Search Results (587)

Process-oriented BIM research has documented coordination workflows and design decisions, but project records rarely show how technical evidence becomes a basis for joint judgment and how confirmed outcomes become controlled project information for subsequent tasks. This study develops an auditable event-level framework and applies a retrospective explanatory embedded single-case design to a 266 m mixed-use supertall building. It reconstructs 31 collaborative decision events from 266 evidence fragments across 41 archival records. Within the case, scheme decision-making and specialist design centered on establishing a defensible design basis; design development linked joint review, responsibility assignment, and controlled updates of models, drawings, and issue records to establish a shared cross-disciplinary baseline; and construction detailing and implementation emphasized translating site and procurement changes into the current executable project state. Changed conditions could reopen joint judgment before controlled project information was updated. Comparable events revealed distinct evidentiary discontinuities: analytical outputs were sometimes retained without traceable use in joint judgment, decisions without traceable controlled write-back, and updated records without documentation of the preceding judgment process. Separating these transitions clarifies why similar digital tools and organizational arrangements produce different information outcomes and provides a traceable, stage-conditioned basis for design justification, baseline formation, implementation control, and cross-stage continuity. Full article

Background: Systemic inflammatory indices derived from routine complete blood counts have been proposed as inexpensive biomarkers in gynecological malignancies. Their ability to discriminate tumor grade in endometrial cancer, however, has rarely been evaluated against routinely available clinical variables under rigorous validation. Methods: We retrospectively analyzed 225 women with endometrial cancer (166 low-grade, G1–G2; 59 high-grade, G3). Nine inflammatory indices were computed. Four feature sets were compared using a prespecified elastic-net logistic regression under repeated nested cross-validation (5 outer × 5 inner folds, 20 repetitions) with 5000 stratified bootstrap confidence intervals: a collinearity-reduced inflammatory panel, the full inflammatory panel, preoperative clinical variables (age, CA-125 and preoperative albumin), and their combination. Selection among nine classifiers was retained as an exploratory analysis. Because 66% of high-grade events were non-endometrioid, every analysis was repeated in three cohorts: the full cohort, endometrioid tumors only, and the subgroup with assessed hormone receptor status. Histologic subtype was deliberately excluded because non-endometrioid carcinomas are high-grade by definition. Model behavior was interpreted with TreeSHAP and cross-checked against permutation importance. Results: Of nine inflammatory indices, only the eosinophil-to-lymphocyte ratio remained statistically associated with grade after false discovery rate correction (AUC 0.626, 95% CI 0.543–0.705; q = 0.036), with all others showing negligible effect sizes (|r| < 0.08); its discrimination was nonetheless modest and insufficient for individual-level prediction. The inflammatory panel achieved AUC 0.533 (95% CI 0.443–0.621), within the range attainable by chance in this design (permutation p = 0.254). Preoperative clinical variables reached AUC 0.687 (95% CI 0.590–0.772) in the full cohort but 0.390 (0.257–0.524) in endometrioid tumors alone and 0.627 (0.485–0.765) in the receptor-assessed subgroup. Adding inflammatory indices to clinical variables did not improve discrimination (ΔAUC = −0.025, 95% CI −0.062 to +0.012; one-sided upper bound +0.007 against a prespecified margin of +0.05). The results were unchanged under an alternative grade dichotomization and after excluding patients with inconsistent blood-count entries. Conclusions: Most inflammatory indices did not demonstrate clinically useful discrimination between G3 and G1–G2 disease in this cohort, and did not add incremental discriminative value beyond the prespecified margin over routinely available preoperative clinical variables. The apparent discrimination of the clinical variables in the full cohort did not survive restriction to endometrioid histology, indicating that it reflected histologic composition rather than grade. Decision curve analysis on recalibrated probabilities showed that no model exceeded the treat-all strategy at thresholds below the prevalence of high-grade disease, and that, above it, any advantage over treating no one was small in the full cohort and absent within endometrioid tumors. Full article

