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PurposeInequality is increasing in Asia and the Pacific. This paper examines how inequality is affecting governments, communities and people in the Asia-Pacific region, given the 2030 Agenda's Sustainable Development Goals and the agenda's commitment to “leave no one behind.” Income inequality is just one element of larger economic and social inequalities in both developed and developing countries. Over the past decade, Bangladesh's economy has experienced one of the fastest growth rates in the world, supported by a narrowing demographic gap. The study focuses largely on the challenges of inequality and wealth distribution and uses the Singaporean experience to reduce inequality.Design/methodology/approachThe study is based on the review of secondary literature and an insightful analysis of the review.FindingsThe Singapore Government has adopted four special budgets coronavirus disease 2019 (COVID-19) to help businesses cope with the economic difficulties caused by the epidemic, protect lives and create an economically and socially resilient Singapore. To sustain this increase in real gross domestic product (GDP) per capita, the Singapore Government continues to pursue growth-oriented policies. Importing technology and skilled labor, investing heavily in research and development, importing technology and developing export markets are some examples of these growth-oriented policies. The Singapore Government is committed to improving human capital through retraining and lifelong learning, which can be seen in all these growth-oriented policies. Bangladesh can learn more about reducing inequality and put these policies into practice.Originality/valueThis study has frankly revealed the inequality issues in Bangladesh. This study has spotted the scarcities of development and the accurate picture of achievement from the perspective of inequality and prosperity dissemination.

Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to target diverse platforms such as PCs, mobile devices, IoT, and cloud platforms. While previous studies have explored single platform-based malware detection, no existing research has comprehensively reviewed malware detection across diverse platforms using machine learning (ML) techniques. With the rise of malware on PC or laptop devices, mobile devices and IoT systems are now being targeted, posing a significant threat to cloud environments. Therefore, a platform-based understanding of malware detection and defense mechanisms is essential for countering this evolving threat. To fill this gap and motivate further research, we present an extensive review of malware detection using ML techniques with respect to PCs, mobile devices, IoT, and cloud platforms. This paper begins with an overview of malware, including its definition, prominent types, analysis, and features. It presents a comprehensive review of machine learning-based malware detection from the recent literature, including journal articles, conference proceedings, and online resources published since 2017. This study also offers insights into the current challenges and outlines future directions for developing adaptable cross-platform malware detection techniques. This study is crucial for understanding the evolving threat landscape and for developing robust detection strategies.

Bangladesh is experiencing rapid urbanization and a growing elderly population, particularly in urban areas. Cognitive decline, ranging from mild cognitive impairment to dementia, is a prevalent issue among elderly populations globally. Understanding the current state of cognitive functioning in this demographic is essential for informing effective healthcare plans and programs. This study aims to investigate the prevalence of cognitive decline and its associated factors among urban-dwelling elderly adults in Bangladesh, using the Rowland Universal Dementia Assessment Scale (RUDAS) to assess cognitive function. This cross-sectional study employed systematic random sampling among 150 elderly participants (aged 60–85 years) from the outpatient department of the National Institute of Traumatology and Orthopaedic Rehabilitation (NITOR) in Dhaka, Bangladesh. The mean age of participants was 67.41 ± 6.31 years, with a male predominance (53.3%). Cognitive function was impaired in a majority of participants, with 53.3% classified as having dementia, 38.7% with MNCD, and only 8% showing normal cognitive function. Significant predictors of cognitive function included age (r = -0.451, P < 0.001), educational level (P = 0.009), and diabetes (P = 0.038). Female participants had lower mean cognitive function scores compared to males (21.16 ± 5.25 vs. 22.03 ± 4.36, P = 0.271). Cognitive impairment is highly prevalent among elderly individuals in urban Bangladesh, with age, educational level, and diabetes being key predictors. These findings highlight the need for public health interventions and policies focused on early screening and targeted healthcare for cognitive decline in this demographic.

Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors.

