Excessive Energy Consumption Research Articles

Optimum Design and Dynamics Analysis of Intelligent Prosthetic Knee Joint

Background: The intelligent prosthetic knee joint is an essential rehabilitation device for amputee patients to recover from mobility disorders. It must ensure excellent performance when installed on the human body. However, issues such as excessive energy consumption and poor stability during walking are challenges in the design of such prosthetic devices. Objective: To address the issues of wasteful energy expenditure and instability in intelligent prosthetic knee joints during walking, an optimized model of a double rocker prosthetic knee joint mechanism was established with the minimum peak driving torque of the active rod as the optimization goal. Methods: The optimal model was established with the minimum peak driving moment of the prosthetic knee joint mechanism as the optimization objective. Under the constraint conditions of performance and structure, the model was optimized by the composite method, and the optimal structural parameters of the prosthetic knee joint mechanism were obtained. Then, according to the trajectory of the instantaneous rotation center of the knee joint, the double rocker mechanism is optimized so that the prosthesis can better simulate the movement trajectory of normal people. Finally, according to the designed virtual prototype model, a physical prototype of the intelligent prosthetic knee joint test platform is built, the knee joint Angle data is collected, and the collected data is analyzed accordingly. Results: ADAMS software was used to simulate the virtual motion of the optimized model. The results showed that the optimized intelligent prosthetic knee joint mechanism has excellent bionic performance. Its peak driving torque is reduced by 40% and the range of driving torque is reduced by 36%, significantly improving the stability and endurance of the intelligent prosthetic knee joint. Through the experimental test of an intelligent prosthetic knee joint, it is verified that the optimized intelligent prosthetic knee joint meets the normal needs of the human body. Conclusion: The results confirm that the optimized prosthetic knee joint mechanism exhibits excellent bionic performance, and the reduction in driving torque enhances its stability and energy efficiency, validating the correctness and rationality of the proposed optimization model.

Ultrafast Synthesis of Hard Carbon Anodes for Sodium-ion Batteries: An Intense-Pulsed-Light-Assisted Approach to Photothermal Carbonization of Polymer/Carbon Nanotube Composite Films.

The conventional carbonization process for synthesizing hard carbons (HCs) requires high-temperature furnace operations exceeding 1000°C, leading to excessive energy consumption and lengthy processing times, which necessitates the exploration of more efficient synthesis methods. This study demonstrates the rapid preparation of HC anodes using intense pulsed light (IPL)-assisted photothermal carbonization without the prolonged and complex operations typical of traditional carbonization methods. A composite film of microcrystalline cellulose (MCC) and single-walled carbon nanotubes (SWCNTs) is carbonized at high temperatures in less than 1 min. The SWCNTs efficiently absorbed light energy, enabling ultrafast heating and eliminating the need for prolonged, high-energy furnace-based processes. The IPL-assisted HC anodes exhibited excellent electrochemical performance, with an initial desodiation capacity of 260.4 mAh g⁻¹anode and 97.5% capacity retention after 200 cycles. These results are comparable to those achieved using traditional furnace-based carbonization processes, such as carbonizing HC anodes at 1200°C, validating the effectiveness of IPL-assisted processes. Additionally, surface and structural analyses revealed the development of pseudo-graphitic domains, crucial for enhanced sodium-ion storage. This research highlights IPL-assisted photothermal carbonization as a viable, time-efficient, and energy-saving alternative to conventional methods, offering a sustainable pathway for the large-scale production of HC anodes for future sodium-ion battery technologies.

Electrocatalytic conversion of nitrate to ammonia on the oxygen vacancy engineering of zinc oxide for nitrogen recovery from nitrate-polluted surface water.

