Faculty Feedback Research Articles

The design of self-healing memristive network circuit based on VTA DA neurons and its application

This paper proposes a self-healing memristive network circuit, which simulates the resilience mechanism of VTA DA neurons. The proposed memristive network circuit mainly consists of four modules: input (synapse) module, damage detection module, threshold trigger module and negative feedback module. The input module is composed of a memristive synaptic circuit, which can react to a harmful initial input rather than a normal initial input. Apart from reacting to initial input, the input module also can receive a feedback signal released by the negative feedback module, and the process of receiving feedback signal is the key of realizing the self-healing function. The damage detection module can output a low level pulse that acts as an input signal of the threshold trigger module when the harmful initial input exists. The threshold trigger module will activate the negative feedback module when the trigger point which corresponds to the positive threshold voltage of memristor is reached. As the last module of the self-healing function, the negative feedback module can transmit the feedback signal, also be called a repairing signal, to the damaged memristor of the input module. The PSPICE simulation results indicate that the proposed memristive network circuit can realize the self-healing function which corresponds to the resilience mechanism of the VTA DA neurons. Meanwhile, when the proposed memristive network circuit has been applied to the damaged electronic machines or robots, the self-healing function of it can make the machines be reusable.

Perceptions and Utility of Course Evaluations in US Pharmacy Schools

ObjectiveThis study aimed to describe the purpose, implementation, and perceived utility of course evaluations in pharmacy programs. MethodsAfter a literature review, a 34-item survey was developed, pretested, and sent to assessment administrators at accredited pharmacy programs (N = 139) with at least 3 follow-ups. Descriptive and inferential statistics were performed in IBM SPSS Statistics software. ResultsA total of 90 programs responded (64.7% response rate). Most students (94%) were offered the opportunity to complete course evaluations. Some students completed evaluations during the course (47%), while others did so within 1 week of completion of the course (49%). Whether or not class time was given for students to complete the survey was often dependent on faculty choice (52.2%). Results were typically released after final grades were posted (92%), in time to use for the next semester of teaching (77%). Faculty were chosen to be evaluated by the number of teaching hours (50%) followed by all instructors (45.6%). Programs used the results for performance reviews by chairs (91%), course coordinator reviews (84%), and committee continuous quality improvement efforts (72%). Most programs did not provide faculty guidance on using evaluations (78%) nor development/mentoring (57%); only 22% of programs offered student development in completing evaluations. ConclusionWhile most programs invite feedback from all students via evaluations, most did not provide guidance to faculty on how to use this feedback for faculty or course development purposes. A more robust process to optimize the use of course evaluations should be developed.

Memristor-Based Neural Network Circuit of Associative Memory With Overshadowing and Emotion Congruent Effect.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module. The generation and recovery of different emotions are realized by the emotion module. The functions of overshadowing under different emotions and recovery from overshadowing are achieved by the inhibition module and the memory module. Finally, the blocking caused by long-term overshadowing is implemented by the feedback module. The proposed circuit can be applied to bionic emotional robots and offers some references for brain-like systems.

Collaborative Approach to the Spatial Journey- An Experiment in First Year Architectural Design Studio.

Abstract: An architectural design studio is a place for learning, experimentation, and exploration. Design of a built environment has constantly remained a collective activity where the architect, client, structural engineer, surveyor, service contractor, mason, and many others work together for a common goal. In the traditional design studio model, a student is expected to follow a set of design procedures like ideation, conceptualization iteration, and finalization to arrive at the outcome. He is supposed to design a built form based on context, climate, socio-economic parameters, and function using appropriate materials and technology. Often the process becomes a drool if the student is allowed to follow the sequence all alone. He or she may get stuck and lack motivation. But if the studio pedagogy involves discussion and an endeavor to explore rational thinking by adopting learning by doing together which may involve peer group discussion and continuous inputs from faculties, the process becomes much more interesting. A pupil studying architecture needs to cooperate and engage in design studios with his or her classmates and faculty. In addition, the students need to confederate, to achieve the objective of learning in a way that prepares them for the extremely synergistic phenomena of the architectural profession. (Crosbie, 1995; Daniels, 2002).This research aims to understand the effectiveness of design studios that can work in collaboration to refine every partaker’s contribution to the design methodology. The research was supervised as a semester-long exercise that integrated forty-six students from the first-year Architectural Design program, at KLE Technological University, India. The author explored quantitative analysis in the form of rubrics along with qualitative parameters like peer assessment, faculty feedback, and student response in the design process. . Keywords— Collaborative Design Studio; Design Methodology; Traditional Design Studio.

