Search Results (76)

Decentralized Autonomous Organizations (DAOs) enable blockchain-based collective governance, yet existing studies often evaluate DAO governance through isolated mechanisms, particularly voting systems. This narrow view does not sufficiently explain recurring problems such as governance capture, weak accountability, inadequate safeguards, and inefficient resource allocation. This paper proposes a Layered Governance Coverage Model that conceptualizes DAO governance as a system of seven interdependent institutional functions spanning participation, agenda formation, collective choice, safeguards, execution, incentives, and meta-governance. The model uses a four-level strength scale to assess not only whether governance functions are present, but also how strongly they are institutionalized. It is empirically applied to thirty-seven active DAOs through evidence-based coding of publicly available governance artifacts. The results show that governance breadth does not necessarily imply governance maturity: collective choice and execution mechanisms are more developed than accountability, safeguards, and meta-governance. Beyond DAO-native settings, the paper positions governance maturity as a trust and resilience regime for blockchain-based IoT and AI infrastructures, where governance affects security, reliability, data integrity, and risk oversight. The paper discusses AI-enabled governance analytics as a support mechanism for monitoring governance activity, detecting anomalies, and improving governance observability. The proposed framework contributes a structured approach for evaluating and designing resilient governance architectures in DAOs and blockchain-based IoT/AI systems. Full article

Background/Objectives: Multiple Sclerosis (MS) is a chronic disease with significant clinical and radiological heterogeneity. This fact, together with the increased number of disease-modifying treatments available, poses challenges in the therapeutic decisions and for the overall management of the disease. In this study, an expert panel on MS from Greece aimed to formulate a consensus, in order to provide recommendation on disease-modifying treatment (DMT) initiation and switching, as well as de-escalation strategies in Relapsing MS (RMS). Methods: The study followed two-round voting based on a modified Delphi setting. A questionnaire was constructed by a subgroup of five experts (core group) and was subsequently administered in a printed form to a group of 12 MS experts in total (panel) in a face-to-face meeting. Consensus required at least 80% agreement within the panel in order to signify strong consensus. Results: The panel agreed that the overall therapeutic plan (DMT choice) must take into consideration the degree of disease activity (low/moderate/high). In certain cases with suboptimal response to a moderate-efficacy DMT, a horizontal switch to another moderate-efficacy DMT may be a valid strategy. However, in cases exhibiting disability accumulation, therapy escalation should be preferred. The concept of de-escalation was suggested as an alternative strategy for cases with stable disease receiving a high-efficacy long-term DMT in the long term. Due to the possibility of rebound phenomena with certain medications (such as fingolimod and natalizumab), a bridging strategy could be applied in cases of family planning and drug-related adverse events (such as lymphopenia and hepatotoxicity), especially in PwMS with recent inflammatory activity. Conclusions: Although novel biomarkers may soon help clinicians predict future disability accumulation, currently, regular and detailed patient monitoring seems to be the optimal way to guide clinicians’ decisions on treatment changes. Full article

This study provides a comprehensive evaluation of bagging ensemble models for financial time series (FTS) classification and addresses a gap in the literature regarding how bootstrap methods, ensemble sizes, voting mechanisms, and loss functions jointly influence model performance. The analysis evaluates decision tree (DT), logistic regression (LR), and multi-layer perceptron (MLP) ensemble models modified by six time series bootstrap methods, five ensemble sizes, and three voting mechanisms across six FTS data sets. The study also examines the influence of entropy- and profit-based loss functions within particle swarm (PSO) and quantum-inspired particle swarm (QPSO) optimization for weighted voting. The results show that LR-based ensembles provide the strongest overall performance and outperform ARIMA, DT, LR, MLP, and LSTM baseline models on both accuracy and profit metrics. Bootstrap effects are model specific. DT and MLP ensembles perform best under the Tukey bootstrap, while LR ensembles achieve strong results under the block bootstrap, the sub-sample bootstrap method, and the Tukey method, and remain the strongest performers across all bootstrap configurations. Optimized voting mechanisms yield clear improvements over equal-weight majority voting, with the profit loss function producing the most consistent gains. The findings also indicate that FTS classification problems exhibit an optimal range of ensemble sizes, as larger ensembles do not always improve performance. The study contributes a systematic assessment of ensemble design choices for FTS classification and highlights the importance of jointly considering bootstrap diversity, ensemble size, and voting strategy when developing ensemble models for financial applications. Full article

