Managing disruptions in the maritime industry – a systematic literature review

PurposeThis paper presents a systematic review of the literature in the domain of maritime disruption management, upon which future research framework and agenda are proposed. Two review questions, i.e. the measures that are employed to manage disruptions and how these contribute to resilience performance, were pursued.Design/methodology/approachThe systematic literature review procedure was strictly followed, including identification and planning, execution, selection, and synthesis and analysis. A review protocol was developed, including scope, databases and criteria guiding the review. Following this, 47 articles were eventually extracted for the systematic review to identify themes for not only addressing the review questions but also highlighting future research opportunities.FindingsIt was found that earlier studies mainly focused on measures, which are designed using mathematical models, management frameworks and other technical support systems, to analyse and evaluate risks, and their impacts on maritime players at the levels of organisation, transport system and region in which the organisation is embedded. There is, however, a lack of research that empirically examines how these measures would contribute to enhancing the resilience performance of maritime firms and their organisational performance as a whole. Subsequently, a Digitally Embedded and Technically Support Maritime Disruption Management (DEST-MDM) model is proposed.Research limitations/implicationsThis review is constrained by studies recorded by the Web of Science only. Nevertheless, the proposed research model would expectedly contribute to enhancing knowledge building in the specific domain of maritime disruption management and supply chain management overall while providing meaningful managerial implications to policymakers and managers in the maritime industry.Originality/valueThis research is perhaps one of the first studies which presents a systematic review of literature in maritime disruption management, and proposes a future research framework that establishes the link between disruption management and resilience and organisational performance for empirical validation.

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

In network security, various protocols exist, but these cannot be said to be secure. Moreover, is not easy to train the end-users, and this process is time-consuming as well. It can be said this way, that it takes much time for an individual to become a good cybersecurity professional. Many hackers and illegal agents try to take advantage of the vulnerabilities through various incremental penetrations that can compromise the critical systems. The conventional tools available for this purpose are not enough to handle things as desired. Risks are always present, and with dynamically evolving networks, they are very likely to lead to serious incidents. This research work has proposed a model to visualize and predict cyber-attacks in complex, multilayered networks. The calculation will correspond to the cyber software vulnerabilities in the networks within the specific domain. All the available network security conditions and the possible places where an attacker can exploit the system are summarized.

Cochlear implantation in a Chinese patient with a novel frameshift variant in POU3F4 gene and incomplete partition type III: a case report

Variations in the POU Class 3 Homeobox 4 ( POU3F4) gene are associated with X-linked mixed deafness. Here, the identification of a novel variant of POU3F4 in a male paediatric patient (the proband) with incomplete partition type III (IP-III) hearing impairment, is described. Clinical data were collected from the proband and his biological parents. Whole exome sequencing of the proband revealed a novel frameshift insertion mutation in POU3F4 (c.717_718ins GTGCCTTGCAG : p.Leu240Valfs*5) in a hemizygous state. This variant likely truncates the protein within the POU-specific domain, and the proband’s biological mother was found to be a carrier of this variant. After excluding all contraindications, the proband underwent cochlear implantation in the right ear in June 2020. Cerebrospinal fluid (CSF) gushing was observed during surgery, but there were no postoperative complications, such as CSF leak, meningitis, or facial nerve stimulation. A novel pathogenic frameshift variant of POU3F4 was identified, enriching the known mutation spectrum of POU3F4. Effective perioperative prevention and response measures should be taken to reduce the incidence of CSF gushing and meningitis in patients receiving IP-III cochlear implantation.

Abstractive Text Summary of COVID-19 Documents based on LSTM Method and Word Embedding

An abstractive summary is a process of producing a brief and coherent summary that contains the original text's main concepts. In scientific texts, summarization has generally been restricted to extractive techniques. Abstractive methods that use deep learning have proven very effective in summarizing articles in public fields, like news documents. Because of the difficulty of the neural frameworks for learning specific domain- knowledge especially in NLP task, they haven't been more applied to documents that are related to a particular domain such as the medical domain. In this study, an abstractive summary is proposed. The proposed system is applied to the COVID-19 dataset which a collection of science documents linked to the coronavirus and associated illnesses, in this work 12000 samples from this dataset have been used. The suggested model is an abstractive summary model that can read abstracts of Covid-19 papers then create summaries in the style of a single-statement headline. A text summary model has been designed based on the LSTM method architecture. The proposed model includes using a glove model for word embedding which is converts input sequence to vector forms, then these vectors pass through LSTM layers to produce the summary. The results indicate that using an LSTM and glove model for word embedding together improves the summarization system's performance. This system was evaluated by rouge metrics and it achieved (43.6, 36.7, 43.6) for Rouge-1, Rouge-2, and Rouge-L respectively.