Multi-objective faculty course assignment problem based on the double parametric form of fuzzy preferences

This paper presents a mathematical model of the multi-objective faculty course assignment problem based on the double parametric form of fuzzy preferences. The fuzzy preferences are based on an analysis of faculty feedback given by students and an analysis of the results of the previous year’s examination of students. The proposed model is developed utilizing faculty members’ preferences, the preferences of an administrator for faculty members to courses, and fuzzy preferences based on faculty feedback and student result analysis. The double parametric approach solves a timetabling problem based on information from a university’s hypothetical numerical data. The fuzzy programming technique with linear membership function is applied to generate efficient and non-dominated allocations with better optimal values and degree of satisfaction of objective functions for different values of parameters α and β for fuzzy preferences. Results are found using LINGO19.0 software.

Instructors Share Techniques for Teaching with Technology: Faculty Connections and Spotlights

This article describes a collaborative project between instructional designers and faculty members in a California public university. It is based on the idea that faculty possess valuable applied knowledge of how to use technology tools to address instructional needs. The sharing of this knowledge via ‘Faculty Spotlights’ fosters technology adoption on campuses. Additionally, the process of co-creating spotlights helps instructional designers see how and why instructors apply technology to pedagogy. This understanding allows designers to collaborate with additional instructors more effectively. This paper describes processes for creating and promoting faculty spotlights, along with a description of early project impact and faculty feedback. It attempts to live its mission, which is to elevate learning from experience.

Effectiveness of task-specific training for improving balance performance in children with cerebral palsy (CP): a narrative systematic review

IntroductionA body of research has focused on helping children with disabilities stay physically active. As poor balance has been found as a major limitation in physical activities, a systematic review exploring balance intervention programs is warranted.MethodsThe primary aim of this is to systematically evaluate evidence for the effect of task-specific training to improve balance outcomes in ambulant children with cerebral palsy. Seven databases were searched for studies involving ambulant children with cerebral palsy (4–15 years old); balance TST; and balance-related outcomes. The quality of the studies was appraised using the JBI Critical Appraisal Checklists for risk of bias, study design, and quality of evidence. Continuous data were transformed and summarised using standardised mean difference and 95%CI.ResultsSeven randomised controlled trials met the inclusion criteria. The risk of bias was deemed low in most of the studies. Due to heterogeneity of study design and outcome measures, a narrative synthesis was conducted. Positive between-group effects favouring TST were found across the six studies (BBS d = 1.528, 95% CI = 0.7747–2.2813) and follow-up (BBS d = –1.667, 95% CI = –2.459 ÷ –0.874). Despite poor reporting of motor learning strategies, repetitive practice, feedback, and task modulation strategies were used consistently in balance TST.ConclusionsTST could be recommended to be a part of an intervention programme to improve balance performance in children with CP. However, more studies with rigorous study design and adequately powered implementation of a more fun task-oriented training programme are needed.

A Dynamic Weights-Based Wavelet Attention Neural Network for Defect Detection.

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise. This article proposes a dynamic weights-based wavelet attention neural network (DWWA-Net) to address these issues, which can enhance the feature representation of defects and simultaneously denoise the image, thereby improving the detection accuracy of weak defects and defects under strong background noise. First, wavelet neural networks and dynamic wavelet convolution networks (DWCNets) are presented, which can effectively filter background noise and improve model convergence. Second, a multiview attention module is designed, which can direct the network attention toward potential targets, thereby guaranteeing the accuracy for detecting weak defects. Finally, a feature feedback module is proposed, which can enhance the feature information of defects to further improve the weak defect detection accuracy. The DWWA-Net can be used for defect detection in multiple industrial fields. Experiment results illustrate that the proposed method outperforms the state-of-the-art methods (mean precision: GC10-DET: 6.0%; NEU: 4.3%). The code is made in https://github.com/781458112/DWWA.

An ecological music education model based on deep learning: a blend of tradition and innovation

Abstract With the improvement of the status of music education, the reform of music education mode is imminent. The intersection of tradition and innovation is achieved by combining ecological education and deep learning concepts to construct an environmental music education model based on the traditional music education model. The research mainly focuses on intelligent music generation and its auxiliary effects in ecological music education. The steps are to complete the intelligent generation of multi-track music through the HL-MTMG model by combining the blended learning module and reward feedback module after defining the problems of music rhythm and melody problems. In analyzing the efficacy of ecological music education based on deep learning, it was concluded that the satisfaction rate of the generated music samples ranged from 66% to 72%, and the average score of students’ satisfaction with the classroom teaching of the ecological music teaching model based on deep learning ranged from 3.313 to 4.253. The environmental music education model through deep learning can significantly enhance the impact of music education and encourage students’ interest in music learning.

