Diverse Scales Research Articles

Effect of tocotrienol and tocopherol regulation by the microbiome on colitis-associated cancer.

e15512 Background: The association between inflammatory bowel disease (IBD) and a higher risk of colorectal cancer (CRC) has been well established, and is commonly attributed to chronic inflammation and a predisposition to genomic instability ( Feagins et al., 2009 ). Independently, an altered gut microbiome has been evidenced in both IBD ( Halfvarson et al., 2017 ) and CRC ( Marchesi et al., 2011 ). It is unknown whether the altered the IBD-associated microbiome is able to directly influence carcinogenesis via an inflammation-independent mechanisms. Methods: Microbiome analysis was performed using BioCorteX’s industry-leading knowledge graph and integrated proprietary engines v20240126_153156. 2,081 stool samples from adults with IBD and 2,000 stool samples from healthy adults were included. Shannon’s Diversity Index was used to consider within-sample diversity, and PERMANOVA and dimensional scaling based (MDS) on Bray-Curtis diversity was used to consider between-sample diversity. Differential abundance analysis was used to identify species significantly associated with IBD, and a database built on existing literature was used to consider their metabolic products and interactions with known human oncogenes and tumour suppressor genes. Results: Our results show a clearly separation of the IBD microbiome from the healthy population (PERMANOVA p = 0.01), as well as a collapse in Shannon diversity (p < 2.2e-16). Compared to the healthy microbiome, 79 species were significantly (FDR < 0.01) enriched in the IBD stool microbiome, including: Bacteroides fragilis, Bacteroides vulgatus, Bacteroides uniformis, Escherichia coli, Veillonella parvula, Enterococcus faecalis, Fusobacterium mortiferum, and Fusobacterium ulcerans, all of which have previously been evidenced to be enriched in CRC ( Randa et al., 2023 , Bi et al., 2022 , Du et al., 2022 ). Conversely, 115 species were enriched in healthy subjects, including several bacteria with probiotic and anti-tumourigeneic potential such as: Segatella copri, Roseburia inulinivorans, Lactobacillus rogosae, and Coprococcus comes ( Sarah et al., 2022 , Kang et al., 2023 , Campisciano et al., 2020 , Yang et al., 2019 ) Two of the bacteria enriched in the healthy group are producers of both delta-tocotrienol and gamma-tocopherol. These vitamin E isoforms have been implicated in suppression of cancer invasion and proliferation ( Ya et al., 2008 ), in part through NFkB signalling ( Ling et al., 2012 ). Conclusions: This work suggests that that IBD-associated microbiome may contribute to colorectal cancer risk via the suppression of vitamin E isoform signalling, a mechanism that is independent from inflammation. This could present a highly targetable way of mediating CRC-risk in the IBD population.

A multidimensional selective landscape drives adaptive divergence between and within closely related Phlox species

Selection causes local adaptation across populations within species and simultaneously divergence between species. However, it is unclear if either the force of or the response to selection is similar across these scales. We show that natural selection drives divergence between closely related species in a pattern that is distinct from local adaptation within species. We use reciprocal transplant experiments across three species of Phlox wildflowers to characterize widespread adaptive divergence. Using provenance trials, we also find strong local adaptation between populations within a species. Comparing divergence and selection between these two scales of diversity we discover that one suite of traits predicts fitness differences between species and that an independent suite of traits predicts fitness variation within species. Selection drives divergence between species, contributing to speciation, while simultaneously favoring extensive diversity that is maintained across populations within a species. Our work demonstrates how the selection landscape is complex and multidimensional.

