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When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region. [ABSTRACT FROM AUTHOR]

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex watershed processes and their responses to a changing climate. However, calibrating these simulators to observed data remains a significant challenge due to several persistent issues including: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified model representations of complex natural hydrologic processes. Here we demonstrate how Simulation-Based Inference (SBI) can help address both these challenges for parameter estimation. SBI uses a learned mapping between parameter space and observed data to estimate parameters for generation of calibrated model simulations. To demonstrate the potential of SBI in hydrologic modelling, we conduct a set of synthetic experiments to infer two common physical parameters, Manning's coefficient and hydraulic conductivity, using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep learning (DL) components to the SBI approach, including an 'emulator' as a surrogate for the process-based simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that where uncertainty about model structure is significant, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework to cases where model structure(s) may not be adequate are introduced using a performance-weighting approach. [ABSTRACT FROM AUTHOR]

Following the recent development trend in the struggle for cleaning the earth’s environment, solar is the one of most promising area that can partially be used as a replaceable energy from non-renewable fuel sources. As such, it plays a significant role in protecting the environment from global warming. As solar power does not emit harmful gases into the atmosphere, its production, distribution, setup, and operation are vital should the production remain constant. Even solar energy waste emissions are small; when compared to current energy sources, the amount of harmful gases is negligible. This paper presented an integrated approach for site of solar plants by using data envelopment analysis (DEA) and Fuzzy Analytical Network Process (FANP). Furthermore, these integrated methodologies, incorporated with the most relevant parameters of requirements for solar plants, are introduced. First, the paper considers an integrated hierarchical DEA and FANP model for the optimal geographical location of solar plants in Mekong Delta Region, Vietnam. Using the proposed model for implementation would allow the renewable energy policy makers to select and control the optimal location for allocating and constructing a solar energy power plant in Vietnam. This is the preferred strategy for location optimization problems associated with solar plant units in Vietnam and around the world.

In recent years, the market of textile and garment materials has been volatile, and the ongoing US-China trade war is creating good opportunities for other markets such as Vietnam, Bangladesh and Mexico to continue to expand their market share in the United States. Vietnam is expected to have great advantages thanks to cheap labor cost and strong production capacity. Raw material supplier selection in a volatile competitive environment is crucial for a company to succeed, and supplier selection is a complicate process in which decision-makers must consider multiple quantitative and qualitative features, along with their symmetrical impact, in order to achieve an optimal result. The purpose of selecting the right supplier is to improve competitiveness and product quality, while satisfying customer demand at a minimum production cost. The aim of this paper is to propose a multicriteria decision making model (MCDM) for garment and textile supplier selection. In the first stage, all criteria affecting this process are defined by using the supply chain operations reference model (SCOR) and experts’ opinion. Incorporating hybrid fuzzy set theory into the analytical network process (ANP) model is the most effective tool for addressing complex problems of decision-making, which has a connection with various qualitative criteria; thus, the Fuzzy Analytical Hierarchy Process (FAHP) was applied for determining the weight of all potential suppliers, and the preference ranking organization method for enrichment of evaluations (PROMETHEE II) was used for ranking the supplier. The results of this research will assist researchers and decision makers in identifying, adapting and applying appropriate methods to identify the optimal material suppliers in the textile and garment industry. This research can also be used to support supplier selection decisions in other industries.

High-resolution, spatially-distributed process-based models are a well-established tool to explore complex watershed processes and how they may evolve under a changing climate. While these models are powerful, calibrating them can be difficult because they are costly to run and have many unknown parameters. To solve this problem, we need a state-of-the-art, data- driven approach to model calibration that can scale to the high-compute, high-dimensional hydrologic simulators that drive innovation in our field today. Simulation- Based Inference (SBI) uses deep learning methods to learn a probability distribution of simulation parameters by comparing simulator outputs to observed data. The inferred parameters can then be used to run calibrated model simulations. This approach has pushed boundaries in simulator-intensive research from cosmology, particle physics, and neuroscience, but is less familiar to hydrology. The goal of this paper is to introduce SBI to the field of watershed modeling by benchmarking and exploring its performance in a set of synthetic experiments. We use SBI to infer two common physical parameters of hydrologic process-based models, Manning’s Coefficient and Hydraulic Conductivity, in a snowmelt-dominated catchment in Colorado, USA. We employ a process-based simulator (ParFlow), streamflow observations, and several deep learning components to confront two recalcitrant issues related to calibrating watershed models: 1) the high cost of running enough simulations to do a calibration; 2) finding ‘correct’ parameters when our understanding of the system is uncertain or incomplete. In a series of experiments, we demonstrate the power of SBI to conduct rapid and precise parameter inference for model calibration. The workflow we present is general-purpose, and we discuss how this can be adapted to other hydrology-related problems. [ABSTRACT FROM AUTHOR]

The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising. [ABSTRACT FROM AUTHOR]

• This paper presents a method for generating local weakest assumptions using a backtracking algorithm. • The backtracking algorithm is based on CDNF algorithm and a variant of membership query answering technique. • The correctness of the backtracking algorithm is presented in the paper • The backtracking algorithm is then integrated into a framework for effectively rechecking evolving software. • The paper presents experimental results for some common systems in the researcher community. This paper presents a framework for verifying evolving component-based software using assume-guarantee logic. The goal is to improve CDNF-based assumption generation method by having local weakest assumptions that can be used more effectively when verifying component-based software in the context of software evolution. For this purpose, we improve the technique for responding to membership queries when generating candidate assumptions. This technique is then integrated into a proposed backtracking algorithm to generate local weakest assumptions. These assumptions are effectively used in rechecking the evolving software by reducing time required for assumption regeneration within the proposed framework. The proposed framework can be applied to verify software that is continually evolving. An implemented tool and experimental results are presented to demonstrate the effectiveness and usefulness of the framework. [ABSTRACT FROM AUTHOR]

Introduction: Ovarian metastatic squamous carcinoma of the cervix is rare, accounting for about 0.4%. This study reports a single case of metastatic recurrent cervical cancer in the ovary., Case Presentation: A 46-year-old patient with a history of cervical cancer T1b2N0M0 underwent a radical hysterectomy, bilateral pelvic lymph node dissection, and ovarian preservation. One year later, the patient was admitted to the hospital because of abdominal pain in the left iliac fossa; the abdominal computed tomography image showed a left ovarian tumour. The patient underwent laparoscopic left oophorectomy. Postoperative histopathology confirmed ovarian squamous cell carcinoma. From this case, we would like to review the literature on epidemiology, diagnosis, treatment, and prognosis., Clinical Discussion: Ovarian preservation during surgery in patients with cervical cancer offers many benefits, but careful patient selection is required. However, it should be selected carefully and closely monitored., Conclusions: Clinicians should be aware of this situation of ovarian metastasis in patients with early cervical cancer undergoing ovarian-conserving surgery., Competing Interests: None., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)