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Based on this holistic model, the authors propose and analyze seven key issues related to the admissibility of digital media in cross-border trials considering four Latin American countries.

The authors apply the modeling process of the soft systems methodology by Checkland in order to develop a holistic model focused on human situation problems involving digital media and information technology devices or systems.

The authors discuss the status of the identified key issues in each country and offer a perspective on the integration of cross-border work analyzing the contribution of these key issues to the collaboration between countries criminal cases or the use of foreign digital artifacts in domestic trials.

In this study, the authors assumed that the problems of official interaction between agencies of different countries are considered solved. However, for future studies or research, the authors recommend that these issues can be considered as relevant, since they are related to cross-border cooperation topics that will necessarily require unavoidable official arrangements, agreements and formalities.

This work is aimed at defining and analyzing the key issues that can contribute to the application of current techniques and methodologies in digital forensics as a tool to support the legal framework of each country, considering cross-border trials. Finally, the authors highlight the implications of this study lie in the identification and analysis of the key issues that must be considered for digital forensics as a support tool for the admissibility of digital evidence in cross-border trials.

The authors consider that digital forensic will have high demand in cross-border trials, and it will depend on the people mobility between the countries considered in this study.

This paper shows that the soft systems methodology allows elaborating a holistic model focused on social problems involving digital media and informatics devices.

Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

A significant number of criminal and deviant acts are investigated by nonpolice actors. These include private investigators who charge fees for their services, professional services firms such as firms of accountants who also charge fees, in-house investigators employed by private organisations and in-house investigators of public sector organisations who are not sworn police officers. Some of these investigators, such as private investigators, have been exposed in unethical activities such as illegal surveillance and blagging to name some. In this respect, this study aims to uncover the ethical orientations of investigators using cluster analysis.

This study is based upon an online survey of private investigators predominantly in the UK, i.e. investigators beyond the public police. An innovate statistical inferential analysis was used to investigate the sample which resulted in the development of three ethical orientations of such investigators.

Based upon a survey response from 331 of these types of investigators this study illustrates the extent they engage in unethical activities, showing a very small minority of largely private investigators who engage in such activities.

A unique feature of this study is the use of an innovative statistical approach using an unsupervised machine learning model, namely, TwoStep cluster analysis, to successfully group and classify respondents based on their ethical orientation. The model derived three types of ethical orientation: ethical, inbetweeners and risk takers.

This study aims to examine the research output on cyber insurance from 2002 to 2021 through an extensive bibliometric analysis. It examines the cyber insurance resources and how the process of cyber insurance works.

This paper uses Scopus and VOSviewer to analyze cyber insurance papers. Using 503 papers from Scopus, this paper enhances the understanding of cyber insurance through collaborative network maps of experts and researchers.

The study comprehensively evaluates the development of cyber research. The results show that the number of research articles on cyber insurance has significantly increased since 2009.

The study's results offer practical implications for researchers to gain knowledge on the latest trends and developments in the domain. In addition, the study highlights the significance of cyber insurance in mitigating financial risks linked to cyberattacks, potentially boosting the investment of more organizations in such policies. Furthermore, practitioners can enhance their understanding of the various types of cyber insurance policies and their coverage.

Our results are likely to encourage practitioners, computer scientists, auditors, accountants and lawyers to contribute further to corporate strategies, data analytics and business operations to mitigate cyber risk consequences. In addition, understanding regarding the cyber insurance concept formed between experts and researchers is limited. This paper fills this gap by evaluating and identifying the development of cyber insurance literature.

This study aims to comprehensively inspect the impact of blockchain technology in the context of globalization, focusing on the most influential research works, scientific production by countries, commonly used keywords, trending topics and the conceptual and intellectual structure of this field.

A bibliometric analysis was conducted in R Studio, involving the review and scrutiny of 194 research papers indexed in the Scopus database. This analytical approach allowed for a systematic examination of the literature and the identification of key patterns and trends in blockchain and globalization research.

The analysis revealed the most influential research contributions, highlighted countries with significant scientific production, identified frequently used keywords and unveiled emerging trends and areas of focus in the field of blockchain and digitization. Additionally, the study explores prospective research directions.

This research provides insights into the current state of blockchain and globalization research, offering valuable information for scholars, policymakers and industry professionals. The recognized themes provide a systematic structure for upcoming inquiries, directing studies toward vital domains like supply chain management and sustainable development. Moreover, the analysis emphasizes the significance of interdisciplinary collaboration and provides practical insights, enabling businesses and policymakers to enhance transparency, counteract fraud and adjust to the digital era for societal and economic advancement.

The study contributes to the prevailing literature by steering a comprehensive analysis of the interplay between blockchain technology and globalization, shedding light on influential works and emerging trends. It adds value by providing a structured overview of the field's intellectual landscape.

