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Liquid chromatography-mass spectrometry (LC–MS) has emerged as a powerful analytical technique for analyzing complex biological samples. Among various chromatographic stationary phases, porous graphitic carbon (PGC) columns have attracted significant attention due to their unique properties—such as the ability to separate both polar and non-polar compounds and their stability through all pH ranges and to high temperatures—besides the compatibility with LC–MS. This review discusses the applicability of PGC for SPE and separation in LC–MS-based analyses of human biological samples, highlighting the diverse applications of PGC-LC–MS in analyzing endogenous metabolites, pharmaceuticals, and biomarkers, such as glycans, proteins, oligosaccharides, sugar phosphates, and nucleotides. Additionally, the fundamental principles underlying PGC column chemistry and its advantages, challenges, and advances in method development are explored. This comprehensive review aims to provide researchers and practitioners with a valuable resource for understanding the capabilities and limitations of PGC columns in LC–MS-based analysis of human biological samples, thereby facilitating advancements in analytical methodologies and biomedical research. [ABSTRACT FROM AUTHOR]

Model transformation (MT) is one of the prominent research problems in the field of software engineering. Model transformation make the source model to target model by considering the fitness function. MT can be developed by optimization criterion by concerning various practical swarm optimization (PSO) methods. The main goal of this research is to convert UML class diagram (CL Model) to relational schema (RS Model) pertaining to Adaptive dragonfly algorithm (ADF), most of the researchers are work on this area by concerning various optimization algorithm. In this proposed research method, model transformation is achieve using Cuckoo search algorithm along with adaptive dragonfly algorithm (ADF), by combining these two algorithms, proposed method will elaborate further. Fitness function of proposed method is taken in to consideration for better performance. By pertaining Levy flight concept try to improve the accuracy for model transformation as well. Further accuracy will check through Automatic Correctness (AC)and fitness value generated from the proposed system, and finally it is compared with existing method and drawn the conclusion based on accumulated result. [ABSTRACT FROM AUTHOR]

Deep learning models are often evaluated in scenarios where the data distribution is different from those used in the training and validation phases. The discrepancy presents a challenge for accurately predicting the performance of models once deployed on the target distribution. Domain adaptation and generalization are widely recognized as effective strategies for addressing such shifts, thereby ensuring reliable performance. The recent promising results in applying vision transformers in computer vision tasks, coupled with advancements in self-attention mechanisms, have demonstrated their significant potential for robustness and generalization in handling distribution shifts. Motivated by the increased interest from the research community, our paper investigates the deployment of vision transformers in domain adaptation and domain generalization scenarios. For domain adaptation methods, we categorize research into feature-level, instance-level, model-level adaptations, and hybrid approaches, along with other categorizations with respect to diverse strategies for enhancing domain adaptation. Similarly, for domain generalization, we categorize research into multi-domain learning, meta-learning, regularization techniques, and data augmentation strategies. We further classify diverse strategies in research, underscoring the various approaches researchers have taken to address distribution shifts by integrating vision transformers. The inclusion of comprehensive tables summarizing these categories is a distinct feature of our work, offering valuable insights for researchers. These findings highlight the versatility of vision transformers in managing distribution shifts, crucial for real-world applications, especially in critical safety and decision-making scenarios. [ABSTRACT FROM AUTHOR]

Micro-expression is a special kind of human emotion. Due to its characteristics of short time, low intensity, and local region, micro-expression recognition is a difficult task. At the same time, it is a natural, spontaneous, and unconcealable emotion that can well convey a person's actual psychological state and, therefore, has certain research value and practical significance. This paper focuses on micro-expression recognition in the field of deep learning through the survey and understanding of existing micro-expression recognition research, as well as grasping the research trend, for the previous literature on micro-expression review ignored the handcrafted features as an important part of the micro-expression recognition framework, and at the same time lacked the analysis of the various enhancement processing, a new micro-expression recognition framework based on deep learning is proposed. The model is designed from the perspective of modularity and streaming data. On the other hand, unlike the previous process of feeding the data directly into the network for training and recognition, the handcrafted features are used as the initial encoding of the micro-expression recognition data, followed by the training and learning of the deep model and at the same time the modular embedding approach is used to incorporate the feature enhancement module, and finally the classification and recognition. The article provides a detailed summary and analysis of each part of the whole framework and a comprehensive introduction to the current problems, experimental protocols, evaluation metrics, and application areas. Finally, it summarizes and gives possible future research directions. Therefore, this paper provides a comprehensive summary and analysis of micro-expression recognition in deep learning so that the related personnel can have a new understanding of the development of this field. On the other hand, it proposes a new recognition framework that also provides a reference for the researchers' later research. [ABSTRACT FROM AUTHOR]

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently. Integrating AlphaFold (AF) predictions with experimental data is not straightforward. Here, authors introduce AF_unmasked, a tool to integrate AF with experimental information to predict large or challenging protein assemblies with high confidence. [ABSTRACT FROM AUTHOR]

Investigating the characteristics of the gut flora in children who go to bed early versus late. The study sample consisted of 88 healthy children aged 2–14 years, with an equal number of boys and girls. The researchers collected faecal samples from all participants and sequenced the genome of their gut flora. Findings indicate that beta diversity was statistically significant at the genus level for both the early and late sleeper groups (P = 0.045). Furthermore, alpha diversity indicators, including Simpson's index (P = 0.0011) and Shannon's index (P = 0.0013), exhibited higher values at the genus level. The differences observed in terms of species diversity, abundance, and metabolic pathways offer potential avenues for implementing pharmacological interventions aimed at addressing sleep disorders in children. [ABSTRACT FROM AUTHOR]

