Search

In recent years, Wi-Fi infrastructures have become ubiquitous, providing device-free passive-sensing features. Wi-Fi signals can be affected by their reflection, refraction, and absorption by moving objects in their path. The channel state information (CSI), a signal property indicator, of the Wi-Fi signal can be analyzed for human activity recognition (HAR). Deep learning-based HAR models can enhance performance and accuracy without sacrificing computational efficiency. However, to save computational power, an inception network, which uses a variety of techniques to boost speed and accuracy, can be adopted. In contrast, the concept of spatial attention can be applied to obtain refined features. In this paper, we propose a human–human interaction (HHI) classifier, CSI-IANet, which uses a modified inception CNN with a spatial-attention mechanism. The CSI-IANet consists of three steps: i) data processing, ii) feature extraction, and iii) recognition. The data processing layer first uses the second-order Butterworth low-pass filter to denoise the CSI signal and then segment it before feeding it to the model. The feature extraction layer uses a multilayer modified inception CNN with an attention mechanism that uses spatial attention in an intense structure to extract features from captured CSI signals. Finally, the refined features are exploited by the recognition section to determine HHIs correctly. To validate the performance of the proposed CSI-IANet, a publicly available HHIs CSI dataset with a total of 4800 trials of 12 interactions was used. The performance of the proposed model was compared to those of existing state-of-the-art methods. The experimental results show that CSI-IANet achieved an average accuracy of 91.30%, which is better than that of the existing best method by 5%.

Abstract Background Healthcare service delivery systems need to ensure standard quality of care (QoC) for achieving expected health outcomes. Although Bangladesh has a good healthcare service delivery system, there are major concerns about the quality of maternal and newborn health (MNH) care services, which is imperative for achievements in health. The study aimed to measure the QoC for different MNH services in two selected public health facilities of Bangladesh. This study also documented the specific areas of each care which needs intervention. Methods The study was conducted in two district-level public health facilities—a district hospital (DH) and a mother and child welfare centre (MCWC). A total of 228 cases of MNH services were observed by using contextualized checklist ‘Standards-based Management and Recognition (S-BMR)’ for 8 selected MNH care services. For scoring, performed activities were calculated as percentages of the total recommended activities and categorized as high (> 80%), moderate (50 to 80%), and low (

Abstract Background Civil registration and vital statistics (CRVS) systems lay the foundation for good governance by increasing the effectiveness and delivery of public services, providing vital statistics for the planning and monitoring of national development, and protecting fundamental human rights. Birth registration provides legal rights and facilitates access to essential public services such as health care and education. However, more than 110 low- and middle-income countries (LMICs) have deficient CRVS systems, and national birth registration rates continue to fall behind childhood immunization rates. Using Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) data in 72 LMICs, the objectives are to (a) explore the status of birth registration, routine childhood immunization, and maternal health services utilization; (b) analyze indicators of birth registration, routine childhood immunization, and maternal health services utilization; and (c) identify missed opportunities for strengthening birth registration systems in countries with strong childhood immunization and maternal health services by measuring the absolute differences between the birth registration rates and these childhood and maternal health service indicators. Methods We constructed a database using DHS and MICS data from 2000 to 2017, containing information on birth registration, immunization coverage, and maternal health service indicators. Seventy-three countries including 34 low-income countries and 38 lower middle-income countries were included in this exploratory analysis. Results Among the 14 countries with disparity between birth registration and BCG vaccination of more than 50%, nine were from sub-Saharan Africa (Tanzania, Uganda, Gambia, Mozambique, Djibouti, Eswatini, Zambia, Democratic Republic of Congo, Ghana), two were from South Asia (Bangladesh, Nepal), one from East Asia and the Pacific (Vanuatu) one from Latin America and the Caribbean (Bolivia), and one from Europe and Central Asia (Moldova). Countries with a 50% or above absolute difference between birth registration and antenatal care coverage include Democratic Republic of Congo, Gambia, Mozambique, Nepal, Tanzania, and Uganda, in low-income countries. Among lower middle-income countries, this includes Eswatini, Ghana, Moldova, Timor-Leste, Vanuatu, and Zambia. Countries with a 50% or above absolute difference between birth registration and facility delivery care coverage include Democratic Republic of Congo, Djibouti, Moldova, and Zambia. Conclusion The gap between birth registration and immunization coverage in low- and lower middle-income countries suggests the potential for leveraging immunization programs to increase birth registration rates. Engaging health providers during the antenatal, delivery, and postpartum periods to increase birth registration may be a useful strategy in countries with access to skilled providers.

