General Issues Research Articles

Shaping Rights Through European Consensus or Trend

Abstract The question of when and how a European consensus or trend contributes to shaping rights guaranteed by the European Convention on Human Rights and its Protocols is controversial. The European Court of Human Rights quite often performs an analysis of the laws and practices of the Council of Europe’s Member States or of relevant international material. However, the cases where rights have actually been shaped by a European consensus or trend are quite rare. In the last twenty-five years, some 27 out of 424 judgments on the merits of the Grand Chamber of the Court established a consensus or trend having a “shaping impact” on these rights. Further, only one advisory opinion based on Protocol No. 16 contained comparative law material having such an influence. This Article assesses the intensity of the impact of a consensus or a trend analysis on a judgment’s or an advisory opinion’s ratio decidendi, shows what shaping a right actually means, and suggests that cases that are more prone to a potentially persuasive consensus or trend analysis will typically deal with matters of political or general policy, sensitive moral or ethical issues, or changes in the case law.

Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.

The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject to variability and may lack precision. Current methodologies leverage conventional deep learning models that, while effective to an extent, often suffer from overfitting and generalization issues on medical image datasets due to the intricate and subtle variations in disease manifestations. These models typically do not fully utilize the potential of transfer learning or advanced data augmentation, leading to less-than-optimal performance, especially in diverse real-world scenarios where data variability is high. This study introduces a robust model using the EfficientNetB5 architecture combined with a sophisticated data augmentation strategy. The model is tailored for the high variability and intricate details present in gastrointestinal tract disease images. By integrating transfer learning with maximal pooling and extensive regularization, the model aims to enhance diagnostic accuracy and reduce overfitting. The proposed model achieved a test accuracy of 98.89%, surpassing traditional methods by incorporating advanced regularization and augmentation techniques. The application of horizontal flipping and dynamic scaling during training significantly improved the model's ability to generalize, evidenced by a low-test loss of 0.230 and high precision metrics across all classes. The proposed deep learning framework demonstrates superior performance in the automated classification of gastrointestinal diseases from image data. By addressing key limitations of existing models through innovative techniques, this study contributes to the enhancement of diagnostic processes in medical imaging, potentially leading to more accurate and timely disease interventions.

Wer Viewership and Queer Imag(in)ing: Thai Soap Opera Shadow of Love and Boys Love Media

Abstract This article brings film/media theory into Southeast Asian research through a revisionist queer approach. It contains two goals: addressing some recent developments about queer imag(in)ing in Thai media whilst reappraising the fundamental question of spectatorship via screen theory. Taking into account the more general issue of media specificity and the particular textual device of identity/gender-switch in several recent Thai television serials, we propose the notion of wer viewership: a mode of viewing practice that features viewer-text interaction through the perceptual-cognitive processes, and is characterised by wer/excessive aesthetics, multiple meanings, and diverse pleasures. Resonant with camp reading, wer viewership underlines how the viewer actively makes sense of the ambiguities about gender, particularly those along the extra-/diegetic interface. We use Thai soap opera Shadow of Love to illuminate the wer/excessive aesthetics rendered through its identity/gender play bordering on the extra-/diegetic divide, and the enhanced pleasures and meanings thus available to its extradiegetic active viewers. We stress, though, the expanded queer imag(in)ing in Shadow is not of total free interpretation, but is animated in relation to both the evolving discourses about gender/sexuality in Thailand, and the popularising homoerotic Boys Love (BL) media across Asia in recent years.