Spin-wave devices have recently become a strong competitor in computing and information processing owing to their excellent energy efficiency. Researchers have explored magnons, the quanta of spin-waves, as an information carrier and significant progress has occurred in both excitation and computation. However, most transmission designs remain immature in terms of data rate and information complexity as they only utilize simple spin-wave pulses and suffer from signal distortion. In this work, using micromagnetic simulations, we demonstrate a spin-wave transmitter that operates reliably at a data rate of 4 Gbps over significant (multi-micron) distances with error rates as low as 10−14. Spin-wave amplitude is used to encode information. Carrier frequency and data rate are carefully chosen to restrict dispersion spreading, which is the main reason for signal distortion. We show that this device can be integrated into either pure-magnonic circuits or modern electronic networks. Our study reveals the potential for achieving an even higher data rate of 10 Gbps and also offers a comprehensive and logical methodology for performance tuning. [ABSTRACT FROM AUTHOR]

The study investigates the feasibility of utilizing 5G and its future iterations (B5G) networks for the transmission of multi-sensory data, including audio, visual, haptic, olfactory, and gustatory data. The advent of 5G and B5G technologies has enabled the transmission of a wide range of sensory information in real-time or near real-time, thanks to their exceptional speed, low latency, and large capacity. The idea behind this review is to explore the technological developments that facilitate the transmission of data, such as layered protocols. This article aims to provide an overall survey and use cases for sensory data transmitted using 5G and B5G networks. The objective of this survey is to focus on challenges for multiple sensor data transmission using 5G and beyond network technologies, as well as how significant is to receive this information without losing them. Moreover, it tackles the inherent difficulties associated with guaranteeing adequate bandwidth, minimal latency, high quality of service, and robust security for the transmission of multi-sensory data via wireless networks. The goal is to provide an introductory overview for researchers, engineers, and practitioners who are interested in exploiting 5G and B5G networks to leverage the revolutionary capabilities of multi-sensory data transmission. The future scope at the end also highlights how to add enhancement in this field. [ABSTRACT FROM AUTHOR]

Introduction: Soft skills, essential for patient care, are relatively unfamiliar to dentists, necessitating investigation for the development of continuing education strategies. This study aimed to map the literature on soft skills usage in dental practice, based on dental professionals' perceptions, acknowledging the importance of conducting a scoping review to understand the current research landscape and identify areas needing attention from researchers and educators. Methods: The research question was: "What are the soft skills investigated in dental practice?". Searches were conducted from November to December 2022 in the following electronic databases: virtual Health Library (BVS), National Library of Medicine (MEDLINE/PubMed), American Psychological Association (APA PsycNET), Scopus (Elsevier), and Portal Capes. Studies with dentists (public) who utilize soft skills (concept) in dental practice in both public and other settings (context), without language restrictions, between 2012 and 2022, were included. Results: After applying the inclusion criteria, 266 articles were included. There was an increasing trend in publications over the last decade (R2 = 0.699). The non-public sector was the most addressed (60.15%). The dimension of intrapersonal skills was the most investigated (37.22%), followed by interpersonal skills (27.44%), communication skills (22.56%), professional ethical values (9.4%), and managerial skills (3.38%). Conclusion: Research on soft skills in dental practice has surged in recent years, with a notable focus on intrapersonal skills. Nonetheless, the scarcity of literature on certain soft skills may impact the continuous education of professionals, highlighting the importance of stimulating further research in this area. [ABSTRACT FROM AUTHOR]

Current evidence suggests that some form of cellular organization arose well before the time of the last universal common ancestor (LUCA). Standard phylogenetic analyses have shown that several protein families associated with membrane translocation, membrane transport, and membrane bioenergetics were very likely present in the proteome of the LUCA. Despite these cellular systems emerging prior to the LUCA, extant archaea, bacteria, and eukaryotes have significant differences in cellular infrastructure and the molecular functions that support it, leading some researchers to argue that true cellularity did not evolve until after the LUCA. Here, we use recently reconstructed minimal proteomes of the LUCA as well as the last archaeal common ancestor (LACA) and the last bacterial common ancestor (LBCA) to characterize the evolution of cellular systems along the first branches of the tree of life. We find that a broad set of functions associated with cellular organization were already present by the time of the LUCA. The functional repertoires of the LACA and LBCA related to cellular organization nearly doubled along each branch following the divergence of the LUCA. These evolutionary trends created the foundation for similarities and differences in cellular organization between the taxonomic domains that are still observed today. [ABSTRACT FROM AUTHOR]

Recognition of the fact that bacterial biofilm might contribute to the development of diseases has prompted a greater emphasis on identifying conditions, that could be linked to the biofilm formation. Microbial biofilms have raised significant concerns within the healthcare, medical, and food industries due to their inherent resistance to conventional antibiotics and cleaning methods, as well as their ability to firmly attach to surfaces, leading to enduring contamination concerns. A biofilm constitute a structural community of microorganisms, encased in a framework of extracellular polymeric substances. Biofilms pose a substantial challenge in the management of bacterial infections and stand as primary factors contribute to the enduring nature of these infections. Depending on their community microbiological makeup and the surrounding conditions, bacteria can create different types of single and multispecies biofilms. Over the past 20 years, interest in the study of bacterial biofilm formation has increased due to the exquisite intricacy of these multicellular communities and their role in infectious illnesses. Biofilms can form on almost any surface and can be advantageous or harmful, depending on the surface and how the community uses it. Extensive researchers have tackled the progressions in comprehending structural and functional variations, along with the roles that biofilms perform in illness and host–pathogen relationships. [ABSTRACT FROM AUTHOR]

misophonia is a sound tolerance disorder which disturbs the emotions of people when exposed to specific triggers. Studies have shown that visual triggers alone can cause misophonia in affected individuals in the absence of auditory triggers. It is also said to affect people's quality of lives affecting social life, work life and personal relationships. Different studies on misophonia indicate that it is prevalent between 5% and 34.67%. These studies help us understand the existence of misophonia across different parts of the world. Studies report factors like gender, age, socioeconomic status, etc. play significant roles in impacting the prevalence of disorder. This study aims to review 12 such English articles available online, to get a comprehensive set of data to make it easy for readers and researchers. Results of the review indicate that not many countries have their prevalence rates established which could be indicating the lack of awareness. In countries that have carried out the study, clinically significant misophonia exists in many individuals with various degrees with multiple factors affecting it. [ABSTRACT FROM AUTHOR]

