Efficient Approach Research Articles

An efficient approach for the preparation of branched starch through thermophilic glycogen branching enzyme modification

Glycogen branching enzymes (GBEs, EC 2.4.1.18) play a crucial role in modifying the starch structure and enhancing its properties. The common approaches to starch modification by GBEs typically involve either fully gelatinization the starch at high temperatures (above 95°C) and then cooling it before adding the enzymes or directly modifying the starch granules at medium-low temperatures (below 60°C). However, both of these methods have their limitations. Therefore, in this study, we utilized thermally stable mutant Pyrococcus horikoshii (Ph) OT3 GBEs (The thermally stable Ph GBEs mutant were named as H415W and T508K, respectively) to directly act on an ungelatinized normal corn starch (NCS) emulsion at 70°C, enabling simultaneous starch swelling and branching modification. The results showed that after modification at 70°C, the crystalline structure of the starch almost completely disappeared, the short-branched chains increased, and the amylose content, rapid digestibility, and retrogradation enthalpy (ΔH) of the H415W- and T508K-modified starch were reduced by 8.2%, 20.35%, and 4.38 J/g when compared to the NCS. Furthermore, when compared to wild-type (WT)-modified starch (modification conducted at 60°C), the amylose content, rapid digestibility, and retrogradation enthalpy (ΔH) of the H415W- and T508K-modified starch were reduced by 4%, 11.71% and 1.52 J/g, respectively. The branching efficiency of the starch was enhanced due to the achievement of simultaneous starch swelling and branching modification at 70°C, resulting in an improved branching efficiency. These findings offer valuable insights for the industrial development of branched starch through enzymatic modification.

Assessment of Deposited Red Clay Soil in Kirkuk City Using Remote Sensing Data and GIS Techniques

This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.

Sustainable and versatile selective oxidation of sulfides to sulfones in deep eutectic solvent

Deep eutectic solvents (DES) have emerged as a promising class of solvents that offer unique properties and applications in various fields. The oxidation of sulfide with readily available oxidants remains a major challenge. Herein, we report a straightforward and efficient approach for selective oxidation of sulfides to sulfones using only DES as a green solvent and catalyst. A variety of sulfides including dialkyl, aryl-alkyl, and diaryl were selectively oxidized to sulfones using H2O2 in good to excellent yields at ambient temperature. This method emphasized, organic solvent-free, and acid-free characteristics making it a new, sustainable, and eco-friendly way to realize direct sulfones. The DES showed a stable catalytic performance through consecutive reuses and were recovered and reused for five times without decrease in the reaction yield.

Synthesis of Methyl N-phenylcarbamate Derivatives by Xphos Pd G2 Catalyzed Intermolecular Amidation Reaction

Abstract: The utilization of palladium catalysts in cross-coupling reactions has emerged as a highly promising method for the facile formation of aryl C-N bonds, operating under mild conditions. In this study, we present an efficient approach for the synthesis of methyl N-phenyl carbamate derivatives through the intermolecular amidation of aryl chlorides, catalyzed by Xphos Pd G2. The developed protocol has demonstrated remarkable efficacy, offering several advantages. Notably, the intermolecular amidation reaction exhibited good chemoselectivity, allowing for the precise targeting of desired C-N bond formations while maintaining the integrity of other functional groups. Additionally, this methodology showcases exceptional functional group compatibility, accommodating a diverse array of moieties, including sensitive groups that are traditionally challenging to handle. The Xphos Pd G2 catalyst has proven to be instrumental in orchestrating this transformation, exhibiting high catalytic activity and selectivity. Furthermore, this protocol stands out for its operational simplicity, making it a practical choice for synthetic chemists seeking a straightforward and reliable route to access methyl N-phenyl carbamate derivatives. Overall, this study not only expands the synthetic toolbox for C-N bond formations, but also underscores the significance of palladium-catalyzed methodologies in modern organic synthesis. The reported findings hold substantial promise for applications in medicinal chemistry and material science, where the facile construction of aryl C-N bonds is of paramount importance.

Assessing the job preferences of senior medical students for mandatory service: a discrete choice experiment.

