Competitive Performance Research Articles

The Contemporary Position of Socially Responsible Marketing – The Past and Present

Many factors drive companies to pursue higher levels of CSR, including changes in employee and customer needs, management and government pressure, business interests in relational models, and compliance with supply chain processes. The foundation of business knowledge as business ethics includes compliance with legal and ecological principles. Businesses should ensure that all employees understand and comply with applicable laws. Businesses should implement and publish a written policy to promote a culture of ethical behaviour within the organisation and to hold employees accountable for legal and ethical compliance. By using these strategies or similar strategies, companies can create opportunities to reduce current and future expenses, increase competitiveness and market performance, and improve results. In today›s business world, ecological concepts are increasingly used in the market as a necessary prerequisite for developing competitive advantage and brand awareness. Business strategies and the need to create products and services that meet customer needs have had a significant impact on the evolution of environmental awareness. This article focuses on the current state and development of the business sector.

Ultrahigh Thermal Robustness of High‐Entropy Spectrally Selective Absorbers for Next‐Generation Concentrated Solar Power System

AbstractSpectrally selective absorbers (SSAs) are a critical component in concentrated solar power (CSP) systems, as they maximize sunlight absorption while suppressing heat radiative loss. Despite various SSAs being demonstrated, the challenges remain on the limitations of thermal instability at elevated operating temperatures especially above 650 °C due to component oxidation and diffusion in the absorption layer. To address these challenges, herein a high‐entropy strategy is resorted. By utilizing high‐entropy nitride film as an absorption layer, a double‐layer SSA is prepared by a facile magnetron sputtering method. High‐entropy engineering intensifies and complicates the localized electronic bands, concurrently exhibiting relatively flat bands around the Fermi level, which can significantly enhance 3d interband transition. The resultant SSA delivers the desired spectral selectivity (α/ɛ82 °C = 92.7%/8.4%). Benefitting from the high‐entropy effect, the SSA maintains outstanding optical properties (α/ɛ82 °C = 93.3%/9.4%) even after a 750 °C vacuum thermal treatment for 120 h. Under 1 kW m−2 simulated illumination, the surface temperature of the absorber can easily rise to 95.3 °C, suggesting its remarkable solar‐thermal performance. Furthermore, its effectiveness in parabolic trough collectors (PTCs) is validated. All these competitive performances make the high‐entropy SSA a potentially promising candidate for PTCs.

HRTracker: Multi-Object Tracking in Satellite Video Enhanced by High-Resolution Feature Fusion and an Adaptive Data Association

Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be directly introduced into remote sensing scenarios. The main reasons for this can be attributed to the following: (1) the existing MOT approaches would cause a significant rate of missed detection of the small targets in satellite videos; (2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion and a two-step association method is proposed. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist detection by maintaining small object features in forward propagation. By utilizing features of different resolutions, the performance of the detection of small targets in satellite videos is improved. Through high-quality detection and the use of an adaptive Kalman filter, the densely packed weak objects can be effectively tracked by associating almost every detection box instead of only the high-score ones. The comprehensive experimental results using the representative satellite video datasets (VISO) demonstrate that the proposed HRTracker with the state-of-the-art (SOTA) methods can achieve competitive performance in terms of the tracking accuracy and the frequency of ID conversion, obtaining a tracking accuracy score of 74.6% and an ID F1 score of 78.9%.

