Increase In Processing Time Research Articles

Comparison of heavy metal contaminants removal using EDTA and Cyanex 302 as chelating agents for supercritical CO2-based soil remediation

Supercritical CO2-based soil remediation technology has become one new method with its advantages of high efficiency, environmental protection, and no secondary pollution. In this study, experiments on a kilogram-scale experimental system have been conducted under different pressures, temperatures, processing times, etc. A wide range of parameter conditions (∼75 °C, ∼40 MPa, ∼40 min) have been tested to explore parameter effects on the removal efficiencies (RE) of metal contaminants: lead, chromium, and arsenic. Comparisons between Cyanex 302 and Ethylene Diamine Tetraacetic Acid (EDTA) as chelators have been conducted: (1) Both Cyanex 302 and EDTA show significant role in promoting the soil remediation process under supercritical CO2 remediation process. Additionally, the removal efficiency of EDTA for the three metals is generally lower than that of Cyanex 302 under the same operating conditions. (2) Cyanex 302 exhibits selectivity for metals. For Cyanex 302, the RE of both lead (Pb) and arsenic (As) was quite high (>93 %), while the removal efficiency of chromium (Cr) showed an obvious regularity with the increase of temperature, pressure and processing time, from 37 % to 83 %. (3) For EDTA cases, the RE of lead (Pb), chromium (Cr) and arsenic (As) showed different trends: the lowest removal efficiency found to be 48 %, 15 %, 28 % at 20 MPa, 35 °C, 20 min conditions, respectively; and the highest of 83 %, 63 %, 64 %, respectively, at 30 MPa, 55 °C conditions. EDTA cases showed different results for all three metals: the influence of temperature on removal efficiency is most obvious, while the influence of processing time on efficiency is neglectable.

Experimental Investigation of Water Jet-Guided Laser Micro-Hole Drilling of Cf/SiC Composites.

In this paper, water jet-guided laser (WJGL) drilling of Cf/SiC composites was employed and the effects of the processing parameters on the depth and quality of the micro-holes were systematically investigated. Firstly, the depth measurement showed that the increase in processing time and power density led to a significant improvement in micro-hole drilling depth. However, the enhancement of the water jet speed resulted in a pronounced decrease in the depth due to the phenomenon of water splashing. In contrast, the scanning speed, path overlap ratio, pulse frequency, and helium pressure exhibited less effect on the micro-hole depth. Secondly, the microstructural analysis revealed that the increase in power density resulted in the deformation and fracture of the carbon fibers, while the augmentation in water jet speed reduced the thermal defects. Finally, based on the optimization of the processing parameters, a micro-hole of exceptional quality was achieved, with a depth-to-diameter ratio of 8.03 and a sidewall taper of 0.72°. This study can provide valuable guidance for WJGL micro-hole drilling of Cf/SiC composites.

Spin-Coating Fabrication Method of PDMS/NdFeB Composites Using Chitosan/PCL Coating.

This paper verified the possibility of applying chitosan and/or ferulic acid or polycaprolactone (PCL)-based coatings to polydimethylsiloxane/neodymium-iron-boron (PDMS/NdFeB) composites using the spin-coating method. The surface modification of magnetic composites by biofunctional layers allows for the preparation of materials for biomedical applications. Biofunctional layered magnetic composites were obtained in three steps. The spin-coating method with various parameters (time and spin speed) was used to apply different substances to the surface of the composites. Scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) were used to analyze the thickness and surface topography. The contact angle of the obtained surfaces was tested. Increasing spin speed and increasing process time for the same speed resulted in decreasing the composite's thickness. The linear and surface roughness for the prepared coatings were approximately 0.2 μm and 0.01 μm, respectively, which are desirable values in the context of biocompatibility. The contact angle test results showed that both the addition of chitosan and PCL to PDMS have reduced the contact angle θ from 105° for non-coated composite to θ~59-88° depending on the coating. The performed modifications gave promising results mainly due to making the surface hydrophilic, which is a desirable feature of projected biomaterials.