Navigating organizational diversity: addressing transphobia among human resources professionals

PropósitoEste estudio tiene como objetivo analizar la relación entre las actitudes hacia la diversidad organizacional y hacia las personas trans, correlacionándolas con los niveles de transfobia entre los profesionales de recursos humanos. Basándonos en la Teoría del Contacto Intergrupal, también buscamos comprender si los resultados pueden estar influenciados por la variable de contacto.Diseño/metodología/enfoqueSe recopilaron datos a través de la administración de un cuestionario a una muestra de 184 profesionales de recursos humanos. El cuestionario estaba compuesto por preguntas autoconstruidas y escalas previamente validadas, que incluían: Attitudes Toward Diversity Scale - ATDS (Montei et al., 1996), Attitudes Toward Transgendered Individuals (ATTI) Scale (Walch et al., 2012) y Genderism and Transphobia Scale - GTS (Hill and Willoughby, 2005).HallazgosLos resultados no muestran altos niveles de actitudes negativas hacia la diversidad organizacional o intolerancia hacia las personas trans, siendo estas variables correlacionadas, con niveles más altos de intolerancia en la porción de la muestra sin contacto con personas transgénero.Originalidad/valorEste artículo contribuye al aumento del conocimiento en el campo de las políticas de desarrollo de recursos humanos, centrándose en la temática de Diversidad e Inclusión, la cual es un tema muy actual e importante en las organizaciones hoy en día. Además, la relevancia y contribución del estudio señalan la necesidad de prácticas de gestión de recursos humanos que asuman la discriminación positiva y/o fomenten la adopción de acciones afirmativas por parte de las organizaciones.

Living in a post‐industrial landscape: repeated patterns of genetic divergence in brown trout (Salmo trutta L.) across the British Isles

AbstractAimThe British Isles have been worked for millennia to extract metal ores to feed industrial development, leaving a legacy of mine water pollution that continues to impact freshwater communities in many regions. Brown trout (Salmo trutta L.) have long been observed to persist in these metal‐impacted systems as apex predators, with previous studies showing a small number of impacted populations to be highly genetically divergent. We sought to understand the scale of genetic diversity across regions and the repeatability of genetic divergence in trout populations affected by metal pollution.LocationWe examined four mine water‐impacted regions across the British Isles: west Wales, northeast England, southwest England and southeast Ireland.MethodsWe employed a panel of 95 SNP loci to screen 1236 individuals from 71 sites representing paired metal‐impacted and clean sites from across the four regions. From these, we obtained diversity statistics, assessed genetic structuring of populations and modelled historical demographic scenarios to understand which factors most credibly explain genetic variation in divergent populations.ResultsWe evidenced hierarchical population structure in the regions studied, in line with expectations from phylogeographic history. However, in a hierarchical analysis of genetic structuring the first level of differentiation was driven by the divergence of the metal‐impacted trout of Cornwall in southwest England. Within regions we observed reduced genetic diversity and repeated patterns of local genetic sub‐structuring between paired samples from metal‐impacted and relatively clean sites. Demographic history analyses suggested the timing of these splits to be relatively recent and to be associated with periods of peak mining activity.Main conclusionsOur findings demonstrate distinct patterns of genetic isolation and reduced diversity arising from legacy pollution in freshwater ecosystems, with impacts being most apparent where both chemical pollution and physical barriers are present. Management should focus on the amelioration of mine water wash‐out and the removal of barriers to fish movement to safeguard genetic diversity in impacted populations.

FMM-VMD-Transformer: A hybrid deep learning model for predicting natural gas consumption

The rapid advancement of energy digitization and intelligence has engendered an escalating demand for accurate and timely prediction of natural gas consumption across diverse temporal scales. This exigency finds solutions in the swift development of intelligent algorithms. Most of the current short-term prediction algorithms are season-dependent and can achieve high prediction accuracy in temperate regions. However, in tropical and subtropical regions where seasonal patterns are not obvious, the prediction accuracy of related studies tends to decrease significantly. To address this challenge, we propose a natural gas hybrid prediction model that is applicable to temperate, subtropical, and tropical regions. This model integrates mode decomposition, feature selection and prediction. Specifically, the Feature Matching Maximization (FMM) optimization algorithm and Variational Mode Decomposition (VMD) are proposed to decompose the natural gas consumption into three sub-series: trend component, feature component, and residue component, thereby enhancing the feature representation capability for complex nonlinear characteristics. Furthermore, the Transformer model based on multi-head attention is enhanced to predict the temporal data of each sub-series. This augmentation capitalizes on the Transformer model’s robust long-term dependence modeling capability, enabling it to extract valuable information from historical data. The hybrid model is experimentally validated on the natural gas consumption dataset of Xiamen, a subtropical region. Compared to other decomposition-based prediction algorithms, our findings possess superior feature extraction capabilities and higher prediction accuracy, especially in the case of nonlinear and non-stationary data. Finally, the robustness of the model is validated using datasets from multiple cities and different time spans.

