Objective Comparison Research Articles

Visual information processing of 2D, virtual 3D and real-world objects marked by theta band responses: Visuospatial processing and cognitive load as a function of modality.

While pictures share global similarities with the real-world objects they depict, the latter have unique characteristics going beyond 2D representations. Due to its three-dimensional presentation mode, Virtual Reality (VR) is increasingly used to further approach real-world visual processing, yet it remains unresolved to what extent VR yields process comparable to real-world processes. Consequently, our study examined visuospatial processing by a triangular comparison of 2D objects, virtual 3D objects and real 3D objects. The theta band response (TBR) was analysed as an electrophysiological correlate of visual processing, allowing for the differentiation of predominantly stimulus-driven processes mirrored in the evoked response and internal, complex processing reflected in the induced response. Our results indicate that the differences between conditions driven by sensory features go beyond a binary division into 2D and 3D materials but are based on further sensory features: The evoked posterior TBR differentiated between all conditions but revealed fewer differences between processing of real-world and VR objects. Moreover, the induced midfrontal TBR indicated higher cognitive load for 2D objects compared to VR and real-world objects, while no difference between both latter conditions was revealed. In conclusion, our results demonstrate that the transferability of 2D- and VR-based findings to real-world processes depends to some degree on whether predominantly sensory stimulus features or higher cognitive processes are examined. Yet although VR and real-world processes are not to be equated based on our results, their comparison yielded fewer significant differences relative to the PC condition, advising the use of VR to examine visuospatial processing.

DRCmpVis: Visual Comparison of Physical Targets in Mobile Diminished and Mixed Reality.

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this article, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

SYMBOLISM IN THE ARCHITECTURAL ENVIRONMENT

Statement of the problem: symbolism in the architectural environment is used to express abstract ideas and create images that carry ideological meaning. The relevance of the study of symbolism in the architectural environment is justified by the need to deepen the understanding of its role and significance in the formation of the aesthetic and ideological content of architectural objects. The purpose of the work: to investigate the history of the emergence and features of the use of symbolism in the architectural environment. Tasks of the research: 1. To consider the essence and history of the emergence of symbolism as a direction of art. 2. Compare architectural objects of different cultures and eras from the point of view of their symbolic load. 3. To study the symbolism of iconic objects of the architectural heritage of Ukraine. Research methods: analysis of literary sources, historical-comparative, iconographic and semiotic analysis. Conclusions: 1. The architectural environment of the world is not only a part of the volume-planning solution created and organized by man, designed to ensure the vital activity of society, but also a complex system of signs and symbols that reveals the worldview of different cultures and eras. 2. Analysis and comparison of architectural objects from different parts of the world makes it possible to understand their symbolic meaning. 3. Symbols in the architectural environment: allow to convey ideas, concepts and concepts without a dictionary description; give architectural objects uniqueness and memorability; create a connection between architecture, history, national culture and traditions, as well as a special emotional atmosphere of the architectural environment. 4. Famous architectural objects and landmarks of Ukraine, such as: Sophia Cathedral, Khotyn Fortress, Lychakiv Cemetery, House of Chimeras and Independence Square, are not only monuments of history and culture, but also symbols that reveal the deep meaning of Ukrainian being. 5. The revival and implementation of traditional cultural images of the Ukrainian people in modern art opens up great prospects for the preservation of Ukrainian identity for future generations and gives symbolism to the architectural environment.

Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring.

Improved differentiation of NPS analogs through the application of chemometric methods to GC-solid deposition-FTIR spectra

