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This paper surveys recent publications on maritime logistics with a focus on blockchain capabilities. Maritime logistics plays an important role in global trade and an important part of the global economy depends on it. The growth of world trade has led to the creation of various systems that are prone to error and fraud, the current supply chain needs to connect and trust many intermediaries which not only increase abuse and errors but also it will increase cost and time in the chain. Hence, transparency and stability are highly demanded. On the other hand, with the onset of the fourth industrial revolution, a new approach called “blockchain” was introduced which can have very important applications in maritime logistics. As this novel technology is in its early stages of development, its applications in maritime logistics have not been specifically investigated. So, this paper takes a deep dive into blockchain technology capabilities for maritime logistics by reviewing relevant scientific journal papers since 2014. The paper also discusses and challenges the current operational paradigms and by categorizing efforts, provides a taxonomy of research topics in maritime logistics. Finally, the paper in addition to presenting challenges, identifies new avenues for academic research based on current trends and development.

The purpose of the present study is to present a model for the use of the Internet of Things in Iranian knowledge-based companies. From a purpose point of view, this research can be classified as applied research and in terms of data collection, it can be categorized as a qualitative and quantitative study. The main approach for achieving the model of using the Internet of Things is the grounded theory methodology which is based on a paradigmatic model. The research population for the qualitative phase is comprised of 25 executives and practitioners of knowledge-based companies who are somehow specialized in Internet of Things topics. The sample of this study was selected using the snowball sampling method and Cochran's formula for finite communities among all the managers and technology experts working in knowledge-based companies. After careful analysis of the interviews, 59 extracted concepts were identified in 5 main categories and 13 subcategories. Management requirements, customer requirements, and resistance economics give rise to the Internet of Things in knowledge-based companies.  The characteristics of the company, the characteristics of the human resources and the characteristics of the managers can provide a suitable ground for using the Internet of Things. And the conditions in the country, international pressures and international cooperation can be among the intermediary factors influencing the use of the Internet of Things. Raw materials and infrastructure can also be effective strategies for using the Internet of Things. Using the Internet of Things, customer-related factors, international collaborations, and expected results can be expected in knowledge-based companies.

According to Gartner’s Hype Cycle, gamification is poised for large-scale adoption in the coming years. The construct of gamification has received considerable interest, especially in the context of learning. Over the past five years, research in the area has grown. The current study looks at consolidating the conducted research and attempts to provide a snapshot of the work carried out in the last five years. The chosen studies have explored the role of gamification in learning activities and have attempted to provide empirical evidence to support gamified learning. The purpose of the study is to review existing literature, in terms of the outcomes, outcome variables, context of the study, and research methodology used. This would not only add to the knowledge in the field but would also guide future research. Additionally, practitioners would benefit from a consolidated view of five years of research into the practice of gamification.

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

To ensure the continuity of the training activity within the framework of the specific precau-tions for the Covid 19 health crisis , the regional centers for training and education profes-sions, such as universities and schools, have radically reversed their training strategy, from face-to-face training to alternating training mode known as `` hybrid training mode '', the training of which is provided remotely one week and face-to-face during the following week and vice versa. In this sense, digital platforms of social networks and videoconferencing soft-ware have been mobilized to succeed in the mission of trainers.  The objective of this article is to verify the contribution of videoconferencing software '' on training practices on the one hand and on the development of trainees' skills on the other hand, in order to trace a training methodology based on technological choices aimed at guar-anteeing maximum socio-professional performance. Finally, we confirm that despite the satisfaction of the majority of trainees 87% with respect to the advantages of videoconferencing tools in the process of assessing skills, technical, spa-tiotemporal and organizational disparities were noted among trainers compared to the use of digital tools in the evaluation (only 53% agree), or also in relation to the transition from one technological choice to another. 

The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.

