Search Results (669)

This paper explores the rise in voice-based social media as a pivotal transformation in digital communication, situated within the broader era of chatbots and voice AI. Platforms such as Clubhouse, X Spaces, Discord and similar ones foreground vocal interaction, reshaping norms of participation, identity construction, and platform governance. This shift from text-centered communication to hybrid digital orality presents new sociological and methodological challenges, calling for the development of voice-centered analytical approaches. In response, the paper introduces a multidimensional methodological framework for analyzing voice-based social media platforms in the context of surveillance capitalism and AI-driven conversational technologies. We propose a high-level reference architecture machine learning for social science pipeline that integrates digital methods techniques, automatic speech recognition (ASR) models, and natural language processing (NLP) models within a reflexive and ethically grounded framework. To illustrate its potential, we outline possible stages of a PoC (proof of concept) audio analysis machine learning pipeline, demonstrated through a conceptual use case involving the collection, ingestion, and analysis of X Spaces. While not a comprehensive empirical study, this pipeline proposal highlights technical and ethical challenges in voice analysis. By situating the voice as a central axis of online sociality and examining it in relation to AI-driven conversational technologies, within an era of post-orality, the study contributes to ongoing debates on surveillance capitalism, platform affordances, and the evolving dynamics of digital interaction. In this rapidly evolving landscape, we urgently need a robust vocal methodology to ensure that voice is not just processed but understood. Full article

The market for digital assets, and more specifically cryptocurrencies, is growing, although their adoption in small island countries remains absent. This paper explores the potential benefits of integrating cryptocurrencies into portfolios alongside stocks, with a focus on Fiji’s stock market. This is the first study on a small market like Fiji, which emphasizes the role of cryptocurrencies in portfolio management. We analyze the outcomes (returns and risks) of combining cryptocurrencies with stocks using 12 different techniques. We use monthly stock returns data of 18 companies listed on the South Pacific Stock Exchange from Aug-2019 to Jun-2025 (71 months) and nine cryptocurrencies from Sept-2019 to Jun-2025 (70 months). Our main analysis shows that only one cryptocurrency, albeit with a small exposure, consistently appears in the stock-cryptocurrency portfolios in the 12 methods. Using the return-to-risk ratio across methods as a guide, we find that the stocks-cryptocurrencies portfolio based on EQW, MinVar, MaxSharpe, MinSemVar, MaxDiv, MaxDeCorr, MaxRMD, and MaxASR offers better outcomes than the stock-only portfolios. Using high returns as a guide, we find that six out of 12 methods (EQW, MaxSharpe, MaxSort, MaxCEQ, MaxOmega, and MaxUDVol) support the stocks-cryptocurrencies portfolios. Portfolios satisfying both conditions (high return-risk ratio and high return) are supported by the EQW and MaxSharpe portfolios. The consistency of assets in both stock and stock−cryptocurrency portfolios is further confirmed by 24-month out-of-sample forecasts and Monte Carlo simulations, although the latter supports small exposures in two out of the nine cryptocurrencies. Based on the results, we conclude that a small exposure to certain cryptocurrencies can strengthen diversification and improve potential returns. Full article

Despite the remarkable success of Deep Neural Networks (DNNs) in Remote Sensing Image (RSI) object detection, they remain vulnerable to adversarial attacks. Numerous adversarial attack methods have been proposed for RSI; however, adding a single large-scale adversarial patch to certain high-value targets, which are typically large in physical scale and irregular in shape, is both costly and inflexible. To address this issue, we propose a strategy of using multiple compact patches. This approach introduces two fundamental challenges: (1) how to optimize patch placement for a synergistic attack effect, and (2) how to retain strong adversarial potency within size-constrained mini-patches. To overcome these challenges, we introduce the Spatially Adaptive and Distillation-Enhanced Mini-Patch Attack (SDMPA) framework, which consists of two key modules: (1) an Adaptive Sensitivity-Aware Positioning (ASAP) module, which resolves the placement challenge by fusing the model’s attention maps from both an explainable and an adversarial perspective to identify optimal patch locations, and (2) a Distillation-based Mini-Patch Generation (DMPG) module, which tackles the potency challenge by leveraging knowledge distillation to transfer adversarial information from large teacher patches to small student patches. Extensive experiments on the RSOD and MAR20 datasets demonstrate that SDMPA significantly outperforms existing patch-based attack methods. For example, against YOLOv5n on the RSOD dataset, SDMPA achieves an Attack Success Rate (ASR) of 88.3% using only three small patches, surpassing other patch attack methods. Full article

