Loop Process Research Articles

Student Feedback Literacy - measurement conceptual framework

One of the key enablers for learning is feedback. The student’s experience receiving feedback can have long-lasting impacts on their beliefs, motivation and behaviour, which influences academic achievement levels and lifetime learning potential (Carless & Boud, 2018). For students to become proactive in the feedback loop process, they require competencies (understandings, capacities and dispositions) for the feedback process, known as Feedback Literacy (Carless & Winstone, 2020). Student Feedback Literacy (SFL) describes the necessary competencies for students to effectively and proactively engage and act on feedback to improve their learning ability (Sutton, 2012). Whilst the student feedback literacy competencies have been well defined (Molloy et al., 2020), these competencies are difficult to quantify. Therefore feedback literacy remains hidden and, for students, is earned as a by-product of student learning that is only acquired subconsciously through student learning experiences.
 The conceptual framework in this proposal contributes to Technology Enhanced Learning (TEL) by quantifying Student Feedback Literacy levels which inform and trigger additional feedback loops that enable students to grow their feedback literacy, which empowers students’ lifetime learning potential

Production of clean hydrogen fuel on a bifunctional iron catalyst via chemical loop reforming of methanol

This study has been proposed to increase the efficiency of the chemical loop process. Iron oxide doped with cobalt was used as a bifunctional material because of catalyst and oxygen carrier properties and methanol was used as a reducing agent. The co-precipitation method was used to synthesis of Co/Fe oxide. To evaluate the performance of the synthesized catalysts, a microchannel reactor was designed and built with a parallel channel arrangement.To increase H2/coke ratio, the effect of different amounts of cobalt (10, 20, and 30 %) in Fe2O3 catalyst has been investigated. Then, the optimal amount of cobalt concentration was selected for investigation of reduction temperature from 350 to 450 °C.The results showed that increasing the Co concentration to 30 % and increasing the temperature of the reduction step to 450 °C causes the catalytic reactions to start in a shorter time and the process efficiency increases due to the high H2/coke ratio. The repeated cycles investigation of the 0.3CoFe2O4 during 10 redox cycles showed decreased oxidation power and low stability. To enhance the oxidation power and stability, applying two-step reoxidation and adding Zr (30, 40 and 50 %) in the catalyst structure were examined.In the next step, using optimized Zr content (40%), to achieve pure H2 stream for fuel cell applications, the reaction time of the reduction step was optimized. The results showed that during reduction times of 7 min, coke formation was prevented and pure H2 (99.9999%) was produced. Finally, the stability of the 0.3CoFe2O4-ZrO2 during 50 redox cycles in reduction time of 7 min showed that the use of Zr maintains the stability of the catalyst.

CARDIOMYOCYTE REPROGRAMMING IN ANIMAL MODELS OF SEPTIC SHOCK.

Cardiomyocyte reprogramming plays a pivotal role in sepsis-induced cardiomyopathy through the induction or overexpression of several factors and enzymes, ultimately leading to the characteristic decrease in cardiac contractility. The initial trigger is the binding of LPS to TLR-2, -3, -4, and -9 and of proinflammatory cytokines, such as TNF, IL-1, and IL-6, to their respective receptors. This induces the nuclear translocation of nuclear factors, such as NF-κB, via activation of MyD88, TRIF, IRAK, and MAPKs. Among the latter, ROS- and estrogen-dependent p38 and ERK 1/2 are proinflammatory, whereas JNK may play antagonistic, anti-inflammatory roles. Nuclear factors induce the synthesis of cytokines, which can amplify the inflammatory signal in a paracrine fashion, and of several effector enzymes, such as NOS-2, NOX-1, and others, which are ultimately responsible for the degradation of cardiomyocyte contractility. In parallel, the downregulation of enzymes involved in oxidative phosphorylation causes metabolic reprogramming, followed by a decrease in ATP production and the release of fragmented mitochondrial DNA, which may augment the process in a positive feedback loop. Other mediators, such as NO, ROS, the enzymes PI3K and Akt, and adrenergic stimulation may play regulatory roles, but not all signaling pathways that mediate cardiac dysfunction of sepsis do that by regulating reprogramming. Transcription may be globally modulated by miRs, which exert protective or amplifying effects. For all these mechanisms, differentiating between modulation of cardiomyocyte reprogramming versus systemic inflammation has been an ongoing but worthwhile experimental challenge.

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class.

