Assignment Process Research Articles

1Hβ chemical shift-based phase modulated NMR methods for fast identification of amino acid types in proteins

Here we report two phase modulated NMR experiments: PM-2D HN(CACBHB) and PM-2D HN(HB), that use 1Hβ chemical shifts to rapidly identify amino acid type in proteins. The magnetization on the 1Hβ spins during the experiments is allowed to evolve for a fixed evolution period that results in phase modulation (positive or negative) of the cross peaks corresponding to various amino acid residues on their 2D HN projections, resembling a typical 2D [1H–15N]-HSQC spectrum. All amino acids except glycine can be categorized into three discernible groups based on their 1Hβ chemical shifts, resulting in unique phase patterns at different fixed evolution periods for 1Hβ, thus facilitating their identification. Remarkably, the PM-2D HN(HB) stands out among all amino acid type identification NMR techniques for its applicability with cost-effective and most routinely employed 15N-labeled protein samples for NMR studies. Furthermore, when combined effectively with the 13Cβ chemical shift-based phase modulated NMR method (PM-2D HN(CACB)), these methods resolved the identification of large groups of amino acids into relatively smaller groups. Moreover, these techniques can accelerate the sequence-specific sequential resonance assignment (SSRA) process and would help in fast tracking of assigned NMR signals exhibiting chemical shift perturbation on the 2D [1H–15N]-HSQC spectrum of proteins during various experiments (e.g., temperature change, pH change, and protein or ligand or cofactor binding) as well as in site-directed mutagenesis.

LabourCentre - Bridging the Gap Between Clients and Skilled Workers

This research paper presents a web application that aims to simplify the hiring and contract assignment of labourers, promoting a more effective and transparent relationship between employers and qualified workers. The application guarantees thorough verification, makes labour registration easier, and makes contract assignments easier. It has an extensive labourer registration module that enables employees to set up profiles with thorough backgrounds and skill sets. Prospective clients are reassured that labourer profiles are authentic by a strong verification mechanism. Customers can peruse an array of au- thenticated workers, making knowledgeable selections predicated on competencies, background, and additional factors. Clients can create, modify, and assign contracts to qualified labourers using an easy-to-use portal that streamlines the contract assignment process. The research explores the impact of the platform on efficiency and transparency, focusing on user satisfaction and adoption rates. Index Terms—labourer, customer, MERN, open source, User friendly, Interface.

Development of an SI-traceable N-terminal pro-B-type natriuretic peptide certified reference material using structure-based impurity-corrected isotope dilution mass spectrometry approaches.

N-Terminal pro-B-type natriuretic peptide (NT-proBNP) is a pivotal biomarker for the diagnosis and prognosis of heart failure (HF). However, no SI-traceable certified reference material (CRM) or reference measurement procedure (RMP) is available for this biomarker, and so clinical testing results obtained in different laboratories cannot be traced to a higher-order standard, leading to incomparable measurements. Protein hydrolysis and protein cleavage isotope dilution mass spectrometry (AAA-IDMS and PepA-IDMS) were used to develop a CRM. Structurally related impurities were identified by high-resolution mass spectrometry. The quantitative AAA-IDMS results were corrected according to the amino acid compositions of the impurities. Using PepA-IDMS, two peptides from the proteolyzed product were confirmed as signature peptides. To obtain traceable and accurate results, the signature peptides were quantified using impurity-corrected AAA-IDMS. The candidate NT-proBNP solution was denatured and enzymatically digested using the Glu-C endoproteinase. The released signature peptides were measured using an isotopic dilution approach. The homogeneity and stability of the candidate CRM were characterized, and their uncertainties were combined with the value assignment process. The developed CRM can be considered a unique SI-traceable NT-proBNP reference material and is expected to be used as a primary calibrator for matrix NT-proBNP CRM development.

