This technical brief considers the distributed consensus problems for multi-agent systems with general linear and Lipschitz nonlinear dynamics. Distributed relative-state consensus protocols with an adaptive law for adjusting the coupling weights between neighboring agents are designed for both the linear and nonlinear cases, under which consensus is reached for all undirected connected communication graphs. Extensions to the case with a leader-follower communication graph are further studied. In contrast to the existing results in the literature, the adaptive consensus protocols here can be implemented by each agent in a fully distributed fashion without using any global information.
Background In recent years, skin health has garnered widespread attention, with hyperpigmentation disorders caused by excessive melanin deposition emerging as a particularly prominent concern. Tyrosinase, as the rate-limiting enzyme in the melanin synthesis process, has long been a major focus in the development of its inhibitors. However, only a limited number of tyrosinase inhibitors are currently available for clinical treatment of such disorders, and they are associated with certain toxicity concerns. Consequently, there is an urgent need to develop novel inhibitors that combine high efficacy with low toxicity. Recent studies have shown that deep learning technology exhibits strong capabilities in uncovering the intrinsic patterns of data and predicting the biological activities of compounds, providing a significant opportunity for the rapid screening of novel tyrosinase inhibitors. Methods Based on a dataset of tyrosinase-related compounds, this study constructed a deep learning model to predict compounds that inhibit tyrosinase activity. Using this model, we conducted activity predictions for 36,585 compound and selected the top 100 molecules with the highest prediction scores for screening and verification. Results Literature comparison revealed that 53 of these molecules had been reported to inhibit tyrosinase activity, providing initial support for the model’s reliability. After further screening based on specific criteria, 10 candidate molecules were ultimately selected for molecular docking studies. The docking results indicated that these molecules had good binding potential with the target protein, indirectly supporting the accuracy of the model’s prediction. The final experimental verification revealed that compounds 5 and 10 significantly inhibited tyrosinase activity and reduced melanin content.
Optical lattice clocks (OLCs) enable us to measure time and frequency with a fractional uncertainty at $10^{-18}$ level, which is 2 orders of magnitude better than Cs clocks. In this article, after briefly reviewing OLCs and the history of testing the fundamental principles of general relativity, we report our experiments of measuring the gravitational redshift between RIKEN and The University of Tokyo, and at Tokyo Skytree using transportable OLCs. We also discuss a couple of future applications of OLCs, such as detecting gravitational waves in space and relativistic geodesy. The possibility of testing second-order parametrized post-Newtonian potential around the Earth is also mentioned.
Jay Lodhia, Mujaheed Suleman, Vanessa Poppe
et al.
Abstract Background Mucoepidermoid carcinoma of the skin is an exceedingly rare neoplasm, with only a limited number of cases documented in the literature. Typically, mucoepidermoid carcinoma arises in the salivary glands, where it accounts for 30% of all malignant salivary gland tumors and generally presents as a low-grade malignancy with a low probability of metastasis. Case presentation We report a case of a 49-year-old female patient of African origin who presented to our tertiary center with a longstanding scalp lesion that had been present for approximately 10 years. Initially, the lesion was roughly the size of a tennis ball and remained stable until about 3 months prior to presentation, when it was excised at a peripheral primary health center. Unfortunately, no histological analysis or follow-up was conducted after the excision, and the lesion recurred, progressively increasing in size. The patient reported no cardinal B-symptoms or central nervous system symptoms at any point. Histopathological analysis of the recurrent mass confirmed a diagnosis of mucoepidermoid carcinoma, and a complete surgical excision was subsequently performed. Following surgery, the patient was referred to the national oncology center for radiotherapy as part of her comprehensive oncological management. She is currently 8 months post-excision, receiving radiotherapy, and exhibits no clinical signs of recurrence. Conclusion Mucoepidermoid carcinoma originating in cutaneous sites is particularly unusual and may stem from the embryonic development of ectopic salivary glands within the skin. In this case report, we describe a unique presentation of primary mucoepidermoid carcinoma on the scalp, which demonstrated aggressive characteristics with suspected metastasis to the vertebral body. This case highlights the importance of considering mucoepidermoid carcinoma in the differential diagnosis of scalp masses and underscores the need for further investigation into its pathogenesis and metastatic potential.
