Big Data-Enabled Authentication Framework for Offshore Maritime Communication Using Drones

Soil salinization poses significant challenges to agricultural productivity, food security, ecological balance, and environmental sustainability. Concerning monitoring and mapping soil salinity, Remote Sensing (RS) has emerged as a pivotal approach, offering cost-effective and scalable solutions compared to conventional laboratory methods. This study aims to present a detailed bibliometric analysis of research articles on soil salinity monitoring and mapping using remote sensing technologies published over the past three decades (1988–2023), leveraging the SCOPUS database and tools like VOSviewer. The analysis examines 566 publications to identify trends, advancements, and research gaps, and it reveals an exponential growth in research outputs, with 82% of studies published in the last decade and 60% in the past five years. Key findings include the dominance of geospatial technologies such as optical and radar remote sensing, as well as emerging methodologies like machine learning algorithms (e.g., Random Forest, Support Vector Machines) and hyperspectral imaging. Collaborative research, particularly among international authors, has significantly contributed to the advancement of the field. Geospatial techniques are frequently employed for salinity monitoring, with research hotspots identified in regions like the Yellow River Delta (China), Bangladesh Coastal Zone, Ebinur Lake, Xinjiang, and Aral Sea. China leads in publication count, while countries like Australia and the Netherlands exhibit higher citation impacts. On the other hand, key findings highlight the utility of remote sensing for addressing soil salinity’s spatial and temporal dynamics, revealing significant gaps in underrepresented regions such as sub-Saharan Africa and South America. Future research directions include improving multi-source data integration, exploring advanced algorithms for big data analytics, and addressing the socioeconomic dimensions of soil salinization. This comprehensive analysis provides a roadmap for leveraging remote sensing technologies to mitigate the impacts of soil salinity on sustainable agriculture and environmental resilience.

A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.

As the scale and dimensionality of data continue to grow in many applications of data analytics (e.g., bioinformatics, finance, computer vision, medical informatics), it becomes critical to develop efficient and effective algorithms to solve numerous machine learning and data mining problems. This tutorial will focus on simple yet practically effective techniques and algorithms for big data analytics. In the first part, we plan to present the state-of-the-art large-scale optimization algorithms, including various stochastic gradient descent methods, stochastic coordinate descent methods and distributed optimization algorithms, for solving various machine learning problems. In the second part, we will focus on randomized approximation algorithms for learning from large-scale data. We will discuss i) randomized algorithms for low-rank matrix approximation; ii) approximation techniques for solving kernel learning problems; iii) randomized reduction methods for addressing the high-dimensional challenge. Along with the description of algorithms, we will also present some empirical results to facilitate understanding of different algorithms and comparison between them.

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Data Stream Partitioning Algorithms for Big Data Analytics: A Review

Application of AHP and VIKOR methods under interval type 2 fuzzy environment in maritime transportation

In this study, it is substantiated that the process of globalization leads to the world economy acquiring the characteristics of a complex multi-level system, which makes it possible to use the properties of systems as tools for researching the effects of negative factors and threats to the development of the world economy and maritime transport as subsystems of the world economy. It was established that not only the rise in prices on the world's leading stock exchanges Using the systems theory methods but also the freight cost of dry cargo ships is a significant influencing factor on food prices for the whole world. It has also been established that since the cargo traffic of food with a substantial level of processing to countries where, due to the level of development of their economy, there are mostly no opportunities for processing products, this affects the increase in the cargo traffic of container transport in global sea transportation. The synergistic nature of the impact of negative factors on the worldwide economy and the maritime transport industry has been outlined. It is singled out that the synergistic negative effect led to a chain reaction to the change of international sea transport routes. The assessment of the effectiveness of management actions of the industry management on external challenges and threats was carried out. It is pointed out that the direction of the global maritime transport industry needs to dynamically adjust the operation of logistics networks to respond effectively to the growing needs of the world market. It is also indicated that the need for more efficiency and capacity of the port infrastructure still predominantly affects container transportation due to the growing demand for the volume of cargo transportation. It is emphasized that the established definition of the world transport system needs to be sufficiently corrected. The conducted research found that the world transport system, under the influence of the globalization of the world market and the internationalization of the cargo transportation process, acquires transnational features and becomes a global structure, working increasingly in the interests of the world market. Keywords: globalization, internationalization, maritime transport, crises, risks, threats, efficiency of industry management.

