Low Error Research Articles

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA.

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1 , RAB25 , NOTCH3 , and ERBB2 . Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting

Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.976, and for Air Quality, it reached an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.94. These results highlight the model’s effectiveness in leveraging the strengths of Temporal Convolutional Networks (TCNs) for capturing multi-scale temporal patterns and Bidirectional Long Short-Term Memory (BiLSTMs) for retaining contextual information, thereby enhancing the accuracy of time series forecasting.

ACSwinNet: A Deep Learning-Based Rigid Registration Method for Head-Neck CT-CBCT Images in Image-Guided Radiotherapy.

Accurate and precise rigid registration between head-neck computed tomography (CT) and cone-beam computed tomography (CBCT) images is crucial for correcting setup errors in image-guided radiotherapy (IGRT) for head and neck tumors. However, conventional registration methods that treat the head and neck as a single entity may not achieve the necessary accuracy for the head region, which is particularly sensitive to radiation in radiotherapy. We propose ACSwinNet, a deep learning-based method for head-neck CT-CBCT rigid registration, which aims to enhance the registration precision in the head region. Our approach integrates an anatomical constraint encoder with anatomical segmentations of tissues and organs to enhance the accuracy of rigid registration in the head region. We also employ a Swin Transformer-based network for registration in cases with large initial misalignment and a perceptual similarity metric network to address intensity discrepancies and artifacts between the CT and CBCT images. We validate the proposed method using a head-neck CT-CBCT dataset acquired from clinical patients. Compared with the conventional rigid method, our method exhibits lower target registration error (TRE) for landmarks in the head region (reduced from 2.14 ± 0.45 mm to 1.82 ± 0.39 mm), higher dice similarity coefficient (DSC) (increased from 0.743 ± 0.051 to 0.755 ± 0.053), and higher structural similarity index (increased from 0.854 ± 0.044 to 0.870 ± 0.043). Our proposed method effectively addresses the challenge of low registration accuracy in the head region, which has been a limitation of conventional methods. This demonstrates significant potential in improving the accuracy of IGRT for head and neck tumors.

Trends analysis of Evapotranspiration and responses to soil moisture and wind speed over the Taklimakan Desert, China

The Tarim Basin is a typical hyper-arid area, 57% of which is occupied by the Taklimakan desert. Because evapotranspiration (ET) is a key factor in determining water demand, accurate estimation of ET is critical for efficient water management in arid regions. In this study, the spatiotemporal dynamics of ET across the Tarim Basin from 2001 to 2020 were simulated based on a revised Surface Energy Balance System (SEBS) algorithm, and the impacts of driving factors on ET trends in the Taklimakan desert were quantified. The findings indicated that the mean annual ET in the entire basin was slightly greater than that in the desert (302 mm vs. 295 mm), although the decreasing rate of annual ET across the entire basin was slower than that in the desert (−0.27 mm/year vs. −0.50 mm/year). Strong increasing trends were found in croplands, whereas decreasing trends, which accounted for 56.5% of the basin, were primarily concentrated in the central and southern parts of the desert. Correlation analysis revealed that wind speed (WS) and soil moisture (SM) were the dominant factors, with a negative contribution of more than 35% and a positive contribution of more than 45%, respectively, to ET trends. Compared with the ERA5-Land and complementary relationship ET using the extended triple collocation method, SEBS ET provided detailed information and a lower random error standard deviation. Our findings contribute to the understanding of how dominant factors impact ET in hyper-arid regions and provide a reference for water management.

تحسين علاقة المرهون لضغط نقطة الفقاعة للنفط الخام الليبي

Accurate estimation of crude oil bubble point pressure plays a vital role in many petroleum engineering calculations, such as reserve estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. The Pb can be measured in the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. In this paper, the Al-Marhoun correlation for bubble point pressure for black oil reservoirs is improved using the linearization of non-linear regression techniques to fit Libyan crudes accurately. A total of 62 PVT data reports taken from various Libyan oil fields were used in the study. The PVT data consist of an oil gravity range of 24.7 ºAPI to 46.8 ºAPI and bubble point pressures of 123 psig to 6100 psig. Statistical error analyses and graphical methods were used to evaluate the original and modified Al-Marhoun correlations. The results showed that the improved Al-Marhoun correlation exhibits significantly lower average absolute error and deviation than the published ones. The correlation coefficient of R2 of the improved Al-Marhoun correlation is 96.70%, and the average percent error (APE) is 0.70% with a standard deviation (SD) of 14.70.

