Search Results (49)

The Apple TV+ series Severance (2022–present) offers a dystopian portrayal of workplace life that intensifies real-world dynamics of control, boundary management, and identity regulation. This paper analyzes Severance as a speculative case study in organizational theory, treating the show’s fictional world as a site for conceptual reflection. Drawing on critical management studies and labor process theory, we examine how mechanisms of control, the regulation of work–life boundaries, and the fragmentation of autonomy and subjectivity are depicted in extreme form. We argue that fiction—particularly speculative satire—can serve as a tool of theoretical production, not merely illustration. Rather than restating familiar critiques, Severance allows us to see workplace norms with renewed clarity, surfacing the moral and psychological consequences of surveillance, coercion, and instrumentalized consent. A methodological note outlines our interpretive approach to narrative fiction, and a discussion of implications situates the analysis within broader debates about organizational ethics, resilience, and critique. Full article

In compressed sensing, it is believed that the L₀ norm minimization is the best way to enforce a sparse solution. However, the L₀ norm is difficult to implement in a gradient-based iterative image reconstruction algorithm. The total variation (TV) norm minimization is considered a proper substitute for the L₀ norm minimization. This paper points out that the TV norm is not powerful enough to enforce a piecewise-constant image. This paper uses the limited-angle tomography to illustrate the possibility of using the L₀ norm to encourage a piecewise-constant image. However, one of the drawbacks of the L₀ norm is that its derivative is zero almost everywhere, making a gradient-based algorithm useless. Our novel idea is to replace the zero value of the L₀ norm derivative with a zero-mean random variable. Computer simulations show that the proposed L₀ norm minimization outperforms the TV minimization. The novelty of this paper is the introduction of some randomness in the gradient of the objective function when the gradient is zero. The quantitative evaluations indicate the improvements of the proposed method in terms of the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR). Full article

Seismic data are easily contaminated by random noise, impairing subsequent geological interpretation tasks. Existing denoising methods like low-rank approximation (LRA) and deep learning (DL) show promising denoising capabilities but still have limitations; for instance, LRA performance is parameter-sensitive, and DL networks lack interpretation. As an alternative, this paper introduces the low-rank tensor network (LRTNet), an innovative approach that integrates low-rank tensor approximation (LRTA) with DL. Our method involves constructing a noise attenuation model that leverages LRTA, total variation (TV) regularization, and weighted tensor nuclear norm minimization (WTNNM). By applying the alternating direction method of multipliers (ADMM), we solve the model and transform the iterative schemes into a DL framework, where each iteration corresponds to a network layer. The key learnable parameters, including weights and thresholds, are optimized using labeled data to enhance performance. Quantitative evaluations on synthetic data reveal that LRTNet achieves an average signal-to-noise ratio (SNR) of 9.37 dB on the validation set, outperforming Pyseistr (6.46 dB) and TNN-SSTV (6.10 dB) by 45.0% and 53.6%, respectively. Furthermore, tests on real field datasets demonstrate consistent enhancements in noise suppression while preserving critical stratigraphic structures and fault discontinuities. The embedded LRTA mechanism not only improves network interpretability, but also reduces parameter sensitivity compared to conventional LRA methods. These findings position LRTNet as a robust, physics-aware solution for seismic data restoration. Full article

In ground-based astronomical observations or artificial space target detections, images obtained from a ground-based telescope are severely distorted due to atmospheric turbulence. The distortion can be partially compensated by employing adaptive optics (pre-detection compensation), image restoration techniques (post-detection compensation), or a combination of both (hybrid compensation). This paper focuses on the improvement of the most commonly used practical post-processing techniques, Richardson–Lucy (R–L) iteration blind deconvolution, which is studied in detail and improved as follows: First, the total variation (TV) norm is redefined using the Gaussian gradient magnitude and a set scheme for regularization parameter selection is proposed. Second, the Gaussian TV constraint is proposed to impose to the R–L algorithm. Last, the Gaussian TV R–L (GRL) iterative blind deconvolution method is finally presented, in which the restoration precision is visually increased and the convergence property is considerably improved. The performance of the proposed GRL method is tested by both simulation experiments and observed field data. Full article

For several decades, Brazil’s Grupo Globo, which controls radio, TV, and newspaper, served as the hegemonic voice controlling the audio, visual, and narrative dimensions of social phenomena that formed and informed social, political, and cultural attitudes among Brazilians. Of all their divisions, none has been more influential than the TV Globo network. Lately, with the popularization of free access to digital media, such as those offered by YouTube, TV Globo’s viewership has substantially declined. This paper discusses the concept of controlling images to analyze examples of TV Globo’s constructed visual image of the hypersexualized Afro-Brazilian female body in the network’s soap operas. It also analyzes cases of TV Globo’s constructed narrative over another subaltern Brazilian group: the LGBTQIA+ community. Recently, Afro-Brazilian and Queer-centric YouTube channels have attracted subscribers by emphasizing content centered on negritude, gender politics, and place of speech while deconstructing and de-normalizing Eurocentric and patriarchal controlling images. Against examples of TV Globo’s normative discourse of the past decades, the YouTube channels discussed in this paper represent alternative mediums for agency, visibility, and unbiased representations of gender and racial identities in Brazil. Full article