Against the backdrop of high digital uncertainty in the 2023 Thai General Election, this study examines how social media reshapes voting intentions through a novel hybrid framework integrating Fuzzy Rough Set Theory (FRST), Hierarchical Confirmatory Factor Analysis (CFA), and Random Forest Regression (RFR). A three-stage design—combining 23 expert opinions with survey data from 812 voters—overcomes expert ambiguity and non-linear dynamics. The findings reveal a hierarchy in digital campaigning: while Party Image (Importance = 0.3056) is the primary predictor for initial voter attention, substantive Campaign Policy (β = 0.98) remains the definitive driver of final commitment. Other perceptual constructs, including Trust, Loyalty, and Perceived Quality, function as reinforcing dimensions that validate policy claims within the digital ecosystem. This suggests a shift where traditional broadcasting is superseded by interactive digital streaming, allowing voters to scrutinize policies through replays and public comments. The model’s robustness, validated through 10-fold Random Forest Cross-Validation, demonstrates high predictive stability (Mean CV R² = 0.840) and minimal error (MAE = 0.064). This study offers a sensitive instrument for emerging democracies and provides actionable insights, showing that substantive policy remains the ultimate driver of voter choice, even when mediated through Party Image in interactive digital environments. Full article

The United States has undergone rapid and, at times, unprecedented political changes in 2025. Recent national polling indicates that many Americans—across political parties—believe that the country is heading in the wrong direction. In a preregistered study with more than 7000 adults residing in the United States, we explored the implications of these widespread concerns for individuals’ psychological functioning. As theorized, individuals who believed that the political climate was worsening and viewed the United States as failing to live up to its core national values experienced lower efficacy, both in terms of their personal ability to influence politics (i.e., individual efficacy) and their confidence in the government to uphold its obligations to the nation and its residents (i.e., government efficacy). In turn, these individuals reported worse overall well-being and less effective coping in response to stressors related to the political climate. These relationships persisted after accounting for the participants’ 2024 presidential vote choice and political party affiliation. Together, these findings suggest that the political turbulence Americans are experiencing exerts a measurable, bipartisan toll on Americans’ psychological and social health. Full article

The government’s scientific disclosure of food safety inspection information can guide consumers toward rational substitution choices, thereby improving food safety while transforming individual decision-making into collective action, thereby achieving social co-governance. This process activates the “voting with their feet” market mechanism, which exerts pressure on supply chain enterprises to improve quality control. However, the current mismatch between disclosed information and consumer demand significantly weakens this effect. Drawing on evolutionary game theory, this study constructs an evolutionary game model involving producers, sellers, and consumers to explore how information alignment shapes stakeholder behavior. The findings indicate that improving information alignment effectively nudges consumers toward informed substitution choices, reinforcing the market-driven pressure on supply chain enterprises to strengthen quality control; reducing quality control costs is a more effective short-term incentive for sellers than increasing market returns; and when information alignment is low, prioritizing inspections of sellers more efficiently enhances co-governance performance, whereas under high alignment, stronger regulation of producers becomes more effective. Aligning the content, channels, and presentation of government-disclosed inspection information with consumer needs is critical to empowering effective social co-governance. These findings provide theoretical foundations and policy insights to optimize information disclosure strategies and regulatory resource allocation. Full article