Investigating the impact of boiling and pressure cooking on resistant starch levels in food

SummaryResistant starch (RS) has been shown to manipulate food digestion and nutrient metabolism as well as being important in promoting gut health. However, the presence of RS varies across diverse food systems depending upon process conditions. To investigate this, chickpeas, potatoes, arrowroots, unripe bananas and oats were subjected to boiling and pressure cooking at various cooking durations and the RS% was determined. After 45 min of boiling and 15 min of pressure cooking, RS% of chickpeas increased by ~29% and ~41%, respectively. Increasing the cooking time, together with boiling and pressure cooking, led to higher RS% in chickpeas, mainly due to the retrogradation of starch after gelatinisation. In contrast, boiling and pressure cooking led to a reduction in RS% for potatoes, arrowroots, unripe bananas and oats, while a further decrease was encountered with the increase in processing time. After 45 min of boiling, RS% of potatoes, arrowroots, unripe bananas, and oats were reduced by 58%, 22%, 80% and 70%, respectively. After 15 min of pressure cooking, RS% of potatoes, arrowroots and unripe bananas were reduced by 41%, 22% and 83%, respectively. This was mainly due to the disruption of the ordered structure of starch molecules during thermal processing in the presence of water. This study clearly elucidates the impact of boiling and pressure cooking on RS% as a function of processing time and type of RS source.

GPU Acceleration for SAR Satellite Image Ortho-Rectification

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

Predicting word vectors for microtext

AbstractThe use of computer‐mediated communication has resulted in a new form of written text called Microtext, which is very different from well‐written text. Most previous approaches deal with microtext at the character level rather than just words resulting in increased processing time. In this paper, we propose to transform static word vectors to dynamic form by modelling the effect of neighbouring words and their sentiment strength in the AffectiveSpace. To evaluate the approach, we crawled Tweets from diverse topics and human annotation was used to label their sentiments. We also normalized the tweets to fix phonetic variations, spelling errors, and abbreviations manually. A total of 1432 out‐of‐vocabulary (OOV) texts and their IV texts made it to the final corpus with their corresponding polarity. To assess the quality of the corpus, we used several OOV classifiers such as linear regression and observed over 90% accuracy. Next, we inferred word vectors using a novel four‐gram model based on sentiment intensity and reported accuracy on both open domain and closed domain sentiment classifiers. We observed an improvement in the range of 4–20 on Twitter, Movie and Airline reviews over baselines.

Impact of microwave heating on product safety and quality in meatballs

The effects of microwave heating at different processing time and power levels (360 W, 600 W and 900 W) on Salmonella Typhimurium, Escherichia coli O157:H7, Listeria monocytogenes and Staphylococcus aureus in Turkish type meatballs were evaluated. Besides, the changes in the textural and sensorial properties of meatballs were determined to reveal the cooking time and temperatures that would make the product safety without affecting the consumer preferences. In this direction, the experimentally contaminated meatballs were subjected to microwave heating at low, medium and high power levels for different processing times and analysed for the survival of the relevant pathogenic bacteria. The total aerobic bacteria count in meatballs could be reduced approximately 3–4 log CFU/g by medium and high microwave powers, whereas 5–7 log CFU/g inhibition was obtained in meatballs inoculated with pathogens. The increase in power level together with the increasing process time resulted in a decrease in the counts of bacteria and an undesirable effect on the textural and sensorial qualities of meatballs. Microwave heating at medium and high power levels as from 3 min, caused excessive drying and blackening of the product, making it difficult to consume due to crust formation. Consequently, to achieve optimal acceptability of the product by consumers, meatballs should be heated with a microwave power of 600 W and 900 W for 3 min.