A discretization-invariant extension and analysis of some deep operator networks

We present a generalized version of the discretization-invariant neural operator in Zhang et al. (2022) and prove that the network is a universal approximation in the operator sense. Moreover, by incorporating additional terms in the architecture, we establish a connection between this discretization-invariant neural operator network and those discussed in Chen and Chen (1995) and Lu et al. (2021). The discretization-invariance property of the operator network implies that different input functions can be sampled using various sensor locations within the same training and testing phases. Additionally, since the network learns a “basis” for the input and output function spaces, our approach enables the evaluation of input functions on different discretizations. To evaluate the performance of the proposed discretization-invariant neural operator, we focus on challenging examples from multiscale partial differential equations. Our experimental results indicate that the method achieves lower prediction errors compared to previous networks and benefits from its discretization-invariant property.

PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection

Additive Manufacturing (AM) has revolutionised the production of custom-shaped samples through direct manufacturing from digital design models. As the internal structural integrity of these printed samples is of critical importance in diverse applications, X-ray radiography and X-ray Computer Tomography (X-CT) have emerged as widely used non-destructive imaging methods for quality control of AM samples. Unfortunately, beam hardening and X-ray scatter often degrade the quality of X-CT images, posing a significant challenge for X-ray based inspection. In addressing X-ray scatter, most of the methodologies assume fixed scanning geometries or stationary/known object characteristics, limiting their practicality in dynamic industrial scenarios where these may change over time. Simulation-based methods have been proposed that estimate and suppress scatter by accurately simulating the forward projection process. Yet, these methods assume the availability of X-CT reconstruction for simulation, thereby requiring a large number of projections (and hence scan time) for faithful X-CT reconstruction. In this work, we propose a simulation-based scatter compensation method (PACS) that eliminates the need for a prior X-CT scan. By employing few projections and nominal surface meshes of the scanned objects, the actual pose of the objects and their superficial deviation (e.g., due to printing) are estimated and used during X-ray simulations. Furthermore, as the pipeline is inherently coupled with a mesh projector, analysis of projective residuals facilitates the inspection for deformities or defects within the scanned object. To demonstrate the versatility of PACS for mitigating scatter, experiments across various inspection scenarios are conducted, and the outcomes are compared with those of a well-established scatter compensation technique. The results consistently show a higher Signal-to-Noise Ratio and Contrast-to-Noise Ratio of pore-defects, as well as lower residual errors in all examined cases.

Sequencing by binding rivals SMOR error-corrected sequencing by synthesis technology for accurate detection and quantification of minor (< 0.1%) subpopulation variants

BackgroundDetecting very minor (< 1%) subpopulations using next-generation sequencing is a critical need for multiple applications, including the detection of drug resistant pathogens and somatic variant detection in oncology. A recently available sequencing approach termed ‘sequencing by binding (SBB)’ claims to have higher base calling accuracy data “out of the box.” This paper evaluates the utility of using SBB for the detection of ultra-rare drug resistant subpopulations in Mycobacterium tuberculosis (Mtb) using a targeted amplicon assay and compares the performance of SBB to single molecule overlapping reads (SMOR) error corrected sequencing by synthesis (SBS) data.ResultsSBS displayed an elevated error rate when compared to SMOR error-corrected SBS and SBB techniques. SMOR error-corrected SBS and SBB technologies performed similarly within the linear range studies and error rate studies.ConclusionsWith lower sequencing error rates within SBB sequencing, this technique looks promising for both targeted and unbiased whole genome sequencing, leading to the identification of minor (< 1%) subpopulations without the need for error correction methods.

