Lower Execution Costs Research Articles

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

#1988 VExUS score as a method to guide diuretic treatment in CKD patients with congestive nephropathy by right heart failure

Abstract Background and Aims Congestive nephropathy is a kidney dysfunction caused by the impact of elevated venous pressures on renal hemodynamics and most of all it is due to right heart failure. Venous congestion can lead to a circulation vicious hormonal activation, increased intra-abdominal pressure, excessive renal tubular reabsorption of sodium and volume overload. In CKD with right heart failure, management of the patient is difficult because may be hard to identify a condition of fluid overload and when to stop intravenous diuretic treatment. Inferior caval vein (ICV) diameter and ICV index (reduction of diameter with respiration) have been used to identify fluid overload and guide diuretic treatment. However, in CKD patients with heart failure the ICV may not change rapidly and could overestimate the need of intravenous diuretics. This is why VExUS has been useful in both diagnosis and monitoring therapeutic. The VExUS method is a new approach, documented in the literature for the first time once in 2020. In this study we report the usefulness of the VExUS (Venous Excess Ultrasound Score) method for the diagnosis of overload volume and venous congestion and in setting an adequate therapy. It consists of Doppler evaluation of the abdominal veins: hepatic, portal and intrarenal. In fact, the acute renal failure, in the patients studied, is to be attributed in the first instance to venous congestion rather than underfilling, resulting from decompensation and therefore requiring diuretic therapy. Method This retrospective study was conducted in CKD patients (stages 3-4) with acute kidney injury (creatinine level: 6.3-2.6 mg/dl) due to right heart failure admitted at the U.O.C. Nephrology at the S. Maria della Pietà Hospital in Nola and UOC Nephrology of Univ. of Campania. The VExUS method defines 3 degrees (score 0-3) of congestion based on the severity of the anomalies on hepatic, portal and intrarenal venous Doppler. All patients have been treated with diuretics i.v. based on a VEXUS score>1, and then returned to oral treatment when VEXUS score returned to 0. VExUS assessment was performed at admission, repeated every 48 hours and the last score has been considered here (on average 1 week). The relationship between change in VExUS score and ICV index vs creatinine levels was studied by correlation analysis. Lung POCUS (point of care pulmonary ultrasonography) has also been used for all patients to identify a possible contemporary overload caused by LV failure. Results Thirty patients have been included. The use of diuretics reduced the VExUS score and the ICV diameter. However, diuretic treatment changed the ICV diameter by a smaller extent (−30%) compared to the VExUS score (−80%), and one patient showed clear reduction of VExUS score, without any modification of the ICV diameter, suggesting that the latter is a slower indicator of recovery compared to VExUS. Indeed, the change of VExUS score was highly correlated to the improvement of creatinine levels (Pearson's correlation 0.837, p < 0.001). The change of IVC diameter explained in comparison a smaller amount of variability of the creatinine improvement (Pearson's correlation 0.65, p = 0.008). Conclusion VExUS is a promising and reliable tool in the management of congestive nephropathy in patients with CKD and right heart failure. VExUS is superior to VCI diameter and should be used routinely, along with clinical, laboratory, and other ultrasound parameters, because of its usefulness, repeatability, low cost and ease of execution.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

COPS: An improved information retrieval-based bug localization technique using context-aware program simplification

Information Retrieval Based Bug Localization (IRBL) techniques are well suited for large-scale software debugging with fewer external dependencies and lower execution costs. However, existing IRBL techniques have several challenges, including localization granularity and applicability. First, existing IRBL techniques have not yet achieved statement-level bug localization. Second, almost all studies are limited to Java-based projects, while their effectiveness for other popular programming languages (e.g., Python) is unknown. The reason for these deficiencies is that existing IRBL techniques mainly rely on conventional NLP techniques to analyze the bug reports and have not yet fully utilized the stack traces attached to the bug reports. To improve the IRBL technique, we propose a context-aware program simplification technique – COPS – that can localize defective statements in suspicious files by analyzing the stack traces in bug reports, enabling statement-level bug localization for Python-based projects. Our experiment is based on 948 bug reports, and the results show that COPS can effectively localize buggy statements. First, compared to the original stack traces, Top@10 is improved by 102.6%, MAP@10 by 56.2%, and MRR@10 by 95.6%. We found that actual buggy code entities are more likely to appear in the first five frames of the stack trace. Second, COPS can achieve equally good localization performance compared to state-of-the-art statement-level bug localization techniques and achieve 92% buggy statement coverage with a full-scope search. Finally, experiments found that the stack trace’s first two-thirds of information is more conducive to localizing buggy statements.

