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This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

The purpose of this conceptual paper is to demonstrate how memes perpetuate trauma with a schematic. This conceptual paper uses the “begin with the end in mind” meme to format the presentation of the necessary components for the schematic of how trauma persists across generations. It is the third paper in a series of applications of the recursive, test-operate-test schematic to the systemic effects of the information processes involved in trauma. The schematic presented permits evaluations of solutions to interrupt the generational trauma cycle.

The required schematic components are described. Trauma (actual or perceived threat to survival) will be briefly discussed. Evolutionary processes that create the psychophysiology necessary to support nominal social expectations (NSEs) memes and metaphors will be summarized. The development of NSEs will be discussed. Metaphors and memes necessary for the creation of the schematic and esoteric events at level Learning IV will be briefly described. Finally, Figure 3, which illustrates the maintenance of NSEs and attempts to prevent their violation, will be explained.

It is asserted that functional human social behavior requires NSEs. Trauma is found to persist through the presence of anti-nominal NSE memes that are transduced by the individual into anti-nominal metaphors, which then damage grid, place and dentate gyrus cell (GPDG) neurophysiology. The damaged neurocircuits allow the use of anti-nominal NSE metaphors within our individual neurophysiology. Furthermore, anti-NSE memes interfere with the self-organized criticalities (SOCs) of genetic-epigenetic processes necessary for the intergenerational transfer of functional social behavior. When anti-NSE nominal metaphors are transduced back into anti-NSEs, social niche memes, the trauma process is reiterated. Anti-NSE memes and metaphors are found to be inappropriate criteria central to the maintenance of persistent trauma. Therefore, anti-NSE memes have hijacked our epigenetics and our social niches. Solutions are available because during our evolution, the Homo clade developed esoteric capabilities and the ability to bring back what information we can from those encounters. This physiology operates around the 5HT2A neural receptors that process hallucinogens, such as psilocybin. Accessing this resource system, either through naturally occurring altered states of consciousness or through micro-dose pharmaceutical psilocybin and related neurotransmitters, produces a significant structural change in the GPDG system to reset the NSE system illustrated in the schematic to its nominal status so that we can maintain nominal NSE relationships within our meme niche(s).

The source of persistent trauma in our social niche(s), the means by which the trauma is maintained and the means to mitigate and perhaps eliminate persistent trauma are identified. Based on these three conclusions, it is difficult to make decisions regarding corrective actions because of ubiquitous anti-NSE memes and because of the limitations of our ordinary consciousness.

If we wish to survive as a species, we will need to discover the criteria necessary to maintain our niche(s) congruent SOCs and use them instead of tyrannical memes described by Dawkins (1989) to make decisions about ourselves and our niche(s).

Significant courage is required to identify the memes that maintain trauma because many of them are culturally sacred cows. Unfortunately, we have known since Bremner's (1995) MRI study of posttraumatic stress disorder that trauma causes brain damage. Fortunately, our NSE genes compel us to pursue restitution of the memes that maintain our trauma-inducing cultures.

Several original assertions are presented. While the Homo clade ancestors began the creation of the social niche(s) that led to Homo sapiens sapiens, it is asserted that the australopiths created the NSE memes which are the foundation behaviors that permit our social niche(s). Furthermore, NSEs were produced by enhanced intentionality skills and NSEs were created by both genetic and memetic processes. The evolution of intentionality-NSE neural networks is asserted as the source of intentional material manipulation. While anti-NSE memes are claimed as the source of persistent trauma, the practice of esoteric technologies is presented as a solution to persistent trauma.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This paper’s objective is to provide a systematic literature review of the contextual factors affecting downward communication from supervisors to subordinates in the audit environment. In addition, this review identifies emerging research themes and directions for future research.

I accomplish this review’s objectives by leveraging communication literature to establish a framework to identify and synthesize contextual factors affecting downward communication in the audit environment. The review identifies 50 published articles in the last 20 years from leading accounting and auditing journals.

