Semiconductor Manufacturing International (China)

To address the challenges of high computational costs, susceptibility to local optima, and heavy reliance on manual intervention in multi-physics parameter optimization for symmetric acoustic metamaterials, an enhanced Chimp Optimization Algorithm (DADCOA) is proposed in this paper. This algorithm integrates the double chaotic initialization strategy (DCS), adaptive multimodal convergence mechanism (AMC), and dual-weight pinhole imaging update operator (DWPI). It employs a Logistic–Tent composite chaotic mapping strategy for population initialization, significantly enhancing distribution uniformity within high-dimensional parameter spaces. An AMC factor is then introduced to dynamically balance global exploration and local exploitation based on the real-time evolutionary state of the population. A dual-weight population update mechanism, incorporating distance and historical contributions, is integrated with a pinhole imaging opposition-based learning strategy to improve population diversity. Additionally, a composite single objective error feedback local differential mutation operation is introduced to improve optimization accuracy for coupled multi-physics objectives. Experimental validation based on the CEC 2022 test function suite and an acoustic metamaterial parameter optimization model demonstrates that compared to the standard COA algorithm and existing improved algorithms, the DADCOA algorithm reduces simulation time by 28.46% to 60.76% while maintaining high accuracy. This approach effectively addresses the challenges of high computational cost, stringent accuracy requirements, and composite single objective coupling in COMSOL physical parameter optimization, providing an effective solution for the design of acoustic metamaterials based on symmetric structures.

Spur gears are commonly used for power transmission in mechanical systems. This research analyzes and compares spur gears made of four different materials - Aluminum alloy, Gray Cast Iron, Structural steel and low alloy steel AISI 4140. FEA was conducted to determine the effects of deformation, von Mises stress, and strain on the gearbox components, which indicated that they will perform adequately in relation to the loads associated with vehicle operation. This comparison evaluated the mechanical behavoir within the operating conditions for gearbox components, such as von Miss stress and Von Miss strain. The FEA and mechanical testing demonstrated that these types of materials had comparable properties. The comparative analysis of mechanical properties of these materials and the design of the gearbox should be useful in helping to determine appropriate material selection for the manufacture of spur gears to be used in mechanical applications to transmit power.

This article presents a globally optimized radio frequency (RF) energy harvesting system that leverages the novel concepts of 3-D maximum power point tracking (3-D MPPT) and collaborative source reconfiguration to achieve high MPPT accuracy and a wide input power range. The proposed 3-D MPPT coordinates the energy sources, optimizes the rectifier outputs, and regulates the rectifier stages for global efficiency optimization. A multi-level regulating DC–DC converter integrated with a reconfigurable rectifier enables synchronized dual-band RF energy harvesting. The reconfigurable buck–boost converter employs hysteretic control to regulate each rectifier output voltage at its respective maximum power point (MPP) and delivers a stable output through pulse-frequency modulation (PFM) with adaptive inductor <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small>-time. The DC–DC converter dynamically connects a rechargeable battery (emulated by a power source and a resistor) to either the source or the load, supplementing insufficient energy or recovering surplus energy, thereby significantly extending the input power range. Fabricated in a 180-nm CMOS process, the prototype dual-band RF energy harvesting interface achieves a peak end-to-end efficiency of 71%, a sensitivity of −24.1 dBm, and wide high-power conversion efficiency (PCE) input power ranges of 15.5 and 20.5 dB at 433 and 900 MHz, respectively.

Addressing the topology identification issue in lowvoltage distribution areas, a dual-mode communication module is utilized to collect power sequence data from branch switches and electricity meters of users, as well as received signal strength indicator (RSSI) data between user electricity meter modules. Based on the power and communication parameter data, a twophase algorithm is designed to solve the problem: In the first phase, clustering based on RSSI is employed to identify the relationship between electricity meters and their respective meter boxes, and then the power sequence data of meters within the same meter box are merged to represent a single branch node. In the second phase, the Pearson coefficient clustering algorithm is applied to the electricity meter sequences to calculate the branch connection relationships. Traditional approaches using quadratic programming algorithms face difficulties in determining the connection relationships of individual users when they consume no electricity. In contrast, the use of RSSI does not require consideration of missing electricity consumption data for individual users. Additionally, after clustering based on RSSI, the number of nodes required for unified calculation can be reduced, thereby enhancing computational efficiency. The effectiveness and reliability of the proposed algorithm are validated through actual power data and RSSI data collected from field distribution areas.

Qinzhou, Guangxi Zhuang Autonomous Region, China, experiences intense seasonal precipitation and relatively high temperatures, which often lead to droughts or floods. Forecasting precipitation and temperature is an essential step in taking precautions against damages caused by weather. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is effective for forecasting time series with regular patterns. This paper uses the SARIMA model to forecast the monthly precipitation and average temperature of Qinzhou. The training set comprises data provided by the National Oceanic and Atmospheric Administration (NOAA) from 2010 to 2022, inclusive, while data from 2023 to 2024 are used as the test set. By analyzing the augmented Dickey-Fuller (ADF) test results, and comparing Akaike information criterion (AIC) values and models' accuracy, sets of reasonable model parameters are selected. Coefficients of determination (R2) suggest the SARIMA model can effectively forecast monthly average temperature and precipitation, but it shows shortcomings in capturing unexpected extreme values.

Crystal glass is characterized by a high transmittance, high refractive index, good hardness, and chemical stability, which makes it an excellent material for use in a wide variety of decoration, lamp, handicrafts, high-grade tableware, and jewelry. Nevertheless, these applications of crystal glass involve complicated mechanical processing, including cutting and polishing. This article examines the morphology, optical, mechanical, and chemical properties of crystal glass following a range of processing treatments. The findings revealed that the surface roughness of the crystal glass subjected to mechanical processing was markedly diminished, while the optical transmittance was enhanced. However, the hardness of the polished sample exhibited a notable decline. Concurrently, the quantity of lead ion precipitation following acid immersion increased considerably, reaching 2.63 mg/L. After acid treatment, many hydrogen ions appeared on the surface, and the water contact angle of the sample significantly increased, especially for untreated crystal glass samples with more surface area.

Carbon emission measurement has become a crucial component of low-carbon management in power systems, with the increasing issue of carbon dioxide emissions. Traditional centralized carbon emission flow calculation methods face the challenges of excessive computational load and declining real-time performance as power grids expand. Consequently, the theory of distributed carbon meter systems based on iterative algorithms has emerged. However, the error propagation characteristics and measurement uncertainty of these iterative calculation processes have not been thoroughly investigated. This paper presents a comprehensive error analysis framework for distributed carbon emission flow iterative algorithms, identifying error sources and tracing their propagation paths throughout the calculation process. Based on a known power system topology, our methodology rigorously quantifies the relative standard uncertainty of carbon emission factors at each node within a distributed carbon metering system. This work provides a theoretical foundation for understanding and improving the accuracy of existing distributed carbon metering systems by analyzing how measurement errors propagate through the iterative algorithm rather than introducing new measurement methods.