Search Results (22)

In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting. Full article

Realization of the goals of precision agriculture is dependent on prescribing irrigation strategies matched to spatiotemporal variations in soil moisture on commercial farms. However, the scale at which these variations occur is not well understood. A high-spatial-density network of sensors with the ability to measure and report data over the course of a growing season is needed. In this work, design of the low-profile Smart Bluetooth Stake spatiotemporal soil moisture mapping system is presented. Smart stakes use Bluetooth Low Energy to communicate 64 MHz soil moisture impedance measurements from ground level to a receiver mounted on the center-pivot irrigation boom and equipped with a rotating high-gain parabolic antenna. Smart stakes can remain in the ground throughout the entire growing season without disrupting farm operations. A system of 86 sensors was deployed on a 50-hectare commercial field near Elberta, Utah, during the final growth stage of a crop of winter wheat. Different receiver antenna configurations were tested over the course of several weeks which included two full irrigation cycles. In the high-gain antenna configuration, data was successfully collected from 75 sensors, with successful packet transmission at ranges of approximately 600 m. Enough data was collected to construct a spatiotemporal moisture map of the field over the course of an irrigation cycle. Smart Bluetooth Stakes constitute an important advance in the spatial density achievable with direct sensors for precision agriculture. Full article

Remote intelligence testing has multiple advantages, but cheating is possible without proper supervision. Proctoring aims to address this shortcoming, yet prior research on its effects has primarily investigated reasoning tasks, in which cheating is generally difficult. This study provides an overview of recent research on the effects of proctoring and on studies in intelligence test settings. Moreover, we conducted an empirical study testing the effects of webcam-based proctoring with a multidimensional intelligence test measuring reasoning, short-term memory, processing speed, and divergent thinking. The study was conducted in a low-stakes context, with participants receiving a fixed payment regardless of performance. Participants completed the test under proctored (n = 74, webcam consent), unproctored random (n = 75, webcam consent), or unproctored chosen (n = 77, no webcam consent) conditions. Scalar measurement invariance was observed for reasoning, processing speed, and divergent thinking, but not for memory. Proctoring had no significant main effect on test performance but showed a significant interaction with test type. Proctored participants outperformed the unproctored chosen group significantly in divergent thinking and scored descriptively higher in reasoning and processing speed, but slightly lower in memory. Observable cheating under proctored conditions was rare (4%), mostly involving note-taking or photographing the screen. We conclude that proctoring is crucial for easily cheatable tasks, such as memory tasks, but currently less critical for complex cognitive tasks. Full article

Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article

This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions—encompassing unimodal, multimodal, and hybrid landscapes—demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges. Full article

Although cognitive ability tests are among the best predictors of job and training performance, their acceptance among applicants is limited. However, with the current talent shortage, applicant reactions to assessments have become increasingly important. Gamification is a promising approach for improving reactions to cognitive ability tests. However, it remains unclear how findings from low-stakes studies of gamified assessments generalize to high-stakes settings. In this quasi-experimental study (N = 210), we compared reactions to a gamified cognitive test from a low-stakes simulated selection setting with experimental participants and from a high-stakes selection setting with real applicants. Test takers in both settings completed the same gamified cognitive ability test and then rated several applicant reactions variables. We found a clear effect of the test setting with real applicants showing more positive reactions to the gamified test concerning perceived fairness, test motivation, organizational attractiveness, behavioral intentions, organizational image, clarity of work activity, and enjoyment compared to participants in the low-stakes setting, whereas there were no differences for perceived job-relatedness and opportunity to perform. These findings highlight the influence of test setting on applicant reactions and underscore the importance of examining environmental factors for research on the effects of gamification in cognitive ability testing. Full article

Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifications. Meta-model-based UQ methods are promising due to the lack of predictive model re-training and low resource requirement. However, there are still several issues present in the training process. (1) Most current meta-models are trained using hard labels that do not allow quantification of the uncertainty associated with a given data sample; and (2) in most cases, the base model has a high test accuracy. Therefore, the samples used to train the meta-model primarily consist of correctly classified samples. This leads the meta-model to learn a poor approximation of the true decision boundary. To address these problems, we propose a novel soft-label formulation that better differentiates between correct and incorrect classifications, thereby allowing the meta-model to distinguish between correct and incorrect classifications with high uncertainty (i.e., low confidence). In addition, a novel training framework using adversarial samples is proposed to explore the decision boundary of the base model and mitigate issues related to training datasets with label imbalance. To validate the effectiveness of our approach, we use two predictive models trained on SVHN and CIFAR10 and evaluate performance according to sensitivity, specificity, an F1-score-style metric, average precision, and the Area Under the Receiver Operating Characteristic curve. We find the soft-label approach can significantly increase the model’s sensitivity and specificity, while the training with adversarial samples can noticeably improve the balance between sensitivity and specificity. We also compare our method against four state-of-the-art meta-model-based UQ methods, where we achieve significantly better performance than most models. Full article

Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students’ performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety predicted high-stakes (but not low-stakes) assessment outcomes in both Physics 1 and Physics 2. Comparison of these findings with prior studies involving physical science and engineering students shows that although women outnumber men in physics courses for bioscience students and the career goals of bioscience students are very different from the earlier researched group, most of the negative trends hold even for this new population. Thus, these findings, in a new context involving bioscience students in physics courses, are very important because they reinforce the systemic nature of women being affected more adversely by anxiety in high-stakes assessments, which is a threat to creating equitable and inclusive learning environments. An important implication is that course instructors should carefully consider how high-stakes and low-stakes assessments are used to determine grades and how to create an overall equitable, inclusive, and low-anxiety learning environment. Full article

The need for sustainability-oriented K–12 engineering education that expands beyond the classroom and the increased accessibility of educational technologies create an opportunity for examining the affordances of educational technologies in low-stakes informal engineering education settings. In this paper, we share our experiences of using novel technologies to develop sustainability-oriented mental models in K–12 informal engineering education. Through the use of technologies including Augmented Reality (AR), Virtual Reality (VR), Minecraft video games, Tinkercad (browser-based application for computer-aided design (CAD)), and physical computing, we have designed and tested approaches to introduce students to engineering design and engineering habits of mind with an overarching theme of developing sustainability-oriented mental models among K–12 youth in informal engineering education spaces. In this paper, we share our approaches, and lessons learned, and outline directions for future research. Full article

This study examined the risk of low energy availability (LEA) and eating disorders among elite adolescent female athletes from a mixed-sport cohort and ballet dancers in Singapore, where the accelerated biological needs of adolescent growth and maturation overlap with the pursuit of sport/ballet excellence and high-stakes academic testing. All of these are competing demands for adequate fuelling and seeking timely treatment, which consequently affect the risk of LEA. Eighty-nine participants (41 athletes, 48 dancers; age 16.00 [3.00] years old) were screened for the risk of LEA and eating disorders using the Low Energy Availability in Females Questionnaire (LEAF-Q) and Brief Eating Disorder in Athletes Questionnaire (BEDA-Q), respectively. The main effects of athlete/ballet status and age (≤15 years old versus 15.1 years–18 years old) on LEAF-Q and BEDA-Q scores was be determined via Spearman’s correlation coefficient and linear regression analyses. The Mann–Whitney U test and Fisher’s exact test were used to compare the groups for the risk of LEA and ED. We observed that adolescent athletes and ballet dancers had a similar prevalence of being at risk of LEA (61.98% versus 54.17%, respectively, p = 0.529), with the risk of eating disorders absent in most of them. It appeared that the risk of LEA is likely of an unintentional nature in this study as the risk of ED was absent in 90.2% of the adolescent athletes/ballet dancers that were at risk of LEA. The age of the participants was significantly correlated with the risk of LEA, while the number of training hours was negatively correlated with the same factor. Age was also found to be negatively correlated with the number of training hours. The findings suggest that the risk of LEA in Singapore youth athletes and dancers are multifaceted, involving factors such as type of activity, age, and training hours. Targeted and tailored interventions and programmes are thus needed to promote adequate energy availability for optimal physical and psychological growth in sport and dance. Full article

In educational settings, students rely on metacognitive processes to determine whether or not to exert effort. We investigated ways to minimize cognitively disengaged responses (i.e., not-fully-effortful responses) during a low-stakes mathematics assessment. Initially, we established theory-driven time thresholds for each item to detect such responses. We then administered the test to 800 eighth-graders across three conditions: (a) control (n = 271); (b) instruction (n = 267); and (c) nudge (n = 262). In the instruction condition, students were told to exert their best effort before starting the assessment. In the nudge condition, students were prompted to give their best effort following each first-attempt response that was both incorrect and not-fully-effortful. Therefore, students had multiple opportunities to adjust their level of effort. Nudges, but not effort instruction, significantly reduced students’ not-fully-effortful responses. Neither the nudges nor the effort instruction significantly impacted performance. In a post-test survey, most students reported that they received nudges whenever they did not know the answer (55%). Overall, these findings suggest that while nudges reduce cognitively disengaged responses, most students appear to strategically modulate their level of effort based on self-monitoring their knowledge and response effort. Full article

