Set Of Configurations Research Articles

Training Effects of Traditional versus Cluster Set Configuration with and without Blood Flow Restriction.

Purpose: This study compared the effects of four different resistance training (RT) programs that differed in the set configuration (cluster vs. traditional) and the blood flow condition [free-flow (FF) vs. blood flow restriction (BFR)] on strength, neuromuscular and hypertrophic adaptations.Methods: Forty-two resistance-trained males were randomly assigned into four protocols that differed in the set configuration (TRA: without rest between repetitions vs. CLU: 30 s rest every 2 repetitions) and in the blood flow condition [FF vs. BFR (50% of arterial occlusion pressure)]. Subjects followed an 8-week RT program, twice per week, with similar intensity (55%-65% 1RM), sets (3), repetitions per set (10-6), and resting time (2 minutes) in the full-squat (SQ) exercise. Before and after the RT program, they were evaluated for: 1) muscle size of the vastus lateralis; 2) vertical jump; 3) maximal isometric contraction; 4) progressive loading test; and 5) fatigue test.Results: BFR-TRA and FF-CLU induced greater increases in 1RM, and velocity against submaximal loads than FF-TRA and BFR-CLU (BFR × time and CLU × time interactions, p = 0.02). The TRA protocols showed greater increases in maximal isometric force than CLU (CLU × time interaction, p = 0.03). BFR did not enhance jump performance unlike the FF protocols (p < 0.01). The TRA protocols induced greater hypertrophy in the distal region of the vastus lateralis than CLU protocols (CLU × time interaction, p = 0.04), with BFR-TRA producing the greatest gains in all vastus lateralis sections.Conclusions: The different combinations of set configurations and blood flow conditions resulted in highly specific adaptations that illustrate the potential of adaptation for each protocol. The divergent underlying mechanisms of CLU and BFR methodologies may offset each other when combined.

Acute cardiovascular responses of postmenopausal women to resistance training sessions differing in set configuration: A study protocol for a crossover trial.

Resistance training is hardly recommended for postmenopausal women to counteract negative effects of hormonal changes. However, some concern exists about the marked hemodynamic responses caused by high-load resistance exercises. In this regard, studies on young, healthy, physically active individuals suggest that set configuration can modulate acute cardiovascular, metabolic, and cardiac autonomic responses caused by resistance training sessions, but this has not yet been explored in postmenopausal women. A sample of 60 physically active postmenopausal women (30 normotensive, 30 hypertensive) will participate in this crossover study. After a medical exam, ergometry, familiarization session, and two testing sessions, participants will complete three experimental sessions and one control session in a randomized order. Each experimental session includes 36 repetitions of four exercises (horizontal leg press, bench press, prone leg curl, and lat pull-down) differing in set configuration: 9 sets of 4 repetitions (i.e., 33% intensity of effort) with 45 s of inter-set recovery, 6 sets of 6 repetitions (50% intensity of effort) with 72 s, and 4 sets of 9 repetitions (75% intensity of effort) with 120 s; with 4 min rest between exercises. Before and immediately after each session, arterial stiffness, hemodynamic variables, cardiac autonomic modulation, baroreflex sensitivity, sympathetic vasomotor tone, and resting oxygen uptake will be evaluated. Furthermore, perceived effort, mechanical performance (e.g., power, velocity), heart rate, and lactatemia will be collected throughout sessions. The impact of set configuration on these variables will be analyzed, along with comparisons between normotensive and hypertensive women. Cardiovascular responses to resistance exercise have been scarcely studied in females, particularly postmenopausal women. The results of this study will provide information about the modulating role of set structure on metabolic and cardiovascular responses of normotensive and hypertensive postmenopausal women to resistance training. NCT05544357 on 7 December 2022.

Estimating Repetitions in Reserve During the Bench Press Exercise: Should We Consider Sex and the Exercise Equipment?