Nitrate pollution in surface water poses a significant threat to drinking water safety. The integration of electrocatalytic reduction reaction of nitrate (NO3RR) to ammonia with ammonia collection processes offers a sustainable approach to nitrogen recovery from nitrate-polluted surface water. However, the low catalytic activity of existing catalysts has resulted in excessive energy consumption for NO3RR. Herein, we developed a facile approach of electrochemical reduction to generate oxygen vacancy (Ov) on zinc oxide nanoparticles (ZnO1-x NPs) to enhance catalytic activity. The ZnO1-x NPs achieved a high NH3-N selectivity of 92.4% and NH3-N production rate of 1007.9 [Formula: see text] h-1 m-2 at -0.65V vs. RHE in 22.5mgL-1NO3--N, surpassing both pristine ZnO and the majority of catalysts reported in the literature. DFT calculations with in-situ Raman spectroscopy and ESR analysis revealed that the presence of Ov significantly increased the affinity for the NO3- (nitrate) and key intermediate of NO2- (nitrite). The strong adsorption of NO3- on Ov decreased the energy barrier of potential determining step (NO3- →∗NO3) from 0.49 to 0.1eV, boosting the reaction rate. Furthermore, the strong adsorption of NO2- on Ov prevented its escape from the active sites, thereby minimizing NO2- by-product formation and enhancing ammonia selectivity. Moreover, the NO3RR, when coupled with a membrane separation process, achieved a 100% nitrogen recycling efficiency with low energy consumption of 0.55 kWh molN-1 at a flow rate below 112mLmin-1 for the treatment of nitrate-polluted lake water. These results demonstrate that ZnO1-x NPs are a reliable catalytic material for NO₃RR, enabling the development of a sustainable technology for nitrogen recovery from nitrate-polluted surface water.

Simulation Study on Heating Performance of Thermal Management in Heat Pump Air Conditioning Systems for Electric Vehicles

Article Simulation Study on Heating Performance of Thermal Management in Heat Pump Air Conditioning Systems for Electric Vehicles Yihan Zhao School of Management, Shenyang Jianzhu University, Shenyang 110168, China; zhaoyihan@sjzu.edu.cn; Tel./Fax: +86-024-2469-2209 Received: 29 September 2024; Revised: 26 November 2024; Accepted: 2 December 2024; Published: 4 December 2024 Abstract: With the advent of the electric vehicle (EV) revolution, the market share of EVs has been steadily increasing, accompanied by growing interest in the heat pump air-conditioning system (including cooling and heating functionalities) of electric vehicles. As the second-largest energy-consuming system in a vehicle, the air-conditioning system significantly impacts driving range, especially under winter heating conditions, where excessive energy consumption can greatly reduce the vehicle’s range. This study addresses the issue of high energy consumption of heat pump air-conditioning systems in winter. By constructing a one-dimensional model of the heat pump air-conditioning system for electric vehicles, the study analyzes the impact of compressor speed, EHX opening, and recirculation ratio on energy consumption and heating capacity under WLTC operating conditions at an ambient temperature of −5 °C. The results indicate that an EHX opening of 450 steps, an external air circulation ratio of 30%, and a compressor speed of approximately 5000 RPM provide optimal heating performance for the heat pump air-conditioning system. This simulation offers insights for optimizing heat pump air-conditioning systems and improving the driving range of electric vehicles. Further optimization could enhance EV range issues and boost the market competitiveness of this type of electric vehicle.

Study on the dynamic characteristics of the revolver cannon under high-speed impact

Abstract Aiming at the problems of discontinuous mechanism action, excessive energy consumption and poor matching with the chainless feeding system under the harsh working conditions of high-speed strong impact, a certain revolver cannon has the problems of discontinuous mechanism action, excessive energy consumption and poor matching with the chainless feeding system. In order to further study the dynamic characteristics of the gas-operated revolver cannon under high-speed impact load, solve the matching problem between the revolver cannon and the chainless feeding system, and improve the firing rate of the revolver cannon, based on the principle of the revolver cannon, the multi-body dynamics and gas dynamics theory are used to couple the thermomechanical coupling calculation model of the revolver cannon gas chamber and the transmission efficiency and impact numerical calculation method between the key parts into the ADAMS dynamic simulation model, and the dynamic analysis and calculation of the whole cannon of the revolver cannon are carried out. The motion characteristic curves of the key parts of the revolver cannon are obtained by simulation, the motion matching between the key parts is analyzed, and the simulation results are verified by experiments. The results show that the error of the motion characteristic parameters such as the active slide displacement and the gun box recoil displacement obtained by simulation and experiment is very small, which verifies the correctness of the calculation model. The simulation calculation method can provide technical support for the matching research between the revolver cannon and the chainless feeding system and the design and structural optimization of the revolver cannon.

