Real-world power distribution data are often inaccessible due to privacy and security concerns, highlighting the need for tools for generating realistic synthetic networks. Existing methods typically overlook critical reliability metrics such as the Customer Average Interruption Frequency Index (CAIFI) and the Customer Average Interruption Duration Index (CAIDI). Moreover, these methods often neglect phase consistency during the design stage, necessitating the use of a separate phase assignment algorithm. This work proposes a Bayesian Hierarchical Model (BHM) that generates phase-consistent unbalanced three-phase distribution systems, and incorporates reliability indices. The BHM learns the joint distribution of phase configuration, power demand, and reliability indices from a reference network, conditioning these attributes on topological features. We apply the proposed methodology to generate synthetic power distribution networks in Brazil, and validated it on known Brazilian networks. The results show that the BHM accurately reproduces the distributions of phase allocation, power demand, and reliability metrics on the training system. Furthermore, in out-of-sample validation on unseen data, the model generates phase-consistent networks and accurately predicts the reliability indices for the synthetic systems. The generated networks are also electrically feasible: three-phase power flows converge and voltages remain within typical operating limits, enabling studies of planning, reliability, and resilience.
This study presents a comparative analysis of lemon peel pectin extracted using a citric acid-glycerol Deep Eutectic Solvents (DES) across four extraction methodologies: conventional heating (CHE), ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and synergistic ultrasonic-assisted microwave extraction (UAME). We aimed to illustrate how extraction technology within DES influences physicochemical properties of pectin and its subsequent performance in biodegradable films. UAME achieved the highest pectin yield (20.18%) and a balanced molecular structure with well-preserved, arabinose-rich side chains (Ara/Gal ratio 1.92). While UAE yielded pectin with the highest molecular weight (1041 kD), it produced the lowest yield and weak film tensile strength (16.4 MPa) due to compact globular conformation. Conversely, CHE and MAE caused thermal degradation, resulting in lower molecular weights but mechanically robust films (∼45 MPa). Notably, UAME-derived CGUMP film exhibited superior functional performance, including the highest hydrophobicity (water contact angle 83.6°) and exceptional barrier performance, with the lowest oxygen transmission rate (15.3 cc/m2/day) and water vapor permeability (0.079 g mm/m2·h·kPa). These enhancements are attributed to the synergistic preservation of molecular branching. Ultimately, UAME is identified as a superior technology for tailoring pectin's molecular architecture to produce high-performance biopolymers for sustainable food packaging.
Slope stability prediction is a critical task in geotechnical engineering, yet regional-scale applications are often prohibitive due to the high computational costs of existing physics-based methods. While data-driven surrogate models offer a computationally efficient alternative, their predictive capabilities are usually hindered by the limited availability of high-quality real-world slope stability datasets. To address this challenge, this study proposes a novel physics-guided neural network (PGNN) framework for slope stability prediction. The novelty of this framework lies in that (1) we integrate classical stability charts as physical constraints via a staged-loss training schedule, and (2) we propose a feature-importance-based clustering strategy for data partitioning to ensure representative coverage of dominant geotechnical variables in both training and validation subsets to further enhance model robustness. The proposed framework is developed and evaluated using a compiled real-world slope dataset, with data consistency ensured through a unified Factor of Safety (FoS) recomputation. Results demonstrate that the proposed PGNN surrogate reduces initialization sensitivity and suppresses extreme prediction outliers in distribution-shifted scenarios compared to purely data-driven baselines. In a regional-scale application across an 8547 km2 topographically complex area in Washington State, the trained surrogate provided rapid post-training inference and achieved high classification agreement with Bishop-based reference calculations (F1-score ≈ 0.89); relative to the single-threaded Python Bishop workflow used in this study, this corresponded to a 15,612-fold reduction in evaluation time. This study provides an accurate and efficient surrogate for rapid Bishop-based regional slope-stability screening.
This study examines the conditional indirect effect of perceived leader greenwashing on employees’ eco-voice behavior and explores the mechanisms through which this effect unfolds. Eco-voice, a discretionary form of pro-environmental action, is critical for translating organizational sustainability goals into frontline practices, yet remains highly sensitive to leadership authenticity and trustworthiness. Data were collected in three phases from 318 full-time employees across 98 green certified hotels and analyzed using partial least squares structural equation modeling (PLS-SEM). Findings reveal that leader greenwashing negatively influences eco-voice both directly and indirectly by eroding trust in leaders’ ability and integrity. Gender differences were observed. Male employees’ eco-voice was more strongly shaped by competence-based trust, whereas female employees were more sensitive to integrity-based trust. Additionally, descriptive social norms moderated these relationships, strengthening the translation of trust into eco-voice. These findings highlight the interplay between leadership authenticity, trustworthiness, and workplace norms in shaping employees’ willingness to raise environmental concerns, and they underscore the fragile yet vital role of eco-voice in advancing organizational sustainability.
Severe obesity (body mass index ≥ 40 kg/m2 or ≥ 35 kg/m2 with obesity-related comorbidities) is increasingly prevalent and independently associated with elevated perioperative morbidity and inferior oncologic outcomes in patients with colorectal cancer (CRC). Despite these risks, intentional preoperative weight optimization is not routinely incorporated into CRC management, owing to concerns regarding treatment delay, absence of guideline endorsement, and limited supporting evidence. A literature review was conducted using PubMed and Embase to evaluate the impact of severe obesity on morbidity, mortality, and oncologic outcomes in CRC. Peer-reviewed English-language studies involving adult human subjects were included, while conference abstracts, non-English publications, and studies unrelated to obesity and CRC were excluded. In the absence of published reports describing synchronized weight loss and CRC management in patients with severe obesity, three novel retrospective case examples were included to demonstrate feasibility during neoadjuvant treatment, with institutional review board approval obtained for all cases. Severe obesity complicates CRC staging due to limitations in cross-sectional imaging and anatomic delineation. Furthermore, severe and particularly visceral obesity is associated with increased rates of anastomotic leak, surgical site infection, and conversion to open surgery. Current CRC guidelines do not incorporate structured weight-loss strategies into standard treatment algorithms. Metabolic bariatric procedures, such as sleeve gastrectomy, achieve rapid and clinically meaningful weight reduction, often resulting in improved operative exposure and technical conditions for subsequent resection. Pharmacologic therapies, while more broadly accessible and less invasive, typically yield more modest reductions in visceral adiposity. Task force members report early experience across three distinct cases of locally advanced CRC in patients with severe obesity, demonstrating successful preoperative visceral fat reduction through multidisciplinary coordination incorporating metabolic bariatric surgery or pharmacologic therapy during neoadjuvant windows, followed by definitive oncologic resection. Severe obesity adversely influences CRC staging, operative complexity, and perioperative outcomes. Intentional metabolic optimization—through bariatric surgery or pharmacologic therapy—may represent a viable adjunct within multidisciplinary, patient-centered CRC care pathways. However, the absence of prospective short- and long-term outcome data underscores the need for systematic investigation to define optimal timing, safety parameters, and oncologic efficacy of weight-loss interventions in this high-risk population.
