Search Results (1,989)

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article

Background/Objectives: Dementia represents a complex syndrome in which biological, psychological, social, and cultural dimensions intersect. While its clinical features are well documented, less is known about how lived experiences, caregiving contexts, and cultural beliefs shape the trajectory of illness. This study explored clinical, behavioral, and socio-cultural dimensions related to the quality of life of people living with dementia from an anthropological perspective, focusing on the interaction between comorbidities, cognition, lifestyle, and caregiving environments as reported by their informal caregivers. Methods: We conducted a single-center, observational cross-sectional study including 73 family caregivers of patients with clinically diagnosed dementia who accessed care at the Neurology–Psychiatry Department of the C.F.2 Clinical Hospital (Bucharest, Romania) between November 2023 and April 2024. Caregivers provided socio-demographic, behavioral, lifestyle, and cultural information using a newly developed anthropological questionnaire. Descriptive and exploratory inferential analyses were performed to examine relationships between cognitive performance, comorbidities, lifestyle factors, and socio-cultural variables. Results: People with dementia had a mean age of 79.2 ± 7.5 years (range 66–95) and were predominantly female (71.2%). Multimorbidity was common, averaging 2.22 ± 1.03 chronic conditions, mainly neurological (84.9%) and cardiovascular (68.5%). The mean BMI was 26.1 ± 3.8 kg/m². Cognitive impairment was substantial (MMSE mean 11.47 ± 7), with descriptively lower scores among older individuals and those with lower education or income, although inferential tests were underpowered. Appetite and sleep disturbances were frequent and tended to co-occur with lower activity levels. Disclosure of diagnosis occurred in 74% of cases; reactions varied widely, ranging from acceptance to denial, confusion, anxiety, and sadness. Family responses likewise reflected a heterogeneous and often ambivalent adjustment process. Cultural beliefs and spirituality played a salient role in shaping explanatory models and coping strategies, with many caregivers attributing importance to religious practices and, to a lesser extent, alternative treatments. Conclusions: In this Romanian cohort, dementia was shaped not only by age-related multimorbidity and cognitive decline but also by caregiving practices, socioeconomic constraints and culturally grounded interpretations of illness. These findings highlight the relevance of integrative approaches to dementia care that consider medical, behavioral, and socio-cultural dimensions and that incorporate caregiver perspectives to improve the quality of life of both patients and families. Full article

Underground tunnel construction projects using tunnel boring machines (TBMs) require a holistic risk perspective. Such projects face various risks arising from social, economic, political, workforce, and regulatory aspects during project execution. It is necessary to develop preventive strategies for managing these risks and thereby ensure timely project delivery, cost efficiency, and safety. In this study, we aimed to develop a comprehensive hybrid decision-making framework for analyzing risks in TBM-based tunnel construction projects. The proposed approach integrates the best–worst method (BWM), data envelopment analysis (DEA) model-based risk assessment, and the preference ranking organization method for enrichment evaluation (PROMETHEE). The BWM was applied to determine the weights of decision criteria with fewer comparisons and improved consistency. Subsequently, the DEA model was then used to compute local risk scores under multiple input and output conditions. Finally, PROMETHEE was employed to analyze the risks based on positive and negative outranking flows. The proposed approach was applied to a realistic metro construction project in Bangkok. The findings indicated that the proposed approach effectively compromised all the decision-making attributes to manage the uncertainties. The proposed methodology can support project managers, stakeholders, engineers, and relevant authorities in identifying high-priority risks and implementing effective mitigation strategies to enhance risk management in tunnel construction. Full article

