bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of the filter media pack configurations on the air filtration performance
Authors: Chong JJ , Poon HM 
Published: July 2024

Abstract
High efficiency particulate air (HEPA) filters are widely used in various industries to contain airborne contamination. The pressure drop of the filters is a crucial performance parameter when designing a HEPA filter as it is the resistance that the filter offers to the flow of air. Thus, it is critical to find the effect of each configuration and integrate an optimal setup for the filter to avoid high-pressure drops. Set against these backgrounds, this study aims to study the effect of different configurations on the total pressure drop of the filter, and to review how each configuration can affect each other using a commercial software ANSYS Fluent. The different filter media pack configurations and boundary conditions investigated here are including pleat shape (V-shaped and U-shaped), pleat density (1-7 pleat/cm), pleat height (1cm, 1.3cm), and inlet velocity (0.02-0.2 m/s) on the pressure drop of the filter. A two-dimensional geometrical model is developed, and then validated with respect to the data obtained from Lydall M3004-06 property sheet. Upon successful validation exercise, a series of parametric studies is conducted to numerically examine the impact of changing each configuration on the total pressure drop of the filter. Results show that an optimal pleat density is attained where the total pressure drop is minimized, i.e., 3 pleat/cm for V-shaped and 4 pleat/cm for U-shaped for inlet velocity of 0.02 m/s. At a constant inlet velocity, the U-shaped pleat has a lower pressure drop than the V-shaped pleat at low pleat densities, but a higher pressure drop at higher densities. This behaviour can be attributed to the balance between inertial and viscous resistance. Increasing the inlet velocity increases the total pressure drop for all pleat heights and decreases the optimal pleat density. This effect is less pronounced on the V-shaped pleat. Increasing the pleat height for the U-shaped pleat decreases the pressure drop and optimal pleat density but increases pressure drop in the viscous dominated region. Conversely, increasing the pleat height for the V-shaped pleat decreases the pressure drop for all pleat counts and only decreases the optimal pleat density at high velocity.
Seawater source heat pump system based on capillary heat exchanger for seepage in submarine tunnel: a case study
Authors: Zhang F J , Zhang L , Li C , Gao S J 
Published: July 2024

Abstract
The discharge of seepage water from undersea tunnel structures, often treated as wastewater, inherently carries a substantial reservoir of untapped low-grade thermal energy. Unfortunately, comprehensive investigations into harnessing this latent potential remain notably limited. This study introduced an innovative strategy through the design of an undersea tunnel seepage seawater source heat pump system. Distinguished by the integration of a capillary front-end heat exchanger, this system aimed to effectively exploit the frequently disregarded low-grade thermal energy present in the seepage water of undersea tunnel structures. The seawater seepage from the tunnel is transported to the car park at the tunnel entrance, and a seawater energy pool is constructed by storing seawater in its underground space. The use of capillary network placed in the energy pool in the front heat exchanger, water source heat pump units, circulating water pumps and fan coil end device composed of underground undersea tunnel seepage seawater source heat pump system for the building heating and cooling. Furthermore, a comparative assessment was conducted, contrasting this novel system with the traditional air-conditioning setup that utilizes chillers and gas boilers as cooling and heating sources. The aim was to evaluate its capacity for energy conservation and emission reduction. The findings from the study strongly affirmed the viability of the proposed seepage seawater source heat pump system within undersea tunnels. It boasted the potential to achieve annual savings of 53.55 tce, highlighting a noteworthy energy-saving rate of 21.2%. Concurrently, reductions in CO2, SO2, and particulate emissions amounted to 132.28 t/a, 1.07 t/a, and 0.54 t/a, respectively. This study not only stands as a reference for the strategic utilization of seepage seawater from undersea tunnel structures, prioritizing energy conservation and emission reduction, but also pioneers innovative approaches toward resource optimization and environmental sustainability, meeting the inherent needs of carbon peaking and carbon neutrality goals.
Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption
Authors: Ghiat Ikhlas , Al-Ansari Tareq 
Published: July 2024

Abstract
Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.
Heat transfer simulation for re-design of tray dryer to reduce the energy consumption in the cocoa bean drying process
Authors: Dharma I A , Haikal M G Z , Ridlwan M , Dewanto S A , Rahman M A 
Published: July 2024

