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A backup virtual sensor (BVS) is widely used as a benchmark, for redundancy, and as a replacement for a physical sensor to implement intelligent operational applications in digital twin-enabled building systems. BVSs are modeled using various approaches and are employed to enhance physical sensor networks. BVSs are conventionally developed with built-in methods, that is, the model is built in a controlled environment, such as an experimental or simulation-based system. However, the built-in BVSs may show performance degradations when applied in real complex systems. This is because the real systems may contain unidentified practical uncertainties. Therefore, BVSs must be developed and managed with in-situ methods, that is, under real system environments. Nevertheless, the performance of an in-situ BVS is largely influenced by the training data and modeling methods. Thus, these factors must be considered in the BVS development and engineering process. Against this backdrop, this study suggests knowledge and industrial guidelines for developing high-performance BVSs in digital twin-enabled systems. Case studies are conducted for 64 cases (different training and testing datasets for 112 days) with a real building system to discuss BVS performance in terms of the training data periods, characteristics of the training data, and modeling methods. The research findings (1) offer modeling knowledge, industrial guidelines, a decision-making algorithm for appropriate modeling approaches, and insights into how building engineers implement virtual sensors and apply them to real building systems, and (2) demonstrate real-world applications of the virtual sensor-enabled digital twin in building automation systems.

This study investigates the inhibitory effects of high-density pulsed electric current (HDPEC) treatment on fatigue crack initiation in directionally solidified nickel-based superalloy. Experiments were conducted under fully reversed conditions (R = -1) using low-cycle fatigue tests to assess the impact of HDPEC on crack initiation. The distribution of misorientation at different stages before and after crack initiation of untreated and HDPEC-treated samples was analyzed through kernel average misorientation maps by electron backscatter diffraction method. The results demonstrate that the HDPEC treatment significantly delays the initiation life of cracks, extending from approximately 2000 cycles to 5000 cycles. Additionally, the average fatigue life of the samples increased from 3851 cycles to 10881 cycles, more than doubling the fatigue life. Furthermore, the formation of persistent slip markings on the surface of the HDPEC-treated samples was completely suppressed, indicating changes in the persistent slip band’s structure. This phenomenon led to a distinct pattern of crack formation and propagation compared to untreated samples, suggesting that HDPEC treatment effectively alters the pattern of crack initiation and propagation by homogenizing dislocation distribution and alleviating residual stresses caused by cyclic loading. The result confirms the potential of HDPEC technology to enhance the fatigue life of nickel-based superalloys, and underscores the potential for further research in this domain.

This study employs artificial neural networks (ANNs) to predict the structural compliance of randomly generated irregular geometries derived from Finite Element (FE) calculations. By imposing volume constraints, the scope of the study is confined to applying ANNs for learning from structural data generated by considering either multiple random walks of a circle or a set of randomly placed circles with allowed overlaps. Numerical results indicate that the learning outcomes of the former approach are more satisfactory than those of the latter. This suggests that the effectiveness of employing ANNs for predicting the structural compliance of irregular geometries is contingent upon how the random geometries are generated and the material volume ratio. The learning outcomes of irregular structures generated by the former approach with a higher volume ratio exhibit greater satisfaction due to a higher degree of structural connectivity.

Abstract Background Although the development of BCR::ABL1 tyrosine kinase inhibitors (TKIs) rendered chronic myeloid leukemia (CML) a manageable condition, acquisition of drug resistance during blast phase (BP) progression remains a critical challenge. Here, we reposition FLT3, one of the most frequently mutated drivers of acute myeloid leukemia (AML), as a prognostic marker and therapeutic target of BP-CML. Methods We generated FLT3 expressing BCR::ABL1 TKI-resistant CML cells and enrolled phase-specific CML patient cohort to obtain unpaired and paired serial specimens and verify the role of FLT3 signaling in BP-CML patients. We performed multi-omics approaches in animal and patient studies to demonstrate the clinical feasibility of FLT3 as a viable target of BP-CML by establishing the (1) molecular mechanisms of FLT3-driven drug resistance, (2) diagnostic methods of FLT3 protein expression and localization, (3) association between FLT3 signaling and CML prognosis, and (4) therapeutic strategies to tackle FLT3+ CML patients. Results We reposition the significance of FLT3 in the acquisition of drug resistance in BP-CML, thereby, newly classify a FLT3+ BP-CML subgroup. Mechanistically, FLT3 expression in CML cells activated the FLT3-JAK-STAT3-TAZ-TEAD-CD36 signaling pathway, which conferred resistance to a wide range of BCR::ABL1 TKIs that was independent of recurrent BCR::ABL1 mutations. Notably, FLT3+ BP-CML patients had significantly less favorable prognosis than FLT3− patients. Remarkably, we demonstrate that repurposing FLT3 inhibitors combined with BCR::ABL1 targeted therapies or the single treatment with ponatinib alone can overcome drug resistance and promote BP-CML cell death in patient-derived FLT3+ BCR::ABL1 cells and mouse xenograft models. Conclusion Here, we reposition FLT3 as a critical determinant of CML progression via FLT3-JAK-STAT3-TAZ-TEAD-CD36 signaling pathway that promotes TKI resistance and predicts worse prognosis in BP-CML patients. Our findings open novel therapeutic opportunities that exploit the undescribed link between distinct types of malignancies. Graphical Abstract