Abstract Background In Bangladesh, while the infrastructure of public health facilities to provide maternal and newborn care services is adequate, services are not always available due to insufficient staffing. A human resource availability index for health facilities is needed for monitoring and advocacy. This study aimed to develop indices for measuring the availability of different types of human resources to provide round-the-clock emergency obstetric and newborn care (EmONC) service at district-level public facilities. Methods As part of a larger intervention study, 30 days of prospective observation of providers was done at a district hospital (DH) and a mother and child welfare centre (MCWC) in one district of Bangladesh using checklists. A scoring system was developed to create an index to quantify the availability of providers for maternal and newborn care. Results Based on the newly developed index, medical doctors in the emergency department of the DH were 100% available, but ranged from 27 to 41% availability in the obstetrics/gynecology (ob/gyn) and pediatric wards. In MCWC, the corresponding indices ranged from 32 to 36%. In the DH, the availability of nurses in the ob/gyn ward (96%) was relatively better than in the pediatric ward (65%) but that in operation theatre was only 31%. In the MCWC, the index for the presence of a paramedic or nursing aid was 82% in the ob/gyn ward and 63% in the operation theatre. However, the availability scores of facility support staff for maintenance and security were generally high (over 90%) in both facilities. Conclusions Our newly developed index on availability of providers demonstrated huge gaps in availability of providers in evening and night shifts in most of the disciplines in the study facilities. This provider availability index is easy to create and can be used as a meaningful tool to quantify gaps in human resources by type in various types of district-level health facilities. Further studies are needed for adaptation of this tool in different types of health facilities and to assess its implication as an advocacy tool.

Abstract Background The capacity to demand and use research is critical for governments if they are to develop policies that are informed by evidence. Existing tools designed to assess how government officials use evidence in decision-making have significant limitations for low- and middle-income countries (LMICs); they are rarely tested in LMICs and focus only on individual capacity. This paper introduces an instrument that was developed to assess Ministry of Health (MoH) capacity to demand and use research evidence for decision-making, which was tested for reliability and validity in eight LMICs (Bangladesh, Fiji, India, Lebanon, Moldova, Pakistan, South Africa, Zambia). Methods Instrument development was based on a new conceptual framework that addresses individual, organisational and systems capacities, and items were drawn from existing instruments and a literature review. After initial item development and pre-testing to address face validity and item phrasing, the instrument was reduced to 54 items for further validation and item reduction. In-country study teams interviewed a systematic sample of 203 MoH officials. Exploratory factor analysis was used in addition to standard reliability and validity measures to further assess the items. Results Thirty items divided between two factors representing organisational and individual capacity constructs were identified. South Africa and Zambia demonstrated the highest level of organisational capacity to use research, whereas Pakistan and Bangladesh were the lowest two. In contrast, individual capacity was highest in Pakistan, followed by South Africa, whereas Bangladesh and Lebanon were the lowest. Conclusion The framework and related instrument represent a new opportunity for MoHs to identify ways to understand and improve capacities to incorporate research evidence in decision-making, as well as to provide a basis for tracking change.

The paper presents findings from a research study of 318 mathematics teachers' experiences of access to, and use of, digital technology in their professional development in marginalised communities in ten districts across Bangladesh. The innovative two-cycle qualitative study design involved early career researchers collaborating with peer-researcher teachers who interviewed their peers about local teachers' experiences. The interview data was analysed through the lens of Van Dijk's model of digital access and use, addressing motivation, physical access, skills, and usage. The findings reveal that most rural teachers were able to overcome barriers to access and complete the online course using their mobile phones, but the aim of making mathematics learning more active and joyful was only realised among the minority of teachers who had a supportive school environment. Promoting and supporting opportunities for praxis in schools is essential if technology enhanced professional development is to lead to improvements in teaching and learning. The findings have important implications for practice and at policy level to rethink access to professional development through 'mobile first' technologies and support for praxis in schools, in marginalised communities in Bangladesh and elsewhere.