Upper limb power-assist wearable robot for handling repetitive medium- to low-weight loads in daily logistics tasks

In this study, we developed an upper-limb power-assisted wearable robot designed to reduce the burden of handling repetitive medium- to low-weight loads for daily logistics workers, thereby enhancing their work efficiency and overall safety. This study proposes a practical wearable robot with a well-designed structure for effectively supporting pick-and-place tasks at waist-to-shoulder height by applying a vertical force directly to the wearer’s wrist. The proposed robot features two active joints that are minimal for vertical assistance, resulting in a lightweight and compact structure. It offers six degrees of freedom per arm, including four passive joints, allowing free end-effector movement. Designed to connect only to the wearer’s wrist, the robot’s linkage is positioned along the wearer’s arm, not requiring alignment with the human–robot joint center, making it easy to wear and having a simple structure. This paper presents a method for calculating the joint torque that accounts for the deformation of the robot’s lightweight and slim links. This approach enhances the gravity compensation accuracy, and the proposed method demonstrates a lower RMS error compared to calculations based on the statics of the rigid link model. Experimental results demonstrated that the robot allowed for a wide range of motion and consistently applied an assistive force of 2 kgf per arm, facilitating the handling of objects weighing several kilograms.

The Effects of Age and Treatment Intensity on Behavioral Target Mastery With Applied Behavior Analysis (ABA) Intervention Using Causal Moderation Models.

Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensityrefers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and massand naturalistic training. Data on the mastery of target behaviors were collected using the Catalystsoftware (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions between age and treatment intensity within ABAinterventions. Practitioners and researchers can develop more tailored and effective therapeutic strategies by identifying and leveraging these interactions. This approach optimizes the treatment process and ensures that interventions are personalized to meet the unique needs of each individual. Ultimately, this leads to more successful outcomes in behavioral therapy, fostering improved adaptive behaviors and overall development.

A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems.

In this work, we resolve the cascaded channel estimation problem and the reflected channel estimation problem for the reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. The novel two-step method contains modified multiple population genetic algorithm (MMPGA), least squares (LS), residual network (ResNet), and multi-task regression model. In the first step, the proposed MMPGA-LS optimizes the crossover strategy and mutation strategy. Besides, the ResNet achieves cascaded channel estimation by learning the relationship between the cascaded channel obtained by the MMPGA-LS and the channel of the user (UE)-RIS-base station (BS). Then, the proposed multi-task-ResNet (MTRnet) is introduced for the reflected channel estimation. Relying on the output of ResNet, the MTRnet with multiple output layers estimates the coefficients of reflected channels and reconstructs the channel of UE-RIS and RIS-BS. Remarkably, the proposed MTRnet is capable of using a lower optimization model to estimate multiple reflected channels compared with the classical neural network with the single output layer. A series of experimental results validate the superiority of the proposed method in terms of a lower norm mean square error (NMSE). Besides, the proposed method also obtains a low NMSE in the RIS with the formulation of the uniform planar array.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Novel sulfonic groups grafted sugarcane bagasse biosorbent for efficient removal of cationic dyes from wastewater

In this research, we reported the synthesis of effective sulphonated sugarcane bagasse (SCB@SA) biosorbent based on agriculture waste materials via a simple diazotization strategy for the removal of methylene blue (MB) and Bismarck Brown R(BB) dyes from waste water samples. First, the sugarcane bagasse (SCB) waste was collected, grinded, and sieved to obtain the desired size. Secondly, the SCB powder is modified with sulfanilinic acid (SA) via the formation of its diazonium salt to introduce sulfonic groups on the SCB surface. Different advanced techniques were applied to characterize the prepared materials before and after the adsorption process viz. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDS) and Thermogravimetric analysis (TGA). Different parameters affecting the adsorption process of both MB and BB were studied. Because of the higher correlation coefficient (R2 ≥ 0.999) and lower error functions, the equilibrium MB and BB adsorption isotherms for a single-dye system fit Langmuir with maximum adsorption capacity reaching to 127.48 and 166.75 mg/g for MB and BB, respectively. Moreover, the RL values obtained for both dyes lie between 0 and 1, indicating that MB and BB adsorption by SCB@SA is a favorable process. Besides, the error functions’ values of the pseudo-2nd-order are significantly lower than those of the pseudo-1st-order, implying that the adsorption MB and BB onto SCB@SA biosorbent fitted the pseudo-2nd-order kinetic model in a chemosorption manner. In the thermodynamic studies, the adsorption process is spontaneous, exothermic, and has less randomness. In addition, the SCB@SA biosorbent could be reused in five cycles maintaining on suitable adsorption efficiency. Finally, the MB and BB dyes could be adsorbed on the SCB@SA biosorbent via three mechanisms including π-π stacking, columbic attraction, and hydrogen bonding.