Abstract
The tray dryer for the cocoa bean drying process is a solution to overcome conventional drying methods that rely on solar heat. The tray dryer is expected to facilitate the cocoa farmers to dry cocoa beans because it is faster and does not depend on weather conditions. However, even though the current condition of the tray dryer can reduce the energy consumption of drying cocoa beans (from Gunung Kidul, Yogyakarta, Indonesia) compared to the solar heat method, it remains unknown whether the cocoa beans drying quality is good enough. Therefore, this research analyzed the heat transfer phenomena of the cocoa drying process using the computational simulation method to re-design the current tray dryer design so that the new design can fit the drying characteristic of the cocoa bean and reduce energy consumption. As a result, for the air velocity of 25.91 m/s and the ½ opening of the gas valve in 10 minutes, which is already at steady-state conditions, the temperature distribution in the existing tray dryer is not suitable with the characteristics of the cocoa beans drying process. The temperature can be increased by reducing the airflow velocity or increasing the heating value by opening the valve. However, with a new design, the temperature can be increased by up to 37.00% at a velocity of 25.91 m/s with a ½ gas valve opening. Furthermore, the new design can achieve the same conditions as the existing design by only using an opening for a 1/8 gas valve. Thus, using the new design, the tray dryer can achieve the drying characteristics of cocoa beans and reduce energy consumption.
Observation of inclusions and intra-granular ferrite evolutions at the HAZ of low-carbon steel with different thermal histories
Authors: Nguyen Hong Hai , Nam Nguyen Duong , Nhung Le Thi , Ha Minh-Tan , Pham Mai Khanh 
Published: July 2024

Abstract
This study investigated the formation and growth of intra-granular ferrite with inclusions (TiN, MnS) in the heat-affected zone (HAZ) of a welding joint in low-carbon steels. The relationship between the cooling rate and the density and size of inclusions was analyzed. An orientation relationship akin to the Baker–Nutting relationship was identified between TiN and ferrite, while the orientation relationship between MnS and ferrite approximated the Kurdjumov–Sachs relationship. At low cooling rates, in the absence of nucleation support from TiN and MnS inclusions, the microstructure in the HAZ comprised Widmanstätten ferrite. Additionally, the shape, size, and density of primary ferrites and Widmanstätten ferrite in steels were influenced by sulfur content.
Behavior of silver tellurite glasses against gamma rays, neutrons, and ions using theoretical and the PHITS Monte Carlo method
Authors: Dulal Apsara , Upadhyay Devendra Raj , Mohamad Tajudin Sufffian , Khanal Raju 
Published: July 2024

Abstract
Present work analyze the ionizing radiations attenuation behaviour, photon trajectories and dose rate reduction properties of silver tellurite glasses to investigate their potential application in radiation shielding. The shielding capability of five glass systems with different elemental compositions have been calculated with the help of theoretical software: Phy-X/PSD and NIST XCOM. For the photon energy range of 1.00 keV to 100.00 GeV, various shielding parameters are calculated, including attenuation coefficients (MAC, LAC), mean free path, half value layer, tenth value layer, effective atomic number, effective electron density, energy build up factors, energy absorption buildup factors, and fast neutron removal cross section. While the energy stopping potentials, projected range of ions (H+, He+, and C+) has been presented with the help of SRIM software. Additionally photon trajectories and dose rate attenuating behavior of 105 photons generated from 137Cs and 60Co sources with activity 200.00 GBq has been studied with the help of particle and heavy ion transport (PHITS) code. It is observed that at both low and high energy levels, the glass composition, labelled S4, (70TeO2-25Ag2O-2Nb2O5-2BaO-1PbO), exhibits comparable attenuation coefficients to previously recommended glass samples for radiation shielding applications. In addition to S4, nine different types of glass samples and polymers with comparable densities to our glass sample, along with water, were included for comparison. The findings indicate that among all the samples tested, S4 demonstrates the highest and comparable radiation-protective performance, positioning it favourably for such applications.
Reciprocating wear behavior of multi-directionally forged and aged Al-Cu-Li alloy
Authors: Jagadeesh C , H Shivananda Nayaka , S Ramesh , Anne Gajanan 
Published: July 2024

