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 approximate to 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.
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 (similar to 45 MPa). Notably, UAME-derived CGUMP film exhibited superior functional performance, including the highest hydrophobicity (water contact angle 83.6 degrees) and exceptional barrier performance, with the lowest oxygen transmission rate (15.3 cc/m(2)/day) and water vapor permeability (0.079 g mm/m(2)& centerdot;h & centerdot;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.
The potato processing industries generate 4 million tons of waste in the United States. As potato production and processing increase, the waste fraction continues to grow, posing a growing challenge for potato companies and society and requiring a sustainable solution. We examined the valorization of potato peel waste (PPW) using anaerobic digestion (AD) after thermal and wet oxidation pretreatments. AD of the pretreated PPW was evaluated by using mesophilic semi-continuous AD. The AD results showed that wet oxidation at a lower concentration of H2O2, specifically 2 % H2O2 at 175 °C for 15 min, resulted in higher conversion of lignocellulosic polymers with 86, 92, and 26 % conversion of cellulose, hemicellulose, and lignin. The higher samplession of polymers resulted in a methane yield of 345 ml/gVS, an increase of 45.6 % compared to the control sample without pretreatment. Results of life cycle assessment showed that the 2 % H2O2 wet oxidation pretreatment reduced the global warming potential by 34.3 % compared to the untreated control (0.000876 vs 0.001334 kg CO2-eq/mL CH4).
Historical and ethnographic evidence suggests that people around the world use supernatural beliefs to explain misfortune. The cross-cultural prevalence of this pattern may arise from our expectations that norm violations lead to sanctioning. In cases where an outcome seems punitive yet lacks an identifiable cause, individuals, motivated to fill this explanatory gap, may rely on beliefs in supernatural punishment to make sense of what has happened. If true, supernatural explanations for misfortunes should be more common when a victim is believed to have not cooperated in the past (i.e., in cases where misfortunes might be unexplained but otherwise appear deserved). Using a vignette experiment across nine diverse populations (N = 714), including African hunter-gatherers and Melanesian horticulturalists, we tested if knowledge of past bad actions increased participants' endorsements of supernatural causes for misfortune. Across all populations, participants rated a misfortune as more likely to be supernaturally caused when the victim was known to have failed to reciprocate a favour or engage in generous actions towards others in the past. This was slightly increased when the behaviour was additionally labelled normative. Responses to open-ended free-listing questions provide additional evidence that when the victim was known to be uncooperative, participants were more likely to propose supernatural causes for misfortune as well as supernatural solutions. Additional normative labelling had only a small effect. Together, these findings suggest that when misfortunes lack clear causes, people's motivation to explain interacts with moral evaluations, leading them to invoke supernatural beliefs most commonly when misfortune appears deserved.