Novel psychoactive substances pose a significant analytical challenge for forensic laboratories. Gas Chromatography with Infrared Spectroscopy (GC-IR) is typically presented as producing visually distinct spectra. However, this is not the case for all compounds. In this study, we showcase three synthetic cathinone analogs (methylone, N-ethylpentylone, and pentylone) which produce extremely visually similar solid state IR spectra.A primary dataset of these analogs was generated at the Amsterdam Police Laboratory. Libraries were created using two different sample preparation methods. The same compounds were also analyzed at the Virginia Department of Forensic Science (DFS). It was observed that changes in either instrument or sample preparation were enough to pose challenges to both the visual assessment and a library matching algorithm. Additionally, week-to-week variation was observed within the primary dataset. Principal Component Analysis (PCA) in combination with mahalanobis distances for objective comparison was assessed. A leave-one-sample-out cross validation accurately identified 100% of the samples in the primary dataset.This study shows how the application of chemometrics to spectral GC-IR data can substantiate the instrument’s differentiation capabilities and provide valuable objective support for a compound identification. In addition, limitations in the consistency of GC-IR spectra over time and across instruments and sample preparation methods were observed which could affect how the forensic community utilizes these techniques. Specifically, this study shows that shared GC-IR libraries might pose selectivity limitations. Therefore, laboratories must exercise caution if shared/generic GC-IR libraries are utilized for casework, and it is recommended that instrument and solvent specific libraries are generated.

PATH-60. AUTOMATED QUANTIFICATION OF KI-67 PROLIFERATION INDEX BASED ON WHOLE SLIDE IMAGES IN PEDIATRIC BRAIN TUMORS

Abstract The use of AI methods with histopathological images (pathomics) has strong potential to aid pathologists in pediatric brain cancer diagnosis and prognosis given the morphological and molecular heterogeneity of these tumors. However, use of pathomics in this context has been relatively limited. One example is the IHC-based tumor proliferation index (percentage of Ki-67 positive cells amongst all malignant cells) which is often routinely reviewed by pathologists clinically but remains to be objectively quantified or reported in a standardized manner. As a result, analysis of this marker across patients at-scale and in data-driven methods remains largely hindered. Here, we explore the extension of an automated deep learning AI tool (DeepLIIF), originally trained and validated with adult breast/prostate/liver data, for quantification of Ki-67 index from digitized whole slide images of a cohort of pediatric brain tumors from the Children’s Brain Tumor Network (N=834). We find that derived Ki-67 scores largely agreed with neuropathologist-reported Ki-67 activity in original clinical notes and could be used to successfully stratify glioma/astrocytoma patients (N=200) into low- and high-grade groups (LGG: M=11.6; SEM=1.2; HGG: M=18.7; SEM=2; p=0.001). Ki-67 scores were also related to the survival status of the patients (Alive: M=11.8; SEM=1.1; Deceased: M=18.8; SEM=2.2; p=0.003). Summary statistics across tumor types (glioma, astrocytoma, medulloblastoma, ATRT, ependymoma, etc.) and molecularly defined subtypes are also compared, providing direct insights into distributional properties between these groups that has not previously been feasible. Our results show the ability of AI-powered methods to quantify routine histopathology metrics with little human input, allowing objective comparisons between patients and opening the door for precision medicine approaches with predictive analytics in clinical contexts.

Comparison of Policy Responses to the COVID‐19 Pandemic. Does It Reproduce the Old Patterns?

ABSTRACTThe research's main objectives are to verify systemic differences between OECD countries in terms of their early responses to the COVID‐19 economic challenge and to compare newly established taxonomy with old patterns determined by classifications that can be found in the literature. Research is based on a few methods of cluster analysis—hierarchical clustering and the k‐means method are supplemented with insights derived from the application of c‐means fuzzy clustering. It also applies Rand and Jaccard indices to compare a new taxonomy with existing classifications of welfare states and capitalistic systems. Objective comparison of the new taxonomy with those existing in the literature with the application of statistical methods is the most significant contribution of the article. Results suggest that it is possible to distinguish various strategies for dealing with the COVID‐19 economic consequences in the early period of the pandemic, but demarcation lines between particular clusters significantly differ from the findings of previous studies.

