Set Of Configurations Research Articles

Vaccination, treatment and containment policy to reduce the effects of the COVID‐19 pandemic in Morocco

AbstractThis paper presents a susceptible–exposed–infected–quarantined–removed (SEIQR) dynamic stochastic general equilibrium model to evaluate the macroeconomic effects of the current COVID‐19 pandemic. The SEIQR configuration setting is calibrated using COVID‐19 data from Morocco. Using the model, we demonstrate how the pandemic might lead to declining consumption and productivity. We illustrate that a combination of quarantine policy, vaccination and treatment of the uninfected person is more successful than using only one of the policies. The combination lowered the number of infected, exposed and quarantined people. We also assessed the economic effects of multiple strategies.

Study of Equipment to Measure Quantities Related to Electricity in a Three-Phase Engine

This study examines the WaveBook/516E device for measuring electrical energy in three-phase engines. Accurate measurement and analysis of electrical energy is critical to engine performance and efficiency. However, existing devices often lack comprehensive data analysis capabilities, which hinders researchers' understanding of engine characteristics and electrical quantities. The study focuses on the WaveBook/516E, which features data storage and customizable analysis options. The methodology involves a theoretical review, including references, data from engines, obtained using the device's standard configuration and flexible settings, underwent detailed statistical analysis. Results demonstrated consistent data for engines under different conditions, validating the device's potential for precise statistical analysis. The WaveBook/516E's data storage and customization options make it a valuable tool for research on electrical energy in three-phase engines, allowing researchers to explore various scenarios. This research helps to improve the understanding of engine behavior and promote efficient engine control practices. The device's ability to change input patterns expands its applicability to broader research in electrical energy measurement and analysis. Overall, the WaveBook/516E expands the capabilities of researchers by facilitating in-depth studies and sound engine control practices.

Playing Various Strategies in Dominion with Deep Reinforcement Learning

Deck-building games, like Dominion, present an unsolved challenge for game AI research. The complexity arising from card interactions and the relative strength of strategies depending on the game configuration result in computer agents being limited to simple strategies. This paper describes the first application of recent advances in Geometric Deep Learning to deck-building games. We utilize a comprehensive multiset-based game representation and train the policy using a Soft Actor-Critic algorithm adapted to support variable-size sets of actions. The proposed model is the first successful learning-based agent that makes all decisions without relying on heuristics and supports a broader set of game configurations. It exceeds the performance of all previous learning-based approaches and is only outperformed by search-based approaches in certain game configurations. In addition, the paper presents modifications that induce agents to exhibit novel human-like play strategies. Finally, we show that learning strong strategies based on card combinations requires a reinforcement learning algorithm capable of discovering and executing a precise strategy while ignoring simpler suboptimal policies with higher immediate rewards.

Effects of different cluster-set rest intervals during plyometric-jump training on measures of physical fitness: A randomized trial.

The optimal intra-set rest for cluster sets (CLS) during plyometric-jump training (PJT) to improve physical fitness remains unclear. The objective of this quasi-experimental study was to compare the effects of PJT with traditional (TRS) vs. CLS structures, using different intra-set rests, on the physical fitness of healthy participants. Forty-seven recreationally active young men performed 3-5 sets of 10-12 repetitions of upper- and lower-body PJT exercises twice a week for six weeks using different set configurations: TRS group (no intra-set rest), and the CLS10, CLS20 and CLS30 groups with 10, 20 and 30 s of intra-set rest, respectively, while the total rest period was equated. Pretest-posttest measurements were carried out 48 h before and after the intervention and the rating of fatigue (ROF) was also assessed using a numerical scale (0-10 points) 20 min after the first and last (i.e., 12th) session. There was no significant difference in the mean energy intake between groups (p > 0.05). The repeated measures ANOVA revealed that all groups showed similar improvements (p < 0.05) in body mass, body mass index, fat-free mass, one repetition maximum (dynamic strength) and repetitions to failure (muscular endurance) in back squat and chest press, handgrip strength, standing long jump, 20 m sprint, 9-m shuttle run (change of direction speed), and ROF. Of note, the ROF was lower for the CLS20 and CLS30 groups, independent from the training effect. The physical fitness of recreationally active young men improved after 6 weeks of PJT involving intra-set rest intervals of 0 s, 10 s, 20 s, or 30 s. However, an intra-set rest of 20 s and 30 s seems to induce lower exercise-induced fatigue perception.

Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.