The global building sector plays a pivotal role in addressing sustainability, climate change, and carbon neutrality. This study introduces the original concept of virtual building models (VBMs) throughout the lifecycle of the building industry, considering the unique characteristics of this industry. VBMs are defined as mathematical models that represent the physical behavior of buildings throughout their lifecycle. These models consist of subvirtual models that represent the physical operational environments of the target building, encompassing physical variables, equipment, systems, indoor environments, and structural and material behaviors. This concept can contribute to achieving the existing concepts of building information modeling, digital twins, and cyber-physical systems within building operations, communities, and environments. In this study, building models are categorized into five aspects: virtual model environments, types, interactions, integrations, and levels of detail (LODs). The LODs are classified into five levels (LOD100, LOD200, LOD300, LOD350, and LOD400) depending on the primary environments of the virtual models and their coverage across the entire building. Virtual modeling methodologies are proposed considering the virtual model aspects to achieve higher or intended LOD states from both academic and industrial perspectives. Finally, future research directions are discussed to drive the concept of virtual buildings to shape the future of the building industry through digital transformations.

In the present paper, the multiple applications of high-density pulsed electric current (HDPEC) with current densities between 100 and 200 A/mm2 made fatigue crack growth (FCG) slow, in which a current density of more than 150 A/mm2 provided high efficiency to refrain from FCG. The application of multiple HDPECs under the current density of 200 A/mm2 showed a significantly reduced fatigue crack growth rate (FCGR). The fracture surfaces without the HDPEC effect showed signs of FCG along the crystal structure elongated in the rolling direction, on the other hand, a series of welded joints were formed at the crack tips due to the effect of applying multiple HDPECs, which provided different patterns of FCG leading to crack arrest. When initial HDPEC is applied in a short crack under the same HDPEC condition, it shows partial welding at the crack tip, while more concentrated welding at the crack tip by moderate Joule-heating is shown as fatigue progresses and the fatigue crack length increases. Elemental analysis by energy-dispersive X-ray spectroscopy and crystallographic analysis by electron backscatter diffraction also provided the microstructure features at the crack tip subjected to the application of multiple HDPECs. It is most efficient to perform in a short crack with multiple applications, and applying multiple manners as the crack progresses contributes to the accumulated improvement of fatigue properties by slowing the FCGR based on each application.

In this paper, a series of molecular dynamics (MD) simulations was performed to identify the high-temperature tensile properties of nanostructured polycrystalline CoCrCuFeNi high entropy alloy (HEA). The mechanism of strength reduction at elevated temperatures was understood through nanostructure observation such as phase transformation and dislocation evolution in MD simulation. The applicability of this material from room temperature to 1200 ℃ as a high-temperature use of structural material was identified. The stress-strain curve was found to gradually decrease ultimate tensile strength and yield stress as the temperature applied to the material increases. The elastic modulus decreases rapidly at slightly high temperatures but decreases gradually as it goes to the extremely high temperatures. Face-centered cubic (FCC)→hexagonal close-packed (HCP) phase transformation, which is energetic process between atoms due to tensile loading, was revealed. From the vicinity of the yield stress to the quasi-plastic regime, it competitively contributes to tensile properties between FCC→HCP phase transformation and growth of voids. In dislocation analysis, the typical partial dislocations such as perfect, Shockley, stair-rod and others were measured, in which Shockley and stair-rod partial dislocations show its characteristics that contribute to tensile properties. The results in this study contribute to understanding the high-temperature applicability of nanostructured polycrystalline CoCrCuFeNi HEA.