Camelina and linseed cakes were included in the diet of Tenebrio molitor (TM) larvae at two levels (5% and 10%) to evaluate their effects on antioxidant and amino acid contents, oxidative stability, water activity (a_w), and sensory attributes. Six experimental diets were tested: a standard diet used by the insect farm (STD), a commercial control diet (CON), and CON with two inclusion levels of camelina (CAM 5, CAM 10) or linseed (LIN 5, LIN 10) cakes. Each treatment consisted of 12 replicates of five-week-old larvae reared until commercial size (9 weeks). Camelina and linseed cake inclusion affected the a_w of dried larvae, with the highest values in CAM 5 and the lowest in LIN 10 (0.69 vs. 0.45, respectively; p = 0.016). The highest linseed inclusion level increased susceptibility to lipid oxidation during storage (11.3 vs. an average 2.93 meq O₂/kg fat, respectively; p < 0.0001), despite elevated antioxidant concentrations (α, δ, γ -tocopherols and β-carotene). Larvae fed with CAM 5 and LIN 5 diets had a higher content of most essential amino acids compared to the other treatments (p < 0.0001). Conversely, increasing the inclusion level to 10% determined a reduction in total amino acid content and in key essential amino acids, particularly lysine (p < 0.0001). Non-essential amino acids displayed a similar trend, except glycine, whose highest value was observed in the LIN 10 group (933 vs. 652 mg/100 g, on average). Sensory evaluation showed that LIN 10 larvae achieved the highest scores for visual and overall acceptability, although some results need further investigation. Overall, camelina and linseed cakes appear to be promising, sustainable agro-industrial by-products to be exploited in TM farming, especially at moderate inclusion levels, as the nutritional quality and market appeal of TM biomass were ensured. Full article

Background: Accurate and timely diagnosis of skin lesions, including Melanoma (MEL), Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), and Nevus (NEV), is often hindered by the severe class imbalance and high morphological similarity among pathologies in clinical practice. Although multimodal learning has shown potential in resolving these issues, existing approaches often fail to address predictive uncertainty or effectively integrate heterogeneous clinical metadata. Therefore, this study proposes DermaCalibra, a robust and explainable multimodal framework optimized for small-scale, imbalanced clinical datasets. Methods: The proposed framework integrates three essential modules: First, the Attention-Based Multimodal Channel Recalibration (AMCR) module introduces a probabilistic Bayesian uncertainty estimation mechanism via Monte Carlo dropout to adjust focal loss weights, prioritizing features from underrepresented classes. Second, the Metadata-Driven Dynamic Feature Modulation and Cross-Attention Fusion (MDFM-CAF) module, designed to resolve inter-class visual ambiguity, dynamically rescales dermoscopic feature maps using non-linear clinical context transformations. Lastly, the Gradient Feature Attribution (GFA) module is implemented to provide pixel-level diagnostic heatmaps and metadata importance scores. Results: Evaluated on the PAD-UFES-20 dataset, DermaCalibra achieves a balanced accuracy (BACC) of 84.2%, outperforming current state-of-the-art (SOTA) methods by 3.6%, and a Macro Area Under the Receiver Operating Characteristic Curve (Macro AUC) of 96.9%. Extensive external validation on unseen hospital and synthetic datasets confirms robust generalizability across diverse clinical settings without the need for retraining. Conclusions: DermaCalibra effectively bridges the gap between deep learning complexity and clinical intuition through uncertainty-aware reasoning and transparent interpretability. The framework provides a reliable and scalable computer-aided diagnostic tool for early skin lesion detection, particularly in resource-limited clinical environments. Full article