Estimating repetitions in reserve (RIR) has been established as a valid and practical method for monitoring set configuration concerning proximity to failure in resistance training. However, factors such as sex and exercise equipment could interfere with the estimation process. Therefore, this study aimed to determine the influence of sex (female vs male) and exercise equipment (free-weight vs Smith machine) on the RIR estimation at different relative loads during the bench press exercise. Men would be more accurate than women in the RIR estimation, and participants would better perceive their actual effort at the Smith machine compared with the free-weight bench press exercise. Repeated measures design. Level 3. In a counterbalanced order, recreationally trained participants (12 female, 14 male) performed 2 identical sessions with the free-weight bench press exercise in 1 week and 2 identical sessions with the Smith machine bench press exercise in another week (first session for familiarization and second session for validity analysis). In each session, participants performed single sets of repetitions to failure against 3 relative loads (65%, 75%, and 85% of the 1-repetition maximum [1RM]). Results revealed a significant interaction between RIR and sex at 65% 1RM (P < 0.01; women: -1.2 [-1.7 to -0.6] repetitions; men: -0.1 [-0.6 to 0.3] repetitions), and between RIR and exercise equipment at 65% 1RM (P < 0.01; free-weight: -1.1 [-1.5 to -0.6] repetitions; Smith machine: -0.2 [-0.6 to 0.2] repetitions), but not at 75% and 85% 1RM. RIR estimation is a valid method for monitoring set configuration. However, this method tends to underestimate RIR at 65%1RM with female athletes and in the free-weight exercise for both sexes. Strength and conditioning coaches should help their athletes understand effort awareness in the aforementioned context.

"My Best Friend's Husband Sees and Knows Everything": A Cross-Contextual and Cross-Country Approach to Understanding Smart Home Privacy

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

TrojanProbe: Fingerprinting Trojan tunnel implementations by actively probing crafted HTTP requests

Trojan is a well-known hidden tunnel protocol widely used to bypass Internet censorship and thus presents a big challenge to transparent network management and forensics. As claimed by the protocol designer, Trojan maintains its anti-identifiability by proxying real HTTPS/TLS traffic to react to unauthenticated requests, eliminating any subtle differences between the Trojan traffic and the legitimate HTTPS. Despite such a protocol seeming unidentifiable by design, the diverse Trojan implementations adopting very different programming languages will likely have varied coding logic and networking API calls, opening a new door to be identified and fingerprinted from the implementation level. In this paper, we propose TrojanProbe, a new class of active probing methods that can be used to fingerprint Trojan implementations by triggering their identifiable responses. Our basic idea is to audit the source code of the Trojan programs and discover the subtle logic discrepancy compared with the legitimate HTTPS counterparts, to craft specific HTTP requests as probes to trigger these differences for fingerprinting. By this idea, we choose the five most popular open-source Trojan programs off-the-shelf as our targets to audit, covering the majority of Trojan market share and the mainstream programming languages from traditional C++ to the cutting-edge Go and Rust, and design a suite of novel HTTP probes to differentiate them from their web server masquerades. Our probes exploit either the different responding/buffering logic to the malformed HTTP requests and the different HTTP versions, or the varied timeouts set in the different networking APIs by default. To this end, we have conducted extensive experiments to evaluate the TrojanProbe against a comprehensive set of configuration and networking conditions. The experimental results show that our TrojanProbe can effectively fingerprint our selected Trojan targets in most conditions, but leave a single Rust implementation with a minority market occupied that can only be identified in some constraint cases. Despite such an exception, our research sheds light on a new kind of possibility to fingerprint Trojans at their implementation level, even if such a hidden tunnel is widely known as unidentifiable at the protocol level.

Metafeature Selection via Multivariate Sparse-Group Lasso Learning for Automatic Hyperparameter Configuration Recommendation.