The main idea of IoT is to connect several objects to each other through Internet. In the field of Computer Network the main problem identified by researchers is network congestion. Now a day’s network congestion is increasing very rapidly because IoT connect a huge number of devices to internet. A transport layer protocol TCP (Transmission Control Protocol) is accountable for network congestion control. The behavior of TCP is not stable as it takes long time to fill the available capacity of the network. It also continuously keeps assessing the capacity of data transmission through increasing the limits.TCP drops its data transmission rate aggressively when packets are dropped, which significantly reduces the throughput. This paper suggests a new approach, stable transmission control protocol for IoT applications. The experimental results show that stable transmission control protocol achieves better performance in terms of goodput.

Blockchain technology was initially implemented for cryptocurrencies in 2009, but it caught the undivided attention of multiple industries such as finance, supply chain management, healthcare, and governments. This research was set out to investigate and evaluate six benefits of blockchain for marketing: fostering disintermediation, combating click fraud, reinforcing trust and transparency, enhancing privacy protection, empowering digital marketing security, and enabling creative loyalty programs. An empirical study in the form of an online survey was conducted to examine the realization of benefits in practice. The research concluded that blockchain does indeed provide promising benefits for marketing, but that depends on whether marketers use public (permissioned) blockchain or private (permissioned) blockchain, and also the ability of the blockchain community to resolve fundamental challenges and pending issues such as scalability, speed, interoperability, and privacy, besides several many others.

Internet of Things could be current paradigm, with promising recent wireless communication. Simultaneously as its will likely deliver compelling and effective arrangement, security of the gadgets and organization could be challenge issue. Therefore security has arisen as quite difficult for the IoT during this paper is expect to create security challenges of the IoT, structural design of IoT through handling security troubles at every layer of the design, key advancements in IoT and use of IoT.

Due to the rapid changes and developments caused by technology in the digital age and the growing importance of achieving sustainability for the survival of businesses, the need to create the capabilities needed to innovate in the business model of organizations has become inevitable. This research is trying to provide a model for business model innovation with the aim of sustainability by considering the external factor of digitalization (digital transformation) and the internal factor of dynamic capabilities. The method of this research is mixed so that in the first stage the qualitative method of Meta-Synthesis is used and in the second stage the quantitative method of Interpretive Structural Modeling (ISM) is used. In this research, based on the systematic review of previous researches in scientific databases, 402 related articles were identified and then 46 final articles were selected during the screening process. Based on the thematic analysis, a total of 4 main categories and 14 factors were extracted. In the second stage of the research, an interpretive structural model (ISM) is developed to show the inter‐relationship of different factors and their levels of importance in the food industry.   The 8-level hierarchy model of factors influencing the sustainable business model innovation in the digital age with a dynamic capabilities approach was proposed. Results show that a new component called network value has been added to the traditional business model components. Furthermore, the MICMAC approach has been used to categorise factors according to their driving power and dependence. finally, digital technologies and Sensing were found to be the factors with the highest independence and the highest driving power, and “Economic Sustainability” was identified as the factor with the highest dependence and the least driving power

Achieving e-commerce success is influenced by a variety of interrelated activities. Identifying, analyzing and prioritizing these activities enable decision-makers firstly to evaluate the current success state of Electronic Commerce (EC) businesses more accurately and then to develop an efficient improvement plan to allocate limited resources to achieve a higher level of success. In this research, a Fuzzy Cognitive Maps- analytical hierarchy process (FCM-AHP) approach is used to address this issue by (1) determining interrelationships between influential activities, (2) determining how much these activities influence each other and the overall success, and (3) prioritizing activities to develop a sound improvement plan. In this approach, the FCM technique is used to indicate all possible causal interrelationships between activities and consider all feedback loops between them. Then, the AHP technique is used to determine the contribution weights of activities to the overall success. The obtained management matrices that make it possible to categorize influential activities and the results of improvement scenarios help the decision-makers to develop the most efficient improvement plan and gain the maximum benefit from the allocation of limited resources. An empirical study is implemented to serve as an example to indicate how the approach works.

In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works.