Drying conditions can markedly reshape the sensory and functional quality of fruit snacks and, ultimately, consumer acceptance. This study compares Royal Gala dried apple snacks produced by indirect sun drying (SDA), oven drying (ODA) and two commercial drying methods (CCA and CFA) using an integrated approach combining instrumental colour and texture analysis, sugar profiling, and the measurement of total phenolics and antioxidant activity along with the recording of consumer hedonic and descriptive responses. Consumers (n = 100) evaluated appearance, aroma, sweetness, texture, overall liking and consumption intention on a 9-point hedonic scale, which was complemented by attribute-selection frequencies. The drying method strongly affected colour development: the SDA samples exhibited the lowest browning index (96.78 ± 2.3) and the lightest colour (L* = 84.53), whereas the ODA, CCA and CFA samples showed progressively higher levels of browning (161.83 ± 3.5 to 194.10 ± 3.7). Total sugars ranged from 25.0 to 33.8 mg/100 g extract, with fructose predominating (≈52–69% of total sugars). Phenolic-related markers also differed significantly: the ODA sample presented with the highest total phenolic content (112.5 ± 2.6 mg GAE/100 g extract) and the SDA with the lowest (78.6 ± 1.9 mg GAE/100 g extract). DPPH inhibition was 75.7%, 71.7%, 68.4% and 63.9% for the SDA, ODA, CCA and CFA samples, respectively. ABTS results were consistent with this pattern, with the SDA sample also exhibiting high antioxidant activity (39.0 ± 2.1 μmol Trolox/g extract). Importantly, the SDA and ODA samples achieved the strongest consumer acceptance, with most participants assigning an overall liking score of 8/9, consistent with higher frequencies of favourable flavour and texture. Overall, the combined physicochemical–sensory evidence indicates that drying approach strongly impacts browning, sugar perception and bioactive-related functionality, with the SDA samples yielding the most preferred product profile among the tested dried apple snacks, outperforming industrial methods in terms of consumer acceptance. Full article

Soil erosion and deposition processes act as key drivers of soil resources distribution across landscapes, affecting soil quality and functionality. However, the impacts of long-term soil erosion on soil quality and degradation in the black soil region remain unclear. Here, we assessed soil quality and degradation as a consequence of historical erosion and soil redistribution in an agricultural catchment in Northeast China. Soil quality indices (SQI) were calculated using both linear and non-linear scoring function methods, along with soil indicator selection approaches, including Total Data Set (TDS) and Minimum Data Set (MDS). Soil degradation indices (SDI), resistance indices (SRI), and the change of SQI (CSQI) were computed and compared. The mean SDI for bulk density (BD) and sand was greater than 0. When BD and sand were excluded, the mean SDI and SRI for the 0–10 cm and 10–20 cm soil layers were −29.8% and −21.9%, and 0.57 and 0.65, respectively. Surface soil (0–10 cm) organic matter (SOM), available potassium (AK), structure stability index (SSI), and total nitrogen (TN) in eroding sites, as well as AK, SSI, SOM, TN, and available phosphorus (AP) in depositional sites, are particularly sensitive to long-term erosion. Field capacity, sand, AK, and SSI were selected to develop the SQI, with the non-linear method utilizing MDS outperforming other SQIs. Most SQIs in eroding sites were lower than those in depositional sites and increased with higher soil redistribution rates. The assessment of soil degradation using SDI, SRI, and CSQI revealed that long-term erosion markedly diminished soil quality, although deposition somewhat alleviated this impact. The lower SQI in the 10–20 cm compared to the 0–10 cm soil layer was primarily attributed to decreased FC, while long-term erosion degraded soil quality by negatively affecting AK and sand content. These findings enhance our comprehension of soil degradation caused by erosion in the Mollisol region of Northeast China. Full article

Due to the rapid spread of disinformation, it is becoming increasingly difficult for the public to understand current events and how discussions and decisions are made in democratic societies. We propose a KG-grounded narrative extraction pipeline to compare disinformation and trustworthy news. English articles (2015–2023), included in EUvsDisinfo cases and matched mainstream coverage, were converted to AMR-based RDF graphs, and LLM ensembles were used to extract characters, events, causal links and framing edges grounded in these graphs. We studied two ensemble policies: a recall-oriented union that retained all model outputs and a precision-oriented consensus that kept only agreed elements, plus an LLM critic that flagged missing links, contradictions and framing inconsistencies. On an expert-annotated subset of 60 articles, the extractor ensemble attained very high precision for characters (0.99) and events (0.97) and solid performance for causal links (0.77) and framing edges (0.84), with similar scores for both classes. Our critic ensemble reached 0.74 precision. Structurally, union and consensus operated over the same grounded nodes but differed significantly in relational density, thus achieving rich vs. skeletal narrative graphs. Linking our narratives to GDELT showed that $\approx 97\%$ of extracted actors and events appeared in global news for both classes, while directional actor pairs from causal links were less often supported for disinformation (0.45) than trustworthy news (0.60). Overall, disinformation and trustworthy articles shared event backbones but diverged in the density and (to a lesser extent) directionality of causal attributions and framing relations. Full article