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically defined port activity anomalies over horizons of 1, 3, 7 and 14 days, under explicit temporal leakage controls and while assessing spatial transferability, the incremental value of port co-movement proxies and the operational utility of warnings. A total of 303,140 port–day observations were used for 115 ports in six selected African countries and Indonesia for the period 2019–2026. A seasonal benchmark, logistic regression and two variants of the Light Gradient Boosting Machine (LightGBM)—including one with a port co-movement proxy—were compared. Separate training, calibration and final-holdout periods were applied, along with isotonic calibration, port bootstrapping, transferability tests and threshold sensitivity analysis. In the main specification, logistic regression demonstrated a bootstrap-supported advantage for 1- and 3-day horizons, whilst differences between the models for 7- and 14-day horizons were not conclusive. A conservative rerun excluding 15 anomaly-derived predictors preserved the bootstrap-supported 1-day advantage, whereas the 3-day margin over the seasonal benchmark became inconclusive. The inclusion of a port co-movement proxy did not yield a confirmed improvement in predictive quality, whilst higher sensitivity required a marked increase in false alarms. Because the target is algorithmically defined and was not validated against an independent event registry, the results concern predictive signals of activity anomalies rather than confirmed port disruptions. The framework provides a transparent research benchmark but is not yet ready for autonomous implementation. Full article

The frequent occurrence of extreme high-temperature events has significantly affected the operating characteristics and distributed photovoltaic (PV) hosting capacity of distribution networks. However, existing hosting capacity assessment methods rarely consider the accumulated heat effect caused by sustained high temperatures. To address this issue, this paper proposes a coordinated planning method for enhancing distributed PV hosting capacity under extreme high-temperature scenarios. First, an accumulated heat load model is developed to characterize the temporal cumulative influence of sustained high temperatures on temperature-sensitive loads. Meanwhile, the uncertainties associated with PV output fluctuations and load demand variations are considered to represent the stochastic characteristics of source-side generation and load-side consumption. Subsequently, a multi-objective source–network–load coordinated planning model is established to maximize distributed PV hosting capacity while minimizing the hosting capacity enhancement cost. A multi-objective artificial bee colony (MO-ABC) algorithm incorporating Sobol sequence-based quasi-Monte Carlo sampling (Sobol-MC) and a constraint domination-based constraint handling strategy are further developed to solve the proposed model efficiently. Simulation results on the modified IEEE 33-bus distribution system show that the proposed method increases distributed PV hosting capacity by 69.52% under extreme high-temperature scenarios through coordinated optimization of PV inverter reactive power control, VAR compensation, and Incentive-based Demand Response (IDR). Full article

Automating the identification of International Classification of Diseases (ICD) codes from electronic health records (EHRs) presents a critical challenge, particularly for rare diseases where existing computational methods severely underperform due to extreme long-tail label distributions. To address this, we propose a multi-modal deep learning framework known as MMAC-Net, designed to enhance the retrospective assignment of ICD-9 codes to admissions involving rare pathologies. The model integrates unstructured clinical narratives with structured auxiliary data, specifically pharmacological prescriptions and microbiology events, using a convolutional attention-based architecture. Through a late fusion mechanism, it synthesizes attention-weighted textual representations with dense embeddings of the structured data types. Validation on the MIMIC-III dataset shows consistent improvements over a matched text-only baseline evaluated under an identical protocol. On the full dataset of 8930 ICD codes, the framework achieved a Micro-AUC of 0.997 and Precision@8 of 0.875. On the subset of admissions carrying at least 1 of 568 rare codes, adding the two structured modalities to the text encoder raises Macro-F1 from 0.011 to 0.084 and Micro-F1 from 0.368 to 0.513 relative to the text-only baseline, corresponding to relative increases of 6.69 and 0.39, respectively, while Precision@8 rises from 0.092 to 0.159 and Micro-AUC from 0.966 to 0.985. While extreme class imbalance remains a formidable obstacle, these findings underscore that incorporating structured clinical context partially mitigates the limitations of purely natural language processing approaches. Practically, the framework is intended as a decision-support component that presents a ranked shortlist of candidate codes to a human coder or clinician; by recovering rare codes that text-only systems miss, it targets the under-coding of low-prevalence conditions that degrades registry completeness and downstream epidemiological estimates. Full article