Study Background and Aims: There are marked barriers to research and publishing for low- and middle- income country (LMIC) ENT researchers. This could be reflected in LMIC journal characteristics and research, which has never been investigated. We aim to characterize differences in the number, geographic distribution, publishing costs, reach, number of articles, citations, and impact factors of high-income country (HIC) journals compared to LMIC journals. Methods: We included journals listed under the category "Otorhinolaryngology' in three major journal databases. From journal websites, we collected data related to financial model, waiver policy, access, and distribution. Additionally, from the Clarivate Journal Citation Reports 2022, we collected the following journal metrics: total articles, total citations, journal citation indicator, journal impact factor (JIF), 5-year JIF, and JIF without self cites. Results: 79.7% HIC journals offered English editing services, compared with 25.0% of LMIC journals. Additionally, 40.0% of HIC journals are solely open access compared with 92.0% in LMICs. Lower journal metrics were seen in LMIC journals, including 2022 mean total articles (107 HICs vs. 60 LMICs), total citations (4296 vs. 751), journal citation indicator (0.88 vs. 0.35), and journal impact factor (12.68 vs. 0.82). Conclusion: We have identified substantial differences in the distribution, English editing services, and journal metrics of HIC journals compared to LMIC journals. These may point to potential barriers to publishing and research access for those in LMICs. To support LMIC research, future work should evaluate opportunities to increase the number of ENT journals in LMICs, expand open access publishing, improve access to language services, and increase LMIC research impact. [ABSTRACT FROM AUTHOR]

An anomaly is a pattern, behavior, or event that does not frequently happen in an environment. Video anomaly detection has always been a challenging task. Home security, public area monitoring, and quality control in production lines are only a few applications of video anomaly detection. The spatio-temporal nature of the videos, the lack of an exact definition for anomalies, and the inefficiencies of feature extraction for videos are examples of the challenges that researchers face in video anomaly detection. To find a solution to these challenges, we propose a method that uses parallel deep structures to extract informative features from the videos. The method consists of different units including an attention unit, frame sampling units, spatial and temporal feature extractors, and thresholding. Using these units, we propose a video anomaly detection that aggregates the results of four parallel structures. Aggregating the results brings generality and flexibility to the algorithm. The proposed method achieves satisfying results for four popular video anomaly detection benchmarks. [ABSTRACT FROM AUTHOR]

As an important underlying visual processing task, underwater image enhancement techniques have received a lot of attention from researchers due to their importance in marine engineering and lake ecosystem optimization. However, various underwater image enhancement algorithms have been proposed to be evaluated mainly with marine water body datasets, and it is not clear whether these algorithms can be performed on datasets collected from inland lakes in the freshwaters. To bridge this gap, for the first time, we construct an underwater image dataset for highland and freshwater-circumstances (HFUI) using 1000 real images to complement the underwater image datasets. In addition, we propose an unsupervised underwater image enhancement algorithm (HUFI-Net) specifically for this dataset to correct the sharpness and color of the images. This algorithm, as well as current advanced underwater image enhancement algorithms, was investigated qualitatively and quantitatively using this dataset to evaluate the effectiveness and limitations of various algorithms and to provide novel ideas for future underwater image enhancement research. We also further validate the generalization and effectiveness of the algorithm on the underwater image datasets of the UIEB and the RUIE. [ABSTRACT FROM AUTHOR]

Surround-view system (SVS) is widely used in the advanced driver assistance system (ADAS). SVS uses four fish-eye lenses to monitor real-time scenes around the vehicle. However, accurate intrinsic and extrinsic parameter estimation is required for the proper functioning of the system. At present, the intrinsic calibration can be pipeline by utilizing checkerboard algorithm, while extrinsic calibration is still immature. Therefore, we proposed a specific calibration pipeline to estimate extrinsic parameters robustly. This scheme takes a driving sequence of four cameras as input. It firstly utilizes lane line to roughly estimate each camera pose. Considering the environmental condition differences in each camera, we separately select strategies from two methods to accurately estimate the extrinsic parameters. To achieve accurate estimates for both front and rear camera, we proposed a method that mutually iterating line detection and pose estimation. As for bilateral camera, we iteratively adjust the camera pose and position by minimizing texture and edge error between ground projections of adjacent cameras. After estimating the extrinsic parameters, the surround-view image can be synthesized by homography-based transformation. The proposed pipeline can robustly estimate the four SVS camera extrinsic parameters in real driving environments. In addition, to evaluate the proposed scheme, we build a surround-view fish-eye dataset, which contains 40 videos with 32,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually labeled with lane annotation, with its GT extrinsic parameters. Moreover, this surround-view dataset could be used by other researchers to evaluate their performance. The dataset will be available soon. [ABSTRACT FROM AUTHOR]

Ancient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model. [ABSTRACT FROM AUTHOR]

This study explores the development of biofoundries, emphasizing the integration of synthetic biology with artificial intelligence (AI) and robotics. It outlines critical challenges such as the necessity for interdisciplinary collaboration and the development of hardware, software, and AI specific to biofoundry operations. To address these challenges, we recommend strategies like rapid prototyping, soft integration, and the strategic implementation of AI. We underscore the vital role of synthetic biology researchers in advancing biofoundry capabilities and advocate for collaborative, multidisciplinary efforts to optimize the development and functionality of biofoundries. [ABSTRACT FROM AUTHOR]