A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of manufacturing. The process variables collected by sensors in real industrial systems typically exhibit diverse time scales and data features. To overcome the above issues, we propose a comprehensive model known as adaptive multiscale CNN and enhanced highway LSTM (ACEL). To begin with, multiscale features under variable operating conditions are automatically extracted by constructing the parallel convolutional module. Then, to selectively focus on the feature representation within the channels, efficient channel attention is introduced to downplay the channels with less important information. In addition, the bidirectional LSTM is designed to generate more fine-grained hybrid features based on contextual information and local features learned by the CNN. An enhanced highway configuration is designed to be used to bolster the global temporal dependencies. Finally, ACEL is applied to the Tennessee Eastman benchmark and CSTR simulation, and multiple statistical metrics show that the proposed model outperforms other advanced fault diagnosis methods.

Do Agile scaling approaches make a difference? an empirical comparison of team effectiveness across popular scaling approaches

With the prevalent use of Agile methodologies, organizations are grappling with the challenge of scaling development across numerous teams. This has led to the emergence of diverse scaling strategies, from complex ones such as “SAFe", to more simplified methods e.g., “LeSS", with some organizations devising their unique approaches. While there have been multiple studies exploring the organizational challenges associated with different scaling approaches, so far, no one has compared these strategies based on empirical data derived from a uniform measure. This makes it hard to draw robust conclusions about how different scaling approaches affect Agile team effectiveness. Thus, the objective of this study is to assess the effectiveness of Agile teams across various scaling approaches, including “SAFe", “LeSS", “Scrum of Scrums", and custom methods, as well as those not using scaling. This study focuses initially on responsiveness, stakeholder concern, continuous improvement, team autonomy, management approach, and overall team effectiveness, followed by an evaluation based on stakeholder satisfaction regarding value, responsiveness, and release frequency. To achieve this, we performed a comprehensive survey involving 15,078 members of 4,013 Agile teams to measure their effectiveness, combined with satisfaction surveys from 1,841 stakeholders of 529 of those teams. We conducted a series of inferential statistical analyses, including Analysis of Variance and multiple linear regression, to identify any significant differences, while controlling for team experience and organizational size. The findings of the study revealed some significant differences, but their magnitude and effect size were considered too negligible to have practical significance. In conclusion, the choice of Agile scaling strategy does not markedly influence team effectiveness, and organizations are advised to choose a method that best aligns with their previous experiences with Agile, organizational culture, and management style.

Densely integrated multi-branch attentive net for image dehazing

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing.

The methods for improving large-scale multi-view clustering efficiency: a survey

The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

Federated Learning of Things - Expanding the Heterogeneity in Federated Learning

The Internet of Things (IoT) has revolutionized how our devices are networked, connecting multiple aspects of our life from smart homes and wearables to smart cities and warehouses. IoT’s strength comes from the ever-expanding diverse heterogeneous sensors, applications, and concepts that are all centered around the core concept collecting and sharing data from sensors. Simultaneously, deep learning has changed how our systems operate, allowing them to learn from data and change the way we interface with the world. Federated learning moves these two paradigm shifts together, leveraging the data (securely) from the IoT to train deep learning architectures for performant edge applications. However, today’s federated learning has not yet benefited from the scale of diversity that the IoT and deep learning sensors and applications provide. This talk explores how we can better tap into the heterogeneity that surrounds the potential of federated learning and use it to build better models. This includes the heterogeneity from device hardware to training paradigms (supervised, unsupervised, reinforcement, self-supervised).

