Search Results (801)

Noisy permutation channels are applied in modeling biological storage systems and communication networks. For noisy permutation channels with strictly positive and full-rank square matrices, new achievability bounds are given in this paper, which are tighter than existing bounds. To derive this bound, we use the $ϵ$-packing with Kullback–Leibler divergence as a distance and introduce a novel way to illustrate the overlapping relationship of error events. This new bound shows analytically that for such a matrix W, the logarithm of the achievable code size with a given block n and error probability $ϵ$ is closely approximated by $\ell log\left({\displaystyle \frac{\sqrt{n}}{-{\Phi }^{-1}(ϵ/G)}}\right)+logV\left(W\right)$, where $\ell =\mathsf{rank}\left(W\right)-1$, $G=2\left({\displaystyle \genfrac{}{}{0pt}{}{\ell +1}{2}}\right)$, and $V\left(W\right)$ is a characteristic of the channel referred to as channel volume ratio. Our numerical results show that the new achievability bound significantly improves the lower bound of channel coding. Additionally, the Gaussian approximation can replace the complex computations of the new achievability bound over a wide range of relevant parameters. Full article

Objectives: An analysis based on epidemiological material to show whether the term Metabolic Syndrome (MS) should be adopted when aiming at predicting coronary heart disease (CHD) and major cardiovascular disease (CVD) fatal events. Material and Methods: MS was defined according to the International Diabetes Federation (IDF) and risk factors were identified in the Italian Risk Factors and Life Expectancy (RIFLE) population study covering over 25,000 adult men from a pool of 19 Italian population samples. The original MS definition and the plain original units of measured risk factors were challenged in Cox proportional hazard models predicting 196 CHD and 412 major CVD fatal events in a seven-year follow-up. Parallel models were run including also total serum cholesterol as a covariate, an unfortunately excluded covariate in the MS definition. The performance of the various models was tested by the log-likelihood statistics treated with the Akaike Information Criterium (AIC). Results: Models using the plain measurements of the risk factors involved were systematically and significantly outperforming any other categorized score based on the IDF-MS classification. An intermediate role was played by a model where the predictive variable was a factor score (derived from a Factor Analysis) where the MS risk factors were linearly combined. The same models also including serum cholesterol provided a significantly better prediction when compared with those without serum cholesterol, based on AIC. Conclusions: The use of a subset of classical CVD risk factors classified according to the IDF-MS criteria adds nothing better than the exclusive use of the risk factors treated by traditional procedures. The addition of serum cholesterol definitely helps in the prediction of the CHD component of major CVD events. Its omission is erroneous. Full article

Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management. Full article

Industrial processes governed by food safety regulations, such as high-temperature short-time (HTST) pasteurization, rely on continuous sensor monitoring to ensure compliance with standards like Hazard Analysis and Critical Control Points (HACCP). However, extracting actionable process insights from raw sensor data remains a non-trivial task, largely due to the continuous, multivariate, and often high-frequency characteristics of the signals, which can obscure clear activity boundaries and introduce significant variability in temporal patterns. This paper proposes a process mining framework to extract activity-based representations from multivariate sensor data in a pasteurization scenario. By modelling temperature, pH, conductivity, viscosity, turbidity, flow, and pressure signals, the approach segments continuous data into discrete operational phases and generates event logs aligned with domain semantics. Unsupervised learning techniques, including Hidden Markov Models (HMMs), are used to infer latent process stages, while domain knowledge guides their interpretation in accordance with critical control points (CCPs). The extracted models support conformance checking against HACCP-based procedures and enable predictive process-monitoring tasks such as next-activity prediction and remaining time estimation. Experimental results on synthetic (literature-grounded data) demonstrated the method’s ability to enhance safety, compliance, and operational efficiency. This study illustrates how integrating process mining with regulatory principles can bridge the gap between continuous sensor streams and structured process analysis in food manufacturing. Full article