First Ideas Matter: A Selection Loop Process for Business Model Adaptation

Experimentation plays a vital role in firms’ activities and strategies. Yet, we still have a scant understanding of the processes underpinning experimentation for business modelling in startups. Through a qualitative longitudinal case, we investigate how a startup reshapes its value proposition and engages with external market stakeholders – in this case end customers – when their heterogeneity could not be catered by the first business model designed by the entrepreneur. We look at cases where a business model, originally envisioned with a single value proposition, needs to cater different groups of customers (i.e., product users, buyers, and beneficiaries) with diverse expectations in terms of value and engagement. Our study identifies a ‘selection loop’ process formed of three different experimentation phases – environment-based stress, cognitive-driven adaptation, and stakeholder-led evolution. This process contributes to better understand how managerial cognition drives business model design and experimentation towards a “fit” between the entrepreneur’s cognitive model and the stakeholders in the external environment. We also advance the cognitive perspective on business models as we take a cognitive lens to explore experimentation as a process to identify and adapt a business model to emerging evidence about a startup’s customers.

Sleepiness in Cognitively Unimpaired Older Adults Is Associated With CSF Biomarkers of Inflammation and Axonal Integrity.

IntroductionSleepiness has been associated with cognitive decline and dementia in the elderly. Older adults with excessive daytime sleepiness appear to be more vulnerable to longitudinal amyloid PET accumulation before the onset of the dementia. However, it remains unclear whether sleepiness is similarly associated with other biomarkers of Alzheimer’s disease (AD), axonal integrity, and inflammation, which may also contribute to neurodegeneration and cognitive decline.MethodsIn this cross-sectional analysis, we identified 260 cognitively unimpaired adults (>60 years) from the Mayo Clinic Study of Aging, a population-based cohort from Olmsted County (MN), who underwent CSF quantification of AD biomarkers (Aβ42, p-tau, p-tau/Aβ42) in addition to at least one of the following biomarkers [neurofilament light chain (NfL) interleukin-6 (IL-6), IL-10, and tumor necrosis factor-α (TNF-α)]. We fit linear regression models to assess associations between sleepiness, as measured by the Epworth Sleepiness Scale (ESS), and CSF biomarkers, controlling for age, sex, APOε4 status, body mass index, hypertension, dyslipidemia, and prior diagnosis of obstructive sleep apnea.ResultsHigher ESS scores were associated with higher CSF IL-6 and NfL, but not with the other CSF biomarkers. For every ESS score point increase, there was a 0.009 ([95% CI 0.001–0.016], p = 0.033) increase in the log of IL-6 and 0.01 ([95% CI 0.002–0.018], p = 0.016) increase in the log of NfL. A sensitivity analysis showed an association between ESS scores and log of p-tau/Aβ42 only in participants with an abnormal ratio (>0.023), highly predictive of amyloid positivity. For every ESS score point increase, there was a 0.006 ([95% CI 0.001–0.012], p = 0.021) increase in the log of CSF p-tau/Aβ42.ConclusionSleepiness was associated with greater CSF IL-6 and NfL levels, which could contribute to neurodegeneration or alternatively cause sleepiness. Higher NfL levels may result from sleep disruption and/or contribute to sleepiness via disturbed connectivity or damage to wake-promoting centers. Associations between sleepiness and p-tau/Aβ42 in participants with abnormal ratio suggest that amyloid positivity contributes to vulnerability to sleep disturbance, which may further amyloid accumulation in a feed-forward loop process. Prospective studies of these markers are needed to determine cause-effect relationships between these associations.

Photon production rate calculation from quark-gluon plasma using MEQM in heavy-ion collision

It is widely accepted that a huge and intense magnetic field is created along with the exotic state of matter in heavy-ion collision. The photon yield from quark-gluon plasma created in a magnetic environment is studied using a thermal quasi-particle mass. The quarks acquire this mass depending on the temperature and nonzero value of quark chemical potential which gets modified as magnetized effective quark mass (MEQM) in the presence of magnetic field. The total photon rate is shown for one loop process incorporating MEQM. It is found that the emission rate to lowest order increases strongly with increasing of the quark chemical potential and temperature. The enhanced yield in photon is also compared with the recently reported theoretical results.

Design of spaceborne self-closed loop extensible temperature control system based on FPGA

Abstract The paper describes the design method and implementation of a self-closed loop extensible temperature control system for spaceborne based on FPGA. FPGA is used as the core of data processing System. Firstly using the high accuracy AD and analog switch for temperature data collection. The FPGA is to filter data by average calculation, voltage correction algorithm and PWM controlling temperature algorithm. Finally, after processing temperature control parameters, according to the corresponding channel relation it outputs temperature control parameters to the corresponding control output on NMOS. It can be uploaded temperature threshold parameters through SPI bus, and the corresponding relationship parameters between temperature control and temperature measurement can be modified, and it carry out the remote sensing function on the earth. The system can realize multi-system cascade temperature control through Rs422 bus. This process realizes a complete self-closed loop process, and finally achieves the system design goal.