PurposeThe purpose of this paper is to present a comparison between the traditional methods for the calculation of customer relationship performance and the modern metrics suggested by the current literature in business-to-business (B2B) markets using the Greek maritime shipping industry as an example.Design/methodology/approachThe primary research was conducted in two phases: quantitative analysis of actual measurements and qualitative evaluation of the results. More specifically, in the first phase, the measures used were a collection of traditional and modern customer relationship management (CRM) metrics applied to actual historical data along with statistical data for actual customers of a company supplying services for maritime transportation of containers in the Greek international trade market. For the qualitative evaluation of these results, a semi-structured interview was carried out with seven “specialists/experts” in this business sector, who provided an assessment of the relative worth of each set of CRM measures.FindingsThe use of modern customer-centred metrics (Share and Size of Wallet, recency, frequency, monetary value) in the shipping sector of Greek industrial activity is the most profitable and efficient means of decision-making. The qualitative research showed that the customer-centred metrics were judged to be more effective and useful, as they provided a multi-dimensional and multi-layered picture of the current and future situation for the company and its customer base.Research limitations/implicationsTo ensure confidentiality of personal information, the research did not use, examine or evaluate the individualized data to preserve the anonymity of the survey sponsor and their specific customers.Originality/valueThis is the first study that examines the effectiveness of different types of CRM metrics in the B2B market, which has, until now, suffered a dearth of empirical studies in the field, especially in the context of national economies that face intense international trading problems and significant reductions in activity in their maritime shipping industry due to the economic recession.

Maritime container supply chains (MCSCs) is exposed to various risks arising from both internal operations and the external environment, and the increasing complexity of the modern global logistics system makes the situation even worse, thus causing a significant challenge to the effective risk management of MCSCs. However, systematic studies on this topic are relatively few. In view of this, this study aims to explore and analyse various MCSC risks, develop suitable risk assessment methods, and evaluate the overall performance of MCSCs from a systematic perspective, so as to ensure the safety, reliability, and resilience of MCSCs. This research starts with the identification and classification of all possible risk factors that may be involved in an MCSC based on a comprehensive literature review, and the research results are further validated through a Delphi expert survey. The identified risk factors are then analysed, screened, and assessed in detail. The novelty of this study lies not only on the risk assessment of MCSCs under an uncertain environment from a supply chain level but also on the consideration of the impact of risk condition of each individual MCSC on the overall performance of the entire container supply network. The research results will provide useful insights and valuable information for both researchers and practitioners on the risk analysis and assessment of MCSCs, which is beneficial to different types of stakeholders involved in the maritime shipping industry. The work is also able to provide a theoretical foundation for risk-based decision making and shipping route optimisation in further work. Although the risk assessment methods are presented on the basis of the specific context in MCSCs, it is believed that, with domain-specific knowledge and data, they can also be tailored for a wide range of applications to evaluate the reliability and performance of other supply chain systems, especially where a high level of uncertainty is involved.

The maritime shipping industry, as a crucial component of the global economic system, is actively involved in solving the major global problems of our time, as more than 80 % of international trade is carried out by sea. Growth in the number of ships requires enhanced measures to improve environmental performance and safety of navigation Throughout the history of navigation, ships have been discharging everything that was considered unnecessary into the ocean: food waste, cargo residues, garbage, polluted water, etc. It was believed that the endless and deep sea would not be harmed by this. However, the possibilities of ocean self-cleaning are not unlimited, while the scale of marine activities is constantly growing. In this article, the authors review and systematize the analytical assessments of domestic and international experts on the problem of pollution and climate change, which requires immediate measures to reduce the negative impact of maritime transport on the environment. The purpose of the article is to outline the issues of main environmental problems in maritime transport and to summarize the results of published scientific research. We emphasize that environmental safety issues include the systematization of the constituent factors of marine pollution by oil, chemicals and other toxic substances, greenhouse gas emissions, loss of fuel tanks during accidents, etc. In our opinion, the identification and classification of the main causes of the impact of negative factors of maritime transport operation on the ecology and the state of the environment with a view to further reducing or eliminating this impact is an important topical problem of our time. To achieve the purpose of the study, theoretical methods of systematization, formalization, statistical processing and generalization of environmental monitoring data of domestic and world scientists were used. It is noted that scientific research in this area is considered to be the most promising.

The maritime transport industry is experiencing rapid growth, but this also brings a number of drawbacks, particularly the issue of illicit wildlife trade and corruption on ships engaged in international maritime traffic. In response to the increasing challenges of wildlife trafficking on sea routes, various efforts and toolkits have been developed to enhance detection, prevention, and prosecution. During the 46th meeting session of its Facilitation Committee, the International Maritime Organization (IMO) approved the Guidelines FAL.5/Circ.50, which provide important guidance for prevention and suppression of the smuggling of wildlife through maritime transport. Vietnam has long been exploited by organized crime syndicates as a strategically located transport hub for the global wildlife trafficking. This article therefore aims to clarify the role of Guidelines FAL.5/Circ.50, and to assess the compatibility of Vietnam's current anti-wildlife-trade legislation with the IMO Guidelines, with a particular focus on the aspects of investigation and prosecution, and prevention and deterrence of corruption, thereby proposing legal and technical recommendations for Vietnam.