University of Surrey’s School of Veterinary Medicine introduced an assessment called On The Spot Presentation-based Assessment (OTSPA) into the 3rd and 4th year of a 5-year veterinary degree programme. The OTSPA is designed as a low-weightage summative assessment, conducted in a supportive learning environment to create a better learning experience. The OTSPA is a timed oral assessment with an ‘on the spot’ selection of taught topics, i.e., students prepare to be assessed on all topics but a subset is chosen on the day. The OTSPA was designed to test the students’ depth of knowledge while promoting skills like communication and public speaking. The aim of this study is to describe the design and operation of the OTSPA, to evaluate student perception of the approach, and to assess the OTSPA’s predictive value in relation to the final written summative assessment (FWSA), which is an indicator of academic performance. This study assessed the student perceptions (N = 98) and predictive value of the OTSPA on the FWSA in three modules: Zoological Medicine (ZM), Fundamentals of Veterinary Practice (FVP), and Veterinary Research and Evidence-based Veterinary Medicine (VREBVM). In the perception study, 79.6% of students felt that their preparation for OTSPAs drove an understanding and learning of topics that formed part of the module learning outcomes. Only a small group (21.4%) reported the assessment to be enjoyable; however, 54.1% saw value in it being an authentic assessment, reflecting real-life situations. The majority of students felt that the OTSPA helped with improving communication skills (80.4%). There was a small but significant positive correlation between the performance in OTSPAs and the FWSA in all modules. This suggests that OTSPAs can be useful in predicting the outcomes of the FWSA and, furthermore, could have utility in identifying where support may be helpful for students to improve academic performance. Outcomes from this study indicate that the OTSPA is an effective low stake summative assessment within the Surrey veterinary undergraduate programme. Full article

The aim of the current study was to examine how reward-associated emotional facial distractors could capture attentional resources in a demanding visual task using event-related potentials (ERPs). In the learning phase, a high- or low-reward probability was paired with angry, happy, or neutral faces. Then, in the test phase, participants performed a face-irrelevant task with no reward at stake, in which they needed to discriminate the length of two lines presented in the center of the screen while faces that were taken from the learning phase were used as distractors presented in the periphery. The behavioral results revealed no effect of distractor emotional valence since the emotional information was task-irrelevant. The ERP results in the test phase revealed a significant main effect of distractor emotional valence for the parieto-occipital P200 (170–230 ms); the mean amplitudes in both the angry- and happy-face conditions were more positive than the neutral-face condition. Moreover, we found that the high-reward association enhanced both the N170 (140–180 ms) and EPN (260–330 ms) relative to the low-reward association condition. Finally, the N2pc (270–320 ms) also exhibited enhanced neural activity in the high-reward condition compared to the low-reward condition. The absence of emotional effects indicated that task-irrelevant emotional facial stimuli did not impact behavioral or neural responses in this highly demanding task. However, reward-associated information was processed when attention was directed elsewhere, suggesting that the processing of reward-associated information worked more in an automatic way, irrespective of the top-down task demand. Full article

Objective: To evaluate the effectiveness of a sequenced drug knowledge pilot in third professional year students in a capstone course. Methods: A three-phase drug knowledge pilot was conducted in spring 2022. Students completed a total of thirteen assessments, including nine low-stakes quizzes, three formative tests, and a final summative comprehensive exam. Results from the previous year’s cohort (historical control) who only completed a summative comprehensive exam were compared to the pilot (test group) results to assess effectiveness. The faculty spent over 300 h developing content for the test group. Results: The pilot group had a mean score of 80.9% on the final competency exam, which was one percent lower than the control group who had a less rigorous intervention. A sub-analysis was conducted that removed the students who failed (<73%) the final competency exam, and no significant difference in the exam score was found. One practice drug exam was found to be moderately correlated and significant (r = 0.62) with the final knowledge exam performance in the control. The number of attempts on the low-stakes assessments had a low correlation with the final exam score in the test group compared to the control (r = 0.24). Conclusion: The results of this study suggest a need to further investigate the best practices for knowledge-based drug characteristic assessments. Full article

Natural polymers have been investigated as part of the endeavors of green chemistry practice in the oil field. However, natural polymer studies are still preliminary. The current study examines okra’s (natural polymer) efficiency for polymer flooding, particularly in Kazakhstan. The evaluation targets the heavy oil trapped in carbonate reservoirs. SEM and FTIR were used to characterize morphology and chemical composition. A rheology study was conducted under different shear rates for three plausible concentrations: 1 wt.%, 2 wt.% and 5 wt.%. The core flooding was challenged by the low porosity and permeability of the core. The results showed that okra’s size is between 150–900 μm. The morphology can be described by rod-like structures with pores and staking as sheet structures. The FTIR confirmed that the solution contains a substantial amount of polysaccharides. During the rheology test, okra showed a proportional relationship between the concentration and viscosity increase, and an inversely proportional relationship with the shear rate. At reservoir temperature, the viscosity reduction was insignificant, which indicated good polymer stability. Okra showed shear-thinning behavior. It was fitted to the Ostwald–de Waele power-law model by a (90–99)% regression coefficient. The findings confirm okra’s pseudo-plasticity, and that it is proportional to the solution concentration. The incremental oil recovery was 7%. The flow was found to be restricted due to the mechanical entrapment resulting from the large molecule size and the low porosity–permeability of the system. This study proves that the dominating feature of natural polysaccharide derivatives is their applicability to moderate reservoir conditions. The current study is a positive attempt at natural polymer application in Kazakhstan and similar field conditions. Full article