Collecting empirical data is essential for understanding the ecology and biology of a species. If the data collection procedure includes direct interaction with the study subject, particularly regarding repeated measures, there may be a risk of introducing biases on the collected dataset due to changes in the behaviour and/or physiology of the studied individuals. This can be particularly relevant during the breeding season when disturbance by researchers can affect birds on various aspects, including nest and chick survival, physiology, and chick body condition. Researcher effects on the chicks' body condition have been shown in some species, but were apparently absent in others. Here, we investigate whether varying levels of handling influences the body condition of Eurasian whimbrel (Numenius phaeopus) chicks, using data collected on 88 individuals handled up to 11 times, during two breeding seasons. We found that neither the handling event (ordinal occasion when a chick was handled) nor the handling interval (period between two consecutive handling events) affected the chick's body condition, but we found evidence that body condition is individually consistent during development. Hence, the levels of handling in our study did not have an apparent effect on Eurasian whimbrel chicks' body condition. Although other potential effects may arise via researchers' disturbance, our findings contribute to the discussion on researchers' impacts on their study subjects, emphasizing the importance of species-specific evaluations to improve research methodologies and ethical practices. [ABSTRACT FROM AUTHOR]

Background: Comprehensive genomic profiling (CGP) can aid the discovery of clinically useful, candidate antitumor agents; however, the variant annotations sometimes differ among the various types of CGP tests as well as the public database. The aim of this study is to clarify the genomic landscape of evaluating detected variants in patients with a malignant solid tumor. Methods: The present, cross-sectional study used data from 57,084 patients with a malignant solid tumor who underwent CGP at the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) between June 1, 2019 and August 18, 2023. The pathogenicity of the variants was annotated using public databases. Results: As a result of re-annotation of the detected variants, 20.1% were pathogenic and 1.4% were benign. The mean number of pathogenic variants was 4.30 (95% confidence interval: 4.27–4.32) per patient. Of the entire cohort, 5.7% had no pathogenic variant. The co-occurrence of the genes depended on the tumor type. Germline findings were detected in 6.2%, 8.8%, and 15.8% of the patients using a tumor/normal panel, tumor-only panel, and liquid panel, respectively, with the most common gene being BRCA2 followed by TP53 and BRCA1. Conclusions: The detected variants should be re-annotated because several benign variants or variants of unknown significance were included in the CGP, and the genomic landscape derived from these results will help researchers and physicians interpret the results of CGP tests. The method of extracting presumptive, germline, pathogenic variants from patients using a tumor-only panel or circulating tumor DNA panel requires improvement. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a complex brain problem that causes significant morbidity and mortality among people of all age groups. The complex pathophysiology, varied symptoms, and inadequate treatment further precipitate the problem. Further, TBI produces several psychiatric problems and other related complications in post-TBI survival patients, which are often treated symptomatically or inadequately. Several approaches, including neuroprotective agents targeting several pathways of oxidative stress, neuroinflammation, cytokines, immune system GABA, glutamatergic, microglia, and astrocytes, are being tried by researchers to develop effective treatments or magic bullets to manage the condition effectively. The problem of TBI is therefore treated as a challenge among pharmaceutical scientists or researchers to develop drugs for the effective management of this problem. The goal of the present comprehensive review is to provide an overview of the several pharmacological targets, processes, and cellular pathways that researchers are focusing on, along with an update on their current state. [ABSTRACT FROM AUTHOR]

Two decades after the International Dyslexia Association (IDA) adopted the 2002 consensus definition of dyslexia, this special issue of the Annals of Dyslexia revisits that definition in light of advances in scientific understanding and evolving needs. Through contributions from leading researchers and interdisciplinary teams, the issue examines the strengths and limitations of the definition as it has been applied in research, policy, and practice. Key themes emerged, which included reconsidering the need to include the neurobiological basis of dyslexia in the definition, the intersection of literacy challenges and mental health, and the role of context in shaping how dyslexia is defined. Contributors to this special issue also reflected on how the definition serves different audiences, including educators, policymakers, and families. As the IDA embarks on a thoughtful reassessment of the 2002 definition, this collection of articles offers insights to guide the path forward, ensuring the definition remains a robust tool for research, identification, intervention, and advocacy in the coming years. [ABSTRACT FROM AUTHOR]

As society's dependence on information and communication systems (ICTs) grows, so does the necessity of guaranteeing the proper functioning and use of such systems. In this context, it is critical to enhance the security and robustness of the DevSecOps pipeline through timely vulnerability detection. Usually, AI-based models enable desirable features such as automation, performance, and efficacy. However, the quality of such models highly depends on the datasets used during the training stage. The latter encompasses a series of challenges yet to be solved, such as access to extensive labelled datasets with specific properties, such as well-represented and balanced samples. This article explores the current state of practice of software vulnerability datasets and provides a classification of the main challenges and issues. After an extensive analysis, it describes a set of guidelines and desirable features that datasets should guarantee. The latter is applied to create a new dataset, which fulfils these properties, along with a descriptive comparison with the state of the art. Finally, a discussion on how to foster good practices among researchers and practitioners sets the ground for further research and continued improvement within this critical domain. [ABSTRACT FROM AUTHOR]

