Search Results (2,086)

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

In southwest China, vegetation restoration is widely used in karst rocky desertification control projects. However, mechanistic evidence explaining how plant community composition and species diversity regulate cadmium (Cd) bioavailability remains limited. Here, the plant community’s species diversity, soil properties, Cd, and available Cd contents were evaluated. Four plant community types, NR (natural recovery), PMC (Pistacia weinmannifolia + Medicago sativa + Chrysopogon zizanioides), and PME (Pistacia weinmannifolia + Medicago sativa + Eragrostis curvula), were selected as the research objects. The species composition was recorded, and dominant plant species and soil samples were collected to analyze Cd accumulation characteristics. Relative to NR, composite restorations increased plant diversity and soil nutrient availability and reduced soil compaction, with PMC showing the strongest remediation, decreasing total Cd by 49.4% and available Cd by 59.5%. Model-averaged regression and hierarchical partitioning analyses further identified nitrogen availability and community structure as the dominant drivers. Specifically, available nitrogen (AN), vegetation coverage, Margalef species richness (DMG), ammonium nitrogen (NH₄⁺–N), and total N (TN) were the main factors of soil total Cd, and BD, TN, nitrate nitrogen (NO₃⁻–N), mean crown diameter (MCD), and Shannon–Wiener index (H′) were the main factors of soil available Cd. The results indicate that PMC provides a plant community structure configuration decisions of a scalable, site-adaptable strategy for durable Cd stabilization and soil conservation in thin, carbonate-rich karst soils. Full article

In this article, the authors address the Serbian media narrative about the EU’s communication on lithium mining in Serbia. In an effort to answer the question of how this narrative can influence the positioning of the EU on Serbia as a candidate country for EU membership, the authors have made a research based on a quantitative–qualitative analysis of media coverage, drawing on a sample of 192 articles (N = 192) published by four Serbian online news portals (RTS, N1, B92, and Blic). The analysis leads to two main conclusions: (1) It indicates an inversion in the general approach to foreign policy orientation across the analyzed media platforms. The customary discourses on Serbia’s foreign policy trajectory temporarily diverged from established patterns—specifically, the fervently pro-Western orientation characteristic of anti-government platforms and the ostensibly West-sceptical orientation typical of pro-government media. This reinforces the argument that the primary structuring line of media discourse in Serbia lies in the division between pro-regime and anti-regime orientations. (2) Media repositioning has exerted a pronounced negative effect on pro-European segments of the Serbian public, reactivating the thesis of “stabilocracy”, conceptualized as the dynamic relationship between authoritarian regimes in the Balkans and their external supporters. According to the authors, the EU’s inability to anticipate the drastic negative shift in public sentiment toward it—particularly among those segments of Serbian society that had been most supportive—or, alternatively, its decision to continue pursuing its own economic interests despite such awareness, underscores the profound flaws in the political communication it employed in this case. Full article

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amphissa, Greece, with validation based on ground samples. We applied the well-established Linear Spectral Unmixing (LSU) and Spectral Angle Mapping (SAM) classification techniques utilizing endmembers of two established spectral libraries and incorporated ground data through geochemical and mineralogical analyses, X-ray fluorescence (XRF), Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), and X-ray Diffraction (XRD), to assess classification performance. The main lithologies in this area are bauxites and limestones; therefore, aluminum oxyhydroxides, calcite, and iron oxide minerals were the dominant phases as indicated by the XRF/XRD results. Almost all target minerals were mapped with the three sensors and both methods. The performance of EnMap is affected by its coarser spatial resolution despite its higher spectral resolution using these methods. Sentinel-2 is most effective for mapping iron-bearing minerals, particularly hematite, due to its higher spatial resolution and the presence of diagnostic iron oxide absorption features in the VNIR. World View 3 Shortwave Infrared (WV3-SWIR) performs better when mapping calcite, benefiting from its eight SWIR spectral bands and very high spatial resolution (3.7 m). Hematite and calcite yield the highest accuracy, especially with SAM, indicating 0.80 for Sentinel-2 (10 m) for hematite and 0.87 for WV3-SWIR (3.7 m) for calcite. AlOOH shows higher accuracy with SAM, ranging from 0.57 to 0.80 across the sensors, while LSU shows lower accuracy, ranging from 0.20 to 0.73 across the sensors. This study showcases each sensor’s ability to map minerals while also demonstrating that spectral coverage and the spatial and spectral resolution, as well as the characteristics of the selected endmembers, exert a critical influence on the accuracy of mineral mapping in mine waste. Full article

