Online Approach Research Articles

Clustering source code from automated assessment of programming assignments

AbstractClustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students’ difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool—AsanasCluster—to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

Online Nucleotide Mapping of mRNAs.

Messenger RNA (mRNA) can be sequenced via indirect approaches such as Sanger sequencing and next generation sequencing (NGS), or direct approaches like bottom-up mass spectrometry (MS). Direct sequencing allows the confirmation of RNA modifications. However, the conventional bottom-up MS approach involves time-consuming in-solution digestions that require a large amount of sample, and can lead to the RNase contamination of the LC-MS system and column. Here, we describe a platform that enables online nucleotide mapping of mRNAs via the use of immobilized RNase cartridges and 2D-LC-MS instrumentation. The online approach was compared to conventional offline digestion protocols adapted from two published studies. For this purpose, five model mRNAs of varying lengths (996-4521 nucleotides) and chemistries (unmodified uridine vs 5-methoxyuridine (5moU)) were analyzed. The profiles and sequence coverages obtained after RNase T1 digestion were discussed. The online nucleotide mapping achieved comparable or slightly greater sequence coverage for the 5 mRNAs (5.8-51.5%) in comparison to offline approaches (3.7-50.4%). The sequence coverage was increased to 65.6-85.6 and 69.7-85.0% when accounting for the presence of nonunique digestion products generated by the RNase T1 and A, respectively. The online nucleotide mapping significantly reduced the digestion time (from 15 to <5 min), increased the signal intensity by more than 10-fold in comparison to offline approaches.

An online approach for cooperative cache updating and forwarding in mobile edge network

An online approach for cooperative cache updating and forwarding in mobile edge network

Generalized multiscale finite element method for language competition modeling II: Online approach

In this paper, we consider language competition in a heterogeneous medium. The mathematical model considers bilingualism, population growth, and social pressure to use specific languages (diglossia). The transition from one language group to another is determined by probabilities that depend on status, group size, and spatial location. The model considers not only diffusive spread but also convective spread, which models seasonal migrations of speaker populations. We develop multiscale approaches for coarse grid modeling while maintaining reasonable accuracy. We present offline and online approaches based on the Generalized Multiscale Finite Element Method. We consider a model problem in a heterogeneous environment to test these approaches. The results show that both approaches can significantly reduce the discrete problem’s size. However, the online approach achieves better accuracy.

Reducing Loneliness through the Power of Practicing Together: A Randomized Controlled Trial of Online Dyadic Socio-Emotional vs. Mindfulness-Based Training.

Loneliness has become a pressing topic, especially among young adults and during the COVID-19 pandemic. In a randomized controlled trial with 253 healthy adults, we evaluated the differential efficacy of two 10-week app-delivered mental training programs: one based on classic mindfulness and one on an innovative partner-based socio-emotional practice (Affect Dyad). We show that the partner-based training resulted in greater reductions in loneliness than the mindfulness-based training. This effect was shown on three measures of loneliness: general loneliness assessed with the 20-item UCLA Loneliness Scale, state loneliness queried over an 8-day ecological momentary assessment in participants' daily lives, and loneliness ratings required before and after daily practice. Our study provides evidence for the higher efficacy of a mental training approach based on a 12 min practice conducted with a partner in reducing loneliness and provides a novel, scalable online approach to reduce the increasing problem of loneliness in society.

Knowledge, Attitude and Perception of Consumers and Stakeholders on Counterfeit Drugs and Anti-Counterfeit Technology in Nigeria

