Proactive Method Research Articles

A novel proactive controller deployment protocol for 5G-enabled software-defined Vehicular Networks

With the vast expansion of data loads across numerous safety and infotainment applications, the software-defined paradigm has become a critical component for 5G-enabled and intelligent vehicular networks. By transforming the network’s rigid hardware devices into programmable units, SDVN offers a broad view of the entire network status and a standard interface between heterogeneous wireless access technologies. However, deploying a centralized control unit carries several problems, such as bottleneck issues and densification issues. A distributed control plane is a potential alternative to a centralized control plane. Still, it poses many questions about the optimal location of local control units and the number of control units required for a given network structure. In this article, we propose an efficient proactive controller deployment and assignment strategy for distributed 5G-enabled software-defined Vehicular Networks based on predictive vehicles’ traffic flow information and communication delays between switch-enabled roadside units. A thorough performance evaluation is conducted under different vehicular network’s mobility scenarios. We assess the performance of the proposed method in terms of communication latency and load on the subsequent control units and their assigned set of switches. The proposed proactive clustering method is compared to different forms of controller placement methods. The results showed that the proposed approach reduced the network’s communication delays while maintaining a balanced distributed load among controllers over the simulation time.

Cyber Threat Hunting

Security and Information Event Management (SIEM) systems require significant manual input; SIEM tools with machine learning minimizes this effort but are reactive and only effective if known attack patterns are captured by the configured rules and queries. Cyber threat hunting, a proactive method of detecting cyber threats without necessarily knowing the rules or pre-defined knowledge of threats, still requires significant manual effort and is largely missing the required machine intelligence to deploy autonomous analysis. This paper proposes a novel and interactive cognitive and predictive threat-hunting prototype tool to minimize manual configuration tasks by using machine intelligence and autonomous analytical capabilities. This tool adds proactive threat-hunting capabilities by extracting unique network communication behaviors from multiple endpoints autonomously while also providing an interactive UI with minimal configuration requirements and various cognitive visualization techniques to help cyber experts quickly spot events of cyber significance from high-dimensional data.

The Potential of Strategic Environmental Assessment to Improve Urban Planning in Sierra Leone.

Strategic Environmental Assessment (SEA) is a proactive and collaborative method for environmental management designed to integrate environmental considerations into decision-making; and it is good for Sierra Leone. To understand whether SEA would be useful in the context of Sierra Leone, the authors interviewed 64 out of 78 experts face to face from March to July 2019. In addition, government policies and regulatory documents on environmental management and sustainable development, published articles served as secondary sources of data. Data were analyzed using descriptive statistics. These Sierra Leonean experts agreed that SEA would be useful for integration and achievement of improved sustainable urban planning strategies. However, the barriers identified to integrating SEA include: not addressing environmental issues during the preparation of policies and programs, insufficient political will, the absence of clear objectives, targets, principles and approaches, overlapping mandates among environmental institutions, and inadequate institutional coordination and non-integrated development framework as barriers to integrating SEA into their work. The study shows that SEA has the potential to have a positive impact on environmental concerns in decision-making, but it would need to be supported by stronger political will, legal frameworks, and improved technical guidance from the policy perspective. Moreover, we propose a conceptual framework for the inclusion of SEA into the urban planning process in Sierra Leone.

A PROACTIVE APPROACH TO NETWORK FORENSICS INTRUSION (DENIAL OF SERVICE FLOOD ATTACK) USING DYNAMIC FEATURES, SELECTION AND CONVOLUTION NEURAL NETWORK

Currently, the use of internet-connected applications for storage by different organizations have rapidly increased with the vast need to store data, cybercrimes are also increasing and have affected large organizations and countries as a whole with highly sensitive information, countries like the United States of America, United Kingdom and Nigeria. Organizations generate a lot of information with the help of digitalization, these highly classified information are now stored in databases via the use of computer networks. Thus, allowing for attacks by cybercriminals and state-sponsored agents. Therefore, these organizations and countries spend more resources analyzing cybercrimes instead of preventing and detecting cybercrimes. The use of network forensics plays an important role in investigating cybercrimes; this is because most cybercrimes are committed via computer networks. This paper proposes a new approach to analyzing digital evidence in Nigeria using a proactive method of forensics with the help of deep learning algorithms - Convolutional Neural Networks (CNN) to proactively classify malicious packets from genuine packets and log them as they occur.