Abstract
Al-Cu-Li alloys have drawn attention because of their decreased density, which is a result of the growing need for lightweight material systems in aerospace and aircraft applications. The alloy was subjected to multi-directional forging (MDF) and post-MDF artificial aging. Reciprocating sliding wear tests were conducted to investigate the effects of these processes on wear properties under different load conditions. After MDF, a decrease in wear resistance was noticed, on the other hand, 12 pass MDF treated samples showed improved wear resistance upon aging treatment. Wear scars and counter ball surfaces were examined using scanning electron microscope (SEM) to understand the wear mechanism and wear mode. The findings demonstrated that, at lower loads, adhesion and abrasion were the main wear processes; at greater loads, delamination, adhesion, and abrasion were clearly visible. The study revealed that the MDF and subsequent aging have a substantial effect on the wear behavior of Al-Cu-Li alloy and is an effective thermomechanical processing route to enhance wear resistance.
Eco-friendly fabrication of novel hydrophobic CMC–C18@MWCNTs nano-sorbent for fat content removal in the analysis of polycyclic aromatic hydrocarbons in fatty-food samples
Authors: Thabet Omar A , Al-Sodies Salsabeel , Al Zahrani Ahmed , Alqahtani Atheer A , Alamry Khalid A , A Hussein Mahmoud , Hoogenboom Richard 
Published: July 2024

Abstract
In this study, carbon nanohybrids was developed by hybridizing carboxymethyl cellulose (CMC) biopolymer with various amounts of multi-walled carbon nanotubes (MWCNTs: 0.2, 0.5, 1, 3, and 5%) in eco-friendly process to produce hydrophobic CMC–C18@MWCNTs, which was then used as a sorbent material for fat content in fatty food samples analysis. The hydrophobic biopolymers were synthesized using an ultrasound-assisted esterification process, and the physicochemical properties were analyzed using FT-IR, XRD, TGA, FE-SEM, and TEM. The performance of the hydrophobic nanocomposites was evaluated by assessing their ability to remove fat content during polycyclic aromatic hydrocarbon (PAHs) analysis in tuna samples. The results showed that CMC–C18–CNT0.2% provided the best peak shapes and highest recoveries for PAHs compounds, ranging between 74.3 and 89.7%, while CMC–C18–CNT5% had the lowest recoveries, ranging between 0 and 35%. Therefore, the lowest amount of MWCNTs was found to be the most efficient for removing fat content with providing high PAHs recovery, while increasing the MWCNTs percentage increased the hydrophobicity and removed PAHs analytes along with fat content. After the investigation, the method was validated using CMC–C18–CNT0.2% in three various levels: 2, 5 and 10 μg kg−1. The obtained results were satisfactory; the average recoveries for all PAHs compounds ranged between 74.3 to 89.7%, and the intra-day precision were estimated by coefficient of variation (%CV), where were less than 10% for all PAHs. The LOD and LOQ were lies between 0.33 to 0.89 μg kg−1 and 1.12 to 1.92 μg kg−1 respectively. For the calibration curve linearity, the correlation coefficient (r2) were higher than 0.999 for all PAHs. Overall, the hydrophobic CMC–C18@MWCNTs are a promising, modifiable, and useful material for fatty food analysis.
Optimization of process parameters in micro electrochemical machining using TOPSIS technique with Analytical Hierarchy Process (AHP)
Authors: M Siva , ArunKumar N , Ganesh M , Sathishkumar N 
Published: July 2024

Abstract
Electro-chemical machining (ECM) is one of the extensively used unconventional machining processes employed in industries to machine difficult-to-machine materials such as super alloys, titanium alloys, stainless steel, etc It does not impart any mechanical stresses on the workpiece. The primary requirement of the work material used in this process should be electrically conductive materials. In favour of these characteristics, Hastelloy C276 is used as a work material because it exhibits excellent physical properties over cast iron. Generally, the quality of machining mostly relies on the electrolyte (mmol L−1), feed rate (mm/rev), and duty ratio. These credential parameters are considered in this present work to enhance the machining quality, such as circularity, taper angle, and metal removal rate (MRR). The Taguchi L9 orthogonal array is used to learn machining behaviour under various circumstances. And this time, an Analytic Hierarchy Process (AHP) is used to allocate the weight for each response in the TOPSIS multi-criteria decision-making method. According to the findings, sodium nitrate (NaNo3) has a higher metal removal rate than sodium chloride (NaCl) and sodium bromide (NaBr). It will, however, result in a low-quality hole. The taper angle is inversely proportional to the feed rate.
Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
Authors: Papacharalampous Georgia , Tyralis Hristos , Doulamis Nikolaos , Doulamis Anastasios 
Published: July 2024