Cloud computing technology delivers services, resources, and computer systems over the internet, enabling the easy modification of resources. Each field has its challenges, and the challenges of data transfer in the cloud pose unique obstacles for forensic analysts, making it necessary for them to investigate and adjust the evolving landscape of cloud computing. This is where cloud forensics emerges as a critical component. Cloud forensics, a specialized field within digital forensics, focuses on uncovering evidence of exploitation, conducting thorough investigations, and presenting findings to law enforcement for legal action against perpetrators. This paper examines the primary challenges encountered in cloud forensics, reviews the relevant literature, and analyzes the strategies implemented to address these obstacles.

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware that aims to steal user’s money. Ransomware is a form of malware that encrypts a victim’s files. The attacker then demands a ransom from the victim to restore access to the data upon a large payment. Ransomware is a way of stealing money in which a user’s files are encrypted and the decrypted key is held by the attacker until a ransom amount is paid by the victim. This systematic literature review (SLR) highlights recent papers published between 2020 and 2024. This paper examines existing research on early ransomware detection methods, focusing on the signs, frameworks, and techniques used to identify and detect ransomware before it causes harm. By analyzing a wide range of academic papers, industry reports, and case studies, this review categorizes and assesses the effectiveness of different detection methods, including those based on signatures, behavior patterns, and machine learning (ML). It also looks at new trends and innovative strategies in ransomware detection, offering a classification of detection techniques and pointing out the gaps in current research. The findings provide useful insights for cybersecurity professionals and researchers, helping guide future efforts to develop strong and proactive ransomware detection systems. This review emphasizes the need for ongoing improvements in detection technologies to keep up with the constantly changing ransomware threat landscape.

The healthcare industry has been shifting toward electronic health records to improve operations, reduce overhead expenditure, and provide better healthcare. Electronic health records (EHRs) are supposed to offer the same levels of confidentiality and privacy as paper records, which have been used for decades. However, this is not the case, as the technology used to access, transmit, and store records poses a high risk to patients and healthcare organizations. Employees are a big risk to EHRs, as they use their devices to access information about a patient and discuss such records with other employees. Healthcare professionals also access patients’ records illegally. Such security loopholes have a high impact on EHRs, as people with malicious intent can use the records to access their financial records or blackmail them. External access to EHRs by cyber attackers poses the highest risk to the records and patients, as attackers are primarily driven by financial gain. On the contrary, internal access to data, though unethical, does not pose a grave danger to patients, as the employees mainly discuss the cases within themselves without any financial incentive to access the data. The current research provides a risk analysis of EHRs, the source of security problems, the impact of the risks involved, and risk management best practices that healthcare organizations can use to protect patients’ data.

Given the widespread use of the internet at the individual, governmental, and nongovernmental levels, and the opportunities it offers, such as online shopping, security concerns may arise. Cyber criminals are responsible for stopping organizations’ access to internet, for stealing valuable and confidential data, and causing other damage. Therefore, the network must be protected and meet security requirements. Network penetration testing is a type of security assessment used to find risk areas and vulnerabilities that threaten the security of a network. Thus, network penetration testing is designed to provide prevention and detection controls against attacks in the network. A tester looks for security issues in the network operation, design, or implementation of the particular company or organization. Thus, it is important to identify the vulnerabilities and identify the threats that may exploit them in order to find ways to reduce their dangers.The ports at risk are named and discussed in this study. Furthermore, we discuss the most common tools used for network penetration testing. Moreover, we look at potential attacks and typical remediation strategies that can be used to protect the vulnerable ports by reviewing the related publications. In conclusion, it is recommended that researchers in this field focus on automated network penetration testing. In the future, we will use machine learning in WLAN penetration testing, which provides new insight and high efficiency in performance. Moreover, we will train machine learning models to detect a wide range of vulnerabilities in order to find solutions to mitigate the risks in a short amount of time rather that through manual WLAN penetration testing, which consumes a lot of time. This will lead to improving security and reducing loss prevention.