Comparative study on a solar-assisted ground source heat pump with CPC solar collector and phase change heat storage

To address the challenges of low solar energy utilization, soil temperature imbalance, and excessive energy consumption in traditional heating systems, a novel solar-assisted ground source heat pump (SAGSHP) with phase change heat storage is proposed. It integrates a compound parabolic concentrator (CPC) solar collector and a buried pipe heat exchanger for combined heating, using RT52, Na₂S₂O₃·5H₂O, and paraffin as phase change materials (PCM) to store excess heat. A TRNSYS model is developed to analyze its performance under different CPC concentration ratios and PCM configurations. Results show that the CPC collector can save approximately 33 % collection area compared to evacuated tube and flat plate collectors, with heat collection efficiencies of 75.5 % and 65.1 % at 2 times and 5 times of the concentration ratios, respectively. The SAGSHP reduces annual energy consumption by about 2000 kWh and improves the average soil temperature stability. The heating time corresponding to RT52, Na2S2O3·5H2O, and Paraffin is 123.75 h, 80.75 h and 75 h, respectively. The average coefficient of performance of SAGSHP with RT52 reaches 3.096, and the energy consumption is reduced by about 24.3 % after ten-year operation. Results confirms the SAGSHP's potential to enhance heating efficiency, optimize solar energy use, and save energy in long-term operation.

Switching On/Off Air Conditioner and Fan Alternately based on IoT Motion Detection and Room Temperature

The Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results.

Identifying Practical Material Retrofit Measures for Energy Efficiency in Existing High-Rise Buildings in the Malaysian Climate

With the global focus on sustainable urban development, many cities are striving to formulate effective urban transformation strategies to transition from traditional urban structures to more sustainable ones. Enhancing the energy efficiency of buildings, particularly existing structures, is recognized as a crucial element in the fight against climate change. In the Malaysian context, improvements in energy consumption are predominantly propelled by retrofitting the country's existing building stock. However, targeted strategies are needed, especially for high-rise buildings, to address the unique challenges posed by the Malaysian climate. This study addresses this gap by focusing on retrofitting strategies specifically tailored for existing high-rise buildings in Malaysia. To evaluate energy efficiency accurately, a selected case study underwent modeling and simulation using Building Information Modeling (BIM) software for energy analysis. The simulation considered crucial aspects of window materials: U-factor, solar heat gain coefficient, visible transmittance, and emissivity. The study identified triple-glazed windows as the most energy-efficient choice, significantly outperforming single-glazed windows due to their lower U-factor and improved insulation properties. Light concrete bricks for walls demonstrated superior heat resistance owing to their lower density. Steel bar joists were found to enhance heat transfer efficiency in roofs. The recommended materials showed promising results, achieving a calculated energy consumption reduction to 266 kWh/m2/year. The study emphasizes that green consultants prioritize elements addressing excessive energy consumption during building upgrades, influencing their choice of retrofit options. Implementing energy-efficient retrofits can significantly reduce buildings' energy dependence, contributing to a substantial decrease in the country's overall energy consumption.

Preparation and Biological Activity of Lignin-Silver Hybrid Nanoparticles.

Silver nanoparticles (AgNPs) are excellent antimicrobial agents and promising candidates for preventing or treating bacterial infections caused by antibiotic resistant strains. However, their increasing use in commercial products raises concerns about their environmental impact. In addition, traditional physicochemical approaches often involve harmful agents and excessive energy consumption, resulting in AgNPs with short-term colloidal stability and silver ion leaching. To address these issues, we designed stable hybrid lignin-silver nanoparticles (AgLigNPs) intended to effectively hit bacterial envelopes as a main antimicrobial target. The lignin nanoparticles (LigNPs), serving as a reducing and stabilizing agent for AgNPs, have a median size of 256 nm and a circularity of 0.985. These LigNPs were prepared using the dialysis solvent exchange method, producing spherical particles stable under alkaline conditions and featuring reducing groups oriented toward a wrinkled surface, facilitating AgNPs synthesis and attachment. Maximum accumulation of silver on the LigNP surface was observed at a mass reaction ratio mAg:mLig of 0.25, at pH 11. The AgLigNPs completely inhibited suspension growth and reduced biofilm development by 50% in three tested strains of Pseudomonas aeruginosa at a concentration of 80/9.5 (lignin/silver) mg L-1. Compared to unattached AgNPs, AgLigNPs required two to eight times lower silver concentrations to achieve complete inhibition. Additionally, our silver-containing nanosystems were effective against bacteria at safe concentrations in HEK-293 and HaCaT tissue cultures. Stability experiments revealed that the nanosystems tend to aggregate in media used for bacterial cell cultures but remain stable in media used for tissue cultures. In all tested media, the nanoparticles retained their integrity, and the presence of lignin facilitated the prevention of silver ions from leaching. Overall, our data demonstrate the suitability of AgLigNPs for further valorization in the biomedical sector.