The threat of counterfeiting medicines is probably growing, particularly in poorer countries with weak regulatory mechanisms and poorly monitored distribution networks. Various interventions, including innovative technologies, have been used to solve problems. The study used a quantitative design method adopting a hybrid quantitative method such as desk, online and physical interviews approach. The selected states for the study are Lagos, Kano, Anambra and FCT Abuja. Data were obtained using structured questionnaires with mainly closed-ended questions and analysed using SPSS (IBM-SPSS version 21). The findings show that the highest proportion of consumers who have heard about counterfeit or fake drugs before was from Lagos (97.1%). The study also found that stakeholders know highly about fake/counterfeit drugs (94.3%). The results show that 62.0% of consumers knew about drug anti-counterfeiting technology, and most stakeholders (79.0) knew about NAFDAC Anti- Counterfeiting Interventions. However, respondents from Lagos had the highest level of awareness (77.3%) of the Anti-Counterfeiting Interventions. The results also revealed that stakeholders exhibited a positive perception of NAFDAC’s innovative techs., (TruScan, RFID, GPHF MINILABS, Black Eye, Others) to curb fake drugs. The findings emphasise raising awareness, implementing effective anti- counterfeiting strategies, and addressing financial limitations to protect public health and combat Nigeria’s growing threat of fake drugs.

Active Learning on Students 'Interpersonal Skills Impact: An Online Theater Approach

This study aims to examine the improvement of interpersonal skills through the application of active learning in the teaching and learning of art education. The approach used is the theatre-play approach. Theatre games are used as a fresh and appropriate approach to learning art education subjects through various types of games that are applied with various theatre techniques, including improvisation, role playing, physical exercise, and voice acting. The subject of art education is a theoretical and practical one that requires high focus during learning. Therefore, theatre games are applied to encourage active learning and prevent students from getting bored when learning is conducted online. Students play, act, improvise, and sing while learning. This qualitative study involved eight Form 5 students at the Malaysian Art School using observation and interview methods. The study applies social learning theory and constructivist learning theory. The data was thematically analysed using Nvivo software. Findings found that active learning takes place in learning through the application of a theatre game approach and highlights the interpersonal skills of students. Two-way communication occurs in every activity because the activity is carried out in groups. Studies on the learning of art subjects that apply this active learning through theatre games are believed to promote confidence and stimulate students to improve their interpersonal skills without realising it.

Evaluation of a Course to Teach Medical Students Latent Hazard Identification in the Operating Room.

To improve situational awareness in the operating room (OR), avirtual online operating room of hazards (ROH) with deliberately placed risks was created. We hypothesized that subjects first participating in the virtual online ROH would identify more hazards during an in-person ROH exercise in a physical OR than those in the control group who only received didactic training. We conducted a randomized controlled trial at a major academic medical center, enrolling 48 pre-clinical medical students with no previous OR exposure during their classes. Control and experimental group subjects participated in a brief, online didactic orientation session conducted live over Zoom (Zoom Video Communications, Inc., San Jose, CA) to learn about latent hazards in the OR. Experimental group subjects further interacted with a virtual onlineoperating ROH in which latent hazards were present. The fraction of deliberately created latent hazards placed in a physical, in-person OR identified by subjects was calculated. Experimental group subjects identified a significantly larger fraction of the created hazards (41.3%) than the control group (difference = 16.4%, 95% CI: 11.3% to 21.4%, P < 0.0001). There was no difference in the number of non-hazards misidentified as hazards between the groups. Participation in the virtual online environment resulted in greater recognition of latent operating room hazards during a simulation conducted in a physical, in-person OR than in a didactic experience alone. Because creating an in-room experience to teach the identification of latent hazards in an OR is resource-intensive and requires removing the OR from clinical use, we recommend the virtual online approach described for training purposes. Adding items most misidentified as hazards is suggested for future implementation.

An Improved On-Line Recursive Subspace Identification Method Based on Principal Component Analysis and Sliding Window for Polymerization

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introduced to calculate the extended observation matrix, and finally the on-line model parameters are extracted. Compared with the traditional subspace schemes, smaller computation complexity and higher identification precision are anticipated in the proposed method. A case study on the modeling of the ethylene polymerization verifies the effectiveness of the developed approach, in which the related statistical indexes of the obtained identification model are better.

Monitoring Food Affordability: Reliability and Validity of an Online Nutritious Food Basket.