Gendered experiences in professional military education: Implications for diversity, equity, and inclusion

AbstractThis multi‐year, cross‐sectional qualitative study investigates gendered experiences of students and faculty at one master's degree‐granting military education institution in the United States. Findings from a grounded theory exploration into institutional climate using focus groups and classroom observations include themes of underrepresentation, tokenization, and dismissal during class conversations, and mischaracterization of diversity of thought. The studied institution responded to these findings by adopting a new gender‐blind class assignment process for students. The authors examined the resultant changes in the learning environment with regards to gender representation in classrooms that had zero, one, two, or three women. A next round of findings reflects students' conformance to gender norms, the prevalence of gatekeeping in class discussion, and the creation of affinity groups as a coping mechanism for underrepresented students. Findings also indicate the burden of intersectional representation falls disproportionately on women students; 73% of women students reflected two or more underrepresented‐group identities compared to just 7% of men students. Encompassing 114 h of classroom observations and 47 interviews with faculty and students, this research represents a rigorous and unprecedented cross‐sectional empirical inquiry into gendered experiences of a master's degree‐granting professional learning environment and has implications for scholars and practitioners working in male‐dominated organizations.

Cross-modal de-deviation for enhancing few-shot classification

Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achieve this, we propose a cross-modal de-deviation framework that leverages class semantic information to provide robust prior knowledge for the samples. This framework begins with a visual-to-semantic autoencoder trained on the labeled samples to predict semantic features for the unlabeled samples. Then, we devise a binary linear programming model to match the initial prototypes with the cluster centers of the unlabeled samples. To circumvent potential mismatches between the cluster centers and the initial prototypes, we perform the label assignment process in the semantic space by transforming the cluster centers into semantic representations and utilizing the class ground truth semantic features as reference points. Moreover, we model a linear classifier with the concatenation of the refined prototypes and the class ground truth semantic features serving as the initial weights. Then we propose a novel optimization strategy based on the alternating least squares (ALS) model. From the ALS model, we can derive two closed-form solutions regarding to the features and weights, facilitating alternative optimization of them. Extensive experiments conducted on few-shot learning benchmarks demonstrate the competitive advantages of our CMDD method over the state-of-the-art approaches, confirming its effectiveness in reducing deviation. The code is available at: https://github.com/pmhDL/CMDD.git.

Mathematical Modeling on Integrated Vehicle Assignment and Rebalancing in Ride-hailing System with Uncertainty Using Fuzzy Linear Programming

The general public frequently uses taxis as local transportation to get from one location to another. Ride-hailing is an innovation in taxi services that lets customers use their smartphones to find drivers, find prices, and submit requests. The two parts of ride-hailing are vehicle assignment and rebalancing. The task of the assignment is to allocate resources as efficiently as possible to fulfil demand. Demand and supply in the area are brought into balance through the rebalancing process. The rebalancing step is frequently carried out independently of the assignment process in ride-hailing pickup systems. However, it can be integrated into a single optimization process to improve system performance. The linear programming approach can integrate assignment and rebalancing. The batch assignment is an assignment algorithm in which each supply and demand is collected within a specific time window. The assignment is done after the vehicles and requests are collected. Fuzzy linear programming is used to deal with environmental uncertainty. Uncertain demand can reduce the reliability of ride-hailing pickup systems in addressing the problem of allocating empty vehicles to areas of high demand. The pickup time may change over time due to traffic conditions. Assignment integration and rebalancing modelling with fuzzy parameters are carried out to obtain a ride-hailing pickup system model that can handle demand uncertainty, pickup travel time, and travel delay times and increase the ride-hailing effectiveness of the pickup system. Numerical simulations were carried out on the assignment and rebalancing integration model without and with uncertainty parameters—numerical simulations based on publicly available taxi travel request data. The model selection for the ride-hailing system can be determined based on the numerical simulation results. This research can potentially advance and expand knowledge, especially in transportation.