A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training

Facial manipulation enables facial expressions to be tampered with or facial identities to be replaced in videos. The fake videos are so realistic that they are even difficult for human eyes to distinguish. This poses a great threat to social and public information security. A number of facial manipulation detectors have been proposed to address this threat. However, previous studies have shown that the accuracy of these detectors is sensitive to adversarial examples. The existing defense methods are very limited in terms of applicable scenes and defense effects. This paper proposes a new defense strategy for facial manipulation detectors, which combines a passive defense method, bilateral filtering, and a proactive defense method, joint adversarial training, to mitigate the vulnerability of facial manipulation detectors against adversarial examples. The bilateral filtering method is applied in the preprocessing stage of the model without any modification to denoise the input adversarial examples. The joint adversarial training starts from the training stage of the model, which mixes various adversarial examples and original examples to train the model. The introduction of joint adversarial training can train a model that defends against multiple adversarial attacks. The experimental results show that the proposed defense strategy positively helps facial manipulation detectors counter adversarial examples.

Application d’une nouvelle méthodologie d’analyse de risques a priori dans un service de radiothérapie

Since 2017, IRSN has been developing a new proactive risk analysis method. The Work Complexity Sharing and Exploration Spaces (EPECT in French) were tested for the first time in 2020 in the radiotherapy department of the Gustave Roussy Institute. The EPECT method makes it possible to anchor the analysis of risks incurred by patients in daily work situations and to involve the top management. The time required to involve the teams is comparable to or even less than that required for a conventional risk analysis. The cost/benefit ratio of the method seems to be advantageous because it provides access to numerous information on daily activities, which makes it possible to improve the safety of patient care in radiotherapy. The information gathered allows relevant actions to be envisaged at both the technical and organizational levels, and to be linked to departmental projects.

NEAR-MISS-INCIDENTS AS AN INDICATOR OF INJURY LEVEL AT THE WORKPLACES OF WORKERS WITH DISABILITIES

Employment of people with disabilities is an important socio-economic issue for every country. In the conditions of constant growth of the number of people with persistent disabilities, due to the increasing share of the population with chronic diseases, industrial and domestic injuries, military conflicts, etc., the problem of socio-economic safety of people with disabilities, increasing their welfare becomes relevant from year to year. The choice of workplace, as well as the necessary measures for its adaptation for worker with disability should be made taking into account the level of injury risk, which can be determined by certain statistical indicators: frequency, severity, etc. Thus, the development of effective management decisions to ensure safety of workers at the workplace, in particular with disabilities, is based on an appropriate information base, which can be formed using both traditional and innovative methods of data analysis. The use of near-miss incidents as indicators of danger at the workplace is the modern proactive method of studying the state of occupational injuries, which has significant advantages over traditional methods of analysis of occupational injuries. This approach is optimal when planning workplace safety measures for workers with disabilities, as it allows taking into account the functional limitations of different groups of workers, and thus - to develop effective solutions to prevent the growth of occupational injuries. This contributes of increasing the level of employment of vulnerable groups and solving a number of important socio-economic problems. The article describes the results of the study of near-miss incidents on the example of an enterprise in the machine-building industry in the context of comparing of data for workplaces of workers with disabilities and workers without persistent disorders of the functional state of the body. It is shown that the presence of disability among workers in most cases affects on the increasing of the frequency of near-miss incidents at the workplaces.

HEALTHCARE FAILURE MODE AND EFFECT ANALYSIS DESIGN FOR INDONESIA HOSPITAL LABORATORIES: A LITERATURE REVIEW

Background: Error rate in medical laboratories is very low. Only one error is identified every 330–1,000 events. The goal of laboratory services should outweigh patient safety in a well-structured manner. Healthcare Failure Mode and Effect Analysis (HFMEA) is a proactive preventive method for identifying and evaluating potential failure.Aims: This study identified factors affecting patient safety in hospital laboratories and described potential risk identification process using the HFMEA.Methods: This study was conducted between March-July 2020 and retrieved data from PubMed, Scopus, and Google Scholar. The data were generalized and extracted into Table 2 based on factors dealing with patient safety in hospital laboratories. This study performed a risk identification design based on the steps of HFMEA.Results: Out of 4,062 articles collected, only 8 articles between 2013–2020 were included for analysis. The highest error rate in laboratories occurred in the pre-analytic phase (49.2%–84.5%). The errors included clotted and inadequate specimen volume, and thus the specimens were rejected. Factors related to patient safety in laboratories were patient condition, laboratory staff performance (including training, negligence, and burnout), facilities, and accreditation.Conclusion: The HFMEA process used the result of hazard analysis with severity and probability criteria categorized into health sector. Decision tree analysis could determine the next step of the work process. The HFMEA must be adjusted to the equipment and technologies in each hospital laboratory. Leader’s commitment in monitoring and evaluation is required to maintain patient safety culture. More comprehensive data from Indonesian hospital laboratories are needed to generate more representative and applicable results.Keywords: error, HFMEA, laboratory, patient safety