The Analysis of the Usage Efficiency of the Time-frequency Resources for Different 5G NR CORESET Configuration Settings

The 5G mobile networks is a New Radio for finding wireless access solutions for Internet access of users with the most demanding requirements for quality service. To implement it radio interface resource allocation functions implemented by hardware and software vendors at base stations must notify subscribers about their decisions with so-called control channels the functions of which are distributed in the CORESET configuration area. The settings of the CORESET parameters affect the efficiency of using radio channel resources. This article discusses CORESET issues and their impact on the efficiency of using radio resources.

Exploring the Impact of Code Clones on Deep Learning Software

Deep learning (DL) is a really active topic in recent years. Code cloning is a common code implementation that could negatively impact software maintenance. For DL software, developers rely heavily on frameworks to implement DL features. Meanwhile, to guarantee efficiency, developers often reuse the steps and configuration settings for building DL models. These may bring code copy-pastes or reuses inducing code clones. However, there is little work exploring code clones’ impact on DL software. In this article, we conduct an empirical study and show that: (1) code clones are prevalent in DL projects, about 16.3% of code fragments encounter clones, which is almost twice larger than the traditional projects; (2) 75.6% of DL projects contain co-changed clones, meaning changes are propagated among cloned fragments, which can bring maintenance difficulties; (3) Percentage of the clones and Number of clone lines are associated with the emergence of co-changes; (4) the prevalence of Code clones varies in DL projects with different frameworks, but the difference is not significant; (5) Type 1 co-changed clones often spread over different folders, but Types 2 and 3 co-changed clones mainly occur within the same files or folders; (6) 57.1% of all co-changed clones are involved in bugs.

Computationally constrained audio-based violence detection through transfer learning and data augmentation techniques

Audio-based violence detection is a critical research area for enhancing public safety and security. This paper delves into comparing machine learning models, specifically Convolutional Neural Networks and Shallow Networks in the context of audio violence detection. We evaluate these models under varying training set configurations and data augmentation techniques, analyzing their impact on model performance and robustness under varying real-world conditions. Specifically, we address the issue of domain shifts, exploring how models perform under different types of noise and reverberation. Our results highlight scenarios where Shallow Networks, despite their lower computational costs, exhibit performance nearly on par with that of high-cost CNNs. Introducing tailored data augmentation techniques significantly enhances the models' performance and stability against domain shifts, providing a promising direction for improving system robustness. Our research underscores the value of careful model selection for real-world audio-based violence detection applications, recognizing the importance of an optimal trade-off between computational cost and performance, especially in resource-constrained scenarios. This research provides valuable insights for researchers and practitioners in developing more efficient, robust and accurate audio-based violence detection systems.

Optimizing the feature set and electrode configuration of high-density electromyogram via interpretable deep forest

Hand gesture recognition via high-density surface electromyogram (HDsEMG) has received increasing attentions in human–machine interactions. However, with an extremely large number of electrodes in the electrode arrays, the computational burden substantially increased in real world mobile computing applications. Additionally, with diverse features extracted from all electrodes, large information redundancy reduced the learning efficiency of machine learning models. Furthermore, most machine learning models were employed as black-box modules, increasing concerns on both ethics and the user trust on machine learning techniques, especially in human-centered applications. In this work, we applied deep forest in HDsEMG-based hand gesture recognition. Deep forest is a new deep learning architecture where information could be processed layer-by-layer via cascaded random forest modules. Built on highly interpretable decision trees, deep forest models retain the interpretability of decision trees. With the inherent interpretability of deep forest, the most important features and electrodes that deep forest focused on in the decision making process can also be used to optimize the feature set and electrode configuration. We also compared the model interpretation results with the anatomy structure of forearm muscles, providing relations between the interpretations of deep forest and the physiological basis of neuromuscular systems. HDsEMG (256 electrodes) from 20 subjects were used in our analyses. To simulate realistic application scenarios, all analyses and evaluations were performed in inter-subject validations. Results showed that with optimized feature set and electrode configuration, the number of features/electrodes was substantially reduced, with the overall classification accuracy improved. Data and codes are available online.