Digital Images Correlation (DIC) measures are widely used in order to calibrate models. It is very common for researchers to use filters on these measurements to improve the measurement resolution (decrease the noise level) before using them with various methodologies. However, the effects of these filters on the tradeoff between spatial resolution and measurement resolution are not fully understood. The objective of the study is to compare seven common denoising/filtering methods for 2D DIC with affine subset shape function, including image filters (Gaussian, Median), frequency filters (Lowpass Butterworth, Holoborodko), local polynomial fitting (Savitzky-Golay filter, cubic splines), and Finite Elements mapping. The proposed methodology to compare the enhancement of these methods is based on the star pattern test cases used in the DIC Challenge 2.0. Hence, this study aims to obtain a general comparison of the filtering methods as opposed to case-dependent approaches. To that end, firstly, the FOLKI-D DIC code is metrologically qualified using the DIC Challenge 2.0 methodology and test cases. Then, the filtering methods are applied to the displacements and strain maps computed with the FOLKI-D code for various DIC subset sizes and filter parameters. It is found that displacement and strain map enhancements vary significantly depending on the filter. Some methods show no improvement (or worsened tradeoff in the strain case), while others achieve similar resolution tradeoffs as DIC codes using quadratic subset shape functions. Finally, it is concluded that the filter choice significantly impacts results, with higher-order lowpass Butterworth, Savitzky-Golay, and Finite Elements mapping methods showing preference over others studied. [ABSTRACT FROM AUTHOR]

Systemic sclerosis is a rare autoimmune condition leading to incurable complications. Therefore fast and precise diagnosis is crucial to prevent patient death and to maintain quality of life. Unfortunately, currently known biomarkers do not meet this need. To address this problem researchers use diverse approaches to elucidate the underlying aberrations. One of the methods applied is metabolomics. This modern technique enables a comprehensive assessment of multiple compound concentrations simultaneously. As it has been gaining popularity, we found it necessary to summarize metabolomic studies presented so far in a narrative review. We found 11 appropriate articles. All of the researchers found significant differences between patients and control groups, whereas the reported findings were highly inconsistent. Additionally, we have found the investigated groups in most studies were scarcely described, and the inclusion/exclusion approach was diverse. Therefore, further study with meticulous patient assessment is necessary. [ABSTRACT FROM AUTHOR]

Evaluating the bearing capacity of strip footing is a classic problem in geotechnical engineering, which has been investigated by many researchers. As the advancement of technology and urbanization, less and less land area can be available for the construction of facilities, which results in that many buildings must be constructed near slopes. The bearing capacity of strip footing constructed near slopes is usually lower than that on flat land. When the soil strength of slopes is not sufficient to support the external loads, it is often necessary to backfill or reinforces the slopes to make the strength of slope meet application requirements. The discontinuity layout optimization (DLO) method is therefore adopted in this paper to investigate the effects of various factors on bearing capacity and failure mechanism of strip footing on inclined multilayered natural slopes. Two conditions, normal slope and backfilled reinforced slope, are considered in the analysis. In addition, the influence of distance between the strip footing and slope, the number of soil layers, the thickness of the interlayer soil layer and earthquake on the unreinforced slope, and the influence of geosynthetic length and burial depth on the reinforced slopes are investigated. Eventually, the results showed that different factors have different impacts on the slope bearing capacity and failure mechanism. [ABSTRACT FROM AUTHOR]

This work gives a rheological study of the flow structure generated by an anchor agitator completely submerged in a cylindrical tank with a flat bottom. To better understand the phenomenon of the fluid mechanics that takes place in the agitated medium, several geometric configurations of anchor have been realized. The investigation of the flow generated was made by using the CFX.12 calculation code, which provides a very good visualization of the phenomenon of fluid mechanics. The results obtained from our work can be very supportive to the existing similar models in the literature, which can mature the knowledge and understanding of researchers on the behaviors of various anchor impellers. [ABSTRACT FROM AUTHOR]

The construction industry has been increasingly concerned with the environment and sustainability, and one solution is engineered cementitious composites (ECC), which have self-healing, ductility, and strength properties. Researchers have been working to make ECC greener by creating synergistic mechanical interactions between fibers, matrices, and fiber–matrix interfaces. This study developed ECC using local river sand, silica fume, moderate to high-volume fly ash (FA), and bulky PVA fibers, evaluating workability and compressive strength at various testing ages (1, 7, 28, 60, and 180 days). According to the test findings, fresh ECC mortar mixtures exhibit an increase in workability with increasing FA content due to the spherical and glossy form of FA particles. All ECC samples have a compressive strength of over 20 MPa after 1 day, making them high-early compressive strength composites. The sample with the highest compressive strength after 28 days was 111.28 MPa. PVA fibers, FA, and SF contribute to increasing strength while using local river sand as an ingredient in composite mixtures is also beneficial. The samples experienced a bulging process during failure due to the use of oiled PVA fibers. The maximum load before the collapse was attained after a longer period of time where smeared cracks were visible on the samples' surface. Based on the analysis of the compressive strength over a period of more than 28 days, it is clear that both ECC mortar and bulky PVA-ECC samples exhibit sufficient strength stability. It is worth mentioning that as the curing age of ECC increases, there is a gradual increase in strength, although the level of increase is relatively slow. [ABSTRACT FROM AUTHOR]

Positive body image, an important perspective in the research literature on body image with adolescents and adults, has become a popular theme in children's picturebooks. This is promising and warrants researchers' attention. Thus this study examined body image-related strategies in 50 children's picturebooks, focusing on body image through content analysis based on the lens of positive body image perspectives. Four main strategies for promoting positive body image emerged from the study including body appreciation, inner positivity, perceived support for protective filtering, and adaptive body investment. This study also examined how these strategies were presented for promoting positive body image through text and pictures. This study fills a gap in the current literature regarding positive body image-related messages for young children. Findings clarify how children's picturebooks can be used to encourage young children to love, respect, and take care of their own and others' bodies physically and mentally. [ABSTRACT FROM AUTHOR]

In this study, researchers examined the math-related expository books found in 23 first-grade classroom libraries to determine specific mathematical concepts related to counting and cardinality and operations and algebraic thinking addressed in each. In addition, they examined how these concepts aligned with the first-grade Common Core State Standards for Mathematics. Results indicate that most of these texts contained concepts related to counting and cardinality but were better aligned with kindergarten standards than the first-grade standards. Very few of the books contained content related to operations and algebraic thinking and again, those that did focused on content more appropriate for kindergarten students. [ABSTRACT FROM AUTHOR]