To investigate the job preferences of senior medical students for mandatory service as general practitioners using discrete choice experiment. Health workforce is directly associated with health service coverage and health outcomes. However, there is a global shortage of healthcare workers (HCWs) in rural areas. Discrete choice experiments can guide the policy and decision-makers to increase recruitment and retention of HCWs in remote and rural areas by determining their job preferences. The aim of this study is to investigate job preferences of senior medical students for mandatory service as general practitioners. This cross-sectional survey was conducted among 144 medical students. To estimate students' preferences for different levels of job attributes, a mixed logit model was utilised. Simulations of job uptake rates and willingness to pay (WTP) estimates were computed. All attributes had an impact on the job preferences of students with the following order of priority: salary, workload, proximity to family/friends, working environment, facility and developmental status. For a normal workload and a workplace closed to family/friends which were the most valued attributes after salary, WTPs were 2818.8 Turkish lira (TRY) ($398.7) and 2287.5 TRY ($323.6), respectively. The preference weights of various job characteristics were modified by gender, the presence of a HCW parent and willingness to perform mandatory service. To recruit young physicians where they are most needed, monetary incentives appear to be the most efficient intervention. Non-pecuniary job characteristics also affected job preferences. Packages of both monetary and non-monetary incentives tailored to individual characteristics would be the most efficient approach.

Efficient Deep Learning Approach for Diagnosis of Attention-Deficit/Hyperactivity Disorder in Children Based on EEG Signals

Efficient Deep Learning Approach for Diagnosis of Attention-Deficit/Hyperactivity Disorder in Children Based on EEG Signals

Duality between Seiberg-Witten theory and black hole superradiance

The newly established Seiberg-Witten (SW)/Quasinormal Modes (QNM) correspondence offers an efficient analytical approach to calculate the QNM frequencies, which was only available numerically before. This is based on the fact that both sides are characterized by Heun-type equations. We find that a similar duality exists between Seiberg-Witten theory and black hole superradiance, since the latter can also be linked to confluent Heun equation after proper transformation. Then a dictionary is constructed, with the superradiance frequencies written in terms of gauge parameters. Further by instanton counting, and taking care of the boundary conditions through connection formula, the relating frequencies are obtained analytically, which show consistency with known numerical results.

Design and Development of Automated Lower Limb Continuous Passive Motion (CPM) Machine

Abstract: Post-operative rehabilitation is critical in orthopedic surgery, especially for hip joint surgeries. Traditional therapies like manual exercises and resource limits in rehabilitation centers might be tough, but early mobilization and rehabilitation are critical for regaining joint function and allowing patient return to normal activities. Continuously Passive Motion (CPM) devices, primarily developed for knee joint rehabilitation, offer a non-invasive method for passive joint mobility, restoring joint range of motion and lowering post-operative stiffness risk. Their application in hip joint healing is gaining pace, offering better results and increased patient satisfaction. The research also investigates abduction mobility in the hip joint, which is necessary for daily actions like stepping to the side, getting out of bed, or exiting a car. Understanding hip joint biomechanics and range of motion parameters is vital for establishing efficient rehabilitation approaches. By merging clinical insights with technology innovations, this research intends to optimize post-surgery rehabilitation regimens, enhancing patient outcomes and quality of life. The subject of orthopedic rehabilitation continues to expand through interdisciplinary collaboration and innovation, bringing new choices for enhancing patient care and recovery trajectories.

Load Behaviour of Steel Beam with Opening using Finite Element Approach

Abstract: This project investigates the structural behaviour of castellated steel beams through a combination of Finite Element Analysis (FEA) and software investigation. Castellated beams, known for their web openings, offer improved structural performance with reduced weight. The study focuses on the deflection patterns of these beams under various loading conditions. ANSYS software was used to model and analyse the beams, both with and without stiffeners around the openings. The results indicate that the incorporation of stiffeners significantly enhances the beams' strength and reduces deflection, optimizing material usage without compromising structural integrity. The strategic placement of stiffeners led to a substantial reduction in steel volume, demonstrating an efficient design approach that balances weight reduction and strength. This work underscores the importance of FEA in optimizing structural designs, providing valuable insights into the behaviour of castellated beams and promoting innovative solutions for industrial applications. The findings contribute to the broader understanding of steel beam behaviour under different loading conditions, highlighting the effectiveness of stiffeners in enhancing performance and efficiency in structural engineering

An optimization framework for efficient and sustainable logistics operations via transportation mode optimization and shipment consolidation: A case study for GE Gas Power

