This study answers a practical question in renewable ventilation design: where does the exergy go when solar–geothermal preconditioning is combined with latent thermal buffering? A hybrid ventilation system coupling an earth–air heat exchanger (EAHE) with an air-based photovoltaic–thermal (PVT) unit is investigated, where phase change material (PCM) is embedded around the buried duct to act as a thermal buffer. Rather than reporting only annual energy savings, the work provides a risk-aware irreversibility breakdown that tracks exergy destruction across preconditioning, mixing, humidity control, and final conditioning. Several scenario analyses are conducted using hourly meteorological records collected over the 2019–2024 period. A bi-level optimization strategy is employed to simultaneously refine the system configuration and time-dependent operating conditions, including ventilation intensity and fresh-air intake ratio. To reduce occupant discomfort under critical conditions, a conditional value-at-risk criterion is incorporated to account for extreme thermo-hygrometric situations. The results indicate that PCM integration lowers system irreversibility. This behavior is mainly attributed to decreased ventilation demand and lower fresh-air intake. As a result, exergy destruction caused by air mixing and dehumidification is reduced. Although CVaR slightly increases during limited hot periods, overall discomfort risk remains about 50–60% below the baseline.
Given the vast array of food options available, food recommender systems play a crucial role in assisting users in discovering desired foods by analyzing their past preferences, represented as interactions between users and food items. To mitigate challenges stemming from data sparsity and cold start problems, these systems incorporate supplementary data resources in conjunction with rating data to enhance the accuracy of their recommendations. Furthermore, deep learning models offer a means to extract latent features from input data resources, thereby enhancing the accuracy of the recommendation process. Nevertheless, the performance of food recommender systems is intricately linked to the judicious selection and fusion of data resources. Previous research has overlooked this critical aspect in the design of food recommender systems. To address this challenge, this paper undertakes a comparative analysis of various data resources applicable to food recommender systems. We delve into the impact of leveraging deep neural networks to obtain deep representations of these data resources, as well as their fusion techniques, on the efficacy of food recommendations. Moreover, we comprehensively investigate the efficacy of various deep data representation fusion models in mitigating the challenges posed by data sparsity and cold start problems. To this end, we design extensive experiments utilizing two widely recognized food datasets. The findings indicate that the integration of additional data resources does not consistently improve recommendation accuracy. Also, varying combinations of these resources exert differential impacts on the performance of food recommender systems.
In transportation systems with mobile and modular facilities, how to make decisions related to facility location and capacity management during the planning horizon is critical; since the effective parameters in making these decisions change over time, it is required to adjust the locations and facilities’ operational capacity within the planning horizon to coordinate with the changing parameters. This paper presents a nonlinear programming formulation for a dynamic, modular, planar hub location problem when transportation demand is stochastic and its change in a continuous-time planning horizon is time-dependent. A dynamic programming-based hybrid solution method is presented to the proposed model, in which some polynomial-time algorithms (drop interchange, simple allocation, allocation improvement, Hyperboloid Approximation Procedure (HAP), and cost calculation) are used to calculate the cost of decisions in different states. The results of instances on the Australian Postal network (AP) dataset with up to a hundred nodes and eight hubs and sensitivity analysis are reported. The findings demonstrate that in the uncertain case, the total selected capacity for each hub in the entire planning horizon is more than in the case where the uncertain parameters take their mean value.
This study presented a procedure for predicting slope stability using four machine learning algorithms (extreme gradient boosting, support vector machine, logistic regression, and random forest). Based on a real-case database of 168 multinational slopes, this study analyzed the impact of six influential inputs: slope angle, friction angle, cohesion, height, pore pressure ratio, and unit weight. Notably, the results showed that the extreme gradient boosting algorithm surpassed other algorithms, achieving approximately 94
This article explores energy planning and management in microgrids using Advanced Dynamic Programming (ADP) to address uncertainties in solar and wind power generation. Probability Distribution Functions (PDFs) are employed to accurately estimate the fluctuations of renewable resources, ensuring a stable and efficient energy management strategy. The proposed approach focuses on optimizing the logistical management of batteries and distributed generation, enhancing microgrid efficiency in power integration while maintaining economic feasibility. MATLAB-based simulations validate the effectiveness of this method, demonstrating key benefits such as reduced curtailment of renewable generation and improved system reliability. A major advantage of ADP is its rapid implementation, making it well-suited for real-time applications and scheduling optimization. The research findings highlight that adopting this strategy not only reduces operational costs but also increases the share of renewable energy while stabilizing power supply. Additionally, sensitivity analysis confirms that the model remains effective across various microgrid configurations and resource conditions. By leveraging ADP, microgrids can enhance their resilience and adaptability in managing fluctuating renewable energy sources, ultimately contributing to a more sustainable and cost-effective power system. This study underscores the potential of ADP as a valuable tool for optimizing energy distribution in modern microgrid networks.
