Multiple Difficulty Research Articles

Phonemic processing is below expectations and linked to word-finding difficulty in multiple sclerosis

Background: Word-finding difficulty is prevalent but poorly understood in persons with relapsing-remitting multiple sclerosis (RRMS). Objective: The objective was to investigate our hypothesis that phonological processing ability is below expectations and related to word-finding difficulty in patients with RRMS. Method: Data were analyzed from patients with RRMS (n = 50) on patient-reported word-finding difficulty (PR-WFD) and objective performance on Wechsler Individual Achievement Test, Fourth Edition (WIAT-4) Phonemic Proficiency (PP; analysis of phonemes within words), Word Reading (WR; proxy of premorbid literacy and verbal ability), and Sentence Repetition (SR; auditory processing of word-level information). Results: Performance (mean (95% confidence interval)) was reliably lower than normative expectations for PP (−0.41 (−0.69, −0.13)) but not for WR (0.02 (−0.21, 0.25)) or SR (0.08 (−0.15, 0.31). Within-subjects performance was worse on PP than on both WR (t(49) = 4.00, p < 0.001, d = 0.47) and SR (t(49) =3.76, p < 0.001, d = 0.54). Worse PR-WFD was specifically related to lower PP (F2,47 = 6.24, p = 0.004, η2 = 0.21); worse PP performance at PR-WFD Often (n = 13; −1.16 (−1.49, −0.83)) than Sometimes (n = 17; −0.14 (−0.68, 0.41)) or Rarely (n = 20; −0.16 (−0.58, 0.27). PR-WFD was unrelated to WR or SR (ps > 0.25). Conclusion: Phonological processing was below expectations and specifically linked to word-finding difficulty in RRMS. Findings are consistent with early disease-related cortical changes within the posterior superior temporal/supramarginal region. Results inform our developing model of multiple sclerosis-related word-finding difficulty.

Research and application of push model based on digital twin for complex solutions

In order to solve the selection problems such as multiple products, complex scenarios, and difficulty in supporting complex solutions, the product selection push model for complex solutions was proposed, the selection cloud platform was set up, and the platform architecture, composition, and operating mechanism were given. Based on the digital twin five-dimensional framework, the modeling process of the product selection cloud platform was detailed, and in the demand stage, the function model of the model was determined by the collaborative filter algorithm, which gave a comprehensive description of its functions. Through the construction of multi-platform fusion and algorithm model, the active push, scheme automatic process, and verification function of solution product selection have been realized. The solution has been applied in the selection of the smart security community scheme, verifying the effectiveness and practicality of the platform.

Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral Sequences

Previous research has not adequately explored students’ behavioral processes when addressing computational thinking (CT) problems of varying difficulty, limiting insights into students’ detailed CT development characteristics. This study seeks to fill this gap by employing gamified CT items across multiple difficulty levels to calculate comprehensive behavioral sequence quality indicators. And then, through latent profile analysis, we identified four distinct latent classes of behavioral process. We then examined the in-game performance differences among these classes, uncovering each class’s unique attributes. Class 1 students consistently demonstrated high-quality, efficient behavioral sequences regardless of item difficulty. In contrast, class 2 students applied significant cognitive effort and trial-and-error strategies, achieving acceptable scores despite low behavioral sequence quality. Class 3 students excelled in simpler items but faltered with more complex ones. Class 4 students displayed low motivation for challenging items, often guessing answers quickly. Additionally, we investigated the predictive value of students’ performance in gamified items and their behavioral process classes for their external CT test scores. The study finally elaborated on the theoretical implications for researchers and the practical suggestions for teachers in CT cultivation.