Background/Objectives: Adult granulosa cell tumors (AGCTs) of the ovary are rare sex cord-stromal malignancies characterized by an indolent clinical course and a persistent risk of late recurrence. Although adjuvant chemotherapy is considered for selected patients with higher-risk disease, the optimal platinum-based regimen remains uncertain because comparative evidence is limited. This study compared disease-free survival (DFS), treatment delivery, and toxicity between bleomycin–etoposide–cisplatin/etoposide–cisplatin (BEP/EP) and carboplatin–paclitaxel (CP) in the postoperative adjuvant setting. Methods: This retrospective, multicenter, nonrandomized comparative cohort study included 58 patients with AGCTs who underwent primary surgery and received adjuvant platinum-based chemotherapy at five tertiary oncology centers in Türkiye between 2010 and 2024. Patients received BEP/EP or CP according to institutional practice and physician discretion. The primary endpoint was disease-free survival (DFS), while secondary endpoints included treatment delivery and treatment-related toxicity. Results: A total of 58 patients were included, with 26 receiving BEP/EP and 32 receiving CP. Patients treated with CP were older, whereas fertility-sparing surgery was more common in the BEP/EP group. After a median follow-up of 75.5 months, 14 recurrences were observed; when deaths without prior recurrence were included according to the prespecified DFS definition, a total of 19 DFS events occurred. Median DFS was 77.2 months in the BEP/EP group and 126.2 months in the CP group. The estimated 5-year DFS rates were 75.5% and 83.5%, respectively, with no statistically significant difference between treatment groups (log-rank p = 0.296). In an exploratory multivariable Cox model restricted to age and treatment regimen, increasing age was associated with a higher risk of a DFS event (adjusted HR 1.046 per year, 95% CI 1.011–1.082; p = 0.010), whereas CP versus BEP/EP was not significantly associated with DFS (adjusted HR 0.426, 95% CI 0.167–1.083; p = 0.073). Treatment completion rates were numerically higher with CP (84.4% vs. 69.2%; p = 0.17). Peripheral neuropathy occurred more frequently with CP (p = 0.007), whereas nephrotoxicity occurred only in the BEP/EP group (p = 0.014). Pulmonary toxicity was observed only with bleomycin-containing treatment, and one patient developed secondary acute myeloid leukemia following BEP. Conclusions: No statistically significant difference in DFS was detected between BEP/EP and CP, while distinct toxicity patterns were observed. Given the retrospective design, limited sample size, and potential for treatment-selection bias and residual confounding, these findings should be considered exploratory and should not be interpreted as evidence of equivalent efficacy between the regimens. Full article

Accurate forecasting of Convective Available Potential Energy (CAPE) is critical for severe weather prediction. However, the operational GFS model exhibits a persistent low-CAPE bias. In this work, we apply a two-step regression–diffusion model (NVIDIA CorrDiff) to address this issue. Our results indicate that while a standard U-Net can successfully reduce the bulk systematic bias, the generated output remains overly smoothed. This occurs because, for data with long-tailed statistical distributions such as CAPE, standard models trained on mean squared error fail to capture rare, high-magnitude events. In contrast, generative diffusion models can reproduce realistic, small-scale features similar to the ground truth by learning to reverse a noise-corruption process through a series of iterative denoising steps. Our study begins with bias correction for the 24 h forecast. We then extend this by applying the model—trained solely on 24 h data—to correct forecasts of up to 120 h. This strategy leverages our finding that the GFS forecast bias is highly persistent over time. Furthermore, our examination of CAPE’s joint Probability Density Functions emphasizes the necessity of matching machine learning models to the target variable’s statistical properties. Ultimately, the effectiveness of CorrDiff highlights its potential for other challenging applications involving small-scale phenomena with long-tailed distributions. Full article

Multimodal Gazepoint Biometrics exports may combine eye-tracking, pupil, electrodermal, cardiovascular, engagement dial, TTL/event, and AOI information, but they are rarely analysis-ready. gpbiometrics provides a Gazepoint-aware R workflow v7.2.0 for importing heterogeneous files and folders, detecting schemas, determining time bases and active channels, auditing data quality, preparing signals, aligning events and AOIs, creating model-ready summaries, generating report-ready outputs, and handing data to specialist external toolchains. Its principal contribution is therefore not a new physiological estimator but an auditable vendor-aware layer between Gazepoint exports and established biosignal and statistical software. In the manuscript demonstration, the common synthetic audit executed 63 functions without error or duplication; the advanced audit reached 89 optional functions, of which 84 completed on the generic CSV demonstration, 4 required richer input objects, and 1 XDF/LSL helper was appropriately not exercised. The experimental cluster-permutation module is restricted to paired two-condition, one-dimensional participant-level time courses and is illustrated with a controlled known-effect simulation; unsupported advanced variants fail explicitly. Synthetic data are used to assess software execution and workflow structure, not physiological or behavioural validity. The revised manuscript also distinguishes core processing, lightweight approximations, external bridges, and guardrails and documents reproducibility and privacy-safe real-data smoke testing. gpbiometrics does not translate signal-derived outputs into direct claims about emotion, stress, cognition, preference, health status, diagnosis, mechanism, or exact temporal onset. Full article

