Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

Off-Street Vehicular Fog for Catering Applications in 5G/B5G: A Trust-based Task Mapping Solution and Open Research Issues

One of the key enablers in serving the applications requiring stringent latency in 5G networks is fog computing as it is situated closer to the end users. With the technological advancement of vehicles’ on-board units, their computing capabilities are becoming robust, and considering the underutilization of the off-street vehicles, we envision that the off-street vehicles can be an enormously useful computational source for the fog computing. Additionally, clustering the vehicles would be advantageous in order to improve the service availability. As the vehicles become highly connected, trust is needed especially in distributed environments. However, vehicles are made from different manufacturers, and have different platforms, security mechanisms, and varying parking duration. These lead to the unpredictable behavior of the vehicles where quantifying trust value of vehicles would be difficult. A trust-based solution is necessary for task mapping as a task has a set of properties including expected time to complete, and trust requirements that need to be met. However, the existing metrics used for trust evaluation in the vehicular fog computing such as velocity and direction are not applicable in the off-street vehicle fog environments. In this paper, we propose a framework for quantifying the trust value of off-street vehicle fog computing facilities in 5G networks and forming logical clusters of vehicles based on the trust values. This allows tasks to be shared with multiple vehicles in the same cluster that meets the tasks’ trust requirements. Further, we propose a novel task mapping algorithm to increase the vehicle resource utilization and meet the desired trust requirements while maintaining imposed latency requirements of 5G applications. Results obtained using iFogSim simulator demonstrate that the proposed solution increases vehicle resource utilization and reduces task drop noticeably. This paper presents open research issues pertaining to the study to lead..

Green demand aware fog computing : a prediction-based dynamic resource provisioning approach

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Commensurate versus incommensurate heterostructures of group-III monochalcogenides

First-principles calculations based on density-functional theory were performed to investigate heterostructures of group-III monochalcogenides (GaS, GaSe, InS, and InSe) and the effects of incommensurability on their electronic structures. We considered two heterostructures: GaS/GaSe, which has a lattice mismatch of 4.7%, and GaSe/InS, with a smaller mismatch of 2.1%. We computed the cost of having commensurate structures, and we also examined the potential energy landscape of both heterostructures in order to simulate the realistic situation of incommensurate systems. We found that a commensurate heterostructure may be realized in GaSe/InS as the interaction energy of this system with the monolayers assuming the average lattice constant is smaller than the interaction energy of an incommensurate system in which each layer keeps its own lattice constant. For GaS/GaSe, on the other hand, we found that the incommensurate heterostructure is energetically more favorable than the commensurate one, even when taking into account the energetic cost due to the lack of proper registry between the layers. Since the commensurate condition requires that one (or both) layer(s) is (are) strained, we systematically investigated the effect of strain on the band gaps and band-edge positions of the monolayer systems. We found that, in all monolayers, the conduction-band minimum is more than two times more sensitive to applied strain than the valence-band maximum; this was observed to strongly affect the band alignment of GaS/GaSe, as it can change from type-I to type-II with a small variation in the lattice constant of GaS. The GaSe/InS heterostructure was found to have a type-II alignment, which is robust with respect to strain in the range of −2% to +2%

An energy efficient interference-aware routing protocol for underwater WSNs

Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one of the key strategies in reducing packet loss in the highly hostile underwater environment. The reduced interference causes efficient utilization of the limited battery power of the sensor nodes that, in consequence, prolongs the entire network lifetime. In this paper, we propose an energy-efficient interference-aware routing (EEIAR) protocol for UWSNs. A sender node selects the best relay node in its neighborhood with the lowest depth and the least number of neighbors. Combination of the two routing metrics ensures that data packets are forwarded along the least interference paths to reach the final destination. The proposed work is unique in that it does not require the full dimensional localization information of sensor nodes and the network total depth is segmented to identify source, relay and neighbor nodes. Simulation results reveal better performance of the scheme than the counterparts DBR and EEDBR techniques in terms of energy efficiency, packet delivery ratio and end-to-end delay

Photoluminescence revealed higher order plasmonic resonance modes and their unexpected frequency blue shifts in silver-coated silica nanoparticle antennas