Sphinx: A transport protocol for high-speed and lossy mobile networks

Modern mobile wireless networks have been demonstrated to be high-speed but lossy, while mobile applications have more strict requirements including reliability, low latency, goodput guarantee, bandwidth efficiency, and computation efficiency. Such a complicated combination of requirements and conditions in networks pushes the pressure to transport layer protocol design. We analyze and argue that few of existing network transport layer solutions are able to handle all these requirements. We design and implement Sphinx to satisfy the five requirements in high-speed and lossy networks. Sphinx has (1) a proactive coding-based method named semi-random LT codes for loss recovery, which estimates packet loss rate and adjusts the redundancy level accordingly, (2) a reactive retransmission method named Instantaneous Compensation Mechanism (ICM), which retransmits precisely and timely once decoding failure occurs, and (3) a parallel coding architecture, which speeds up coding process by leveraging multi-core, shared memory and kernel-bypass Data Plane Development Kit (DPDK). Prototype and evaluation show that Sphinx outperforms TCP schemes and other coding solutions significantly in microbenchmarks across all five requirements, and improves the performance of the video streaming application.

Aspergillus flavus and Aspergillus ochraceus inhibition and reduction of aflatoxins and ochratoxin A in maize by irradiation

Grains are susceptible to contamination by molds; some cause spoilage and others produce certain mycotoxins that cause a serious health threat to humans and animals. Aspergillus flavus and Aspergillus ochraceus and their mycotoxins, aflatoxins and ochratoxin A, are natural contaminants of various agricultural commodities. Control of these molds and their mycotoxins in food commodities is of utmost importance; therefore, the target of this research was to explore the effects of gamma irradiation doses on the growth of A. flavus and A. ochraceus in artificially inoculated yellow maize as well as on the production of aflatoxin B1, ochratoxin A, and the formation of toxins in maize. The irradiated dose of 6.0 kGy was found to completely inhibit the growth of the two molds, while a dose of 4.5 kGy reduced the production of their mycotoxins. Maximum degradation of the formed aflatoxins and ochratoxin A in maize occurred at 20 kGy, with best reduction rates of 40.1%, 33.3%, and 61.1% observed for aflatoxin B1, aflatoxin B2, and ochratoxin A, respectively. We recommend grains irradiation by gamma radiation at 6.0 kGy to decontaminate mycotoxin-producing molds before they produce mycotoxins. The study represents a proactive, efficient, and potent method for avoiding potential contamination of fungus during grains storage and transfer for one to two months.

SecureRx: A blockchain-based framework for an electronic prescription system with opioids tracking

ObjectivesThe proposed research aims at attaining several objectives. To start with, it assesses the limitations associated with the current electronic prescription systems. Accordingly, it proposes a proactive method for providing real-time monitoring to prevent overdose prescriptions. The proposed approach should ensure that health providers have access to a comprehensive dataset without state or geographical limitations. More importantly, the research aims to improve drug monitoring to enhance accountability and trust, which are central to promoting drug legitimacy. MethodsA blockchain-based framework for secure, interoperable, and efficient access to prescription records. The proposed solution, SecureRx, is a web-based application developed on the Ethereum blockchain and RxCheck hub. ResultsBased on the evaluation results, SecureRx is functional as it allows health providers to verify patient history and make informed decisions regarding whether to prescribe opioids. The proposed solution exhibits adequate scalability and handles transactions quickly when the number of nodes is reduced. It is also robust and secure, supports HIPAA compliance, and requires minimal operational expenses. ConclusionsThe safe utilization of medications, especially opioids, continues to be a major problem in the United States and around the world. Part of the reason for this is the inability of health providers to access health databases for patients. Current drug prescription systems are limited in terms of tracking the usage of prescription drugs.The SecureRx framework has highlighted the potential of the record-keeping technology to nurture effective and efficient sharing of information about prescriptions while ensuring the safety and privacy of the original sources of the data.

