bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Investigation of the Vibration Control of Nonrotating Periodic Drill Strings
Authors: Sassi Sadok , Renno Jamil , Zhou Han , Baz Amr 
Published: January 2021

Abstract
During the drilling process in oil and gas fields, slender drill strings often experience a multitude of complex and simultaneous vibrational phenomena. Drill string vibrations hinder the drilling process and can cause premature wear and damage to the drilling equipment. Here, the suppression of drill string vibrations during drilling operations is experimentally investigated using a novel drill string design, based on the use of innovative periodic inserts that control the vibration transmissibility in different directions. These inserts are equipped with viscoelastic rings that act as sources of local resonances, surrounding piezoelectric actuators that generate internal axial loading when electrically excited. An experimental prototype that combined all these details was constructed and tested to demonstrate the periodic drill string's feasibility and effectiveness in minimizing undesirable vibrations. The obtained results indicate that the periodic inserts’ careful design can effectively enhance the drill strings’ dynamic behavior and conveniently regulate its bandgap characteristics. Both radial and axial vibrations were controlled, and the vibrations’ amplitude was reduced significantly over a wide range of frequencies. The proposed approach appears to present a viable means for designing intelligent drill strings with tunable bandgap characteristics.
Modeling Analysis of the Wrist Dynamics via an Ellipsoidal Joint
Authors: Wang Jiamin , Gupta Sunit K. , Barry Oumar 
Published: January 2021

Abstract
An accurate wrist model is crucial to the understanding of human wrist mechanics and the development of forearm rehabilitation devices. This paper studied the nonlinear dynamics of the wrist through an ellipsoidal joint model. Compared to many studies where a universal joint is used to model the wrist, the proposed ellipsoidal model intends to better approximate the human wrist biomechanics with the use of kinematic constraints. The constraint on the original 3-dimensional rotation of the wrist is realized based on a quaternion formulation, reducing the wrist kinematics to the coupled 2-degree-of-freedom motions of flexion-extension and radial-ulnar deviation. The ellipsoidal joint also introduces additional coupling from the translational motion constraints. The multibody modeling of the wrist model is then established. The stability and control of the model are analyzed based on a constrained state-space model. Numerical simulations validate the analytical results and demonstrate the coupled dynamical behavior of the wrist. The simulations also show that the proposed model constraint is an ideal base regression function for wrist joint parameter identification. Finally, with the involvement of nonlinear stiffness and damping, chaotic-like behaviors and limit cycles are observed. The approach in this study is also generally applicable to a family of ellipsoidal joint systems.
On a Gamified Brain-Computer Interface for Cognitive Training of Spatial Working Memory
Authors: Liu Ziming , Bryan Jonathan , Borkoski Robert , Yuan Fengpei , Li Yansong , Zhao Xiaopeng 
Published: January 2021

Abstract
In the United States, there are a large number of people suffering from memory and attention deficit problems. For example, patients with attention-deficit hyperactivity disorder (ADHD) and dementia have difficulties in performing activities of daily living and have a low quality of life. Currently, there exist no effective treatment for these memory and attention issues in specific cognitive impairments. In this paper, we developed a gamified platform of brain-computer interface (BCI) for cognitive training, which can engage users in the training and provide users qualitative and quantitative feedback for their training of spatial working memory. The user is able to control the movement of a drone using motor imager, which is imagined movement of body part. Sensorimotor rhythms of the user are calculated using the user’s EEG to drive the movement of the drone. Twenty normal healthy subjects were recruited to test the user experience. Our system showed the capability of engaging users, good robustness, user acceptability and usability. Therefore, we think our platform might be an alternative to provide more accessible, engaging, and effective cognitive training for people with memory and attention problems. In future, we will test the usability and effectiveness of the system for cognitive training in patients with ADHD and dementia.
Adaptive Equivalent Factors of Multi-Objective Energy Management for Fuel Cell Hybrid Electric Vehicles
Authors: Ma Yan , Chen Jian , Wang Junmin 
Published: January 2021

Abstract
In this paper, a multi-objective energy management strategy with an adaptive equivalent factor is proposed to improve the fuel economy, system durability, and charge-sustenance performance of fuel cell hybrid electric vehicles. Firstly, the total hydrogen consumption and degradation cost of power sources can be calculated by flexible empirical models. Then, the multi-objective optimization problem can be transformed into an objective function, which can be solved by quadratic programming to improve the real-time performance. Furthermore, an adaptive Unscented Kalman filter is designed to estimate the aging state of the fuel cell system. The equivalent factor in the objective function can be adaptively updated by the estimated aging state, which can balance the conflict between the fuel economy and the system durability while keeping the state-of-charge in an ideal range. Finally, simulation results show that when the fuel cell system is obviously damaged during the operation, the proposed energy management strategy still can minimize the total cost and maintain the charge-sustenance performance under different driving cycles compared with other methods.
Optimal Control and Energy Management in Hybrid Turbojet Engines
Authors: Richter Hanz , Connolly Joseph W. , Simon Donald L. 
Published: January 2021