Polymeric biomaterials do not degrade in vivo through isolated chemical or mechanical events. Hydrolysis, oxidation, enzymatic cleavage, fatigue, wear, protein adsorption, and lipid uptake often interact across the material surface, bulk, and surrounding biological environment, producing time-dependent changes in both structure and function. Here, we propose a conceptual framework for organizing polymer degradation under biomedical conditions as a directional network of coupled mechanisms, emphasizing how established degradation pathways can influence one another and collectively contribute to time-dependent functional loss. The framework distinguishes degradation reactions from interfacial modulators and links molecular damage to functionality retention, defined according to application-specific endpoints such as mechanical strength, mass retention, dimensional stability, or interfacial performance. We introduce a qualitative interaction matrix to describe how individual mechanisms can amplify or modulate downstream pathways, and we use this matrix to identify recurrent degradation archetypes across major biomedical polymer classes, including polyesters, polyolefins, polyamides, polyurethanes, silicones, polyacrylates, polyvinyl polymers, polyimides, and natural polymers. This perspective emphasizes that material optimization strategies rarely eliminate degradation; instead, they shift the hierarchy of active pathways. By reframing biocompatibility as a dynamic, functionality-dependent property, the proposed framework provides a structured basis for comparing polymeric biomaterials, designing more realistic in vitro tests, and developing future data-driven models of long-term implant performance. Full article

Background/Objectives: Diazoxide is used for persistent or recurrent hypoglycemia when the clinical and biochemical pattern is compatible with hyperinsulinism, but neonatal treatment populations are heterogeneous. We evaluated the clinical and hormonal characteristics and short-term outcomes associated with diazoxide use in hypoglycemic neonates. Methods: This single-center retrospective cohort included 124 hypoglycemic neonates treated between January 2020 and January 2026; 36 received diazoxide and 88 did not. Critical hormonal samples were obtained concurrently with glucose < 47 mg/dL; in treated infants, sampling preceded the first diazoxide dose. Hormonal effects were estimated with parsimonious multivariable models. Diazoxide-selection factors were examined with a reduced logistic model and Firth sensitivity analysis. Results: Treated infants were more frequently preterm (80.6% vs. 52.3%; p = 0.003) and had greater pre-treatment sepsis and ventilation burden. Severe hypoglycemia < 25 mg/dL did not differ significantly (27.8% vs. 17.0%; p = 0.176). Insulin was higher with diazoxide (median 6.05 vs. 2.77 mU/L; adjusted GMR 2.23, 95% CI 1.38–3.61; p = 0.001), as was C-peptide (adjusted GMR 1.59, 95% CI 1.03–2.47; p = 0.038). Among treated infants, 35/36 had insulin > 1.25 mU/L; 17/29 with available maximum glucose-infusion-rate data required >8 mg/kg/min. Diazoxide began at a median postnatal age of 10.5 days; 30/36 infants remained on therapy at discharge. Fluid retention requiring diuretics occurred in 11/36 (30.6%) versus 11/88 (12.5%; p = 0.017). Pulmonary hypertension was not documented on routine day 5–7 echocardiography (0/36; exact 95% CI 0.0–9.7%). Conclusions: Diazoxide use identified a clinically vulnerable, treatment-selected subgroup with evidence of inadequate insulin suppression during hypoglycemia. The data do not establish uniformly confirmed hyperinsulinism. Fluid retention was the main documented treatment-period adverse event; rare or later cardiopulmonary complications cannot be excluded by this retrospective cohort. Full article