159 Good Clinical Practice training assignment and tracking completions improvement project

OBJECTIVES/GOALS: We will reduce the number of research staff by 50% within 6 months that are non-compliant with completing the Good Clinical Practice (GCP) course without increasing costs as measured by salary and benefits for staff involved in the tracking and assignment process. We will also introduce a GCP policy to define the education requirements for GCP training METHODS/STUDY POPULATION: We touted focus groups to stakeholders for input on workflows with graphs, surveys, and meetings in the onset and throughout the project. This input prompted us to develop a staff guide outlining the modification process of removing a staff name from an IRB trial if they are no longer active in the trial. A workflow and root cause analysis were done resulting in the implementation of one gatekeeper for assigning and tracking completions, instead of three staff. Successfully reconciling the non-compliant report, which extracted data from three databases, allowed us to eliminate 174 names from the report, which originally comprised 792 names. IRB modifications were entered into the IRB system for these 174 names. We also put into effect a GCP policy for the institution, where none had previously existed. RESULTS/ANTICIPATED RESULTS: * In 3.5 months, we decreased the GCP non-compliance rate by 50% from 792 to 399. * We removed 22% of names from the 792 from the report due to their status of being non-employees, or Emeritus, or not in research anymore. * We discovered a data type issue in the non-compliance report that shuffled the MAX calculation, therefore not requiring some staff to complete the training. * We developed a new process for assigning training, resulting in faster compliance rates for the institution. It included sending emails to users two months before their training expired before we assigned the course to them. * We reconciled the non-compliance report, and it decreased the effort with staff involved in GCP for grant renewals and audits in numerous other departments. * Developed an escalation procedure for non-compliant staff. DISCUSSION/SIGNIFICANCE: Failure to address our GCP non-compliance rates could have put our institution at risk for potential penalties. With 4400+ research personnel listed on active trials the interventions we implemented accelerated our compliance rate by assigning the refresher course monthly and this also resulted in no disruption to staff completing the training.

Cross-linguistic effects in grammatical gender assignment and predictive processing in L1 Greek, L1 Russian, and L1 Turkish speakers of Norwegian as a second language

The present study examines grammatical gender knowledge in offline production (gender marking on indefinite articles) and online gender processing (visual world paradigm) in adult second language (L2) learners of Norwegian with three different first languages (L1s): Greek, Russian, and Turkish. In particular, it investigates the role of the following factors: (1) presence vs. absence of grammatical gender in L1 (Norwegian, Greek and Russian have gender, whereas Turkish does not), (2) lexical gender congruency, (3) structural similarity between L1 and L2 in the realization of gender, and (4) proficiency in L2. In offline production, no difference was found between the three L2 groups: they all overused the default gender (masculine). However, L1 effects were observed in the eye-tracking task, where the high-proficiency L1 Greek and L1 Russian speakers showed earlier and more prominent signs of predictive gender processing compared to the high-proficiency L1 Turkish speakers. There were no effects of lexical gender congruency or structural similarity. This suggests that, when it comes to predictive gender processing, what matters is proficiency and the presence vs. absence of grammatical gender in the L1. We interpret the findings in the context of current approaches to predictive processing emphasizing the role of cue reliability and utility.

Comparative evaluation of antimicrobial efficacy of QMix 2in1, Octenidine Dihydrochloride irrigants, 940nm diode laser with and without NaOCl, against mature E. faecalis biofilm: An in vitro study

To compare the antimicrobial efficacy of NaOCl, QMix 2in1, Octenidine dihydrochloride, 940nm diode laser with and without NaOCl against matured E. faecalis biofilm.Ninety extracted single-rooted teeth were instrumented and autoclaved. The specimens were exposed to E. faecalis contamination over a period of 21 days and subsequently divided into six distinct groups through a random assignment process. Group I-Control, Group II-NaOCl, Group III-940nm Diode laser, Group IV-940nm Diode laser + NaOCl, Group V-QMix 2in1, Group VI-Octenidine Dihydrochloride. Microbial specimens were gathered, cultured, and the quantification of colony-forming units was conducted.ANOVA, Post hoc test, And Tuckey test were applied to find significance.940nm Diode laser +NaOCl group showed a greater antimicrobial effect compared to other groups. No significant difference was observed between 940nm Diode laser and 940nm diode laser +NaOCl (p=.550), NaOCl and QMix 2 in 1 (p=.121), NaOCl and 940nm Diode laser groups (p =.680)The concurrent use of both sodium hypochlorite and 940nm Diode laser light manifests a synergistic influence, enhancing the bactericidal efficacy. The antibacterial effect of QMix 2in1 is comparable to that of NaOCl, 940nm Diode laser, more effective than OCT and less effective than the combination of sodium hypochlorite and 940nm Diode laser.A combination of 940nm laser and NaOCl is effective in reducing the bacterial count because of their capacity to penetrate effectively deep into dentinal tubules, resulting in intensified bactericidal action compared to newer irrigants used alone.