Abstract
Speed regulation with simultaneous electric and fuel energy management in electrified aircraft engines is considered. An optimal control approach is formulated and solved that uses a weighted measure of stored energy and fuel consumption as cost function. The semiactive virtual control technique, originally developed for robots with regenerative drives, is suitably extended and used to treat the torque applied by the electromechanical subsystem upon the engine as an independent control input to be optimized. The standard proportional-integral loop used for the primary fuel flow control is maintained for compatibility with industry practice and gain scheduling and limit management logic schemes. A single tunable weight reflects design preferences for fuel or stored electric energy preservation. The magnitude of the weight yields feedback gains which can effectively reverse the direction of power flow between the engine and the electromechanical system, providing a direct way to induce power extraction or injection in transient and steady regimes. In the steady-state, infinite-horizon case, the optimal torque controller is shown to reduce to simple proportional action on the shaft speed plus a bias, paralleling standard linear quadratic tracking with a custom cost function. The paper includes simulation results using the gas turbine model included in NASA’s open-source T-MATS package (Toolbox for the Modeling and Analysis of Thermodynamic Systems) and a representative electromechanical system.
Multi-Car Convex Feasible Set Algorithm in Trajectory Planning
Authors: Huang Jing , Liu Changliu 
Published: January 2021

Abstract
Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.
An Online Transfer Learning Approach for Identification and Predictive Control Design With Application to RCCI Engines
Authors: Bao Yajie , Mohammadpour Velni Javad , Shahbakhti Mahdi 
Published: January 2021

Abstract
This paper presents a framework to refine identified artificial neural networks (ANN) based state-space linear parameter-varying (LPV-SS) models with closed-loop data using online transfer learning. An LPV-SS model is assumed to be first identified offline using inputs/outputs data and a model predictive controller (MPC) designed based on this model. Using collected closed-loop batch data, the model is further refined using online transfer learning and thus the control performance is improved. Specifically, fine-tuning, a transfer learning technique, is employed to improve the model. Furthermore, the scenario where the offline identified model and the online controlled system are “similar but not identitical” is discussed. The proposed method is verified by testing on an experimentally validated high-fidelity reactivity controlled compression ignition (RCCI) engine model. The verification results show that the new online transfer learning technique combined with an adaptive MPC law improves the engine control performance to track requested engine loads and desired combustion phasing with minimum errors.
Application of MIMO Data Driven Feedback Control Design to Dual Stage Hard Disk Drives
Authors: Shah Prateek , Chen Zhi , Horowitz Roberto 
Published: January 2021

Abstract
With increasing data density on hard disk drives, there is need to develop more robust and better performing track following control systems. We present a multi-input multi-output (MIMO) data driven feedback control design methodology. The design considers multiple frequency response measurements of all actuators, simultaneously, ensuring robustness of the control design system. A mixed H2 – H∞ norm locally convex optimization algorithm is used to synthesize the feedback controllers for MIMO systems.
Feedback controllers are developed for dual stage hard disk drives using the MIMO data driven control design technique. A dual stage hard disk drive comprises of two actuators in series, controlling a read/write head onto a rotating disk. Our objective is to stabilize the closed loop of the actuators and minimize the error position signal of the read/write head. H2 norm and H∞ norm control objectives are used to formulate the MIMO data driven control problem. The design is based on a set of five frequency response measurements of the two actuators. We also compare the MIMO design methodology to a single-input multi-output (SIMO) design methodology presented earlier [1].
Constrained Control of Input Delayed Systems With Partially Compensated Input Delays
Authors: Abel Imoleayo , Janković Mrdjan , Krstić Miroslav 
Published: January 2021

Abstract
Control Barrier Functions (CBFs) have become popular for enforcing — via barrier constraints — the safe operation of nonlinear systems within an admissible set. For systems with input delay(s) of the same length, constrained control has been achieved by combining a CBF for the delay free system with a state predictor that compensates the single input delay. Recently, this approach was extended to multi input systems with input delays of different lengths. One limitation of this extension is that barrier constraint adherence can only be guaranteed after the longest input delay has been compensated and all input channels become available for control. In this paper, we consider the problem of enforcing constraint adherence when only a subset of input delays have been compensated. In particular, we propose a new barrier constraint formulation that ensures that when possible, a subset of input channels with shorter delays will be utilized for keeping the system in the admissible set even before longer input delays have been compensated. We include a numerical example to demonstrate the effectiveness of the proposed approach.
Passivity-Based Disturbance Observer Design
Authors: Chen Ying-Chun , Woolsey Craig 
Published: January 2021

