Darkweb research: Past, present, and future trends and mapping to sustainable development goals

Exploring the role of technology in human trafficking in Pakistan: A qualitative study of lived experiences of victims

With the ongoing digital transformation, the internet, mobile, social media and other computer technologies are being increasingly used in the targeting, recruitment, transportation and exploitation of human trafficking victims. The current study is the first of its kind which uses a qualitative method to comprehensively investigate the role of technology in the lived experiences of human trafficking victims in Pakistan. This qualitative study was carried out with a phenomenological approach in two provinces of Pakistan. Data was collected using in-depth interviews with 13 victims who were selected using purposive and snowball sampling methods. The data was then analyzed using Braun and Clarke's thematic analysis approach. The data analysis results were divided into four main themes and ten sub-themes. The main themes are: Recruitment approaches, transportation process, exploitation process, and mental health consequences. The analysis show that traffickers extensively use technology across all phases of human trafficking. This study aims to support a range of relevant institutional stakeholders, including law enforcement agencies, legislative bodies, policymakers, civil society and SDG (2030) goals, in Pakistan and globally, in their efforts to address and enhance the learning and awareness level regarding related technologies in order to improve investigation in combating technological based human trafficking.

Mapping biomimicry research to sustainable development goals

This study systematically evaluates biomimicry research within the context of sustainable development goals (SDGs) to discern the interdisciplinary interplay between biomimicry and SDGs. The alignment of biomimicry with key SDGs showcases its interdisciplinary nature and potential to offer solutions across the health, sustainability, and energy sectors. This study identified two primary thematic clusters. The first thematic cluster focused on health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's role in healthcare innovations, sustainable collaboration, and land management. This cluster demonstrates the potential of biomimicry to contribute to medical technologies, emphasizing the need for cross-sectoral partnerships and ecosystem preservation. The second thematic cluster revolves around clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), showcasing nature-inspired solutions for sustainable development challenges, including energy generation and water purification. The prominence of SDG 7 within this cluster indicates that biomimicry significantly contributes to sustainable energy practices. The analysis of thematic clusters further revealed the broad applicability of biomimicry and its role in enhancing sustainable energy access and promoting ecosystem conservation. Emerging research topics, such as metaheuristics, nanogenerators, exosomes, and bioprinting, indicate a dynamic field poised for significant advancements. By mapping the connections between biomimicry and SDGs, this study provides a comprehensive overview of the field's trajectory, emphasizing its importance in advancing global sustainability efforts.

Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin

The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.