Power-oriented resistance training combined with high-intensity interval training in pre-frail and frail older people: comparison between traditional and cluster training set configurations on the force–velocity relationship, physical function and frailty

ObjectivesTo analyse the force–velocity relationship changes in response to two different training programmes differing in the set configuration (cluster vs. traditional), and their impact on physical function and frailty in pre-frail and frail older adults.Methods43 pre-frail and frail (Frailty Phenotype ≥ 1 criteria) older adults (81.4 ± 5.1 years) participated in this study. Participants were assigned to cluster (CT; n = 10; 10-s intra-set rest), traditional (TT; n = 13; no intra-set rest) or control (CON; n = 20) groups. Force–velocity relationship (F0, V0 and Pmax), physical function (Short Physical Performance Battery, SPPB) and frailty (Frailty Phenotype, FP) were assessed at baseline and after the training programme.ResultsBoth CT and TT groups showed similar improvements in Pmax after training (CT = + 36.7 ± 34.2 W; TT = + 33.8 ± 44.6 W; both p < 0.01). V0 was improved by both CT (+ 0.08 ± 0.06 m s−1; p < 0.01), and TT (+ 0.07 ± 0.15 m s−1, p > 0.05). F0 remained unchanged in CT (+ 68.6 ± 224.2 N, p > 0.05) but increased in TT (+ 125.4 ± 226.8 N, p < 0.05). Finally, SPPB improved in both training conditions (CT = + 2.3 ± 1.3 points; TT = + 3.0 ± 1.2 points; both p < 0.05) and in the CON group (+ 0.9 ± 1.4 points, p < 0.05). CT and TT reduced their FP (CT = − 1.1 criteria; TT = − 1.6 criteria; both p < 0.01), while no changes were observed in the CON group (− 0.2 criteria, p = 0.38).ConclusionsBoth training methods were equally effective for improving Pmax, physical function and reducing frailty in pre-frail and frail older people. TT may be effective for improving both force and velocity parameters, while CT may be effective for improving velocity parameters alone, although further research is required to confirm these findings.

Granular convergence as an iterated local map.

Granular convergence is a property of a granular pack as it is repeatedly sheared in a cyclic, quasistatic fashion, as the packing configuration changes via discrete events. Under suitable conditions the set of microscopic configurations encountered converges to a periodic sequence after sufficient shear cycles. Prior work modeled this evolution as the iteration of a pre-determined, random map from a set of discrete configurations into itself. Iterating such a map from a random starting point leads to similar periodic repetition. This work explores the effect of restricting the randomness of such maps in order to account for the local nature of the discrete events. The number of cycles needed for convergence shows similar statistical behavior to that of numerical granular experiments. The number of cycles in a repeating period behaves only qualitatively like these granular studies.

Volume integral model for algebraic image reconstruction and computed tomography

Industrial computed tomography (CT) has seen widespread adoption as an inspection technique due to its ability to resolve small defects and perform high-resolution measurements on complex structures. The reconstruction of CT data is usually performed using filtered back-projection (FBP) methods, such as the Feldkamp-Davis-Kress (FDK) method, and are selected as they offer a good compromise between reconstruction time and quality. More recently, iterative reconstruction algorithms have seen a resurgence in research interest as computing power has increased. Iterative reconstruction algorithms, such as the algebraic reconstruction technique (ART), use a reconstruction approach based on linear algebra to determine voxel attenuation coefficients based on the measured attenuation of the sample at the detector and calculation of the ray paths traversing the voxel grid. This offers a more precise model for CT reconstruction but at the cost of computational complexity and reconstruction time. Existing ART implementations are based on the 2D weighting models of the binary integral method (BIM), line integral method (LIM) and area integral method (AIM). For full 3D reconstruction, BIM and LIM only offer approximations leading to numerical inaccuracies. AIM for 2D reconstruction is mathematically exact but considers the divergent nature of a fan beam for 2D only. For a full 3D volumetric reconstruction, the X-ray cone beam is divergent in all directions and therefore AIM cannot be applied in its current form. A novel voxel weighting method for 3D volumetric image reconstruction using ART and providing a mathematically exact fractional volume weighting is introduced in this paper and referred to as the volume integral method (VIM). A set of algorithms is provided based on computer graphics techniques to determine ray/voxel intersections with volume reconstruction computed based on the divergence theorem. A set of experimental configurations is developed to provide a comparison against existing methods and conclusions are provided. Optimisation is achieved through graphic acceleration.

Modified embedded atom method interatomic potential for FCC γ-cerium

As interest in Cerium containing alloys and Al-Ce alloys in particular grows, the need to generate predictions of mechanical, thermodynamic and transport properties across a compositional space becomes more pronounced. The absence of a reliable interaction potential to be used in classical molecular dynamics (MD) simulation of γ-Ce is a fundamental bottleneck to subsequent alloy simulation. In this work, a Modified Embedded Atom Method (MEAM) potential to be used in MD simulation for γ-Ce has been generated. The parameterization process involved two steps. First, the iterative Latin hypercube sampling (LHS) method was used to generate MEAM parameter sets based on an automated optimization of energies and forces relative to a training set of small configurations evaluated through Density Functional Theory (DFT). Second, a human-guided optimization in that local parameter space was performed to refine the parameters to satisfy an array of mechanical, thermodynamic and transport properties available from the experimental literature. When used in an MD simulation, the resulting potential provides excellent estimates of all tested material properties across a broad temperature range. This γ-Ce potential, when combined with existing MEAM potential for Al, will form the necessary foundation for the subsequent development of a mixture potential, enabling the simulation of Al-Ce and Al-Ce-X alloys.