Characterization with geoelectrical methods of fissural porosity and flow pattern in physical models of fractured rock masses

Understanding water flow through discontinuities in rock masses is essential for ensuring the safety of engineering works, understanding the transport of contaminants, and directing the prospection and production of water in fractured aquifers. Investigation methods such as boreholes are expensive and not always effective. An alternative can be indirect investigation with electrical geophysical methods, such as Electrical Resistivity (ER) and Streaming Potential (SP), owing to their technical feasibility, low cost, and speed of execution. Although well established, doubts remain about these surveys owing to field limitations. In this sense, simulations in reduced physical models, where the internal and boundary conditions tend to be known, are ideal for testing these surveys. In this study, a rock mass model was developed with vertical fracturing patterns covered by soil; the entire set was saturated, thus reproducing the conditions common to areas of the crystalline basement with a tropical climate. With the use of ER and SP, the objective was to map the preferential flow directions with and without deep tube well influences and estimate fissure porosity. ER results showed an efficiency of 85% in the estimation of porosity, clearly distinguishing the main directions of anisotropy by the fractures. With SP, it was possible to identify flow directions through the fractures and define the geometry of the depression cone caused by pumping a deep tube well. Overall, these findings demonstrate the feasibility of physical models for understanding media with fissural porosity.

Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-Learning Environment

To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving the accuracy of performance prediction. A novel approach via neutrosophic cognitive diagnosis is proposed to predict student performance in a personalized e-learning environment. This approach innovatively employs the neutrosophic set (NS) theory to comprehensively measure the students' cognitive status on knowledge concepts from three features, i.e., understanding level, degree of misunderstanding, and uncertainty. A new measurement method of NS similarity is used to identify top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> neighboring students for one student. Then, we predict a student's performance on exercises in future exams by integrating a neighborhood-based collaborative filtering algorithm with a probabilistic matrix factorization method to address the data sparsity problem. Finally, the experiments based on real-world datasets demonstrate that the proposed approach provides higher prediction accuracy than the existing ones with a low execution cost.

Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates

Manufacturing tolerances and uncertainties concerning material parameters, e.g., operating conditions or substrate permittivity are detrimental to characteristics of microwave components. The knowledge of relations between acceptable parameter deviations (not leading to violation of design specifications) and the nominal performance (not considering uncertainties), and is therefore indispensable. This paper proposes a multi-objective optimization technique of microwave components with tolerance analysis. The goal is to identify a set of trade-off designs: nominal performance versus robustness (quantified by the maximum input tolerance values that allow for achieving 100-percent fabrication yield). Our approach exploits knowledge-driven regression predictors rendered using characteristic points (features) of the component’s response for a rapid evaluation of statistical performance figures, along with trust-region algorithm to enable low execution cost as well as convergence. The proposed methodology is verified with the use of three microstrip circuits, a broadband filter, and two branch-line couplers (a single- and a dual-band one). It is demonstrated that a Pareto set w.r.t. nominal performance and robustness objectives can be produced using only 40 to 60 EM simulations of the respective structure (per design). Reliability of the proposed algorithm is corroborated with the use of EM-based Monte Carlo simulation.

Banning dark pools: Venue selection and investor trading costs

We analyze the relation between transaction costs and venue choice using proprietary transaction-level data from institutional trade executions in the U.K. equity market. We show that, for a given investor, a higher share of dark trading (midpoint dark pools) is associated with lower execution costs. In the context of a recent ban on dark trading, we provide evidence consistent with migration towards substitute trading venues such as periodic auctions, which has mitigated adverse effects on trading costs for large investors. We also provide micro-evidence on Menkveld et al.’s (2017) pecking order theory of venue choice over the life-cycle of large parent orders.

IoT-RRHM: Provably secure IoT-based real-time remote healthcare monitoring framework

We have designed an IoT-based real-time remote healthcare monitoring (IoT-RRHM) framework to monitor the healthcare conditions of a patient who lives in a remote location. While monitoring the health conditions of a patient remotely via the Internet, the privacy and security of the patient data may be breached through eavesdropping, masquerading, fabrication, and replaying. The proposed IoT-RRHM framework provides health data security and privacy using elliptic curve cryptography and collision-resistant one-way hash function. It also ensures low execution costs compared to state-of-the-art schemes. The proposed IoT-RRHM framework is provably secure in the random oracle model. The AVISPA simulation results, confirm that the proposed IoT-RRHM framework can withstand the known passive and active attacks. The proposed IoT-RRHM is simulated in an IoT environment that used LM35 and MAX30100 sensor devices, Amica ESP 8266 NodeMCU micro-controller device, Raspberry Pi3, and ACS ACR38U-I1 38U contact smartcard reader/writer device for real-time implementation. This implementation shows that the proposed IoT-RRHM framework is feasible in an IoT-based healthcare environment.

