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This paper analyses container throughput developments in the East Asia container port system. Throughput evolutions and concentration/deconcentration patterns in the multi-range container port system of East Asia are analysed. The paper also provides a more in-depth qualitative analysis of the reasons underlying the observed trends and results. It is demonstrated that the East Asian port system is undergoing major structural shifts in cargo patterns and is witnessing a cargo deconcentration trend as a result of the rise of the Chinese ports and the relative stagnation of the Japanese range.

This paper deals with network configurations in liner shipping and inland transportation from a carrier's perspective. The cost efficiency of different possible network configurations in the foreland-hinterland continuum is discussed based on a cost model and on a qualitative analysis. It is demonstrated that the tendency towards cargo concentration in a limited number of ports has led to the redesign of collection and distribution networks in the hinterland. Further cargo bundling in the foreland-hinterland continuum towards even fewer ports and inland centres is only interesting from a cost perspective if considerable economies of scale and density can be realised in the associated hinterland networks. The more cost efficient the network becomes, the less convenient that network could be for the shippers ' needs in terms of frequency and flex ibility. As such, the future configuration of liner shipping networks and inland transport networks will largely depend on the balance of power between carriers and shippers.

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.

The purpose of the present study is to examine the relationship between spiritual leadership and employees' alienative commitment to the organization, both directly and indirectly, via employee social capital. We also test the role of employee political skill as a boundary condition of the indirect spiritual leadership–alienative commitment link.

Time-lagged data were collected from 491 employees in various manufacturing and service organizations. Data were analyzed using structural modeling equation in Mplus (8.6).

Spiritual leadership was negatively associated with alienative commitment, both directly and indirectly, via social capital. Employee political skill moderated the indirect relationship between spiritual leadership and alienative commitment, such that the relationship was stronger when employee political skill was high (vs low).

The demonstration of spiritual leadership's behaviors by both managers and employees can develop employees' social capital at work, which in turn can reduce employees' negative commitment to the organization. Likewise, improving employees' political skills can help leadership diminish alienative commitment.

The present work contributes to the literature on spiritual leadership by foregrounding how and why spiritual leadership undermines employee alienative commitment to the organization. By doing so, the study also enhances the nomological networks of the antecedents and outcomes of social capital and contributes to the scant literature on negative alienative commitment. Given the prevalence and negative repercussions of alienative commitment for employees' and organizations' productivity and performance, our findings are timely and relevant.

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Customer experience research predominantly anchors the customer journey on a specific offering, implying an inherently firm-centric perspective. Attending calls for a more customer-centric approach, this study aims to develop a goal-oriented view of customer journeys.

This study interprets the results of a phenomenological study of a transformative journey toward a sober life with the self-regulation model of behavior to advance understanding of customer journeys.

The consumer's journey toward a higher-order goal encompasses various customer journeys toward subordinate goals, through which consumers engage in iterative cognitive and behavioral processes to adjust or maintain their experienced situation vis-à-vis the goal. Experiences drive behavior toward the goal. It follows that negative experiences may contribute to goal attainment.

This study highlights the importance of looking at the consumers' higher-order goals to obtain a more holistic understanding of the customer journey.

Companies and organizations should extend their view beyond the immediate goals of their customers to identify relevant touchpoints and other customer journeys that affect the customer experience.

This study proposes conceptualization of the customer journey, comprising goal-oriented processes at different hierarchical levels, and it demonstrates how positive and negative customer experiences spur behaviors toward the higher-order consumer goal. This conceptualization enables a more customer-centric perspective on journeys.

The purpose of this paper is to test whether managers’ political skill is relevant for employees’ authentic leadership perceptions. Political influence theory assumes that political tactics seek to affect others’ interpretations of a person or situation. Thus, what matters for employees’ perceptions of their manager’s authentic leadership may be whether the manager actively seeks to show behavior that can be interpreted as authentic leadership. Combining political influence theory and gender stereotypes research, it is further suggested that manager gender moderates the employees’ interpretation of political influence attempts that are ambiguous.

Managers (n=156; 49.5 percent female) completed measures of their political skill. Employees (n=427; 39.1 percent female) completed measures of the manager’s authentic leadership.

Managers’ apparent sincerity was positively related to employees’ perceptions of managers’ authentic leadership; managers’ networking ability was negatively related to employees’ perceptions of female managers’ authentic leadership, but not of male managers.

The methodology does not allow claims about causality.

Findings add knowledge of authentic leadership, such as difficulties that female managers face, and show the value of a fine-grained approach to political skill. Female managers should be aware that networking might have disadvantageous side effects. Conversely, sincere behavior attempts seem favorable for authentic leadership perceptions.