Background/Objectives: This study examined the prevalence of adverse childhood experiences (ACEs) among patients with myasthenia gravis (MG) and explored associations between ACE exposure and psychological outcomes. In addition, this study conducted a preliminary evaluation of an online “Positive Mental Health BMI Learning Program” and its association with changes in psychological well-being. Methods: A total of 77 patients with MG were included, with data collected between January 2024 and January 2025. Sociodemographic characteristics, ACE exposure, and psychological and disease-related indicators were assessed, including the Myasthenia Gravis Activities of Daily Living Scale (MG-ADL), the Myasthenia Gravis Quality of Life 15-item scale (MG-QOL15), the indicator of mental health BMI on well-being (mBMI), and the Patient Health Questionnaire-9 (PHQ-9). Using a single-group pre–post design, this exploratory pilot study examined associations between ACEs and psychological outcomes, along with pre–post changes among participants who completed the online program. Results: Among the 32 participants who completed the online program, mBMI scores showed an increase, primarily reflecting improvements in emotional stability (21.41 ± 4.70 to 23.03 ± 4.49, p < 0.01); however, in the absence of a control group, these changes cannot be attributed solely to the intervention. In contrast, no significant pre–post changes were observed in PHQ-9, MG-ADL, and MG-QOL15. Across the full sample, higher ACE exposure was associated with greater depressive symptom severity, as measured by the PHQ-9 (p < 0.05). Overall, 42.9% of participants reported at least one ACE, with emotional abuse being the most frequently endorsed, followed by parental separation or divorce and emotional neglect. Conclusions: ACE exposure was common among patients with MG and was associated with greater depressive symptoms. Participation in the online positive mental health BMI learning program was associated with improvements in positive psychological well-being. Full article

Existing Bayesian network-based differential privacy algorithms predominantly employ uniform privacy budget allocation. However, since attribute nodes carry heterogeneous information loads, the traditional privacy budget allocation strategy may result in insufficient noise being added to important attributes, while excessive noise is added to less important attributes. To optimize privacy budget utilization, we propose SA-PrivBayes, a scoring-function-driven allocation method. To enhance Bayesian network precision, we introduce a threshold mechanism during network construction that pre-filters low-scoring attribute pairs before applying the exponential mechanism for selection. Subsequently, during parameter learning, privacy budgets are dynamically allocated to low-dimensional attribute sets based on node-specific scoring functions. Under identical privacy budgets, our algorithm demonstrates stronger data protection capabilities compared to the PrivBayes algorithm. Experimental results indicate that, compared to traditional differential privacy methods based on Bayesian networks under identical privacy budgets, our algorithm better meets the privacy protection requirements of high-dimensional data while maintaining higher data utility. Full article