Generation of visible Raman operation laser by a fiber electro-optical modulator feedback loop

Generation of visible Raman operation laser by a fiber electro-optical modulator feedback loop

Enhancing Faculty Development Through Compiled Verbal Feedback on Clinical Teaching From Trainees.

Feedback from fellows-in-training (FITs) is important for faculty development and to enrich clinical teaching. We sought to evaluate the effectiveness of traditional online evaluations and a novel compiled verbal feedback mechanism. An annual feedback system was implemented in our rheumatology division in which FITs provided verbal feedback on all faculty to a facilitator who compiled, deidentified, and shared the feedback with individual faculty members. FITs also completed standard online annual evaluations of faculty. FITs and faculty completed surveys assessing the perceived effectiveness and confidentiality of each feedback mechanism. Thirteen of 15 eligible faculty and all 4 eligible FITs completed both surveys. Responses by FITs and faculty regarding the quality of online evaluations were generally unfavorable or neutral. Faculty responses regarding compiled verbal feedback were more favorable in all questions and significantly more favorable with respect to the feedback's ability to explain strengths (54% favorable for online evaluations vs 100% for compiled verbal feedback), the feedback's specificity (0% vs 54%), and the feedback's actionable nature (15% vs 62%). All FITs' responses regarding quality of compiled verbal feedback were favorable. FITs had concerns regarding confidentiality with both online evaluations (0% favorable) and compiled verbal feedback (25% favorable), though FITs had less concern for future faculty interactions with compiled verbal feedback (100% favorable) than with online evaluations (0% favorable). Compiled verbal feedback by FITs produced more actionable and effective feedback for faculty, with less concerns regarding future faculty interactions compared with traditional online evaluations. Further study of this method across different programs and institutions is warranted.

Compensated acceleration feedback based active disturbance rejection control for launch vehicles

In this paper, the attitude tracking and load relief control problems against wind disturbances and uncertain aerodynamics as well as the engine thrust of launch vehicles are studied. Firstly, a framework of Compensated Acceleration Feedback based Active Disturbance Rejection Control (CAF-ADRC) is established to achieve both desired attitude tracking and load relief performances. In particular, the total disturbance that includes the effects caused by both aero-coefficient perturbations and disturbances is estimated by constructing an Extended State Observer (ESO) to achieve attitude tracking. Furthermore, combined with the normal acceleration due to the engine thrust, the accelerometer measurement is also compensated to enhance the load relief effect. Secondly, the quantitative analysis of ESO and the entire closed-loop system are studied. It can be concluded that the desired attitude tracking and load relief performances can be achieved simultaneously under the proposed approach. Besides, tuning laws of the proposed approach are systematically given, which are divided into ESO, Proportional Derivative (PD) and Compensated Acceleration Feedback (CAF) modules. Moreover, the performances under CAF-ADRC approach can be better than those under CAF based PD (CAF-PD) approach by tuning load relief gain. Finally, the approach presented is applied to a typical control problem of launch vehicles with wind disturbances and parameter uncertainties.

Integration of a dental hygienist into the interprofessional long-term care team.

To address poor oral health of residents in long-term care homes (LTCH), this study explored the process of integrating an educational resource and a dental hygienist on the interprofessional care team. This convergent mixed-methods study took place at a 472-bed LTCH in Toronto, Canada from February to August 2018. Nurses employed at the LTCH participated in the study. During the study period, a dental hygienist was integrated into an interprofessional LTCH team. Nurses completed an online eLearning module about using the Oral Health Assessment Tool (OHAT) when referring residents' oral health concerns to a. Pre/post knowledge quizzes, module feedback and satisfaction surveys were administered. A retrospective chart review examined OHAT use and compared nurse and dental hygienist oral health assessments. Two cycles of semi-structured interviews with five nurses explored experiences with the eLearning module, OHAT and integration of the dental hygienist into the team. Nurses scored well on the knowledge quizzes and reported comfort in using the OHAT to refer oral concerns to a dental hygienist; however, actual use was minimal. oral health issues were under-reported by nurses on the Resident Assessment Instrument-Minimum Data Set (RAI-MDS); the dental hygienist reported significantly more debris, teeth lost and carious teeth (all P < 0.0001). Qualitative analysis indicated that the nurses valued dental hygienist integration into the team. Using knowledge mobilisation practices, a new oral health referral tool was developed. This study highlights the feasibility and desirability of an oral health eLearning module, practical assessment tools and participation of a dental hygienist on the LTCH interprofessional care team.

