Manual measurement of retinal bifurcation features

This paper introduces a new computerized tool for accurate manual measurement of features of retinal bifurcation geometry, designed for use in investigating correlations between measurement features and clinical conditions. The tool uses user-placed rectangles to measure the vessel width, and lines placed along vessel center lines to measure the angles. An analysis is presented of measurements taken from 435 bifurcations. These are compared with theoretical predictions based on optimality principles presented in the literature. The new tool shows better agreement with the theoretical predictions than a simpler manual method published in the literature, but there remains a significant discrepancy between current theory and measured geometry

BIST hardware synthesis for RTL data paths based on test compatibility classes

New BIST methodology for RTL data paths is presented. The proposed BIST methodology takes advantage of the structural information of RTL data path and reduces the test application time by grouping same-type modules into test compatibility classes (TCCs). During testing, compatible modules share a small number of test pattern generators at the same test time leading to significant reductions in BIST area overhead, performance degradation and test application time. Module output responses from each TCC are checked by comparators leading to substantial reduction in fault-escape probability. Only a single signature analysis register is required to compress the responses of each TCC which leads to high reductions in volume of output data and overall test application time (the sum of test application time and shifting time required to shift out test responses). This paper shows how the proposed TCC grouping methodology is a general case of the traditional BIST embedding methodology for RTL data paths with both uniform and variable bit width. A new BIST hardware synthesis algorithm employs efficient tabu search-based testable design space exploration which combines the accuracy of incremental test scheduling algorithms and the exploration speed of test scheduling algorithms based on fixed test resource allocation. To illustrate TCC grouping methodology efficiency, various benchmark and complex hypothetical data paths have been evaluated and significant improvements over BIST embedding methodology are achieved

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

Currently, with the widespread of the intelligent Internet of Things (IoT) in beyond 5G, wireless federated learning (WFL) has attracted a lot of attention to enable knowledge construction and sharing among a huge amount of distributed edge devices. However, under unstable wireless channel conditions, existing WFL schemes exist the following challenges: First, learning model parameters will be disturbed by bit errors because of interference and noise during wireless transmission, which will affect the training accuracy and the loss of the learning model. Second, traditional edge devices with CPU acceleration are inefficient due to the low throughout computation, especially in accelerating the encoding and decoding process during wireless transmission. Third, current hardware-level GPU acceleration methods cannot optimize complex operations, for instance, complex wireless coding in the WFL environment. To address the above challenges, we propose a software-defined GPU-CPU empowered efficient WFL architecture with embedding LDPC communication coding. Specifically, we embed wireless channel coding into the server weight aggregation and the client local training process respectively to resist interruptions in the learning process and design a GPU-CPU acceleration scheme for this architecture. The experimental results show its anti-interference ability and GPU-CPU acceleration ability during wireless transmission, which is 10 times the error control capability and 100 times faster than existing WFL schemes

Energy-Efficient End-to-End Security for Software Defined Vehicular Networks

One of the most promising application areas of the Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN’s flexible network management, network performance, and energy-efficient end-toend security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 & MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDV

Deep information fusion-driven POI scheduling for Mobile Social Networks

With the growing importance of green wireless communications, point-of-interest (POI) scheduling in the mobile social network (MSN) environment has become important in addressing the high demand for innovative scheduling solutions. To enhance feature expressions for the complicated structures in MSNs, this article explores a deep information, fusion-based POI scheduling system of the MSN environment via the implementation of an edge-cloud deep hybrid sensing (PS-MSN) framework. Cloud sensing modules utilize the explicit contextual real-time information for each user, while edge sensing modules detect the real-time implicit linkages among users. Based on these two types of modules, a deep representation scheme is embedded into the hybrid sensing framework to improve its feature expression abilities. As a result, this type of framework is able to integrate multisource information so that more fine-grained feature spaces are built. In this work, two groups of experiments are conducted on a real-world dataset to evaluate the efficiency, as well as stability, of the designed PS-MSN. Using three benchmark methods to make comparisons, the excellent overall performance of PS-MSN is properly verified

Biofuel production using cultivated algae: technologies, economics, and its environmental impacts

The process of looking for alternative energy sources is driven by the increasing demand for energy and environmental contamination caused by using fossil fuels. Recent investigations reported the efficiency of microalgae for biofuel production due to its low cost of production, high speed of growth, and ability to grow in harsh environments. In addition, many microalgae are photosynthetic, consuming CO2 and solar light to grow in biomass and providing a promising bioenergy source. This review presents the recent advances in the application of microalgae for biofuel production. In addition, cultivation and harvesting systems and environmental factors that affect microalgae cultivation for biofuel production have also been discussed. Moreover, lipid extraction and conversion technologies to biofuel are presented. The mixotrophic cultivation strategy is promising as it combines the advantages of heterotrophy and autotrophy. Green harvesting methods such as using bio-coagulants and flocculants are promising technologies to reduce the cost of microalgal biomass production. In the future, more investigations into co-cultivation systems, new green harvesting methods, high lipids extraction methods, and the optimization of lipid extraction and converting processes should be implemented to increase the sustainability of microalgae application for biofuel production