Deep Transfer Learning for Biology Cross-Domain Image Classification

Automatic biology image classification is essential for biodiversity conservation and ecological study. Recently, due to the record-shattering performance, deep convolutional neural networks (DCNNs) have been used more often in biology image classification. However, training DCNNs requires a large amount of labeled data, which may be difficult to collect for some organisms. This study was carried out to exploit cross-domain transfer learning for DCNNs with limited data. According to the literature, previous studies mainly focus on transferring from ImageNet to a specific domain or transferring between two closely related domains. While this study explores deep transfer learning between species from different domains and analyzes the situation when there is a huge difference between the source domain and the target domain. Inspired by the analysis of previous studies, the effect of biology cross-domain image classification in transfer learning is proposed. In this work, the multiple transfer learning scheme is designed to exploit deep transfer learning on several biology image datasets from different domains. There may be a huge difference between the source domain and the target domain, causing poor performance on transfer learning. To address this problem, multistage transfer learning is proposed by introducing an intermediate domain. The experimental results show the effectiveness of cross-domain transfer learning and the importance of data amount and validate the potential of multistage transfer learning.

The Notion of Common and Social Representations

The recent emergence of social and political movements calling for common sense and the use of the notion of common in philosophy and social sciences has led to the opening of a reflection on the social and scientific representations concerning them. After having mentioned some political uses of the notions of common sense and common, we examine a notion that is closely associated with them: that of community on which S. Moscovici expresses a reserved position but introduces a new perspective on cybercommunities and the importance attached to affectivity in community groups. The ways of dealing with common sense, identified over time, from antiquity to the present day, highlight certain recurrences from a double perspective. From a typological point of view, several characterizations are distinguished: through simple sharing, through the sameness of moral values and emotional dimensions, through rooting in daily experience, through its devaluation as a form of knowledge in relation to science, through rationality, through its potential for revolt or on the contrary through conformity. From a conceptual point of view, common sense is analyzed as an epistemic characteristic of a group, in its content, formation, transmission, and role in social cohesion. The latest developments in the reflection highlight its link with democracy and populism. The term common of recent appearance is situated opposite the notion of common goods which, after having focused on material realities, now integrates the facts and practices of knowledge, being the subject of a specific domain: the commons of knowledge. The common appears as a new way of approaching social relationships and responds to the desire to introduce a relational, ethical and political dimension into the analysis of social and change processes. In this respect, the call to the common presents affinities with the approach of social representations. The examination of the different scientific and secular representations regarding the notions of community, common sense and common makes it possible to establish connections with the perspective of the study of social representations and to open the way for new investigations.

Vitamin D Status and Its Association with Multiple Intelligence among Arab Adolescents

Studies investigating the association of vitamin D on intelligence is limited. The present study therefore aims to determine the association of vitamin D status with the different domains of intelligence among Saudi Arabian adolescents. This study used relational survey method among 1864 Saudi adolescent, including 549 boys and 1315 girls (mean age 14.7 ± 1.7 years) recruited using a multistage, stratified cluster randomization of 47 public and private schools in Riyadh in Saudi Arabia. A general questionnaire was used to collect demographic information. Intelligence was assessed using multiple intelligence inventory. Anthropometrics were measured and fasting blood samples collected for assessment of glucose and lipid profile. Vitamin D deficiency (25(OH)D <50 nmol/L) was observed in 84.2% of boys and 93.5% of girls. Girls had higher levels of verbal, kinesthetic, musical, naturalist and existential intelligence than boys, while boys have higher logical intelligence than girls (p-values < 0.05). Mixed regression analysis controlled for age, BMI and sex revealed that kinesthetic intelligence was significantly associated with 25(OH)D in boys (β 5.6 (2.8–8.5; p < 0.001)) and inversely associated with musical intelligence (β −1.2 (−2.3–0.1; p = 0.03)) and positively with naturalist (β 2.3 (0.5–4.2; p = 0.01)) in girls. Vitamin D status is associated with several domains of intelligence in adolescents and is sex-specific. Development a specific domain of intelligence may indirectly affect vitamin D status among adolescents, but needs to be proven prospectively.