Slow magnetic relaxation in a 3D dysprosium(III)-fluoro-oxalate framework containing zig-zag [Dy–F]n chains

Dysprosium complexes based on [F–Dy] or near-linear [F–Dy–F] unit are of great concern in the field of single-molecule magnets due to their large magnetic anisotropy. Here, the crystal structure and the magnetic relaxation dynamics were reported for a three-dimensional (3D) metal–organic framework (MOF): [DyF(C2O4)(H2O)2]n·2nH2O (1), which is the unique MOF containing zig-zag [Dy–F]n chains. Magnetic susceptibility characterization reveals that 1 is one of the few 3D MOFs which show slow magnetic relaxation under zero dc field. And the effective energy barrier of 72 K for 1 is also higher than most Dy-based 3D MOFs. The diamagnetic ion dilution study shows that the ferromagnetic exchange couplings mainly transmitted by F− bridges in 1 contribute little to the energy barrier, but effectively suppress the quantum tunneling process and result in a smooth hysteresis loop with no waist-restricted step.

Secondary void interactions under shear in thin porous sheets

A compressive stress state decreases the mechanism of void growth in metals. However, in reality, void sheets are evidenced on the fracture surface of metallic shear-compression specimens. The prevailing stress state in the gauge is in contrast to the features observed on the fracture surface. This work examines the mechanical fields in the surroundings of a void under shear as a realization of the mechanical behavior of a ligament between coalescing voids. It is shown that the presence of secondary voids in the surroundings of one primary void reduces the average triaxiality in the cell and homogenizes its distribution; thus, it can be surmised to be mechanically favored. This paper examines this point from a micromechanical perspective utilizing the periodic unit cell method with a single void in its center as a reference case to be compared with other configurations of voids distributed in the cell. It is hypothesized that nucleated voids grow and promote nucleation and growth of voids around them – a mechanism to describe void sheet formation as a positive feedback loop process. The effect of extra secondary voids on the mechanical fields is examined and correlated to their size and location with respect to the existing primary void.

FPGA Hardware in the Loop Validation of Asynchronous Machine with Full Direct Torque Control Implementation

This article provides a full hardware implementation for direct torque control (DTC) of an asynchronous motor (AM) on the Field Programmable Gate Array (FPGA). Due to its high processing frequency, the FPGA circuit presents an alternative for achieving a high performance DTC implementation. This cannot be achieved by any DSP or microcontroller application. The proposed hardware architecture implements three components of DTC control strategy (the integral proportional speed regulator, the estimation and the switching blocks). This implementation has the advantage of being faster, more efficient and with minimum hardware resources on the target FPGA board. The hardware architecture of the DTC control was designed and simulated in Matlab / Simulink using XSG blocks, then synthesized with the Xilinx ISE 14.2 tool, implemented and validated on the Xilinx Virtex-4 FPGA circuit by Hardware in the Loop process.

Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing

Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in choosing the shortest path with least energy consumption.

Технология программной имплементации цифрового двойника в контур IoT управления HVAC-системой

The development of application software for cyber-physical systems of buildings involves the widespread use of Internet of Things (IoT) integration platforms. In practice, the flexible functionality of IoT platforms often leads to additional costs for software enhancement of existing and connection of new units, in particular digital twins. The paper proposes a technological solution for the implementation of a digital twin of the ventilation process in the IoT control loop of heating, ventilation and air conditioning (HVAC) systems for buildings and industrial facilities. The implementation and execution of the digital twin in the form of a dynamic simulation model in the object-oriented modelling language Modelica in the OpenModelica environment is considered. The IoT platform InfluxData, based on the TICK stack, is considered as an example of an integration environment. It is a horizontally-oriented IoT platform that contains the mechanism for collecting data from devices and the InfluxDB time-series database for storing metrics. To integrate simulation models on Modelica with InfluxDB, an OMPython server is proposed. In this case, the integration scripts are executed in the Python language, which as a result extends the traditional capabilities of the IoT platform significantly to the level of a digitally twinned control system. This HVAC control involves adapting control loops by taking into account the dynamics of the air distribution process over the ventilation network, evaluating and compensating for process inertia. The publication was prepared within the framework of the Academic Fund Program at the HSE University in 2020–2021 (grant № 21-04-039).