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.

Non-convex tensorial multi-view clustering by integrating [formula omitted]-based sliced-Laplacian regularization and [formula omitted]-sparsity

Consider the recent upswing in interest around multi-view clustering procedures. Such methods aim to boost clustering efficiency by leveraging information from numerous perspectives. Much research has been devoted to tensorial representation to exploit high-order correlations underlying disparate views while preserving the local geometric structure inside each view. Our research introduces a novel multi-view clustering approach. This approach creates a 3rd-order tensor, assimilating features from all perspectives. We use the t-product in the tensor space to generate the self-representation tensor from the tensorial data. We incorporate the ℓ1-based sliced-Laplacian regularization to increase our model’s resilience and introduce a fresh column-wise sparse norm: the ℓ2,p-norm with 0<p<1. This norm displays attributes of invariance, continuity, and differentiability. We present a closed-form answer to the ℓ2,p-regularized shrinkage problem, broadening its relevance to other generalized problems. Simultaneously, we propose a tensorial arctan-function as an improved surrogate for the tensor rank. This function has proven more proficient at assessing consistency across multiple viewpoints. By integrating these two components, we formulate an effective algorithm that refines our suggested model, ensuring that the constructed sequence gravitates toward the stationary KKT point. Our team conducts extensive experiments on various datasets to evaluate our model’s effectiveness, spanning diverse situations and scales. Results from these experiments emphasize that our approach establishes a novel performance standard.

CONTEXTS.py (CS.py): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse conceptual spaces. By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements.This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.).Computationally, our method is operationalised by CONTEXTS.py (CS.py), a plugin written in Python for QGIS. CS.py relies on training areas, defined by the user at diverse scales, to identify and extract in the input image conceptual spaces whose spatial contexts have the same spatial features present in the training areas.Applied to a case study on the island of Sicily, where millennial land use dynamics have resulted in a mosaic landscape, CS.py could detect from an orthophoto diverse conceptual spaces of land use in an area ordinarily classified as one land cover, thus expanding the capabilities of geospatial analysis to reach additional qualitative dimensions of information from image data.•CS.py simplifies a supervised contextual post-classification routine in an easy-to-use, practical and accessible QGIS plugin;•CS.py joins a family of tools for supervised object-based classification (e.g. OTB, GRASS), providing, additionally, the possibility to include contextual information as spatial criteria to train the classification routine.•CS.py has broad applications in different disciplines investigating landscape from quantitative and qualitative perspectives, allowing both, as in multiple environments.

SLMFNet: Enhancing land cover classification of remote sensing images through selective attentions and multi-level feature fusion.

Land cover classification (LCC) is of paramount importance for assessing environmental changes in remote sensing images (RSIs) as it involves assigning categorical labels to ground objects. The growing availability of multi-source RSIs presents an opportunity for intelligent LCC through semantic segmentation, offering a comprehensive understanding of ground objects. Nonetheless, the heterogeneous appearances of terrains and objects contribute to significant intra-class variance and inter-class similarity at various scales, adding complexity to this task. In response, we introduce SLMFNet, an innovative encoder-decoder segmentation network that adeptly addresses this challenge. To mitigate the sparse and imbalanced distribution of RSIs, we incorporate selective attention modules (SAMs) aimed at enhancing the distinguishability of learned representations by integrating contextual affinities within spatial and channel domains through a compact number of matrix operations. Precisely, the selective position attention module (SPAM) employs spatial pyramid pooling (SPP) to resample feature anchors and compute contextual affinities. In tandem, the selective channel attention module (SCAM) concentrates on capturing channel-wise affinity. Initially, feature maps are aggregated into fewer channels, followed by the generation of pairwise channel attention maps between the aggregated channels and all channels. To harness fine-grained details across multiple scales, we introduce a multi-level feature fusion decoder with data-dependent upsampling (MLFD) to meticulously recover and merge feature maps at diverse scales using a trainable projection matrix. Empirical results on the ISPRS Potsdam and DeepGlobe datasets underscore the superior performance of SLMFNet compared to various state-of-the-art methods. Ablation studies affirm the efficacy and precision of SAMs in the proposed model.