Purpose: This study aimed to assess the reliability and validity of an online approach to monitoring food affordability in Ontario using the updated Ontario Nutritious Food Basket (ONFB).Methods: The ONFB was priced online in 12 large multi-chain grocery stores to test intra-/inter-rater reliability using percent agreement and intra-class correlations (ICCs). Then, the ONFB was priced in-store and online in 28 stores to estimate food price differences using paired t-tests and Pearson's correlation for all (n =1708) and matched items (same product/brand and purchase unit) (n = 1134).Results: Intra-/inter-rater agreement was high (95.4%/81.6%; ICC = 0.972, F = 69.9, p < 0.001). On average, in-store prices were less than $0.02 lower than online prices. There were no significant differences between mean in-store and online prices for all items (t = 0.504 p = 0.614). The mean price was almost perfectly correlated between in-store and online (fully matched: R = 0.993 p < 0.001; all items: R = 0.967 p < 0.001). Online monthly ONFB estimates for a family of four were strongly correlated (R = 0.937 p < 0.001) with estimates calculated using in-store data.Conclusions: Online pricing is a reliable and valid approach to food costing in Ontario that contributes to modernizing the monitoring of food affordability in Canada and abroad.

Online State Estimation for Supervisor Synthesis in Discrete-Event Systems With Communication Delays and Losses

In the context of networked discrete-event systems (DESs), communication delays and losses exist between the plant and the supervisor for observation and between the supervisor and the actuator for control. In this paper, we first introduce a new framework for supervisory control of networked DESs. Under the introduced framework, we address the state estimation problem for supervisor synthesis of networked DESs with both communication delays and losses. The estimation algorithm considers the effect of the controls imposed on the system. Additionally, the estimation algorithm is based on the control decisions available up to the moment, and all the future control decisions are assumed to be unknowable. Two notions, called “observation channel configuration” for tracking observation delays and losses and “control channel configuration” for tracking control delays and losses, are defined. Then, we introduce an online approach for state estimation of the controlled system. Compared with the existing approach, the proposed approach under the introduced framework can estimate the states of the controlled system more accurately. As an application of the proposed approach, we finally show that the existing methods can be applied to synthesize maximally permissible and safe networked supervisors.

Quantifying the Chemical Composition and Real-Time Mass Loading of Nanoplastic Particles in the Atmosphere Using Aerosol Mass Spectrometry.

Plastic debris, including nanoplastic particles (NPPs), has emerged as an important global environmental issue due to its detrimental effects on human health, ecosystems, and climate. Atmospheric processes play an important role in the transportation and fate of plastic particles in the environment. In this study, a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was employed to establish the first online approach for identification and quantification of airborne submicrometer polystyrene (PS) NPPs from laboratory-generated and ambient aerosols. The fragmentation ion C8H8+ is identified as the major tracer ion for PS nanoplastic particles, achieving an 1-h detection limit of 4.96 ng/m3. Ambient PS NPPs measured at an urban location in Texas are quantified to be 30 ± 20 ng/m3 by applying the AMS data with a constrained positive matrix factorization (PMF) method using the multilinear engine (ME-2). Careful analysis of ambient data reveals that atmospheric PS NPPs were enhanced as air mass passed through a waste incinerator plant, suggesting that incineration of waste may serve as a source of ambient NPPs. The online quantification of NPPs achieved through this study can significantly improve our understanding of the source, transport, fate, and climate effects of atmospheric NPPs to mitigate this emerging global environmental issue.