Abstract
A design method is proposed for a nonlinear disturbance observer based on the notion of passivity. As an initial application, we consider here systems whose structure comprises a set of integrator cascades, though the proposed approach can be extended to a larger class of systems. We describe an explicit procedure to choose the output of the system and to design the nonlinear feedback law used by the observer, provided the system satisfies a sufficient condition for output feedback semi-passification. The output injection term in the observer scales the measurement residual with a nonlinear gain that depends on the output and a set of static design parameters. We provide guidance for parameter tuning such that the disturbance tracking performance and the transient response of the estimation error can be intuitively adjusted. Example applications to two nonlinear mechanical systems illustrate that the proposed nonlinear observer design method is quite effective, producing an observer that can estimate a wide range of disturbances without any need to know or assume the disturbance dynamics.
Revisiting Ediacaran sulfur isotope chemostratigraphy with in situ nanoSIMS analysis of sedimentary pyrite
Authors: Wang Wei , Hu Yongliang , Muscente A. Drew , Cui Huan , Guan Chengguo , Hao Jialong , Zhou Chuanming 
Published: January 2021

Abstract
Reconstructions of ancient sulfur cycling and redox conditions commonly rely on sulfur isotope measurements of sedimentary rocks and minerals. Ediacaran strata (635–541 Ma) record a large range of values in bulk sulfur isotope difference (Δ34S) between carbonate-associated sulfate (δ34SCAS) and sedimentary pyrite (δ34Spy), which has been interpreted as evidence of marine sulfate reservoir size change in space and time. However, bulk δ34Spy measurements could be misleading because pyrite forms under syngenetic, diagenetic, and metamorphic conditions, which differentially affect its isotope signature. Fortunately, these processes also impart recognizable changes in pyrite morphology. To tease apart the complexity of Ediacaran bulk δ34Spy measurements, we used scanning electron microscopy and nanoscale secondary ion mass spectrometry to probe the morphology and geochemistry of sedimentary pyrite in an Ediacaran drill core of the South China block. Pyrite occurs as both framboidal and euhedral to subhedral crystals, which show largely distinct negative and positive δ34Spy values, respectively. Bulk δ34Spy measurements, therefore, reflect mixed signals derived from a combination of syndepositional and diagenetic processes. Whereas euhedral to subhedral crystals originated during diagenesis, the framboids likely formed in a euxinic seawater column or in shallow marine sediment. Although none of the forms of pyrite precisely record seawater chemistry, in situ framboid measurements may provide a more faithful record of the maximum isotope fractionation from seawater sulfate. Based on data from in situ measurements, the early Ediacaran ocean likely contained a larger seawater sulfate reservoir than suggested by bulk analyses.
Co-designing organisational improvements and interventions to increase inpatient activity in four stroke units in England: a mixed-methods process evaluation using normalisation process theory
Authors: Clarke David , Gombert-Waldron Karolina , Honey Stephanie , Cloud Geoffrey , Harris Ruth , Macdonald Alastair , McKevitt Christopher , Robert Glenn , Jones Fiona 
Published: January 2021

ObjectiveTo explore facilitators and barriers to using experience-based co-design (EBCD) and accelerated EBCD (AEBCD) in the development and implementation of interventions to increase activity opportunities for inpatient stroke survivors.DesignMixed-methods process evaluation underpinned by normalisation process theory (NPT).SettingFour post-acute rehabilitation stroke units in England.ParticipantsStroke survivors, family members, stroke unit staff, hospital managers, support staff and volunteers. Data informing our NPT analysis comprised: ethnographic observations, n=366 hours; semistructured interviews with 76 staff, 53 stroke survivors and 27 family members pre-EBCD/AEBCD implementation or post-EBCD/AEBCD implementation; and observation of 43 co-design meetings involving 23 stroke survivors, 21 family carers and 54 staff.ResultsFormer patients and families valued participation in EBCD/AEBCD perceiving they were equal partners in co-design. Staff engaged with EBCD/AEBCD, reporting it as a valuable improvement approach leading to increased activity opportunities. The structured EBCD/AEBCD approach was influential in enabling coherence and cognitive participation and legitimated staff involvement in the change process. Researcher facilitation of EBCD/AEBCD supported cognitive participation, collective action and reflexive monitoring; these were important in implementing and sustaining co-design activities. Observations and interviews post-EBCD/AEBCD cycles confirmed creation and use of new social spaces and increased activity opportunities in all units. EBCD/AEBCD facilitated engagement with wider hospital resources and local communities, further enhancing activity opportunities. However, outside of structured group activity, many individual staff–patient interactions remained task focused.ConclusionsEBCD/AEBCD facilitated the development and implementation of environmental changes and revisions to work routines which supported increased activity opportunities in stroke units providing post-acute and rehabilitation care. Former stroke patients and carers contributed to improvements. NPT’s generative mechanisms were instrumental in analysis and interpretation of facilitators and barriers at the individual, group and organisational level, and can help inform future implementations of similar approaches.
Students’ top 10 priorities of research uncertainties on students’ sleep: a pragmatic James Lind Alliance approach
Authors: Holmen Heidi , Riiser Kirsti , Løyland Borghild , Sandbekken Ida Hellum , Almendingen Kari , Andenæs Randi , Bjørnnes Ann Kristin , Fagerlund Bettina Holmberg , Früh Elena Albertini , Helseth Sølvi , Kvarme Lisbeth Gravdal , Leegaard Marit , Misvær Nina , Pallesen Ståle , Rasalingam Anurajee , Ravn Ingrid , Ribu Lis , Rostad Hanne Marie , Sparboe-Nilsen Bente , Sæterstrand Torill Margaret , Torbjørnsen Astrid , Utne Inger , Valeberg Berit Taraldsen , Valla Lisbeth , Winger Anette , Grov Ellen Karine 
Published: January 2021