Geospatial technologies are rapidly emerging as pivotal tools for advancing sustainable urban and rural development through citizen empowerment in India and worldwide. This study systematically reviews peer-reviewed and grey literature to examine their integration with global frameworks, such as the SDGs, Paris Agreement, and Sendai Framework, while aligning with Indian initiatives like NAPCC, Smart Cities, Digital India, SVAMITVA, AMRUT, and the National Geospatial Policy 2022, with emphasis on the citizen as a crucial feedback factor. Employing thematic mapping and comparative analysis between the Global North and South, we evaluate applications in urban planning, mobility, energy, resilience, and health, highlighting platforms like PPGIS, VGI, Bhuvan, and 'Know Your DIGIPIN' for participatory data collection and decision-making.</p>
<p>Our analysis reveals regional disparities in India, with the southern zone leading in innovation (35% adoption) and the eastern region focussing on disaster management (15%), along with global successes in disaster relief, welfare targeting, and immunisation tracking. Quantitative impacts include India's geospatial market growth to ₹63,000 crores by 2025 and AMRUT 2.0's rapid water and sewerage coverage expansion in many major cities. However, persistent challenges include technical knowledge gaps in academia, insufficient institutional support for geospatial startups, and barriers like low digital literacy and language limitations that restrict broader participation.</p>
<p>We recommend enhanced geospatial education, open data policies, vernacular interfaces, and inclusive citizen science frameworks to bridge these gaps, foster equitable participation, and realise geospatial intelligence's full potential for resilient, data-driven sustainability.
This is not a study, but a research note on academic writing practices in our field, whose purpose it is to serve as a foundation for discussion. It provides a brief introduction into researcher reflexivity, my own positioning towards the topic, and a numerical thematic overview of authorial presence (pronouns, third-person terms, and their semantic functions) in data-based research articles published in the Scenario journal over the last ten years. I do not draw conclusions, but from the angle of researcher reflexivity, I submit questions with respect to clarity of premises and ethics, for possible consideration by future authors in our field.
In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.
Text generation has become more accessible than ever, and the increasing interest in these systems, especially those using large language models, has spurred an increasing number of related publications. We provide a systematic literature review comprising 244 selected papers between 2017 and 2024. This review categorizes works in text generation into five main tasks: open-ended text generation, summarization, translation, paraphrasing, and question answering. For each task, we review their relevant characteristics, sub-tasks, and specific challenges (e.g., missing datasets for multi-document summarization, coherence in story generation, and complex reasoning for question answering). Additionally, we assess current approaches for evaluating text generation systems and ascertain problems with current metrics. Our investigation shows nine prominent challenges common to all tasks and sub-tasks in recent text generation publications: bias, reasoning, hallucinations, misuse, privacy, interpretability, transparency, datasets, and computing. We provide a detailed analysis of these challenges, their potential solutions, and which gaps still require further engagement from the community. This systematic literature review targets two main audiences: early career researchers in natural language processing looking for an overview of the field and promising research directions, as well as experienced researchers seeking a detailed view of tasks, evaluation methodologies, open challenges, and recent mitigation strategies.
Kanankira A Nnko, Raphael T Pima, Calvin J Baraka
et al.