Characterisation and emulsifying properties of microwave pretreated camelina seed mucilage

SummaryIn this study, microwave (MW)‐assisted extraction was used to obtain mucilage from Camelina sativa seeds. MW pretreatment was applied for 0, 2.5, 5 and 7.5 min and then conventional heating was conducted (55 °C for 2 h). The extracted mucilage was spray dried. The MW‐assisted extraction method (2.02%–2.50%) had higher extraction yield for camelina seed mucilage than the conventional heating method (1.91%) (P < 0.05). The emulsifying properties of the mucilages were compared with those of gum Arabic and all emulsions displayed shear thinning behaviour (pseudoplastic, n < 1). The apparent viscosity of the conventional, 2.5, 5 and 7.5 min MW mucilage emulsions were 15.83, 14.48, 7.91 and 7.56 cP, respectively, at 10 s−1. MW pretreatment improved physical stability and resulted in a reduction in the droplet size of mucilage‐stabilised emulsions with increasing process time. The highest droplet size belonged to the conventional mucilage emulsion (d32, 4.43 μm), while the lowest belonged to the 7.5 min MW mucilage emulsion (d32, 2.08 μm) (P < 0.05). The results highlight the advantages of using MW pretreatment for mucilage extraction and the potential of using camelina seed mucilage as a natural emulsifier.

Neurocognitive Changes in Patients with Post-COVID Depression.

Background: Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD, n = 25) patients using neuropsychological testing. Methods: The study involved 71 post-COVID patients with matched control groups: recovered COVID-19 individuals without complications (n = 18) and individuals without prior COVID-19 history (n = 19). A post-COVID depression group (PCD, n = 25) was identified based on psychiatric diagnosis, and a comparison group (noPCD, n = 46) included participants with neurological COVID-19 complications, excluding clinical depression. Results: The PCD patients showed gender-dependent significant cognitive impairment in the MoCA, Word Memory Test (WMT), Stroop task (SCWT), and Trail Making Test (TMT) compared to the controls and noPCD patients. Men with PCD showed worse performances on the SCWT, in MoCA attention score, and on the WMT (immediate and delayed word recall), while women with PCD showed a decline in MoCA total score, an increased processing time with less errors on the TMT, and worse immediate recall. No differences between groups in Sniffin's stick test were found. Conclusions: COVID-related direct (post-COVID symptoms) and depression-mediated (depression itself, male sex, and severity of COVID-19) predictors of decline in memory and information processing speed were identified. Our findings may help to personalize the treatment of depression, taking a patient's gender and severity of previous COVID-19 disease into account.

A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

Artificial neural networks (ANNs) is an exponentially growing field, mainly because of its wide range of applications to everyday life such as pattern recognition or time series forecasting. In particular, reservoir computing (RC) arises as an optimal computational framework suited for temporal/sequential data analysis. The direct on-silicon implementation of RCs may help to minimize power and maximize processing speed, that is especially relevant in edge intelligence applications where energy storage is considerably restricted. Nevertheless, most of the RC hardware solutions present in the literature perform the training process off-chip at the server level, thus increasing processing time and overall power dissipation. Some studies integrate both learning and inference on the same chip, although these works are normally oriented to implement unsupervised learning (UL) [with a lower expected accuracy than supervised learning (SL)], or propose iterative solutions (with a subsequent higher power consumption). Therefore, the integration of RC systems including both inference and a fast noniterative SL method is still an incipient field. In this article, we propose a noniterative SL methodology for RC systems that can be implemented on hardware either sequentially or fully parallel. The proposal presents a considerable advantage in terms of energy efficiency (EE) and processing speed if compared to traditional off-chip methods. In order to prove the validity of the model, a cyclic echo state NN with on-chip learning capabilities for time series prediction has been implemented and tested in a field-programmable gate array (FPGA). Also, a low-cost audio processing method is proposed that may be used to optimize the sound preprocessing steps.

Degradation of Stains from Metal Surfaces Using a DBD Plasma Microreactor.