In the chemical industry, hydrogen (H2) production through steam-methane reforming is a well-established process. With the growing demand for H-fueled vehicles and charging stations, there is a need for compact reformers with efficient heat transfer capabilities. In this study, computational fluid dynamics simulations were performed to evaluate the methane (CH4) conversion and heat transfer efficiency of various reformer designs. These designs include single, double, and triple tubes, each with parallel- and counter-flow configurations between the reformate feed and heat source. The findings revealed substantial disparities in methane conversion between the tube designs and flow configurations. Notably, the triple-tube design outperforms single and double tubes, exhibiting higher methane conversion and improved heat transfer efficiency. This superior performance is attributed to the larger wall area facing the heat source and additional heat recovery from the reformate flowing in the inner annulus. This led to the highest temperature at the catalyst exit among the cases, increasing methane conversion, and the lowest reformate temperature at the reformer tube exit, which is also beneficial for the subsequent water–gas shift reaction process. Installing external fins on the reformer tube provided a more effective enhancement of heat transfer than using internal fins in the catalyst section. Regardless of the tube design employed, the counter-flow configuration consistently enhanced the heat transfer efficiency, resulting in 4.6–11.9% higher methane conversion than the parallel-flow configuration. Consequently, the triple-tube design with the counter-flow configuration achieved the highest methane conversion, offering flexibility in the reformer design, including the potential for lower heat input and a reduced catalyst volume.

In this study, the Liu–Murakami (LM) creep damage-coupled model was considered to evaluate the creep properties of martensitic stainless steel. The degree of creep damage was examined at two temperatures (565 ℃ and 593 ℃) to assess mechanically and thermally activated processes. A series of high applied stresses (applied stress/ultimate strength > 0.5) was considered for accelerated creep loadings. A full set of creep constants was determined by combining the Norton and LM models. Constitutive equations were used to quantitatively estimate experimental creep curves. The variation in creep constants was discussed based on stress sensitivity, such as stress triaxiality and applied stress, depending on the power of stress. The creep strain–time curves were successfully estimated. The comparison between the experimental and analytical results was in good agreement in the tertiary regime. In addition, the compensation of the two applied temperatures provides a supplementary explanation of the relationship between the ultimate strength and rupture time in terms of temperature sensitivity. The analytical results show that different applied stresses and temperatures could be compensated to characterize the creep behavior of the material. Thus, the creep strain–time and creep strain rate–certain rupture time curves were finally achieved. The analytical process in this study provides a laboratory-scale assessment of creep properties using the accelerated creep test and LM model.

The stack effect is dominant in multifamily high-rise buildings (MFHRBs) in winter because of the considerable height of MFHRBs, which causes a difference in the infiltration amount between floors. This difference causes a heating load difference between floors in a MFHRB. However, there are no indicators to quantify the heating load differences in previous studies. In this article, an indicator—the thermal draft load coefficient (TDLC)—is proposed that can be used to estimate and evaluate the differences between floors in a MFHRB. The TDLC is built on a theoretical model of the stack effect and leakage area of the airflow paths, considering the entire building airflow in a MFHRB. The theoretical model was validated by comparison with a simulation model. The winter average coefficient of variation of the root mean square error and the normalized mean bias error of the theoretical model were acceptable (17.1% and 9.3%, respectively). The TDLC resulted in a maximum of 2.5 and a minimum of approximately 0.1 in the target MFHRB. The TDLC can pre-evaluate the load difference in the building design stage and can be utilized to build design standards or guidelines.

Intelligent building systems require a data-rich environment. Virtual sensors can provide informative and reliable sensing environments for operational datasets in building systems. In particular, backup virtual sensors that are in situ are beneficial for developing the counterparts of target physical sensors in the field, thus providing additional information about residuals between both types of sensors for use in data-driven modeling, analytics, and diagnostics. Therefore, to obtain virtual sensor potentials continuously during operation, we proposed an in situ calibration method for in situ backup virtual sensors (IBVS) in operational building energy systems, based on virtual in situ calibration (VIC). The proposed method was applied using operational datasets measured by a building automation system built into a target system. In a case study, the in situ virtual sensor showed large errors (the root mean squared error (RMSE) was 0.97 °C) on certain days. After conducting the proposed VIC, the RMSE of virtual sensor errors decreased by 22.7% and 18.7% from the perspective of sensor error types such as bias and random error, respectively, in the validation month. The subsequent virtual measurements could be considerably and effectively improved without retraining the specific in situ backup virtual sensor.