Today, humanity has faced a fundamentally new catastrophe - the Covid-19 pandemic. Quarantine, closing borders, creating numerous problems for almost all states - all these are the realities of today. Experts will still be able to fully assess the scale of the economic shock associated with the epidemic and all its long-term consequences. But it is already clear that the recovery of the global economy is not an easy one. And already tested anti-crisis measures will not always work. It is necessary to look for fundamentally new, non-standard solutions. The aim of the research is to analyze the state of trade against the background of the global world economic situation. Identify the shortcomings of the industry, development prospects and highlight the economic benefits that need to be addressed as a matter of priority in order to improve performance and improve the situation during a pandemic. The article examines the current state of the global economic situation in the maritime transport industry during the Covid-19 pandemic. The international market of maritime trade is based on certain patterns, among which there is a clear focus on the parameters of international economic relations, the constant use of innovative technologies and the cyclical changes in key indicators. The trend of development of the maritime transport industry and its regionalization is due to a complex set of external and internal factors. Therefore, the article considers the relationship and impact of both negative and positive factors on the state of the world economy. The data of the interim economic forecasts for 2019-2022 in the estimated comparison of countries that have a global impact on the economic development of partner countries are studied. The interrelation of economic and maritime industries is analyzed, the state of modern problems of sea transportation is investigated, perspective directions are revealed and the necessity of concrete decisions for large-scale development of competitiveness of the transport industry is emphasized. As a result of the research it is suggested: in order to automate the process of sea transportation to accelerate the introduction of contactless document management, to consolidate the position of maritime logistics, which plays an important organizational and cost-effective role in modern economic relations and trade flows.

With the prosperity and development of the Maritime Silk Road, China's maritime industry has reached a new height. While the maritime transport industry has been vigorously developed, it has also brought great challenges to safe navigation. To realize intelligent navigation, effectively prevent maritime collision accidents, and improve navigation safety, a structural model of intelligent navigation obstacle avoidance platform based on Internet of Things technology is first proposed. Then the research combines the analytic hierarchy process, artificial neural network and BP neural network algorithm, and introduces environmental factors to design an optimized intelligent navigation obstacle avoidance algorithm, so that the algorithm can make real-time intelligent adjustment strategies according to the changes of the actual environment. Finally, the collision risk at the location of the research ship is judged, and the priority list of obstacle avoidance is constructed by the risk value between different ships and the research ship, providing reference for the pilot. The research results show that the prediction accuracy of I-INOA algorithm is 97.83%. In the two obstacle avoidance experiments, the decision-making efficiency of the four ships based on I-IONA algorithm is the highest, which is 1. In practical application, the priority list of obstacle avoidance is P, O and S2. In conclusion, I-INOA algorithm has better performance and practicability, enabling the research ship to respond more intelligently and quickly.

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

The concept of shared IoT devices has attracted much attention from the industry sector, academia, and financial institutions, providing various benefits, such as saving resources, reducing personal expenses, and providing convenience. Although shared IoT devices facilitate people’s lives and work, the information exchange is over wireless networks that may suffer from some security attacks such as unauthorized access to a shared device or some private information of legitimate users being leaked. It makes the secure access control to the shared IoT devices become an intractable issue. In order to guarantee the access right of the legitimate users, to prevent the problems of privacy leakage and unnecessary economic disputes, a secure decentralized access control scheme for shared IoT devices is proposed leveraging the technologies of blockchain and a proposed authentication protocol in this paper. The new lightweight authentication protocol is proposed to perform mutual authentication between the user and the IoT device. To protect the privacy of the user, the instruction data are encrypted by a temporary session key negotiated between the user and the IoT device with the help of blockchain which enables nontamperable transactions and prevents central corruption and single point of failure. In our scheme, blockchain is maintained by the gateway nodes an acts as a distributed database and a smart contract for shared service is deployed on it. The smart contract has three functions in our scheme: (1) achieving the prepayment of users and settlement for the service contributor, (2) participating in a verification step during the key negotiation to prevent some malicious behaviour from users or devices, (3) recording the workload of the gateway. Finally, a comprehensive analysis on the safety and reliability of the entire scheme is carried out; extensive simulation experiments are conducted to reveal the authentication protocol is efficient and the scheme is feasible.