A railway track reconstruction method using robotic vision on a mobile manipulator: A proposed strategy

Autonomous robot integration in railways infrastructure maintenance accelerates the digitization and intelligence of infrastructure survey & maintenance, providing high-efficiency and low-cost execution. This paper proposes a health assessment based on 3D reconstruction technology for railway track maintenance using a mobile robotic sensing platform. By combining multiple sensing and taking advantage of a robotic manipulator, a digital model of the target track components is built by a robot-actuated vision system which provides better 3D structural and surface condition reconstruction. Global geo-location and surrounding laser scanning are integrated to reinforce the digital completeness of the model for intelligent management. The new method consists of the following steps: First, according to scheduled maintenance tasks, a Robotics Inspection and Repair System (RIRS) navigates to the task location and uses the onboard depth camera for positioning. Then robot-mounted vision system is guided with an automated trajectory to build the 3D reconstruction of the track or repair object using the vision modeling technique. Finally, the 3D reconstructed model is fused with surrounding mapping of depth vision and Lidar scanning. Both laboratory tests and a realistic track test validated the feasibility of the proposed method by creating an accurate 3D reconstructed model. The modeled rail steel section size is quantitively compared with the ground truth in dimension, demonstrating good accuracy with a size error of less than 0.3 cm. The main contribution includes: (1) unmanned automatic 3D reconstruction by a robotic mobile manipulator, (2) the technique trims the reconstruction details & data to the specific maintenance goal or components, which supports the infrastructure maintenance towards the high-detailed & target-oriented digital management. This combination strategy of robotic automation and sensor fusion lies down a promising foundation for automated digital twin establishment for railway maintenance with autonomous RIRS, and upgrades technology readiness and digital intelligence for maintenance management

Enhancing QoS with Resource Optimization Technique Based on Harmony Search in Cloud Environment

In cloud environment, enhancing QoS is a challenging task to solve the multi objective problems. Effective load balancing and scheduling is a solution to enhance QoS. Job scheduling is a NP complete problem and is difficult to get the best scheduling especially for large number of jobs to enhance QoS. Multiple activities may need to be scheduled efficiently on various resources (CPU, virtual machines) by reducing completion time and maximizing resource utilization. A meta-heuristic method has been proposed for optimizing multi-objective scheduling problems in cloud systems using harmony search algorithm. According to cloud user's expectations, throughput should be high such that most of the user tasks are completed before the deadline expires thereby resulting in a low task rejection rate. Major purpose of the HS-based, NEW HS scheduling algorithm is to arrange upcoming cloud applications at virtual machines in such a way that tasks are completed in least amount of time (execution and makespan time) with low execution cost and take deadline of cloud application as a QoS constraint.

Project for a reconnaissance unmanned aerial vehicle

Abstract The paper presents the process of designing an unmanned Micro class aircraft, from the analysis of the dynamically developing market and the condition of the Polish Armed Forces to construction of objects and flight test. The possibilities and limitations of using miniature UAVs on the modern battlefield were determined. For the designed UAV the propulsion was selected based on tests carried out on the engine test bench. The avionics equipment was selected based on components readily available on the market. The object was then made and inspected in flight. During the flight tests, the aircraft performance was verified and compared with the assumptions. It has been shown that the developed object is able to fulfill the reconnaissance tasks entrusted to it, while maintaining the assumed simplicity of construction and low cost of execution and service.

Immersive Virtual vs. Real Environment: a validation field-study to assess occupants’ work productivity and comfort

In order to maximise occupants’ well-being and work efficiency, keep satisfying environmental conditions, and minimise costs and impact related to the operative and energy use, a “human-centric” approach is more and more pursued in buildings’ design and performance assessment. In this context, the use of Virtual Reality (VR) is emerging due to its advantages (low cost, repeatability, and speed of execution) compared to physical study settings. However, in Immersive Virtual Environments (IVE), it is important to ensure that data represented and collected faithfully replicate the physical environments. In order to provide a further contribution in terms of IVE validation process in the building field, this research presents results from an experimental study, where subjects’ performance tests and comfort assessments were compared in real and virtual office settings under three different walls colour layouts and two air temperature levels. “Internal”, “ecological” and “construct” validity of the IVE have been demonstrated. Findings revealed no statistically significant differences in productivity and sensation votes and in the impact of colour and temperature variables. Results then highlight a strong agreement of the two tested environments, revealing that VR is a potentially reliable tool to measure its real counterparts in terms of occupants’ productivity, perception, and behaviour under different test conditions.

A CFD-based surrogate model for predicting flow parameters in a ventilated room using sensor readings

In this work, we develop a computational fluid dynamics (CFD)-based surrogate model, which predicts flow parameters under different geometrical configurations and boundary conditions in a benchmark case of a mechanically ventilated room with mixed convection. The model inputs are the temperature and velocity values in different locations, which act as a surrogate of the sensor readings. The model’s output is a set of comfort-related flow parameters, such as the average Nusselt number on the hot wall, jet separation point, average kinetic energy, average enstrophy, and average temperature. We tested four different machine learning methods, among which we chose the gradient boosting regression due to its accurate performance. We also adapted the developed model for indoor environment control applications by determining the optimal combinations of sensor positions which minimize the prediction error. This model does not require the repetition of CFD simulations in order to be applied since the structure of the input data imitates sensor readings. Furthermore, the low computational cost of the model execution and good accuracy makes it an effective alternative to CFD for applications where rapid predictions of complex flow configurations are required, such as model predictive control.

