Additional Delay Research Articles

Metamorphic Testing and Serverless Computing: A Basic Architecture

Automated testing of complex software systems can be a challenging task, and today there are a large number of methods for its implementation. One such method is metamorphic testing, which effectively solves the problems of usual methods and is gaining popularity. However, performing metamorphic tests can take a long time, so the question arises of their distributed running, including in the cloud. Thus, the authors of this study considered the designing of a cloud serverless architecture of software for metamorphic testing. The serverless architecture for metamorphic testing is proposed, which is based on the composition of the entire system from 5 individual components: models, data generator, software artifact under test, metamorphic relations, and serverless functions. For each of the main possible types of software artifacts, the possibility of using a serverless architecture for metamorphic testing is considered. The developed architecture is presented in the form of component, deployment, and sequence diagrams. The use of the proposed architecture in practice is shown by the example of testing two software artifacts – a class library and a web application. Performance measurements have shown that despite the additional network delay when running one test, the performance of all tests in general in the case of a serverless architecture is closer to local startup and will be faster with increasing complexity and number of tests.

Incidental Findings in Low-Dose Full-Body Imaging Taken for Total Hip and Knee Arthroplasty

AbstractDetection of incidental findings (IFs) in preoperative imaging for total joint arthroplasty (TJA) patients may lead to additional testing, cost, and surgical delay. The purpose of this study is to identify the incidence and clinical significance of IFs on preoperative low-dose full-body imaging (LFI) in the total joint population. Furthermore, we aim to determine whether these findings lead to additional imaging, delay, or cancelation in TJA cases. A retrospective review of 2,183 patients planned for total hip arthroplasty or total knee arthroplasty, who had preoperative LFI imaging obtained from 2013 to 2020 was conducted. LFI radiographic reports were reviewed to identify any flagged radiographs. Flagged IFs were divided into thoracic, spinal, lower limb, and abdominal IFs. Charts were reviewed to identify if any additional imaging was completed, the case was delayed or canceled, or a new pathology requiring continued follow-up or treatment was discovered. Of the 2,183 LFI images taken prior to TJA, 41 cases qualified as having IF (1.9%, 41/2,183), 18 thoracic, 11 spinal, 11 lower limb, and 1 abdominal. Overall, 15 (0.7%, 15/2,183) IF cases were clinically determined to require at least one additional form of imaging. Two had a significant finding; a schwannoma near the distal femoral shaft and a squamous cell carcinoma of the lung. Three TJA cases were delayed, and no cases were cancelled due to the IFs. The possible increase in IF detection by preoperative LFI does not greatly impact TJA case completion. Though rare, providers should pay close attention as clinically significant IFs can be discovered and lead to early treatment. This was a retrospective cohort study with level III evidence.

The Effects of Vehicle-to-Infrastructure Communication Reliability on Performance of Signalized Intersection Traffic Control

Vehicle-to-infrastructure communications can inform an intersection controller about the location and speed of connected vehicles. Recently, the design of adaptive intersection control algorithms that utilize this information received substantial research attention. These studies typically assume perfect communications. This study explores the possible effects of a temporal decrease in the reliability of the communication channel, on the intersection throughput. Road traffic and DSRC-VANET communications are modelled by integrating traffic and communication simulation tools (Vissim and OMNeT++, respectively). Simulations of scenarios with challenging, but realistic communication distortions conditions show significantly larger average delays to vehicles compared to scenarios with perfect communication conditions. These additional delays are largely independent of whether all or only some of the intersection approaches are affected by the communication distortions. Furthermore, delays do not increase uniformly on all signal groups. They may even decrease for some, which causes unfair allocation of green times. The control may be corrected for the lost communications using the information received in previous time intervals and simple assumptions about the vehicle movements. This correction decreases delays in all scenarios both for isolated and connected intersections. It performs similarly to the case with perfect communications when the communication distortions are distributed uniformly among all intersection approaches. Overall, the results demonstrate that the impact of the communication distortions should be considered in the design of the adaptive intersection control algorithms.

Towards Optimal Parallelism-Aware Service Chaining and Embedding

Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.