Industrial heritage embodies the complex interplay between historical continuity, technological development, and social spatial transformation. However, existing assessment methods often rely on qualitative judgments or fragmented criteria, limiting their ability to systematically evaluate the reuse potential in the context of heterogeneous heritage. To overcome this limitation, this study constructs an empirical evaluation framework that defines heritage value through quantifiable indicators and examines how different value dimensions affect reuse potential. Based on a dataset of 124 industrial heritage sites located on saline–alkali soil along the coast of Jiangsu Province, this study integrates multiple data sources such as archival records, field surveys, spatial data, and questionnaire surveys to construct a multidimensional indicator system. This system quantifies and analyzes four value dimensions: historical, architectural, technological, and socio-cultural, and employs machine learning methods for analysis. The study utilizes a Random Forest model to examine the relative impact of each dimension and assess their comprehensive explanatory power in classifying the potential for heritage reuse. The performance of the model is evaluated through cross-validation, yielding robust results (accuracy = 0.833, macro F1 = 0.812). A five-fold cross-validation is conducted to train a Random Forest classifier. The model achieves an accuracy of 0.833, a macro F1 score of 0.812, and an AUC of 0.871, outperforming the baseline classifier and validating the reliability of the analytical framework. The research findings indicate that the impact of architectural integrity and technical characteristics on reuse potential significantly outweighs symbolic or perceptual attributes, unveiling structural biases present in traditional heritage assessment practices. This study transcends descriptive assessments by empirically examining the operational modes of different value dimensions within a unified analytical framework, offering empirical insights into the mechanisms influencing the reuse of industrial heritage. The proposed framework provides a reproducible and transparent approach to support heritage conservation and adaptive reuse strategies in industrial transformation areas. Full article

Objectives: We compared patient-reported outcomes (PROs) in persons with hemophilia A (PwHA) by inhibitor status and prescribed treatment products. Methods: Hematology Utilization Group VIII study enrolled PwHA aged ≥ 2 years to collect PRO data via surveys. A clinical chart review documented the hemophilic severity, inhibitor level and treatment regimen. PROs were compared across inhibitor status and prescribed treatment products. Results: Among 85 enrolled PwHA, 9 (10.6%) had active inhibitors, 22 (25.9%) had tolerized inhibitors, and 54 (63.5%) had no inhibitors. The no-inhibitor group was significantly older (mean: 29.3 ± 13.5 years) than the tolerized (16.3 ± 9.5 years) and active inhibitor (21.9 ± 19.1 years; p = 0.001) groups. A larger proportion of participants with active inhibitors (66.7%) and no inhibitors (53.7%) reported having bleeds in the previous month compared to those with tolerized inhibitors (22.7%, p = 0.02). After covariate adjustment for age and hemophilia severity, the tolerized inhibitor group showed the lowest estimated number of joint bleeds compared to those of the no inhibitor and active inhibitor groups (p = 0.08), and the highest EQ-5D index score (p = 0.09). Emicizumab users reported significantly fewer bleeds in the previous months than those who were prescribed standard or extended half-life factor VIII (33.3% vs. 58.6%, 64.3%, p = 0.04). Conclusions: Participants with active inhibitors experienced joint bleeding rates similar to those of participants without inhibitors, likely attributable to emicizumab use. Tolerized participants reported the fewest joint bleeds and highest quality-of-life scores, potentially reflecting younger age and possible greater prophylaxis adherence. Emicizumab was associated with lower bleed rates compared to standard or extended half-life factor VIII products. Full article

