Search Results (9,281)

Traditional journalists now utilize social media platforms such as Facebook, Instagram, Twitter, and TikTok to disseminate information. With the emergence of TikTok as a prominent social network for entertainment and information, many journalists worldwide, including in Israel, have begun leveraging it to create and share short video content. This study presents a qualitative case study of journalists operating within the Israeli media system, examining why and how journalists use TikTok, the professional challenges they face on the platform, and how they address these challenges. Specifically, it focuses on how journalists perceive TikTok as a journalistic space and their professional role within it. Focusing on the Israeli context, which is both digitally advanced and characterized by a democratic and pluralistic media environment, semi-structured in-depth interviews were conducted with 15 prominent journalists from traditional Israeli media outlets who are extensively active and considered at least micro-influencers on TikTok. The findings reveal several key themes regarding journalists’ use of TikTok. These include the platform’s role as a tool for reaching younger audiences and maintaining relevance; and the journalists’ self-perception as gatekeepers combating fake news. However it was found that they face ethical dilemmas and an absence of the structural and ethical foundations necessary for serious investigative journalism. This is the result of adapting their work to the platform’s light, fast-paced, and visually engaging format, favoring content that is entertaining and often sensational, to meet the expectations of TikTok audiences. While grounded in the Israeli case, the findings contribute to broader discussions on the platformization of journalism and the transformation of professional norms in media environments. Full article

Understanding the spatial organization of environmental sounds is essential for linking acoustic patterns with landscape structure and ecological processes. While ecoacoustics has made substantial progress in the temporal and spectral analysis of soundscapes, their directional and spatial components remain comparatively underexplored, particularly through low-cost and scalable approaches. Here we introduce the Sonic Explorer, a lightweight rotational sonic device designed to explore the angular structure and the spatial dynamics of sonotopes, defined as homogeneous spatial sonic units within a soundscape. The system is based on two opposed supercardioid microphones mounted on a rotating platform, coupled with a custom signal-processing framework that analyzes directional variations in sound intensity across frequency classes. Rather than aiming at sound pressure level measurements or full-sphere sound field reconstruction, the Sonic Explorer focuses on detecting spatial contrasts, dominant sound directions, and angular sound patterns relevant to ecological interpretation. Field tests conducted in a human-modified environment demonstrate the ability of the device to identify coherent directional acoustic structures associated with landscape configuration and dominant sound sources. The proposed approach provides a new practical and exploratory tool for landscape and soundscape research, enabling spatially explicit interpretations of sonic environments while maintaining low cost, portability, and adaptability. Full article

The platformization of urban space is opening new frontiers of capital accumulation, particularly through short-term rentals. Airbnb plays a central role in this process by commodifying housing in tourist destinations. Despite its rapid growth, research on Airbnb in Latin America—especially in small tourist cities—remains limited and largely focused on metropolitan contexts. This article addresses this gap with the objective of analyzing how platform-mediated short-term rentals reorient housing markets beyond traditional urban cores. It is hypothesized that Airbnb expands housing commodification by extending tourism-oriented uses into new residential areas and by redistributing returns unevenly across actors. Using a quantitative and geospatial approach, the results reveal a strong presence of Airbnb in rural and natural areas, from which the highest returns are extracted, as well as a high concentration of accommodation supply among professional hosts. These dynamics reconfigure housing use toward asset-based logics, posing challenges for housing security and social and territorial sustainability in small tourist cities. Full article

Spam consists of unsolicited messages, and the posting of such irrelevant messages often presents significant challenges in technical forums. Two particular challenges are the dynamic nature of spamming tactics and the inadequacy of adaptable spam databases for automated classifiers. Our work addresses the need for a robust spam classification solution that can be seamlessly integrated with database, SQL, and APEX applications. We developed a labeled spam database by asking experts to categorize 1916 posts as spam or regular posts to ensure accurate classification and then created an SVM-based spam classification model that achieves an average validation accuracy of 90%. Our research enhances the current understanding of spam in technical forums and represents a solution for embedding spam classifiers into widely used platforms with an accuracy of 98.1%. Furthermore, we explore the incorporation of generative topics into our approach by integrating generative topic modeling techniques, such as latent Dirichlet allocation. In our work, the spam classifier is dynamically updated to account for emerging spam patterns and topics based on a generative approach that improves the robustness of the classifier against new spamming tactics and enables nuanced, context-aware filtering of messages. In addition, our experiments highlight the potential of text SVM classifiers for real-time applications through the fine-tuning of text features. Full article