Case-Control Studies in Neurosurgery: The Issue of Effect Estimates

Clinical research questions are commonly answered using a case-control design. The decision to use this design is usually justified due to low cost, feasibility, and ease of execution. However, the case-control design presents challenges in execution, selection of cases/controls, and interpretation of effect measures (odds ratios, among others). In this paper, we clarify for a neurosurgical audience the design and appropriate effect size measures obtained from case-control studies. A narrative review was conducted of published literature on the topic. The future implementation of such studies was discussed and highlighted with several examples from neurosurgical practice. In a case-control design, participants are selected for a study based on their outcome status. Some participants have the outcome of interest (cases), whereas others do not (controls). Controls can be selected from a variety of sources, such as the general population, relatives/friends, or hospital patients without the disease under investigation. The most important criterion is that these controls come from the same study base as cases. Furthermore, it is essential to realize that measures of association obtained from a case-control study depend on the sampling strategy of the controls and, as such, have equivalent counterparts available from cohort studies. We delineate traditional case-control, case-cohort, and incidence density-sampled case-control studies and their applicability to common conditions encountered in daily neurosurgical practice (e.g., glioblastoma, aneurysms, and epilepsy). Neurosurgeons must understand the types of case-control studies and their associated effect measures to properly conduct research and incorporate research findings into clinical practice.

Frequency modelling and dynamic identification of cross-flow water turbines

The cross-flow turbines cover from the point of view hydraulic power the running domain of some well-known turbines such as Pelton, Francis or Kaplan. This type of turbine has a simple construction, long life and low execution cost, which makes it very suitable for on and off grid small to medium hydro power plants. It is quite difficult to establish an exact theoretical dynamic model for this type of turbines, due to the complex flow phenomenon (bi phase flow water and air). In order to obtain the exact dynamic behavior of the hydraulic machine, experimental dynamic identification will be done. In automation, the dynamic properties, represent the fundamental characteristic of the object which must be regulated. When the dynamic properties of the regulated object are obtained experimentally, we analyze the characteristics of the transient regime, which appears because of the application at the system inlet of some stochastic or deterministic signals (sine waves for our case). The hydraulic turbine is modeled as an informational quadrupole having the inlet parameters the movement of the wicket gate and the turbine head and outlet parameters the torque and the speed. In this paper it will be presented the frequency modelling of the cross-flow turbine and the validation of the mathematical model through experimental dynamic identification.

AutoRank: Automated Rank Selection for Effective Neural Network Customization

Tensor decomposition is a promising approach for low-power and real-time application of neural networks on resource-constrained embedded devices. This paper proposes AutoRank, an end-to-end framework for customizing neural network decomposition using cross-layer rank-selection. For many-layer networks, determining the optimal decomposition ranks is a cumbersome task. To overcome this challenge, we establish a state-action-reward system that effectively absorbs inference accuracy and platform specifications into the rank-selection policy. Our proposed framework brings platform characteristics and performance in the customization loop to enable direct incorporation of hardware cost, e.g., runtime and memory footprint. By means of this hardware-awareness, AutoRank customization engine delivers high accuracy decomposed deep neural networks with low execution cost. Our framework minimizes the engineering cost associated with rank selection by providing an automated API for AutoRank that is compatible with popular deep learning libraries and can be readily used by developers.

A Decentralized Framework for Managing Task Allocation in Distributed Software Engineering

In distributed software development, planning and managing fair and transparent task allocation is both critical and challenging. The objective of this paper is to propose a decentralized blockchain-oriented, transparent task allocation framework to improve the quality of the task allocation process. It addresses the concerns of (i) enhancing collaboration, (ii) inhibiting knowledge vaporization, and (iii) reducing documentation problems. The proposed method is a novel two-fold process: First, it identifies and categorizes tasks exhibiting different dependencies and complexities to create equal task clusters based on their dependency type, difficulty, cost, and time. Second, it uses a blockchain-oriented framework to broadcast, check bid validity, allow developers to bid on tasks matching their roles and expertise, evaluate, and announce the winner for task allocation using smart contracts. Results of experimentation, surveys, and interviews with software practitioners conclude that the proposed solution is transparent and effective in allocating tasks (with Cranach’s alpha of 0.894) at a low cost of contract execution in a distributed software development environment. Overall, the proposed approach will have a positive and significant impact in industrial settings.

Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

AbstractThe internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.