Parametric Machine Learning-Based Adaptive Sampling Algorithm for Efficient IoT Data Collection in Environmental Monitoring

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA); however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change.

Underground compressed air energy storage (CAES) in naturally fractured depleted oil reservoir: Influence of fracture

In the last decade, the sources of clean energy supply to meet human needs have been given much attention by researchers worldwide. Compressed air storage in underground formations is an excellent way to balance energy production and consumption. During off-peak hours, with the consumption of excess electrical energy, the air is temporarily stored at high pressure in the desired environment. The stored compressed air produces recovered electrical power during the needed hours and peak energy consumption. Compressed air energy storage in underground structures, including depleted hydrocarbon reservoirs, due to having a suitable storage capacity for air and because their geological characteristics have already been well identified, is one of the storage methods. In order to underground storage of compressed air in aquifers and salt caverns, research have been carried out, but so far, studies have yet to be carried out regarding the storage of compressed air in depleted natural fractured oil reservoirs. This study simulated the storage of compressed air in a naturally fractured depleted oil reservoir, the effect of fracture on the rate of oxidation reactions, air dissolution and air diffusion in the oil and water phases. Also, for the first time, an examination of the fracture properties, including porosity, permeability, and fracture spacing on the amount of air recovery during CAES, was numerically simulated. Significantly Increasing the fracture porosity and permeability improves the air recovery and leads to a 19% and 16% respectively increase in the air recovery factor. In the fractured reservoir, increasing the fracture porosity has the most significant effect on reducing the air recovery factor and reducing the fracture spacing has the most negligible effect on the air recovery. Finally, the results of this study showed that due to the consumption and loss of air in the fractured reservoir compared to the conventional reservoir, the air recovery factor in the fractured reservoirs is less than the conventional reservoirs.

Towards carbon neutrality: asymmetric impact of financial development and digitalization on carbon dioxide emissions in Mediterranean countries

The current research investigates the impact of financial development, digitalization, green trade, manufacturing, and national income on carbon dioxide (CO2) emissions of six Mediterranean countries (MEDIT-6). The study uses a nonlinear panel quantile regression model with panel data of MEDIT-6 countries from 1994 to 2022. The study asserts that higher financial development will reduce CO2 emissions for MEDIT-6 countries, as it provides more financing options to invest in green energy and potentially curb excessive energy consumption which in turn reduces CO2 emissions. The study also provides evidence that digitalization in MEDIT-6 countries has led to dematerialization, thereby reducing CO2 emissions. Digitalization makes trade and commerce platforms more efficient by facilitating the smooth flow of information and enhancing the efficiency of production processes. The positive relationship between manufacturing and national income and CO2 emissions exhibits a U-shaped pattern, which supports the existence of Environmental Kuznets Curve (EKC) hypothesis. The study shows how the MEDIT-6 countries have been successful in promoting financial development and digitalization, which helps reduce their CO2 emissions. However, it also raises concerns for policymakers as promoting developmental activities such as manufacturing is inevitable, but it comes with environmental challenges such as higher CO2 emissions. The current study contributes to the reservoir of existing literature by providing fresh evidence from the Mediterranean region on the impact of financial development and digitalization on CO2 emissions.Graphical

Preparation and characteristics of CuS-CNTs modified PVDF-based flexible composite phase change films