Fault diagnosis of rolling bearings under variable operating conditions based on improved graph neural networks

Abstract To address the issues of low diagnostic accuracy, insufficient generalization, and poor robustness in traditional fault diagnosis methods across different equipment and varying operating conditions, this paper proposes an improved graph neural network-based fault diagnosis method for rolling bearings to enhance model performance under complex conditions. First, the optimized wavelet transform coefficient features are used as nodes, and by exploring the correlations between features, node adjacency relationships are constructed. The associations between fault modes and feature node graphs under different conditions are studied, and a fault feature graph sample set based on subgraph structures is built, providing data for the subsequent graph neural network learning. Then, a multi-head attention mechanism (MHGAT) and multi-scale feature adaptive perception pooling (MSF-ASAP) are integrated to construct a multi-head graph attention mechanism model based on multi-scale feature adaptive perception pooling (MSM-GAT). MHGAT enhances the model's ability to perceive global information by learning different features from multiple perspectives and dimensions, thus improving the model's generalization. MSF-ASAP adaptively selects and aggregates information at multiple scales, enabling the model to effectively extract key features under various operating conditions, resist noise interference, and adapt to local information changes, thus improving the model's robustness under varying conditions and noisy environments. Experimental results under multiple and continuously varying conditions demonstrate that the proposed method outperforms traditional methods in terms of diagnostic accuracy and robustness. Notably, it exhibits excellent generalization when identifying unknown conditions, achieving over 95% accuracy in recognizing new conditions and maintaining over 92.5% accuracy in noisy environments.

Response to Ho Dah-an

Ho Dah-an’s 2016 review of our book Old Chinese: A New Reconstruction (2014) contains little discussion of the book’s main themes or proposals: he focuses instead on “errors” which, according to him, “reflect the outdated concepts of the authors and the insufficiency of their basic training.” In this response to Ho’s review, we consider his discussion of these “errors.” On two points (discussed below), he has actually found errors (a faulty interpretation of an entry in the Shuōwén jiězì and one incorrect citation), which we are glad to be able to correct. Neither materially affects our conclusions. The other “errors” that Ho mentions are not errors at all: in most cases they reflect basic misunderstandings on Ho’s part: of our book, of other sources, and of basic principles of historical linguistics. Since some of these misunderstandings relate to general issues in the reconstruction of Old Chinese, we attempt here to correct these misunderstandings in order to set the record straight.

A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.

Criteria for Assessing Exposure to Biomechanical Risk Factors: A Research-to-Practice Guide—Part 1: General Issues and Manual Material Handling

Musculoskeletal disorders are the most prevalent occupational health problem all over the world and are often related to biomechanical risk factors; to control these risk factors, several assessment methods (mostly observational) have been proposed in the past 40 years. An in-depth knowledge of each method to evaluate biomechanical risk factors is needed to effectively employ them in the field, together with a robust understanding of their effective predictive value and limitations. In Part 1, some general issues relevant to biomechanical risk assessment are discussed, and the method for assessing manual material handling after receiving more robust validation data is reviewed (Revised NIOSH Lifting Equation), together with a discussion about variability of tasks. Similarly, for the assessment of the biomechanical exposure of the upper limb, the TLV for Hand activity (ACGIH®) is presented in Part 2 of this guide, together with criteria to proportion risk assessment to the working duration in part-time jobs.

The Local Declines in Child SSI Applications and Awards During COVID

Child applications and awards for Supplemental Security Income (SSI) fell sharply at the outset of the COVID-19 pandemic. Cumulative applications from April to September 2020 were about 30% lower than applications over the same period in 2019 with substantial variation in rates of decline across local areas. In this article, we explore the factors correlated with the change in applications and awards across localities at the beginning of the pandemic. Our findings point to three particularly important factors influencing the magnitude of the change in applications and awards: (a) the restriction of in-person services in all Social Security Administration field offices in March 2020, (b) the pandemic’s disruptions to social and service networks through which people may learn about SSI, and (c) new economic stabilization policies, such as economic impact payments and supplemental unemployment insurance payments. These results highlight the substantial local variation in SSI participation. This variation is especially important in considering general issues around access to SSI, particularly for policies that aim to improve SSI access through outreach. Our findings underscore the importance of local networks in creating program awareness at local levels, and that they might be fruitful avenues for further outreach efforts.

Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization

Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting.Scientific contributionNeural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it.

HIGH-DIMENSIONAL FORECASTING WITH KNOWN KNOWNS AND KNOWN UNKNOWNS

Abstract Forecasts play a central role in decision-making under uncertainty. After a brief review of the general issues, this article considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and One Covariate at a time Multiple Testing, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches to forecasting. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1–2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.