Background/Objectives: The aim of this study was to determine whether incomplete follow-up and competing event risks may be associated with the clinical course after treatment and with known risk factors, such as chronic limb-threatening ischemia (CLTI), in peripheral artery disease (PAD) patients. Methods: Patients hospitalized with PAD who were treated by endovascular or open-surgical means and followed up were included in this retrospective observational study. The primary outcome was reintervention-free survival (RFS); the secondary outcomes and competing events were major amputation and death. The follow-up index (FUI), defined as the ratio between the real and the maximal follow-up interval, was determined for each patient. Results: The FUI depended significantly on the disease stage of CLTI (estimate: −0.16; p: 0.003), endovascular (0.17; p: 0.007) or open-surgical intervention (0.21; p: 0.007) and intra-hospital re-operation (−0.29; p: 0.002) and tended to decrease with age (−0.004; p: 0.09). Independent of disease stage, patients with claudication or CLTI with an FUI < 0.5 had shorter RFS than patients with a FUI ≥ 0.5 (Cox regression, p: 0.07; log-rank test, p: 0.03). When both the FUI and competing risks were considered using Fine–Gray regression analysis, CLTI was associated with RFS (p: 0.016), while FUI (p: 0.004), CLTI (p < 0.001), and the involvement of common femoral (p < 0.001) and posterior tibial arteries (p < 0.001) were associated with major amputation-free survival. Conclusions: Incomplete follow-up is associated with advanced PAD and may itself mask a worse outcome, such as reintervention, restenosis, major amputation, or death. Competing events should also be considered potential sources of bias. Therefore, the FUI and competing events should be reported, and conclusions should be drawn cautiously in both observational and randomized prospective clinical studies. Full article

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection. Full article

Background: Hypertrophic cardiomyopathy (HCM) is associated with sudden cardiac death (SCD). The HCM Risk-SCD model has been widely used in Western populations, but its performance in Chinese patients remains unclear. Methods: This retrospective cohort study evaluated 534 HCM patients (348 males and 186 females) at Fuwai Hospital from 1992 to 2010. We calculated the HCM Risk-SCD score for each patient and categorized them into low-risk (<4%) and intermediate–high-risk (≥4%) groups. The primary endpoint was SCD events, defined as unexpected sudden death within one hour of symptom onset, successful resuscitation after cardiac arrest, appropriate ICD discharge, or sustained ventricular tachycardia. Model performance was assessed using Cox regression analysis, Kaplan–Meier survival analysis, ROC curve analysis, and subgroup analyses with interaction tests. Results: During a mean follow-up of 6.96 ± 4.16 years, 31 SCD events occurred. The intermediate–high-risk group had significantly higher SCD incidence than the low-risk group (8.68% vs. 3.42%, p = 0.01). This association remained significant after multivariate adjustment (HR 2.718, 95% CI: 1.264–5.848, p = 0.011). Kaplan–Meier analysis showed significant differences in SCD-free survival between risk strata (log-rank p = 0.01). The actual 5-year SCD event rate (4.31%) closely aligned with the model-predicted rate (4.65 ± 3.26%). ROC analysis demonstrated moderate discriminative ability in the overall population (AUC = 0.660, p = 0.003). The optimal cutoff value was 3.23 for the overall population. Conclusions: The HCM Risk-SCD model demonstrates acceptable performance in Chinese HCM patients. Full article

The method addresses the challenges of non-steady conditions at an early age by combining wavelet filtering and empirical mode decomposition (EMD) to separate strain components arising from shrinkage, expansive agent compensation, temperature variations, construction disturbances, and live loads. The approach incorporates construction logs as external constraints to ensure accurate correspondence between signal features and physical events. Scientifically, this study addresses the fundamental problem of identifying and quantifying multi-source strain components under transient and non-steady construction conditions, which remains a major challenge in the field of structural monitoring. Field monitoring was conducted on typical cast-in-place concrete components: a full-width bridge deck in the negative moment region. The results show that both structural types exhibit a distinct shrinkage–recovery process at an early age but differ in amplitude distribution, recovery rate, and restraint characteristics. During the construction procedure stage, the cast-in-place segment in the negative moment region was sensitive to prestressing and adjacent segment construction. Under variable loads, the former showed higher live load sensitivity, while the latter exhibited more pronounced temperature-driven responses. Total strain decomposition revealed that temperature and dead load were the primary long-term components in the structure, with differing proportional contributions. Representative strain variations observed in the field ranged from 10 to 50 µε during early-age shrinkage–expansion cycles to 80–100 µε reductions during prestressing operations, quantitatively illustrating the evolution characteristics captured by the proposed method. This approach demonstrates the method’s capability to reveal transient stress mechanisms that conventional steady-state analyses cannot capture, providing a reliable basis for strain monitoring, disturbance identification, and performance evaluation during construction, as well as for long-term prediction and optimization of operation–maintenance strategies. Full article

