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Intelligent reflecting surface (IRS) is expected to play a pivotal role in future wireless sensing networks owing to its potential for high-resolution and high-accuracy sensing. In this letter, we investigate a multi-target direction-of-arrival (DoA) estimation problem in a semi-passive IRS-assisted sensing system, where IRS reflecting elements (REs) reflect signals from the base station to targets, and IRS sensing elements (SEs) estimate DoA based on echo signals reflected by the targets. First, instead of solely relying on IRS SEs for DoA estimation as done in the existing literature, this letter fully exploits the DoA information embedded in both IRS REs and SEs matrices via the atomic norm minimization (ANM) scheme. Subsequently, the Cramér-Rao bound for DoA estimation is derived, revealing an inverse proportionality to $MN^{3}+NM^{3}$ under the case of identity covariance matrix of the IRS measurement matrix and a single target, where M and N are the number of IRS SEs and REs, respectively. Finally, extensive numerical results substantiate the superior performance of the proposed method over representative baselines.

Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in the far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is introduced for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only $5\times 10^{-4}$ bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 1 dB lower normalized-mean-square error than SSE.

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in the millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen’s inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms.

Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-Terahertz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we consider the ergodic achievable rate of RIS-assisted mmWave multiple-input multiple-output orthogonal frequency division multiplexing communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We first derive the approximations of the ergodic achievable rate by means of the majorization theory and Jensen’s inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic achievable rate by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-filling. The reflection coefficients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the effectiveness of the proposed algorithms.

In order to respond to low carbon manufacturing, from energy, materials and process carbon emission, etc., this paper constructs a carbon footprint model for wave soldering process in the module workshop of H Company, which belongs to instrument industry. On the basis of selecting appropriate parameter factors, a parameter optimisation model of wave soldering process life cycle carbon footprint is constructed, and the optimum combination of parameters is analysed by response surface method, which contain surface area, clip velocity, clipping angle, flux, purity of solder, temperature of tin furnace, height of wave peak, etc. and then the response values obtained are verified. The experimental results show that the optimised parameters are used to process and manufacture the wave soldering process, and the carbon emission produced by wave welding can be controlled from above 15 kg CO2to 12 kg CO2.

Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the data set and comparing several single-objective data and normalised multi-objective data.

This paper describes an analysis of the connotation of process optimisation driven by Digital Twin (DT) and puts forward the framework design of a five-dimensional assembly system driven by DT. Based on the assembly framework, the DT technology is constructed based on key features from the exploration of physical space and virtual space. A method for optimising the assembly process is put forward through assembly hierarchy division, process preparation and information collection, and process execution process feedback.

In the process of mechanical product configuration, if there is more than one feasible scheme, it is required to choose the one which is most consistent with the actual conditions of core enterprise. These actual conditions include cost, quality, delivery time and other factors that are related to the interests of the core enterprise. We propose to find the most suitable scheme from the feasible scheme set by using data envelopment analysis. By comparing the projected variable and the original data, we also know how much improvement can be achieved for a specific scheme. The above algorithm is verified and illustrated using a case study.

In order to improve the resources and energy efficiency of digital device during the repair process, this paper take selected phase of S layer maintenance mode as device important degree analysis, considering the impact of environment during the repair process, use the modified fuzzy comprehensive evaluation method to describe the various elements of criticality for comprehensive evaluation, establish the digital equipment maintenance decision model for low-carbon environment; combine the qualitative and quantitative analysis to determine the level of membership evaluation factors, use the quasi excellent consistent matrix and variable weight comprehensive to improve the conventional weights, then, select the appropriate comprehensive evaluation model for solving the criticality, use the logic method to determine maintenance mode based on the degree of importance. Finally, verify the correctness and practicability of the model through an example.

As global warming becomes increasingly severe, environmental consciousness has become critical in the design and operation of globally integrated supply chain networks. Product Carbon Footprint is defined as the life cycle Greenhouse Gas (GHG) emissions of goods and services and it can be considered as a simplified life cycle assessment restricted to a single impact category. In order for companies to better confront GHG emission issues, calculating product carbon footprint and analysing how various parameters affect the carbon footprint over the entire life cycle of a product is necessary. This paper studies the carbon footprint across supply chains and proposes a method of production carbon footprint analysis in a supply chain based on life cycle assessment, including the following: taking product life cycle as the span of carbon footprint analysis, with all kinds of complex information system as objects and then carrying out the carbon footprint knowledge extraction according to the concept model format in physical database; building a carbon footprint analysis ontology, which is related to product life cycle in supply chain environment; calculating the quantification of carbon footprint through GHG emissions over the entire life cycle, and designing a tool for product carbon footprint in supply chain environment.

To mitigate global warming, companies are being compelled to control greenhouse gas (GHG) emission in the supply chain. As a fundamental quantisation parameter of carbon emission in product life cycle (PLC), carbon footprint (CF) captures the interest of governments, firms and consumers. However, calculating CF is a prerequisite to confirm or refute best practices and policies for controlling CF. This research examines the method of calculating CF of tobacco industry based on PLC. For different stages of PLC, it presents different models to measure CF at four stages respectively, through which firms especially tobacco industry could find out where carbon emits too much and then could implement corresponding measures to control or even reduce CF. Customers could consider the low-carbon product through carbon labels. For government or related organisations, it could open a new way to promote efficient emission reduction policies, which can control GHG and mitigate global warming.