Digital transformation has profoundly reshaped caregiving practices, yet its influence on family cohesion within disability contexts remains underexplored, particularly in Arab societies. This qualitative phenomenological study examines how digital technologies shape family cohesion among Jordanian caregivers of children with disabilities. In-depth, semi-structured interviews were conducted with 22 primary caregivers, and data were analyzed using reflexive thematic analysis. The findings reveal a central tension of being “between connectivity and care,” articulated through four interrelated themes: (1) a digital double-bind in which online support networks function as a vital “virtual village” while simultaneously contributing to intra-familial fragmentation; (2) the reconfiguration of care labor, whereby digital management emerges as an invisible and gendered form of caregiving work, often positioning mothers as primary digital coordinators; (3) the translation of traditional social capital (wasta) into digital spaces to navigate systemic resource constraints, producing new moral and emotional burdens; and (4) the strategic use of digital platforms to preserve cultural, religious, and familial identity in the face of stigma, thereby reinforcing internal cohesion. These findings suggest that digital technologies do not merely facilitate connection but actively reconfigure family dynamics through ongoing negotiation between support and strain. The study underscores the need for family-centered digital inclusion policies and support interventions that mitigate digital burdens while harnessing technology’s potential to strengthen culturally grounded resilience among families of children with disabilities. Full article

Exploring public awareness, participation, and emotional inclination toward intangible cultural heritage (ICH) clarifies public attitudes and demands toward traditional culture, providing a crucial basis for targeted ICH protection and inheritance. Based on ICH text big data collected from China’s mainstream social media platform Weibo, this study improves the TF-IDF algorithm, integrates LDA topic analysis for semantic feature mining, and trains a new sentiment analysis model to explore public emotional attitudes and their formation mechanisms. The study is geographically limited to China and covers the entire year of 2023. The results show that: (1) Public ICH perception is multi-dimensional, with close attention to crafts like paper-cutting and traditional Chinese medicine; action-oriented terms reflect dynamic inheritance demands. Public discussions focus on three dimensions: ICH inheritance and development (39%), introduction and promotion (45%), and public experience and participation (16%), with the latter accounting for a low proportion. (2) Public sentiment toward ICH is predominantly positive, with all regions scoring above 0.730 (full score = 1), and Zhejiang (0.751) and Jiangsu (0.750) ranking significantly higher. (3) Spatial econometric analysis reveals marked regional differences in ICH sentiment distribution, mainly affected by three key factors—the number of ICH projects, the number of inheritors, and regional GDP—with regression coefficients of 0.699, 0.632, and 0.458 (p < 0.01). This finding provides a basis for formulating targeted ICH protection strategies. Full article

Background/Objectives: Leishmaniasis is a group of diseases caused by obligate intracellular parasites of the Leishmania genus and is classified by the World Health Organization as a category I neglected tropical disease. Leishmania infantum predominantly affects children under five years of age and shows an increasing incidence of cutaneous and visceral forms. The development of new therapeutic alternatives remains challenging, making in silico approaches valuable for accelerating antileishmanial drug discovery. This study aimed to identify new compounds with potential activity against Leishmania infantum amastigotes using artificial intelligence-based classification models. Methods: A curated database of compounds with reported biological activity was constructed. Molecular representation employed zero- to two-dimensional descriptors calculated with Dragon software (v 7.0.10). Unsupervised k-means cluster analysis was applied to define training and external prediction sets. Supervised models were developed on the WEKA platform using IBk, J48, multilayer perceptron, and sequential minimal optimization algorithms. Model performance was assessed through internal cross-validation and external validation procedures. Results: All models achieved classification accuracies above eighty percent for both training and prediction sets, indicating consistent predictive performance and good generalization ability. The validated models were applied to virtual screening of the DrugBank database and a collection of synthetic compounds. This screening campaign enabled the identification of one hundred twenty compounds with potential activity against the amastigote form of Leishmania infantum. Conclusions: Artificial intelligence-based QSAR models proved to be useful tools for prioritizing antileishmanial candidates. The integration of molecular descriptors, machine learning, and virtual screening offers an efficient strategy for drug discovery. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