Therapeutic doses of ketamine acutely attenuate the aversive effect of losses during decision-making.

The discovery of rapid-acting antidepressant, ketamine has opened a pathway to a new generation of treatments for depression, and inspired neuroscientific investigation based on a new perspective that non-adaptive changes in the intrinsic excitatory and inhibitory circuitry might underlie the pathophysiology of depression. Nevertheless, it still remains largely unknown how the hypothesized molecular and synaptic levels of changes in the circuitry might mediate behavioral and neuropsychological changes underlying depression, and how ketamine might restore adaptive behavior. Here, we used computational models to analyze behavioral changes induced by therapeutic doses of ketamine, while rhesus macaques were iteratively making decisions based on gains and losses of tokens. When administered intramuscularly or intranasally, ketamine reduced the aversiveness of undesirable outcomes such as losses of tokens without significantly affecting the evaluation of gains, behavioral perseveration, motivation, and other cognitive aspects of learning such as temporal credit assignment and time scales of choice and outcome memory. Ketamine's potentially antidepressant effect was separable from other side effects such as fixation errors, which unlike outcome evaluation, was readily countered with strong motivation to avoid errors. We discuss how the acute effect of ketamine to reduce the initial impact of negative events could potentially mediate longer-term antidepressant effects through mitigating the cumulative effect of those events produced by slowly decaying memory, and how the disruption-resistant affective memory might pose challenges in treating depression. Our study also invites future investigations on ketamine's antidepressant action over diverse mood states and with affective events exerting their impacts at diverse time scales.

Therapeutic doses of ketamine acutely attenuate the aversive effect of losses during decision-making

The discovery of rapid-acting antidepressant, ketamine has opened a pathway to a new generation of treatments for depression, and inspired neuroscientific investigation based on a new perspective that non-adaptive changes in the intrinsic excitatory and inhibitory circuitry might underlie the pathophysiology of depression. Nevertheless, it still remains largely unknown how the hypothesized molecular and synaptic levels of changes in the circuitry might mediate behavioral and neuropsychological changes underlying depression, and how ketamine might restore adaptive behavior. Here, we used computational models to analyze behavioral changes induced by therapeutic doses of ketamine, while rhesus macaques were iteratively making decisions based on gains and losses of tokens. When administered intramuscularly or intranasally, ketamine reduced the aversiveness of undesirable outcomes such as losses of tokens without significantly affecting the evaluation of gains, behavioral perseveration, motivation, and other cognitive aspects of learning such as temporal credit assignment and time scales of choice and outcome memory. Ketamine’s potentially antidepressant effect was separable from other side effects such as fixation errors, which unlike outcome evaluation, was readily countered with strong motivation to avoid errors. We discuss how the acute effect of ketamine to reduce the initial impact of negative events could potentially mediate longer-term antidepressant effects through mitigating the cumulative effect of those events produced by slowly decaying memory, and how the disruption-resistant affective memory might pose challenges in treating depression. Our study also invites future investigations on ketamine’s antidepressant action over diverse mood states and with affective events exerting their impacts at diverse time scales.

Multispectral Pedestrian Detection Based on Prior-Saliency Attention and Image Fusion

Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds in far-infrared (FIR) images by employing saliency attention derived from FIR images via UNet. However, extracting salient regions of diverse scales from FIR images poses a challenge for saliency attention. To address this, we integrate Simple Linear Iterative Clustering (SLIC) superpixel segmentation, embedding the segmentation feature map as prior knowledge into UNet’s decoding stage for comprehensive end-to-end training and detection. This integration enhances the extraction of focused attention regions, with the synergy of segmentation prior and saliency attention forming the core of Prior-AttentionNet. Moreover, to enrich pedestrian details and contour visibility in low-light conditions, we implement multispectral image fusion. Experimental evaluations were conducted on the KAIST and OTCBVS datasets. Applying Prior-Attention mode to FIR-RGB images significantly improves the delineation and focus on multi-scale pedestrians. Prior-AttentionNet’s general detector demonstrates the capability of detecting pedestrians with minimal computational resources. The ablation studies indicate that the FIR-RGB+ Prior-Attention mode markedly enhances detection robustness over other modes. When compared to conventional multispectral pedestrian detection models, Prior-AttentionNet consistently surpasses them by achieving higher mean average precision and lower miss rates in diverse scenarios, during both day and night.