Insights into charge transfer dynamics of Li batteries through temperature-dependent electrochemical impedance spectroscopy (EIS) utilizing symmetric cell configuration

Studying charge transfer processes in Li batteries poses a challenge due to their complicated nature and the battery's inherent inertness. While electrochemical impedance spectroscopy (EIS) offers a promising approach to investigating these processes, its effectiveness is hindered by the complexity of the data generated and the ambiguity in the analyses. Here, we demonstrate that the assignment process is not straightforward for complex secondary Li batteries, and it necessitates additional measurements and in-depth analysis. We first performed simplification of the impedance data by employing symmetric cell configurations to obtain charge transfer resistance values for each electrode. We second took advantage of altering three parameters during the EIS measurement: electrolyte composition, measurement temperature, and the state of health to extract important parameters related to both the anodic and cathodic charge transfer mechanisms. The analysis enabled the correlation of the observed charge transfer resistances to the corresponding electrochemical process and to obtain the kinetic parameters of the studied process. Our work illustrates how EIS can be used to study and understand the intricate electrochemical charge transfer processes in Li batteries, which are difficult to explore using alternative techniques.

Prescriptive analytics models for vessel inspection planning in maritime transportation

Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict-then-optimize approach, predicting the number of vessel deficiencies using a k-nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate-then-optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate-then-optimize models surpass the predict-then-optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications.

An Investigation on Students Learnings & Mental Health Influenced by Artificial Intelligence

Artificial intelligence has impacted student’s social, emotional, and physical well-being. It has resulted in how a person thinks and reacts to a particular situation. The Purpose of this investigation is to test the effectiveness of learning patterns on our mental health. It can affect a student’s energy level, concentration, dependability, and mental ability. On the other hand, AI can automate the process of grading assignments and providing feedback to students, which can save teachers a lot of time and can focus more on student interaction. It is essential to ensure that AI systems used in education are unbiased and fair. We read articles about the connection between artificial intelligence and mental health that use mood rating scales and classified them into the pros and cons of artificial intelligence. In our college, we will also carry out a poll that shows how Artificial intelligence has harmed and benefited the students. Therefore, it is crucial to carefully examine the benefits and risks of AI in education and implement appropriate safeguards to ensure that it is used ethically and responsibly. Public mental health based on artificial intelligence engineering has a wide range of application and development prospects in enhancing the effectiveness of instruction, achieving high efficiency and intelligence, and enhancing specialized education. The use of artificial intelligence-based public mental health in business administration teaching effects has highly favorable importance for precisely determining the mental health and teaching effects of business administration students. We develop a method for the influence of public mental health on the teaching effect of the business administration profession based on artificial intelligence engineering to improve the teaching- learning effect of the business administration major. ( Keywords:- Learnings, Mental Health, and Artificial Intelligence )

Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)

Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load- and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems. Doi: 10.28991/ESJ-2024-08-01-018 Full Text: PDF

The Evaluation of Payment for Obstetric and Gynecology Services: From ACOG.