Speaker change detection (SCD) in long, multi-party meetings is essential for diarization, Automatic speech recognition (ASR), and summarization, and is now often performed in the space of pre-trained speech embeddings. However, unsupervised approaches remain dominant when timely labeled audio is scarce, and their behavior under a unified modeling setup is still not well understood. In this paper, we systematically compare two representative unsupervised approaches on the multi-talker audio meeting corpus: (i) a clustering-based pipeline that segments and clusters embeddings/features and scores boundaries via cluster changes and jump magnitude, and (ii) a multi-scale jump-based detector that measures embedding discontinuities at several window lengths and fuses them via temporal clustering and voting. Using a shared front-end and protocol, we vary the underlying features (ECAPA, WavLM, wav2vec 2.0, MFCC, and log-Mel) and test the model’s robustness under additive noise. The results show that embedding choice is crucial and that the two methods offer complementary trade-offs: the pipeline yields low false alarm rates but higher misses, while the multi-scale detector achieves relatively high recall at the cost of many false alarms. Full article

This article examines how social media and digital channels are related to information behavior and voting among young voters (aged 18–30) during the 2025 German federal election. Based on an online survey (n = 673) conducted after the election among participants from southwest Germany and three diverse educational backgrounds, our exploratory study found no overarching generational effect in social media use or political socialization. Instagram emerged as the most important platform for political information. TikTok played a limited role overall; however, the Left Party was the only party able to gain visible support from it. In contrast, voters of the radical right Alternative for Germany (AfD) often reported receiving political content via private messenger groups, highlighting the role of non-public channels in political communication. Concerning vote choice, we find that it depends on the platform to what extent a party can benefit from digital campaigning, e.g., the Left Party benefits from frequent TikTok usage, while YouTube correlates with voting for the Greens, and messenger usage with voting for the AfD. The findings, therefore, suggest a more professionalized and targeted approach to digital campaigning, with specific parties reaching distinct voter groups through tailored platform strategies. Full article

This study discusses the asymmetric dominance effect in the context of political elections with third-party candidates. Animal and human research both show that the addition or removal of a third option influences choices between the remaining two options. The direction of sway created by the addition/removal of the 3rd option is context-dependent and unconsciously regulated. The results confirmed our hypotheses that both the timing and perceived viability of third-party candidates significantly influence voter preferences, with the strongest effects observed when third-party candidates remain present through election day. These findings suggest that the impact of third-party candidates extends beyond simple vote-splitting and is at least partly unconscious, though direct implicit measures were not employed. This study is situated in the context of U.S. presidential elections and focuses on moderate voters. Full article

Background/Objectives: Prevention and treatment of venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major clinical issue in hospitalized patients. Some aspects of VTE management lack clarity due to differing physicians’ opinions and behaviors. Methods: A multidisciplinary steering committee identified two main areas of uncertainty: VTE prophylaxis and PE management in special settings. A multiple-choice questionnaire including 10 statements was circulated to 183 doctors trained in VTE management. The expected benefit-to-harm ratio was represented on a nine-point Likert scale, with consensus (≥75% agreement) on scores of 1–3 indicating inappropriate and 7–9 indicating appropriate care measures. Results: In online voting, a consensus was reached for 9/10 statements. Respondents considered the following to be appropriate: risk assessment of VTE (93.44%) and bleeding (91.6%) in hospitalized medical patients; low-molecular weight heparin (LMWH) prophylaxis for inpatients with pneumonia and malignancy (82.78%); therapeutic doses of LMWH/fondaparinux in patients with intermediate/high risk of PE with (80.9%) or without (77.97%) instability criteria; and echocardiography to manage patients with a post-PE syndrome (93.99%). Respondents considered the following to be inappropriate: use of 4000 IU LMWH in chronic renal failure (80.46%); use of 2000 IU LMWH in persons on dual antiplatelet therapy (77.01%); and use of low-dose apixaban (2.5 mg) in pregnancy (88.57%) or in subsegmental PE with hypoxemia (82.46%). No consensus was reached on the identification of PE cases eligible for outpatient treatment. Conclusions: Our findings show persistent gaps between guideline recommendations and clinical implementation despite improved awareness among physicians. Uncertainty persists regarding criteria for outpatient PE eligibility and/or for validation of bleeding-risk models. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