A Multi‐Module Sensing and Bi‐Directional HMI Integrating Interaction, Recognition, and Feedback for Intelligent Robots

AbstractRapid advances in robotics have placed urgent demands on more intelligent human‐machine interaction technologies. Specifically, the way of establishing dual‐way intuitive communication with a consistent sensory system can greatly enhance efficiency and reliability. Here, a bi‐directional human‐machine interface (HMI) is designed by applying starch‐based hydrogel sensors. The whole system consists of a multi‐modal wearable sensory exoskeleton with a haptic feedback module and sensory robotic hand. The sensory exoskeleton with strain‐sensing glove and rotation‐sensing arm can capture and project the motion of the entire upper limb. The system offers object recognition functions by utilizing a sensing array on the robotic hands and machine learning algorithms, which can identify the shape and hardness information. The recognized results can be delivered back to the operator via vision and vibrational haptic feedback, respectively. This dual‐way intelligent sensory system shows potential application in many key fields such as the Internet of Things, teleoperation, and medical robotics.

Crosstalk of CGMP and CAMP in the vertebrate phototransduction cascade

The conventional phototransduction cascade suggests that cyclic guanosine monophosphate (cGMP) functions as the chief secondary messenger while intracellular calcium concentration dominates as the central feedback modulator. Countless years and several scientific institutions have led to this conclusion, and it stands as the most extensively researched and explicit among all sensory transduction schemes. However, experimental evidence suggests that our understanding of the phototransduction cascade mechanisms remains significantly incomplete [1]. According to the canonical cascade scheme, all transients should be completed within a second once the light stimulus is no longer present. However, our data indicates that there are long-lasting changes in cell sensitivity and dark current parameters after the stimulus, which can last for more than 10 s. Phenomena that deviate from the standard phototransduction cascade behavior can potentially be clarified by an alternative regulatory mechanism that is based on cyclic adenosine monophosphate (cAMP). Previous research has provided convincing evidence that the intracellular levels of cAMP can significantly impact the functioning of the phototransduction cascade on both slow (day) [2] and relatively fast (minutes) [3] time scales. Additionally, there is phenomenological evidence indicating the existence of other regulatory signaling pathways in the phototransduction cascade without a corresponding mechanism in the classical phototransduction scheme, including inositol triphosphate (IP3) and diacylglycerol (DAG). We investigate whether cAMP, IP3, and DAG regulate the phototransduction cascade during photoresponse. For this regulatory effect to occur, there must be a change in the signaling molecule concentration during the process. These processes occur in less than a second, and it is crucial for the presence of the regulatory effect. Given that traditional fluorescence methods cannot measure the concentration of any signaling molecule in the retina, a hardware-software setup has been developed that allows cryofixation of retinal samples at the required speed. The setup permits fixing up to six samples in a series with a delay of no more than 80 milliseconds after light stimulation. The concentration of signal molecules is assessed using high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry. The results demonstrate a 4.5-fold elevation in cAMP concentration 1.1 s after switching on a light with an intensity close to saturation. The concentration of cAMP is directly proportional to the intensity of the stimulating light; there is no increase in cAMP at lower light intensities. No noteworthy changes in IP3 and DAG concentration were detected in response to light stimulation. The findings align with existing literature on the kinetics of light-triggered protein kinase A (PKA) activity [3], which indicated an initial decrease followed by an increase in PKA activity. These results could potentially inform the revision and expansion of the phototransduction cascade model.

Improving Balance and Movement Control in Fencing Using IoT and Real-Time Sensorial Feedback.

Fencing, a sport emphasizing the equilibrium and movement control of participants, forms the focal point of inquiry in the current study. The research endeavors to assess the efficacy of a novel system designed for real-time monitoring of fencers' balance and movement control, augmented by modules incorporating visual feedback and haptic feedback, to ascertain its potential for performance enhancement. Over a span of five weeks, three distinct groups, each comprising ten fencers, underwent specific training: a control group, a cohort utilizing the system with a visual real-time feedback module, and a cohort using the system with a haptic real-time feedback module. Positive outcomes were observed across all three groups, a typical occurrence following a 5-week training regimen. However, noteworthy advancements were particularly discerned in the second group, reaching approximately 15%. In contrast, the improvements in the remaining two groups were below 5%. Statistical analyses employing the Wilcoxon signed-rank test for repeated measures were applied to assess the significance of the results. Significance was solely ascertained for the second group, underscoring the efficacy of the system integrated with visual real-time feedback in yielding statistically noteworthy performance enhancements.