DOLL

Most large-scale ML implementations scale to large amounts of data by utilizing multiple servers or virtual machines (VMs) that iteratively compute model updates on local data that are periodically synchronized. Due to the complexity of managing the resulting computing infrastructure, many companies run their ML jobs on external cloud providers' servers. However, cloud resources can be expensive, particularly for large ML jobs with long runtimes. A particularly popular method to limit the costs of training ML jobs is to utilize preemptible cloud instances. These may be interrupted at the cloud provider's discretion, but they are significantly (up to 90%) cheaper than conventional on-demand instances. Most studies of these ML methods, however, assume the availability of large datasets at training time. In practice, training data may arrive at irregular intervals and models may be trained online as new data samples arrive, e.g., when monitoring data from IoT sensors. While some software frameworks like Apache Kafka can feed online data arrivals to ML algorithms, they provide little insight into the resulting costs of ML training. We extend prior work on provisioning preemptible instances to analyze available pools of data in order to run online ML on incoming datastreams, which presents new challenges due to the need to carefully handle data arrivals. We design, analyze, and optimize DOLL, which to the best of our knowledge is the first system that provides provable performance guarantees for Distributed OnLine Learning over preemptible instances. Research Challenges and Our Contributions: When pools of data are readily available, the bottleneck to distributed ML training often lies in the time required for each VM to compute its model updates. In our scenario, however, the arrival rate of incoming data may also bottleneck data processing. An intuitive strategy would then be for each VM to process each data point as it arrives. However, since arrivals at different VMs may not be coordinated, synchronizing the model parameters at each VM between data arrivals may introduce additional delays, while asynchronous SGD methods can lead to slow convergence [1]. DOLL uses a batching and grouping process to limit the synchronization delay, which naturally realizes traditional mini-batch SGD so as to provide provable model convergence guarantees. Handling online data arrivals becomes particularly challenging when we use preemptible instances to compute model updates. Existing methods utilizing preemptible instances for ML jobs largely focus on mitigating training interruptions [2] and their effects on model convergence [3]. When used on datastreams, we face an additional challenge of interruptions pausing the data arrival process, which impedes the rate at which we can compute model updates and thus model convergence. Thus, one should ensure that preemptions do not happen "too often," e.g., by computing some updates on on-demand instances. Our work is the first to optimize the number of preemptible VMs used and demonstrate that we can meet ML convergence guarantees.

BDS/GPS Multi-Baseline Relative Positioning for Deformation Monitoring

The single-baseline solution (SBS) model has been widely adopted by the existing global navigation satellite system (GNSS) deformation monitoring systems due to its theoretical simplicity and ease of implementation. However, the SBS model neglects the mathematical correlation between baselines, and the accuracy and reliability can be degraded for baselines with long length, large height difference or frequent satellite signal occlusion. When monitoring large-area ground settlement or long-spanned linear objects such as bridges and railroads, multiple reference stations are frequently utilized, which can be exploited to improve the monitoring performance. Therefore, this paper evaluates the multi-baseline solution (MBS) model, and constrained-MBS (CMBS) model that has a prior constraint of the spatial-correlated tropospheric delay. The reliability and validity of the MBS model are verified using GPS/BDS datasets from ground settlement deformation monitoring with a baseline length of about 20 km and a height difference of about 200 m. Numerical results show that, compared with the SBS model, the MBS model can reduce the positioning standard deviation (STD) and root-mean-squared (RMS) errors by up to (47.4/51.3/66.2%) and (56.9/60.4/58.4%) in the north/east/up components, respectively. Moreover, the combined GPS/BDS positioning performance for the MBS model outperforms the GPS-only and BDS-only positioning models, with an average accuracy improvement of about 13.8 and 25.8%, with the highest accuracy improvement of about 41.6 and 43.8%, respectively. With the additional tropospheric delay constraint, the CMBS model improves the monitoring precision in the up direction by about 45.0%.

Statistical model calculation of the compound nuclear fission timescale in fusion-fission reactions

In the decay of an excited heavy nucleus, both the fission or evaporation residue (ER) cross sections and the fission time are determined by the competition between the various evaporation channels and fission. Statistical model (SM) calculations for several heavy-ion-induced fusion-fission reactions are presented which reproduce the fission or ER excitation functions. The corresponding calculated fission times are found to depend upon the compound nuclei and their excitation energies. The multichance fission probabilities and the corresponding fission times are also obtained, and they are shown to depend sensitively on the shell correction energies and the neutron separation energies of the compound nuclei involved. The experimental neutron multiplicities are reproduced by adjusting the saddle-to-scission transition time interval. An additional delay time for compound nuclear formation is found necessary to fit the experimental neutron multiplicities for a system with higher entrance channel mass symmetry.

Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I)

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance.

Mitigation strategy of cascading failures in urban traffic congestion based on complex networks

Urban road traffic network is becoming increasingly complex. The increasing travel demand has exceeded facility supply of transportation system. Thus, a scientific way of transportation can solve the problem of traffic congestion. If some central intersections are congested, the road network could easily lose its travel efficiency and further lead to cascading failure. In this regard, from the viewpoint of complex networks, this paper provides a cascading failure mitigation strategy for the urban road traffic network. A load redistribution strategy is represented which can redistribute congestion load reasonably. Random and intentional failures are simulated with MATLAB on Barabási and Albert (BA) scale-free network. The indices related to complex network of different methods are compared on BA network and part of the real road network in Baoding City. The load redistribution strategy in this paper is implemented combined with user equilibrium assignment, then the travel time related to urban road network is compared with another method on the Sioux Falls network. The simulation results suggest that when the capacity parameter reaches the threshold, further increase has little effect on the mitigation time. Comparisons of other mitigation approaches verify the effectiveness of our approach in lessening cascading failure caused by traffic congestion. The increase in total network travel time and additional delay by reason of load redistribution is acceptable.

Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication

Diverse IoT applications, such as unmanned driving, intelligent video, unmanned working face, industrial control, and intelligent robot inspection, are the key technologies in the field of intelligent mines. In order to fully meet the requirements of underground IoT systems of high bandwidth, low latency, and massive connections, it is necessary to study 5G technologies suitable for underground environments to achieve effective deployment in mines. In key areas, such as main transport roadways, fully mechanized mining faces, and underground substations, both spurs and crosstalk in the frequency domain are the dominant factors affecting the stability and reliability of 5G signals. For the purpose of improving the performance of mine 5G, a fusion anti-interference scheme is designed here. Based on a deep complex network and blind source separation, periodic monitoring and filtering suppression of signal interference can be achieved. The test results show that the frequency domain spurs’ suppression capability of the proposed method is 20% higher than that of the traditional equalization method. For frequency domain crosstalk, 90% interference elimination could be achieved by the proposed method without additional delays when compared with the conventional blind source separation. The high-bandwidth and low-latency characteristics of 5G communication can be guaranteed by this method.

Results on passivity and design of passive controller for fuzzy neural networks with additive time-varying delays

Results on passivity and design of passive controller for fuzzy neural networks with additive time-varying delays

Fuzzy control of a landing gear system with oleo-pneumatic shock absorber to reduce aircraft vibrations by landing impacts

PurposeThe purpose of this paper is to control a landing gear system with an oleo-pneumatic shock absorber with the fuzzy controller.Design/methodology/approachThe landing gear system with an oleo-pneumatic shock absorber is modeled mathematically. A fuzzy controller is designed for reducing aircraft vibrations. Stroke velocity and main mass velocity parameters were used to decide variable gas pressure with the fuzzy controller.FindingsThe fuzzy controller, designed according to stroke velocity and main mass velocity, reduces aircraft vibrations by the landing impacts. The controller can provide strong robustness because it shows similar good performance for different descent speeds.Research limitations/implicationsThis study was carried out through simulations in a computer environment and has not been experimentally tested in a real environment. In addition, signal and measurement delays are not taken into account. In future models, the effects of these signal delays can be added, and the controller can be tested on a real model.Originality/valueIn this study, to the best of the authors’ knowledge, for the first time, the gas pressure for the landing gear system using an oleo-pneumatic shock absorber was controlled by a fuzzy controller that adjusts the stroke velocity and the main mass velocity. Although the oleo-pneumatic shock absorber model contains high nonlinearities, the designed fuzzy controller gave successful results as robust.