The increasing frequency of ransomware attacks necessitates the development of more effective detection methods. Existing image-based ransomware detection approaches have largely focused on static analysis, overlooking specialized ransomware behaviors such as encryption, privilege escalation, and system recovery disruption. Although dynamic and memory forensics-based visualization methods exist in the broader malware domain, they primarily target generic malware families and often rely on memory dumps or system snapshots without transforming behavioral features into spatially meaningful representations. Moreover, traditional machine learning methods such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) typically depend on manual feature engineering and large labelled datasets, limiting scalability and adaptability. To address these limitations, we propose a novel behavior-to-image ransomware detection framework that transforms dynamic behavioral features extracted from sandbox-generated JSON reports into two-dimensional (2D) grayscale and color image representations, optimized for transfer learning (TL), enabling effective classification under small-data conditions. Our approach integrates domain-specific feature filtering and impact analysis to ensure the selection of the most ransomware-relevant attributes. TL subsequently automates feature extraction and classification, eliminating the need for separate feature selection procedures and overcoming the time-consuming process of manual feature engineering. Furthermore, by leveraging prior knowledge from large-scale image datasets, TL significantly mitigates the need for extensive labelled data while maintaining high detection accuracy and strong generalization. Experimental results demonstrate that fine-tuned pretrained models, notably ResNet50, achieve up to 99.96% accuracy with a minimal loss factor of 0.0026, even with a small dataset of 500 ransomware and 500 benign samples. We further validated the model’s interpretability through t-SNE visualizations and saliency maps, confirming its ability to focus on class-discriminative behavioral patterns. The low misclassification rate, along with the transparency of the model, highlights its potential for practical deployment in ransomware detection systems.

The SH-SY5Y cell line is widely used in neurotoxicity studies. However, the effects of inducing cell differentiation on the cytotoxic effects of heavy metals are unclear. Therefore, we investigated the effects of mercuric chloride (HgCl 2 ), cadmium chloride (CdCl 2 ), arsenic trioxide (As 2 O 3 ), and methylmercury (MeHg) on SH-SY5Y cells differentiated in the presence of insulin-like growth factor-I (IGF-I) or all-trans retinoic acid (ATRA). Neurite outgrowth with distinct changes in neuronal marker expression, phenotype, and cell cycle was induced in SH-SY5Y cells by IGF-I treatment for 1 day or ATRA treatment for up to 7 days. The cytotoxic effects of HgCl 2 decreased at lower concentrations and increased at higher concentrations in both IGF-I- and ATRA-differentiated cells compared with those in undifferentiated cells. Differentiation with IGF-I, but not with ATRA, increased the cytotoxic effects of CdCl 2 . Decreased cytotoxic effects of As 2 O 3 and MeHg were observed at lower concentrations in IGF-I-differentiated cells, whereas increased cytotoxic effects of As 2 O 3 and MeHg were observed at higher concentrations in ATRA-differentiated cells. Changes in the cytotoxic effects of heavy metals were observed even after 1 day of ATRA exposure in SH-SY5Y cells. Our results demonstrate that the differentiation of SH-SY5Y cells by IGF-I and ATRA induces different cellular characteristics, resulting in diverse changes in sensitivity to heavy metals, which depend not only on the differentiation agents and treatment time but also on the heavy metal species and concentration.

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. “Bare lands” decreased by 21% being occupied by other land uses, especially by “shrimp farms.” Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in “settlement” area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

The Rohingya people have sought refuge in Bangladesh following decades of ethnic and religious persecution in Myanmar. After the mass exodus in August 2017, Bangladesh launched emergency repatriation initiatives. In November 2017, Bangladesh and Myanmar reached a preliminary agreement on repatriation, despite widespread concerns from human rights organisations. This article examines the stalled repatriation of Rohingya refugees by analysing diplomatic challenges, regional geopolitical dynamics, and potential solutions. Using a qualitative approach and secondary sources, the study explores how geopolitical tensions, Myanmarʼs unwillingness to ensure safe returns, and security concerns have blocked progress. These factors have contributed to deteriorating conditions in the refugee camps, including overcrowding, increased crime, and a sharp decline in international aid. The ongoing crisis has exacerbated Bangladesh while economic and security burdens, while regional powers such as China and India continue to prioritise strategic interests over humanitarian responsibilities. Thus, the present study from a policy standpoint advocates for greater diplomatic pressure on Myanmar, stronger regional cooperation, and the development of a comprehensive refugee policy. Furthermore, empowering Rohingya refugees through education and economic opportunities can mitigate security risks while fostering sustainable repatriation models. Additionally, third‐country resettlement and international burden‐sharing must be prioritised to achieve long‐term and dignified solutions.