The surface cleaning of metals plays a pivotal role in ensuring their overall performance and functionality. Dielectric barrier discharge (DBD) plasma, due to its unique properties, has been considered to be a good alternative to traditional cleaning methods. The confinement of DBD plasma in microreactors brings additional benefits, including excellent stability at high pressures, enhanced density of reactive species, reduced safety risks, and less gas and energy consumption. In the present work, we demonstrated a DBD plasma-based method for the degradation of stains from metal surfaces in a microreactor. Aluminum plates with capsanthin stains were used to investigate the influence of operational parameters on the decolorization efficiency, including plasma discharge power, plasma processing time, and O2 content in the atmosphere. The results revealed that an increase in plasma discharge power and plasma processing time together with an appropriate amount of O2 in the atmosphere promote the degradation of capsanthin stains. The optimum processing condition was determined to be the following: plasma power of 11.3 W, processing time of 3 min, and Ar/O2 flow rate of 48/2 sccm. The evolution of composition, morphology, bonding configuration, and wettability of aluminum plates with capsanthin and lycopene stains before and after plasma treatment were systematically investigated, indicating DBD plasma can efficiently degrade stains from the surface of metals without damage. On this basis, the DBD plasma cleaning approach was extended to degrade rhodamine B and malachite green stains from different metals, suggesting it has good versatility. Our work provides a simple, efficient, and solvent-free approach for the surface cleaning of metals.

Formation and prediction of heterocyclic amines and N-nitrosamines in smoked sausages using back propagation artificial neural network.

Heterocyclic amines (HAs) and N-nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP-ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat-to-lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP-ANN model. With an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP-ANN model was 5-11-2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance. The contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP-ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

Analisis Faktor Penyebab Keterlambatan Proyek “XYZ”

A project tends to experience delays if planning and control are not carried out properly. Various things can happen in a construction project that can cause an increase in processing time so that project completion is delayed. The purpose of this research is to identify and classify the factors that cause delays in the "XYZ" project. This data processing uses the help of the SPSS (Statistical Product and Service Solution) computer program version 23.00. Data analysis techniques using validity, reliability and factor analysis techniques. The results of this study obtained 3 new factors from the extraction results of factor analysis, namely: Lack of Expertise and Slow Material Mobilization (Xb1), Lack of Planning and Inadequate Quality of Workforce (Xb2), Changes in Scope and Work Documents (Xb3).

Temporally-aware node embeddings for evolving networks topologies

Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. Aiming at fill the gap regarding the inability of static methods to capture evolving processes on dynamic networks, we propose a biased random walk method named Evolving Node Embedding (EVNE). EVNE leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. It learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines by up to 20% in a downstream node classification task. EVNE’s embeddings achieve better performance than others, based on experiments with four classifiers and five datasets. In addition, we present seven variations of our model to show the impact of each of EVNE’s mechanisms.

Electrochemical treatment of plastic industry wastewaters powered by photovoltaic solar energy

Electrochemical processes combined with renewable energy sources are considered sustainable and efficient for wastewater treatment. The combined photovoltaic – electrochemical (PV-EC) method can produce electrolytic hydrogen and purify industrial wastewaters by combining solar energy with electrocoagulation and electrooxidation processes. The PV-EC system's adaptability resides in its capacity to modify wastewater flow rate in response to the PV array's current intensity and instantaneous solar radiation. Operating parameters that affect process efficiency include wastewater flowrate, applied current intensity, electroprocessing time and solar irradiance were examined and optimal conditions were identified. The experimental results revealed that the initial chemical oxygen demand (COD) of 19 460 mg L-1 of the plastic industry wastewater, were decreased to 8000 mg L-1, after 2 h electroprocessing time using iron or aluminium electrodes at 600 mA current intensity. Further increase of COD removal efficiency was not possible either with increasing processing time, current intensity or using electrooxidation process. The results of our study indicate that the electrochemical treatment can reduce COD from wastewater generated during the production of plastic products. Moreover, the proposed method is applicable in decentralized areas without access to the electricity grid.