Abstract
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States, we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machine (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Integrated Multi-satellitE Retrievals (IMERG), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.
Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems
Authors: Jang Seohee , Jadidi Mohammad , Rezaeiravesh Saleh , Revell Alistair , Mahmoudi Yasser 
Published: July 2024

Abstract
This study explores the implementation of physics-informed neural networks (PINNs) to analyze turbulent flow in composite porous-fluid systems. These systems are composed of a fluid-saturated porous medium and an adjacent fluid, where the flow properties are exchanged across the porous-fluid interface. The segregated PINN model employs a novel approach combining supervised learning and enforces fidelity to flow physics through penalization by the Reynolds-averaged Navier-Stokes (RANS) equations. Two cases were simulated for this purpose: solid block, i.e. porous media with zero porosity, and porous block with a defined porosity. The effect of providing internal training data on the accuracy of the PINN predictions for prominent flow features, including flow leakage, channeling effect and wake recirculation was investigated. Additionally, L2 norm error, which evaluates the prediction accuracy for flow variables was studied. Furthermore, PINN training time in both cases with internal training data was considered in this study. Results showed that the PINN model predictions with second-order internal training data achieved high accuracy for the prominent flow features compared to the RANS data, within a 20% L2 norm error of second-order statistics in the solid block case. In addition, for the porous block case, providing training data at the porous-fluid interface showed errors of 18.04% and 19.94% for second-order statistics, representing an increase in prediction accuracy by 7% compared to without interface training data. The study elucidates the impact of the internal training data distribution on the PINN training in complex turbulent flow dynamics, underscoring the necessity of turbulent second-order statistics variables in PINN training and an additional velocity gradient treatment to enhance PINN prediction.
Towards an open pipeline for the detection of critical infrastructure from satellite imagery—a case study on electrical substations in The Netherlands
Authors: De Plaen Joël J-F G , Koks Elco E , Ward Philip J 
Published: July 2024

Abstract
Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap, processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.
Autumn phenology consistently delays in subtropical forests in China based on a new process-based model integrating temperature, photoperiod and precipitation
Authors: Xu Yue , Li Mingwei , Wu Zhaofei , Li Xiran , Liu Zunchi , Nie Xingyu , Zhang Xuan , Chen Jiahao , Hao Fanghua , Fu Yongshuo H 
Published: July 2024

Abstract
Climate warming has delayed vegetation autumn phenology, which in turn influences terrestrial carbon and water cycles and their feedback to the climate. However, the performance of autumn phenology models, especially for subtropical forests, remains poor. In this study, we extracted the end-of-photosynthetic-growing-season (EOPS) dates in subtropical China over the period 2001–2018 based on high-resolution solar-induced chlorophyll fluorescence (SIF) dataset using three fitting methods. We developed a new autumn phenology model (DMP model) that integrates precipitation and photoperiod into the classic cold-degree days model, and the new model outperformed the classic model reducing the RMSE by approximately 2 d. We found that the EOPS date was delayed by an average of 4.1 d per decade in the subtropical forests of China from 2001 to 2018, and the precipitation (partial correlation coefficient, r = 0.45), rather temperature (r = 0.29) determined the EOPS processes. We further studied future EOPS using the DMP model and found that the EOPS will delay by 1.0 d per decade under the shared socioeconomic pathway (SSP2-4.5) scenario and 2.7 d per decade under the SSP5-8.5 scenario from 2030 to 2100. Our study highlighted the role of precipitation in regulating EOPS in the subtropical forests of China and provided valuable insight for integrating multiple climatic determinants into autumn phenology models.