Background: Mucous stools in infancy are commonly attributed to non–IgE-mediated gastrointestinal food allergies and are generally considered transient and benign. However, whether mucous stools may indicate an atopy-prone clinical phenotype and relate to later respiratory atopy remains insufficiently explored. Objective: To evaluate the long-term risk of respiratory atopy (asthma and/or allergic rhinitis) in infants presenting with mucous stools during the first year of life and to identify early clinical predictors of this risk. Methods: This retrospective cohort study included infants who presented with mucous stools within the first 12 months of life and were followed for four years. Baseline demographic, clinical, dietary, and laboratory data were extracted from standardized medical records. Mucus severity was graded using a pragmatic 0–3 clinical mucus score. The primary outcome was physician-diagnosed asthma and/or allergic rhinitis at four years. Multivariable logistic regression was used to identify independent predictors, with model discrimination assessed by the area under the receiver operating characteristic curve (AUC). Results: A total of 142 infants with complete follow-up data were analyzed. At four years, respiratory atopy was observed in 45 infants (31.7%). In multivariable analysis, family history of atopy (adjusted odds ratio [aOR] 2.68, 95% CI 1.20–5.98, p = 0.016) and wheezing at presentation (aOR 3.74, 95% CI 1.56–8.94, p = 0.003) were independent predictors of respiratory atopy. The mucus score was associated with respiratory atopy in univariable analysis but did not remain an independent predictor in multivariable modeling. The model showed good discrimination (AUC = 0.769). Conclusions: In this cohort of infants presenting with mucous stools in the first year of life, respiratory atopy was observed in nearly one-third by age 4. While mucous stool burden was associated with the outcome in univariable analyses, it did not remain an independent predictor after adjustment. Early wheezing and a family history of atopy were the strongest clinical predictors and may help identify infants who warrant closer follow-up. These findings should be interpreted as associative and hypothesis-generating in the absence of a mucous-stool–free comparison group. Full article

Six different vinification treatments were applied to evaluate the effect of prefermentative ultrasound treatments on bioactive compounds and taste sensory attributes of autochthonous Croatian grape variety Malvazija istarska. Four of them were based on the application of a prefermentative ultrasound technique on cooled cryomacerated mash (at 10 °C for 24 h) as follows: ultrasound treatments of 70% amplitude for 80 min (US80-70%) and 160 min (US160-70%) and ultrasound treatments of 100% amplitude for the same durations as the previous (US80-100% and US160-100%). The research also included a control treatment C (wine produced using standard white winemaking technology without maceration) and a cryomaceration treatment lasting one day at 10 °C (CRIO). Phenolic compounds in wine were analyzed by HPLC-DAD-FLD, total phenolic content (TPC), antioxidant activity and color intensity by UV/VIS spectrophotometry, and sensory evaluation was performed using the QDA and 100-point O.I.V./U.I.O.E. methods. Ultrasound-treated wines exhibited the most pronounced increases in TPC, antioxidant activity and color intensity, as well as total hydroxycinnamic and hydroxybenzoic acids, flavonols, flavan-3-ols, stilbenes and the total HPLC phenolic concentration. All wines obtained after ultrasound treatments received the highest scores by both sensory methods, in the majority of sensory attributes, especially the US160-100% treatment. The differences observed between treatments indicate that both ultrasound amplitude and duration play a key role in optimizing extraction of phenolic compounds and improving sensory attributes of the wine. The results indicate that ultrasound treatments significantly enhance the bioactive composition and sensory profile of Malvazija istarska wines, highlighting their nutritional, health-related, and market potential. Full article

The early diagnosis of depression is often impeded by the subjectivity inherent in traditional clinical assessments. To advance objective screening, this study proposes a lightweight neural network framework designed to discriminate between pathological depressive states and non-pathological transient negative emotions using EEG signals. Diverging from conventional methods that rely on single-domain features, we construct a comprehensive multi-domain feature space via Wavelet Packet Decomposition. Specifically, the framework integrates frequency (α/β power spectral density ratio), spatial (normalized α-asymmetry), and non-linear (Sample Entropy) attributes to capture the heterogeneous neurophysiological dynamics of depression. To effectively synthesize these diverse features, a multi-head additive attention mechanism is introduced. This mechanism empowers the model to adaptively recalibrate feature weights, thereby prioritizing the most discriminative patterns associated with the disorder. Experimental validation on the DEAP (negative emotion) and HUSM (major depressive disorder) datasets demonstrates that the proposed method achieves a classification accuracy of 92.2% and an F1-score of 93%. Comparative results indicate that our model significantly outperforms baseline SVM and standard deep learning approaches. Furthermore, the architecture exhibits high computational efficiency and rapid convergence, highlighting its potential as a deployable engine for real-time mental health monitoring in clinical scenarios. Full article