Piano score rearrangement into multiple difficulty levels via notation-to-notation approach

Musical score rearrangement is an emerging area in symbolic music processing, which aims to transform a musical score into a different style. This study focuses on the task of changing the playing difficulty of piano scores, addressing two challenges in musical score rearrangement. First, we address the challenge of handling musical notation on scores. While symbolic music research often relies on note-level (MIDI-equivalent) information, musical scores contain notation that cannot be adequately represented at the note level. We propose an end-to-end framework that utilizes tokenized representations of notation to directly rearrange musical scores at the notation level. We also propose the ST+ representation, which includes a novel structure and token types for better score rearrangement. Second, we address the challenge of rearranging musical scores across multiple difficulty levels. We introduce a difficulty conditioning scheme to train a single sequence model capable of handling various difficulty levels, while leveraging scores from various levels in model training. We collect commercial-quality pop piano scores at four difficulty levels and train a MEGA model (with 0.3M parameters) to rearrange between these levels. Objective evaluation shows that our method successfully rearranges piano scores into other three difficulty levels, achieving comparable difficulty to human-made scores. Additionally, our method successfully generates musical notation including articulations. Subjective evaluation (by score experts and musicians) also reveals that our generated scores generally surpass the quality of previous rule-based or note-level methods on several criteria. Our framework enables novel notation-to-notation processing of scores and can be applied to various score rearrangement tasks.

Multiple Choice Question Difficulty Level Classification with Multi Class Confusion Matrix in the Online Question Bank of Education Gallery

Multiple Choice Question Difficulty Level Classification with Multi Class Confusion Matrix in the Online Question Bank of Education Gallery

Identifying the Science and Art of Interactive Experiences

In game design, player engagement and game attractiveness are important components that influence player interaction and interest. This study examines player interaction with different game design aspects, including different races, difficulty levels, novice tutorials, and character design. A mixed-methods research approach was employed, combining qualitative and quantitative methods. A literature review was conducted to gather existing knowledge and insights. Expert interviews, surveys, and user testing sessions were also shown to collect data. The research findings indicate that allowing players to customize characters of different races enhances player immersion and immersiveness. In addition, multiple difficulty levels tailored to player preferences increase player satisfaction and motivation towards the game. And engaging character design, including unique traits and visually appealing aesthetics, can enhances player enjoyment and emotional connection. The research also emphasized the importance of player-centric design in game development. Developers can create immersive experiences that foster player engagement and enjoyment by incorporating these design elements such as player preferences, motivations, and engagement factors into games.

Heterogeneous graph-based knowledge tracing with spatiotemporal evolution

Knowledge tracing (KT), in which the future performance of students is estimated by tracing their knowledge states based on their responses to exercises, is widely applied in the field of intelligent education. However, existing mainstream KT models explore the importance of knowledge relations but ignore the key role of cognitive factors. According to the knowledge construction theory, the human cognitive system performs both spatial accommodation and temporal assimilation to internalize knowledge. In this paper, we propose an innovative heterogeneous graph-based Knowledge tracing method with spatiotemporal evolution (TSKT), in which knowledge state evolution is traced along both temporal and spatial dimensions. We construct a heterogeneous graph with multiple exercise attributes, including content, concepts, and difficulty, to obtain a knowledge space with richer exercise representations through hierarchical aggregation. We design a spatial updating module in which each interaction updates the current node’s state of the knowledge space and transfers its influence to its neighbors. We also design a temporal updating module to further update the knowledge state through short-term memory enhancing and long-term memory forgetting. Finally, we stack these modules to obtain deeper features by using alternate spatiotemporal updating. Extensive experiments on three datasets reveal the superiority of the proposed method and its variants in terms of future performance prediction.

Design of Automatic Disconnection Device for Non-power Outage Operation of High-voltage Line

A set of remote electric control disconnection device for high-voltage line with strong adaptability, easy operation, and high safety has been designed to address the problems of multiple spans, wide coverage, and difficulty in coordinating power failure with construction crossing operation. The design process aimed to reduce the weight and improve the reliability of the mechanical structure, and conducted computation and simulation of the traction force and safety load of the electrified tight cable. Based on the designed live disconnection device, experiments were conducted for different spans and cable diameters, while meeting the scope of infrastructure crossing sections. The experiments proved that the designed disconnection device has good effects in practical applications, and can select any point in the straight span to break the cable, in order to achieve safe and convenient uninterrupted operations in power recovery service.

Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP

Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations, reduce data dimensionality, and simplify the model structure, a BP neural network model for air quality level prediction optimized by stepwise discriminant analysis (STEPDISC) and principal component analysis (PCA) is proposed with 12 factors of historical daily meteorology and air pollutants in Bayannur city as samples. The results showed that, at the significance level of 0.01, the STEPDISC method retained 9 significant impact factors. The PCA method made an orthogonal linear combination of the 9 factors to form the principal components, and the contribution of the top 5 principal components were 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%. At a contribution threshold of 0.85, the top 5 principal component scores were used as input nodes to construct the STEPDISC-PCA-BP model, which had a prediction accuracy of 85.5%.Compared with the PCA-BP and BP model, which had a prediction accuracy of 61.8% and 56.7%, respectively, the STEPDISC-PCA-BP model has a higher prediction accuracy, shorter time, and lower complexity of structure and data dimensionality, and can provide the necessary technical support for the local air quality improvement.

Dungeons &amp; Replicants II: Automated Game Balancing Across Multiple Difficulty Dimensions via Deep Player Behavior Modeling

Video game testing has become a major investment of time, labor and expense in the game industry. Particularly the balancing of in-game units, characters and classes can cause long-lasting issues that persist years after a game's launch. While approaches incorporating artificial intelligence have already shown successes in reducing manual effort and enhancing game development processes, most of these draw on heuristic, generalized or optimal behavior routines, while actual low-level decisions from individual players and their resulting playing styles are rarely considered. In this paper, we apply Deep Player Behavior Modeling to turn atomic actions of 213 players from 6 months of single-player instances within the MMORPG Aion into generative models that capture and reproduce particular playing strategies. In a subsequent simulation, the resulting generative agents (replicants) were tested against common NPC opponent types of MMORPGs that iteratively increased in difficulty, respective to the primary factor that constitutes this enemy type (Melee, Ranged, Rogue, Buffer, Debuffer, Healer, Tank or Group). As a result, imbalances between classes as well as strengths and weaknesses regarding particular combat challenges could be identified and regulated automatically.

Multiple Instance Complementary Detection and Difficulty Evaluation for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has attracted lots of attention because it solely employs image-level labels to drive the model training. Most of the WSOD methods incline to mine salient object as positive instance, and the less salient objects are considered as negative instances, which will cause the problem of missing instances. In addition, the quantity of hard and easy instances is usually imbalanced, consequently, the cumulative loss of a large amount of easy instances dominates the training loss, which limits the upper bound of WSOD performance. To handle the first problem, a complementary detection network (CDN) is proposed, which consists of a complementary multiple instance detection network (CMIDN) and a complementary feature learning (CFL) module. The CDN can capture robust complementary information from two basic multiple instance detection networks (MIDNs) and mine more object instances. To handle the second problem, an instance difficulty evaluation metric named instance difficulty score (IDS) is proposed, which is employed as the weight of each instance in the training loss. Consequently, the hard instances will be assigned larger weights according to the IDS, which can improve the upper bound of WSOD performance. The ablation experiments demonstrate that our method significantly increases the baseline method by large margins, i.e. 23.6% (10.2%) mAP and 32.4% (13.1%) CorLoc gains on the NWPU VHR-10.v2 (DIOR) dataset. Our method obtains 58.1% (26.7%) mAP and 72.4% (47.9%) CorLoc on the NWPU VHR-10.v2 (DIOR) dataset, which achieves better performance compared with seven advanced WSOD methods.

A failure assessment method of island distribution lines based on model‐data hybrid drive

Open circuit faults of island distribution lines account for more than 70% of the total number of faults in the distribution network. Failure assessment of lines plays a crucial role in the operational safety of the island distribution network. Aiming at the problems of multiple operational failure factors and difficulty in failure assessment, a failure assessment method of island distribution lines based on model-data hybrid drive is proposed in this paper. Specifically, in order to accurately describe the actual operating status and failure mechanism of distribution lines in the typical island environment, model-driven method is used to formulate a comprehensive operational failure index of distribution lines, which combines with the impact of strong wind and corrosion operation scenarios and the operating characteristics of lines. On this basis, to improve the accuracy of failure assessment under the influence of multiple factors in the island environment, an improved light gradient boosting machine model is constructed in a data-driven manner. Finally, based on actual data from distribution network in Zhoushan, China, the case study reveals that the proposed method could effectively assess the operational failure of island distribution lines.