Verrucous carcinoma is a well-recognized low-grade variant of squamous cell carcinoma. Cutaneous, oral, and anogenital forms exist. Exposure to persistent chronic irritation, inflammation, and repeated injury, as well as carcinogenic agents such as human papillomavirus infection, smoking, and alcohol use, are established risk factors. These neoplasms occur mostly in the oral cavity. The usual extraoral sites include the larynx, esophagus, genitals, and perineum. It is an extremely uncommon site of occurrence for the extraoral chin region. This unusual location makes the index case unique. Other uncommon sites reported include finger and foot. Case studies of verrucous carcinoma with huge tumor sizes are rare. Although it can be destructive locally, verrucous carcinoma typically does not spread to distant sites. Wide surgical excision with free margins is the most common treatment approach with a favorable prognosis. These tumors are likely to recur if they are incompletely excised, and recurred lesions tend to be more aggressive clinically as compared to their original counterparts. Herein, the authors describe a case of a huge oral verrucous carcinoma localized on the chin of a 43-year-old female patient. The clinical course, diagnostics, and proposed treatment have been discussed with the existing available literature.
The aim of the study is to generalize the available interdisciplinary data on the formation of graphomotor skills in older preschoolers and use them as a basis for describing the process of forming graphomotor skills in left-handed children. The essence and structure of graphomotor skills as a component of written activity is considered. In the structure of writing, graphomotor skills represent the final link in writing activity and are associated with the drawing of graphic symbols. Based on neuropsychological data on interhemispheric asymmetry in children, the necessity of developing a graphomotor skill in older preschoolers is substantiated, taking into account the dominant profile of lateralization. We consider the provisions of the theory of the level organization of movements by N.A. Bernstein as a support in the development of a graphomotor skill. Noting the level nature of the organization of written activity, attention is focused on the role of gross motor skills, posture retention and other components of background levels, without which the very implementation of the graphic skill is difficult. A scheme is proposed that reflects the indicators of the formation of graphomotor skills in older preschoolers. The following components have the leading importance in it: general motor readiness, visual-object gnosis and visual-motor coordination, elementary graphic skill. The features of pedagogical work in the formation of graphomotor skills in left-handed children are described. The novelty of the study lies in modeling the system of work on the formation of this skill of written activity based on the theory of the level organization of movements by N.A. Bernstein. The proposed system model can be used to form graphic-motor skills in left-handed children, which reflects its practical significance.
This study investigates news audiences’ platform preferences, usage patterns, and factors affecting their mobile news consumption through news apps. Four explanatory factors, news app users’ demographics, news media usage, perceptions, and motivations, are proposed to predict adoption intention. By surveying 698 mobile news app users in the US, this study’s findings indicate that user perceptions of news apps (i.e., perceived ease of use, compatibility, relative content advantage, and observability) and instrumental motivations of news consumption (i.e., information-seeking and opinion needs) best predict news consumers’ willingness to continue using mobile news apps. Theoretical and practical implications are discussed to offer new insights into mobile news audience behavior and inform current digital publishers on cross-media strategies in the highly competitive mobile news market.
Journalism. The periodical press, etc., Communication. Mass media
Nour Neifar, Afef Mdhaffar, Achraf Ben-Hamadou
et al.
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG) and electromyogram (EMG). Compared to the existing review papers, we present the first review that summarizes the recent state-of-the-art deep generative models. By analyzing the state-of-the-art research related to deep generative models along with their main applications and challenges, this review contributes to the overall understanding of these models applied to physiological signals. Additionally, by highlighting the employed evaluation protocol and the most used physiological databases, this review facilitates the assessment and benchmarking of deep generative models.
The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin, and charge of the black hole, gives rise to a variety of phenomena in the motion of particles and photons outside the horizons that have no Newtonian counterparts. Moreover, the Kerr-Newman spacetime remains remarkably resilient to many attempts in modifying the underlying theory of gravity. The monitoring of stellar orbits around supermassive black holes, the detection of gravitational waves from the coalescence of stellar-mass black holes, and the observation of black-hole shadows in images with horizon-scale resolution, all of which have become possible during the last decade, are offering valuable tools in testing quantitatively the predictions of this remarkable solution to Einstein's equations.