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Engineering components/structures with geometric discontinuities normally bear complex and variable loads, which lead to a multiaxial and random/variable amplitude stress/strain state. Hence, this study aims how to effectively evaluate the multiaxial random/variable amplitude fatigue life.

Recent studies on critical plane method under multiaxial random/variable amplitude loading are reviewed, and the computational framework is clearly presented in this paper.

Some basic concepts and latest achievements in multiaxial random/variable amplitude fatigue analysis are introduced. This review summarizes the research status of four main aspects of multiaxial fatigue under random/variable amplitude loadings, namely multiaxial fatigue criterion, method for critical plane determination, cycle counting method and damage accumulation criterion. Particularly, the latest achievements of multiaxial random/variable amplitude fatigue using critical plane methods are classified and highlighted.

This review attempts to provide references for further research on multiaxial random/variable amplitude fatigue and to promote the development of multiaxial fatigue from experimental research to practical engineering application.

This paper presents a numerical and experimental investigation of ceramic shell cracking during the burnout process in investment casting with internally webbed laser stereolithography patterns. Considered are the cracking temperature of the ceramic shell, the buckling temperature of the web link, and the glass transition temperature of the epoxy resin. Our hypothesis is that shell cracking will occur if the ceramic rupture temperature is lower than the temperature of glass transition and the temperature of web buckling. This hypothesis is validated by a good agreement we obtained between experimental observations and numerical simulations. It is found that the shell cracking and web link buckling are strongly related to the cross‐sectional dimensions and span length of the web structure and the shell thickness, and that shell cracking can be prevented by buckling of the epoxy webbed pattern in early stages of the burnout process.

One of the fundamental problems in control systems engineering is the problem of sensors and actuators placement. Decisions in this context play a key role in the success of control process. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive. The computational burden is significant, in particular, for large-scale systems. The purpose of this paper is to improve and extend the state-of-the-art methods within this field.

In this paper, a new technique is developed for placing sensor and actuator in large-scale systems by using restricted genetic algorithm (RGA). RGA is a kind of genetic algorithm which is developed specifically for sensors and actuator placement.

Unlike its other counterparts, the proposed method not only supports unstable systems but also requires significantly lower computations. The numerical investigations have confirmed the advantages of the proposed methods which are clearly significant, in particular, in dealing with large-scale unstable systems.

The proposed method is novel, and compared to the methods which have already been presented in literature is more general and numerically more efficient.

This paper reviews studies that have examined how accounting information impacts commercial lending judgments. Issues discussed involve the usefulness of accounting data in lending decisions, effects of different accounting methods on lenders’ judgments, bankruptcy and default judgments, and decision processes pertaining to the use of accounting information in lending decisions. Additionally, the paper reviews the research on how audits and other forms of assurance influence commercial loan officers’ judgments. Topics include the way perceived auditor independence influences loan officers’ judgments, the impact of financial statement audits and audit opinions on lending decisions, how internal control reports and other CPA firm reports influence loan decisions, ways in which audit report disclosures and wording impact lending decisions, how perceived auditor quality affects lending decisions, and the effects of limited assurance engagements on loan officers’ judgments.

Climatic changes caused by greenhouse gases (GHGs) emissions are an urgent challenge for all regions around the globe while the livestock sector is an important source of GHGs emissions. The adoption of low-carbon manure treatment technology (LMTT) by farmers is emerging as an effective remedy to neutralize the carbon emissions of livestock. This paper aims to incorporate environmental literacy and social norms into the analysis framework, with the aim of exploring the impact of environmental literacy and social norms on farmers' adoption of LMTT and finally reduce GHGs emission and climate effects.

This research survey is conducted in Hebei, Henan and Hubei provinces of China. First, this research measures environmental literacy from environmental cognition, skill and responsibility and describes social norms from descriptive and imperative social norms. Second, this paper explores the influence of environmental literacy and social norms on the adoption of LMTT by farmers using the logit model. Third, Logit model's instrumental approach, i.e. IV-Logit, is applied to address the simultaneous biases between environmental skill and farmers’ LMTT adoption. Finally, the research used a moderating model to analyze feasible paths of environmental literacy and social norms that impact the adoption of LMTT by farmers.

