Bangladesh University of Professionals (BUP) is the 31st public university of Bangladesh (according to University Grants Commission of Bangladesh), located in Mirpur Cantonment, Dhaka. It is the first publicuniversity of Bangladesh to be run by the Bangladesh Armed Forces. It was established under the Bangladesh University of Professionals Act, 2009..
Colorectal cancer (CRC) is the most prevalent cancer that affects both genders, making it among the top three cancers in terms of both incidence and mortality. Natural products, known for their bioactive compounds, are essential resources for treating various health disorders, including cancer. This systematic review investigates the effectiveness and safety of these natural products in managing CRC that underwent clinical studies and examines their mechanisms of action. PubMed, Google Scholar databases, Science Direct, and Scopus were examined thoroughly for relevant literature using terms like “Natural products against colorectal cancer” and “Clinically investigated medicinal plant against colorectal cancer,” among others. The current study aimed at understanding the beneficial impacts and mechanisms of action of medicinal plants that have undergone clinical study for their efficacy in the fight against CRC, including Green tea extract, B6-pyridoxine, Aged Garlic, Berberine, Camptothecin, Curcumin, Epothilones, Korean Red Ginseng, Fucoidan, Lycopene, Resveratrol, Rhus verniciflua Stokes (RVS), Silybum marianum L., Probiotics, Ganoderma lucidum, Plocabulin, Everolimus, Anthocyanin. The chosen natural products showed a substantial protective impact against CRC in a varied study. These products demonstrated beneficial effects mediated by numerous physiological processes associated with cell cycle, apoptosis, inflammation, angiogenesis, cell growth and survival, etc, and importantly, a few of them exhibited mild undesirable effects in patients. This review indicates that the medicinal plants discussed have the potential to lower CRC. Nonetheless, comprehensive investigations are warranted to clarify their underlying molecular pathways and assess their safety profiles for clinical application.
This study examines the impact of technological advancements on the sustainable performance of supply chains in the fast-moving consumer goods (FMCG) industry, with a particular focus on the mediating roles of Green Innovation and Green Supply Chain Practices. Data were collected from 313 supply chain executives in Bangladesh using a structured questionnaire. A purposive sampling technique was employed to ensure responses from professionals directly involved in supply chain operations and technology adoption. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS4 to assess the relationships between technological advancements, mediators, and sustainable performance. SPSS was used for descriptive analysis, including the demographic profiling of respondents. The findings indicate that Artificial Intelligence (AI) and Autonomous Robots significantly enhance sustainable supply chain performance, whereas the effect of Supply Chain Information Systems (SCIS) is less pronounced, highlighting the need for further investment in SCIS integration in Bangladesh. The study further confirms that Green Innovation and Green Supply Chain Practices act as mediators, strengthening the relationship between technology adoption and sustainability. This research underscores the importance of aligning technological advancements with sustainability-driven strategies to optimize supply chain performance. It recommends investing in AI and robotics, promoting eco-friendly innovations, acquiring ISO 14001 certification, and enhancing SCIS implementation. Ultimately, this study advocates for a holistic approach that integrates technology and sustainability to address contemporary business challenges and contributes to broader environmental objectives.
Recent breakthroughs in artificial intelligence (AI) are revolutionizing several areas and integrating into daily life. Large language models (LLMs) are a significant part of this transformation and reducing human intervention. Recent research shows that agentic AI is the next breakthrough, which can operate independently and make decisions without human involvement. This chapter provides a comprehensive understanding of the security threats, risks, and challenges related to agentic AI. Thus, when each of the components of the AI agents work flow is reduced to its first principles, we are in a position to evaluate the risks and vulnerabilities as the research progress. In this way, this study fills the current shortage of literature and combines knowledge available and serves as a background for further research in this field. This chapter further represents the findings with the ongoing work of the OWASP agentic AI, which is building one such top 10 for these systems. The primary contribution of this chapter lies in understanding the distinctive security challenges of agentic AI, proposing possible solutions, and presenting the adaptive secure agent framework (ASAF) as a comprehensive blueprint for establishing robust security norms. This research is critically important for stakeholders who wish to leverage the powerful capabilities of agentic AI while ensuring effective protection against emerging risks.
Federated Learning (FL) and Transfer Learning (TL) are essential for ensuring data privacy and enhancing Machine Learning (ML) models. Small datasets often hinder effective model training, highlighting the importance of data sharing. However, security concerns and privacy constraints lead to the emergence of data island, where feature differences can negatively impact ML performance. Federated Transfer Learning (FTL) addresses these challenges by allowing devices to collaborate on model improvement without sharing raw data, thus preserving privacy and scalability in distributed ML scenarios. TL reduces the computational costs associated with developing models for new applications by transferring knowledge from pre-trained networks, enabling strong performance even with limited data. FTL is particularly valuable for transferring knowledge between domains with minimal feature overlap, saving time and resources. However, its broader adoption faces challenges, including ineffective knowledge transfer, scalability issues, privacy concerns, and the absence of a standardized framework. This work reviews 25 FTL models across various sectors, including machine fault diagnosis, healthcare, wind and photovoltaic power forecasting, disaster prediction, finance, and image steganalysis. These models were sourced from open-access repositories like Google Scholar, IEEE Xplore, and ScienceDirect. Key challenges such as data heterogeneity, label scarcity, communication overhead, and scalability remain open areas for further FTL research. This review aims to provide a unified roadmap to inspire future researchers to explore FTL methodologies for addressing sparse overlapping features in diverse data distributions.
Accidental falls have emerged as a major public health concern, especially among individuals aged 65 and older, due to their high incidence and severe consequences. Without timely intervention, such falls can result in fractures, traumatic brain injuries, and long-term complications. As a result, considerable research has focused on developing automated fall detection systems that integrate intelligent algorithms with sensor-based data acquisition to enable rapid response and medical assistance. This study follows the PRISMA framework to conduct a systematic literature review. A comprehensive search was performed across major databases including PubMed, Google Scholar, Scopus, and IEEE Xplore using fall detection–related keywords. From an initial pool of 596 articles, duplicates were removed and strict inclusion/exclusion criteria were applied, resulting in 182 relevant articles for in-depth analysis. This review examines a wide range of Artificial Intelligence (AI) and Machine Learning (ML) approaches applied to fall detection using diverse sensor modalities, including wearable, vision-based, ambient, and multimodal systems. Additionally, it summarizes the publicly available datasets and sensor configurations used in existing studies. This broad perspective is essential to compare trade-offs across sensing modalities and support effective system development for real-world deployment. This review provides a comparative overview of AI/ML-based fall detection approaches, highlighting differences in accuracy, sensitivity, and dataset usage as reported in existing literature. In the end, many open research challenges in using AI and ML to detect falls are outlined, along with potential future perspectives.