Digital Transformation Analysis of Retail Enterprises: A Case Study of Tianhong

In the context of the digital economy, digital transformation is an inevitable trend for the future development of retail enterprises. Based on a case analysis of the digital transformation of Tianhong, this study presents a digital path for retail enterprises. Additionally, the study identified several general issues that retail enterprises encounter during digital transformation. These include a need for more clarity regarding the digital transformation strategy. This organizational structure needs to be aligned with the digital transformation, a shortage of digital talent, and the existence of short boards that oversee the digital transformation of enterprises. Based on this, and in conjunction with the case of the digital transformation of Tianhong, this study puts forward several recommendations for the digital transformation of retail enterprises. Firstly, enterprises should develop a clear digital transformation strategy. Secondly, they should speed up organizational change and reshape the corporate culture. Thirdly, they should strengthen the cultivation of composite talents. Fourthly, they should build a good team of digital talents. Finally, they should adhere to the nature of consumer-centric business.

Current issues of using metronidazole in clinical practice. Effects and directions of clinical application

Metronidazole is an important drug in clinical and outpatient practice due to its broad antibacterial and antiprotozoal properties. Metronidazole is effective against anaerobic bacteria and some protozoa such as Trichomonas vaginalis and Giardia lamblia. This makes it indispensable in the treatment of infections of various etiologies. The review article contains current information about the clinical pharmacology of metronidazole and its place in the pharmacotherapy of infectious diseases. General and specific issues of the use of metronidazole in the practice of GPs, surgeons, obstetrician-gynecologists, and other specialists are considered. Information is provided on the possibilities and indications for the use of metronidazole in the eradication of Helicobacter pylori, in radiation therapy for cancer patients, dermatovenereology, dentistry, treatment of protozoal infections, as well as in alcoholism. Metronidazole is often used in combination with other antibiotics to enhance the antimicrobial effect, especially in cases of mixed infections or when it is necessary to cover a wider range of pathogens. In surgical practice, metronidazole is used to prevent infectious complications after operations on the abdominal and pelvic organs, where the risk of anaerobic infection is high. In an environment of increasing antibiotic resistance, metronidazole remains an effective treatment against many anaerobic pathogens, highlighting its place in modern practice.

Health information, what do people search and where? a cross-sectional online survey study in the adult Swiss population.

Health promotion and disease prevention are crucial for improving public health and alleviating the burden of illness in the population. This study aimed to investigate, the sources of health information most used and trusted, and the health topics most searched, by means of a nationwide cross-sectional online survey of a representative sample of the adult Swiss general population. Overall, complete surveys of 2020 participants were analysed (mean age 47 years old, 51% male, and matching the Swiss population regarding age, sex, and language-speaking region). Sources' use were calculated with descriptive statistics per sex and age groups. The most frequently used sources were general practitioners (min: 46% to max: 73%), government websites (40% to 55%), family and friends (37% to 63%), pharmacy (33% to 46%), and television (21% to 57%). The most trusted sources were specialised physicians (94% to 98%) general practitioners (90% to 96%), and pharmacies (81% to 89%). Based on multivariable controlled regression, age (per five years increase) was associated with increased odds of using television (Odds Ratio (OR): 1.19, 95% Confidence Interval (CI): 1.12 to 1.21), print media (OR: 1.15, 95% CI: 1.10 to 1.19), radio (OR: 1.15, 95%CI: 1.10 to 1.20), and the general practitioner (OR: 1.11, 95% CI: 1.07 to 1.15), and decreased odds of using news websites (OR: 0.94, 95% CI:0.9 to 0.98), family and friends (OR: 0.93, 95% CI: 0.9 to 0.98), foreign authorities websites (OR: 0.91, 95%CI: 0.86 to 0.97), and social media (OR: 0.88, 95% CI: 0.84 to 0.92). Women were more likely to seek health information in the pharmacy (OR: 1.39, 95% CI: 1.15 to 1.68), specialised physicians (OR: 1.39, 95% CI:1.13 to 1.72), television (OR: 1.41, 95% CI: 1.16 to 1.72), and books (OR: 1.9, 95% CI: 1.44 to 2.5). The most searched health topics, based on the International Classification of Primary Care, 2nd edition (ICPC2), were general and unspecified symptoms (20.9%) and musculoskeletal issues (19.4%). The use of these findings by policymakers and health care providers could potentially enhance the effectiveness of health-related education strategies, by aligning communication efforts with the populations' preferences and content needs, and allocating resources where they are most commonly accessed and trusted, namely in the general practice.