The results showed that environmental literacy and social norms significantly and positively affect the adoption of LMTT by farmers. In particular, the effects of environmental literacy on the adoption of LMTT by farmers are mainly contributed by environmental skill and responsibility. The enhancement of social norms on the adoption of LMTT by farmers is mainly due to the leading role of imperative social norms. Meanwhile, if the endogeneity caused by the reverse effect between environmental skill and farmers’ LMTT adoption is dealt with, the role of environmental skill will be weakened. Additionally, LMTT technologies consist of energy and resource technologies. Compared to energy technology, social norms have a more substantial moderating effect on environmental literacy, affecting the adoption of farmer resource technology.

To the best of the authors’ knowledge, a novel attempt is made to examine the effects of environmental literacy and social norms on the adoption of LMTT by farmers, with the objective of identifying more effective factors to increase the intensity of LMTT adoption by farmers.

Rapid Prototyping (RP) technology is being widely used in diverse areas including mold manufacturing. However, the quality of RP parts is significantly affected by the property of adopted material and process parameters of the rapid prototyper. The aim of this paper is to investigate the powder material and to optimize the process parameters for Zcorp 402 3DP rapid fabricator. Taguchi's method was employed to investigate the possible process parameters including binder setting saturation value (shell & core), layer thickness, and location of made‐up parts. The experimental result shows that these optimal parameters can shorten parts building time and reduce the use of powder and glue about 20 per cent for ZP100 and 10 per cent for ZP11. Additionally, the quality of RP parts is also improved dramatically. The observation of experiments also shows that the parts made by ZP11 powder is difficult to clean up because of its starch‐based property.

Recently, the concept of frugal innovation (FI) has gained relevance in both academic and societal discourse. Whether disruptive frugal digital innovation is contributing to the growth of small and medium-sized businesses in Africa is a subject of debate. Generally speaking, frugal inventions are linked to developing nations where sizable consumer groups at the base of the pyramid have unfulfilled needs. Africa, a continent with growing economies, has witnessed a surge in disruptive FI, which makes this chapter an important discourse. Digital technologies such as the Internet of Things (IoT), social media, cloud computing, and Artificial Intelligence (AI) are finding their way in the market, and how it is affecting human development and small enterprises has been a subject for debate. This book chapter presents findings on how FI has led to sustainable human development and growth of micro, small-, and medium-sized enterprises (MSMEs) as revealed by a review of existing literature on the subject. According to findings from the literature, MSMEs' connections and interactions with partners, suppliers, and consumers have improved as a result of the adoption of disruptive digital platforms. Additionally, while operating in challenging settings in Africa, digital technologies offer certain intrinsic traits and information technology capabilities that allow firms to grow and flourish. Digital platforms are viewed as transparent, collaborative instruments that provide economic opportunities throughout the ecosystem. The results also show that disruptive digital technologies offer MSMEs an inexpensive, simple-to-use, and simple-to-deploy solution in order to compete with stronger incumbents across a range of industries.

In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches.

A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper.

The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems.

The usage of the proposed approach can be valuable to industrial corporations, as it allows for improved workflows, maximization of available robotic operations and improvement of efficiency.

To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Over the recent years, solar energy has received outstanding attention from researchers. Solar energy applications and related large-scale projects are increasing to meet growing global energy demand as an economical, non-polluting and renewable energy source. The purpose of this study is investigating different plenum and absorber configurations of solar air heating wall (SAHW) experimentally and numerically.

In this study, various configurations of SAHW have been numerically simulated to determine the most effective design. According to the simulation results, two SAHWs with various plenum thicknesses have been fabricated and tested at different conditions.

Numerical simulation results indicated that parallel-flow SAHWs exhibited better performance in comparison with other placements of absorber plate. Regarding to the experimentally attained results, the highest thermal efficiency was reached to 80.51%. Also, the average deviation between experimentally and numerically obtained outlet temperature is 5.5%.

Considering the obtained results in the present study, designed SAHW has admissible efficiency to be used in various industrial and residential applications such as; air preheating, space heating and drying.