To perform an environmental scan of the current status of reimbursement for obstetric and gynecology services and identify problematic issues and opportunities for change. The areas that were evaluated include the American Medical Association (AMA) relative value unit assignment process, payer rates (where available), and trends in employment and salary determination for obstetrician-gynecologists (ob-gyns). This report was developed by members of the American College of Obstetricians and Gynecologists' (ACOG) Committee on Health Economics and Coding using public-facing payment data from the Medicare Physician Fee Schedule and state Medicaid programs, as well as published research and commentary on payment for physicians, maternal health, and gynecologic surgery. Data from the Centers for Disease Control and Prevention were used to describe typical patient characteristics, and practice survey reports from the AMA were analyzed. Finally, an anonymous online survey was distributed to 27,854 members of ACOG in March 2022, with a response rate of 10.8% (3,018 members) and a CI of ±1.7%. The evaluation found that payment for ob-gyns is heavily influenced by the values and rates set by third-party payers, a patient case-mix that includes a higher-than-average number of patients with Medicaid insurance, and the increase of employed physicians reliant on salary contracts that include productivity requirements and bonuses. The Committee identified action items, including payment reform for obstetric services; advocating for gynecologic surgery time as a priority for hospital administration; developing resources to assist employed physicians with payment, practice, and business management; developing a business and coding curriculum for students and early-career physicians; and continued advocacy with private and public policymakers who influence physician payment.

Design-based identification with formula instruments: A review

Summary Many studies in economics use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. We compare this design-based approach with conventional estimation strategies based on conditional unconfoundedness, and contrast it with alternative strategies that leverage a model for unobservables.

DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients

In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large language model (LLM) fine-tuned on clinical notes to enhance DRGs assignment. Utilizing LLaMA as the foundational model and optimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV discharge summaries, our DRG-LLaMA -7B model exhibited a noteworthy macro-averaged F1 score of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged Area Under the Curve (AUC) of 0.986, with a maximum input token length of 512. This model surpassed the performance of prior leading models in DRG prediction, showing a relative improvement of 40.3% and 35.7% in macro-averaged F1 score compared to ClinicalBERT and CAML, respectively. Applied to base DRG and complication or comorbidity (CC)/major complication or comorbidity (MCC) prediction, DRG-LLaMA achieved a top-1 prediction accuracy of 67.8% and 67.5%, respectively. Additionally, our findings indicate that DRG-LLaMA ’s performance correlates with increased model parameters and input context lengths.

Association Between Nurse Copatient Illness Severity and Mortality in the ICU.

In the context of traditional nurse-to-patient ratios, ICU patients are typically paired with one or more copatients, creating interdependencies that may affect clinical outcomes. We aimed to examine the effect of copatient illness severity on ICU mortality. We conducted a retrospective cohort study using electronic health records from a multihospital health system from 2018 to 2020. We identified nurse-to-patient assignments for each 12-hour shift using a validated algorithm. We defined copatient illness severity as whether the index patient's copatient received mechanical ventilation or vasoactive support during the shift. We used proportional hazards regression with time-varying covariates to assess the relationship between copatient illness severity and 28-day ICU mortality. Twenty-four ICUs in eight hospitals. Patients hospitalized in the ICU between January 1, 2018, and August 31, 2020. None. The main analysis included 20,650 patients and 84,544 patient-shifts. Regression analyses showed a patient's risk of death increased when their copatient received both mechanical ventilation and vasoactive support (hazard ratio [HR]: 1.30; 95% CI, 1.05-1.61; p = 0.02) or vasoactive support alone (HR: 1.82; 95% CI, 1.39-2.38; p < 0.001), compared with situations in which the copatient received neither treatment. However, if the copatient was solely on mechanical ventilation, there was no significant increase in the risk of death (HR: 1.03; 95% CI, 0.86-1.23; p = 0.78). Sensitivity analyses conducted on cohorts with varying numbers of copatients consistently showed an increased risk of death when a copatient received vasoactive support. Our findings suggest that considering copatient illness severity, alongside the existing practice of considering individual patient conditions, during the nurse-to-patient assignment process may be an opportunity to improve ICU outcomes.