Background/Objectives: Seasonal influenza imposes a significant burden on pediatric public health. Despite official recommendations and full insurance coverage, vaccination rates among children in Slovakia remain critically low. This study aims to analyze the attitudes, beliefs, and determinants of parental hesitancy regarding childhood influenza vaccination in the post-pandemic context. Methods: A single-center cross-sectional survey was conducted between February and March 2025 using convenience sampling among parents of children attending a pediatric immunoallergology center. An anonymous questionnaire collected data on demographics, risk perception, and attitudes. Data from 301 parents were analyzed using descriptive statistics, chi-squared tests, and odds ratios (OR) to identify key predictors of hesitancy. Results: Only 27.6% of parents expressed willingness to vaccinate their children, while 42.5% were opposed and 29.9% hesitant. Statistical analysis revealed no significant association between parental university education and vaccination intent (p > 0.05), indicating that vaccine hesitancy in this specific setting was present across all educational backgrounds. However, the source of information proved to be a critical determinant: consulting a pediatrician significantly increased the odds of acceptance (OR = 6.32; 95% CI: 3.54–11.28), whereas reliance on the internet and social media was a significant predictor of refusal (OR = 0.29; 95% CI: 0.17–0.50). The primary reported barrier was fear of adverse effects (70.4%), which significantly outweighed doubts about efficacy (30.2%). Conclusions: Parental hesitancy in Slovakia is a widespread phenomenon pervasive across all educational backgrounds, driven primarily by safety concerns and digital misinformation. The contrast between the protective influence of pediatricians and the negative impact of digital media underscores that clinical encounters are currently the most effective firewall against hesitancy. Public health strategies must therefore pivot from general education to empowering pediatricians with active, presumptive communication strategies. Full article

One of the challenges post-COVID-19 is reducing the negative impacts on quality of life, performance, and independence in activities of daily living. Assessing functional dependence in adults one year after acute infection can help to understand the long-term consequences, evaluate the impact on quality of life, plan rehabilitation and healthcare, identify the most vulnerable groups, measure the socioeconomic impact, and support public policies and clinical decisions. Objectives: The objectives of this study are as follows: (a) to assess the prevalence of functional dependence in Brazilian adults with COVID-19; (b) to analyze the association between the study variables; and (c) to determine the factors associated with functional dependence. Methods: This was an observational, cross-sectional study with 987 adults (18 to 59 years old) living in the State of Paraná (Brazil) hospitalized for COVID-19 between March and December 2020. Data were collected by telephone 12 months after the acute infection using an instrument to retrieve sociodemographic and health information, and a functional dependence scale to assess dependence before COVID-19 retrospectively (using participant recall information) and at the time of the interview. Data were analyzed using penalized logistic regression after imputing missing data. Data were analyzed using penalized logistic regression after imputing missing data. Results: Functional dependence after COVID-19 was 5.0% and was associated with low levels of education, not having a partner, living with someone, not owning a home, experiencing job changes, requiring care, obesity, smoking, multimorbidity, ICU admission in the acute phase, use of invasive ventilation, or having Long COVID. Individuals who required care or used invasive ventilation support were, respectively, 9.3 and 6.5 times more likely to develop dependence after COVID-19. Despite adjustment for multiple factors, the magnitude of the observed effects warrants cautious interpretation, as unmeasured or residual confounding effects may still be present. Sample recall bias due to collection after 12 months and the presence of the alpha variant without COVID-19 vaccination coverage may limit data generalization. Conclusions: The results highlight the need to emphasize the public health implications of identifying functional dependence. In this vein, it is necessary to implement preventive measures, identify and monitor more vulnerable groups, plan rehabilitation programs, and develop public health policies. Full article