Influence of aging on the resistive switching behavior of epitaxial strontium titanate based heterostructures

In this work, we investigated the impact of aging on the resistive switching behavior of high quality epitaxial heterostructure resistive switching devices [Pt/SrTiO3/LaNiO3/LaAlO3 (001) with SrTiO3 (STO) as switching layer, Pt as top electrode and LaNiO3 as bottom electrode]. For this study, the resistive switching memory devices of two different ultrathin switching layer (STO) thicknesses (5 and 10 nm) were fabricated with different shape and size of top electrode (Pt) i.e. 20 µm (circular shape) and 50 µm (square shaped). Subsequently, their resistive switching behavior at different instant of time i.e. freshly prepared, 4 months and 16 months were studied. With long aging, we find a decrease in the positive bais voltages for which current start rising exponentially (set voltages) for most of the devices besides emergence of various features such as multiple crossovers, noisy/spike region during set process and openness in the switching hysteresis loop. The existence of these behaviors is attributed to the fact that the moisture permeates through the interfacial region between top electrode (Pt) and switching layer (STO) irrespective of the thickness of switching layer and dimensions of top electrode.

An updated parametric hysteretic model for steel tubular members considering compressive buckling

An updated parametric hysteretic model for steel tubular members, in which the member compressive buckling is considered, is proposed in this paper. The model can be used for simulating the complex inelastic force-displacement hysteresis behaviour of steel tubular members. Based on the general aspects of hysteretic behaviour and the traditional Marshall model, the modified Marshall model is developed to improve the simulation accuracy, whose control parameters are determined by batch parameter analysis results. For the applicability in multiple hysteresis loop process, the initial member curvature under compression-tension during hysteretic loop process is studied and the computation methods under compression and tension are given individually based on the finite element analysis results and theoretical derivation. On this foundation, the model control parameter correction and axial compression capacity redefined with large initial member curvature during hysteretic loop process are executed in the updated parametric hysteretic model proposed later in this paper. Finally, in addition to the comparison with the finite element results, the rationality and applicability of the proposed model are also verified by several experiments and the good agreement in loop curve trend and hysteretic energy consumption are found between the simulated and experimental results.

Study protocol: A stepped-wedge, cluster-randomized trial of the effectiveness of a deliberative loop in identifying implementation strategies for the adoption of a dental sealant guideline in dental clinics

BackgroundThe American Dental Association (ADA) recommends dental providers apply dental sealants to the occlusal surfaces of permanent molars for the prevention or treatment of non-cavitated dental caries. Despite the evidence-based support for this guideline, adherence among general dentists is low, ranging from less than 5 to 38.5%. Thus, an evidence-to-practice gap exists, and it is unclear which implementation strategies would best support providers in adopting and implementing the evidence-based practice. One potential approach to selecting and tailoring implementation strategies is a deliberative loop process, a stakeholder-engaged approach to decision-making. This trial aims to test the acceptability, feasibility, and effectiveness of using a deliberative loop intervention with stakeholders (i.e., providers and staff) to enable managers to select implementation strategies that facilitate the adoption of an evidence-based dental practice.MethodsSixteen dental clinics within Kaiser Permanente Northwest Dental will be cluster randomized to determine the timing of receiving the intervention in this stepped-wedge trial. In the three-part deliberative loop intervention, clinic stakeholders engage in the following activities: (1) receive background information, (2) participate in facilitated small-group discussions designed to promote learning from each other’s lived experiences and develop informed opinions about effective clinic-level implementation strategies, and (3) share their informed opinions with clinic leaders, who may then choose to select and deploy implementation strategies based on the stakeholders’ informed opinions. The primary outcome of Reach will be defined as patient-level receipt of guideline-concordant care. Secondary outcomes will include the cost-effectiveness, acceptability, and feasibility of the deliberative loop process. Implementation strategies deployed will be catalogued over time.DiscussionThese results will establish the extent to which the deliberative loop process can help leaders select and tailor implementation strategies with the goal of improving guideline-concordant dental care.Trial registrationThis project is registered at ClinicalTrials.gov with ID NCT04682730. The trial was first registered on 12/18/2020. https://clinicaltrials.gov/ct2/show/NCT04682730

From Buildings’ End of Life to Aggregate Recycling under a Circular Economic Perspective: A Comparative Life Cycle Assessment Case Study

The demolition of buildings, apart from being energy intensive and disruptive, inevitably produces construction and demolition waste (C&Dw). Unfortunately, even today, the majority of this waste ends up underexploited and not considered as valuable resources to be re-circulated into a closed/open loop process under the umbrella of circular economy (CE). Considering the amount of virgin aggregates needed in civil engineering applications, C&Dw can act as sustainable catalyst towards the preservation of natural resources and the shift towards a CE. This study completes current research by presenting a life cycle inventory compilation and life cycle assessment case study of two buildings in France. The quantification of the end-of-life environmental impacts of the two buildings and subsequently the environmental impacts of recycled aggregates production from C&Dw was realized using the framework of life cycle assessment (LCA). The results indicate that the transport of waste, its treatment, and especially asbestos’ treatment are the most impactful phases. For example, in the case study of the first building, transport and treatment of waste reached 35% of the total impact for global warming. Careful, proactive, and strategic treatment, geolocation, and transport planning is recommended for the involved stakeholders and decision makers in order to ensure minimal sustainability implications during the implementation of CE approaches for C&Dw.