Vector-borne parasitic diseases (VBPDs), including malaria, schistosomiasis, leishmaniasis, Chagas disease, African trypanosomiasis, lymphatic filariasis, and onchocerciasis, impose a significant global health burden. This study analyzes the global disease burden of VBPDs from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data and projects trends to 2036. Metrics include prevalence, deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) across regions, sexes, age groups, and Socio-demographic Index (SDI) levels. Key findings reveal persistent disparities: malaria dominated the burden (42% of cases, 96.5% of deaths), disproportionately affecting sub-Saharan Africa. Schistosomiasis ranked second in prevalence (36.5%). While African trypanosomiasis, Chagas disease, lymphatic filariasis, and onchocerciasis declined significantly, leishmaniasis showed rising prevalence (EAPC = 0.713). Low-SDI regions bore the highest burden, linked to environmental, socioeconomic, and healthcare access challenges. Males exhibited greater DALY burdens than females, attributed to occupational exposure. Age disparities were evident: children under five faced high malaria mortality and leishmaniasis DALY peaks, while older adults experienced complications from diseases like Chagas and schistosomiasis. ARIMA modeling forecasts divergent trends: lymphatic filariasis prevalence nears elimination by 2029, but leishmaniasis burden rises across all metrics. Despite overall progress, VBPDs remain critical public health threats, exacerbated by climate change, drug resistance, and uneven resource distribution. Targeted interventions are urgently needed, prioritizing vector control in endemic areas, enhanced surveillance for leishmaniasis, gender- and age-specific strategies, and optimized resource allocation in low-SDI regions. This analysis provides a foundation for evidence-based policy and precision public health efforts to achieve elimination targets and advance global health equity. Full article

The healthcare industry faces mounting pressure to enhance efficiency and accuracy in logistics operations. Despite its critical role, the sector demonstrates a low adoption rate of logistics automation, with the investment ratio at 14.9%, significantly lower than the industrial average of 18%. This study explores the current state and strategic application of logistics automation in healthcare through 20 in-depth interviews with stakeholders across manufacturers, wholesalers, hospitals, clinics, and pharmacies in South Korea. Analysis revealed that automation adoption is largely contingent on two key factors: annual order volumes and inventory complexity. Companies handling over 100,000 order lines annually and managing over 1000 SKUs were more likely to have adopted or planned automation systems such as AS/RSs, AMRs, or Cube-based AS/RS. The research culminates in a directional map that aligns automation strategies with operational scale and product characteristics. This study contributes novel empirical insights into the fragmented healthcare logistics sector, offering actionable guidance for phased automation implementation based on contextual constraints and stakeholder typologies. Full article

Concrete sleepers are essential components of railroad infrastructure, yet their service life has been reduced by one-third due to deterioration caused by internal swelling reactions (ISR), leading a major Brazilian railroad to replace millions of sleepers within a decade. This study investigates the reliability of various non-destructive testing (NDT) techniques to estimate damage levels in concrete sleepers. The methods evaluated include surface hardness testing, stress wave propagation, electromagnetic wave propagation using ground-penetrating radar (GPR), electrical resistivity, and resonant frequency. These techniques were applied to assess sleepers diagnosed as affected by alkali-silica reaction (ASR) and delayed ettringite formation (DEF) at different deterioration degrees. Although findings indicate that most NDT methods are limited and unreliable for quantifying ISR-induced damage, resonant frequency testing combined with energy dissipation analysis provided the highest accuracy across all damage stages and was able to capture microstructural changes before significant expansion occurred. These results support the use of vibration-based screening tools to enhance early detection and guide condition assessment of railroad infrastructure, helping to reduce the premature replacement of ISR-affected concrete sleepers. Full article