The paper proposes a new multilevel inverter (MLI) topology that is designed to overcome the challenges posed by conventional MLIs, which can have high costs and complexity due to the large number of power components. The proposed MLI is designed to work with both symmetric and asymmetric DC sources, and uses fewer power semiconductor switches compared to recently propose reduced components MLIs. The paper presents the ratio of voltage levels generated per power semiconductor switch, and the gate pulses for the inverter are generated using a DSPACE1103 controller. A prototype of the 7-level inverter is developed, and experimental outcomes are provided to evaluate the simulated results. The paper highlights the importance of reducing the number of power components in MLIs to reduce costs and improve reliability. The proposed MLI offers a promising solution to this challenge, while also maintaining the advantages of MLIs such as reduced harmonic distortion and electromagnetic interference. Overall, the research offers valuable insights into the design and performance of multilevel inverters, and the proposed topology has the potential to be a useful tool for researchers and engineers working in the field of power electronics. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

The recent focus on cloud computing is due to its evolving platform and features like multiplexing users on shared infrastructure and on-demand resource computation. Efficient use of computer resources is crucial in cloud computing. Effective task-scheduling methods are essential to optimize cloud system performance. Scheduling virtual machines in dynamic cloud environments, marked by uncertainty and constant change, is challenging. Despite many efforts to improve cloud task scheduling, it remains an unresolved issue. Various scheduling approaches have been proposed, but researchers continue to refine performance by incorporating diverse quality-of-service characteristics, enhancing overall cloud performance. This study introduces an innovative task-scheduling algorithm that improves upon existing methods, particularly in quality-of-service criteria like makespan and energy efficiency. The proposed technique enhances the Snake Optimization Algorithm (SO) by incorporating sine chaos mapping, a spiral search strategy, and dynamic adaptive weights. These enhancements increase the algorithm's ability to escape local optima and improve global search. Compared to other models, the proposed method shows improvements in cloud scheduling performance by 6%, 4.6%, and 3.27%. Additionally, the approach quickly converges to the optimal scheduling solution. [ABSTRACT FROM AUTHOR]

In the contemporary era, online social networks have become the prevalent medium for interpersonal interactions, encompassing a multitude of virtual social relationships. To prevent attackers from inferring more sensitive information through social relationships, numerous researchers have devised privacy protection methodologies tailored to these social relationships. However, most of research achievements have tended to overlook considerations pertaining to algorithmic efficiency and the delicate balance between privacy and utility. To address this issue, we first identify vulnerable nodes within social networks using two indicators, namely, the Marchenko–Pastur law rate and the mean spectral radius. This approach aims to enhance algorithm efficiency. Furthermore, we propose a privacy-preserving algorithm based on vulnerable nodes, PPVN, which classifies the friendship links associated with vulnerable nodes into three distinct levels, thereby ensuring precise safeguarding of social relationship privacy while striking an optimal equilibrium between privacy and structural utility. To bolster privacy safeguards, we develop a replacement index θ, designed explicitly to preclude replaced friendship links from evolving into newly sensitive links. Empirical findings substantiate the remarkable efficacy of the PPVN algorithm in preserving user privacy while concurrently upholding data utility. Compared with other privacy-preserving methods, the PPVN algorithm demonstrates superior privacy protection within the shortest computational timeframe. [ABSTRACT FROM AUTHOR]

PageRank can be viewed as a hyperlink-based method for estimating the importance of nodes in a network, and has attracted a lot of attention from researchers. In this paper, we first propose an extrapolation procedure for PageRank vector estimation based on the linear combination of the Ritz values computed from the Hessenberg process and eigenvectors from the classical power method. In order to improve the convergence rate of the PageRank computations, the extrapolation procedure is introduced into the Hessenberg-type algorithm by Gu et al. (Numer. Algorithms, 89(4): 1845–1863, 2022), then a new algorithm is derived and named Hessenberg-extrapolation algorithm whose convergence and construction are investigated in detail. In addition, we further analyze the convergence of the Hessenberg-type algorithm and discuss the relationship between the approximate vectors of the Hessenberg-type algorithm and the Arnoldi-type algorithm. Numerical experiments on several examples demonstrate that our presented algorithm has great potential in PageRank approximation. [ABSTRACT FROM AUTHOR]

Since the introduction of differential evolution (DE) algorithms, they have achieved remarkable success in the field of evolutionary algorithms and engineering applications. In single-objective DE algorithms, most researchers tend to focus on improving mutation operators and parameter control, while overlooking the study of selection operators. However, the study of selection operators still holds great potential in enhancing the performance of DE algorithms. This study proposes a fitness-distance-based selection (FDS) strategy and a new scaling factor control method. FDS is divided into two stages. The first stage is to determine whether an individual needs to accept discarded trial vectors. The second stage involves selectively accepting these discarded trial vectors, which is based on the information related to the discarded trial vectors and the corresponding target vector. A new setting for the scaling factor parameter is proposed, designed to more effectively assist FDS in enhancing algorithm performance. Based on these strategies, an improved variant of the DE algorithm, called fitness-distance-based DE (FDDE) algorithm, is further proposed by this study. To verify the performance of FDDE, we conducted an in-depth study comparing it with six other advanced DE variants and four famous evolutionary algorithms using the CEC 2017, CEC 2022, and CEC 2011 benchmark sets. The experimental results demonstrate that the FDS strategy and the new scaling factor can significantly improve the performance of DE algorithms, and FDDE is significantly better than other advanced algorithm. [ABSTRACT FROM AUTHOR]