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

Using Nonlinear Features and Logistic Regression for Epilepsy Detection with Linear Complexity

Introduction: In this specific research study, a remarkably accurate and significantly simplified approach has been presented.Material and Metods: this research is encompasses three crucial stages. Firstly, the length of the signal is effectively diminished to an optimal magnitude through the utilization of a technique widely known as windowing. This technique plays a pivotal role in reducing the signal to an ideal size, ensuring the subsequent stages are executed with utmost precision. Secondly, the pertinent features are extracted from the shortened signal, specifically focusing on the Fractal Dimension, the Hurst Exponent, and the Ratio of Determinism to Recurrence Rate. These features are chosen due to the inherent nonlinear nature of the signal, as they provide valuable insights into the complex patterns and structures present within. Lastly, the extracted features undergo Logistic Regression, a widely employed classification algorithm, to effectively categorize and classify them. This step plays a crucial role in providing a clear and concise understanding of the underlying characteristics of the signal.Results: The implementation of the proposed method not only achieves an outstanding accuracy rate of 99.66%, but it also exhibits a linear time complexity, ensuring efficient processing. Additionally, this method leads to a significant reduction in the length of EEG signals, which is of utmost importance in practical applications.Conclusion: The primary objective of this proposed method revolves around the introduction of an online approach that can seamlessly integrate into healthcare systems. This objective is derived from a comprehensive analysis and evaluation of the obtained results, ensuring the method's practicality and effectiveness are thoroughly assessed.

Reinforcement Learning Building Control: An Online Approach With Guided Exploration Using Surrogate Models

Abstract The incorporation of emerging technologies, including solar photovoltaics, electric vehicles, battery energy storage, smart devices, Internet-of-Things devices, and sensors in buildings, desirable control objectives are becoming increasingly complex, calling for advanced controls approaches. Reinforcement learning (RL) is a powerful method for this. RL can adapt and learn from environmental interaction, but it can take a long time to learn and can be unstable initially due to limited environmental knowledge. In our research, we propose an online RL approach for buildings that uses data-driven surrogate models to guide the RL agent during its early training. This helps the controller learn faster and more stably than the traditional direct plug-and-learn online learning approach. In this research, we propose an online approach in buildings with RL where, with the help of data-driven surrogate models, the RL agent is guided during its early exploratory training stage, aiding the controller to learn a near-optimal policy faster and exhibiting more stable training progress than a traditional direct plug-and-learn online learning RL approach. The agents are assisted in their learning and action with information gained from the surrogate models generating multiple artificial trajectories starting from the current state. The research presented an exploration of various surrogate model-assisted training methods and revealed that models focusing on artificial trajectories around rule-based controls yielded the most stable performance. In contrast, models employing random exploration with a one-step look-ahead approach demonstrated superior overall performance.

Differentially Private Federated Learning With Stragglers’ Delays in Cross-Silo Settings: An Online Mirror Descent Approach

Differentially Private Federated Learning With Stragglers’ Delays in Cross-Silo Settings: An Online Mirror Descent Approach

Design and development of an arena blended connected learning model for faculty development in health professions education: A step forward.

Current medical education and clinical practice has led to a need for advanced faculty development for medical teachers to effectively play the role of educators, researchers and administrators. There is large variability in the teaching programmes across countries, which range from a one-time activity to regularly scheduled workshops and seminars, to a highly advanced course spanning a few months to a year. Several healthcare institutes around the world offer faculty training programmes in health professions education, where the curriculum varies in design as they are developed and implemented by their own institutional body or education unit. Following a discussion of arena blended connected (ABC) learning design during a faculty training programme (Postgraduate Diploma in Health Professions Education) and the subsequent move towards an online approach to education due to the pandemic in 2019, the advisory faculty and students started to envision designing the already existing Postgraduate Diploma in Health Professions Education curriculum along the ABC model favouring blended and outcome-based education. Criteria were set for each topic with clearly defined learning levels to be implemented and the frequency of implementation. We describe the design and development of a curriculum for faculty development of health professions education using the ABC model.

Online versus in-person surgical near-peer teaching in undergraduate medical education during the COVID-19 pandemic: A mixed-methods study.