It is indisputable that the impact of buildings on global energy demand. To address the issue of excessive energy consumption in buildings, this manuscript proposes the preparation of a flexible composite phase change films with excellent solar energy absorption conversion and thermal management capabilities. In this paper, composite phase change material was obtained by adsorbing paraffin into expanded graphite (EG) using vacuum adsorption method. The photothermal material copper sulfide‑carbon nanotubes (CuS-CNTs) was synthesized by hydrothermal reaction. The dispersion of EG and carbon nanotubes (CNTs) in polyvinylidene fluoride (PVDF) matrix was enhanced under the influence of polyvinylpyrrolidone (PVP). The flexible and bendable films were finally prepared by using the non-solvent-induced phase separation method. The latent heat of the film reaches 63.63 J/g, and the thermal conductivity of the film system approaches to 0.53 W/(m·K) through the adsorption of the high thermal conductivity enhanced phase EG and the intervention of CNTs. The photothermal conversion ability of CuS-CNTs improves the photothermal performance of the film, and the solar energy utilization reaches 46.2 % under simulated sunlight. The experimental results show that the prepared films can effectively convert solar energy into thermal energy and store it in the form of latent heat, and the films can release the energy in the form of heat again at low temperatures. Utilizing this property, the film can be applied to the field of building materials, which demonstrates great potential and prospect in reducing energy consumption and carbon emission in buildings. The prepared composite phase change film for photothermal conversion provides a new idea for modern building thermal management materials.

Fuzzy Logic Approach for Modeling of Heating and Scale Formation in Industrial Furnaces.

Heating steel charges is essential for proper charge formation. At the same time, it is a highly energy-intensive process. Limiting the scale formed is critical for reducing heat consumption in this process. This paper applies fuzzy logic to model heating and scale formation in industrial re-heating furnaces. Scale formation depends on the temperature of the initial charge, heating time, excess air coefficient value, and initial scale thickness. These parameters were determined based on experimental tests, which are also the inputs in the model of the analyzed process. The research was carried out in walking beam furnaces operating in hot rolling mill departments. To minimize the excess energy consumption for heating a steel charge in an industrial furnace before forming, a heating and scale formation (HSF) model was developed using the fuzzy logic-based approach. The developed model allows for the prediction of the outputs, i.e., the charge's final surface temperature and the scale layer's final thickness. The comparison between the measured and calculated results shows that the model's accuracy is acceptable.

Enhanced Ant Colony Based VM Selection and Consolidation for Energy Conservation

Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments.

Photoinhibition Sensitivity of Abies sachalinensis Saplings Under Different Leaf Mass per Area Conditions: Insights into Acclimation to Current and Future Light Environments

Photoinhibition, a common physiological obstacle in forest regeneration, results from rapid light elevations after removing upper-layer trees, thereby impeding sapling growth and survival. The leaf mass per area (LMA), which reflects the current light environment, may predict saplings’ responses to future elevations in light intensity. However, the relationship between the LMA and photoinhibition sensitivity in boreal forest evergreen conifers remains unclear. In this study, we explored whether the LMA was related to photoinhibition sensitivity in A. sachalinensis saplings. Saplings with a high LMA exhibited higher excitation pressure (1−qP) under the current light environment, leading to increased stress even at low relative light intensities of 3%–17%. A model illustrating the dependence of photosynthetic physiological parameters on the photosynthetic photon flux density (PPFD) revealed that 1−qP significantly decreased with an increasing LMA, while the electron transfer rate (ETR) and non-photochemical quenching (NPQ) significantly increased. Additionally, regardless of the LMA, 1−qP reached near saturation (≈1.0) at a PPFD of 1000 μmol m−2 s−1, likely owing to the inefficient consumption of excess energy by electron transfer, as indicated by the low maximum ETR (approximately 25 μmol m−2 s−1). These findings suggest that although A. sachalinensis exhibits high sensitivity to photoinhibition, a high LMA may reflect physiological acclimation to forthcoming elevations in light exposure, thereby reducing the susceptibility to photoinhibition at present and in the future.

