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For mechanical equipment, the wear particle in the lubrication system during equipment operation can reflect the lubrication condition, wear mechanism, and severity of wear between equipment friction pairs. To solve the problems of false detection and missed detection of small, dense, and overlapping wear particles in the current ferrography wear particle detection model in a complex oil background environment, a new ferrography wear particle detection network, EYBNet, is proposed. Firstly, the MSRCR algorithm is used to enhance the contrast of wear particle images and reduce the interference of complex lubricant backgrounds. Secondly, under the framework of YOLOv5s, the accuracy of network detection is improved by introducing DWConv and the accuracy of the entire network is improved by optimizing the loss function of the detection network. Then, by adding an ECAM to the backbone network of YOLOv5s, the saliency of wear particles in the images is enhanced, and the feature expression ability of wear particles in the detection network is enhanced. Finally, the path aggregation network structure in YOLOv5s is replaced with a weighted BiFPN structure to achieve efficient bidirectional cross-scale connections and weighted feature fusion. The experimental results show that the average accuracy is increased by 4.46%, up to 91.3%, compared with YOLOv5s, and the detection speed is 50.5FPS.

Object detection has made widespread development and remarkable progress in various fields, but, in complex application scenarios, often encounters the situation that the target features are inconspicuous and the scale range is large, making it incapable of achieving the desirable results, especially for small targets. This paper proposes a multi-scale object detector MSSD based on spatial pyramid depthwise convolution (SPDC) and efficient channel attention mechanism (ECAM) from the optimization of SSD. Firstly, use ResNet50 to replace VGG as backbone to obtain more representative features. Secondly, a plug-and-play spatial pyramid depthwise convolution module SPDC is proposed to enhance perceptual field and multi-scale feature extraction capabilities. Furthermore, we design a straightforward efficient channel attention mechanism (ECAM) to scale the weights of features on channels to derive more robust features. Finally, the feature pyramid network (FPN) with ECAM (ECAM-FPN) module is introduced in the prediction feature layer for deep feature fusion to obtain multi-scale features rich in semantic and detail information. For 300 × 300 input, MSSD achieves 82.5 % mAP on PASCAL VOC07+12 dataset at 56 FPS and 48.2 % mAP on MS COCO2017 dataset, which are 8.2 % and 7.0 % higher than SSD(300), respectively. Detection of small targets is improved by 0.8 % on COCO and by 6.5 % when scaled to 512 × 512. The proposed method has significant gains in cross-scale target detection while satisfying real time and is comparable with other methods.

Greenhouse gas (GHG) production is one of the urgent problems to be solved in the wastewater treatment industry in the context of “carbon neutrality”. In this study, the carbon emissions and energy consumption of typical wastewater treatment processes in China were evaluated, starting from different cities and water treatment plants. Tool of Energy Performance and Carbon Emission Assessment and Monitoring (ECAM) was used. By comparing the influent BOD5, it was found that the energy consumption for wastewater treatment was positively correlated with the influent organic load. The annual CH4 emission of Xi’an WWTP can reach 19,215 t CO2eq. Moreover, GHGs are closely related to the wastewater treatment process chosen. WWTP B of Kunming used only an anaerobic process without continuous aeration, with an average monthly energy consumption of 8.63 × 105 kW·h. The proportion of recoverable biogas was about 90% in the GHG discharged by the traditional process. However, the anaerobic digestion-thermoelectric cogeneration process can make the recovery of the biogas utilization ratio reach 100%. Compared to the Shuozhou WWTP and WWTP A of Kunming, the Strass WWTP served the smallest population and had the largest treatment capacity, reaching the lowest energy consumption, consuming only 23,670 kW·h per month. The evaluation and analysis of ECAM provide data support and research foundation for the wastewater treatment plants to improve energy utilization and reduce greenhouse gas emissions.

Urban water systems are essential infrastructure but significantly contribute to greenhouse gas emissions through their operation. This study analyzed the greenhouse gas emissions of Incheon’s water system and proposed effective reduction strategies. In 2021, total greenhouse gas emissions from Incheon’s water system are 410,407 tCO2eq, with the sanitation sector accounting for 82.1% and water supply for 17.9%. N2O from wastewater treatment contributes 59.2% of total emissions, followed by CO2 (36.6%) and CH4 (4.2%). Sensitivity analysis using system dynamics identified per capita water consumption (LPCD) reduction as the most impactful mitigation strategy, surpassing widely adopted strategies such as renewable energy adoption. Scenario analysis showed that an aggressive policy could reduce emissions by 28.8% by 2050 compared to the baseline scenario. These findings provide a decision-making policy for carbon-neutral urban water management, emphasizing the need for integrated approaches to water management, emphasizing water demand reduction, energy efficiency, and sludge management.