Tangential-firing boilers develop large swirling fireballs by using pulverized coal and air from the corners of the burner zone. During operation, however, the boiler may experience an uneven air supply between corners; this deforms the fireball, raising various issues concerning performance and structural safety. This study investigated the characteristic boiler performance and the role of burner tilting in a 500 MWe boiler with secondary air (SA) in two corners that are up to 1.9 times larger than those in the other corners. Computational fluid dynamics simulations with advanced coal combustion sub-models were employed with the following two sets of cases: (i) six cases of actual operation to validate the modeling and (ii) sixteen cases for the parametric study of SA flow ratio and burner tilt between −15° and +26°. The results showed that the uneven SA supply deteriorated the boiler performance in various aspects and the burner tilt can be used to alleviate its impact. With a larger SA supply from the left wind box, the mass flow, heat absorption, and O2 concentration were larger in the right half of the heat exchanger sections owing to the rotating flow. The corresponding imbalance in the reaction stoichiometry increased the peak temperature entering the tube bundles by up to 60 °C and NO emissions by 6.7% as compared with normal operations. The wall heat absorption was up to 19% larger on the right and front walls. The high burner tilt of +26° helped alleviate the impact of uneven SA supply on the heat distribution and uniformity of the flow pattern and temperature, whereas a +15° burner tilt was the least favorable.

Capacitive deionization (CDI) technology is currently considered a potential candidate for brackish water desalination. In this study, we designed iron oxide nanoparticle-incorporated activated carbon (AC/Fe2O3) via a facile and cost-effective hydrothermal process. The as-synthesized material was characterized using several techniques and tested as electrodes in CDI applications. We found that the distinctive properties of the AC/Fe2O3 electrode, i.e., high wettability, high surface area, unique structural morphology, and high conductivity, resulted in promising CDI performance. The electrosorptive capacity of the AC/Fe2O3 nanocomposite reached 6.76 mg g−1 in the CDI process, with a high specific capacitance of 1157.5 F g−1 at 10 mV s−1 in a 1 M NaCl electrolyte. This study confirms the potential use of AC/Fe2O3 nanocomposites as viable electrode materials in CDI and other electrochemical applications.

Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models’ objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information.

Although three-dimensional (3D)-printed anatomic models are not new to medicine, the high costs and lengthy production times entailed have limited their application. Our goal was developing a new and less costly 3D modeling method to depict organ-tumor relations at faster printing speeds. We have devised a method of 3D modeling using tomographic images. Coordinates are extracted at a specified interval, connecting them to create mesh-work replicas. Adjacent constructs are depicted by density variations, showing anatomic targets (i.e., tumors) in contrasting colors. An array of organ solid-tumor models was printed via a Fused Deposition Modeling 3D printer at significantly less cost ($0.05/cm3) and time expenditure (1.73 min/cm3; both, p < 0.001). Printed models helped promote visual appreciation of organ-tumor anatomy and adjacent tissues. Our mesh-work 3D thyroidal prototype reproduced glandular size/contour and tumor location, readily approximating the surgical specimen. This newly devised mesh-type 3D printing method may facilitate anatomic modeling for personalized care and improve patient awareness during informed surgical consent.

District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets for intelligent management applications. Based on the existing virtual sensor capabilities to estimate physical variables, performance, etc., and to detect the anomaly detection in building energy systems, this paper proposes a virtual sensor-based method for the estimation of DH energy consumption in a residential building. Practical issues, including sensor absences and limited datasets corresponding to actual buildings, were also analyzed to improve the applicability of virtual sensors in a building. According to certain virtual sensor development processes, model-driven, data-driven, and grey-box virtual sensors were developed and compared in a case study. The grey-box virtual sensor surpassed the capabilities of the other virtual sensors, particularly for operation patterns corresponding to low heating, which were different from those in the training dataset; notably, a 16% improvement was observed in the accuracy exhibited by the grey-box virtual sensor, as compared to that of the data-driven virtual sensor. The former sensor accounted for a significantly wider DHS operation range by overcoming training data dependency when estimating the actual DH energy consumption. Finally, the proposed virtual sensors can be applied for continuous commissioning, monitoring, and fault detection in the building, since they are developed based on the DH variables at the demand side.