Size scaling of large landslides from incomplete inventories

Abstract. Landslide inventories have become cornerstones for estimating the relationship between the frequency and size of slope failures, thus informing appraisals of hillslope stability, erosion, and commensurate hazard. Numerous studies have reported how larger landslides are systematically rarer than smaller ones, drawing on probability distributions fitted to mapped landslide areas or volumes. In these models, much uncertainty concerns the larger landslides (defined here as affecting areas ≥ 0.1 km2) that are rarely sampled and often projected by extrapolating beyond the observed size range in a given study area. Relying instead on size-scaling estimates from other inventories is problematic because landslide detection and mapping, data quality, resolution, sample size, model choice, and fitting method can vary. To overcome these constraints, we use a Bayesian multi-level model with a generalised Pareto likelihood to provide a single, objective, and consistent comparison grounded in extreme value theory. We explore whether and how scaling parameters vary between 37 inventories that, although incomplete, bring together 8627 large landslides. Despite the broad range of mapping protocols and lengths of record, as well as differing topographic, geological, and climatic settings, the posterior power-law exponents remain indistinguishable between most inventories. Likewise, the size statistics fail to separate known earthquakes from rainfall triggers and event-based triggers from multi-temporal catalogues. Instead, our model identifies several inventories with outlier scaling statistics that reflect intentional censoring during mapping. Our results thus caution against a universal or solely mechanistic interpretation of the scaling parameters, at least in the context of large landslides.

The scope and limits of fine-grained image and category information in the ventral visual pathway.

Humans can easily abstract incoming visual information into discrete semantic categories. Previous research employing functional MRI (fMRI) in humans has identified cortical organizing principles that allow not only for coarse-scale distinctions such as animate versus inanimate objects but also more fine-grained distinctions at the level of individual objects. This suggests that fMRI carries rather fine-grained information about individual objects. However, most previous work investigating fine-grained category representations either additionally included coarse-scale category comparisons of objects, which confounds fine-grained and coarse-scale distinctions, or only used a single exemplar of each object, which confounds visual and semantic information. To address these challenges, here we used multisession human fMRI (female and male) paired with a broad yet homogenous stimulus class of 48 terrestrial mammals, with 2 exemplars per mammal. Multivariate decoding and representational similarity analysis (RSA) revealed high image-specific reliability in low- and high-level visual regions, indicating stable representational patterns at the image level. In contrast, analyses across exemplars of the same animal yielded only small effects in the lateral occipital complex (LOC), indicating rather subtle category effects in this region. Variance partitioning with a deep neural network and shape model showed that across exemplar effects in EVC were largely explained by low-level visual appearance, while representations in LOC appeared to also contain higher category-specific information. These results suggest that representations typically measured with fMRI are dominated by image-specific visual or coarse-grained category information but indicate that commonly employed fMRI protocols may reveal subtle yet reliable distinctions between individual objects.Significance Statement While it has been suggested that functional MRI (fMRI) responses in ventral visual cortex carry fine-grained information about individual objects, much previous research has confounded fine-grained with coarse-scale category information or only used individual visual exemplars, which potentially confounds semantic and visual object information. Here we address these challenges in a multisession fMRI study where participants viewed a highly homogenous stimulus set of 48 land mammals with 2 exemplars per animal. Our results reveal a strong dominance of image-specific effects and additionally indicate subtle yet reliable category-specific effects in lateral occipital complex, underscoring the capacity of commonly employed fMRI protocols to uncover fine-grained visual information.

Parameter analysis in stereoelectroencephalography-guided radiofrequency thermocoagulation: A common basis for objective comparison between protocols

ObjectiveStereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) is an invasive procedure based on stereotactic lesioning of cortical targets in the brain using bipolar current through electrode contacts within the SEEG implant. To date, several RF-TC protocols have been described in the literature; however, a consensus has yet to be reached. This work aims to analyze the electrical parameters during RF-TC processes, offering a method to objectively describe and compare different SEEG-guided RF-TC protocols. MethodsThe study included patients who underwent RF-TC procedures at the IRCCS Istituto delle Scienze Neurologiche di Bologna from February 2022 to May 2023. During each procedure, modifications of the following parameters were measured: voltage, current, impedance, and electric power. An ad-hoc algorithm was implemented to detect abrupt impedance raises, which reflects the occurrence of the thermocoagulation. A two-sample t-test was used to compare parameter curves in RF-TC of different brain structures. ResultsA total of ninety-two RF-TC procedures were performed in eight patients according to a standardized protocol. During each procedure, impedance levels started at about 700Ω and rose up to 1300Ω, displaying an erratic pattern characterized by one or multiple raises. All measured parameters exhibited similar trends until the first peak, after which changes were influenced by the frequency of impedance raises. No significant correlations were observed between parameter modifications in distinct anatomical sites of RF-TC. SignificanceThe systematic analysis of electrical parameters may represent a reliable tool to compare different RF-TC protocols, paving the way for identifying optimal configurations for SEEG-guided RF-TC procedures in the future.