Back to the Basics: Uncovering the Rhetoric Student Learning Outcome

Using an evaluative approach within a professional communication service course, we used student documents and instructor feedback to uncover how students and instructors were understanding the rhetoric student learning outcome (SLO). Because rhetoric is central to the course, our driving questions were, Can we locate language that actualizes the rhetoric SLO in student documents? How does faculty feedback articulate the rhetoric SLO to facilitate effective revision? Overall, we found that whether identifying rhetoric in student documents or instructor feedback, the interpretation was varied and opens up room in pedagogical practices. We offer three implications for teaching: enhancing attention to teaching rhetoric, improving assignment design, and focusing on professional development for faculty.

Auxin homeostasis is maintained by sly-miR167-SlARF8A/B-SlGH3.4 feedback module in the development of locular and placental tissues of tomato fruits.

The locular gel, produced by the placenta, is important for fruit flavor and seed development in tomato. However, the mechanism underlying locule and placenta development is not fully understood yet. Here, we show that two SlARF transcription factors, SlARF8B and SlARF8A (SlARF8A/B), promote the development of locular and placenta tissues. The expression of both SlARF8A and SlARF8B is repressed by sly-microRNA167 (sly-miR167), allowing for the activation of auxin downstream genes. In slarf8a, slarf8b, and slarf8a/b mutants, the auxin (IAA) levels are decreased, whereas the levels of inactive IAA conjugates including IAA-Ala, IAA-Asp, and IAA-Glu are increased. We further find that SlARF8B directly inhibits the expression of SlGH3.4, an acyl acid amino synthetase that conjugates the amino acids to IAA. Disruption of such auxin balance by the increased expression of SlGH3.4 or SlGH3.2 results in defective locular and placental tissues. Taken together, our findings reveal an important regulatory module constituted by sly-miR167-SlARF8A/B-SlGH3.4 during the development of locular and placenta tissues of tomato fruits.

Federated Recognition Architecture Based on Voting and Feedback Mechanisms for Accuracy Object Classification in Distributed Edge Intelligence Environment

Deep learning has recently become a crucial tool to solve many complex problems and has the potential to revolutionize industries. With the widespread adoption of the Internet of Things, there are now many devices with limited computational resources that are capable of running deep learning models. It has opened up new opportunities to implement deep learning in the edge environment so that decisions can be made locally without sending data to a cloud server for processing. However, because of limited resources, the model’s performance and communication overhead are challenges when deploying learning models to edge devices. The focus of this paper is to investigate and propose a federated recognition architecture for object classification in a distributed edge intelligence environment. Specifically, we build an edge server that includes a voting module and a feedback module to improve the overall accuracy of object classification. The voting module aggregates predictions of multiple edge devices whereas the feedback module sends the voting results to edge devices to adjust the local deep learning model. We build edge devices based on the EdgeX platform which makes it easy to manage data and optimize communication overheads. Because the edge server and edge nodes only exchange prediction results, our proposed architecture ensures security with sensitive data as well as deep learning model architecture. By testing on the image dataset, we evaluate the proposed architecture’s performance and show that it outperforms individual local models in terms of accuracy. Furthermore, our experiments demonstrate that with the feedback mechanism, the deep learning model is constantly updated with new data to maintain accuracy and avoid being outdated. Besides, we prove the real-time processing speed by collecting the delay time of the proposed model. The results show that our proposed architecture has the potential to be deployed in practical applications such as smart cities, and surveillance systems.

Load Modulation Feedback in Adaptive Matching Networks for Low-Coupling Wireless Power Transfer Systems

This paper explores the use of load modulation feedback (LMF) in adaptive matching networks (MN) for low-coupling inductive wireless power transfer systems, with an emphasis on its use in implantable medical devices. After deriving the handy expressions of link efficiency and modulation depth in the case of LMF in the case of loose coupling, a brief overview of the most common capacitive resonance networks is presented. In particular, the MN employing two capacitors in Series–Parallel and in Parallel–Series configurations allow adaptivity with a wide range of load conditions. Then, the authors describe an effective design procedure of an adaptive matching network with LMF for an inductive wireless power transfer system, exploring the trade-off between power efficiency and modulation depth. Analytical and electrical simulations show that the proposed simple modulation strategy can successfully achieve high power transfer efficiency while maintaining steady back telemetry under varying loading conditions.