The increased use of Information Systems (IS) as a working tool for employees increases the number of accounts and passwords required. Despite being more aware of password entropy, users still often participate in deviant password behaviors, known as “password workarounds” or “shadow security.” These deviant password behaviors can put individuals and organizations at risk, resulting in a data breach. This paper aims to engage IS users and Subject Matter Experts (SMEs), focused on designing, developing and empirically validating the Password Workaround Cybersecurity Risk Taxonomy (PaWoCyRiT) – a 2x2 taxonomy constructed by aggregated scores of perceived cybersecurity risks from Password Workarounds (PWWAs) techniques and their usage frequency.

This research study was a developmental design conducted in three phases using qualitative and quantitative methods: (1) A set of 10 PWWAs that were identified from the literature were validated by SMEs along with their perspectives on the PWWAs usage and risk for data breach; (2) A pilot study was conducted to ensure reliability and validity and identify if any measurement issues would have hindered the results and (3) The main study data collection was conducted with a large group of IS users, where also they reported on coworkers' engagement frequencies related to the PWWAs.

The results indicate that statistically significant differences were found between SMEs and IS users in their aggregated perceptions of risks of the PWWAs in causing a data breach, with IS users perceiving higher risks. Engagement patterns varied between the two groups, as well as factors like years of IS experience, gender and job level had statistically significant differences among groups.

The PaWoCyRiT taxonomy that the we have developed and empirically validated is a handy tool for organizational cyber risk officers. The taxonomy provides organizations with a quantifiable means to assess and ultimately mitigate cybersecurity risks.

Passwords have been used for a long time to grant controlled access to classified spaces, electronics, networks and more. However, the dramatic increase in user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure a high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. Thus, the taxonomy that the authors have developed and empirically validated provides broader implications for society, as it assists organizations in all industries with the ability to mitigate the risks of data breaches that can result from PWWAs.

The taxonomy the we have developed and validated, the PaWoCyRiT, provides organizations with insights into password-related risks and behaviors that may lead to data breaches.

This paper aims to examine the relationship between the readability of annual reports and corporate performance in Chinese listed firms.

This research examined the annual report readability factors of Chinese listed companies by using a textual analysis method using Python to extract the text from the annual reports, convert it into numerical form to facilitate statistical analysis and then merge the results with data from the Chinese stock market to explain the impact on corporate performance and predict future earnings in the Chinese financial markets from 2008 to 2021.

Study findings indicate that firms with better financial reporting readability are more profitable, incur lower agency costs and have low earnings in the Chinese stock markets when readability is low (i.e. more complexity and length of annual reports). It was also found that when a listed company has a good performance, it prefers to use a short space to explain its operating and financial status. More generally, the means of the report length are short, and accounting terms are used less frequently; in the case of a poor company, the annual report is particularly long and accounting terms are more frequently used. In the context of the COVID-19 crisis, this study served as a proxy measure of returns prior to the announcement of the COVID-19 pandemic. In addition, an instrumental variable approach is used, which helps results to remain robust and control for fixed effects and potential endogeneity problems.

Although this study’s results cannot be generalised globally because of their limited scope, they can still be generalised across non-English speaking countries. Thus, future cross-country research is encouraged to examine the textual analysis of financial reports across those countries.

This study conveys two messages to investors and policymakers within the Chinese market. First, investors ought to pay greater attention to the nonfinancial information contained in annual reports to improve the accuracy of their predictions regarding future firm performance. Second, Chinese policymakers are encouraged to instate a policy for the use of plain English in annual reports to make them more readable by international investors.

This study contributes to the paucity of research that examines English-written annual reports in non-English speaking countries by examining the readability of annual reports in the Chinese market.

In the absence of new funding dedicated to cold case investigation, innovation is required.

The number of unresolved homicides in the USA has surpassed a quarter million, and the figure grows by thousands every year. Homicides that do not yield a quick arrest are time and labor intensive. This creates a staffing and resource dilemma for law enforcement administrators, as allocating time for older cases comes at the expense of investigating current ones, and vice versa.

Universities offer the enthusiastic labor of college students to “defrost” cold cases. One such partnership has been in place for nearly three years in an unusual collaboration between a state police agency and a regional state university. Small groups of students systematically organize, review and present case files. They create investigative recommendations and prioritize cases by solvability. Investigators can then select a case that may be relatively close to an arrest, access the case details very quickly and have the investigative recommendations as a place to begin a renewed investigation. Additionally, cases that are appropriate for new forensic testing or new forensic tools are identified and advanced.

Partnerships such the one described here are rare but lucrative. We recommend new collaborations like ours to reduce the number of unresolved homicide cases.