Nickel–titanium (NiTi) shape memory alloy has diverse applications, especially in areas such as the medical, aerospace, and aeronautical industries. Due to this alloy’s excellent fatigue strength, high mechanical properties even at higher temperatures, and tendency to corrosion resistance, NiTi alloy is considered difficult to machine. In the present scenario, electrochemical arc machining ECAM (hybrid of electric discharge erosion and electrochemical dissolution) is an evolving procedure for difficult to machine the materials due to constraints of existing processes. The present research aims to investigate the machinability of Ni 55.7 Ti alloy through electrochemical arc drilling using molybdenum electrode. Electrolyte concentration (ethanol with ethylene glycol and sodium chloride), supply voltage, and tool rotation are considered as the variable factors in order to evaluate the ECAM performance characteristics in drilling blind hole operation concerning overcut, tool wear rate, and materials removal rate. Consequently, response surface methodology is implemented for predictive modeling of various performance characteristics. Finally, multi-objective optimization through DFA has produced a set of optimal parameters to improve the productivity along with the accuracy, which is the prime requirement for the industrial applicability of the ECAM process. Results demonstrated that supply voltage is the influential key factor for improvement of machining rate. SEM photographs revealed the development of HAZ, white layer, melted droplet, craters, re-solidified material, ridge-rich surface, and voids as well as cavities around the end-boundary surfaces of a blind hole. Composition analysis through EDS indicated the oxygen content on the machined surface because electrolyte breakdown causes oxidation to take place at elevated temperatures across the machining zone. Moreover, carbide precipitation like TiC was found in the melting zone of the drilled hole which has the affinity to reduce the SMA properties in HAZ.

There are examples in aerodynamics that take advantage of electric-to-aerodynamic analogies, like the law of Biot–Savart, which is used in aerodynamic theory to calculate the velocity induced by a vortex line. This article introduces an electric-to-aerodynamic analogy that models the lift, drag, and thrust of an airplane, a helicopter, a propeller, and a flapping bird. This model is intended to complement the recently published aerodynamic equation of state for lift, drag, and thrust of an engineered or a biological flyer by means of an analogy between this equation and Ohm’s law. This model, as well as the aerodynamic equation of state, are both intended to include the familiar and time-proven parameters of pressure, work, and energy, analytical tools that are ubiquitous in all fields of science but absent in an aerodynamicists’ day-to-day tasks. Illustrated by various examples, this modeling approach, as treated in this article, is limited to subsonic flight.

Municipal wastewater treatment plants (MWWTPs) are essential infrastructures in any urban context, but they may be considered as a potential source of greenhouse gas (GHG) emissions and should be coherent with European Union (EU) policy on energy efficiency. This study presents a sustainability evaluation of four Italian and Romanian MWWTPs in terms of energy efficiency and greenhouse gas emissions using Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool software. The obtained results indicated that biogas recovery improved energy performances, while the largest contributions in terms of GHG emissions were in all cases caused by energy consumption and methane produced during wastewater treatment. The Romanian plants exhibited higher GHG emissions, compared to the Italian plants, mainly because of the different values of national conversion factors for grid electricity (0.41 kg CO2/kWh for Italy and 1.07 kg CO2/kWh for Romania). Two scenarios aimed at enhancing the overall sustainability were hypothesized, based on increasing the serviced population or energy efficiency, achieving significant improvements. A sustainability assessment of MWWTPs should be adopted as a useful tool to help water utilities to introduce low-energy, low-carbon management practices as well as being useful for policy recommendations.

This study presents a baseline assessment of carbon emissions in water utilities in Madaba, Jordan. The Energy Performance and Carbon Emissions Assessment and Monitoring Tool (ECAM) is applied in the present study in order to reduce indirect and direct emissions. Input data for the assessment included inter alia, population, water volumes, energy consumption, and type of wastewater treatment. The methodology focuses on the greenhouse gas (GHG) emissions and energy use that is directly associated with the utility operations covering the whole water cycle. The ECAM's Quick Assessment revealed that 89.7% of the energy is consumed in abstraction and distribution systems of water supply, whereas wastewater collection, treatment, and discharge consumed only 10.3% in Madaba. The detailed ECAM tool assessment results showed that total GHG emissions from the entire water and wastewater system in Madaba are approximately 28.122 million kg CO2/year. The water supply is the major contributor to GHG accounting for 62.4%, while 37.6% of GHG emissions result from sewage treatment, and are associated with treatment process requirements considered in this work, in addition to sludge transport from septic tanks to the wastewater treatment plant. The findings of this work can help the utility to undertake energy efficiency and GHG reduction measures.

This paper details the fundamental principles of the machining method spark assisted electrochemical machining (SAEM). SAEM is a further development of the electrochemical arc machining (ECAM) which makes use of the electrochemical discharge phenomenon to machine electrically conductive materials. ECAM is not able to drill micro-holes, but now, using SAEM a postponed electrochemical finishing of the SAEM drilled micro-hole can be realized. The mass flow calibration of a throttle plate can be done immediately after SAEM microdrilling by electrochemical machining (ECM) with a still axis-symmetrically located tool electrode with the same machining equipment, working media, and power source. To enable ECAM to drill micro-holes, its material removal mechanism is improved by combination with the contact arc as an additional mechanism. This mechanism is presented and explained herein. The machining efficiency of the newly developed SAEM is optimized by adjusting the gap control in such a way that the contact arc is generated with an adequate frequency. This reduces the machining time by almost 50 %. Some examples of SAEM drilled micro-holes, for which different electrolytes are used, are presented and the surface and heat affected zone of those micro-holes are examined.

In this work, single discharges of electrochemical arc machining are examined. The heat-affected zone is analyzed, and a model is set up to simulate the heat transfer into the workpiece. As an input parameter of the simulation, the temperature of the electrochemical arc machining process was determined to be 3,500 K by means of emission spectroscopy. The simulation shows that the diameter of the heat-affected zone is less dependent on discharge duration and heat transfer due to heat flux than on the arc spot diameter. As a result of the investigation, it became clear that varying diameters of the heat-affected zone have to evolve from different diameters of the plasma channel’s arc spot. Understanding the heat distribution into the workpiece in electrochemical arc machining with micro-machining parameters allows the further development of a micro-drilling process for electrically conductive materials based on electrochemical arc machining.