Background/Objectives—Traditional forensic investigations often analyze digital, physical, and criminological evidence separately, leading to fragmented timelines and reduced accuracy in reconstructing complex events. To address these gaps, this study proposes the Digital Stratigraphy Framework (DSF), inspired by archaeological stratigraphy, to integrate heterogeneous evidence into structured, temporally ordered layers. DSF aims to reduce asynchronous inconsistencies, minimize false associations, and enhance interpretability across digital, behavioral, geospatial, and excavation evidence. Methods—DSF employs Hierarchical Pattern Mining (HPM) to detect recurring behavioral patterns and Forensic Sequence Alignment (FSA) to synchronize evidence layers temporally and contextually. The framework was tested on the CSI-DS2025 dataset containing 25,000 multimodal, stratified records, including digital logs, geospatial data, criminological reports, and excavation notes. Evaluation used 10-fold cross-validation, Bayesian hyperparameter tuning, and structured train-validation-test splits. Metrics included accuracy, precision, recall, F1-score, and Stratigraphic Reconstruction Consistency (SRC), alongside ablation and runtime assessments. Results—DSF achieved 92.6% accuracy, 93.1% precision, 90.5% recall, 91.3% F1-score, and an SRC of 0.89, outperforming baseline models. False associations were reduced by 18%, confirming effective cross-layer alignment and computational efficiency. Conclusions—By applying stratigraphic principles to forensic analytics, DSF enables accurate, interpretable, and legally robust evidence reconstruction. The framework establishes a scalable foundation for real-time investigative applications and multi-modal evidence integration, offering significant improvements over traditional fragmented approaches. Full article

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterprets spectrograms as visual objects and applies Feature Coding for Machines (FCM) to anomalous sound detection (ASD). In our approach, audio signals are transformed log-mel spectrograms, from which intermediate feature maps are extracted, compressed, and reconstructed through the FCM pipeline. For comparison, we implement AAC-LC (Advanced Audio Coding Low Complexity) as a representative perceptual audio codec and VVC (Versatile Video Coding) as spectrogram-based video codec. Experiments were conducted on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2023 Task 2 dataset, covering four machine types (fan, valve, toycar, slider), with anomaly detection performed using the official Autoencoder baseline model released in DCASE 2024. Detection scores were computed from reconstruction error and Mahalanobis distance. The results show that the proposed FCM-based ACoM (Audio Coding for Machines) achieves comparable or superior performance to AAC at less than half the bitrate, reliably preserving critical features even under ultra-low bitrate conditions (1.3–6.3 kbps). While VVC retains competitive performance only at high bitrates, it degrades sharply at low bitrates. These findings demonstrate that feature-based compression offers a promising direction for next-generation ACoM standardization, enabling efficient and robust ASD in bandwidth-constrained industrial environments. Full article

Preschool children develop essential mathematical concepts through play, yet little is known about how traditional board games can support this process. This small-scale microgenetic case study investigates how children unfamiliar with the Ethiopian game Gebeta (a mancala-type game) learn to play the game and what mathematical competencies emerge. Video observations were conducted with 5-year-olds in Norway as they engaged in repeated play sessions. Event logs and transcripts were analysed with a focus on children’s developing strategies and difficulties. The results show that children quickly understood the basic rules but faced challenges with directionality (i.e., maintaining the correct direction of play) and differentiating between different types of game moves. Over time, they demonstrated evident progress in following the rules. They began to treat groups of counters as units, moving from one-by-one counting to bundling and unitising. These developments align with Bishop’s fundamental activities locating and counting. We conclude that Gebeta provides a playful context in which children can practice directionality and bundling, both of which are crucial for avoiding common errors in later school mathematics, such as off-by-one counting errors and misunderstandings of the number system. The study suggests that Gebeta can be introduced in early childhood settings without simplifying the rules, providing an engaging resource for early mathematics education. Full article