Separation of C6 hydrocarbons on sodium dithionite reduced graphene oxide aerogels

The ability of reduced graphene oxide aerogels (rGOAs) for challenging gas-phase separation was investigated with hexane isomers and benzene (C6 hydrocarbons) using inverse gas chromatography (IGC). For the first, rGOAs were synthesized with sodium dithionite (DTN) as a reductant. Experiments revealed that the most optimal DTN to graphene oxide mass ratio was 2:1, resulting in the highest specific surface area of 432.3 m2 g−1 and the highest degree of graphitization among analyzed samples. C6 hydrocarbon adsorption tests demonstrated the dominant role of the kinetic effect for the adsorption of branched and cyclic hexane isomers - the partition coefficient decreased as the molecule kinetic diameter increased. The contribution of thermodynamic effects was distinguished for molecules with uneven charge distribution. A comparison of the partition coefficient ratios for different pairs of hydrocarbons demonstrated the potential of rGOAs in separating various C6 hydrocarbons. The selectivity, calculated from binary-component adsorption tests of benzene (Bz)/cC6 equimolar mixture, was 13.7, 8.5 and 2.8 for DTN4, DTN2, and DTN1. The research indicates that rGOAs may have potential as adsorbents for the selective separation of hydrocarbons, however, the competitive adsorption and performance at high surface coverages of adsorbates have to be accounted for in further research to assess the applicability of rGOAs reliably.

A global reweighting approach for cross-domain semantic segmentation

Unsupervised domain adaptation semantic segmentation attracts much research attention due to the expensive pixel-level annotation cost. Since the adaptation difficulty of samples is different, the weight of samples should be set independently, which is called reweighting. However, existing reweighting methods only calculate local reweighting information from predicted results or context information in batch images of two domains, which may lead to over-alignment or under-alignment problems. To handle this issue, we propose a global reweighting approach. Specifically, we first define the target centroid distance, which describes the distance between the source batch data and the target centroid. Then, we employ a Fréchet Inception Distance metric to evaluate the domain divergence and embed it into the target centroid distance. Finally, a global reweighting strategy is proposed to enhance the knowledge transferability in the source domain supervision. Extensive experiments demonstrate that our approach achieves competitive performance and helps to improve performance in other methods.

Fusing infrared polarization images for road detection via denoising diffusion probabilistic models.

Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods. In this Letter, we present a denoising diffusion model aimed at improving the performance of road detection in infrared polarization images. This model achieves effective integration of infrared intensity and polarization information through forward and reverse diffusion processes. Furthermore, we propose what we believe to be a novel method to augment polarized images from different orientations based on the angle of polarization. The augmented polarized image serves as the guiding condition, enhancing the robustness of the diffusion model. Our experimental results validate the effectiveness of the proposed method, demonstrating competitive performance compared to state-of-the-art methods, even with fewer training samples.

CT and MRI Image Fusion via Coupled Feature-Learning GAN

The fusion of multimodal medical images, particularly CT and MRI, is driven by the need to enhance the diagnostic process by providing clinicians with a single, comprehensive image that encapsulates all necessary details. Existing fusion methods often exhibit a bias towards features from one of the source images, making it challenging to simultaneously preserve both structural information and textural details. Designing an effective fusion method that can preserve more discriminative information is therefore crucial. In this work, we propose a Coupled Feature-Learning GAN (CFGAN) to fuse the multimodal medical images into a single informative image. The proposed method establishes an adversarial game between the discriminators and a couple of generators. First, the coupled generators are trained to generate two real-like fused images, which are then used to deceive the two coupled discriminators. Subsequently, the two discriminators are devised to minimize the structural distance to ensure the abundant information in the original source images is well-maintained in the fused image. We further empower the generators to be robust under various scales by constructing a discriminative feature extraction (DFE) block with different dilation rates. Moreover, we introduce a cross-dimension interaction attention (CIA) block to refine the feature representations. The qualitative and quantitative experiments on common benchmarks demonstrate the competitive performance of the CFGAN compared to other state-of-the-art methods.