Energy-Efficient Random Access for LEO Satellite-Assisted 6G Internet of Remote Things

Satellite communication system is expected to play a vital role for realizing various remote internet of things (IoT) applications in 6G vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this paper, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods

Securing radio resources allocation with deep reinforcement learning for IoE services in next-generation wireless networks

The next generation wireless network (NGWN) is undergoing an unprecedented revolution, in which trillions of machines, people, and objects are interconnected to realize the Internet of Everything (IoE). with the emergence of IoE services such as virtual reality, augmented reality, and industrial 5G, the scarcity of radio resources becomes more serious. Moreover, there are hidden dangers of untrusted terminals accessing the system and illegally manipulating interconnected devices. To tackle these challenges, this paper proposes a securing radio resources allocation scheme with Deep Reinforcement Learning for IoE services in NGWN. First, the solution uses a BP neural network based on multi-feature optimized Firefly Algorithm (FA) for spectrum prediction, thereby improving the prediction accuracy and avoiding interference between unauthorized and authorized users with efficient radio utilization. Then, a spectrum sensing method based on deep reinforcement learning is proposed to identify the untrusted users in system while fusing the sensing results, to enhance the security of the cooperative process and the detection accuracy of spectrum holes. Extensive simulation results show that the proposal is superior to the traditional solutions in terms of prediction accuracy, spectrum utilization and energy consumption, and is suitable for deployment in future wireless systems

Thermal based remediation technologies for soil and groundwater: a review

Thermal remediation technologies are fast and effective tools for the remediation of contaminated soils and sediments. Nevertheless, the high energy consumption and the effect of high temperature on the soil properties may hinder the wide applications of thermal remediation methods. This review highlights the recent studies focused on thermal remediation. Eight types of thermal remediation processes are discussed, including incineration, thermal desorption, stream enhanced extraction, electrical resistance heating, microwave heating, smoldering, vitrification, and pyrol-ysis. In addition, the combination of thermal remediation with other remediation technologies is presented. Finally, thermal remediation sustainability is evaluated in terms of energy efficiency and their impact on soil properties. The developments of the past decade show that thermal-based technologies are quite effective in terms of contaminant removal but that these technologies are associated with high energy use and costs and can has an adverse impact on soil properties. Nonetheless, it is anticipated that continued research on thermally based technologies can increase their sustainability and expand their applications. Low temperature thermal desorption is a prom-ising remediation technology in terms of land use and energy cost as it has no adverse effect on soil function after treatment and low temperature is required. Overall, selecting the sustainable remediation technology depends on the contaminant properties, soil properties and predicted risk level. © 2022 Desalination Publications. All rights reserved

Association of aromatase with bladder cancer stage and long-term survival: new insights into the hormonal paradigm in bladder cancer

BACKGROUND Hormonal factors may play a role in bladder cancer (BCa). We investigated the expression of aromatase and estrogen receptor (ER)β and its association with pathological variables and survival outcomes. PATIENTS AND METHODS BCa specimens from 40 patients were evaluated. Immunohistochemistry was performed for aromatase and ERβ. Descriptive statistics and univariate analyses assessed the association of these markers with pathologic variables and survival outcomes. RESULTS Aromatase expression was significantly associated with tumor stage; muscle-invasive disease was found in 15 of 19 (79%) patients with positive staining and in 7 of 18 (39%) patients with negative staining (P = .02). Node-positive disease was found in 8 of 19 (42%) patients with positive staining and 1 of 18 (6%) patients with negative staining (P = .01). After a median follow-up of 112 months, Cox regression analysis demonstrated that aromatase expression was associated with a more than 2-fold risk of cancer recurrence (hazard ratio, 2.37; confidence interval, 0.92-6.08; P = .07) and an almost 4-fold higher risk of cancer-specific death (hazard ratio, 3.66; 95% confidence interval, 1.19-12.06; P = .02). Muscle-invasive disease was found in 15 of 18 (83%) ERβ-positive specimens and 4 of 12 (33%) ERβ-negative specimens (P = .0009). Hierarchical clustering analysis demonstrated a 4-fold up-regulation of ERβ gene expression in tumor versus adjacent, non-tumor urothelium (P < .05). However, no significant association with survival outcomes was found. CONCLUSION Aromatase expression in BCa may be associated with advanced tumor stage and poorer survival outcomes. ERβ is upregulated in malignant tissue, and its expression is associated with muscle-invasive disease. These findings provide further evidence for the hormonal paradigm in BCa