Background/Objectives: In-hospital cardiac arrest (IHCA) remains a critical event with high mortality, requiring coordinated multidisciplinary response. Return of spontaneous circulation (ROSC) and hospital discharge rates are key quality indicators in resuscitation efforts. In Costa Rica, there is limited published data on team performance, protocol adherence, and the pharmacist’s role in code blue events, despite similar evidence gaps across Latin America. This study aimed to evaluate clinical outcomes and operational performance of in-hospital cardiac arrest events at a Costa Rica hospital. Methods: This retrospective cohort study included 77 adult patients who experienced IHCA at Clínica Bíblica between 2020 and 2024. Data collection was conducted between February and May 2025 from electronic medical records and code blue activation logs. Clinical variables, comorbidities, pharmacologic interventions, and outcomes were analyzed. Predictive models (Charlson Comorbidity Index [CCI], IHCA-ROSC, RISQ-PATH) and Kaplan–Meier survival analysis were applied. Results: ROSC was achieved in 55.8% of patients, and 21% were discharged alive. Asystole was the predominant initial rhythm (76.6%), and comorbidities such as renal disease and myocardial infarction were most frequent. A higher comorbidity burden was significantly associated with lower discharge rates (p = 0.032). Despite 98.7% of patients being classified as low probability for ROSC by the IHCA-ROSC model, observed outcomes exceeded expectations (predicted: 5.53% vs. actual: 55.84%; p < 0.000001). The code team adhered to institutional protocols in 100% of cases, with clinical pharmacists playing a key role in documentation and medication tracking. Conclusions: Structured multidisciplinary response was associated with ROSC rates notably higher than predicted by validated models. Opportunities for improvement include post-event laboratory testing, pharmacist-led documentation, and therapeutic hypothermia in shockable rhythms. Full article

Background/Objectives: Cancer patients using implanted venous access devices (ICVADs) for chemotherapy are at increased risk of venous thromboembolism (VTE), but the performance of risk assessment models (RAMs) in this setting is understudied. This study evaluated VTE incidence, risk factors, and the predictive performance of the Khorana, COMPASS-CAT, and ONKOTEV models. Methods: We retrospectively reviewed records of adult cancer patients treated with chemotherapy via ICVADs. The cumulative incidence (CI) of VTEs was estimated using the Fine–Gray method, and RAM performance was assessed by sensitivity, specificity, predictive values, accuracy, and AUC. Overall survival (OS) was analyzed using Kaplan–Meier and log-rank tests. Results: A total of 446 patients were included. The most common cancers were colorectal (29.6%), gastric (26%), pancreatic (18.4%), and breast (13.9%). During a median follow-up of 16.5 months, VTEs occurred in 82 patients (18.4%), including 43 (9.6%) that were ICVAD-related. Median time to VTE was 117 days and 68 days for ICVAD-related events. The CI of VTEs was 9% at 1 year and 18.4% at 2 years. ONKOTEV showed the best performance (accuracy of 74.4%, specificity of 85.7%, and AUC of 0.607), with 1-year incidence higher in the high-risk group (28.5% vs. 12.4%, p < 0.001). In contrast, all RAMs showed limited ability for ICVAD-related VTEs. VTE was independently associated with inferior OS (HR 1.39, p = 0.037). Conclusions: Cancer patients with ICVADs face a substantial risk of early VTEs. Among evaluated RAMs, ONKOTEV performed best for overall but not ICVAD-related events. Prospective studies are needed to guide prophylaxis strategies using validated RAMs. Full article

