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This paper aims to develop an experimental paradigm to assess effects of degrees of logo change on logo processing speed to provide rigid tests of the effects of objectified degrees of logo changes and to understand how degrees of logo change interact with consumer and market conditions.

Experiment 1 (N = 120) used a 3 (degree of change: no vs small vs substantial change) unifactorial between-subjects design to develop the experimental paradigm for the effects of degrees of logo change. Experiment 2 (N = 148) examined effects of brand consciousness and exposure in a 3 (degree of change: original vs small vs substantial change) × 2 (exposure: 1 vs 3 exposures) between-subjects design with brand consciousness as a continuous moderator to extend the paradigm to a more naturalistic marketing communication setting.

Substantial logo changes harmed processing speed of highly brand conscious consumers in particular. Furthermore, substantial logo changes decreased processing speed, which was compensated by repeated exposure to the redesigned brand logo.

The findings suggest that brand consciousness creates a tunnel vision, which impairs openness to changes in brand image. Furthermore, the findings imply that brand logos can be (substantially) changed without hurting logo processing speed: only a few exposures are needed to neutralize these effects.

This is the first study that provides rigid tests of objectified degrees of logo changes, extended to a more naturalistic marketing communication setting, by examining brand consciousness and exposure effects.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

First, this study expands knowledge on the strategic decision process dimension decision-making speed by analyzing decision-making speed and two possible antecedents in a purchasing context. Second, it takes an additional step toward clarifying the relationship between strategic and lateral integration. Specifically, the purpose of this paper is to analyze the potential mediating effect of lateral purchasing integration on the relationship between strategic purchasing integration and purchasing decision-making speed.

This research analyzes survey data of 152 firms from Austria, Germany, and Switzerland using covariance-based structural equation modeling.

The results of the structural equation model provide strong support for the hypothesized relationships. Strategic purchasing integration drives lateral purchasing integration, which in turn positively influences purchasing decision-making speed.

This study focusses solely on internal types of integration. A logical next step would be to further enrich the model by including external dimensions, such as supplier or customer integration.

This study should help managers gain a better understanding of the relationship between strategic and lateral purchasing integration, highlighting their positive impact on decision-making speed. Decision-making speed is particularly important for companies operating in volatile markets and time-constrained business environments.

This study offers new insights into the theoretical and empirical connection between intra-organizational purchasing integration, unpacked as strategic purchasing integration and lateral purchasing integration, and purchasing decision-making speed. Furthermore, it offers insights into decision-making speed in a purchasing context.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Comorbidity of attention deficit hyperactivity disorder (ADHD) and reading disabilities (RD) is greater than what would occur by chance. Considering the well-documented adverse impact of both ADHD and RD on development, the presence of both conditions may lead to particularly poor outcomes for affected people. This chapter, which reviews 43 research studies carried out in the last decade that have focused on the link between ADHD and RD, is divided into two broad nuclei of contents. First, studies are described that contribute information about characteristics of the comorbid phenotype. Second, studies related to procedures directed toward evaluation and intervention in this problem are analyzed. The review carried out does not make it possible to extract definitive results on the exact nature of ADHD and RD comorbidity or, even less, reach conclusions about its causes. However, the literature-based evidence shows a cognitive profile of ADHD+RD characterized by failure of various functions that can produce more severe functional deficits and worse neuropsychological, academic, and behavioral outcomes. Furthermore, the analysis of the set of results from the studies shows a limited efficacy of pharmacological and psychopedagogical treatments, and highlights the need for continued research on this topic. From a clinical and educational standpoint, the conclusions derived from this review underline the importance of performing an exhaustive evaluation of children and adolescents with symptoms of ADHD and/or RD, in order to be able to plan interventions with greater possibilities of success in each case.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.

The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.

The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.

As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This text proposes that time is essentially related to one's visual metabolic rate. Metabolic rate is regulated by the speed of the intake of energy, the rate of the production of adenosine triphosphate (ATP) to the visual areas. The author's working hypothesis is that the visual area of the brain known as human V5, the region involved in motion detection, may be the region most responsible for time perception. Our time sense from all the senses is, thus, compiled in the visual region, the speed of the human perception of reality. Time is the relationship of the human perception of reality and the rate that the reality itself is taking place (given by light waves in the environment). Hence, vision (and the visual cortex area V5) may be the vitally important aspects in answering the question: what is time? When we are not looking at a clock, time may be governed by our rate of metabolism; rate of the production of ATP by the mitochondria in V5. For example, when general human metabolism (and V5) is fast, time runs slow. When metabolic rate is relatively slow, time runs relatively faster. Many factors enter into the speed of metabolism such as age, sex, drug effects, velocity compared to speed c, states of boredom or excitement, darkness or light and mental states such as sleep. The relationship between time and space is discussed with the metabolic rate of V5 in mind. Because the uncertainty principle and the quantum picture of reality are adopted, this model qualitatively quantizes space and time, showing why they must forever be connected i.e. space‐time. This idea is discussed in relation to Zeno's paradox, which suggests that space and time are indeed quantized. Events, instants and entropy are defined. Reality can be understood in terms of the speed of the processing of instants. The arrow of time is pictured as caused by long‐term potentiation of synaptic neurons within the brain. Minkowski‐Einstein space‐time is analyzed and compared with the visual metabolic rate. The probable consequences of this model are proposed.