GCNT: Identify influential seed set effectively in social networks by integrating graph convolutional networks with graph transformers

Exploring effective and efficient strategies for identifying influential nodes from social networks as seeds to promote the propagation of influence remains a crucial challenge in the field of influence maximization (IM), which has attracted significant research efforts. Deep learning-based approaches have been adopted as an alternative promising solution to the IM problem. However, a robust model that captures the associations between network information and node influence needs to be investigated, while concurrently considering the effects of the overlapped influence on training labels. To address these challenges, a GCNT model, which integrates Graph Convolutional Networks with Graph Transformers, is introduced in this paper to capture the intricate relationships among the topology of the network, node attributes, and node influence effectively. Furthermore, an innovative method called Greedy-LIE is proposed to generate labels to alleviate the issue of overlapped influence spread. Moreover, a Mask mechanism specially tailored for the IM problem is presented along with an input embedding balancing strategy. The effectiveness of the GCNT model is demonstrated through comprehensive experiments conducted on six real-world networks, and the model shows its competitive performance in terms of both influence maximization and computational efficiency over state-of-the-art methods.

Analysis of sales techniques from the perspective of entrepreneurship in MSMEs (MIPYMEs)

This study analyzes and evaluates sales techniques from the perspective of entrepreneurship in micro, small and medium-sized enterprises (MSMEs) with the objective of identifying effective strategies to improve their competitiveness and commercial performance. Critical to economic development and employment generation, MSMEs face significant challenges, including limited resources and intense competition. The research focuses on traditional and digital sales techniques, highlighting the importance of innovation and entrepreneurship. A quantitative and documentary methodology was used to collect and analyze data from 40 MSMEs. The results indicate that digital techniques, such as social media marketing and e-commerce, have surpassed traditional techniques, such as print advertising and direct sales, in effectiveness. MSMEs that adopt an entrepreneurial and flexible approach show better business performance and higher customer satisfaction. Ongoing training and the use of emerging technologies are crucial to the successful implementation of current sales strategies. However, MSMEs face barriers such as resistance to change and scarcity of financial resources. To overcome these challenges, it is recommended to encourage innovation, invest in staff training, and optimize available resources.

Spatial awareness enhancement based single-stage anchor-free 3D object detection for autonomous driving

The real-time and accurate detection of three-dimensional (3D) objects based on LiDAR is a focal problem in the field of autonomous driving environment perception. Compared to two-stage and anchor-based 3D object detection methods that suffer from inference latency challenges, single-stage anchor-free 3D object detection approaches are more suitable for deployment in autonomous driving vehicles with the strict real-time requirement. However, they face the issue of insufficient spatial awareness, which can result in detection errors such as false positives and false negatives, thereby increasing the potential risks of autonomous driving. In response to this, we focus on enhancing the spatial awareness of CenterPoint, a widely used single-stage anchor-free 3D object detector in the industry. Considering the limited allocation of computational resources and the performance bottleneck caused by pillar encoder, we propose an efficient SSDCM backbone to strengthen feature representation and extraction. Furthermore, a simple BGC neck is devised to weight and exchange contextual information in order to deeply fuse multi-scale features. Combining improved backbone and neck networks, we construct a single-stage anchor-free 3D object detection model with spatial awareness enhancement, named CenterPoint-Spatial Awareness Enhancement (CenterPoint-SAE). We evaluate CenterPoint-SAE on two large-scale and challenging autonomous driving datasets, nuScenes and Waymo. It achieves 53.3% mAP and 62.5% NDS on nuScenes detection benchmark, and runs inference at a speed of 11.1 FPS. Compared to the baseline, the upgraded networks deliver a performance improvement of 1.6% mAP and 1.2% NDS at minor cost. Notably, on Waymo dataset, our method achieves competitive detection performance compared to two-stage and point-based methods.

Sparsesurv: a Python package for fitting sparse survival models via knowledge distillation.