Feasibility of loop-gain tuning for general measurement systems inspired by quantum locking for DECIGO

Abstract A series of quantum locking theories have been proposed to enhance the quantum-noise-limited target sensitivity of the DECi-hertz Interferometer Gravitational wave Observatory. The quantum locking that uses a square completion optimizes the sensitivity across all frequencies. However, a substantial amount of data-series must be post-processed since the square completion is a form of signal processing technique. This paper approaches the optimal sensitivity across all frequencies from an alternative perspective: by optimizing the frequency dependence of a servo gain in a feedback loop. The optimal servo gain is formulated by comparing the alternative method with the square completion method for the same optical setup. This will be shown in general noise issues extending the framework of the quantum locking. We find that the optimal servo gain forms a non-feasible filter but has certain characteristics. We also find that the noise of the measurement signal deteriorates proportionally to the noise measured in the feedback loop when the servo gain is slightly imperfect.

Reclaiming Desire and Defying Conventions: An Analysis of Female Sexuality in Mrs Dalloway and the God of Small Things

The paper offers a detailed analysis of the complexities of female sexuality in Mrs Dalloway and The God of Small Things from a feminist perspective. It investigates how female protagonists overcome and defy societal norms and expectations concerning their sexual desires. The obstacles of conventional sexual norms align with the ‘repressive hypothesis’, coined by Michele Foucault in The History of Sexuality which posits that the modern discourse on sexuality is characterized by repression. This theory is used to depict how societal repression shapes and is shaped by the narratives of Clarissa and Ammu. The paper is qualitative and uses close textual analysis as an analytical tool. For the study, primary data were collected from textbooks, and secondary sources including various books, websites, articles, and journals were used to gather secondary data. The clear finding is that Clarissa has a sexual desire for Sally which is not accepted by post world war British society and Ammu, being a divorcee and upper-class woman, reclaims her sexual agency and attempts to fulfill her sexual desire by making a romantic relationship with Velutha which is prohibited in the post-colonial Indian society. Through the blending of sexual desires with more general societal issues like race, class, and cultural identities, both novels deconstructed conventional depictions of female sexuality. Thus, the study contributes to the ongoing conversation on the role of women’s sexual desires and their place in a postmodern world where autonomy, self-expression, and the deconstruction of conventional norms are of paramount importance.

Mental Health Literacy Reduces Mental Disorder Stigma in Society

The general background mental health issues remain a major global health challenge, including in Indonesia, where mental illness is often stigmatized. Specific background mental illness stigma is prevalent in society, manifesting in negative attitudes towards individuals with mental disorders, thus affecting their acceptance and treatment. Knowledge gap there is limited research exploring the relationship between mental health literacy and mental illness stigma within community settings in Indonesia. This study aims to investigate the correlation between mental health literacy and mental disorder stigma among residents of a selected community. Results the findings reveal a significant negative correlation between mental health literacy and mental disorder stigma, indicating that higher levels of mental health literacy are associated with lower levels of stigma. Novelty this study contributes to the limited body of knowledge by demonstrating that improving mental health literacy can effectively reduce mental disorder stigma in community settings. Implications these results suggest that public health interventions focusing on mental health education may help reduce stigmatization, fostering a more supportive environment for individuals with mental health disorders, which may ultimately lead to better access to mental health services and outcomes. Highlights: Higher mental health literacy lowers mental disorder stigma. Stigma reduction improves mental health support in communities. Public health education can reduce mental disorder misconceptions. Keywords: Mental Health, Mental Disorder, Stigma, Community Attitudes, Education