Predation of sea turtles by jaguars (Panthera onca) in the Santa Rosa National Park (SRNP) has been well documented over the past decade. However, the factors that influence jaguar feeding behavior, including environmental factors or characteristics of the beaches and the adjacent forest, are poorly known. This study aimed to identify the relationship between vegetation density and human activity on the distribution of feeding sites of jaguar on sea turtles at nesting beaches in Santa Rosa National Park, Costa Rica. We sampled three beaches (Naranjo, Nancite, and Colorada), where we identified and registered sea turtle carcasses preyed on by jaguars between June and November 2019. Through systematic searches of the forest adjacent to the beach, we documented the species, geographic coordinates, carcass length and width, vegetation cover at the carcass site, and the average vegetation coverage corresponding to the date and beach of each sea turtle carcass. In total, we recorded 338 sea turtle carcasses preyed on by jaguars, 156 at Naranjo beach, 103 at Nancite beach, and 89 at Colorada beach. The beach with the highest average density of carcasses was Colorada (8.7 (SD = 5.42)/ha), followed by Nancite (6.06 (SD = 5.58)/ha) and Naranjo (2.64 (SD = 1.79)/ha). The dragging distance from the beach line to sea turtle carcasses was best explained by the interaction of nesting beach and canopy cover at the carcass. Our canopy cover results may reflect that jaguars select sites that better hide their prey, in the same way that green turtles (Chelonia mydas) usually prefer areas with good coverage to nest in, contrasting to the nesting behavior of olive ridleys (Lepidochelys olivacea). On beaches, higher concentrations were observed where there was less human presence and this may reflect both turtle nesting and jaguar predation activity. Full article

The influence of nitrate (NO₃⁻) concentration on the corrosion resistance of the Inconel 625 (superalloy) was investigated. The surface of Inconel 625 was chemically modified by oxidation in an alkaline sodium nitrate(V) solution. The surface and microstructure of specimens were observed by a scanning electron microscope (SEM). The mechanical properties of Inconel 625 were characterized by microhardness (HV) measurements. The corrosion tests of materials were carried out by using the electrochemical method in the acidic chloride solution. The adsorption of the (Me_mO_n)_ads layer effectively separates the Inconel 625 surface from contact with the aggressive corrosive environment. The microhardness (HV10) value increased (about 13%) with the increase in nitrate concentration. A more-than-five-times-lower corrosion rate (CW) value was obtained for the Inconel 625 sample, whose surface was modified in an alkaline solution with the highest NO₃⁻ concentration. Chemical modification improves the structure and surface topography of the superalloy. After exposing Inconel 625 to an oxidizing environment (1.00 M NO₃⁻), the surface coverage degree (SC) was 80%. Full article

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

Background: Men who have sex with men (MSM) using HIV pre-exposure prophylaxis (PrEP) experience a high burden of human papillomavirus (HPV) infection and related diseases, yet data on HPV vaccination among this group in Brazil remain limited. Aims: The aims of this study were to estimate the prevalence of complete HPV vaccination and to identify factors associated with vaccination completion among MSM using PrEP in Brazil. Methods: We conducted a cross-sectional online survey between May and September 2025 among MSM aged ≥18 years, residing in Brazil and currently using oral PrEP. Participants were recruited through virtual snowball sampling and targeted advertisements on social media and a gay geosocial networking application. Data were collected using a structured, self-administered questionnaire hosted on REDCap^®. Complete HPV vaccination was defined as self-reported receipt of all doses recommended according to the participant’s age and clinical condition. Sociodemographic characteristics, relationship patterns, sexual behaviors, lubricant use during sexual activity, and history of sexually transmitted infections (STIs) were assessed. Adjusted prevalence ratios (aPRs) and 95% confidence intervals (95% CIs) were estimated using Poisson regression with robust (sandwich) variance. Results: A total of 872 MSM using PrEP were included, of whom 59.4% reported complete HPV vaccination. In adjusted analyses, complete vaccination was more frequent among participants reporting both steady and casual partners (aPR = 1.90; 95% CI: 1.36–2.65) or only casual partners (aPR = 1.72; 95% CI: 1.24–2.39), those reporting lubricant use during sexual activity (aPR = 1.41; 95% CI: 1.23–1.61), and those with a diagnosis of chlamydia and/or gonorrhea in the previous 12 months (aPR = 1.22; 95% CI: 1.08–1.36). Conclusions: Although HPV vaccination coverage among MSM using PrEP in Brazil is higher than that reported for MSM in general, it remains incomplete in a population with regular contact with specialized health services. Integrating systematic assessment and delivery of HPV vaccination into PrEP care may help increase vaccination completion and reduce missed opportunities for prevention. Full article

