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Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This paper proposes a holistic, proactive and adaptive approach to cybersecurity from a service lens, given the continuously evolving cyber-attack techniques, threat and vulnerability landscape that often overshadow existing cybersecurity approaches.

Through an extensive literature review of relevant concepts and analysis of existing cybersecurity frameworks, standards and best practices, a logical argument is made to produce a dynamic end-to-end cybersecurity service system model.

Cyberspace has provided great value for businesses and individuals. The COVID-19 pandemic has significantly motivated the move to cyberspace by organizations. However, the extension to cyberspace comes with additional risks as traditional protection techniques are insufficient and isolated, generally focused on an organization's perimeter with little attention to what is out there. More so, cyberattacks continue to grow in complexity creating overwhelming consequences. Existing cybersecurity approaches and best practices are limited in scope, and implementation strategies, differing in strength and focus, at different levels of granularity. Nevertheless, the need for a proactive, adaptive and responsive cybersecurity solution is recognized.

This paper presents a model that promises proactive, adaptive and responsive end-to-end cybersecurity. The proposed cybersecurity continuity and management model premised on a service system, leveraging on lessons learned from existing solutions, takes a holistic analytical view of service activities from source (service provider) to destination (Customer) to ensure end-to-end security, whether internally (within an organization) or externally.

Various organizational landscapes have evolved to improve their business processes, increase production speed and reduce the cost of distribution and have integrated their Internet with small and medium scale enterprises (SMEs) and third-party vendors to improve business growth and increase global market share, including changing organizational requirements and business process collaborations. Benefits include a reduction in the cost of production, online services, online payments, product distribution channels and delivery in a supply chain environment. However, the integration has led to an exponential increase in cybercrimes, with adversaries using various attack methods to penetrate and exploit the organizational network. Thus, identifying the attack vectors in the event of cyberattacks is very important in mitigating cybercrimes effectively and has become inevitable. However, the invincibility nature of cybercrimes makes it challenging to detect and predict the threat probabilities and the cascading impact in an evolving organization landscape leading to malware, ransomware, data theft and denial of service attacks, among others. The paper explores the cybercrime threat landscape, considers the impact of the attacks and identifies mitigating circumstances to improve security controls in an evolving organizational landscape.

The approach follows two main cybercrime framework design principles that focus on existing attack detection phases and proposes a cybercrime mitigation framework (CCMF) that uses detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface. The methods and implementation processes were derived by identifying an organizational goal, attack vectors, threat landscape, identification of attacks and models and validation of framework standards to improve security. The novelty contribution of this paper is threefold: first, the authors explore the existing threat landscapes, various cybercrimes, models and the methods that adversaries are deploying on organizations. Second, the authors propose a threat model required for mitigating the risk factors. Finally, the authors recommend control mechanisms in line with security standards to improve security.

The results show that cybercrimes can be mitigated using a CCMF to detect, assess, analyze, evaluate and respond to cybercrimes to improve security in an evolving organizational threat landscape.

The paper does not consider the organizational size between large organizations and SMEs. The challenges facing the evolving organizational threat landscape include vulnerabilities brought about by the integrations of various network nodes. Factor influencing these vulnerabilities includes inadequate threat intelligence gathering, a lack of third-party auditing and inadequate control mechanisms leading to various manipulations, exploitations, exfiltration and obfuscations.

Attack methods are applied to a case study for the implementation to evaluate the model based on the design principles. Inadequate cyber threat intelligence (CTI) gathering, inadequate attack modeling and security misconfigurations are some of the key factors leading to practical implications in mitigating cybercrimes.

There are no social implications; however, cybercrimes have severe consequences for organizations and third-party vendors that integrate their network systems, leading to legal and reputational damage.

The paper’s originality considers mitigating cybercrimes in an evolving organization landscape that requires strategic, tactical and operational management imperative using the proposed framework phases, including detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface, which is currently inadequate.

Studies suggest deep brain stimulation (DBS) as a treatment modality for the refractory obsessive-compulsive disorder (OCD). It is unclear where to place the DBS. Various sites are proposed for placement with the ventral capsule/ventral striatum (VC/VS) among the most studied. Herein, we aim to summarize both quantitative Yale-Brown Obsessive-Compulsive Scale (YBOCS) data and qualitative descriptions of the participants' symptoms when given. A literature search conducted via PubMed yielded 32 articles. We sought to apply a standard based on the utilization of YBOCS. This yielded 153 distinct patients. The outcome measure we focused on in this review is the latest YBOCS score reported for each patient/cohort in comparison to the location of the DBS. A total of 32 articles were found in the search results. In total, 153 distinct patients' results were reported in these studies. Across this collection of papers, a total of 9 anatomic structures were targeted. The majority of studies showed a better response at the last time point as compared to the first time point. Most patients had DBS at nucleus accumbens followed by VC/VS and the least patients had DBS at the bilateral superolateral branch of the median forebrain bundle and the bilateral basolateral amygdala. The average YBOCS improvement did not seem to directly correlate with the percentile of patients responding to the intervention.

Well-controlled, randomized studies with larger sample sizes with close follow up are needed to provide a more accurate determination for placement of DBS for OCD.

As the 4th industrial revolution (4IR) unfolds, there is an increasing awareness that its implications for workforce transformation and shifts in workforce demand will profoundly impact the future of work. Specifically, the paper seeks to answer the following research questions: i) how does Students’ Industrial Work Experience Scheme (SIWES) equip young people for the real world of work, especially in the era of the third industrial revolution?; ii) does SIWES support the exposure of young people to the world of digitalization?; and iii) what are the effects of the SIWES exposure on the employability of young people? This paper aims to evaluate the University Internship system and preparation of young people for the world of work in the 4th industrial revolution.

This paper used a mixed method to unravel the objectives of this study, that is, quantitative and qualitative methods. For the former, structured questionnaires were used to elicit a response from 249 young people drawn from tertiary institutions across Lagos State, Nigeria. The latter used an in-depth interview method conducted among 45 respondents (25 employers of labor and 20 lecturers).

The findings reveal that: SIWES contributes meaningfully to the advancement of knowledge and capacity building among young people; SIWES exposes young people to the world of digitalization, depending on the organization where the internship takes place; and SIWES pays little attention to financial rewards and more attention to the acquisition of skills that are relevant to the world of work. The practical and policy implications of the findings are critically discussed.

This paper critically evaluates the SIWES policy amidst the growing threats of widening skills gap, greater inequality and broader polarization.