Backgrounds and Aim: Pancreatic cancer is frequently diagnosed at advanced stages, highlighting the need for biomarkers that are capable of detecting early-stage disease in asymptomatic individuals. Recently, apolipoprotein A2 isoforms (ApoA2-ATQ/AT) have been reported as a new blood biomarker for pancreatic cancer. We recently developed diagnostic models based on 100 highly expressed serum microRNAs (miRNAs) combined with CA19-9; these models achieved high accuracy in terms of distinguishing individuals with pancreatic cancer from healthy individuals. This study aimed to compare the diagnostic performance of these miRNA-based models with that of the ApoA2-ATQ/AT biomarker. Methods: Comprehensive sequencing of serum miRNAs was conducted using samples from 120 pancreatic cancer patients recruited across 14 hospitals, along with 93 healthy controls without cancer. Serum CA19-9 levels, miRNA index values, miRNA+CA19-9 index values, and ApoA2 index values were assessed. miRNA-based indices were derived from classification models built on an automated machine-learning platform. Results: The miRNA model (AUC 0.94; 95% CI 0.91–0.97) and the miRNA+CA19-9 model (AUC 0.99; 95% CI 0.98–1.00) outperformed ApoA2 (AUC 0.89; 95% CI 0.84–0.93) in terms of distinguishing individuals with pancreatic cancer from healthy controls across all stages. In early-stage disease (stages 0–I and 0–II), both miRNA-based models also demonstrated superior performance. Strong negative correlations were observed between the ApoA2 index and both the miRNA model index (r = −0.62) and the miRNA+CA19-9 index (r = −0.63). Conclusions: These findings suggest that miRNA-based diagnostic models, particularly when combined with CA19-9, could serve as powerful tools for the early detection of pancreatic cancer. Full article

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

Background/Objectives: Poly(ADP-ribose) polymerase 1 (PARP1) is an important therapeutic target in DNA repair-deficient cancers, but discovery of new inhibitors remains constrained by scaffold convergence, tolerability limits, and acquired resistance. This study aimed to develop an interpretable, reliability-stratified cheminformatics workflow for PARP1 potency prioritization and structure-based follow-up. Methods: A curated ChEMBL dataset of 3339 PARP1 inhibitors was encoded using RDKit 2D descriptors and Avalon fingerprints (1143 initial features), then reduced to 132 informative variables by Random Forest-based feature selection. Five regression models were optimized, including a stacked ensemble. Model interpretation was performed using permutation feature importance and SHAP. External near-domain corroboration was assessed using a stringent PubChem similarity expansion (Tanimoto > 0.90) around sub-10 nM seed compounds, followed by comparison with retrievable experimental PARP1 activity values. Top scaffold-diverse candidates were further evaluated by complementary docking against PARP1 (PDB: 4R6E) using AutoDock Vina and cavity-guided docking through the SwissDock platform. Results: The stacked ensemble achieved the best held-out performance (test R² = 0.723; RMSE = 0.610 pIC₅₀ units), with 83.7% of test predictions within ≤0.75 pIC₅₀ units and only 2.7% exceeding 1.5 pIC₅₀ units. PubChem similarity expansion retrieved approximately 32,450 analogs, of which 3349 were predicted to have IC₅₀ ≤ 10 nM. Among 366 compounds with retrievable experimental PARP1 activity values, predicted versus experimental pIC₅₀ showed a positive association (R² = 0.124; Pearson r = 0.479), with RMSE = 0.491 and MAE = 0.330 pIC₅₀ units. Three ligands—CID 168873053, CID 175154210, and CID 172894737—showed the strongest complementary docking support and pocket-consistent poses relative to niraparib. Conclusions: This workflow provides a transparent and practically useful framework for near-domain PARP1 inhibitor prioritization. The combined QSAR, explainability, external corroboration, and docking strategy supports shortlist generation for experimental follow-up. Full article

