Open Issues Research Articles

Toward security quantification of serverless computing

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.

Decompositions of Hyperbolic Kac–Moody Algebras with Respect to Imaginary Root Groups

We propose a novel way to define imaginary root subgroups associated with (timelike) imaginary roots of hyperbolic Kac–Moody algebras. Using in an essential way the theory of unitary irreducible representation of covers of the group SO(2, 1), these imaginary root subgroups act on the complex Kac–Moody algebra viewed as a Hilbert space. We illustrate our new view on Kac–Moody groups by considering the example of a rank-two hyperbolic algebra that is related to the Fibonacci numbers. We also point out some open issues and new avenues for further research, and briefly discuss the potential relevance of the present results for physics and current attempts at unification.

Influence of Perceived Health Risk Factors on Adoption of Community-Led Total Sanitation: A Case of Turkana Central Sub-County, Turkana County, Kenya

The primary objective of Sustainable Development Goal 6 target 2 is to achieve widespread accessibility to sufficient sanitation and hygiene facilities, therefore eliminating the practice of open defecation by the year 2030. One strategy for achieving this objective involves the implementation of Community-Led Total Sanitation (CLTS), a participatory method that allows communities to take on accountability for their sanitation behaviors. However, sustaining favorable changes after the implementation of the intervention continues to be a significant obstacle. The objective of this study was to investigate the influence of perceived health risk factors on adoption of Community-Led Total Sanitation. The study was carried out on the household members of Lodwar township and Kalokol ward, targeting a sample size of 382 respondents The research utilized a convergent design methodology, which combined quantitative and qualitative data collection methods, enabling the simultaneous gathering of both types of data. Quantitative data were collected using structured questionnaires and observation checklist, while qualitative data were collected from Focus Group Discussions. A stratified sampling technique was applied to group the village units into strata and simple random sampling used in selection of household heads. Qualitative results were structured into themes and direct quotes. The study determined that an increase in perceived knowledge regarding health risks was associated with a higher likelihood of latrine presence (p-value< 0.05). Findings from multiple regression analysis also showed that education level was a significant predictor for awareness, understanding, and knowledge (p=0.000<0.05), while gender demonstrated a significant negative relationship with awareness and understanding p= -0.2573, suggested males score lower in these areas compared to females. Age correlated positively with awareness and was marginally significant for understanding (p=0.043), indicating that older individuals had higher scores. The study concluded that increased knowledge regarding health risk in terms of vulnerability and susceptibility influenced latrine presence at the households and thus the adoption of community-led total sanitation programs in Turkana Central Sub-County. The study highlighted the critical role of knowledge in achieving open defecation-free status in the area. The study recommended that CLTS interventions by the Ministry of Health and sanitation implementers place a particular focus on knowledge enhancement, to ensure that communities are better informed, which could lead to a higher likelihood of adopting improved sanitation practices, thereby reducing the prevalence of open defecation and related health issues.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

This paper presents an open and interesting issue for missiles, i.e., achieving collaborative parameters constrained cooperative guidance, despite the interference of pursing interceptors (INTs) and the maneuvering target, by the fact that the target-missile-interceptor (TMI) engagement induces their complex and time-varying relationships. The Memory-Extraction-based Soft-Actor-Critic (ME-SAC) approach is proposed, which enhances the collaborative performance of missiles by implicitly extracting coupling motion characteristics among TMI from historical state, achieving the joint optimization of situation awareness and strategy. Firstly, the cooperative guidance task is formulated as a multi-order Markov decision process (MOMDP) to better represent the dynamic evolution of engagement, and a memory-extraction process is introduced to alleviate the curse of dimensionality. Secondly, a memory-decision-oriented maximum entropy framework combined with memory update modules is designed for enhancing strategy search ability. Then, a domain-knowledge-based pre-training is implemented to improve convergence speed. Finally, in simulation evaluation with various scenarios, the proposed ME-SAC shows more promising than the typical DRL-based and model-based algorithms in task success rate, learning efficiency, and adaptability.

Catheter-related deep vein thrombosis: Where are we at and where are we going? Updates and ongoing unmet clinical needs.