Impacts of Connected and Automated Vehicles on Travel Demand and Emissions in California

The emerging connected and automated vehicle (CAV) technology is likely to bring significant changes to the transportation world. This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. We design a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050. These scenarios consider the ownership and operational type (e.g., private and shared) of CAVs, as well as additional policies such as pricing and the use of zero-emission vehicles (ZEVs) to curb potential impacts. The scenarios are: 0. Baseline (no automation); 1. Private CAV; 2. Private CAV + Pricing; 3. Private CAV + ZEV; 4. Shared CAV; 5. Shared CAV + Pricing; 6. Shared CAV + ZEV. Impacts of the introduction of CAVs across the transportation system are quantitatively estimated. The results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled (VMT) and emissions will likely increase, because of the relative convenience of CAVs. The results also show that VMT could be substantially affected by a modification in auto travel costs. This means that the implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help reduce tailpipe pollutant emissions in future scenarios.

Per-fraction planning to enhance optimization degrees of freedom compared to the conventional single-plan approach.

&#xD;In conventional radiotherapy, a single treatment plan is generated pre-treatment, and delivered in daily fractions. In this study, we propose to generate different treatment plans for all fractions ('Per-fraction' planning) to reduce cumulative OAR doses. Per-fraction planning was compared to the 'Conventional' single-plan approach for non-coplanar 4x9.5 Gy prostate SBRT.&#xD;&#xD;Approach:&#xD;An in-house application for fully automated, non-coplanar multi-criterial treatment planning with integrated beam angle and fluence optimization was used for plan generations. For the Conventional approach, a single 12-beam non-coplanar IMRT plan with individualized beam angles was generated for each of the 20 included patients. In Per-fraction planning, four fraction plans were generated for each patient. For each fraction, a different set of patient-specific 12-beam configurations could be automatically selected. Per-fraction plans were sequentially generated by adding dose to already generated fraction plan(s). For each fraction, the cumulative- and fraction dose were simultaneously optimized, allowing some minor constraint violations in fraction doses, but not in cumulative.&#xD;&#xD;Main results:&#xD;In the Per-fraction approach, on average 32.9 ± 3.1 [29;39] unique beams per patient were used. PTV doses in the separate Per-fraction plans were acceptable and highly similar to those in Conventional plans, while also fulfilling all OAR hard constraints. When comparing total cumulative doses, Per-fraction planning showed improved bladder sparing for all patients with reductions in Dmean of 22.6% (p=0.0001) and in D1cc of 2.0% (p=0.0001), reductions in patient volumes receiving 30% and 50% of the prescribed dose of 54.7% and 6.3%, respectively, and a 3.1% lower rectum Dmean (p=0.007). Rectum D1cc was 4.1 % higher (p=0.0001) and Urethra dose was similar. &#xD;&#xD;Significance:&#xD;In this proof-of-concept paper, Per-fraction planning resulted in several dose improvements in healthy tissues compared to the Conventional single-plan approach, for similar PTV dose. By keeping the number of beams per fraction the same as in Conventional planning, reported dosimetric improvements could be obtained without increase in fraction durations. Further research is needed to explore the full potential of the Per-fraction planning approach.&#xD.

Magnetism and Electronic State of Iron Ions on the Surface and in the Core of TiO2 Nanoparticles

In this paper, the electron and magnetic state of iron placed either on the surface or in the core of TiO2 nanoparticles were investigated using magnetometric methods, electron paramagnetic resonance (EPR) and Mössbauer spectroscopy. It was demonstrated that the EPR spectra of TiO2 samples with iron atoms localized both on the surface and in the core of specific features depending on the composition and size of the nanoparticles. Theoretical calculations using the density functional theory (DFT) method demonstrated that the localization of Fe atoms on the surface is characterized by a considerably larger set of atomic configurations as compared to that in the core of TiO2 nanoparticles. Mössbauer spectra of the samples doped with Fe atoms both on the surface and in the core can be described quite satisfactorily using two and three doublets with different quadrupole splitting, respectively. This probably demonstrates that the Fe atoms on particle surface and in the bulk are in different unlike local surroundings. All iron ions, both on the surface and in the core, were found to be in the Fe3+ high-spin state.