Operational risk management (ORM) in financial institutions has traditionally relied on quantitative loss event databases that capture what went wrong, but rarely why from a human factors perspective. This paper proposes a comprehensive Human Factors Framework for ORM that integrates the Human Factors Analysis and Classification System (HFACS) taxonomy with deep learning and large language models (LLMs) to extract, classify, and predict human-factor-driven operational risks from unstructured consumer complaint narratives. Using the U.S. Consumer Financial Protection Bureau (CFPB) Consumer Complaints Database (300,000 banking narratives), we (1) define and validate an eight-factor HFACS-Banking taxonomy through a Delphi study with five domain experts (Cohen’s κ = 0.86), (2) compare three extraction approaches—regex, supervised BERT, and Mistral-7B zero-shot LLM—achieving 0.81 average F1 with the LLM-distilled BERT relative to Mistral-generated reference labels, (3) propose a hybrid HF-BERT-BiLSTM-Attention architecture that fuses contextual text embeddings with structured features and HFACS factor probabilities for predicting a four-class company-response-based complaint severity proxy, and (4) provide explainability through SHAP feature attribution applied to the Random Forest baseline and attention-weight visualization of the proposed neural model. Under a temporal split comprising training data from 2014 to 2022, validation data from 2023H1, and held-out test data from 2023H2 to 2025, the proposed model achieves 91.42% accuracy and 89.78% macro F1 (95% CI from 1000-iteration paired bootstrap: [89.34, 90.21]), outperforming Random Forest (+9.84% F1, p < 0.001 Bonferroni-corrected), BiLSTM (+5.46%, p < 0.001), FinBERT (+3.21%, p = 0.003), and BERT-only (+3.92%, p = 0.002) baselines. Under the secondary random-split ablation analysis, removing the HFACS features and replacing additive attention with mean pooling reduced macro-F1 by 3.79 and 2.35 points, respectively. The findings demonstrate the retrospective feasibility of integrating theory-grounded human-factor representations with neural language models for complaint-outcome analysis; prospective institutional validation is required before operational use. Full article

Hydrological seasonality is a major driver of microbial community dynamics in river ecosystems, yet how seasonal flow regimes regulate protistan diversity, community assembly, and functional organization remains poorly understood. In this study, environmental DNA metabarcoding targeting the 18S rRNA gene was employed to investigate seasonal variations in protistan communities across 17 sites along the Hongshuihe River, a subtropical tributary of the Pearl River, during flood and normal seasons. Flood events significantly altered environmental conditions and increased protistan alpha diversity, while community composition showed clear seasonal differentiation. Although dominant protistan lineages remained relatively stable, seasonal turnover was primarily associated with rare and low-abundance taxa, highlighting their critical role in maintaining biodiversity and driving community reassembly. Null model analyses revealed a seasonal shift in community assembly mechanisms, with deterministic processes, particularly heterogeneous selection, dominating during the flood season, whereas stochastic drift prevailed during the normal season. Distance–decay and variance partitioning analyses further demonstrated that environmental and spatial processes jointly regulated protistan community variation, with stronger environment–spatial coupling during flood periods. Functional trait analysis revealed an increased proportion of consumer taxa and a reduced contribution of phototrophic taxa during floods, suggesting a shift toward stronger heterotrophic pathways under hydrological disturbance. Collectively, our findings demonstrate that hydrological seasonality regulates riverine protistan communities through coordinated changes in rare taxa dynamics, assembly processes, and functional restructuring, emphasizing the importance of incorporating protistan diversity and functional traits into long-term river ecosystem monitoring. Full article

Background: Chordoma is a rare malignant bone tumor thought to arise from remnants of the embryonic notochord. Its management remains challenging because of its proximity to critical neurovascular structures and its high propensity for local recurrence. Current standard treatment consists of maximal safe resection followed by radiotherapy, yet durable disease control remains difficult to achieve in many patients. Emerging evidence suggests that cyclin-dependent kinase inhibitor 2A (CDKN2A) loss is a recurrent molecular event in chordoma and may serve as both a prognostic biomarker and a therapeutic target. Methods: A literature search was performed by acquiring articles containing “(CDKN2A or p16) AND (chordoma or notochordal tumor)”, and “chordoma 9p21”. Of the 41 articles retrieved, 17 met the inclusion criteria. Results: Homozygous and heterozygous CDKN2A deletions were frequently identified in chordoma using fluorescence in situ hybridization and genomic sequencing approaches. These alterations were commonly associated with loss of p16^INK4A expression, the protein product of CDKN2A, supporting a role in tumorigenesis and disease progression. Epigenetic mechanisms may also contribute to reduced p16^INK4A expression in a subset of tumors. Preclinical studies in CDKN2A-deficient chordoma cell lines and patient-derived xenografts demonstrated sensitivity to cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, including palbociclib, flavopiridol, and abemaciclib. Combination strategies pairing palbociclib with buparlisib or rapamycin produced greater antitumor effects, particularly in p16^INK4A- and PTEN-deficient models. In addition, CDKN2A loss has been associated with adverse clinical features in selected cohorts, although its independent prognostic significance remains unclear. Available preclinical evidence further suggests that chordomas lacking p16^INK4A expression and retaining retinoblastoma pathway dependence may be particularly susceptible to CDK4/6 inhibition. Conclusions: CDKN2A loss is a recurrent molecular alteration in chordoma that is associated with reduced p16^INK4A expression, adverse clinicopathologic features, and less favorable outcomes in selected cohorts, although its independent prognostic significance remains inconsistent. Preclinical data support CDK4/6 inhibition, particularly in biomarker-selected CDKN2A-deficient tumors, and suggest that combination approaches targeting complementary pathways such as PI3K/mTOR may further enhance therapeutic efficacy. Together, these findings support the clinical relevance of CDKN2A as both a prognostic biomarker and a promising therapeutic target in chordoma. Full article

