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BACKGROUND RESULTS CONCLUSION Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time‐consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small‐scale traders. Therefore, developing a user‐friendly, rapid and economical method is of particular importance.This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively.Our study provides a feasible and practical non‐destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions. [ABSTRACT FROM AUTHOR]

As one of the important electronic components, the dielectric capacitors for energy storage applications have been extensively studied in recent years. Among various dielectric materials, the perovskite oxide Bi0.5Na0.5TiO3‐based ceramics have become promising candidates due to their high polarization, dielectric tunability, environment‐friendly composition, and so forth. Herein, the relaxor ferroelectric ceramics of Bi0.5Na0.5TiO3–SrTiO3–Bi(Mg0.5Zr0.5)O3 were prepared via solid‐state reaction method. The sample with optimal composition not only retains the high polarization by introducing Bi3+ ions, but also achieves the reduced remanent polarization by enhancing relaxor behavior and the promoted electric breakdown strength by the grain size refinement. Accordingly, a high recoverable energy density of 8.3 J/cm3 under 450 kV/cm and the superb charge/discharge properties (current density CD = 1200 A/cm2, power density PD = 150 MW/cm3, charge/discharge time t0.9 = 0.15 µs) are achieved, revealing great prospect in energy storage applications. [ABSTRACT FROM AUTHOR]

Bismuth sodium titanate (Bi0.5Na0.5TiO3)-based relaxor ferroelectric ceramics have received ever-increasing interest for their potential application in dielectric capacitors owing to their sterling energy storage capability. Herein, the perovskite end-member Ba(Fe0.5Nb0.5)O3 (BFN) was incorporated into 0.7Bi0.5Na0.5TiO3-0.3SrTiO3 (0.7BNT-0.3ST) ceramics to improve the relaxor characteristics and refine the grain, leading to slim polarization–electric field (P–E) hysteresis loops and enhanced electric breakdown strength. Particularly, the 0.85(0.7BNT-0.3ST)-0.15BFN ceramics achieved a high recoverable energy density of 5.7 J/cm3 and a high energy storage efficiency of 86.4% under a moderate electric field of 390 kV/cm. Additionally, remarkable stability in frequency, cycling, and temperature and excellent charge/discharge behavior were achieved at the same time. The above findings reveal that BFN-modified BNT-ST ceramics display greatly improved comprehensive energy storage properties, making them promising candidates in the field of electrostatic energy storage. [ABSTRACT FROM AUTHOR]

With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage. [ABSTRACT FROM AUTHOR]

The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability. [ABSTRACT FROM AUTHOR]

As one of the important electronic components, the dielectric capacitors for energy storage applications have been extensively studied in recent years. Among various dielectric materials, the perovskite oxide Bi0.5Na0.5TiO3‐based ceramics have become promising candidates due to their high polarization, dielectric tunability, environment‐friendly composition, and so forth. Herein, the relaxor ferroelectric ceramics of Bi0.5Na0.5TiO3–SrTiO3–Bi(Mg0.5Zr0.5)O3 were prepared via solid‐state reaction method. The sample with optimal composition not only retains the high polarization by introducing Bi3+ ions, but also achieves the reduced remanent polarization by enhancing relaxor behavior and the promoted electric breakdown strength by the grain size refinement. Accordingly, a high recoverable energy density of 8.3 J/cm3 under 450 kV/cm and the superb charge/discharge properties (current density CD = 1200 A/cm2, power density PD = 150 MW/cm3, charge/discharge time t0.9 = 0.15 µs) are achieved, revealing great prospect in energy storage applications. [ABSTRACT FROM AUTHOR]

Piezocatalysis offers an encouraging alternative for the sustainable, on-demand, and decentralized production of hydrogen peroxide (H 2 O 2 ), underscoring the importance of enhancing piezocatalytic efficiency. Enhancing piezocatalysts through defect engineering has shown considerable potential in boosting H 2 O 2 production efficiency. However, the impact of oxygen vacancies on piezocatalytic activity remains unclear. Herein, we used a chemical probe method to quantify negative charges (q - ) and superoxide radicals (O 2 - ) to explore the relation between the oxygen vacancy concentration and piezocatalytic performance of bismuth titanate (Bi 4 Ti 3 O 12 ) based catalysts. Results indicate that piezocatalytic H 2 O 2 production in pure water demonstrates a volcanic trend with increasing oxygen vacancy concentration. This trend is attributed to the dual role of oxygen vacancies, which reduce the piezoelectric property of the piezocatalyst while simultaneously increasing the concentration of O 2 - , which is crucial for H 2 O 2 formation through the O 2 reduction pathway. This study provides insights into the interplay between oxygen vacancies, piezoelectric properties, and piezocatalytic activity, offering valuable guidance for the design of piezocatalysts for sustainable H 2 O 2 production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)