121 year after Einthoven: show me the normal ECG?

Abstract Background The clinical electrocardiogram (ECG) is measured about 120 years. The interpretations of the ECG is an art mastered by only few. Rhythm and Interval analysis is relative reliable and well supported by the available ECG machines. However, the interpretation of the ECG waveform and where it starts to deviate from the normal waveforms is poorly defined for the clinical practice. To support the anatomical relationship of the ECG, the PathECG has been developed as part of the CineECG tool, expanding the view on the ECG. In this study normal ECG distributions were created from a public available database to enable a) visual comparison of the ‘normality’ of the ECG and PathECG and b) build a predictive model to classify an ECG as normal or abnormal. Purpose To create an objective and 3D visual comparison of an ECGs to "normal ECGs". Methods From the (Physionet) PTB-XL database (Germany) all verified ECGs with known gender were selected. From the these ECGs a median ventricular beat was automatically constructed and subsequently resampled to have a standardized number of samples (Figure: Normal ECGs on top of abnormal ECGs). These resampled ECGs were also converted to their equivalent PathECG. From the normal labeled ECGs (3124 female, 2283 male) distributions were created, representing the normal ECG for male, female or the combined group. With these distributions a percentage of ECG segments (QRS, ST, T wave) can be identified as being outside this distribution, and thus as potentially abnormal. A logistic regression model was fitted to determine the optimal outlier removal per segment for the ECG waveforms and the PathECG to classify and ECG as normal or abnormal per gender group. The performance was tested on the CPSC (Hong Kong) public available ECG database. Results The abnormal ECG data in both databases contained approximately 1/3 normal ECG, atrial and ventricular arrhythmias and 1/3 ischemia and conduction disorders. The performance (AUC) of the logistic regression model on the PTB-XL and CPSC data respectively was MALES: 87.5% - 81.5%, FEMALES: 86.7% - 83.7%, and COMBINED: 86.8%-82.9%. The visual overlay of the ECG significantly supports the identification of abnormal segments (Figure: Normal ECG distribution example). Conclusions The overlay of an ECG and PathECG on the normal distribution supports the identification and verification of abnormal ECGs. This method might increase the ability of less experienced ECG readers, like general practitioners, to identify patients that need to be transferred to a cardiologist. This method could also be used for the classification of P-waves, paediatric ECGs or ECGs in specific patient groups.Normal ECGs on top of abnormal ECGsNormal ECG distribution example

A comparison of head impact characteristics during elite men’s Rugby Union fifteens and sevens match play

Different forms of rugby may pose distinct risks to head injury. Video of rugby match footage was analyzed using head impact magnitude, frequency, and time interval for 15s and 7s athletes. Impacts were reconstructed in laboratory, and finite element modeling was used to estimate maximum principal strain. No difference was found in impact frequency or time interval between the two forms. Significantly more head impacts of higher severity levels were documented during 7s. These findings provide objective comparisons between 7s and 15s which may guide risk mitigation strategies in managing brain trauma for specific forms of rugby.

A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles

The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking. Here, we perform an in-depth comparison of 12 state-of-the-art deep learning architectures that use different protein and compound representations. The models were selected for their reported performance and architectures. To reliably compare model performance, we curated over 300 thousand binding and non-binding CTIs and established several gold-standard datasets of varying size and information. Based on our findings, DeepConv-DTI consistently outperforms other models in CTI prediction performance across the majority of datasets. It achieves an MCC of 0.6 or higher for most of the datasets and is one of the fastest models in training and inference. These results indicate that utilizing convolutional-based windows as in DeepConv-DTI to traverse trainable embeddings is a highly effective approach for capturing informative protein features. We also observed that physicochemical embeddings of targets increased model performance. We therefore modified DeepConv-DTI to include normalized physicochemical properties, which resulted in the overall best performing model Phys-DeepConv-DTI. This work highlights how the systematic evaluation of input features of compounds and targets, as well as their corresponding neural network architectures, can serve as a roadmap for the future development of improved CTI models.Scientific contributionThis work features comprehensive CTI datasets to allow for the objective comparison and benchmarking of CTI prediction algorithms. Based on this dataset, we gained insights into which embeddings of compounds and targets and which deep learning-based algorithms perform best, providing a blueprint for the future development of CTI algorithms. Using the insights gained from this screen, we provide a novel CTI algorithm with state-of-the-art performance.