An ontology-based approach to the analysis of the acid-base state of patients at operative measures

Researchers working in various domains are focusing on extracting information from data sets by data mining techniques. However, data mining is a complicated task, including multiple complex processes, so that it is unfriendly to non-computer researchers. Due to the lack of experience, they cannot design suitable workflows that lead to satisfactory results. This article proposes an ontology-based approach to help users choose appropriate data mining techniques for analyzing domain data. By merging with domain ontology and extracting the corresponding sub-ontology based on the task requirements, an ontology oriented to a specific domain is generated that can be used for algorithm selection. Users can query for suitable algorithms according to the current data characteristics and task requirements step by step. We build a workflow to analyze the Acid-Base State of patients at operative measures based on the proposed approach and obtain appropriate conclusions.

Genome-Wide Analysis of Gene Families of Pattern Recognition Receptors in Fig Wasps (Hymenoptera, Chalcidoidea)

Pattern recognition receptors (PRRs) play important roles in detecting pathogens and initiating the innate immune response. Different evolutionary histories of pollinators and non-pollinators may result in different immune recognition systems. A previous study had reported that there were significant differences in peptidoglycan recognition proteins (PGRPs) between pollinators and non-pollinators in gene number and lineage of specific genes. In this study, based on the genomic data of 12 fig wasp species, with seven pollinators and five non-pollinators, we investigated the evolution patterns of PRRs, such as Gram-negative bacteria-binding proteins (GNBPs), C-type lectins (CTLs), scavenger receptors class B (SCRBs), fibrinogen-related proteins (FREPs), galectins, and thioester-containing proteins (TEPs). Our results showed that pollinators had no GNBP, but non-pollinators all had two gene members, which were clustered into two different clades in the phylogenetic tree, with each clade having specific domain and motif characteristics. The analysis of CTL and SCRB gene families also showed that there were lineage-specific genes and specific expansion in non-pollinators. Our results showed that there were significant differences in immune recognition between pollinators and non-pollinators, and we concluded that they had undergone flexible adaptive evolution in different environments. Our study can provide more molecular evidence for future functional studies on the immune system of fig wasps.

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture

The Internet of Things (IoT) allows the sharing of information among devices in a network. Hardware evolutions have enabled the employment of cognitive agents on top of such devices, which could help to adopt pro-active and autonomous IoT systems. Agents are autonomous entities from Artificial Intelligence capable of sensing (perceiving) the environment where they are situated. Then, with these captured perceptions, they can reason and act pro-actively. However, some agent approaches are created for a specific domain or application when dealing with embedded systems and hardware interfacing. In addition, the agent architecture can compromise the system’s performance because of the number of perceptions that agents can access. This paper presents three engineering approaches for creating IoT Objects using Embedded Multi-agent systems (MAS)—as cognitive systems at the edge of an IoT network—connecting, acting, and sharing information with a re-engineered IoT architecture based on the Sensor as a Service model. These engineering approaches use Belief-Desire-Intention (BDI) agents and the JaCaMo framework. In addition, it is expected to diversify the designers’ choice in applying embedded MAS in IoT systems. We also present a case study to validate the whole re-engineered architecture and the approaches. Moreover, some performance tests and comparisons are also presented. The study case shows that each approach is more or less suitable depending on the domain tackled. The performance tests show that the re-engineered IoT architecture is scalable and that there are some trade-offs in adopting one or another approach. The contributions of this paper are an architecture for sharing resources in an IoT network, the use of embedded MAS on top IoT Objects, and three engineering approaches considering agent and artifacts dimensions.