Traditional Chinese paper cutting represents an important intangible cultural heritage. Can artificial intelligence (AI) reactivate the heritage in a new style? The aim of this study was to use AI to reactivate temple arts and paintings by converting them into the style of traditional Chinese paper cuttings. Thirty sets of old images taken 18 years ago and 10 images of ancient paintings from the National Palace Museum were restyled in Nano Banana (Pro)^®. Related design elements included integrated isolated parts, visual depth, details, and solid and void alternation. Three-dimensional stone and wood sculptures were reconstructed using Rodin^® or Meshy^® and converted into AR models in Sketchfab^®. From the generated 2D images and their 3D representations, a reactivated style of Chinese paper cutting was developed that can be interacted with in the AR smartphone platform or RP in the physical world. Approximately 370 images were regenerated, and 167 versions of models were reconstructed. AI should be considered part of culture. Rethinking traditional folk art highlights demand for the cross-reference and cross-reactivation of heterogeneous art forms. This AI model interprets novel 3D structural and visual details and creates a unique 2D and 3D identity for each subject. Full article

This article investigates whether increasing economic status through adopting digital payment capabilities in Delhi fosters economic and financial inclusion among urban women street vendors in Mahila Haat. Digital freedom is a new step forward in technology for everyone. Still, a woman not only balances the social responsibilities of childbearing, caring for her children and family, and struggling with economic issues, health issues, and undernourishment, but can also balance the household job of street vending to increase self-esteem and financial independence. This research work conducted a sampling survey and applied the Kruskal–Wallis H-test with a p-value (0.05) significance level by evaluating 11 variables to investigate the relationship between the digital capabilities and economic independence of street vendors in Mahila Haat (a women’s market where the vendors are all women) in the Red Fort area of New Delhi. UPI systems were created using measurements based on a five-point Likert scale to analyze different levels of satisfaction in clusters of digital capabilities on digital platforms. Further, the ordinary least squares (OLS) method was used to estimate quality of life and social happiness in the context of digital empowerment. Digital payment systems positively influence women’s empowerment. Women vendors can adopt digital payment methods, making them economically independent. The positive relationship between women vendors and customer satisfaction before UPI use and after UPI use is also analyzed. This research will be helpful for both government and non-government organizations to provide financial assistance, informational awareness, skill development training, and advocacy for gender equality to increase women’s empowerment. Full article

Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems, especially in multilingual contexts. This paper introduces a reliability-aware transformer-based framework for analyzing sentiment in agricultural market intelligence across multiple languages. The framework leverages weakly supervised multilingual transformers to extract sentiment signals from large-scale unlabeled Thai and English texts about major agricultural commodities found online. To enhance robustness under weak supervision, the framework incorporates reliability-aware mechanisms, including confidence-based pseudo-label filtering, cross-source consistency refinement, and expert-guided calibration to reduce noise and account for bias between different data sources. Sentiment predictions are further aligned with market intelligence objectives through reliability-weighted aggregation, yielding interpretable sentiment indices that enable cross-lingual and cross-source comparability. We tested the framework extensively using a multilingual agricultural corpus derived from social media and news coverage of agriculture. The results show consistent improvements over both classical machine learning approaches and standard multilingual transformer baselines. Additional ablation studies and sensitivity analyses confirmed that reliability-aware mechanisms, particularly confidence thresholding, play a crucial role in getting the right balance between label quality and data coverage. Overall, the results indicate that reliability-aware multilingual sentiment analytics provide robust and actionable insights for agricultural market monitoring and policy analysis. Full article

Digital signal processing (DSP) methods and artificial intelligence (AI) serve as a unifying platform across diverse research areas and educational courses based on analysis of signals acquired by appropriate sensors and their time-synchronized systems. Autonomous sensor systems having their own batteries, memories, and possibilities of wireless communication form the core of modern technological systems. The interconnection of sensors for data acquisition, methods for advanced analysis of signal features, and collaborative evaluation promotes both theoretical learning and practical problem solving in professional practice. This paper emphasizes a common mathematical foundation for the processing of data acquired by different sensor systems, and it presents the integration of DSP and AI, enabling the use of similar theoretical methods in different applications, including robotics, digital twins, neurology, augmented reality, and energy optimization. Through selected case studies, it shows how a combination of sensor technology for data acquisition and the use of similar computational methods, visualization, and real-world case studies strengthens interdisciplinary collaboration. Findings of this paper demonstrate how integrating AI with DSP supports innovative research and teaching strategies, redefines the field’s educational role in the digital era, and points to the development of new digital technologies. Full article