Background/Objectives: Adrenocortical carcinoma (ACC) is a rare malignancy in which overexpression of steroidogenic factor-1 (SF-1/NR5A1) and aberrant activation of canonical Wnt/β-catenin signaling are important oncogenic events. However, the extent to which these regulatory axes converge or interact at the transcriptional level remains unclear. We investigated their relationship using bulk transcriptomic and regulatory-network approaches. Methods: Regulatory network inference using RTN/ARACNe was applied to the TCGA-ACC cohort. NR5A1 and β-catenin-associated TCF/LEF regulon activities were evaluated in perturbation datasets and tested for associations with CpG island methylator phenotype (CIMP), overall survival, and gene-level interaction effects. Candidate modulators of NR5A1 activity were assessed using the MINDy algorithm. The effects of cBAF inhibition on NR5A1 regulon activity were also evaluated in H295R and CU-ACC1 cells. Results: NR5A1 knockdown repressed steroidogenic pathways, whereas β-catenin knockdown predominantly suppressed Wnt/β-catenin signaling. In TCGA-ACC, NR5A1 regulon activity was associated with CIMP-high status and overall survival. In contrast, β-catenin-related regulons were not significantly associated with CIMP-high status after adjustment for NR5A1 activity, and no significant multiplicative interaction effects were identified. Fewer than 3% of protein-coding genes showed improved fit in models including NR5A1 × TCF/LEF interaction terms, without enrichment for steroidogenic or Wnt/β-catenin-related pathways. CTNNB1 was identified as a statistically significant but low-ranking positive modulator of NR5A1 activity, whereas CTNNBIP1 was a top-decile negative modulator. cBAF inhibition was associated with NR5A1 regulon repression in both cell models. Conclusions: These findings indicate limited detectable global transcriptional convergence between NR5A1 and canonical β-catenin-related regulons in the evaluated bulk transcriptomic frameworks. Potential relationships between these pathways may depend on more localized, chromatin-dependent, protein-level, or context-specific regulatory mechanisms. NR5A1 regulon activity showed more consistent associations with CIMP-high status and clinical outcome than the β-catenin/TCF-LEF regulons in the evaluated TCGA-ACC models. The modulation patterns associated with CTNNB1 and CTNNBIP1, together with the repression of NR5A1 regulon activity following cBAF inhibition, raise the possibility that NR5A1 acts as a context-dependent regulatory node connecting oncogenic signaling and chromatin-remodeling mechanisms in ACC. These findings support further investigation into the role of chromatin-remodeling complexes in sustaining NR5A1-driven transcriptional programs in ACC. Full article

This paper studies a risk-averse stochastic unit commitment framework for an energy aggregator, operating a portfolio of conventional generators, renewable units, and battery energy storage in a network-constrained environment. Renewable generation and demand uncertainty are represented through a scenario-based extensive-form mixed-integer linear program. To avoid exposure to rare but high cost events, the model incorporates conditional value-at-risk as part of the objective function. The approach captures key market interactions, including day-ahead commitments, imbalance penalties, and power exchange with a neighboring network, while respecting generator constraints, storage dynamics, line flow limits, and bus voltage security. A comprehensive parametric study is conducted to quantify the influence of two risk parameters: the conditional value-at-risk confidence level $\alpha$ and the risk-aversion weight $\lambda$. Using a 300-scenario test set on a modified IEEE 9-bus system, the results show that risk-neutral scheduling exposes the aggregator to larger operational costs in extreme scenarios. Minor levels of risk aversion (0.1–0.5) reduce CVaR and tighten the distribution of costs. Increasing $\lambda$ further yields diminishing returns, while higher $\alpha$ values focus risk mitigation on the most severe outcomes. The results demonstrate how CVaR-based stochastic scheduling can support aggregator decision-making by quantifying downside risk under renewable uncertainty. Full article