YOLO_MRC: A fast and lightweight model for real-time detection and individual counting of Tephritidae pests

Tephritidae pests severely affect the quality and safety of various melons, fruits and vegetable crops. However, many agricultural managers lack an adequate understanding of the level of pest occurrence, resulting in the misuse of pesticides, which ultimately leads to environmental pollution and economic loss. Therefore, real-time detection and counting of Tephritidae pests are important for timely pest spotting and control. This work helps quickly determine the distribution and abundance of pests in the current environment, thus providing data on pest conditions for agricultural management to optimize pesticide use. Nevertheless, the fast speed, high accuracy, and lightweight performance of real-time detection and counting are difficult to balance. To address this problem, based on the YOLOv8n model, this paper takes Bactrocera cucurbitae pests as the detection target and proposes a fast and lightweight real-time detection and individual counting model for Tephritidae pests, named YOLO_MRC. This paper introduces three key improvements: (1) Constructing a new module called Multicat into the neck network increases the focus on the detection target by incorporating an attention mechanism; (2) Reducing the original three detection heads to two and then adjusting their sizes to decrease the number of parameters in the network model; (3) Devising a novel module, C2flite, to enhance the deep feature extraction capability of the backbone network. According to the above points, this paper conducts ablation experiments to compare the performances of different models. Experiments showed that the Multicat module significantly offsets the large increase in GFLOPs and processing time caused by reducing the detection head and can further reduce the number of parameters and improve the accuracy when combined with the C2flite module. On our Bactrocera cucurbitae pest dataset, the mAP@0.5 of the YOLO_MRC model reached 99.3%. Simultaneously, as the number of parameters decreases by 63.68%, GFLOPs is reduced by 19.75%, and the processing time is shortened by 5%. To ensure the validity of the model, YOLO_MRC is compared with four excellent detection models by using manual counting results as the benchmark. YOLO_MRC achieves an average pest counting accuracy of 94%, demonstrating superior performance in terms of model size and processing time. To further explore the performance of YOLO_MRC in multiclass insect detection tasks, we choose the public dataset Pest_24_640 for comparison with four state-of-the-art models. YOLO_MRC achieves a 3.6 ms processing time and 70.4% accuracy with only a 2.4 MB model size, which demonstrates the potential of YOLO_MRC in multiclass pest detection.

IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY

Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter

Effect of Changes Turbidity on Oxygen Solubility in Fish Pond Water Electrocoagulation Process

The water used for fish farming by the Mina Lestari is very dirty, because many soil particles are dissolved in water. The water with high dirty in generally has very high turbidity. So that oxygen is difficult to dissolve in water and is not sufficient for fish life. So many fish die, due to insufficient oxygen for breathing. To reduce water turbidity is treated by electrocoagulation process combined with the aeration process. The aim of the research was to study the effect of changes in turbidity on dissolved oxygen in water. The study began by measuring the turbidity using a turbidimeter and dissolved oxygen using a DO meter. Furthermore, pour 10 liters of water into the electrocoagulation process tank. The electrocoagulation was carried out at 12 volt or 0,3 ampere for 10 minutes. The process was stopped, then the water flowed into the settling tank and left for 30 minutes to precipitate of the dirt. The water flowed into a holding tank and followed aeration process by flowing air at a rate of 500 cc per minute for 30 minutes. Then followed measuring turbidity by turbidimeter and dissolved oxygen by DO meter. Subsequent studies were carried out using the same procedure with an interval of 10 minutes processing time for electrocoagulation process. The results showed that an increase in processing time can reduce water turbidity from 68 NTU to 45 NTU or 33.82% and increase dissolved oxygen from 2.8 mg/L to 5.3 mg/L or 89.29% at 50 minutes processing.

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time.

To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.