The performance of classification algorithms is mainly governed by the hyperparameter settings deployed in applications, and the search for desirable hyperparameter configurations usually is quite challenging due to the complexity of datasets. Metafeatures are a group of measures that characterize the underlying dataset from various aspects, and the corresponding recommendation algorithm fully relies on the appropriate selection of metafeatures. Metalearning (MtL), aiming to improve the learning algorithm itself, requires development in integrating features, models, and algorithm learning to accomplish its goal. In this article, we develop a multivariate sparse-group Lasso (SGLasso) model embedded with MtL capacity in recommending suitable configurations via learning. The main idea is to select the principal metafeatures by removing those redundant or irregular ones, promoting both efficiency and performance in the hyperparameter configuration recommendation. To be specific, we first extract the metafeatures and classification performance of a set of configurations from the collection of historical datasets, and then, a metaregression task is established through SGLasso to capture the main characteristics of the underlying relationship between metafeatures and historical performance. For a new dataset, the classification performance of configurations can be estimated through the selected metafeatures so that the configuration with the highest predictive performance in terms of the new dataset can be generated. Furthermore, a general MtL architecture combined with our model is developed. Extensive experiments are conducted on 136 UCI datasets, demonstrating the effectiveness of the proposed approach. The empirical results on the well-known SVM show that our model can effectively recommend suitable configurations and outperform the existing MtL-based methods and the well-known search-based algorithms, such as random search, Bayesian optimization, and Hyperband.

Acute Metabolic Response, Neuromuscular Activity, and Mechanical Performance to Different Set.

The aim of this study was to examine the acute metabolic response, neuromuscular activity, and mechanical performance of different set configurations in bench-press (BP). Twenty-two resistance-trained men performed three resistance exercise protocols consisting of 3 x 12 BP repetitions at 60% 1RM, with 4 minutes of rest between sets, but with different set configurations: (a) traditional set (TS), without rest within the set; (b) cluster-6 (CS6), with 30-second intraset rest after the sixth repetition in each set; and (c) cluster-2 (CS2), with 30-second intraset rest every two repetitions. Mean propulsive force (MPF), velocity (MPV), power (MPP), and electromyography (EMG) values were recorded for each repetition. Blood lactate, maximal voluntary isometric BP contraction, and dynamic strength in BP were assessed pre- and post-exercise. The CS2 protocol resulted in greater mechanical performance (i. e. MPF, MPV, and MPP) and lower alterations of EMG parameters (i. e. root mean square and median frequency) during the exercise compared to CS6 and TS (TS<CS6<CS2). The CS2 protocol induced smaller increases in lactate compared to TS and CS6. No significant "protocol x time" interactions were observed for the MVIC (maximal voluntary isometric BP contraction) variables. Introducing short but frequent intraset rest periods alleviates training-induced fatigue assessed by better performance maintenance.

The Acute Effect of Ascending-Pyramid, Descending-Pyramid, and Constant-Load Set Configurations on Repetition Performance, Training Volume, and Barbell Velocity During Bench Press Exercise

The Acute Effect of Ascending-Pyramid, Descending-Pyramid, and Constant-Load Set Configurations on Repetition Performance, Training Volume, and Barbell Velocity During Bench Press Exercise

Untraded inter‐dependencies as auxiliary production factors for informal artisans in urban industrial clusters: An empirical study in Kumasi, Ghana

AbstractThe concept and influence of untraded inter‐dependencies are under‐explored in the industrial cluster and informal economy literature. In this paper, we attempt to bridge this gap by extending the conventional Cobb‐Douglas production function to operationalising and testing the moderating effects of two forms of untraded inter‐dependencies in informal production. Based on the diffusion theory, we argue that knowledge‐sharing would favour informal production, as opposed to tool‐sharing. Using quantitative methods, 334 informal artisans were randomly sampled from three clusters in the Kumasi Metropolis of Ghana and analysed data obtained with moderating models and simple slopes plots. The tested models and fuzzy set configurations confirmed that knowledge‐sharing, but not tool‐sharing, has significant interaction effects on informal production. The optimal model derived would be one in which capital interacts with incremental knowledge‐sharing at the existing level of tool‐sharing among the artisans. This paper serves as an extension of new economic geography with an additional factor of untraded inter‐dependencies. As part of post‐COVID restoration and enhancement of informal production processes, we recommend city officials to deliberately plan and space clustered enterprises that enable the observed rates of interactions between production factors and untraded inter‐dependencies.

Impact of squat set configuration on mechanical performance in paired sets of upper-body exercises.