The National Risk Register 2023: some reasoned reflections

ABSTRACT We provide a constructive response to the recent National Risk Register 2023, highlighting a number of generic issues. These primarily include reflections on the deployed methodology, and its possible biases. We suggest that the National Risk Register should be extended to adopt a whole systems perspective, reasoning that it is unlikely that the acute risks will each occur in an isolated way, and additionally there may be (non-risk) precursor contexts and events which might amplify many acute risks and/or nullify planned responses. We discuss some potential causes of a lack of national resilience; and some possible generic failures in acute risk response. We make a number of recommendations for consideration by HM Government.

Parental involvement in the inclusive transition to school: preschool teachers’ transition practices in a German context

ABSTRACT Parental involvement (PI) in the transition from preschool to primary school is an important prerequisite for children with disabilities to start school successfully. Therefore, the present study looks at PI practices that are conducted to support successful inclusive transition processes in Germany. The focus is on practices that take place in the institutional context of early childhood facilities. The data base consists of qualitative guideline-based interviews with n = 22 preschool teachers. The data analysis comprises two steps: 1) a qualitative content analysis and 2) a systematic comparison of teachers and their transition practices according to specific teacher characteristics, such as professional experience. The results show that communication formats between teachers and parents regarding a child’s development or general issues such as the course of the last preschool year are most widely established to involve parents in the transition process. Some teachers also claim to inform parents about additional support for their child and involve parents in preschool or school visits. Moreover, findings indicate that parental involvement hardly differs systematically between teachers with different backgrounds (e.g., different levels of professional experience).

PAOFCDN: A novel method for predictive analysis of solar irradiance

Solar photovoltaic power is the most feasible Renewable Energy Source (RES) for Pakistan, due to ample sunlight availability throughout the year. Since solar photovoltaic power is primarily dependent on solar irradiance, forecasting of solar irradiance is essential for reliable, secure and effective incorporation of solar photovoltaic power in power systems. Considering the importance of solar irradiance forecasting, in this study, predictive analysis of Islamabad’s solar irradiance is performed by using a novel proposed model named as Pelican Algorithm-based Optimized Fully-Connected Deep Network (PAOFCDN). The initial weights of Fully-Connected Deep Network (FCDN) are optimized through an effective optimization technique known as Pelican optimization. The accuracy of the optimized network PAOFCDN is enhanced many fold as compared to the FCDN network trained with randomly initialized weights. The inherent issue of poor generalization in FCDN is also resolved by optimization. The superior performance of PAOFCDN is evident from its comparative evaluation with existing benchmark methods, i.e., Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Least Square Boosting (LSBoost) and standard FCDN. PAOFCDN achieves the least Normalized Root Mean Square Error (NRMSE) of 0.0503 as compared to 0.1179 of LSTM, 0.1256 of FCDN, and 0.2992 of SVR and LSBoost. The proposed model is applied to three real-world solar irradiance datasets having different resolutions of 10-minutes, hourly and daily. This study took the initiative of performing predictions on three datsets having multiple resolutions in perspective of south asia.

A speech separation model improved based on Conv-TasNet network

Abstract In the field of single-channel speech separation, the extraction and separation of features from mixed audio have always been the focus and difficulty of research. Currently, mainstream methods mainly suffer from poor generalization ability and issues such as inadequate feature extraction, which leads to the models’ inferior separation capability. This paper proposes an improved DConv-TasNet network model, focusing on the optimization of the encoder/decoder modules and separation modules and utilizing deep dilated encoders/decoders to extract features from mixed speech signals. It enhances feature extraction capability and generalization compared to conventional encoders/decoders. In terms of the separation module, improvements were made to the convolutional blocks within the module by enhancing feature extraction in the channel dimension, leading to improved performance of the separation network. Validation of the model’s performance was conducted using the WSJ0-Mix2 dataset, demonstrating superior performance compared to the Conv-TasNet network.