With the explosive growth in the quantity of electrical equipment in distribution networks, traditional manual inspection struggles to achieve comprehensive coverage due to limited manpower and low efficiency. This has led to frequent equipment failures including partial discharge, insulation aging, and poor contact. These issues seriously compromise the safe and stable operation of distribution networks. Real-time monitoring and defect identification of their operation status are critical to ensuring the safety and stability of power systems. Currently, commonly used methods for defect identification in distribution network electrical equipment mainly rely on single-image or voiceprint data features. These methods lack consideration of the complementarity and interleaved nature between image and voiceprint features, resulting in reduced identification accuracy and reliability. To address the limitations of existing methods, this paper proposes distribution network electrical equipment defect identification based on multi-modal image voiceprint data fusion and channel interleaving. First, image and voiceprint feature models are constructed using two-dimensional principal component analysis (2DPCA) and the Mel scale, respectively. Multi-modal feature fusion is achieved using an improved transformer model that integrates intra-domain self-attention units and an inter-domain cross-attention mechanism. Second, an image and voiceprint multi-channel interleaving model is applied. It combines channel adaptability and confidence to dynamically adjust weights and generates defect identification results using a weighting approach based on output probability information content. Finally, simulation results show that, under the dataset size of 3300 samples, the proposed algorithm achieves a 8.96–33.27% improvement in defect recognition accuracy compared with baseline algorithms, and maintains an accuracy of over 86.5% even under 20% random noise interference by using improved transformer and multi-channel interleaving mechanism, verifying its advantages in accuracy and noise robustness. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article

This study presents the first comprehensive assessment of per- and polyfluoroalkyl substances (PFAS) in surface waters of northern Serbia (Middle Danube region), combining targeted analysis of 25 PFAS with high-resolution mass spectrometry suspect screening (SSA) at 12 settlement-adjacent sites on major rivers and part of the Danube–Tisa–Danube (DTD) canal network. The sum of 10 quantified PFAS showed pronounced spatial variability: the Great Bačka Canal (GBC) exhibited the highest mean and maximum values (18.4 ng/L and 52.6 ng/L, respectively); the Danube averaged 9.05 ng/L (2.92–22.2 ng/L); the Tisa averaged 10.5 ng/L (4.53–16.5 ng/L); and the Sava and Tamiš exhibited the lowest means (~5.4 ng/L each). In total, 19 of 24 sites exceeded the proposed EU group Environmental Quality Standard (EQS) of 4.4 ng/L, expressed as PFOA-equivalents, with exceedances of 5.4–20.2 ng/L; PFOS exceeded the 0.65 ng/L inland surface water annual average (AA) EQS in 17 samples. SSA expanded coverage beyond targets, revealing ultra-/short-chain PFAS and replacements, with TFA as the most abundant (337–1165 ng/L; mean 513 ng/L) and notable maxima for PFPrA (51.3 ng/L), ADONA (24.9 ng/L), and TFMS (11.2 ng/L). Compared with European freshwaters, the maximum obtained here lies in the lower-mid part of the reported range, consistent with short-chain perfluoroalkyl carboxylic acids (PFCA) dominance and diffuse-source influences. Full article

The essay delves into the impact of environmental uncertainty on asymmetric mispricing, utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improving the efficiency of emerging capital markets. Full article