Infrared dim small target detection has received a lot of attention, because it is a crucial component of the IR search and track systems (IRST). The robust principal component analysis (RPCA) is a common detection framework, which works with poor performance with complex background edges and sparse clutters due to the inappropriate approximation of sparse items. A nonconvex constraint detection method based on the difference between the L1 and L2 (L1–L2) norm and total variation (TV) is presented. The L1–L2 norm is a more accurate sparse item approximation of L0 norm, which can achieve a better description of the sparse item to separate the target from the complex backgrounds. Then, the total variation norm is conducted on the target image to suppress the sparse clutters. The new model is solved using the alternating direction method of multipliers (ADMM) method. Then, the subproblems in the model are tackled by the difference of convex algorithm (DCA) and the Newton conjugate gradient (Newton-CG) solving L1–L2 norm and TV norm, respectively. In the experiment, we conducted experiments on multiple and single target datasets, and the proposed model outperforms the state-of-the-art (SOTA) methods in terms of background suppression and robustness to accurately detect the target. It can achieve a higher true position rate (TPR) with a low false position rate (FPR). Full article

There is evidence that Iranian preschool children are increasingly spending their time in front of screens (screen time: ST; time spent with any screen such as TVs, computers, tablets, smartphones, game consoles, or video games), but few studies have explored the possible causes of such an increase. Given this, the present study aimed to qualitatively explore determinants of excessive ST in Iranian children. To this end, parents of preschool children were interviewed, and their answers were qualitatively clustered to identify additional important factors. Key informant interviews were conducted with parents of preschool children in Tehran (Iran). A semi-structured interview was developed to assess child and family life, daily routine, family rules, family interactions, and home climate as possible contributing factors to ST. Parents’ audiotaped statements were transcripted verbatim, coded, and clustered into main themes using thematic analysis with the MaxQda^® software. A total of 20 parents of children aged 2 to 7 were interviewed, and a total of 6 key themes and 28 subthemes were extracted from their interviews. The results of the analysis identified a broad range of both independent and interrelated factors leading to the development and maintenance of ST behaviors among preschool children. Our findings indicate that the central concept is the family. Considering screen-related behaviors, family life encompasses parental health literacy (e.g., parenting pattern, monitoring standards, thoughtful parenting), family psychological atmosphere (e.g., presence of parents, family norms, parent–parent and parent–child interaction, congruency/incongruency of parents with each other) and the digital structure of the home. The child’s and parents’ actions and characteristics can influence family interactions. A child’s and parent’s behavior is also influenced by social/cultural factors. Parents’ behaviors and attitudes, family communications, and interactions contribute to healthy ST habits in children. It is not possible to examine the child’s behavior without considering the family and the dominant environment, since the behavior of family members as a whole affects each family member. Given this, interventions should make parents aware of their role and responsibilities in reducing children’s ST and consider the family system as a whole, and interventions also can benefit from considering the parental perceptions of children’s behaviors. Full article

In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex $\gamma$-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the $\gamma$-norm to outliers. Secondly, we use the $l_1$-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing. Full article

Mobile streaming is increasingly viewed as a major advancement in the wireless industry, as it enables users to consume content without any time or space restrictions. Mobile TV serves as an excellent example of streaming, providing services for watching TV content on mobile devices. While previous studies have explored video-on-demand (VOD) purchasing factors in mobile TV, it is rare to find research examining differences based on users’ mobile TV usage types, such as subscribers and free users. Consequently, we investigated VOD purchase factors for 310 subscribers and 311 free mobile TV users. In other words, using 621 survey responses, we analyze the influence of personality traits, intrinsic and extrinsic motivations, and mobile-related factors on users’ VOD purchase intentions and behavior. Our findings indicate that mobile TV utilization, hedonic needs, and subjective norms are positively related to VOD purchases, and that neuroticism, extraversion, openness, and conscientiousness positively impact mobile TV utilization. We also examine the relationships between the constructs within two sub-groups to highlight the differing perceptions and behavioral patterns of these groups regarding mobile TV utilization and VOD purchases. Theoretical and practical implications are discussed as well. Full article

Pansharpening refers to the fusion of a panchromatic (PAN) and a multispectral (MS) image aimed at generating a high-quality outcome over the same area. This particular image fusion problem has been widely studied, but until recently, it has been challenging to balance the spatial and spectral fidelity in fused images. The spectral distortion is widespread in the component substitution-based approaches due to the variation in the intensity distribution of spatial components. We lightened the idea using the total variation optimization to improve upon a novel GIHS-TV framework for pansharpening. The framework drew the high spatial fidelity from the GIHS scheme and implemented it with a simpler variational expression. An improved L1-TV constraint to the new spatial–spectral information was introduced to the GIHS-TV framework, along with its fast implementation. The objective function was solved by the Iteratively Reweighted Norm (IRN) method. The experimental results on the “PAirMax” dataset clearly indicated that GIHS-TV could effectively reduce the spectral distortion in the process of component substitution. Our method has achieved excellent results in visual effects and evaluation metrics. Full article