Multilevel Disparities of Sex-Differentiated Human Papilloma Virus-Positive Oropharyngeal Cancers in the United States.

Objectives: This study used multilevel social determinants of health (SDoH) models to determine how SDoH influence different sexes of patients diagnosed with HPV-positive oropharyngeal squamous cell cancers (OPSCC) across the US. Methods: This was a retrospective cohort study assessing HPV-confirmed patients with oropharyngeal squamous cell cancers from 2010 to 2018 using census-level Yost Index socioeconomic status (SES) score and rurality-urbanicity measures alongside individual-level race-ethnicity while stratifying by biological sex. Age-adjusted multivariate regressions were performed for survival, treatment receipt, and delay of treatment initiation (of 3+ months). Results: Across 14,076 OPSCC-HPV-positive patients, delay of treatment uniquely featured positive predictors for males of black race-ethnicity (OR, 2.07; 95% CI, 1.68-2.54) and poor Yost SES (1.43; 1.24-1.65). Five-year all-cause mortality uniquely showed positive predictors of females of black race-ethnicity (2.74; 1.84-4.71) and of males with poor Yost SES (1.98; 1.79-2.19). Three-year all-cause mortality shared positive predictors across sexes but were exacerbated in females of black race-ethnicity (2.50; 1.82-3.44) compared to males (2.23; 1.91-2.60); this was reversed for poor Yost SES (male, 1.92, 1.76-2.10; female, 1.60, 1.32-1.95). Surgery showed negative predictors of black race-ethnicity that displayed worsened effects in females (0.60, 0.44-0.79) versus males (0.75, 0.66-0.86). First-line radiation receipt uniquely featured negative predictors for males of black race-ethnicity (0.73; 0.62-0.86) with poor Yost SES (0.74; 0.68-0.82). Conclusions: Comprehensive models of multilevel SDoH displayed exacerbated disparity effects of community-level SES in males and black race-ethnicity among female HPV-positive OPSCC patients. These objective comparisons of specific SDoH factors inform providers and policy direction on how to strategically target the most pertinent SDoH factors affecting a rapidly growing cancer population.

Statistical monitoring applied to data science in classification: continuous validation in predictive models

PurposeThis study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.Design/methodology/approachThe research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.FindingsValidation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.Originality/valueThe main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion from the MICCAI2022 challenge

Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image. Preoperative 3D models extracted from Computed Tomography (CT) or Magnetic Resonance (MR) imaging data are registered to the intraoperative laparoscopic images during this process. Regarding 3D–2D fusion, most algorithms use anatomical landmarks to guide registration, such as the liver’s inferior ridge, the falciform ligament, and the occluding contours. These are usually marked by hand in both the laparoscopic image and the 3D model, which is time-consuming and prone to error. Therefore, there is a need to automate this process so that augmented reality can be used effectively in the operating room. We present the Preoperative-to-Intraoperative Laparoscopic Fusion challenge (P2ILF), held during the Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) conference, which investigates the possibilities of detecting these landmarks automatically and using them in registration. The challenge was divided into two tasks: (1) A 2D and 3D landmark segmentation task and (2) a 3D–2D registration task. The teams were provided with training data consisting of 167 laparoscopic images and 9 preoperative 3D models from 9 patients, with the corresponding 2D and 3D landmark annotations. A total of 6 teams from 4 countries participated in the challenge, whose results were assessed for each task independently. All the teams proposed deep learning-based methods for the 2D and 3D landmark segmentation tasks and differentiable rendering-based methods for the registration task. The proposed methods were evaluated on 16 test images and 2 preoperative 3D models from 2 patients. In Task 1, the teams were able to segment most of the 2D landmarks, while the 3D landmarks showed to be more challenging to segment. In Task 2, only one team obtained acceptable qualitative and quantitative registration results. Based on the experimental outcomes, we propose three key hypotheses that determine current limitations and future directions for research in this domain.