Paired sets and alternative set configurations (e.g., cluster sets) are frequently employed by strength and conditioning practitioners; however, their synergistic impact remains underexplored in research. This study aimed to elucidate whether the set configuration used in a lower-body exercise affects mechanical performance during paired sets of upper-body exercises. Twenty-one resistance-trained individuals (14 men and 7 women) randomly completed three experimental sessions that involved four sets of five repetitions at 75%1RM during both the bench press and bench pull exercises. The three experimental sessions varied solely in the activity conducted during the inter-set rest periods of each upper-body exercise: (i) Traditional squat - six squat repetitions without intra-set rest at 65%1RM; (ii) Rest redistribution squat - two clusters of three repetitions of the squat exercise at 65%1RM with 30s of intra-set rest; and (iii) Passive rest - no exercise. The rest redistribution set configuration allowed the sets of the squat exercise to be performed at a faster velocity than the traditional set configuration (p = 0.037). However, none of the mechanical variables differed between the exercise protocols neither in the bench press (p ranged from 0.279 to 0.875) nor in the bench pull (p ranged from 0.166 to 0.478). Although rest redistribution is an effective strategy to alleviate fatigue during the sets in which it is implemented, it does not allow subjects to perform better in subsequent sets of the training session.

Acute Responses to Traditional and Cluster-Set Squat Training With and Without Blood Flow Restriction.

Cornejo-Daza, PJ, Sánchez-Valdepeñas, J, Páez-Maldonado, J, Rodiles-Guerrero, L, Boullosa, D, León-Prados, JA, Wernbom, M, and Pareja-Blanco, F. Acute responses to traditional and cluster-set squat training with and without blood flow restriction. J Strength Cond Res 38(8): 1401-1412, 2024-To compare the acute responses to different set configurations (cluster [CLU] vs. traditional [TRA]) under distinct blood flow conditions (free vs. restricted) in full-squat (SQ). Twenty resistance-trained males performed 4 protocols that differed in the set configuration (TRA: continuous repetitions; vs. CLU: 30 seconds of rest every 2 repetitions) and in the blood flow condition (FF: free-flow; vs. blood flow restriction [BFR]: 50% of arterial occlusion pressure). The relative intensity (60% 1RM), volume (3 sets of 8 repetitions), and resting time (2 minutes) were equated. Mean propulsive force (MPF), velocity (MPV) and power (MPP), and electromyography (EMG) parameters were recorded during each repetition. Tensiomyography (TMG), blood lactate, countermovement jump (CMJ) height, maximal voluntary isometric contraction, in SQ, and movement velocity against the load that elicited a 1 m·s -1 velocity at baseline (V1-load) in SQ were assessed at pre- and post-exercise. The CLU protocols allowed a better maintenance of MPF, MPV, MPP, and EMG median frequency during the exercise compared to TRA (clu-time interaction, p < 0.05). The TRA protocols experienced greater impairments post-exercise in TMG- and EMG-derived variables (clu-time interaction, p < 0.05) and SQ and CMJ performance (clu-time interaction, p = 0.08 and p < 0.05, respectively), as well as higher blood lactate concentrations (clu-time interaction, p < 0.001) than CLU. Moreover, BFR displayed decreases in TMG variables (bfr-time interaction, p < 0.01), but BFR-CLU resulted in the greatest reduction in twitch contraction time ( p < 0.001). Cluster sets reduced fatigue during and after the training session and BFR exacerbated impairments in muscle mechanical properties; however, the combination of both could improve contraction speed after exercise.

