Search Results (9)

A Smart campus increasingly operates on the basis of data-driven operations, but an increasing demand for energy puts their control over costs and sustainability at risk. This study addresses the challenge of anticipating and managing energy consumption peaks in multi-campus environments by proposing a hybrid framework that combines advanced time-series forecasting models with a large language model (LLM)-driven multi-agent system. Based on the UNICON dataset, LSTM, CNN, GRU, and a combination architecture are trained and compared in terms of MAE and RMSE. The hybrid configuration achieves the greatest forecasting results by returning the minimum loss values. For the identification of critical periods, we employed a strategy based on median thresholding, which offers a categorization into low, normal, and extreme category, allowing the targeting of peak mitigation actions. We also introduce a multi-agent system based on the LLM, including the data aggregator, the forecaster, and the policy advisor, which create actionable policies informed by context. We also compare LLMs (Qwen-2.5, Gemma-2, Phi-4, Mistral, Llama-3.3) in terms of context accuracy, response relevance, semantic similarity, and retrieval/recall accuracy and fidelity, with Llama-3.3 achieving the best overall results. This framework has shown great potential, not only for energy consumption forecasting but also for developing precise policies on how to effectively manage energy consumption peaks. Full article

Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region. Full article

Analysis of pile bearing capacity is an important task in the investigation of soil-structure interaction. The paper is dedicated to the prediction methods for the pile bearing capacity calculation based on the cone penetration test (CPTu) results, namely UniCone method, Laboratoire Central des Ponts et Chaussées method (LCPC), and the method involved in the Eurocode 7—2. A set of CFA piles was tested to obtain reference bearing capacity. The ability of the prediction methods to determine the bearing capacity of the pile was investigated. In each evaluation criteria using statistical tools, the methods were ranked based on their performance. The results of the study indicate that the UniCone method is most applicable for the given conditions. The EC 7—2 method showed the largest variability of results, and we do not recommend its application without a deeper analysis. The applicability of any presented method cannot be considered final or universal. It is advisable to use more modern and updated methods which have been developed from a larger database of pile tests. The development of these methods should continue by expanding the database of tested piles together with the application of more advanced rock environment testing procedures. Full article

The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water to the people and environment and are critical to moisture recycling and transport, and represents an important variable for climate models to simulate accurately. This paper evaluates the performance of 13 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylor skill score, annual climatology comparison, cumulative distribution analysis, and empirical orthogonal function (EOF) analysis. Precipitation analysis shows: (1) This region displays higher rainfall in the north-northwest and drier conditions in the south. Models tend to underestimate northern values or overestimate the central to northwest averages. (2) The southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, and February) and models simulate this well. The northern Amazon dry season tends to occur in August, September, and October and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well. Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dry season, although ensemble means typically display the overall pattern more precisely. (3) Models struggle to capture extreme values of precipitation except when precipitation values are close to 0. (4) EOF analysis shows that models capture the dominant mode of variability, which was the annual cycle or South American Monsoon System. (5) When all evaluation metrics are considered, the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. This paper supports research in determining the most up-to-date CMIP6 model performance of precipitation regime for 1981–2014 for the Brazilian Amazon. Results will aid in understanding future projections of precipitation for the selected subset of global climate models and allow scientists to construct reliable model ensembles, as precipitation plays a role in many sectors of the economy, including the ecosystem, agriculture, energy, and water security. Full article

This paper presents different CPT methodologies for the prediction of the pile shaft resistance in tension on the example of three reference screw piles of the Jazowa test site in Poland. The shaft capacity was estimated based on the cone resistance, sleeve friction and CPT excess pore water pressure. Three piles with a diameter of 0.4 m and the length varied from 8 m to 14.6 m were subjected to static load tests in tension. Their results were used to determine the ultimate bearing capacity of the reference piles. The pile shaft resistance was estimated according to the AFNOR standard, Doan and Lehane 2018 centrifuge tests based method (Delft University of Technology approach), the Modified Unicone method, KTRI (Kajima Technical Research Institute) and LCPC (Laboratoire Central des Ponts et Chaussées) method. Then, the ultimate bearing capacity determined in static load tests was compared to the estimated values according to five different methods. The best estimation, fitting almost perfectly to static load test values, was obtained with the AFNOR method, whereas the other predictions significantly underestimated the ultimate bearing capacity. Full article