Stabilized Time Transfer via a 1000-km Optical Fiber Link Using High-Precision Delay Compensation System

Variations in optical fiber length and refractive index are induced by environmental perturbation, resulting in an additional dynamic propagation delay in fiber-based time synchronization systems, which deteriorate their transfer stability. This disadvantage can be significantly reduced by transmitting the time signal in both directions through fiber and constructing a feedback loop to compensate the propagation delay at the remote end of the link. This paper proposes an analog-digital hybrid proportional integral derivative (PID) control compensation system based on the time-frequency phase-locked loop (TF-PLL). The system is designed to keep the merits of wide servo bandwidth, servo accuracy, and a large dynamic delay compensation range up to 1 s, which is much greater than that reported in previous studies. For proving the validity of this proposed scheme, a self-developed optical fiber time synchronization equipment based on the delay compensation system is applied. The delay compensation system is used on a 1100-km long laboratory optical fiber, and the results show that the time synchronization stability in terms of time deviation (TDEV) is less than 5.92 ps/1 s and 2.56 ps/10,000 s. After successful laboratory evaluation, the proposed system is installed on a real 988.48-km line between the Xi’an Lintong branch of the National Time Service Center (NTSC) and Linfen City, Shanxi Province, realizing the time synchronization of 10 stations along the optical fiber link. The experimental results in the 988.48-km link illustrate that the measured time difference with a peak-to-peak value of 176 ps, the standard deviation of 19.3 ps, and a TDEV of less than 10.49 ps/1 s and 2.31 ps/40,000 s is achieved. The high-precision time delay compensation system proposed in this paper is simple, reliable, and accurate; has a wide range of compensation; and opens up a feasible scheme for providing synchronized time signals to multiple users over the long-distance field optical fiber networks.

Differentiation of Apricots of Different Geographic Origin in Central and Southern Europe by Applying 87Sr/86Sr Analysis: Potential and Limitations.

Consumers prefer food commodities of certain origins over the same products of other provenances and are willing to pay higher prices for them. Thus, it is possible to increase profit simply by giving an incorrect geographic origin to a product. To effectively control the declared geographic origin of food, the product itself has to be investigated to discover whether it actually originates from the declared origin, or if it has been mislabeled. Conventionally, control of a geographic origin is conducted by stable isotope analysis of the main elements, which has proven to be successful in numerous cases, but often reference data have to be produced anew for every harvest to control, resulting in additional costs and delays. Applying entirely geogenic parameters for the control of provenance requires reference data to be produced only once. As they do not vary between years and harvests, they can often be used for different (food) commodities. Here, we investigate whether the geographic origin of apricot samples can be controlled by their 87Sr/86Sr ratios measured by TIMS. The results show that Slovak and Hungarian apricots can be differentiated from the Wachau apricots, a well-known regional Austrian brand, and those from other regions in Austria, even though the differentiation from the latter is only partial. 87Sr/86Sr investigations can be a very potent tool; however, its success depends on the exact question that needs to be answered.

An IoV-PBFT Consensus-Based Blockchain for Collaborative Congestion Avoidance and Simulation Test

This paper proposes a novel Internet of Vehicles (IoV) practical Byzantine fault tolerance (PBFT) consensus mechanism suitable for the distributed vehicle-to-vehicle (V2V) scenario and the V2V collaborative congestion avoidance mechanism based on blockchain. Compared with the traditional PBFT consensus mechanism, the proposed IoV-PBFT consensus mechanism has been improved in three aspects. (1) The authenticated vehicles are divided into three types including common nodes, production nodes, and verification nodes. The vehicles in the same congestion road section are used as the consensus participation nodes, which are divided into the production node and verification node. Since the vehicles in this section are moving slowly or even stopping and the number of newly added and departing nodes are less, the identity of the previous nodes is verified before each round of consensus, which ensures the dynamic tolerance with less additional delay. (2) The verification nodes are grouped based on path loss, and the nodes with good channel quality are selected as the leader nodes. (3) The main node in traditional PBFT is omitted, and the two-stage process is used instead of the traditional three-stage process, which effectively reduces the consensus delay and communication complexity. The theoretical derivation and simulation results show that the proposed IoV-PBFT has greatly improved the V2V link quality compared with traditional PBFT, and the success ratio of links between nodes can reach more than 93%, which has met the requirements of establishing blockchain in IoV.

Building Information Modeling Using Constraint Logic Programming

AbstractBuilding Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety, to name just a few. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore’s CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information about building objects, a separate, ad-hoc model pre-processing is required to determine, for example, evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.

Population pharmacokinetic of paracetamol and atorvastatin with co-administration of semaglutide.