50 Years of Context Effects: Merging the Behavioral and Quantitative Perspectives

Abstract Over the past 50 years, consumer researchers have presented extensive evidence that consumer preference can be swayed by the decision context, particularly the configuration of the choice set. Importantly, behavioral research on context effects has inspired prominent quantitative research on multialternative decision-making published in leading psychology, management, economics, and general interest journals. While both streams of research seem to agree that context effects are an important research area, there has been relatively limited interaction, communication, and collaboration between the two research camps. In this article, we seek to initiate an active dialogue between the two sides. We begin by providing a critical overview of the two literatures on context effects, discussing both their strengths and weaknesses, as well as disparities and complementarities. Here, we place particular emphasis on deepening consumer researchers’ understanding of context effects by drawing on prominent quantitative research published in non-marketing journals over the last decades. Importantly, we provide a roadmap for the future that can inspire further research and potential collaborations between the two camps, overcoming silos in knowledge creation.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

Background1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits.MethodsTo ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel.ResultsOur findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results.ConclusionsOur study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

TEFu-Net: A time-aware late fusion architecture for robust multi-modal ego-motion estimation

Ego-motion estimation plays a critical role in autonomous driving systems by providing accurate and timely information about the vehicle’s position and orientation. To achieve high levels of accuracy and robustness, it is essential to leverage a range of sensor modalities to account for highly dynamic and diverse scenes, and consequent sensor limitations.In this work, we introduce TEFu-Net, a Deep-Learning-based late fusion architecture that combines multiple ego-motion estimates from diverse data modalities, including stereo RGB, LiDAR point clouds and GNSS/IMU measurements. Our approach is non-parametric and scalable, making it adaptable to different sensor set configurations. By leveraging a Long Short-Term Memory (LSTM), TEFu-Net produces reliable and robust spatiotemporal ego-motion estimates. This capability allows it to filter out erroneous input measurements, ensuring the accuracy of the car’s motion calculations over time. Extensive experiments show an average accuracy increase of 63% over TEFu-Net’s input estimators and on par results with the state-of-the-art in real-world driving scenarios. We also demonstrate that our solution can achieve accurate estimates under sensor or input failure. Therefore, TEFu-Net enhances the accuracy and robustness of ego-motion estimation in real-world driving scenarios, particularly in challenging conditions such as cluttered environments, tunnels, dense vegetation, and unstructured scenes. As a result of these enhancements, it bolsters the reliability of autonomous driving functions.

Investigate of unsimilar effect of longer rest interval in multiple sets by performing deload repetition training regimes to micro strength gain change on light weeks of weightlifters

Continuum zone repetition training strategies are one of repetition manipulation both multiple set and repetition zone periodization on weightlifters. The present study was aimed to strength level on weightlift exercise condition of weightlifting athletes, deload repetition resistance training regimes involved in proper rest interval and repetition range by performing multiple set and longer rest interval within different maximize performance on light weeks. The experimental study was conducted to 2 olympic, 1 national, 6 professional weightlifters participated on age (16-21 years) this study. With study maximize performance condition over 3 week resistance training finished to weightlifters. Priority experimental pre-test and post-test outcomes after resistance training showed significant different (p &lt; .05) in dynamic strength to countermovement jump shrug (p = .035; d = 0.69; = 17.78%). However, significant different in low load repetition failure to overhead press (p = .007; d = 0.86; = 5.56%), high load repetition failure to bent-over row (p = .017; d = 1.14; = 8.00%), low and high load repetition failure to countermovement jump shrug (p = .017; d = 1.17; = 13.78% - p = .048; d = 0.73; = 5.44%), in localize endurance not significant performance level to resistance exercise performance. However, isometric strength detected to overhead press (p = .016; d = 1.30; = -0.72%), bent-over row (p = .012; d = 1.67; = -0.51%) and countermovement jump shrug (p = .004; d = 2.45; = -0.48%) in weightlifters. Unsimilar longer rest interval multiple set configuration suggested to maximize strength gain on short time deload repetition training regimes by manipulated repetition and set addition rest interval currently manipulation of weightlifters.

Influence on Sample Determination for Deep Learning Electromagnetic Tomography.