High-current decoupled hydrogen and oxygen evolution via nickel–cobalt based redox mediators and bifunctional catalyst of 3D printing substrates

The conversion of renewable energy sources with relatively large energy fluctuations into hydrogen represents a crucial aspect of energy storage. Nevertheless, the direct water electrolysis process is known to require excessive instantaneous energy consumption and high cost. Two-step alkaline water electrolysis is regarded as a secure and effective method of generating hydrogen from renewable energy sources when compared to direct water electrolysis. Here we propose a two-step alkaline water electrolysis using nickel–cobalt based hydroxide (Ni0.9Co0.1(OH)2) as a redox mediator, and a high-performance bifunctional catalyst as gas evolution electrodes (GEE). The substrates for the GEE were prepared using 3D printing and then loaded with in-situ grown Ru-doped MoS2/NiFe-LDH hierarchical heterostructure catalysts (MS-NiFe-Ru-3D). The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) of the MS-NiFe-Ru-3D catalyst can reach up to 500 mA cm−2 at 300 and 250 mV overpotentials, respectively. It can meet the requirement of high catalyst performance for two-step alkaline water electrolysis. The direct water electrolysis using the bifunctional MS-NiFe-Ru-3D catalyst only requires a voltage of 1.85 V at 500 mA cm−2 with minimal attenuation over 300 h. For the two-step alkaline water electrolysis using MS-NiFe-Ru-3D as bifunctional catalysts and Ni0.9Co0.1(OH)2 as redox mediator, only 1.70 V and 0.27 V were required for HER and OER at 500 mA cm−2, respectively. This work offers a promising avenue for the efficient conversion of renewable secondary energy sources into hydrogen.

New Solution to 3D Projection in Human-like Binocular Vision

A human eye has about 120 million rod cells and 6 million cone cells. This huge number of light-sensing cells inside a human eye will continuously produce a huge quantity of visual signals that flow into the human brain for daily processing. However, the real-time processing of these visual signals does not cause excessive energy consumption by the human brain. This fact tells us the truth which is to say that human-like vision processes do not rely on complicated and expensive formulas to compute depth, displacement, and colors. On the other hand, the human eye is like a camera with pan-tilt (PT) motions. We all know that in computer vision, each set of PT parameters (i.e., coefficients of pan motion as well as tilt motion) requires a dedicated calibration to determine a camera’s projection matrix. Since there is an infinite number of PT parameters that could be produced by a human eye, it is unlikely that a human brain stores an infinite number of calibration matrices for each human eye. These observations inspire us to look for a simpler and computationally non-expensive solution which is to undertake three-dimensional (3D) projection in human-like binocular vision. In other words, it is an interesting question for us to answer, which is to say whether simpler and learning-friendly formulas for computing depth and displacement exist or not. If the answer is yes, these formulas must also be calibration-friendly (i.e., easy process on the fly or on the go). In this paper, we present an important discovery of a new solution to 3D projection in a human-like binocular vision system. This solution is computationally simpler and could be easily learned or calibrated on the fly. We know that the purpose of doing 3D projection in binocular vision is to undertake forward and inverse transformations (or mappings) between coordinates in 2D digital images and coordinates in a 3D analogue scene. The formulas underlying the new solution are accurate, easily computable, easily tunable (i.e., to be calibrated on the fly or on the go) and could be easily implemented by a neural system (i.e., a network of neurons or a network of computational flows). Experimental results have validated the newly derived formulas which are better than textbook solutions.

Energy-saving, high-pressure resistant mini valve based on a bistable electromagnetic actuator

As core components of microfluidic and wearable pneumatic electronic systems, mini valves are attracting a growing intellectual interest. However, most existing mini valves exhibit either excessive energy consumption or limited flow rate and holding pressure. To address these issues, this study proposes an energy-saving mini valve with large flow rate and high holding pressure. The implementation of electromagnetic bistable structure allows the valve to remain open or closed without energy consumption. In contrast to the small and intricate flow channels seen in conventional mini valves, the straight-through flow channel design with reduced flow resistance improves its flow rate properties. And the pressure resistance characteristics are enhanced by the use of permanent magnetic attraction for sealing. Furthermore, the valve is driven by a single coil to switch between two steady states, resulting in a more compact structure. We have developed valve prototypes with diameters of 10 mm and 6 mm. Performance evaluation tests have shown that it sustains a holding pressure as high as 100 kPa and a flow rate of 1.5 L/min (@ 3 kPa), while only expending an energy of 0.37 J during switch transitions. Given these attributes, this valve demonstrates significant potential for integration within wearable pneumatic electronic systems.

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.