Understanding the Dynamics of Ocean Wave-Current Interactions Through Multivariate Multi-Step Time Series Forecasting

ABSTRACT Understanding ocean wave-current interactions’ complex dynamics is crucial for coastal engineering, marine operations, and climate research applications. This study introduces a pioneering data-driven approach by employing advanced deep learning techniques, specifically Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, to forecast both wave and current parameters at varying depths. The models are designed to capture the complex temporal relationships inherent in ocean dynamics, considering wave speed and direction, current speed, and direction as multivariate time series inputs. Two comprehensive experiments are conducted, one utilizing historical values of all parameters and another focusing on using wave parameters to forecast current parameters. Model performance is rigorously evaluated across forecast horizons of 5, 12, and 24 hours ahead using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). BiLSTM emerges as the superior model, demonstrating lower errors, particularly at higher depths, while nearshore predictions reveal challenges in shallower waters. Furthermore, the methodology incorporates hyperparameter optimization and cross-validation techniques to enhance the model’s robustness. Ultimately, this work represents a transformative leap toward smarter oceans, emphasizing the fusion of fluid dynamics and bathymetry to advance our understanding of coupled wave-current dynamics. The results showcase high accuracy and reliability across various forecast horizons and depths, signifying the method’s potential applications in oceanography, hydrodynamics, coastal engineering, and ocean renewable energy.

Novel multi-degree-of-freedom force–displacement mixed control framework for low loading rate substructure hybrid testing

In substructure hybrid tests, imposing realistic multi-degree-of-freedom (MDOF) displacement and force boundary conditions on a specimen involves controlling several actuators to emulate the MDOF boundary conditions of the experimental substructure. This setup presents significant challenges for decoupling control in MDOF systems. Due to the typically high stiffness of the specimen in the axial direction, force control is preferred over displacement control, which has limited accuracy in this direction. Typically, force control in the axial direction is combined with simultaneous displacement control in the softer direction, forming an MDOF force-displacement mixed control scheme. This scheme transforms force control into displacement control in the inner loop in the axial direction to ensure safe loading. The scheme involves addressing two major challenges: the nonlinear force–displacement transformation and the nonlinear coordinate transformation from the local actuator space (LAS) to the global Cartesian coordinate (GCC) system. To address these challenges, the Modified Newton–Raphson iteration integrated with proportional integral plus feed-forward (MNR-PIFF) and incremental kinematic transformation integrated with proportional integral plus feed-forward (IKT-PIFF) methods are proposed. Based on these methods, a novel MDOF force–displacement mixed control (NMFDC) framework is developed for substructure hybrid testing. A 6-DOF cyclic test simulation using the NMFDC framework was conducted to demonstrate its superiority. Furthermore, a 6-DOF cyclic test and substructure hybrid test at low loading rates were performed to validate the NMFDC framework because of the loading equipment limit. Experimental results and numerical simulations indicate that the NMFDC framework achieves swifter response with lower error levels. For applications in fast and real-time hybrid testing, dynamic loading equipment need to be used, the PIFF controller in the NMFDC framework should be replaced by adaptive time delay compensation in future developments.

Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran

AbstractClimate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (&gt;10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5.

The influence of radiation modeling on flame characteristics and emissions prediction in microcombustors with Ammonia/Hydrogen blends

This study advances numerical methodologies for designing microcombustor-based thermophotovoltaic systems by incorporating comprehensive analyses of radiation models and absorption coefficients. It examines the impact of non-premixed hydrogen-ammonia-air fuel blends on the micro combustion process using three-dimensional CFD simulations on a Y-shaped microcombustor under atmospheric conditions.Key to this research is modeling radiative heat transfer from multiple species, including ammonia (NH3), nitric oxide (NO), and water vapor, ensuring accurate predictions and optimization of thermal behavior and efficiency. Three modeling approaches were compared: a multispecies Planck-mean approximation radiation model (PMAC), a narrowband model for water vapor, and a scenario excluding radiation effects.The PMAC model, incorporating the absorption coefficients of ammonia and its products, consistently produced lower errors in flame length compared to the NB method across all equivalence ratios, with errors ranging from 0 % to 7 % for PMAC and 10 % to 30 % for NB, with the lowest error observed at Φ = 1.0. Additionally, the prediction of laminar burning velocity (LBV) differed by approximately 4 % between the two models.Considering different fuel blends, two scenarios were compared: constant input thermal power and constant fuel flow rate. Efficiency in the constant input thermal power scenario maintained higher radiation efficiency, in the range of 32–38%. Additionally, radiation efficiency initially increased with increasing H2 content but subsequently decreased for high H2 values due to effects on flame area, flame position, and temperature.