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions. Full article

This study investigates sustainable mortars using lightweight synthetic sand (LSS), made from dune sand and recycled PET bottles, to replace natural sand (0–100% by volume). This aligns with circular economy principles by valorizing plastic waste into a construction aggregate. LSS is produced via controlled thermal treatment (250 ± 5 °C, 50–60 rpm), crushing, and sieving (≤3.15 mm), leading to a significantly improved interfacial transition zone (ITZ) with the cement matrix. The evaluation included physico-mechanical tests (density, strength, UPV, dynamic modulus, ductility), thermal properties (conductivity, diffusivity, heat capacity), porosity, sorptivity, alkali–silica reaction (ASR), and SEM. The results show LSS incorporation reduces mortar density (4–23% for 25–100% LSS), lowering material and logistical costs. While compressive strength decreases (35–70%), these mortars remain suitable for low-stress applications. Specifically, at ≤25% LSS, composites retain 80% of their strength, making them ideal for structural uses. LSS also enhances ductility and reduces dynamic modulus (18–69%), providing beneficial flexibility. UPV decreases (8–39%), indicating improved acoustic insulation. Thermal performance improves (4–18% conductivity reduction), suggesting insulation applicability. A progressive decrease in sorptivity (up to 46%) enhances durability. Crucially, the lack of ASR susceptibility reinforces long-term durability. This research significantly contributes to the repurposing of plastic waste into sustainable cement-based materials, advancing sustainable material management in the construction sector. Full article

Geopolymers are an environmentally sustainable class of low-calcium alkali-activated materials (AAMs), distinct from high-calcium C–A–S–H gel systems. Synthesized from aluminosilicate-rich precursors such as fly ash, metakaolin, slag, waste glass, and coal gasification fly ash (CGFA), geopolymers offer a significantly lower carbon footprint, valorize industrial by-products, and demonstrate superior durability in aggressive environments compared to Ordinary Portland Cement (OPC). Recent advances in thermodynamic modeling and phase chemistry, particularly in CaO–SiO₂–Al₂O₃ systems, are improving precursor selection and mix design optimization, while Artificial Neural Network (ANN) and hybrid ML-thermodynamic approaches show promise for predictive performance assessment. This review critically evaluates geopolymer chemistry and composition, emphasizing precursor reactivity, Si/Al and other molar ratios, activator chemistry, curing regimes, and reaction mechanisms in relation to microstructure and performance. Comparative insights into alkali aluminosilicate (AAS) and aluminosilicate phosphate (ASP) systems, supported by SEM and XRD evidence, are discussed alongside durability challenges, including alkali–silica reaction (ASR) and shrinkage. Emerging applications ranging from advanced pavements and offshore scour protection to slow-release fertilizers and biomedical implants are reviewed within the framework of the United Nations Sustainable Development Goals (SDGs). Identified knowledge gaps include standardization of mix design, LCA-based evaluation of novel precursors, and variability management. Aligning geopolymer technology with circular economy principles, this review consolidates recent progress to guide sustainable construction, waste valorization, and infrastructure resilience. Full article

This study introduces an amphibious spherical robot equipped with a dual-propulsion system (ASR-DPS) and investigates its water-surface motion characteristics. Due to its distinctive spherical geometry, the robot exhibits markedly different hydrodynamic behavior compared to conventional vessels. A comparative analysis of the frontal wetted area is performed, followed by computational fluid dynamics (CFD) simulations to assess water-surface performance. The results indicate that the hemispherical bow increases hydrodynamic resistance and generates large-scale vortex structures as a consequence of intensified flow separation. Although the resistance is higher than that of traditional hulls, the robot’s greater draft and dual-propulsion configuration enhance stability and maneuverability during surface operations. To validate real-world performance, standard maneuvering tests, including circle and zig-zag maneuvers, are conducted to evaluate the effectiveness of the propeller-based propulsion system. The robot achieves a maximum surface speed of 1.2 m/s and a zero turning radius, with a peak yaw rate of 0.54 rad/s under differential thrust. Additionally, experiments on the pendulum-based propulsion system demonstrate a maximum speed of 0.239 m/s with significantly lower energy consumption (220.6 Wh at 60% throttle). A four-degree-of-freedom kinematic and dynamic model is formulated to describe the water-surface motion. To address model uncertainties and external disturbances, two control strategies are proposed: one employing model simplification and the other adaptive control. Simulation results confirm that the adaptive sliding mode controller provides precise surge speed tracking and smooth yaw regulation with near-zero steady-state error, exhibiting superior robustness and reduced chattering compared to the baseline controller. Full article