ObjectivesInspired by the James Lind Alliance (JLA) user involvement approach, the aim of the present study was to identify the top 10 uncertainties for sleep research raised by students in higher education, and to discuss our experiences with adapting the JLA method to a student population.DesignThe study design is a pragmatic JLA approach, including a priority setting partnership within the field of sleep, collection of sleep-related research uncertainties as reported by students in higher education, sorting of the uncertainties and a final identification of the top 10 uncertainties through collaborative work between researchers, students, stakeholders and experts in the field. Uncertainties were collected using a one-question online survey: ‘as a student, which question(s) do you consider to be important with regards to sleep?’. A variety of approaches were applied to promote the survey to the students, including social media, radio, the university website, stands in university cafeterias and a sleep stunt. NVivo V.12 was used to code and sort the questions.SettingA higher education institution in Norway.Participants555 students.ResultsThe data collection provided 608 uncertainties, and the following prioritised top 10: (1) screen time, (2) stress, (3) educational achievements, (4) social relations, (5) mental health, (6) physical activity, (7) indoor environment, (8) substance abuse, (9) shift work and (10) sleep quality. Despite successful data collection, we found sleep to be a broad topic, and defining specific questions throughout the sorting and verification process proved difficult.ConclusionsWe identified the prioritised top 10 research uncertainties as reported by students in higher education, ranking screen time first. However, the process was time and resource consuming. The research uncertainties addressed by the students showed great diversity, characterised by heterogeneity and a lack of specificity, making verification of the uncertainties challenging.
Cardiac Arrest Nurse Leadership (CANLEAD) trial: a simulation-based randomised controlled trial implementation of a new cardiac arrest role to facilitate cognitive offload for medical team leaders
Authors: Pallas Jeremy David , Smiles John Paul , Zhang Michael 
Published: January 2021

BackgroundMedical team leaders in cardiac arrest teams are routinely subjected to disproportionately high levels of cognitive burden. This simulation-based study explored whether the introduction of a dedicated ‘nursing team leader’ is an effective way of cognitively offloading medical team leaders of cardiac arrest teams. It was hypothesised that reduced cognitive load may allow medical team leaders to focus on high-level tasks resulting in improved team performance.MethodsThis randomised controlled trial used a series of in situ simulations performed in two Australian emergency departments in 2018–2019. Teams balanced on experience were randomised to either control (traditional roles) or intervention (designated nursing team leader) groups. No crossover between groups occurred with each participant taking part in a single simulation. Debriefing data were collected for thematic analysis and quantitative evaluation of self-reported cognitive load and task efficiency was evaluated using the NASA Task Load Index (NTLX) and a ‘task time checklist’ which was developed for this trial.ResultsTwenty adult cardiac arrest simulations (120 participants) were evaluated. Intervention group medical team leaders had significantly lower NTLX scores (238.4, 95% CI 192.0 to 284.7) than those in control groups (306.3, 95% CI 254.9 to 357.6; p=0.02). Intervention group medical team leaders working alongside a designated nursing leader role had significantly lower cognitive loads than their control group counterparts (206.4 vs 270.5, p=0.02). Teams with a designated nurse leader role had improved time to defibrillator application (23.5 s vs 59 s, p=0.004), faster correction of ineffective compressions (7.5 s vs 14 s, p=0.04), improved compression fraction (91.3 vs 89.9, p=0.048), and shorter time to address reversible causes (107.1 s vs 209.5 s, p=0.002).ConclusionDedicated nursing team leadership in simulation based cardiac arrest teams resulted in cognitive offload for medical leaders and improved team performance.