As a growing up-and-coming branch of the Internet of Things and traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) is intended to perform as a core information carrying and processing platform for Intelligent Transport Systems. However, owing to its dynamic topological structures, large network scale, and mobile limitation, IoV systems still struggle with many unresolved challenges, especially those concerning security and privacy. Therefore, researchers consider security a significant concern due to the variety of vulnerabilities. Recently, existing security researchers have worked extensively to guarantee IoV security. However, multiple challenges in terms of security and privacy are generated by various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, and data authenticity attacks. In this context, we provide a systematic review of emerging security and privacy vulnerabilities IoV and identify current challenges and the remaining open issues. Furthermore, we will discuss the future of this area. The paper contributes by presenting significant issues and solutions in a clear, smooth, comprehensive, and integrated way. [ABSTRACT FROM AUTHOR]

Background: Though proposed by Lake and Yeoh in 1978 for vulcanized rubber characterization and possessing unique advantages with respect to traditional fracture characterization approaches, the Y-shaped cutting technique has been applied to a limited number of materials. Objective: This limited implementation may be due to researchers' unfamiliarity with the effects of Y-shaped cutting conditions and technique limitations, as well as a lack of standards. This review and best practices guide aims to provide a detailed road-map of the capabilities of Y-shaped cutting, with guidance for designing, executing, and interpreting its results. Method: By performing Y-shaped cutting at a constant blade propagation rate, fracture initiation effects encountered in many 'tearing' tests are bypassed. Meanwhile, unlike other contact-driven fracture conditions (needle insertion or cutting), the 'leg' separation renders the cutting nearly 'frictionless' under a variety of conditions. Results: Y-shaped cutting possesses two unique attributes. Under certain conditions (Zhang and Hutchens in Soft Matter 17(28):6728–6741, 2021), it can yield a fracture energy independent of sample and cutting implement geometry. In contrast to soft solid crack blunting, cutting reduces both the finite stretch and failure process zones to within a field of view readily imaged on a microscope, useful for microstructural studeis. To facilitate access to the above advantages, we summarize experimental variables and their role the fracture response and/or successful cutting and establish a pseudo-standard using silicone. Conclusion: This overview and recommendations empowers researchers to implement this highly-tunable cutting method in order to provide insights into other classes of materials in the future. [ABSTRACT FROM AUTHOR]

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity – their emotional response to social situations – can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes – a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs – such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches. [ABSTRACT FROM AUTHOR]

This research is an extension of the recent scale development efforts for the marker variable Attitude Toward the Color Blue (ATCB), which addresses the efficacy of multiple shorter permutations of the scale. The purpose of this study is to develop a shorter version of an ideal marker variable scale used to detect common method variance. Potential uses of the shorter version of ATCB include intensive longitudinal studies, implementation of experience sampling methodology, or any brief survey for which the original version might be cumbersome to implement repeatedly or appear very odd to the respondent when paired with only a few other substantive items. Study 1, uses all six-, five-, and four-item versions of ATCB in confirmatory factor analysis (CFA) marker technique tests on a bivariate relationship. Study 2 analyzes the best- and worst-performing versions of reduced lengths of the ATCB scale found in the first study on another bivariate relationship. Study 3 compares the original seven-item version, as well as randomly selected reduced length versions in a data set with 15 model relationships. Study 4 uses an experiment to determine the efficacy of providing respondents with one of three shorter ATCB scales in a model of three substantive variables. Our findings indicate that ATCB of different permutations and lengths can detect CMV successfully, and that researchers should choose the length of scale based on their survey length. We conclude that ATCB is adaptable for a variety of research situations, presenting it as a valuable tool for high-quality research. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed. [ABSTRACT FROM AUTHOR]

Online experiments are increasingly gaining traction in the behavioral sciences. Despite this, behavioral researchers have largely continued to use keyboards as the primary input devices for such online studies, overlooking the ubiquity of touchscreens in everyday use. This paper presents an open-source touchscreen extension for jsPsych, a JavaScript framework designed for conducting online experiments. We additionally evaluated the touchscreen extension assessing whether typical behavioral findings from two distinct perceptual decision-making tasks – the random-dot kinematogram and the Stroop task – can similarly be observed when administered via touchscreen devices compared to keyboard devices. Our findings indicate similar performance metrics for each paradigm between the touchscreen and keyboard versions of the experiments. Specifically, we observe similar psychometric curves in the random-dot kinematogram across the touchscreen and keyboard versions. Similarly, in the Stroop task, we detect significant task, congruency, and sequential congruency effects in both experiment versions. We conclude that our open-source touchscreen extension serves as a promising tool for data collection in online behavioral experiments on forced-choice tasks. [ABSTRACT FROM AUTHOR]

To detect careless and insufficient effort (C/IE) survey responders, researchers can use infrequency items – items that almost no one agrees with (e.g., "When a friend greets me, I generally try to say nothing back") – and frequency items – items that almost everyone agrees with (e.g., "I try to listen when someone I care about is telling me something"). Here, we provide initial validation for two sets of these items: the 14-item InvalidResponding Inventory for Statements (IDRIS) and the 6-item Invalid Responding Inventory for Adjectives (IDRIA). Across six studies (N1 = 536; N2 = 701; N3 = 500; N4 = 499; N5 = 629, N6 = 562), we found consistent evidence that the IDRIS is capable of detecting C/IE responding among statement-based scales (e.g., the HEXACO-PI-R) and the IDRIA is capable of detecting C/IE responding among both adjective-based scales (e.g., the Lex-20) and adjective-derived scales (e.g., the BFI-2). These findings were robust across different analytic approaches (e.g., Pearson correlations; Spearman rank-order correlations), different indices of C/IE responding (e.g., person-total correlations; semantic synonyms; horizontal cursor variability), and different sample types (e.g., US undergraduate students; Nigerian survey panel participants). Taken together, these results provide promising evidence for the utility of the IDRIS and IDRIA in detecting C/IE responding. [ABSTRACT FROM AUTHOR]