Comparison of Friction Resistance Between Different Bracket Systems: An In Vitro Study

Introduction The efficiency of tooth movement associated with orthodontic sliding mechanics can be compromised by friction between archwire and tube or bracket slot. Orthodontic brackets and archwires are important constituents of an orthodontic appliance system. Friction is an uncontrolled variable of particular interest when continuous arch techniques are used to align and move teeth by way of sliding a tube or bracket along an arch. Aim and Objectives Comparison of frictional resistance between different bracket systems of 3M and MO brands in combination with different wires of 3M, MO, and G&H brands. Evaluation of frictional resistance between two different types of bracket systems and different types of wire combinations. Materials and Methods Two types of bracket systems were used: metal bracket system and ceramic bracket system (3M and MO brands). Four different orthodontic wire alloys were used: stainless steel (SS), TMA, NiTi, and CuNiTi (3M, MO, and G&H brands). A rectangular block made up of self-cure acrylic resin was designed to hold the bracket during the mechanical test. Each bracket base was glued to an acrylic block with cyanoacrylate glue. The wire segment was inserted into the bracket and was ligated passively with SS ligature wire. The bracket–wire combination was submitted to mechanical tests with a universal testing machine. One end of the acrylic block was mounted to the lower grip of the universal testing machine. The free end of the archwire was fixed to the upper grip of the universal testing machine. The friction which was generated between the wire and bracket was measured under dry conditions and at room temperature. The test wire was pulled through the bracket at a speed of 0.5 cm/min. The load cell registered a maximum force level. This data was stored in the computer and was submitted to statistical analysis, and parameter tests were applied. Results The highest mean friction resistance of 5.998 N was generated by group B2-E2 (ceramic bracket – MO + TMA archwire – MO). The lowest mean friction resistance of 2.552 N was generated by group A1-C1 (metal bracket – 3M + SS wire – 3M). There was a statistically significant difference between the friction generated by the metal bracket on SS archwire and the ceramic bracket on TMA archwire regardless of 3M or MO brand material ( p value < .0001) and metal bracket ( p = .0004).

Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

When feedback backfires: Knowledge of results can impair cognitive strategy choice.

Frequently, problems can be solved in more than one way. In modern computerised environments, more ways than ever exist. Naturally, human problem solvers do not always decide for the best-performing strategy available. One underlying reason might be the inability to continuously and correctly monitor each strategy's performance. Here, we supported some of our participants' monitoring ability by providing written feedback regarding their speed and accuracy. Specifically, participants engaged in an object comparison task, which they were asked to solve with one of two strategies: an internal strategy (mental rotation) or an extended strategy (manual rotation). After receiving no feedback (30 participants), trialwise feedback (30 participants), or blockwise feedback (30 participants) in these no choice trials, all participants were asked to estimate their performance with both strategies and were then allowed to freely choose between strategies in choice trials. Results indicated that written feedback improves explicit performance estimates. However, results also indicated that such increased awareness does not guarantee improved strategy choice and that attending to written feedback might tamper with more adaptive ways inform the choice. Thus, we advise against prematurely implementing written feedback. While it might support adaptive strategy choice in certain environments, it did not in the present setup. We encourage further research that improves the understanding of how we monitor the performance of different cognitive strategies. Such understanding will help create interventions that support human problem solvers in making better choices in the future.

Comparative analysis of CNN and TNN models in environmental noise source identification

This paper presents a focused examination of environmental noise, an issue of relevance due to its implications for human health and well-being, compliant with ISO 1996-2:2017 standards. Moving beyond the traditional methodologies that primarily rely on level exceedance, our study integrates advanced machine learning techniques to address the challenges in noise source identification. With the advent of IoT Noise Monitoring Terminals, the conventional manual auditory analysis methods have become less feasible. Our research explores the application of convolutional neural networks (CNNs), a standard in environmental noise analysis, and assesses the emerging utility of Transformer Neural Network (TNN) models in this domain. The aim is to conduct an objective comparison of these models, applying them to identical datasets to determine their effectiveness in identifying noise sources. Through this analysis, the study seeks to contribute to the field of environmental acoustics by offering insights into the comparative strengths and limitations of CNN and TNN models.