Sparse survival models are statistical models that select a subset of predictor variables while modeling the time until an event occurs, which can subsequently help interpretability and transportability. The subset of important features is often obtained with regularized models, such as the Cox Proportional Hazards model with Lasso regularization, which limit the number of non-zero coefficients. However, such models can be sensitive to the choice of regularization hyperparameter. In this work, we develop a software package and demonstrate how knowledge distillation, a powerful technique in machine learning that aims to transfer knowledge from a complex teacher model to a simpler student model, can be leveraged to learn sparse survival models while mitigating this challenge. For this purpose, we present sparsesurv, a Python package that contains a set of teacher-student model pairs, including the semi-parametric accelerated failure time and the extended hazards models as teachers, which currently do not have Python implementations. It also contains in-house survival function estimators, removing the need for external packages. Sparsesurv is validated against R-based Elastic Net regularized linear Cox proportional hazards models as implemented in the commonly used glmnet package. Our results reveal that knowledge distillation-based approaches achieve competitive discriminative performance relative to glmnet across the regularization path while making the choice of the regularization hyperparameter significantly easier. All of these features, combined with a sklearn-like API, make sparsesurv an easy-to-use Python package that enables survival analysis for high-dimensional datasets through fitting sparse survival models via knowledge distillation. sparsesurv is freely available under a BSD 3 license on GitHub (https://github.com/BoevaLab/sparsesurv) and The Python Package Index (PyPi) (https://pypi.org/project/sparsesurv/).

Synergistic Regulation of Polyselenide Dissolution and Na‐Ion Diffusion of Se‐Vacancy‐Rich Bismuth Selenide toward Ultrafast and Durable Sodium‐Ion Batteries

AbstractMetal selenides (MSes) have great potential as candidate anode materials in high‐specific‐energy sodium‐ion batteries (SIBs) but are plagued by rapid capacity degradation and slow kinetics. Here, it is reveal that the Bi2Se3 anode discharge process involves multiple‐types of sodium polyselenides (Na‐pSex) which suffer from terrible dissolution and shuttling properties. Based on these observations, a nanoflower‐like composite of dual carbon‐confined Bi2Se3−x crystallites is designed via facile defect chemistry. The robust dual N‐doped carbon layer suppresses the precipitation and aggregation of Bi2Se3, significantly alleviating the dissolution and shuttle effect of Na‐pSex. Theoretical calculations indicate that the pyridine/pyrrole nitrogen sites exhibit strong van der Waals resistance and chemisorption properties against Na2Se4 and Na2Se2. Furthermore, the abundant Se vacancies improve the inherent conductivity of Bi2Se3, reduce the diffusion barrier of Na+, and accelerate the reaction kinetics. Consequently, the resulting Bi2Se3−x@DNC electrode exhibits extraordinary durability (over 2000 cycles at 10.0 A g−1) and high‐rate capability (354.4 mAh g−1 at 75.0 A g−1), propelling the battery performance to new heights. Encouragingly, the assembled hybrid capacitor displays competitive rate performance and an ultra‐long lifespan exceeding 40 000 cycles, making the Bi2Se3−x@DNC electrode a promising candidate for SIBs.

CSLNSpeech: Solving the extended speech separation problem with the help of Chinese sign language

Previous audio-visual speech separation methods synchronize the speaker's facial movement and speech in the video to self-supervise the speech separation. In this paper, we propose a model to solve the speech separation problem assisted by both face and sign language, which we call the extended speech separation problem. We design a general deep learning network to learn the combination of three modalities, audio, face, and sign language information, to solve the speech separation problem better. We introduce a large-scale dataset named the Chinese Sign Language News Speech (CSLNSpeech) dataset to train the model, in which three modalities coexist: audio, face, and sign language. Experimental results show that the proposed model performs better and is more robust than the usual audio-visual system. In addition, the sign language modality can also be used alone to supervise speech separation tasks, and introducing sign language helps hearing-impaired people learn and communicate. Last, our model is a general speech separation framework and can achieve very competitive separation performance on two open-source audio-visual datasets. The code is available at https://github.com/iveveive/SLNSpeech