The discriminability measure d ′ is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate d ′ involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate d ′ , but these corrections distort the estimate. In three simulation studies, we show that distortion in d ′ estimation can arise from other properties of the experimental design (number of trials, sample size, sample variance, task difficulty) that, when combined with application of the correction method, make d ′ distortion in any specific experiment design complex and can mislead statistical inference in the worst cases (Type I and Type II errors). To address this problem, we propose that researchers simulate d ′ estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates d ′ distortion, providing researchers the means to identify distortion and take steps to minimize its impact. [ABSTRACT FROM AUTHOR]

The tendency for individuals to mind wander is often measured using experience sampling methods in which probe questions embedded within computerized cognitive tasks attempt to catch episodes of off-task thought at random intervals during task performance. However, mind-wandering probe questions and response options are often chosen ad hoc and vary between studies with extant little guidance as to the psychometric consequences of these methodological decisions. In the present study, we examined the psychometric properties of several common approaches for assessing mind wandering using methods from item response theory (IRT). IRT latent modeling demonstrated that measurement information was generally distributed across the range of trait estimates according to when probes were presented in time. Probes presented earlier in time provided more information about individuals with greater tendency to mind wandering than probes presented later. Furthermore, mind-wandering ratings made on a continuous scale or using multiple categorical rating options provided more information about individuals' latent mind-wandering tendency – across a broader range of the trait continuum – than ratings dichotomized into on-task and off-task categories. In addition, IRT provided evidence that reports of "task-related thoughts" contribute to the task-focused dimension of the construct continuum, providing justification for studies conceptualizing these responses as a kind of task-related focus. Together, we hope these findings will help guide researchers hoping to maximize the measurement precision of their mind wandering assessment procedures. [ABSTRACT FROM AUTHOR]

There have been many published picture corpora. However, more than half of the world's population speaks more than one language and, as language and culture are intertwined, some of the items from a picture corpus designed for a given language in a particular culture may not fit another culture (with the same or different language). There is also an awareness that language research can gain from the study of bi-/multilingual individuals who are immersed in multilingual contexts that foster inter-language interactions. Consequently, we developed a relatively large corpus of pictures (663 nouns, 96 verbs) and collected normative data from multilingual speakers of Kannada (a southern Indian language) on two picture-related measures (name agreement, image agreement) and three word-related measures (familiarity, subjective frequency, age of acquisition), and report objective visual complexity and syllable count of the words. Naming labels were classified into words from the target language (i.e., Kannada), cognates (borrowed from/shared with another language), translation equivalents, and elaborations. The picture corpus had > 85% mean concept agreement with multiple acceptable names (1–7 naming labels) for each concept. The mean percentage name agreement for the modal name was > 70%, with H-statistics of 0.89 for nouns and 0.52 for verbs. We also analyse the variability of responses highlighting the influence of bi-/multilingualism on (picture) naming. The picture corpus is freely accessible to researchers and clinicians. It may be used for future standardization with other languages of similar cultural contexts, and relevant items can be used in languages from different cultures, following suitable standardization. [ABSTRACT FROM AUTHOR]

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis. [ABSTRACT FROM AUTHOR]

Surface facial electromyography (EMG) is commonly used to detect emotions from subtle facial expressions. Although there are established procedures for collecting EMG data and some aspects of their processing, there is little agreement among researchers about the optimal way to process the EMG signal, so that the study-unrelated variability (noise) is removed, and the emotion-related variability is best detected. The aim of the current paper was to establish an optimal processing pipeline for EMG data for identifying emotional expressions in facial muscles. We identified the most common processing steps from existing literature and created 72 processing pipelines that represented all the different processing choices. We applied these pipelines to a previously published dataset from a facial mimicry experiment, where 100 adult participants observed happy and sad facial expressions, whilst the activity of their facial muscles, zygomaticus major and corrugator supercilii, was recorded with EMG. We used a resampling approach and subsets of the original data to investigate the effect and robustness of different processing choices on the performance of a logistic regression model that predicted the mimicked emotion (happy/sad) from the EMG signal. In addition, we used a random forest model to identify the most important processing steps for the sensitivity of the logistic regression model. Three processing steps were found to be most impactful: baseline correction, standardisation within muscles, and standardisation within subjects. The chosen feature of interest and the signal averaging had little influence on the sensitivity to the effect. We recommend an optimal processing pipeline, share our code and data, and provide a step-by-step walkthrough for researchers. [ABSTRACT FROM AUTHOR]

Computer code plays a vital role in modern science, from the conception and design of experiments through to final data analyses. Open sharing of code has been widely discussed as being advantageous to the scientific process, allowing experiments to be more easily replicated, helping with error detection, and reducing wasted effort and resources. In the case of psychology, the code used to present stimuli is a fundamental component of many experiments. It is not known, however, the degree to which researchers are sharing this type of code. To estimate this, we conducted a survey of 400 psychology papers published between 2016 and 2021, identifying those working with the open-source tools Psychtoolbox and PsychoPy that openly share stimulus presentation code. For those that did, we established if it would run following download and also appraised the code's usability in terms of style and documentation. It was found that only 8.4% of papers shared stimulus code, compared to 17.9% sharing analysis code and 31.7% sharing data. Of shared code, 70% ran directly or after minor corrections. For code that did not run, the main error was missing dependencies (66.7%). The usability of the code was moderate, with low levels of code annotation and minimal documentation provided. These results suggest that stimulus presentation code sharing lags behind other forms of code and data sharing, potentially due to less emphasis on such code in open-science discussions and in journal policies. The results also highlight a need for improved documentation to maximize code utility. [ABSTRACT FROM AUTHOR]

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18–64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = –.27, p =.03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98–1.00 vs. 0.75–0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95–0.99 vs. 0.85–0.98; concurrent validity: 0.95–1.00 vs. 0.75–0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99–1.00 vs. 0.89–0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest. [ABSTRACT FROM AUTHOR]