The coronavirus disease 2019 (COVID-19) pandemic stimulated a paradigm shift in medical and surgical education from in-person teaching to online teaching. It is unclear whether an in-person or online approach to surgical teaching for medical students is superior. We aim to compare the outcomes of in-person versus online surgical teaching in generating interest in and improving knowledge of surgery in medical students. We also aim the quantify the impact of a peer-run surgical teaching course. A six-session course was developed by medical students and covered various introductory surgical topics. The first iteration was offered online to 70 UK medical students in March 2021, and the second iteration was in-person for 20 students in November 2021. Objective and subjective knowledge was assessed through questionnaires before and after each session, and also for the entire course. Data were analyzed from this mixed-methods study to compare the impact of online versus in-person teaching on surgical knowledge and engagement. Students in both iterations showed significant improvement of 33%-282% across the six sessions in knowledge and confidence after completing the course (p < 0.001). There was no significant difference in the level of objective knowledge, enjoyment, or organization of the course between online and in-person groups, although the in-person course was rated as more engaging (mean Likert score 9.1 vs. 9.7, p = 0.033). Similar objective and subjective surgical teaching outcomes were achieved in both iterations, including in "hands-on"topics such as suturing, gowning, and gloving. Students who completed the online course did not have any lower knowledge or confidence in their surgical skills;however, the in-person course was reported to be more engaging. Surgical teaching online and in-person may be similarly effectiveand can be delivered according to what is most convenient for the circumstances, such as in COVID-19.

Reconciliation as an Essential Ingredient in Peace Building: A Case Study of Crude Oil-Related Conflicts

Reconciliation is a vital component of conflict resolution. This is true because, dispute settlement without genuine reconciliation will only result in compromise without necessarily uniting the aggrieved and the aggressor. This study has explored the extent of oil related conflict and effectiveness of reconciliation initiatives in Ilaje and Ese-Odo Local Government areas in Ondo State. Both online and physical approach was used to gather data from a total sample of 150 participants drawn across the study areas and descriptive research approach was adopted. Five research questions were raised and answered. Descriptive analysis reveals the following: factors contributing to oil related conflict in region include unequal distribution of royalties, government and company policies as well as competition for oil related resources. Oil related conflict is an occasional incident in Ilaje and Ese-Odo Local Government Areas of Ondo State. There have been reconciliation efforts (64% agreement) in Ilaje and Ese Odo aiming to address oil related conflict. The effectiveness of the reconciliation attempts was judged moderate pointing to the fact that they are still unresolved grievances among the affected parties. The major setbacks found to effective reconciliation initiatives include greed, cultural differences, distrust, political interferences and sentiment. The study recommends, among others, that oil companies, government agencies fulfil should fully implement all the promises/agreements reached with the oil producing region and that the royalties should not only end in the pockets of selected few.

Interacting within an asynchronous online interprofessional education workshop focused on social determinants of health

BackgroundMeaningful interprofessional education (IPE) involves students from at least two professions interacting to learn with, about, and from one another. Our objective was to describe a novel online approach used to create meaningful IPE within a social determinants of health (SDoH) workshop. Interprofessional education activityThis online workshop integrated four different professions' perspectives on SDoH (social-work, public-health, nursing, and pharmacy). Each six-student interprofessional team was assigned a local neighborhood. This week-long workshop had numerous activities (pre- and post-workshop quizzes, a SDoH-primer video, video self-introduction to teammates, a windshield questionnaire with two subsequent clinical cases, a post-workshop reflection, and post-workshop evaluation). For discussion, asynchronous video-based responses were used instead of traditional text-based discussion-boards. DiscussionQuantitatively comparing quiz scores, students' SDoH knowledge increased with this workshop. Qualitatively from evaluations, most students found this workshop helpful and meaningful. Supporting use of video-based responses, many students' favorite aspect was interacting and collaborating within their interprofessional teams, although some students desired synchronous activities instead. Faculty facilitators confirmed that meaningful IPE interactions occurred. ImplicationsIn short, students from multiple health-professions learned SDoH-content and, using video-based responses, interacted asynchronously during this online workshop. This report demonstrated one tool available to help facilitate meaningful IPE asynchronously. This asynchronous, online IPE workshop appears to be a promising format to be integrated with other in-person IPE sessions.