Twisted magnetic knots and links

For magnetic knots and links in plasmas, we introduce an internal twist and study their dynamical behavior in numerical simulations. We use a set of helical and non-helical configurations and add an internal twist that cancels the helicity of the helical configurations or makes a non-helical system helical. These fields are then left to relax in a magnetohydrodynamic environment. In line with previous works, we confirm the importance of magnetic helicity in field relaxation. However, an internal twist, as could be observed in coronal magnetic loops, does not just add or subtract helicity, but also introduce an alignment of the magnetic field with the electric current, which is the source term for helicity. This source term is strong enough to lead to a significant change in magnetic helicity, which for some cases leads to a loss of the stabilizing properties expressed in the realizability condition. Even a relatively weak internal twist in these magnetic fields leads to a strong enough source term for magnetic helicity that for various cases even in a low diffusion environment, we observe an inversion in sign of magnetic helicity within timescales much shorter than the diffusion time. We conclude that in solar and stellar fields, an internal twist does not automatically result in a structurally stable configuration and that the alignment of the magnetic field with the electric current must be taken into account.

Rockfall in open pit mines: management of the pit geometry and protection measures design

Rockfall events represent a serious hazard for people, structures, and infrastructures. The phenomenon can occur in several environments, natural and artificial. In surface mining operations, blasting, bench clean-up and scaling can induce rock blocks detachment, compromising operators’ and operations’ safety. The installation of passive mitigation measures coupled with a specific design of the pit geometry can mitigate this risk. The bench height, width, and slope angle, the number of benches, together with the material characterizing the slope, affect in different ways the kinematics of the possible detached blocks. In this paper, a large set of configurations is studied and trajectory analyses are performed to evaluate the influence of each geometrical or material variable. Results are analysed considering blocks kinematics both on the pit floor and at each bench. The findings provide a useful a tool for a preliminary design of pit geometry and mitigation measures. Design charts are proposed for different slope materials, reporting, as function of the total pit height, the blocks stopping distance, their velocity at the pit floor, their passing height and velocity at each bench, for each geometrical configuration. The obtained charts can be effectively implemented in a quantitative risk assessment procedure for mining activities.

Expansivity and Periodicity in Algebraic Subshifts

A d-dimensional configuration c:mathbb {Z}^dlongrightarrow A is a coloring of the d-dimensional infinite grid by elements of a finite alphabet Asubseteq mathbb {Z}. The configuration c has an annihilator if a non-trivial linear combination of finitely many translations of c is the zero configuration. Writing c as a d-variate formal power series, the annihilator is conveniently expressed as a d-variate Laurent polynomial f whose formal product with c is the zero power series. More generally, if the formal product is a strongly periodic configuration, we call the polynomial f a periodizer of c. A common annihilator (periodizer) of a set of configurations is called an annihilator (periodizer, respectively) of the set. In particular, we consider annihilators and periodizers of d-dimensional subshifts, that is, sets of configurations defined by disallowing some local patterns. We show that a (d-1)-dimensional linear subspace Ssubseteq mathbb {R}^d is expansive for a subshift if the subshift has a periodizer whose support contains exactly one element of S. As a subshift is known to be finite if all (d-1)-dimensional subspaces are expansive, we obtain a simple necessary condition on the periodizers that guarantees finiteness of a subshift or, equivalently, strong periodicity of a configuration. We provide examples in terms of tilings of mathbb {Z}^d by translations of a single tile.

Determination of pairwise interactions via the radial distribution function in equilibrium systems interacting with the Mie potential

Particle interactions play a fundamental role in condensed matter physics because they determine both the dynamical behavior and the equilibrium physical properties of a given system at temperature T and density ρ. However, these interactions are not always precisely known in experiments, or in simulations of coarse-grained systems. A direct determination of the pair interaction potential in a given system could help understand observed behaviors and make further predictions. Given a number of equilibrium configurations of a system, it would be desirable to find a method to directly determine the pair potential by only using these snapshots. We propose two simple methods towards this goal for the specific case of the systems in 3 dimensional space with the Mie potential, which includes two exponents as m and s. Well-equilibrated system configurations are produced by molecular dynamical simulations using the Mie potential with different exponent combinations (m,s). In the first method, we construct a correspondence between the value and location of the first peak of the radial distribution function and the couple (m,s), which allows us to determine the potential with an accuracy of 100% when given a set of equilibrium configurations for an unknown potential. In the second method, we train an artificial neural network to learn this correspondence. We find that all (m,s) combinations are correctly predicted. Both methods support the idea that the pairwise interaction can often easily be inferred by using equilibrium snapshots.