Impulse noise removal is an important problem in the field of image processing. Although many methods exist to remove impulse noise, there is still room for improvement. This paper proposes a new method for removing impulse noise that combines the nuclear norm and the detection ${\ell }_0$TV model while considering the low-rank structure commonly found in visual images. The nuclear norm maintains this structure, while the detection ${\ell }_0$TV criterion promotes sparsity in the gradient domain, effectively removing impulse noise while preserving edges and other vital features. To solve the non-convex and non-smooth optimization problem, we use a mathematical process with equilibrium constraints (MPEC) to transform it. Subsequently, the proximal alternating direction multiplication algorithm is used to solve the transformed problem. The convergence of the algorithm is proven under mild conditions. Numerical experiments in denoising and deblurring show that for low-rank images, the proposed method outperforms ${\ell }_1$TV with detection, ${\ell }_0$TV and ${\ell }_0$OGSTV. Full article

X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression. Full article

Given the vast scholarly attention paid to multilingualism on traditional media over the years, it seems timely to focus on streaming platforms. This paper sets out to identify potential norms for the rendering of multilingual occurrences in the localised content of Netflix series. It also seeks to explore whether streaming translation practices related to multilingualism differ from the consolidated norms and practices for TV and cinema content. The chosen data sample consists of the Italian dub streams of five TV Netflix-produced shows featuring multilingualism as a main characteristic. The strategies and techniques adopted in each series are singled out, quantified, and labelled according to a combination of taxonomies. These include dubbing, revoicing, subtitling, part-subtitling, diegetic interpreting, unchanged speech transfer, and no translation. A wider analysis is also carried out across all the data sample to draw patterns on a macro level. The findings reveal a strong tendency to mark and preserve multilingualism, in line with Netflix’s own policies and dubbing specifications. Transfer unchanged combined with subtitles emerges as the most recurrent strategy, while the dub-over strategy accounts for 13% of the multilingual occurrences in the data sample. Extensive neutralisation is therefore not encountered. That said, a certain degree of overlap between multilingual translation norms on Netflix and conventional Italian dubbing practices (which tend to neutralise) can still be observed. Full article

(1) Background: In propionic acidemia (PA), myocardial involvement often leads to progressive cardiac dysfunction of the left ventricle (LV). Cardiomyopathy (CM) is an important contributor to mortality. Although known to be of prognostic value in CM, there are no published data on right ventricular (RV) function in PA patients. (2) Methods: In this cross-sectional single-center study, systolic and diastolic RV function of PA patients was assessed by echocardiography, including frequency, onset, and combinations of echocardiographic parameters, as well as correlations to LV size and function. (3) Results: N = 18 patients were enrolled. Tricuspid annulus S’ was abnormal in 16.7%, RV-longitudinal strain in 11.1%, tricuspid annular plane systolic excursion (TAPSE) in 11.1%, Tricuspid valve (TV) E/e’ in 33.3%, and TV E/A in 16.7%. The most prevalent combinations of pathological parameters were TV E/A + TV E/e’ and TAPSE + TV S’. With age, the probability of developing abnormal RV function increases according to age-dependent normative data. There is a significant correlation between TAPSE and mitral annular plane systolic excursion (MAPSE), and RV/LV-longitudinal strain (p ≤ 0.05). N = 5 individuals died 1.94 years (mean) after cardiac evaluation for this study, and all had abnormal RV functional parameters. (4) Conclusions: Signs of diastolic RV dysfunction can be found in up to one third of individuals, and systolic RV dysfunction in 16.7% of individuals in our cohort. RV function is impaired in PA patients with a poor outcome. RV functional parameters should be used to complement clinical and left ventricular echocardiographic findings. Full article

The sparsity regularization based on the L₁ norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduce the blurry effect of the convolution kernel. In practice, the total variation (TV) and TV-sparsity (TVS) regularizations based on the L₁ norm are widely adopted in solving the ill-posed problem. Generally, however, the existence of bias is ignored, which is incomplete in theory. This paper places emphasis on analyzing the partially biased property of the L₁ norm. On this basis, we derive the partially bias-corrected solution of TVS and TV, which improves the rigor of the theory. Lastly, two groups of experimental results reflect that the proposed methods with partial bias correction can preserve higher quality than those without bias correction. The proposed methods not only distinguish the adjacent targets, suppress the noise, and preserve the shape and size of targets in visual terms. Its improvement of Peak Signal-to-Noise Ratio, Structure-Similarity, and Sum-Squared-Errors assessment indexes are overall 2.15%, 1.88%, and 4.14%, respectively. As such, we confirm the theoretical rigor and practical feasibility of the partially bias-corrected solution with sparsity regularization based on the L₁ norm. Full article