This study consolidates research findings on downward communication under two primary contextual factors: (1) message and (2) channel. Findings indicate that empirical research examining communication in audit is fragmented and limited. Studies examining the message focus heavily on its content and treatment in the areas of feedback, nonverbal cues, and fraud brainstorming, and a handful of additional studies examine the effectiveness of the channel in these areas. Additional research is needed to understand a broader set of supervisor–subordinate communication practices, including those that are computer-mediated, and their effect on subordinate auditors’ judgments and behaviors in the contemporary audit environment.

Much of the audit literature examining communication to date is topic-versus construct-based, making it difficult to see how the research findings relate to one another. This review is the first to synthesize the literature to provide academics recommendations for a way forward, and inform practitioners of communication practices whereby supervisors can be trained to improve audit quality.

Due to the global COVID-19 pandemic, UNESCO Landscapes and World Heritage sites have faced unstable situations. Both at the sites themselves and in the research centres, universities and even the homes of the people involved, they have acted and responded to the best of their ability. In this context, the aim of the comparative analysis of different cases carried out here is to understand the main effects of the pandemic in the short term. On the one hand, the purpose is to determine what the general response trends have been and, on the other, to measure the resilience capacity in each case.

Up to eight cases studies representing different and diverse kinds of Heritage and Protected Natural sites from Southern Europe and America are compared.

In a context of uncertainty, new responses, unique opportunities and hitherto unseen weaknesses have arisen in research and management of natural and cultural heritage. In general terms, the dialogue between officials, technicians and researchers that have put together this article underlines the need to work towards a governance model that engages everyone in dialogue. Discrepancies between overlapping strategies and plans, which is the main conflict detected, should be avoided while a decentralisation of policies could be more operational. In this sense, situated knowledge may be of help in configuring practical management tools.

This paper compares and contrasts for first time the effects of the pandemic in Europe and Latin America. This exercise has provided a valuable diagnostic for present and future heritage management.

Emergent research suggests that compulsive social media usage (CSMU) has a correlational link with well-being. Previous research in this area primarily focused on the prevalence, dynamics and consequences of social media usage. However, the knowledge of these occurrences among school and university students is still in its infancy stage. This research study addresses the knowledge gap by investigating the nexus between fear of missing out (FOMO), phubbing, CSMU and well-being.

Cross-sectional surveys were conducted for collecting the data of school students and university students during COVID-19 when the exposure to the Internet and social media among the students had increased tremendously. Multivariate analysis and Moderated Mediated analysis techniques were performed to analyze the data using the structural equation modeling approach.

The results indicated that while on one side, students experience “FOMO”, on the other, they phone snub the individuals available to them to interact. FOMO significantly influences well-being; phubbing also has a significant impact on well-being; phubbing partially mediates the relationship between CSMU and well-being. However, for university students, the full mediation of phubbing in the relationship between CSMU and well-being was confirmed. It was also found that sleep fully mediated the relationship between CSMU and well-being.

This study provides novel highlights of the differential effects of FOMO, phubbing, sleep hygiene and well-being among the university and school-attending cohorts.

This paper posits the need for English language arts (ELA) teachers to foster students’ use of languaging about their relations with ecosystems and peers, leading to their engaging in collective action to critique and transform status-quo systems impacting the climate crisis.

This paper reviews the current theory of languaging theory and research that focuses on the use of languaging to enact relations with ecosystems and others and voice emotions for transforming communities and reducing emissions contributing to climate change.

This review of languaging theory/research leads to identifying examples of teachers having students critique the use of languaging constituting status quo energy and community/transportation systems, respond to examples of characters using languaging in literary texts, using languaging in discussing or writing about the need to address climate change, critiquing languaging in media promoting consumption, using media to interact with audiences and using languaging through engaging in role-play activities.

This focus on languaging in ELA classrooms is a unique perspective application of languaging theory, leading students to engage in collective, communal action to address the climate crisis.