G2-MonoDepth: A General Framework of Generalized Depth Inference From Monocular RGB+X Data.

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This article investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.

Design of sports training information analysis system based on a multi-target visual model under sensor-scale spatial transformation

In the contemporary realm of athletic training, integrating technology is a pivotal determinant for augmenting athlete performance and refining training outcomes. The amalgamation of multi-target visual modeling with sensor technology imparts an enriched stratum of sports training data. Subsequently, the sensor scale-space transformation accentuates the comprehensive apprehension of data across diverse scales and angles. Hence, within this manuscript, addressing the multi-target tracking intricacies during sports training and competition, we posit a framework that amalgamates the shortest path elucidated by the K shortest paths (KSP) methodology with the pose information emanating from the Alphapose network. This framework recognizes the athlete’s shortest path through a convolutional neural network and KSP, followed by the amalgamation of these divergent data sources. The fusion unfolds by incorporating the athlete’s pose information grounded in Alphapose, culminating in a comprehensive integration of the two data streams. Consequently, synthesizing alpha-derived athlete information precipitates the ultimate amalgamation of the two information streams. The accomplished fusion, premised on Alphapose, forms the bedrock for multi-target tracking, culminating in a feature-rich synthesis. Empirical results reveal that after integrating these information streams, the Multiple Object Tracking Accuracy (MOTA) index and Global Multiple Object Tracking Accuracy (GMOTA) index surpass those of the solitary information tracking methods, thereby furnishing a technical underpinning and a foundation for information fusion within prospective sports training analysis systems.

THE ROLE OF INTERNATIONAL ACCOUNTING STANDARDS IN FOSTERING CORPORATE REPORTING TRANSPARENCY

This research investigates the multifaceted impact of International Accounting Standards (IAS) on corporate reporting transparency. Amidst the interconnected global business landscape, the study aims to discern global adoption trends, financial reporting quality, stakeholder perceptions, implementation challenges, and the responsiveness of IAS to industry dynamics. Through meticulous analyses spanning the years 2010 to 2020, the research unfolds key insights.The adoption of IAS is a critical facet of global financial reporting, influencing business practices, investor decisions, and regulatory frameworks. Understanding its impact is paramount for policymakers, standard-setters, and businesses navigating an increasingly interconnected and diverse financial ecosystem.This research seeks to comprehensively examine the intricate relationship between IAS and corporate reporting transparency. By delving into adoption trends, financial metrics, stakeholder perspectives, implementation challenges, and update responsiveness, the study aims to provide a holistic view of the global accounting landscape.The analysis reveals a consistent upward trajectory in global IAS adoption, with North America and the Asia-Pacific region playing pivotal roles. Financial reporting quality experiences substantial improvements, particularly benefiting smaller enterprises. Stakeholder perceptions vary across regions and professional roles, emphasizing the need for tailored communication strategies. Implementation challenges, including legal framework complexities and cultural differences, underscore the intricate nature of global adoption. The frequency of IAS updates showcases the adaptability of standards to emerging trends, emphasizing sector-specific implications.This research concludes that IAS significantly influences corporate reporting transparency, offering a standardized framework for diverse business scales. Challenges in implementation necessitate targeted interventions, with recommendations focusing on stakeholder communication, tailored support for small enterprises, and addressing legal and cultural complexities. The adaptability of IAS to industry dynamics reaffirms its role as a responsive and evolving standard. As businesses, regulators, and standard-setters move forward, continuous collaboration and flexibility become imperative for navigating the complexities of a globally harmonized financial reporting landscape.