Code smells, which represent poor design choices or suboptimal code implementations, reduce software quality and hinder the code maintenance process. Detecting code smells is, therefore, essential during software development. This study introduces a Python-based code smell dataset targeting two smell types: Large Class and Long Method. Five ensemble learning methods—Bagging, Gradient Boost, Max Voting, AdaBoost, and XGBoost—were employed to detect code smells within these datasets. The ten most significant features were selected using the Chi-square feature selection technique. To address the class imbalance, the SMOTE algorithm was applied. Experimental results yielded a best accuracy score of 0.96 and an MCC of 0.85 for the Large Class dataset using the Max Voting model. For the Long Method dataset, a best accuracy score of 0.98 and an MCC of 0.94 were achieved using the Gradient Boost model in conjunction with Chi-square feature selection. These results highlight the effectiveness of the proposed methodology and its potential to enhance code smell detection in Python significantly, reinforcing confidence in the approach’s thoroughness and applicability. Full article

This paper investigates the growing political alignment of Latino Evangelicals with the Republican Party, particularly their support for Donald Trump in the 2020 election. Historically, Latino political behavior has been studied with an assumption of religious homogeneity, largely focusing on the Catholic majority. However, the rise of the Latino Evangelical population has coincided with increasing Latino support for the GOP. Former President Obama attributed this shift in support to the growing Evangelical demographic. Building on Chaturvedi’s (2014) work, which found that Evangelical Latinos’ conservative views on issues like same-sex marriage vary by age, this study tests Obama’s assertion using data from the 2020 USC Dornsife Presidential Poll. Logistic regressions show that older Latino Evangelicals were significantly more likely to support Trump, driven by their desire to elect officials who align with their Evangelical policy preferences. The findings explain that the political behavior of older Latino Evangelicals is more strongly related to religious values compared to their younger counterparts. These results highlight the importance of considering religious diversity within Latino politics, pointing to religious identity as a key factor in shaping Latino political behavior and emphasizing the need for further exploration of religious variation in Latino voting patterns. Full article

We have employed a time-domain behavioral simulator to analyze how different design options for bang-bang Clock and Data Recovery (CDR) impact the Jitter Tolerance (JTOL) performance of High-Speed Serial Interfaces (HSSIs) with PAM-4 signaling. The simulator includes the effect of Inter-Symbol Interference (ISI) due to the transmission channel, various equalization schemes and a detailed description of the CDR architecture. Many design options have been investigated, with particular focus on transition filtering and on the algorithm to identify the Early/Late (E/L) information from data and edge samples after deserialization. It has been found that if majority voting is employed to derive a single set of E/L information from an array of phase detectors working on deserialized data and edges, the different filtering strategies provide the same JTOL, meaning that one can avoid transition filtering and furthermore use a single edge sampler with a zero threshold, significantly simplifying the CDR architecture. Instead, if summation of the E/L information from deserialized data and edges is performed, the decision to use one or three thresholds for the edge sampling and the choice of whether to implement transition filtering both impact JTOL; however, better performance is achieved under these conditions than when employing majority voting on the deserialized E/L signals. Full article

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolution of 10 m, 5-day revisit period and rich spectral information of Sentinel-2 imagery. This study integrated Sentinel-2 images and uncrewed aerial vehicle (UAV) RGB imagery to develop six regression models at the field scale, which were employed for the estimation of seasonal and annual fresh tea yields of the Yunlong Tea Cooperatives in Yixiang Town, Pu’er City, China. Firstly, we gathered fresh tea production data from 133 farmers in the cooperative over the past five years and obtained UAV RGB and Sentinel-2 imagery. Secondly, 23 spectral features were extracted from Sentinel-2 images. Based on the UAV images, the parcel of each farmer was positioned and three topographic features of slope, aspect, and elevation were extracted. Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. Thirdly, we applied six different regression algorithms to establish fresh tea yield models for each season and evaluated their estimation accuracy. The results showed that random forest regression models were the optimal choice for estimating spring and summer yields, with the spring model achieving an R² value of 0.45, an RMSE of 40.38 kg/acre, and an rRMSE of 40.79%. Similarly, the summer model achieved an R² value of 0.5, an RMSE of 78.46 kg/acre, and an rRMSE of 39.81%. For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R² value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R² value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. This study provides a promising new method for estimating fresh tea yield in different seasons at the field scale. Full article