A comparison of occupancy-sensing and energy-saving performance: CO2 sensors versus fisheye cameras

A number of occupancy-sensing methods have been proposed to date with the goal of saving HVAC energy in commercial spaces by means of demand-controlled ventilation. Currently, CO2 measurement is the most common approach deployed in practice. However, recently a number of other approaches have been proposed, such as using surveillance cameras, depth sensors, thermal imagers, etc. In this paper, we compare the occupancy-sensing performance of high-accuracy commercial CO2 sensors and overhead fisheye cameras supported by deep-learning algorithms. Our experiments are conducted over 3 days in a large university classroom with highly-dynamic occupancy. First, we assess the impact of parameter selection and sensor placement on CO2-to-occupancy conversion accuracy. Then, for the best-performing setup, we compare this accuracy against that of fisheye cameras. Subsequently, we estimate the potential energy savings offered by both systems. Our results show that overhead fisheye cameras produce on average a 20% lower occupancy-estimation error than CO2 sensors in steady-state periods, and almost 70% lower error in transient periods across 4 performance metrics used in this work. In terms of potential energy savings, fisheye cameras offer on average 6 percentage points of additional savings compared to CO2 sensors. Given their relatively low cost and implementation simplicity, CO2 sensors are an attractive approach to saving HVAC energy, however their occupancy-estimation accuracy is highly sensitive to parameter selection that is challenging in practice. Occupancy sensing based on fisheye cameras is not subject to similar parameterization sensitivity and in addition to outperforming CO2 sensors allows precise localization of occupants in a space thus potentially enabling additional space-management and safety/security services, that CO2 sensors cannot offer.

AI-based non-linear models for mechanical and toughness properties of sustainable fiber-reinforced geopolymer concrete (FRGPC)

The incorporation of fibers into geopolymer concrete (GPC) produces FRGPC which mitigates brittle failure and restrains macro crack propagation; however, research on predicting the mechanical and toughness properties of FRGPC remains limited. This study addresses this gap by developing prediction models for compressive strength (CS), flexural strength (FS), flexural toughness (FT), and fracture toughness (FR). A dataset of 600 data points was compiled from the published literature, encompassing various constituent proportions, fiber shapes, fiber dosages, aspect ratios, and curing conditions. Employing an artificial neural network (ANN) methodology, 10 independent variables (g1, g2, …., g10) were utilized to predict four dependent variables (CS, FS, FT, and FR), resulting in the development of eight non-linear ANN models for both straight fibers (SFs) and hooked fibers (HFs). Each model has shown a higher R 2 value and lower root mean square error (RMSE) for training (70%), testing (15%), and validation (15%) datasets. The prediction model for the CS of FRGPC with HFs (CS-HF) and SF (CS-SF) showed R 2 values of 0.983 and 0.973, and RMSE of 2.088 and 2.435 MPa highlighting the accuracy of the ANN models. Similarly, the comparative analysis for FR models exhibited R 2 values of 0.997 and 0.987, and RMSE values of 0.045 Mpa √m and 0.032 Mpa √m for FR-HF and FR-SF, highlighting that fiber addition strongly impacts improving toughness properties. To identify the most influential independent variable(s), sensitivity analysis revealed g10, g1, g8, and g9 as the most influential parameters for mechanical and toughness properties with SFs. For HFs, g1, g2, and g3 were most influential for mechanical properties, and g8, g9, and g10 for toughness. This study also presented the mathematical formulation of the developed models for better interpretability and to facilitate optimal and economical FRGPC mix design selection, highlighting the potential of AI-based models in advancing sustainable construction materials.

Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns

This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.