Investigating and designing an online psychosocial support platform for caregivers

Background: Healthcare professionals are prone to many psychosocial issues including job burnout, depression, and anxiety, which can affect the healthcare systems and societies. This has been intensified by the outbreak of the Covid-19 pandemic. Many of the caregivers' problems are due to insufficient psychosocial competence and lack of suitable training for basic skills such as self-awareness, rapport, empathy, compassion, reasoning, decision-making, etc. The main purpose of this study was to design a psychosocial service package for caregivers to help them throughout their lives. Methods: Data gathering was performed by conducting 2 focus groups, searching for studies to identify urgent and important healthcare needs, and finding the best way to address their necessities through a proactive and sustainable method. Results: The online healers' healing platform is designed with 3 general objectives. The first objective was to present practical and effective materials to support caregivers in order to improve their psychosocial competence. The second objective was to monitor the member's progress and measure the effectiveness of the program. Moreover, to make the members more active in training and their own health promotion, this platform leads and supports self-help groups for caregivers as the third purpose of this project. Furthermore, healers can be active in content development and community education in order to experience a more productive interaction within the communities of caregivers. © 2021, Vesnu Publications. All rights reserved.

Capacitor Commutation Method for MVDC Hybrid Circuit Breakers

The medium voltage DC (MVDC) type system can connect multiple terminals to a common MVDC bus, so it is possible to connect several renewable DC power sources to the common MVDC bus, but a DC circuit breaker is needed to isolate short circuit accidents that may occur in the MVDC bus. For this purpose, the concept of a hybrid DC circuit breaker that takes advantage of a low conduction loss contact type switch and an arcless-breaking semiconductor switch has been proposed. During break the hybrid switch, a dedicated current commutation device is required to temporarily bypass the load current flowing through the main switch into a semiconductor switch branch. Existing current commutation methods include a proactive method and a reverse current injection method by a LC (Inductor-capacitor) resonant circuit. This paper proposes a power circuit of a new MVDC hybrid circuit breaker using a low withstanding voltage capacitor branch for commutation and a sequence controller according to it, and verifies its operation through an experiment.

A proactive selection method for dynamic access points grouping in user-centric UDN

Network densification is a promising solution to fulfill network capacity requirement and transmission rate for beyond 5G and 6G wireless communications. Ultra-dense network (UDN) integrates heterogeneous network resources and coordinates technologies on quality of service controlling, to provide users with flexible service. However, dense deployment reduces coverage radius of the cell, resulting in an increase on handover frequency, which makes a serious impact on service continuity. In this paper, we propose a proactive selection method for dynamic access points grouping (DAPGing) in accordance with "user-centric" philosophy, which selects target Access Points (AP) and reduces handover times to ensure communication continuity. This method includes two criteria: 1) the user's sojourn time, which is determined by analyzing the AP coverage area; 2) neighbor relationship between APs, which is determined by coverage area and signal strength characteristics between neighboring APs. Therefore, candidate APs become the proactive selected ones to update the AP group. Stochastic geometry is used to build system model and performance metrics are analyzed, including AP group coverage probability and average update frequency. Experimental analysis shows that the proposed proactive selection method brings similar coverage probability to traditional handover method, while average update frequency is reduced more than 20% selection criteria.

Online and Proactive Vehicle Rerouting with Uppaal Stratego

Modern navigation systems warn the user of traffic jams ahead and suggest alternative routes. However, a lemming effect can cause the alternative routes also to become congested, as the system suggests the same route to all users. As such, in an attempt to optimize for the individual driver, the welfare of the traffic network is punished. In this paper we introduce an online and proactive method for collective rerouting recommendations based on real-time data and stochastic optimization. Our system periodically monitors the status of the network to identify potentially congested roads together with vehicles affected by them. The system then uses Uppaal Stratego to perform machine learning and approximate the best rerouting scenarios. As a proof of concept, we build a SUMO model of a representative traffic network. We perform exhaustive experiments considering different traffic loads and different traffic light controllers. Our results are promising, showing considerable improvement in travel times, queue lengths, and CO2 emissions.

Implementing biofeedback as a proactive coping strategy: Psychological and physiological effects on anticipatory stress

Anticipating a stressful situation involves psychophysiological reactions before the occurrence of the overt stress event. The current challenge in the stress domain is to characterize anticipatory stress reactions and how to effectively modulate them. The present study aimed to characterize the anticipation period and evaluate the benefits of a heart-rate variability biofeedback (BFB) intervention designed to manage anticipatory stress. Healthy participants were exposed to an anticipation stress period (15 min) during which they either practised BFB (stress + bfb, n = 15) or watched a neutral video (stress + video, n = 14). Anticipatory stress was effectively induced by the Trier Social Anticipatory Stress (TSAS) protocol, specifically designed for this study. Control participants, without anticipation stress, practised BFB for an equivalent time (ctrl + bfb, n = 15). Subsequently, all participants performed a set of cognitive tasks assessing executive functions. Heart-rate variability (cardiac coherence, standard deviation of the R–R intervals, root mean square of successive difference measure) and the evolution of the perceived psychological state were measured during the anticipation period. Self-reported judgements of how the intervention influenced stress and performance were further assessed. The main result showed that BFB is a relevant proactive stress-coping method. Compared with the stress + video group, participants who practised BFB attained higher cardiac coherence scores. Post-intervention self-reported measures revealed that BFB contributed to reduce psychological stress and increase perceived levels of performance. Together, these findings provide practical guidelines for examining the stress anticipation period by means of the TSAS protocol.