RSFD: A rough set-based feature discretization method for meteorological data

Meteorological data mining aims to discover hidden patterns in a large number of available meteorological data. As one of the most relevant big data preprocessing technologies, feature discretization can transform continuous features into discrete ones to improve the efficiency of meteorological data mining algorithms. Aiming at the problems of high interaction of multiple attributes, noise interference, and difficulty in obtaining prior knowledge in meteorological data, we propose a rough set-based feature discretization method for meteorological data (RSFD). First, we calculate the information gain of each candidate breakpoint in the meteorological attribute to split the intervals. Then, we use chi-square test to merge these discrete intervals. Finally, we take the variation of indiscernibility relation in rough set as the evaluation criterion for the discretization scheme. We scan each attribute in turn by using the strategy of splitting first and then merging, thus obtaining the optimal discrete feature set. We compare RSFD with the state-of-the-art discretization methods on meteorological data. Experiments show that our method achieves better results in the classification accuracy of meteorological data, and obtains a smaller number of discrete intervals while ensuring data consistency.

New magnetic isolation winding for modular multiple three‐phase permanent‐magnet machine with mixed‐layer

In large scale permanent magnet (PM) machines, such as direct drive wind power generators, stator modularity is widely used for easy manufacturing and to protect the winding from mechanical damage due to being exposed to air. Multiple three-phase systems that use commercial inverters provide not only a fault-tolerant operation but also price competitiveness. Although the conventional modular structure of a concentrated single-layer or distributed winding shows good performance, a large magnetic mutual interference between coils is inevitable. Consequently, if conventional windings are applied to multiple three-phase systems, complexity and difficulty for controlling the machine can arise. This study proposes a new magnetic isolation winding scheme for modular multiple three-phase PM machines with a mixed layer to deal with the abovementioned limitation. For comparison, five different modular multiple-three phase PM machines with a mixed-layer winding (MMPM) are presented and three conventional modular types are selected. Then, several simulation results and experimental data confirm the validity of the proposed structure.

Massive haematometrosalpinx in a young girl with cloacal anomalies: Laparoscopic management

A cloacal anomaly refers to the collection of defects that occur during fetal development, where the intestinal, urinary and reproductive tracts open into a common cavity. It is imperative to address this condition since despite its fairly low incidence and limited data, it has a very high impact on the patient's quality of life, and therefore mandates further study to effectively manage these patients. This study is regarding a 13-year-old girl diagnosed to have a cloacal anomaly since birth presenting with cyclical abdominal and pelvic pain for 6 months. She had undergone multiple abdominal and pelvic corrective surgeries since birth, leading to a colostomy and Mitrofanoff channel for bowel and bladder control, respectively. Diagnostic imaging revealed bilateral haematosalpinx for which ultrasound guided aspiration followed by subcutaneous administration of GnRH analogues was given as a temporary mode of relief which eventually recurred within 4 months. Following an extensive Multidisciplinary discussion, the team proceeded with the crucial decision of a hysterectomy and bilateral salpingectomy. The surgery itself was challenging due to multiple adhesions, scarring and difficulty in port placement. Following surgery, the patient's quality of life significantly improved allowing her to attend to her activities of daily living and maintain a social life. The need for future appointments to address sexual functions and fertility wishes was explained.

Synchrony as a measure of conversation difficulty: Movement coherence increases with background noise level and complexity in dyads and triads.