Unmanned aerial vehicle (UAVs) is capable of adding significant potential to the internet of thing (IoT) devices and hence smart UAV–IoT collaborative system has attracted the attention of many researchers. This system has to be energy efficient for its nature and functionalities. Optimized trajectory planning is a significant area of research for any automatic movable device. In this article, we propose a technique, called EETO-GA for energy-efficient trajectory optimization of UAV–IoT using a genetic algorithm (GA). This technique prescribes each device of: (i) the next timestamp of arrival on the present cluster of IoT devices, at which the task queue of its header contains the highest possible number of tasks, and (ii) the minimum amount of energy that requires to complete all the tasks present in task queue of the IoT device. This technique uses a GA for optimization where the fitness function is designed by optimizing objectives: (i) the total number of tasks that can be completed, (ii) minimization of consumed energy, and (iii) the number of devices that could be served. A GA is applied here to accommodate a large number of IoT devices. A binary method of encoding is applied and methods like cross-over and mutation are used to arrive at the optimal solution. Through a simulation study, the proposed technique shows significant improvement in terms of UAV energy saved (UAVE), energy saving in IoT devices (IoTDEC), the average delay in execution of the task (ADET), and the percentage of tasks that could be completed (PTSK). Proposed EETO-GA improved average UAVE: 43%, IoTDEC: 56%, PTSK: 7.5%, and ADET: 38% over the state of the art.

From disaster prevention to mitigation, drug analysis to drug design, agriculture to food security, IoT to AI, and big data analysis to knowledge or sentiment mining, a high computation power is a prime necessity at present. As such, massively parallel computer (MPC) systems comprising a large number of nodes are gaining popularity. To interconnect these huge numbers of nodes efficiently, hierarchical interconnection networks are an attractive and feasible option. A Tori-connected flattened butterfly network (TFBN) has been proposed by the authors in a prior work for future generation MPC systems. In the previous study, the static network performance and static cost-effectiveness were evaluated. In this research, a novel trade-off factor named message traffic congestion vs. packing density trade-off factor has been proposed, which characterizes the message congestion in the network and its packing density. The factor is used to statically assess the suitability of the implementation of an interconnection network. The message traffic density, packing density, and new factor have been evaluated for the proposed network and similar competitive networks such as TTN, TESH, 2D-Mesh, 3D-Mesh, 2D-Torus, and 3D-Torus. It has been found that the performance of the TFBN is superior to the other networks.

In order to fulfill the increasing demand for computation power to process a boundless data concurrently within a very short time or real-time in many areas such as IoT, AI, machine learning, smart grid, and big data analytics, we need exa-scale or zetta-scale computation in the near future. Thus, to have this level of computation, we need a massively parallel computer (MPC) system that shall consist of millions of nodes; and, for the interconnection of these massive numbers of nodes, conventional topologies are infeasible. Thus, a hierarchical interconnection network (HIN) is a rational way to connect huge nodes. Through this article, we are proposing a new HIN, which is a tori-connected flattened butterfly network (TFBN) for the next generation MPC system. Numerous basic modules are hierarchically interconnected as a toroidal connection, whereby the basic modules are flattened butterfly networks. We have studied the network architecture, static network performance, and static cost-effectiveness of the proposed TFBN in detail; and compared static network and cost-effectiveness performance of the TFBN to those of TTN, torus, TESH, and mesh networks. It is depicted that TFBN possesses low diameter and average distance, high arc connectivity, and temperate bisection width. It also has better cost-effectiveness and cost-performance trade-off factor compared to those of TTN, torus, TESH, and mesh networks. The only shortcoming is that the complexity of wiring of the TFBN is higher than that of those networks; this is because the basic module necessitates some extra short length link to form the flattened butterfly network. Therefore, TFBN is a high performance and cost-effective HIN, and it will be a good option for the next generation MPC system.

Diaryl(iodo)bismuthanes possessing a hypervalent C=O•••Bi–I bond were conveniently synthesized in a one-pot reaction by using arylzinc reagents generated from o-carbonyl iodobenzenes and zinc powder under ultrasonication. This method is superior to the conventional organolithium and Grignard methods because it has a wide functional group tolerance, requires no protecting group manipulations, and proceeds under mild reaction conditions that do not need low temperature control. Furthermore, no intermediate triarylbismuthane precursor for the hypervalent iodobismuthane is necessary.