Catheter-related thrombosis (CRT) is one of the major complications affecting patients with indwelling venous catheters, usually involving the upper extremity deep venous system. This condition can lead to potentially life-threatening complications such as pulmonary embolism and sepsis. The risk of developing CRT varies depending on type of catheters and patient characteristics. Despite advances in materials and technologies, the actual incidence of CRT is still considerable. Available evidence on CRT management remains controversial, and clinical guidelines base their recommendations on data from non-catheter related upper extremity or lower extremity deep venous thromboses. This narrative review aims to describe the epidemiology of CRT, to review the available evidence on its management and to highlight the current unmet needs. No formal search strategy was applied for the revision of the literature. The main sources of information used were Medline and guidelines from international societies. The management of CRT requires a careful balance between the risk of thrombus progression, recurrent events, and systemic embolization and the increased bleeding risk in often fragile patients. Open issues include the optimal management of the catheter and the type and duration of anticoagulant therapy. Direct oral anticoagulants are increasingly prescribed, representing an important alternative to the standard of care low molecular weight heparins in selected cases. The development of new anticoagulant drugs such as factors XI and XII inhibitors may offer further advantages in this context. The management of CRT is still challenging with constant need for updated evidence to support tailored approaches.

Geospatial insights into open defecation vulnerability in Gulumbe, Nigeria

Open defecation (OD) poses a serious public health risk by exposing communities to diseases and compromising the well-being of people. One potential approach to mitigating its impact is the application of spatial planning techniques. However, spatial data paucity is one of the major problems in addressing open defecation issues in Nigeria. This study employed spatial analysis to identify areas particularly susceptible to OD using Geographical Information System (GIS) including Merginmap applications on Android and QGIS software, which were used with field observations to georeferenced residential areas, public toilets, and instances of open defecation. Proximity analysis and local indicators of spatial autocorrelation, based on Moran’s I, were employed to assess vulnerability. Buffer analysis was conducted to identify residential areas within 100 m of an OD site, and the findings indicated residential areas at high risk of experiencing negative impacts from OD. The LISA analysis revealed a high-to-high clustering pattern (p < 0.05) in the southern region of the study area, which aligns with the results of the buffer analysis and further suggests consistent spatial patterning. Furthermore, OD was more prevalent near water bodies and inner residential areas, increasing the risk of water and air pollution. This pollution could facilitate the spread of harmful bacteria, such as E. coli and Salmonella, from OD sites to nearby households, increasing the risk of disease. This study stresses the potential of GIS in evaluating areas vulnerable to OD and highlights the need to expand awareness campaigns regarding proper hygiene and sanitation practices in rural communities.

Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.

“Green” particle technology: Its history, successes, and open issues

“Green” particle technology: Its history, successes, and open issues

Temporal trends of suicidality among hospitalised adolescents during COVID-19 pandemic: A Bayesian framework

ObjectiveLiterature on temporal patterns of suicidality among youths during the COVID-19 pandemic is growing. The present work proposes a Bayesian approach to assess temporal patterns of suicide-related behaviours among inpatient adolescents during the COVID-19 pandemic. MethodologyData referred to the first hospital discharge record with ICD9-CM codes related to suicide-related behaviour and/or suicidal ideation among adolescents aged 13–19 between 1 January 2017 and 31 March 2021 were collected in the Piedmont region, Italy (n = 334; median age: 15 years, IQR: 14–16; 80% girls). A Poisson Bayesian regression model performed on pre-COVID-19 data (2017–2020), adjusted by seasonality and stratified by sex, was adopted to provide the probability that the predicted counts exceed the observed ones in each pandemic year quarter. ResultsA declining trend of suicidality was observed in April–June 2020 among both sex groups. Among females, an increasing pattern of suicidality was registered in early 2021 (January–March) compared to the pre-pandemic period. ConclusionThe present findings contributed to a growing literature on the COVID-19 pandemic's impact on adolescents' suicide-related behaviours from a gender perspective and encouraged wider adoption of Bayesian approaches as valuable tools to explore rare events and deeply enlighten open public health issues.

In Vitro Simulated Ketogenic Diet Inhibits the Proliferation and Migration of Liver Cancer Cells by Reducing Insulin Production and Down-regulating FOXC2 Expression.