When people interact, they fall into synchrony. This synchrony has been demonstrated in a range of contexts, from walking or playing music together to holding a conversation, and has been linked to prosocial outcomes such as development of rapport and efficiency of cooperation. While the basis of synchrony remains unclear, several studies have found synchrony to increase when an interaction is made challenging, potentially providing a means of facilitating interaction. Here we focus on head movement during free conversation. As verbal information is obscured when conversing over background noise, we investigate whether synchrony is greater in high vs low levels of noise, as well as addressing the effect of background noise complexity. Participants held a series of conversations with unfamiliar interlocutors while seated in a lab, and the background noise level changed every 15-30s between 54, 60, 66, 72, and 78 dB. We report measures of head movement synchrony recorded via high-resolution motion tracking at the extreme noise levels (i.e., 54 vs 78 dB) in dyads (n = 15) and triads (n = 11). In both the dyads and the triads, we report increased movement coherence in high compared to low level speech-shaped noise. Furthermore, in triads we compare behaviour in speech-shaped noise vs multi-talker babble, and find greater movement coherence in the more complex babble condition. Key synchrony differences fall in the 0.2–0.5 Hz frequency bands, and are discussed in terms of their correspondence to talkers’ average utterance durations. Additional synchrony differences occur at higher frequencies in the triads only (i.e., >5 Hz), which may relate to synchrony of backchannel cues (as multiple individuals were listening and responding to the same talker). Not only do these studies replicate prior work indicating interlocutors’ increased reliance on behavioural synchrony as task difficulty increases, but they demonstrate these effects using multiple difficulty manipulations and across different sized interaction groups.

Validation of the IWATE criteria as a laparoscopic liver resection difficulty score in a single North American cohort.

Laparoscopic liver resection (LLR) involves a difficult learning curve, for which multiple difficulty scores have been proposed to assist with safe adaptation. The IWATE Criteria is a 4-level difficulty score shown to correlate with conversion to open surgery, estimated blood loss (EBL), and operative time in Japanese and French cohorts. We set out to validate the IWATE Criteria in a North American cohort, describe the evolution of our LLR program, and analyze the IWATE Criteria's ability to predict conversion to open surgery. Patients that underwent LLR between January 2006 and December 2019 were selected from a prospectively maintained database. Difficulty outcomes, including conversion to open surgery, EBL, operative time, and post-operative complications were analyzed according to IWATE difficulty level, both overall and between chronological eras. The IWATE Criteria's ability to predict conversion to open surgery was assessed with a receiver operating characteristic (ROC) analysis. A total of 426 patients met inclusion criteria. Operative time, EBL, and conversion to open surgery increased in concordance with low to advanced IWATE difficulty. ROC analysis for conversion to open surgery demonstrated an overall area under the curve (AUC) of 0.694. Predictive performance was superior during the first two eras, with AUCs of 0.771 and 0.775; predictive value decreased as the LLR program gained experience, with AUCs of 0.708 and 0.551 for eras three and four. This study validated the IWATE Criteria in a North American population distinct from previous Japanese and French cohorts, based on its correlation with operative time, EBL, and conversion to open surgery. The IWATE Criteria may be of utility for identification of LLR cases appropriate for surgeon experience, as well as determination of laparoscopic feasibility. Interval difficulty score recalibration may be warranted as surgeon perception of difficulty evolves.

A modified equilibrium optimizer using opposition-based learning and novel update rules

Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that is based on control volume mass balance models, and has shown competitive performance with other state-of-the-art algorithms. However, the original EO has the disadvantages of a low exploitation ability, ease of falling into local optima, and an immature balance between exploration and exploitation. To address these shortcomings, this paper proposes a modified EO (m-EO) using opposition-based learning (OBL) and novel update rules that incorporates four main modifications: the definition of the concentrations of some particles based on OBL, a new nonlinear time control strategy, novel population update rules and a chaos-based strategy. Based on these modifications, the optimization precision and convergence speed of the original EO are greatly improved. The validity of m-EO is tested on 35 classical benchmark functions, 25 of which have variants belonging to multiple difficulty categories (Dim = 30, 100, 300, 500 and 1000). In addition, m-EO is used to solve three real-world engineering design problems. The experimental results and two different statistical tests demonstrate that the proposed m-EO shows higher performance than original EO and other state-of-the-art algorithms.

Optimizing Mental Workload Estimation by Detecting Baseline State Using Vector Phase Analysis Approach.

Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.

Do's and Don'ts of the Virtual Interview: Perspectives From Residency and Fellowship Applicants.

Do's and Don'ts of the Virtual Interview: Perspectives From Residency and Fellowship Applicants.