One-step selective recovery and cyclic utilization of valuable metals from spent lithium-ion batteries via low-temperature chlorination pyrolysis

An efficient and environmentally friendly closed loop process combing low-temperature chlorination pyrolysis with water leaching is proposed for one-step selective recovery and cyclic utilization of valuable metals from spent lithium-ion batteries (LIBs). First, the spent LiNixCoyMnzO2 (s-NCM) cathode materials are pyrolyzed with NH4Cl as a chlorination agent and the valuable metals (Ni, Co, Mn, Li) can be converted into readily soluble chloride which attribute to the release of HCl and NH3 from NH4Cl. After the s-NCM cathode materials treated via low-temperature chlorination pyrolysis at 300 °C for 30 min with NH4Cl/s-NCM mass ratio of 3:1, more than 99.9% of the valuable metals can be recovered by simple water leaching. Moreover, thermodynamic analysis show that the valuable metals can be converted to chloride while the impurity metals (Fe, Al) do not participate in the reaction at this temperature, which can achieve the one-step selective recovery of valuable metals. Therefore, the water leaching solution can be directly used to synthesize regenerated pristine NCM (r-NCM) cathode materials which exhibit excellent cycle and rate performance. In summary, one-step selective leaching and regeneration of valuable metals can be accomplished by low-temperature chlorination pyrolysis. The whole process reduces the energy consumption and the environmental pollution caused by alleviating the introduction of acidic or alkaline chemicals and realize the high-value utilization of waste resources.

Efficient extraction of metals from thermally treated waste printed circuit boards using solid state chlorination: Statistical modeling and optimization

Formal recycling of e-waste is necessary for recovery of valuable metals and to avoid environmental damage due to use of toxic chemicals and illegal disposal of the leftovers. This study focused on development of an effective and environment-friendly method to extract valuable metals from waste printed circuit boards (WPCBs) using low temperature solid state chlorination followed by water leaching method. The shredded WPCBs were thermally pre-treated at 600 °C to convert plastic fraction into oil and combustible gases. The acquired metal rich residue (T-WPCBs) was chlorinated subsequently with NH4Cl. A combination of central composite design-response surface methodology (CCD-RSM) was successfully implemented into modeling the chlorination process for extraction of Au and Cu as a function of the process variables viz. temperature, time and NH4Cl dose. More than 96% of Cu and 67% of Au were extracted with water, when the T-WPCBs powder was chlorinated with 3 g/g of NH4Cl at 300 °C for 3 h. However, the proposed process has potential to extract other metals also; hence, the added credibility of the process was revealed by reporting extraction of other valuable metals such as Ni, Fe, Pb, Ag, and Zn at optimized conditions. This study strives to develop a closed loop process for extraction of metals; therefore, NH4Cl, the lone chemical used herein, was recycled for an economically viable process. Unlike the traditional pyrometallurgical process, which operates above 1000 °C and produces toxic fumes and soot, this process works at a low temperature range of 275–325 °C and able to extract out more than 90% of the total metal content without generating any toxic fume and soot.

Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems

The urgency of solving the problem of ensuring the security of cyber-physical systems is due to ensure their correct functioning. Cyber-physical system applications have a significant impact on different industrial sectors. The number and variety of cyber-attacks are growing, aimed not only at obtaining data from cyber-physical systems but also managing the production process itself. Detecting and preventing attacks on cyber-physical systems is critical because they can lead to financial losses, production interruptions, and therefore endanger national security. This paper proposes a deep hybrid model based on three parallel neural architectures: a one-dimensional convolutional neural network, a gated recurrent unit neural network, and a long short-term memory neural network. The SPOCU activation function is considered in hidden layers of the proposed model and improves its performance. Furthermore, to improve the classification accuracy, a modified version of Adam optimizer is considered. The experiments are conducted on two datasets: raw water treatment plant and gasoil heater loop process as the cyber-physical system applications. They contain information about the normal behavior of these systems and their failures caused by cyber-attacks. The results show that the proposed model outperforms the recent works using machine learning techniques.