© 2019 by the authors. Higher order plasmonic resonance modes and their frequency blue shifts in silver-coated silica nanoparticle antennas are studied. Synthesizing them with a wet chemistry method, silica (SiO2) nanoparticles were enclosed within silver shells with different thicknesses. A size-dependent Drude model was used to model the plasmonic shells and their optical losses. Two higher order plasmonic resonances were identified for each case in these simulations. The photoluminescence spectroscopy (PL) experimental results, in good agreement with their simulated values, confirmed the presence of those two higher order resonant modes and their resonance frequencies. When compared with pure metallic Ag nanoparticles, size-induced blue shifts were observed in these resonance frequencies

Find My Trustworthy Fogs: A Fuzzy-based Trust Evaluation Framework

The growth of IoT is proven with the massive amount of data generated in 2015, and expected to be even more in the years to come. Relying on the cloud to meet the expanding volume, variety, and velocity of data that the IoT generates may not be feasible. In the last two years, fog computing has become a considerably important research topic in an attempt to reduce the burden on cloud and solve cloud's inability to meet the IoT latency requirement. However, fog environment is different than in cloud since fog environment is far more distributed. Due to the dynamic nature of fog, backups such as redundant power supply would deem unnecessary, and relying on just one Internet Service Provider for their fog device would be sufficient. If obstacles arise in this fog environment, factors such as latency, availability or reliability would in turn be unstable. Fogs become harder to trust, and this issue is more complicated and challenging in comparison to the conventional cloud. This implies that trustworthiness in fog is an imperative issue that needs to be addressed. With the help of a broker, managing trust in a distributive environment can be tackled. Acting as an intermediary, a broker helps in facilitating negotiation between two parties. Although the brokering concept has been around for a long time and is widely used in the cloud, it is a new concept in fog computing. As of late, there are several research studies that incorporates broker in fog where these brokers focus towards pricing management. However to the best of our knowledge there is no literature on broker-based trust evaluation in fog service allocation. This is the first work that proposes broker-based trust evaluation framework that focuses on identifying a trustworthy fog to fulfi ll the user requests. In this paper, fuzzy logic is used as the basis for the evaluation while considering the availability and cost of fog. We propose Request Matching algorithm to identify a user request, and Fuzzy-based Filtering algorithm to match the request with one of the predefi ned sets created and managed by the broker. In this paper, we present a use case that illustrates how fuzzy logic works in determining the trustworthiness of a fog. Our findings suggest that the algorithms can successfully provide users a trustworthy fog that matches their requirement

Women’s gainful employment in ‘gher’ farming system (prawn–carp–rice integrated culture) in Bangladesh: trends and determinants

© 2019, Springer Nature Switzerland AG. The paper examines trends in women’s gainful employment in ‘prawn-carp’ and ‘high yield variety (HYV) rice’ enterprises of the ‘gher’ farming system and jointly identifies the determinants of employing female and male labor in these enterprises using a farm-level panel data of 90 producers covering a 14-year period (2002–2015) from southwest Bangladesh by applying a multivariate Tobit approach. Results revealed that women’s gainful employment and real wage increased significantly with a substantial reduction in wage gap between female and male labor. The decision to employ female and male labor was found to be positively correlated thereby confirming jointness in decision-making and validity of our chosen approach. Prices and socio-economic factors exerted varied influences on female and male labor demand. Output prices, cultivated area, education, and experience positively increased women’s gainful employment whereas larger family size reduced it. Policy implications include investments in education targeted at the ‘gher’ farming households and land reform policies to consolidate farm size and facilitate smooth functioning of the land market to increase women’s gainful employment

An Update on Hepatorenal Syndrome

Hepatorenal syndrome (HRS) is one of the many potential causes of acute kidney injury (AKI) in patients with decompensated liver disease. HRS is associated with poor prognosis and represents the end-stage of a sequence of reductions in renal perfusion induced by progressively severe hepatic injury. The pathophysiology of HRS is complex with multiple mechanisms interacting simultaneously, although HRS is primarily characterised by renal vasoconstriction. A recently revised diagnostic criteria and management algorithm for AKI has been developed for patients with cirrhosis, allowing physicians to commence treatment promptly. Vasopressor therapy and other general management, such as antibiotic prophylaxis, need to be initiated whilst patients are assessed for eligibility for transplantation. Liver transplantation remains the treatment of choice for HRS but is limited by organ shortage. Other management options, such as transjugular intrahepatic portosystemic shunt, renal replacement therapy and molecular absorbent recirculating system, may provide short-term benefit for patients not responding to medical therapy whilst awaiting transplantation. Clinicians need to be aware of the pathophysiology and management principles of HRS to provide quality care for patients with multi-organ failure