Deep learning (DL) has been frequently applied in the image reconstruction of electromagnetic tomography (EMT) in recent years. It offers the potential to achieve higher-quality image reconstruction. Among these, research on samples is relatively scarce. Samples are the cornerstone for both large and small models, which is easy to ignore. In this paper, a deep learning electromagnetic tomography (DL-EMT) model with nine elements is established. Complete simulation and experimental samples are obtained based on this model. On the sample sets, the reconstruction quality is observed by adjusting the size and configuration of the training set. The Mann-Whitney U test shows that beyond a certain point, the addition of more samples to the training data fed into the deep learning network does not result in an obvious improvement statistically in the quality of the reconstructed images. This paper proposes a CC-building method for optimizing a sample set. This method is based on the Pearson correlation coefficient calculation, aiming to establish a more effective sample base for DL-EMT image reconstruction. The statistical analysis shows that the CC-building method can significantly improve the image reconstruction effect in a small and moderate sample size. This method is also validated by experiments.

OMNeT++ Simulation Framework for Avionics Full-Duplex Switched Ethernet

Avionics Full-Duplex Switched Ethernet (AFDX) is a popular network technology for integrated modular avionics (IMA). When designing an AFDX network, it is important to fulfill the real-time communication requirements of avionic systems. To this end, simulation tools offer a low-cost solution for the performance evaluation of a given AFDX network and message set configuration at the design stage. In this paper, we develop an improved AFDX model for the widely used OMNeT++ (extensible, modular, component-based C++ simulation library and framework) network simulator. Our model exhibits high fidelity to the standard, adds detailed parameter configuration and data logging capabilities, and is compatible with the most recent OMNeT++ version. Our model is accepted by the OMNeT++ community and published as the current AFDX simulation model. Moreover, we propose a complete AFDX simulation framework by augmenting our improved OMNeT++ models with a new network configuration and analysis software tool that we call ANCAT. ANCAT enables users who lack familiarity with the OMNeT++ interface and specific file formats to provide simulation parameters in generic file formats such as.xlsx. Subsequently, ANCAT runs the simulation and generates reports for the results. We first systematically verify the correctness of our AFDX simulation framework by custom-designed experiments. Then, we investigate the practicability of our proposed framework with the performance analysis of a realistic AFDX network topology and message set. In particular, we demonstrate that our proposed framework can be efficiently used to explore the real-time capabilities and relevant features of AFDX networks, as well as provide basic guidelines for network configuration.

Design and Characterization of Soft Fabric Omnidirectional Bending Actuators

Soft robots, inspired by biological adaptability, can excel where rigid robots may falter and offer flexibility and safety for complex, unpredictable environments. In this paper, we present the Omnidirectional Bending Actuator (OBA), a soft robotic actuation module which is fabricated from off-the-shelf materials with easy scalability and consists of three pneumatic chambers. Distinguished by its streamlined manufacturing process, the OBA is capable of bending in all directions with a high force-to-weight ratio, potentially addressing a notable research gap in knit fabric actuators with multi-degree-of-freedom capabilities. We will present the design and fabrication of the OBA, examine its motion and force capabilities, and demonstrate its capability for stiffness modulation and its ability to maintain set configurations under loads. The mass of the entire actuation module is 278 g, with a range of omnidirectional bending up to 90.80°, a maximum tolerable pressure of 862 kPa, and a bending payload (block force) of 10.99 N, resulting in a force-to-weight ratio of 39.53 N/kg. The OBA’s cost-effective and simple fabrication, compact and lightweight structure, and capability to withstand high pressures present it as an attractive actuation primitive for applications demanding efficient and versatile soft robotic solutions.

Robust errant beam prognostics with conditional modeling for particle accelerators

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

Evaluation of security framework for BYOD device in cloud environment

The widespread practice of Bring your own device (BYOD) to work significantly raises cybersecurity risks. All organization’s employees and employers will greatly benefit from this trend. The growth of spyware, malware and other dubious downloads on individual devices has compelled the government to review its data security guidelines. Without user knowledge, hazardous apps are downloaded to personal devices. Both people and governments could suffer tragic consequences as a result of this. In this situation, BYODs are problematic since they can alter policies without permission and release private information. The main goal of the research was to detect fraudulent communications coming from BYOD environments. A network monitoring and managing information configuration settings of mobile devices in the network is established by providing policies and authenticating endpoint devices in the BYOD network using a mix of NAC and MDM. This framework deals with Security Manager (SaaS) as the second module is briefly described. The study's early results were positive and suggested that the framework would lessen access control-related issues.