Semaglutide is a glucagon‐like‐peptide‐1 (GLP‐1) analogue marketed for once‐weekly subcutaneous administration for type 2 diabetes mellitus. Like other long‐acting GLP‐1 analogues, semaglutide reduces gastric emptying and, potentially, alters the rate of absorption of orally co‐administered drugs. The objective of the current analysis was to evaluate the effects on the gastric emptying rate caused by semaglutide on pharmacokinetic model parameters of paracetamol and atorvastatin in healthy subjects. Non‐linear mixed effect modeling was used to estimate population pharmacokinetic model parameters of paracetamol and atorvastatin after single doses with or without semaglutide. The absorption rate (ka) of paracetamol decreased by 53% when co‐administered with semaglutide. For atorvastatin, ka and transit compartment rate (ktr) decreased by 72% and 91%, respectively. Thus, gastric emptying, measured as T50, i.e., drug disappearance from the absorption compartments, showed an additional 5‐min delay for paracetamol and a 67‐min delay for atorvastatin when co‐administered with semaglutide. Semaglutide affected pharmacokinetic model parameters of paracetamol and atorvastatin, and minor quantitative differences in gastric emptying between placebo vs. semaglutide administration were observed. However, these effects of semaglutide were considered not to be of clinical relevance.

Online model-based reinforcement learning for decision-making in long distance routes

In road transportation, long-distance routes require scheduled driving times, breaks, and rest periods, in compliance with the regulations on working conditions for truck drivers, while ensuring goods are delivered within the time windows of each customer. However, routes are subject to uncertain travel and service times, and incidents may cause additional delays, making predefined schedules ineffective in many real-life situations. This paper presents a reinforcement learning (RL) algorithm capable of making en-route decisions regarding driving times, breaks, and rest periods, under uncertain conditions. Our proposal aims at maximizing the likelihood of on-time delivery while complying with drivers’ work regulations. We use an online model-based RL strategy that needs no prior training and is more flexible than model-free RL approaches, where the agent must be trained offline before making online decisions. Our proposal combines model predictive control with a rollout strategy and Monte Carlo tree search. At each decision stage, our algorithm anticipates the consequences of all the possible decisions in a number of future stages (the lookahead horizon), and then uses a base policy to generate a sequence of decisions beyond the lookahead horizon. This base policy could be, for example, a set of decision rules based on the experience and expertise of the transportation company covering the routes. Our numerical results show that the policy obtained using our algorithm outperforms not only the base policy (up to 83%), but also a policy obtained offline using deep Q networks (DQN), a state-of-the-art, model-free RL algorithm.

Physical geography and traffic delays: Evidence from a major coastal city

Traffic congestion is a major environmental and social problem whose causes include urban sprawl, imbalanced home-job distributions, increased car ownership, and lack of public transportation. We focus on a relatively understudied factor: the existence of geographic barriers. We study traffic times and flows in the Boston metropolitan area, a major coastal city with substantial shape non-convexities. We show that natural barriers not only cause additional delays to the trips affected directly, but also worsen downtown congestion for everyone. Additionally, commuter flows between places separated by barriers decrease, generating additional traffic elsewhere. We also find that places next to geographic obstacles suffer from higher risks of congestion, due to their lower traffic-diffusion ability. Policymakers may consider specific solutions for congestion arising from constraining physical geographies.

A resilient bi‐level control strategy for power sharing and voltage balancing in bipolar DC microgrids

In the three-level DC-DC converter-based bipolar DC microgrids, accurate power sharing among distributed generators and voltage balancing between the positive and negative poles are two main control objectives. This article presents a resilient bi-level control strategy which simultaneously satisfies both aforementioned objectives. The proposed control structure is a combination of a distributed consensus-based cooperative method and a washout filter-based method. In the first level, a distributed consensus-based cooperative control method with considering time delays and communication noises is developed to realize proportional power sharing. At this level, DGs operate collaboratively using only neighbour-to-neighbour feedback over a sparse communication network, even if the communication network is susceptible to additive noise and time delay. In the second level, a decentralized control method is employed to restore the voltage of each pole of a bipolar DC microgrid to the desired value. The second level, which works without any communication links and additional control loops, utilizes a dynamic feedback incorporating washout filters. The whole proposed control strategy is robust against a single point of link-failure and resilient against the communication time delays and noises as well. Comprehensive simulation studies are carried out in PLECS software to verify the effectiveness of the proposed control scheme.