Rancang Bangun Manajemen Bimbingan Tugas Akhir Pada Jurusan Teknik Industri Universitas Muhammadiyah Malang

The final assignment is a scientific paper, in the form of writing research results that must be completed by each student to get a bachelor's degree. In the Department of Industrial Engineering there are several stages of the process that must be done by students, including registration of supervisors, seminar proposals and final hearings. So far the registration is still using google form where it is only limited to registration, and for the final assignment guidance report has not been recorded properly and done manually using the assistance sheet in the thesis logbook so the final assignment supervisor and coordinator has difficulty monitoring the student's guidance. The purpose of this study is to build a website-based system using the codeigniter framework and the waterfall method. The stages passed in the waterfall method include needs analysis, system design, implementation and system testing. The results of this research are the final assignment guidance management system in the industrial engineering department of Muhammadiyah Malang University which can monitor, assist in reporting guidance activities, and registration in the final assignment process.The final assignment is a scientific paper, in the form of writing research results that must be completed by each student to get a bachelor's degree. In the Department of Industrial Engineering there are several stages of the process that must be done by students, including registration of supervisors, seminar proposals and final hearings. So far the registration is still using google form where it is only limited to registration, and for the final assignment guidance report has not been recorded properly and done manually using the assistance sheet in the thesis logbook so the final assignment supervisor and coordinator has difficulty monitoring the student's guidance. The purpose of this study is to build a website-based system using the codeigniter framework and the waterfall method. The stages passed in the waterfall method include needs analysis, system design, implementation and system testing. The results of this research are the final assignment guidance management system in the industrial engineering department of Muhammadiyah Malang University which can monitor, assist in reporting guidance activities, and registration in the final assignment process.

Peer-reviewed presentation exchange in an undergraduate classroom.

Reading, presenting, and discussing peer-reviewed scientific reports, case studies, and reviews are essential to modern biology education. These exercises model crucial aspects of students' future professional activities and introduce the students to the current scientific concepts and methodology, data analysis, and presentation. A common format for working with primary literature is a journal club: presenting and discussing research literature in front of peers, which has many merits. However, in large modern classrooms, this format is very time-consuming and stressful, especially since presenting is not a commonly taught skill. We argue that student groups for whom the current educational and professional paradigms present a challenge due to a historical lack of representation or wellness issues are deprived of a key educational opportunity. To solve this problem, we formulated an approach called Peer-Reviewed Presentation Exchange (PRPE), which focuses on collaborative analysis, presentation, and review of research literature that includes (i) voice-narrated research presentations by students, (ii) checklists generated by the instructor to establish expectations for an informative presentation or review, and (iii) presentation assignment and peer review process. We tested this approach in an undergraduate cell biology class over 3 years. Pre- and post-assessments show significant gains in self-efficacy and knowledge not only by students who presented but also by the students who reviewed the presentations; therefore, peer-reviewed presentations are an effective tool for learning. Exit surveys show that the approach is seen as beneficial by most students. Our approach allows every student to speak and ask questions in a low-stress creative environment. It is an excellent customizable, trackable, and scalable low-stakes assessment tool.

A comprehensive health diagnosis method for expansive soil slope protection engineering based on supervised and unsupervised learning

ABSTRACT There is a complex multifactorial coupling effect among the damages of various protection structures on slopes. Existing research focused on the health assessment of individual structures is often insufficient in representing the overall health status of the protection engineering system. Considering the characteristics of expansive soil slope protection engineering, this study proposes a health diagnosis method using combined weights and binary K-means clustering algorithm. The method quantifies the damage data of protection structures based on subjective and objective weights, and clusters the data by combining the binary K-means method and target vector layer to obtain the diagnosis results. Furthermore, an XGBoost-based surrogate diagnosis model is constructed to omit the repetitive modelling process in practical applications to achieve dynamic diagnosis. The proposed method is validated to an expansive soil slope in Gaochun district, Nanjing. The results show that the proposed method can accurately evaluate protection engineering with different degrees of damage; the surrogate model follows the same weight assignment process as the diagnostic method to establish reliable prediction. Based on the proposed method, damage coupling effects between individual protection structures are captured, and targeted maintenance and repair can be implemented. The proposed method can be further extended to other slope engineering.