Ketogenic diet (KD) may benefit patients with liver cancer, but the underlying mechanism of its anti-cancer effect remains an open issue. This work aimed to explore the influence of simulated KD on the proliferation and migration of cultured hepatoma cells. The low-glucose medium supplemented with β-hydroxybutyrate (BHB-Glow) was utilized to simulate clinical KD treatment. Western blot was utilized for detecting the expression of glycolysis-related proteins, Seahorse XF96 for oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), and ELISA for insulin content. Expression of FOXC2 in liver cancer cells was analyzed by bioinformatics and qPCR. Cell Count Kit-8 (CCK-8) testing kit was utilized for testing cell viability. KD treatment significantly reduced the expression of glycolysis-related proteins in Huh-7 cells, inhibited insulin production in β islet cells, reduced ECAR, and increased OCR. FOXC2 was significantly up-regulated in Huh-7 cell line, and sh-FOXC2 hindered the proliferation and migration of Huh-7 cells. The exogenous addition of insulin promoted the malignant progression of Huh-7 cells. Together, the medium simulating KD environment strengthened the protection of liver cancer cells by reducing insulin production and down-regulating FOXC2 expression. This study confirmed through in vitro cell experiments that KD could inhibit the proliferation and migration of liver cancer cells by targeting down regulation of insulin and FOXC2 expression, providing new theoretical basis for the treatment of liver cancer patients.

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

Monosodium glutamate (MSG) is a widely used food additive with conflicting evidence regarding its potential effects on human health, with proposed relevance for obesity and metabolic syndrome (MetS) or chronic kidney disease. As being able to accurately quantify the MSG dietary intake would help clarify the open issues, we constructed a predictive formula to estimate the daily intake of MSG in a rat model based on the urinary metabolic profile. Adult male Wistar rats were divided into groups receiving different daily amounts of MSG in drinking water (0.5, 1.5, and 3.0 g%), no MSG, and MSG withdrawal after 3.0% MSG treatment for 4 weeks. We then analyzed 24-hour urine samples for chemistries and metabolites using 1H NMR spectrometry and observed a strong correlation between urine pH, sodium, bicarbonate, alpha-ketoglutarate, citrate, fumarate, glutamate, methylamine, N-methyl-4-pyridone-3-carboxamide, succinate, and taurine and the daily MSG intake. Following the multiple linear regression analysis a simple formula model based on urinary Na+, citrate, and glutamate was most accurate and could be validated for estimating daily MSG intake. In conclusion, we propose that the daily MSG intake correlates with urinary metabolites in a rat model and that this new tool for monitoring the impact of MSG on health measures.

An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: “perception, edge computing, and services”. The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environment.

Wireless quantum optical communications with uncorrelated scattering multipath reception

AbstractWireless quantum optical communications over multipath channels with uncorrelated scattering still seem to be an open issue, although classical optical transmission was already considered, however, neglecting Bose–Einstein distributed thermal noise photons, leading to results that cannot be applied here. A viable way forward is proposed.

Deciphering TADF Mechanisms in Metal-Free Organic Emitters BrA-HBI: Utilizing Optimally Tuned Range-Separated Functionals for Insight

Abstract Metal-free organic emitters, characterized by their thermally activated delayed fluorescence (TADF) properties, offer considerable promise for the creation of highly efficient organic light-emitting diodes (OLEDs). Recently, Shao et al. presented a novel excited state intramolecular proton transfer (ESIPT) system BrA-HBI, demonstrating an emission quantum yield of up to 50% [Adv. Funct. Mater. 32, 2201256 (2022)]. However, many open issues cannot be answered solely by experimental means only and require detailed theoretical investigations. For instance, what causes the activation of TADF from the Keto* tautomer and leads to fluorescence quenching in the Enol* form? Herein, we provide a theoretical investigation on the TADF mechanism of the BrA-HBI molecule by optimally tuned range-separated functionals. Our findings reveal that ESIPT occurs in the BrA-HBI molecule. Moreover, we have disclosed the reason for the fluorescence quenching of the Enol* form and determined that the T 2 state plays a dominant role in the TADF phenomenon. In addition, double hybrid density functionals method was utilized to verify the reliability of optimally tuned range separation functionals on the calculation of the TADF mechanism in BrA-HBI. These findings not only provide a theoretical reference for development of highly efficient organic light-emitting diodes, but also demonstrate the effectiveness of the optimally tuned range-separated functionals in predicting the luminescence properties of TADF molecules.