Temporal Graph Network for continuous-time dynamic event sequence

Continuous-Time Dynamic Graph (CTDG) methods have shown their superior ability in learning representations for dynamic graph-structured data, the methods split the sequential updating process into discrete batches to reduce the computation costs, as a result, the message constructor in existing CTDG methods cannot be optimized by gradient descent and is designed to be parameter-free. In particular, this layer fails to embed complex event subgraphs and ignores the structure information, while most real-world events are structured and complex. For example, a paper publication event in an academic graph contains different relations like authorship and citations. Furthermore, the corresponding nodes could not receive position-wise messages to make precise representation updates. To tackle this issue, we propose a new method called Temporal Graph Network for continuous-time dynamic Event sequence (TGNE) with a structure-aware message constructor to update node representation with complex event subgraph, by treating message construction and delivery as a message-passing process, in this way, the message constructor can be formalized as a graph neural network layer. TGNE extends the input of CTDG methods to subgraphs with complex structures and preserves more information in message delivery. Extensive experiments demonstrate that the proposed method can achieve competitive performance on traditional tasks on bipartite graphs and event sequence learning tasks on heterogeneous graphs.

Bit Reduction for Locality-Sensitive Hashing.

Locality-sensitive hashing (LSH) has gained ever-increasing popularity in similarity search for large-scale data. It has competitive search performance when the number of generated hash bits is large, reversely bringing adverse dilemmas for its wide applications. The first purpose of this work is to introduce a novel hash bit reduction schema for hashing techniques to derive shorter binary codes, which has not yet received sufficient concerns. To briefly show how the reduction schema works, the second purpose is to present an effective bit reduction method for LSH under the reduction schema. Specifically, after the hash bits are generated by LSH, they will be put into bit pool as candidates. Then mutual information and data labels are exploited to measure the correlation and structural properties between the hash bits, respectively. Eventually, highly correlated and redundant hash bits can be distinguished and then removed accordingly, without deteriorating the performance greatly. The advantages of our reduction method include that it can not only reduce the number of hash bits effectively but also boost retrieval performance of LSH, making it more appealing and practical in real-world applications. Comprehensive experiments were conducted on three public real-world datasets. The experimental results with representative bit selection methods and the state-of-the-art hashing algorithms demonstrate that the proposed method has encouraging and competitive performance.

Assessing risk indicators of intimate partner femicide considering victim's coping strategies to violence: A dynamic multilevel linear mixed model based on genetic algorithms

Intimate partner femicide (IPF) is a grave social and health concern affecting women worldwide, with approximately 30,000 deaths annually at the hands of their current or former intimate partners. Previous studies have focused on identifying risk factors for IPF and developing risk assessment tools to identify high-risk cases. However, an important aspect that has been overlooked in these studies is victims' coping strategies in response to intimate partner violence. Understanding victims' coping strategies can provide valuable insights into how they deal with the abuse and can inform the development of effective interventions and prevention strategies. This study aims to address this gap by developing a multilevel linear mixed model (LMM) to analyze the impact of engagement and disengagement coping on the likelihood of IPF and identify common and specific IPF risk indicators for these coping strategies. A total of 491 Spanish cases of violence against women by current or former intimate partners were analyzed from penal sentences issued by Spanish provincial and supreme courts from 2019 to 2022. The LMM model obtained from the study has competitive performance in identifying IPF and non-IPF cases, including risk indicators of prior history of injuries, history of sexual aggression, frequency and escalation of violence, physical violence, place of crime, lonely place of crime, and community presence. Victims with engagement coping and disengagement coping share some risk indicators, while others belong to just one category. Overall, the results suggest that victims with disengagement coping are more predisposed to suffer IPF than victims with engagement coping. This evidenced-based knowledge emphasizes the significance of considering coping strategies in predicting and preventing IPF, with further implications discussed at the end of this paper.