This study focuses on the development and evaluation of automatic speech recognition (ASR) systems for Kazakh child speech, an underexplored domain in both linguistic and computational research. A specialized acoustic corpus was constructed for children aged 2 to 8 years, incorporating age-related vocabulary stratification and gender variation to capture phonetic and prosodic diversity. The data were collected from three sources: a custom-designed Telegram bot, high-quality Dictaphone recordings, and naturalistic speech samples recorded in home and preschool environments. Four ASR models, Whisper, DeepSpeech, ESPnet, and Vosk, were evaluated. Whisper, ESPnet, and DeepSpeech were fine-tuned on the curated corpus, while Vosk was applied in its standard pretrained configuration. Performance was measured using five evaluation metrics: Word Error Rate (WER), BLEU, Translation Edit Rate (TER), Character Similarity Rate (CSRF2), and Accuracy. The results indicate that ESPnet achieved the highest accuracy (32%) and the lowest WER (0.242) for sentences, while Whisper performed well in semantically rich utterances (Accuracy = 33%; WER = 0.416). Vosk demonstrated the best performance on short words (Accuracy = 68%) and yielded the highest BLEU score (0.600) for short words. DeepSpeech showed moderate improvements in accuracy, particularly for short words (Accuracy = 60%), but faced challenges with longer utterances, achieving an Accuracy of 25% for sentences. These findings emphasize the critical importance of age-appropriate corpora and domain-specific adaptation when developing ASR systems for low-resource child speech, particularly in educational and therapeutic contexts. Full article

This paper presents a real-time speech-to-text (STT) system designed for edge computing environments requiring ultra-low latency and local processing. Differently from cloud-based STT services, the proposed solution runs entirely on a local infrastructure which allows the enforcement of user privacy and provides high performance in bandwidth-limited or offline scenarios. The designed system is based on a browser-native audio capture through WebRTC, real-time streaming with WebSocket, and offline automatic speech recognition (ASR) utilizing the Vosk engine. A natural language processing (NLP) component, implemented as a microservice, improves transcription results for spelling accuracy and clarity. Our prototype reaches sub-second end-to-end latency and strong transcription capabilities under realistic conditions. Furthermore, the modular architecture allows extensibility, integration of advanced AI models, and domain-specific adaptations. Full article

This paper refers to the discussion on how to implement socially responsible measures in agribusiness, a complex and often heterogeneous system. It indicates the similarities between Corporate Social Responsibility and Agribusiness Social Responsibility as well as the unique characteristics that distinguish agribusiness. The focus was on the analysis of the processes taking place in the supply chain of the pig market operating in Poland, due to the author’s detailed knowledge of the phenomena taking place there. As part of these considerations, the following three key questions were asked: (1) What are the differences between the definitions of CSR and ASR, and is there any reason to define the two concepts separately? (2) Which links in the food supply chain require particular attention when implementing social responsibility? (3) To what extent should social responsibility principles be adhered to on a voluntary basis? The analyses were based on a critical review of the literature on the subject, inspired by Denyer and Tranfield’s literature review structure. The following two repositories were used: Google Scholar, which is publicly available, and Web of Science, which is a licensed network. The study found that, despite significant similarities between ASR and CSR, fundamental differences exist. Understanding the specific nature of agribusiness social responsibility requires not only accepting its differences but, above all, taking a holistic view of the processes accompanying food production, processing, and distribution. Furthermore, it requires considering the economic, organizational, and social diversity of entities comprising the food supply chain. Full article

Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running. Full article

Background/Objectives: Late embryogenesis abundant (LEA) proteins regulate stress responses and contribute significantly to plant stress tolerance. As a model species for stress resistance studies, foxtail millet (Setaria italica) lacks comprehensive characterization of its LEA gene family. This study aimed to comprehensively identify SiLEA genes in foxtail millet and elucidate their functional roles and tissue-specific expression patterns. Methods: Genome-wide identification of SiLEA genes was conducted, followed by phylogenetic reconstruction, cis-acting element analysis of promoters, synteny analysis, and expression profiling. Results: Ninety-four SiLEA genes were identified and classified into nine structurally distinct subfamilies, which are unevenly distributed across all nine chromosomes. Phylogenetic analysis showed closer clustering of SiLEA genes with sorghum and rice orthologs than with Arabidopsis thaliana AtLEA genes. Synteny analysis indicated the LEA gene family expansion through tandem and segmental duplication. Promoter cis-element analysis linked SiLEA genes to plant growth regulation, stress responses, and hormone signaling. Transcriptome analysis revealed tissue-specific expression patterns among SiLEA members, while RT-qPCR verified ABA-induced transcriptional regulation of SiLEA genes. Conclusions: This study identified 94 SiLEA genes grouped into nine subfamilies with distinct spatial expression profiles. ABA treatment notably upregulated SiASR-2, SiASR-5, and SiASR-6 in both shoots and roots. Full article