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

Blockchain technology, based on decentralized data storage and distributed consensus design, has became a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), It optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.

Insider threat mitigation: Systematic literature review

The increasing prevalence of cybercrime necessitates the implementation of robust security measures. The majority of these attacks are initiated by authorized users who possess knowledge of the system vulnerabilities. Thus, insider attack prevention (ITP) strategies need to be explored to ensure the security of organizations. This review comprehensively examines the existing literature on ITP methods, focusing on recent developments and their implementation in various corporations. This review categorizes and classifies different types of insider attacks and their corresponding prevention and detection techniques. This paper also introduces a multi-tiered activity monitoring model that integrates network, system, and physical security measures to provide comprehensive defense against insider threats. This review also provides a detailed taxonomy that classifies insider threats based on insider type, access level, and targeted security objectives. Finally, future directions for ITP are explored, highlighting the open issues and challenges that need to be addressed.

Towards a Fracture Mechanics-Based Assessment for Fatigue Life Prediction of Ni–Ti Stents

The fatigue failure of Ni–Ti peripheral stents still represents an open issue of major concern due to the non-linear material behavior, the complex loads acting in vivo, and the manufacturing process. The fatigue assessment currently exploits total-life methodologies devoted to preventing crack nucleation. This work investigates a complementary fracture mechanics-based approach accounting for crack propagation from pre-existing manufacturing defects. Fatigue crack growth tests were performed on rolled Ni–Ti samples with a thickness and microstructure comparable to that of stents. A fracture mechanics-based assessment was implemented to predict the fatigue durability of surrogate samples tested at different mean and alternate strains. The fracture surfaces of the samples were inspected to determine a statistical distribution of defect size at the fracture origin. The cyclic J-integral was adopted as the crack driving force parameter, and it allowed to account for the complex response of the material, undergoing energy dissipations during phase transformation. Encouraging fatigue life predictions conforming to experimental data were obtained in the finite-life regime, whereas conservative estimates were computed below the fatigue threshold. This approach can be reverted to determine the maximum acceptable material defects for specific applications, providing a useful tool to manufacturing companies.

Prandtl-Meyer Reflection Configurations, Transonic Shocks, and Free Boundary Problems

We are concerned with the Prandtl-Meyer reflection configurations of unsteady global solutions for supersonic flow impinging upon a symmetric solid wedge. Prandtl (1936) first employed the shock polar analysis to show that there are two possible steady configurations: the steady weak shock solution and the steady strong shock solution, when a steady supersonic flow impinges upon the solid wedge – the half-angle of which is less than a critical angle (i.e., the detachment angle), and then conjectured that the steady weak shock solution is physically admissible since it is the one observed experimentally. The fundamental issue of whether one or both of the steady weak and strong shocks are physically admissible has been vigorously debated over the past eight decades and has not yet been settled in a definitive manner. On the other hand, the Prandtl-Meyer reflection configurations are core configurations in the structure of global entropy solutions of the two-dimensional Riemann problem, while the Riemann solutions themselves are local building blocks and determine local structures, global attractors, and large-time asymptotic states of general entropy solutions of multidimensional hyperbolic systems of conservation laws. In this sense, we have to understand the reflection configurations in order to understand fully the global entropy solutions of two-dimensional hyperbolic systems of conservation laws, including the admissibility issue for the entropy solutions. In this monograph, we address this longstanding open issue and present our analysis to establish the stability theorem for the steady weak shock solutions as the long-time asymptotics of the Prandtl-Meyer reflection configurations for unsteady potential flow for all the physical parameters up to the detachment angle. To achieve these, we first reformulate the problem as a free boundary problem involving transonic shocks and then obtain appropriate monotonicity properties and uniform a priori estimates for admissible solutions, which allow us to employ the Leray-Schauder degree argument to complete the theory for all the physical parameters up to the detachment angle.