Optimizing sports performance in China: Investigating the influence of training, recovery, motivation, and environmental support within a moderation-mediation framework

The expansion of sports in China is astounding. Given the country's focus on athletic achievement, it is crucial to comprehend the different facets of sports. This research examines China's athletic success in training, recuperation, motivation, and environment. The moderation-mediation strategy is used to understand how these variables affect athletic performance. This study fills a gap in understanding Chinese players' on-field performance. The study examined 355 Chinese athletes from various regions and sports. The sample has a balanced gender distribution, diverse age distribution, and representation from various regions. Furthermore, training programs, recovery strategies, motivation, psychological well-being, support systems, and experience levels influence athletes' sports performance. The study reveals that the coefficients indicate that training, recuperation tactics, motivation, and support systems affect athletic performance. Psychological health emerges as a critical mediator in explaining the effects of these factors on performance outcomes. Additionally, athlete experience moderates the influence of these variables, highlighting nuanced differences. These findings offer crucial insights for enhancing the performance of Chinese athletes. By adopting a moderation-mediation approach, this research illuminates the interconnected dynamics among key components, enriching our understanding of avenues for athlete improvement. Specifically, the results underscore the interconnectedness of athletes' psychological well-being, on-field experience, and competitive performance, emphasizing the importance of tailored interventions. This study could help Chinese politicians, sports psychologists, and coaches enhance athletes' performance.

Permutation Equivariant Graph Framelets for Heterophilous Graph Learning.

The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network (GNN) models and suggests aggregations beyond the one-hop neighborhood. In this article, we develop a new way to implement multiscale extraction via constructing Haar-type graph framelets with desired properties of permutation equivariance, efficiency, and sparsity, for deep learning tasks on graphs. We further design a graph framelet neural network model permutation equivariant graph framelet augmented network (PEGFAN) based on our constructed graph framelets. The experiments are conducted on a synthetic dataset and nine benchmark datasets to compare the performance with other state-of-the-art models. The result shows that our model can achieve the best performance on certain datasets of heterophilous graphs (including the majority of heterophilous datasets with relatively larger sizes and denser connections) and competitive performance on the remaining.

The Low-Attenuation Endfire Leaky-Wave State on an Optically Transparent Lossy Film

The development of high-performance optically transparent radio frequency (RF) radiators is limited by the intrinsic loss issue of transparent conductive films (TCFs). Instead of pursuing expensive endeavors to improve the TCFs’ electrical properties, this study introduces an innovative approach that leverages leaky-wave mode manipulation to mitigate the TCFs’ attenuating effect and maximize the RF radiation. Our finding reveals that the precise control of the mode confinement on glass-coated TCFs can create a low-attenuation window for leaky-wave propagation, where the total attenuation caused by TCF dissipation and wave leakage is effectively reduced. The observed low-attenuation leaky-wave state on lossy TCFs originates from the delicate balance between wave leakage and TCF dissipation, attained at a particular glass cladding thickness. By leveraging the substantially extended radiation aperture achieved under suppressed wave attenuation, this study develops an optically transparent antenna with an enhanced endfire realized gain exceeding 15 dBi and a radiation efficiency of 66%, which is validated to offer competitive transmission performance for advancing ubiquitous wireless communication and sensing applications.

TransC-ac4C: Identification of N4-Acetylcytidine (ac4C) Sites in mRNA Using Deep Learning.

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA that is critical in mRNA translation in terms of stability and regulation. In the past few years, numerous approaches employing convolutional neural networks (CNN) and Transformer have been proposed for the identification of ac4C sites, with each variety of approaches processing distinct characteristics. CNN-based methods excel at extracting local features and positional information, whereas Transformer-based ones stands out in establishing long-range dependencies and generating global representations. Given the importance of both local and global features in mRNA ac4C sites identification, we propose a novel method termed TransC-ac4C which combines CNN and Transformer together for enhancing the feature extraction capability and improving the identification accuracy. Five different feature encoding strategies (One-hot, NCP, ND, EIIP, and K-mer) are employed to generate the mRNA sequence representations, in which way the sequence attributes and physical and chemical properties of the sequences can be embedded. To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the CNN is employed to process five single features, stitch them together and feed them to the Transformer layer. Then, our approach employs CNN to extract local features and Transformer subsequently to establish global long-range dependencies among extracted features. We use 5-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 81.42% and 80.69%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model.