Background: Macular edema (ME) secondary to retinal vein occlusion (RVO) is a significant cause of vision impairment. Many patients show suboptimal responses to anti-vascular endothelial growth factor (anti-VEGF) monotherapy, prompting the exploration of alternative treatments. Faricimab is a bispecific antibody that targets VEGF-A and angiopoietin-2. We report 9-month real-world outcomes of switching to faricimab in therapy-resistant RVO-associated ME. Methods: In this retrospective study at a single tertiary center, patients with persistent or recurrent ME despite prior treatments (ranibizumab, aflibercept, or dexamethasone implant) were switched to faricimab. All eyes received a loading phase of four monthly faricimab injections, followed by a treat-and-extend regimen individualized per response. Key outcomes included best-corrected visual acuity (BCVA, logMAR), the central subfield thickness (CST, μm), and the intraretinal fluid (IRF) status on optical coherence tomography, assessed from the baseline (month 0, mo0) through the loading phase (mo1–mo3) and at month 9 (mo9). Results: Nineteen eyes (19 patients, mean age 64.8 years) were analyzed. The median BCVA improved from 0.20 to 0.00 logMAR by month 3 (p < 0.01) and was maintained at month 9. The median CST decreased from 325 μm at the baseline to 285 μm at month 3 (p < 0.01) and remained at 285 μm at month 9. IRF was present in 100% of eyes at the baseline, 26% at month 3, and 26% at month 9 (p < 0.01 for the baseline vs. month 9). Among eyes previously on anti-VEGF therapy (n = 14), the median treatment interval increased from 45.50 days at the baseline to 56.50 days at month 9 (p = 0.01; δ = 0.86). No intraocular inflammation or other adverse events were observed in this cohort over nine months. Conclusions: In this retrospective series, switching to faricimab was associated with improvements in vision and retinal anatomy that were maintained over 9 months; injection intervals were extended in a subset of eyes. These exploratory findings warrant confirmation in larger, controlled studies to define long-term effectiveness, safety, and dosing strategies. Full article

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with a fivefold increase in stroke risk. Direct oral anticoagulants (DOACs), including apixaban, are now the preferred therapy for stroke prevention in patients with non-valvular AF (NVAF). However, interindividual variability in drug response and safety remains a major challenge, particularly in elderly patients with comorbidities and polypharmacy. Genetic polymorphisms in drug-metabolizing enzymes and transporters may contribute to variability in apixaban exposure and bleeding risk. This study aimed to evaluate the association of polymorphisms in ABCB1, CYP3A4, and CYP3A5 with steady-state plasma concentrations of apixaban (Cssmin) and hemorrhagic complications in elderly patients with NVAF. Methods: This cross-sectional study included 197 patients (mean age 83 ± 8 years; 67% women) with NVAF treated with apixaban (5 mg twice daily). Genotyping of ABCB1 (rs1045642, rs2032582, rs1128503), CYP3A4*22 (rs35599367), and CYP3A5*3 (rs776746) was performed using allele-specific real-time PCR. Cssmin of apixaban was determined by high-performance liquid chromatography coupled with tandem mass spectrometry. Associations with bleeding events were evaluated. Results: Bleeding events were recorded in 40 patients (20.3%). An association signal was observed for ABCB1 rs1045642, where carriers of the CC genotype had a higher risk of bleeding compared with alternative alleles (OR = 2.805; 95% CI: 1.326–5.935; p = 0.006). After correction for multiple testing, the association remained significant only under the log-additive model (OR = 1.93 per C allele; 95% CI: 1.17–3.20; q = 0.0275; p_adj = 0.044), while recessive and codominant effects did not withstand Bonferroni adjustment. No significant associations were observed for rs2032582, rs1128503, CYP3A4*22, or CYP3A5*3. None of the studied polymorphisms, including rs1045642, significantly affected Cssmin. Concomitant therapy, particularly with antiarrhythmic drugs and statins (rosuvastatin), also increased bleeding risk. Conclusions: The findings highlight the potential contribution of ABCB1 rs1045642 and specific drug–drug interactions to the risk of hemorrhagic complications in elderly NVAF patients receiving apixaban. Full article