Instrument separation during root canal therapy is inevitable in endodontics with several unfavorable clinical consequences. Therefore, examining the cyclic flexural fatigue resistance of commonly used rotary endodontic files is crucial. This study aimed to determine the cyclic flexural fatigue resistance of four nickel–titanium (NiTi) rotary files used as a single canal preparation technique: WaveOne, Reciproc, Protaper F2, and Unicone medium instruments. According to the manufacturer’s instructions, each file was rotated freely within a 1.3 mm deep and 1.3 mm wide V-shaped groove in a stainless-steel block with a 40° and 5 mm radius of curvature. Cyclic fatigue resistance was compared between the NiTi files by verifying the time needed to crack. The data were analyzed using one-way analysis of variance (ANOVA) followed by Scheffé post hoc with a significant level set at p < 0.05. Our results demonstrated that the WaveOne instrument had the highest cyclic flexural fatigue resistance among the tested groups (p ≤ 0.05), while Unicone had the lowest cyclic flexural fatigue resistance. This study concluded that WaveOne size 25/0.08 could illustrate a superior cyclic flexural fatigue resistance when instrumenting root canals with the lowest possibility to cause instrument separation. Full article

Instrument fracture ranks among the most crucial complications during the endodontic treatment of a tooth. In order to better understand the practical limits of the instrument, the relation between the cyclic fatigue resistance and physical properties such as hardness, modulus of elasticity, creep and surface roughness were explored. Cyclic fatigue testing in an artificial root canal at intracanal temperature, nanoindentation and 3D microscopy were used for evaluation of four commonly used thermomechanically treated NiTi endodontic instruments (Unicone Plus 6/025, Unicone 6/025, Reciproc Blue R25 and WaveOne Gold Primary). Cyclic fatigue results were analyzed using the Kruskal–Wallis, Mann–Whitney and Bonferroni corrections. The wear resistance of Unicone 6/025 instruments was significantly lower compared to all other instruments (p < 0.05). WaveOne Gold Primary was significantly less resistant than Unicone Plus 6/025 and Reciproc Blue R25, while the difference between Reciproc Blue R25 and Unicone Plus 6/025 was insignificant (p > 0.05). These results are in correlation with measurements of local mechanical properties (hardness, elastic modulus and their ratios H/E and H³/E²). Even though surface roughness, area of cross-section and shape of instruments are important factors affecting instruments behavior, thermal processing appears to be the most important. Full article

This study investigated the cyclic fatigue resistance of three brands of nickel-titanium rotary files—Unicone (size 25 0.06; Medin), Navigator Evo (W-4; size 25 0.06; Medin) and Protaper Next (X2; size 25 0.06 Dentsply Tulsa Dental)—in reciprocating and continuous motion, during artificial canal instrumentation. Seventy-two samples—Unicone (n = 24), Navigator Evo (n = 24), and Protaper Next (n = 24)—each measuring 25 mm in length, were allocated to reciprocating (n = 36) and continuous motion (n = 36) experimental subgroups, and rotated in a simulated steel curved canal until fracture occurred. Fracture times and fragment lengths of samples in the experimental subgroups were recorded. One of the Unicone, Navigator Evo and Protaper Next fractured samples was randomly selected and analyzed for topographic characteristics by using scanning electron microscopy. Times to fracture and fragment lengths of samples were evaluated by analysis of variance and Tukey’s tests. Independent sample t test was used to compare mean values between the different groups. Protaper Next samples displayed significantly higher resistance to cyclic fatigue in reciprocating motion than Unicone and Navigator Evo samples (p < 0.001). Unicone samples exhibited the least fracture-resistant in continuous and reciprocating motion. Full article

This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation–model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler—the KPA Unicon BioGrate boiler. Full article