding box information, have not been discussed in previous studies. In this paper, we take advantage of this prior and further extend it to 3D detection tasks. In 3D object detection, the ground-truth bounding boxes are hardly overlapping. Therefore, the queries should be more diverse under this hypothesis. To achieve this goal, we propose a Plug-in Discrimination Module (PDM) to discriminate learnable queries from all the other queries with a discrimination loss to ensure the diversity of queries. The PDM is a simple train-time-only module. It contains a query projection head to project all the object queries into a common latent space. In the latent space, the discrimination loss is conducted on all the queries. Experimental results show that this design can directly improve the 3D detector’s performance without modifying the detector’s architecture and adding extra inference costs. The NDS improvement on the nuScenes dataset is up to a maximum of 1.62% in the 8th training epoch and remains an average 0.64% improvement in the following epochs, compared with the baseline model.

Electric Vehicles (EVs) are gaining momentum due to several factors, including the price reduction as well as the climate and environmental awareness. This paper reviews the advances of EVs regarding battery technology trends, charging methods, as well as new research challenges and open opportunities. More specifically, an analysis of the worldwide market situation of EVs and their future prospects is carried out. Given that one of the fundamental aspects in EVs is the battery, the paper presents a thorough review of the battery technologies—from the Lead-acid batteries to the Lithium-ion. Moreover, we review the different standards that are available for EVs charging process, as well as the power control and battery energy management proposals. Finally, we conclude our work by presenting our vision about what is expected in the near future within this field, as well as the research aspects that are still open for both industry and academic communities.

The current processing cabinet mainly adopts the distributed heat dissipation architecture, the plug-in unit is forced by air cooling by the fan equipped with the plug box, then the fan at the top of the cabinet will discharge the heat out of the plug box. Under this architecture, there are many internal noise sources in the cabinet, and the whole machine is noisy. The noise of the cabinet when the door opens or closes is inconsistent; when the door opens, the noise is louder. In this paper, a kind of integrated independent air duct heat dissipation architecture is proposed, the fan provided by the plug box is eliminated, the feasibility of the structure is verified by the software of thermal analysis software, a low-noise cabinet is designed to reduce the noise of the whole machine and solve the problem of excessive door opening noise.

This study compared the effects of white and red thyme oil against natural biofilm. Thyme oil contains various volatile biologically active substances that can affect the growth of biofilm (algae, cyanobacteria, bacteria). In the first step, white thyme oil was therefore tested in flasks using a plastic lid (air-tight) or a cotton plug (air-permeable). The aim was to determine whether the method of closing the test containers affects the amount of biomass obtained. This was measured using a spectrometer at a wavelength of 750 nm. The results showed a clear positive effect of the cotton plug on the growth of the biofilm without a significant effect on the inhibition rate of the tested oil concentration. In the next step, the algicidal effectiveness of white and red thyme oil (10 mg/L and 100 mg/L) against biofilm was compared. The results showed a higher efficiency of red oil. Therefore, red thyme oil was selected for further research.

A hydraulic back pressure valve with a large flow range up to not less than 100L/min was designed. In order to meet the requirements of low cracking pressure and large applicable flow range, the valve was designed in a pilot operated style with a flat valve port of main valve. Area difference between the front and rear of main spool was adopted to keep the valve port self-sealing and adjustable damping plugs were used to easily regulate the dynamic performance of the valve. The mathematic models of the valve were established and simulations were carried out. Then, the influences of some major structural parameters such as volume before main throttle, volume of main spring chamber, dimensions of slender holes of damping plugs and the main throttle were obtained. The simulation results show that the valve has good flow adaptability, and the pressure shock can be cut down remarkably by reasonably setting the parameters.

In the US, over 400 state and local incentives have been issued to increase the adoption of plug-in electric vehicles (PEVs) since 2008. This article quantifies the influence of key incentives and enabling factors like charging infrastructure and receptive demographics on PEV adoption. The study focuses on three central questions. First, do consumers respond to certain types of state level vehicle purchase incentives? Second, does the density of public charging infrastructure increase PEV purchases? Finally, does the impact of various factors differ for plug-in hybrid electric vehicles (PHEV), battery electric vehicles (BEV) and vehicle attributes within each category? Based on a regression of vehicle purchase data from 2008-2016, we found that tax incentives and charging infrastructure significantly influence per capita PEV purchases. Within tax incentives, rebates are generally more effective than tax credits. BEV purchases are more affected by tax incentives than PHEVs. The correlation of public charging and vehicle purchases increases with the battery-only driving range of a PHEV, while decreasing with increasing driving range of BEVs. Results indicate that early investments in charging infrastructure, particularly along highways; tax incentives targeting affordable BEVs and PHEVs with higher battery only range, and better reflection of the environmental cost of owning gasoline vehicles are likely to increase PEV adoption in the US.

This article presents a systematic literature review on the Plug and Produce concept in advanced automated manufacturing control systems. Over recent decades, this concept has evolved significantly, with researchers focusing on enhancing its applicability and improving its conceptual, logical, and physical aspects across various sub-areas such as system design, methodologies, and supporting tools within the Industry 4.0 and Industry 5.0 frameworks. The review offers technical insights on the research domain of Plug and Produce accompanied by an analytical schematic outlining five key evolving research streams ranging from system design framework, and functionality features, up to the empirical application. Additionally, the article discusses important issues surrounding the evolution of Plug and Produce in alignment with emerging trends within Industry 5.0 automation. By analyzing the literature and current trends in industrial automation, the article highlights critical key development directions for shaping the future of manufacturing systems focusing on smart, circular, and human-centric solutions using Plug and Produce.

Hydraulic selection is a key feature of aerobic granular sludge (AGS) systems but existing aerobic granular sludge (AGS) models neglect those mechanisms: gradients over reactor height (Hreactor), selective removal of slow settling sludge, etc. This study aimed at evaluating to what extent integration of those additional processes into AGS models is needed, i.e., at demonstrating that model predictions (biomass inventory, microbial activities and effluent quality) are affected by such additional model complexity. We therefore developed a new AGS model that includes key features of full-scale AGS systems: fill-draw operation, selective sludge removal, distinct settling models for flocs/granules. We then compared predictions of our model to those of a fully mixed AGS model. Our results demonstrate that hydraulic selection can be predicted with an assembly of four continuous stirred tank reactors in series together with a correction code for plug-flow. Concentration gradients over the reactor height during settling/plug-flow feeding strongly impact the predictions of aerobic granular sludge models in terms of microbial selection, microbial activities and ultimately effluent quality. Hydraulic selection is a key to predict selection of storing microorganisms (phosphorus-accumulating organisms (PAO) and glycogen-accumulating organisms (GAO)) and in turn effluent quality in terms of total phosphorus, and for predicting effluent solid concentration and dynamic during plug-flow feeding.