Fruits and vegetables are used not only for nutritional purposes but also as therapeutics to treat various diseases and ailments. These food items are prominent sources of phytochemicals that exhibit chemopreventive and therapeutic effects against several diseases. Hirsutine (HSN) is a naturally occurring indole alkaloid found in various Uncaria species and has a multitude of therapeutic benefits. It is found in foodstuffs such as fish, seafood, meat, poultry, dairy, and some grain products among other things. In addition, it is present in fruits and vegetables including corn, cauliflower, mushrooms, potatoes, bamboo shoots, bananas, cantaloupe, and citrus fruits. The primary emphasis of this study is to summarize the pharmacological activities and the underlying mechanisms of HSN against different diseases, as well as the biopharmaceutical features. For this, data were collected (up to date as of 1 July 2023) from various reliable and authentic literature by searching different academic search engines, including PubMed, Springer Link, Scopus, Wiley Online, Web of Science, ScienceDirect, and Google Scholar. Findings indicated that HSN exerts several effects in various preclinical and pharmacological experimental systems. It exhibits anti-inflammatory, antiviral, anti-diabetic, and antioxidant activities with beneficial effects in neurological and cardiovascular diseases. Our findings also indicate that HSN exerts promising anticancer potentials via several molecular mechanisms, including apoptotic cell death, induction of oxidative stress, cytotoxic effect, anti-proliferative effect, genotoxic effect, and inhibition of cancer cell migration and invasion against various cancers such as lung, breast, and antitumor effects in human T-cell leukemia. Taken all together, findings from this study show that HSN can be a promising therapeutic agent to treat various diseases including cancer.

Now-a-days, various types of antibiotics are being used worldwide in veterinary sector indiscriminately for promotion of growth and treatment of the livestock. Significant portions of antibiotics are released through milk of dairy animals unaltered and exert serious harmful effects on human health. This review evaluates and compare researches on antibiotic residues in milk in published literatures from Pubmed, CrossRef, CAB direct, DOAJ, JournalTOCs, AGRICOLA, ScientificGate, Electronic Journals Library, CAB abstracts, Global Health Databases, Global Impact Factor, Google Scholar, Park Directory of Open Access Journals, BanglaJOL and ISC E-Journals. Antibiotics residue in milk was first detected in 60s and then with an increasing trend with highest after 2,000 (188). The highest no. of works, 49 (21.87%) were accomplished in China, followed by Spain, 30 (13.39%); Germany, 11 (4.91%); and USA, 10 (4.46%). Continent-wise highest researches are published from Europe, 105 (46.88%), followed by Asia, 77 (34.38%); South America, 18 (8.04%); North America, 16 (7.14%); and Africa, 8 (3.57%). For detection, Bovine milk sample is mostly used, 193 (86.16%), followed by ovine, 19 (8.48%); and caprine, 14 (6.25%). Acetonitrile was used in maximum cases (77) for processing the samples. Chromatographic technique was the highest, 115 (51.34%) for detection. Residue of ß-lactam group have been detected mostly 133 (36.54%), followed by tetracyclines, 51 (14.01%); fluoroquinolones, 49 (13.46%); sulfonamides, 46 (12.64%); and aminoglycosides, 38 (10.44%). This review observe that antibiotics residues are more common in milk samples that are being manifested in increasing researches on antibiotic detection and measures should adopt to cease this residue.