A Novel Approach Based on Fluid Dynamics for On-Site Safety Assessment

Construction safety assessment is a major component of safety management in construction projects. However, thus far, most assessment studies have focused on either cross-sectional or longitudinal performance. This study aims to develop a proactive and preliminary safety assessment method using a fluid dynamics (FD) approach by integrating longitudinal contractor performance with cross-sectional safety conditions. To this end, an FD framework was first developed using three processes: (a) identifying the connections between FD principles and safety analogs, (b) incorporating the Darcy-Weisbach equation into the framework, and (c) modifying FD equations for construction safety assessment. Subsequently, two case studies were investigated, and the results obtained were compared and verified with one existing assessment results. The results indicate that the state of safety at a construction site incorporates both the occurrence of hazards and conservation of energy. Thus, the proposed FD approach can be used to preliminarily assess and predict safety conditions by combining safety-related measures with the dynamic characteristics of construction processes. The approach considers a comprehensive range of indicators and parameters, which enables the comparison of safety performance between projects or assessment periods by independently changing parameters.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

An Online Proactive CTR Monitoring Method for Optocoupler in Automotive Auxiliary Converter

Automotive auxiliary converter empowers the electronic system, the failure of which will affect the system performance. As a critical component in the feedback loop of the converter, real-time health monitoring for optocoupler is of great importance. The degraded optocoupler mainly shows a falling current transfer ratio (CTR). This article proposes an online proactive CTR monitoring method for optocoupler via voltage measurement of critical nodes. The monitored CTR can indicate the health condition of the optocoupler. First, the principle of a boost- LLC converter is analyzed in detail. Then, the influences of degraded CTR on the feedback loop are derived. An online proactive CTR monitoring scheme is introduced. The realization of voltage measurement, signal filtering, wireless communication, and time synchronization are in two independent embedded systems. CTR will be calculated in the local server and be stored in the local database. The proposed method can monitor CTR for both unaged and aged optocouplers. Effects of switching frequency and ambient temperature are also incorporated for online CTR monitoring. Experimental results validate the superiority of this online proactive monitoring method. The method could be extended and further evaluate the health condition of the overall automotive auxiliary converter.

Modified Optical Burst Switching (OBS) Based Edge Node Architecture Using Real-Time Scheduling Techniques

Contention is the main problem in optical burst switching (OBS) systems that lead to loss more burst by dropping the contended burst. To deal with this problem, we propose a new proactive method which is based on the integration of Real-Time Scheduling Techniques: Earliest-deadline-first (EDF) and round-robin (RR). Based on this consideration, this paper proposes a series of novel coherent interdependence methods to propose a novel OBS edge node Architecture: (i) An adaptive priority system to obtain QoS guarantees, (ii) Admission control and bandwidth distribution to maximize the link throughput. The proposed technique classifies the incoming data packets into two categories: granted service (GS) for high priority and best effort (BE) for low priority. The RR technique is used for scheduling different BE and GS packets when they have the same deadlines while the EDF is used to manage GS packets with different deadlines. In addition, the admission and bandwidth assignment policies vary in function of traffic characteristics. For GS traffic, only the bandwidth used is needed, while the remaining bandwidth is assigned to best effort traffic. With the EDF and RR scheduling algorithms, the proposed edge node design obtains a brand new architecture. The proposed OBS architecture has been tested for dynamic traffic in which both BE and GS traffic arrives according to a Poisson distribution for different scenarios based on traffic distribution and switch resources reservation. In scenario#1, 90% GS traffic and 10% BE traffic and reserve 7% of the switch resources for BE traffic are considered. Therefore, the proposed architecture provides a higher grade of service to the end-users with the guaranteed service level of agreements than the existing architecture that does not implement resource reservation protocols. In addition, the modified OBS-edge node is designed and implemented on FPGA Virtex-11C2V40. The hardware performance analysis shows that the network dimension is a key factor in latency measurement. A 2×2 network dimension can serve approximately 230 Mbps throughput and approximately 400 Mbps in the case when the number of flits (segment of burst) is twice.