General Electric (GE) Gas Power, a leading manufacturer of gas and steam turbines, manufactures and installs these turbines in power generation plants worldwide. They source components for these turbines from suppliers globally and transport these components to manufacturing and assembly locations in the United States using various modes of transportation, including ocean, air, and ground. These transportation options have different lead times and costs. The challenge lies in identifying the most cost-effective solution that meets the assembly requirements, given the high volume of shipments and the complexity of the intermodal freight network. To address this challenge, a customized, multi-period (dynamic), multi-commodity network flow model and a novel heuristic approach with a rolling time horizon are developed. This model incorporates consolidation and storage options at intermediate nodes, allowing the business to optimize its shipments. This research addresses a unique real-world logistics problem and offers an optimization model together with an efficient solution approach for industry. Computational experiments indicate that the heuristic approach proposed can yield results closely aligned with those produced by state-of-the art commercial solvers. Furthermore, the experiments show the criticality of full container load shipments in achieving substantial cost-saving in global shipments.

Facile preparation of zwitterionic core-shell nanogels for dual-functional coatings: Combining antifouling and antibacterial features in silicon implant model surface modification

To develop surface-coating technology for fabricating functionalized biomedical implant and overcome implant-associated bacterial infections, this study presented an innovative antifouling and antibacterial dual-functional nanogel coating methodology for engineering silicon-based biomedical implants/biochips. Firstly, zwitterionic core-shell PAA@(GMA/SBMA) nanogels were designed and prepared via reflux-precipitation polymerization and were characterized through NMR, FTIR, DLS, AFM and TEM. The nanogels exhibited uniform and spherical morphology, salt-ion responsiveness, and high lysozyme loading capability. Subsequently, the PAA@(GMA/SBMA) nanogels were covalently grafted onto the surface of silicon wafers, via an epoxy-amine ring-opening reaction, to prepare nanogel-coated silicon wafers (Si@nanogels). A comprehensive array of characterization techniques, including SEM-EDS, contact angle measurements, and XPS, validated the effective nanogel-coating. These coatings demonstrated notable resistance to protein adsorption, highlighting their robust antifouling properties. Upon loading with the antibacterial agent lysozyme, the nanogel coating displayed remarkable antibacterial efficacy against both Gram-negative (E. coli) and Grampositive (S. aureus) bacteria and effectively inhibited biofilm formation. Furthermore, the nanogel coatings exhibited good biocompatibility and dynamic stability, thus facilitating the surface-mediated adhesion and growth of fibroblast 3T3 cells. Significantly, this work provided a facile, economic and efficient surface modification approach to fabricate nanogel-coated antifouling/antibacterial dual-functional biomedical implant model towards potential clinical applications.

An efficient approach for preparation of battery-grade Li2CO3 from intermediate product Li2SiO3

The continuous development of lithium-ion batteries (LIBs) manufacturing industry calls for the mass production of Li2CO3, which serves as a pivotal raw material for LIBs. In our previous study, Li was successfully precipitated as intermediate product Li2SiO3 from an alkaline solution containing Si by the hydrothermal precipitation method, realizing the preliminary separation of Li and impurities. In this research, H2SO4 digestion-water leaching-Na2CO3 precipitation process was proposed to manufacture Li2CO3 from the intermediate product Li2SiO3. Firstly, the reaction feasibility of H2SO4 digestion-water leaching and direct H2SO4 leaching for Li2SiO3 were comparatively evaluated through thermodynamic calculation and experimental research. Compared to direct H2SO4 leaching, H2SO4 digestion-water leaching process is preferred in view of its excellent filtration properties. Subsequently, systematic digestion and leaching experiments showed that up to 97.1 % of Li was extracted under the optimized conditions, and the concentration of Li in the leach liquor is as high as 29.33 g/L. After pH adjustment and Al removal, Na2CO3 precipitation were adopted for Li2CO3 production. The equilibrium relationship in Li2SO4-Na2CO3-H2O system was analyzed to predict the precipitation behavior of Li and select appropriate operation window for Li2CO3 preparation. Finally, with an additional carbonation-decomposition process, a Li2CO3 product with a purity of 99.51 % is produced. This process realizes efficient synthesis of battery-grade Li2CO3 from the intermediate product Li2SiO3.

Online Rental Website for Travelers in MERN

Abstract: In the contemporary digital landscape, the travel industry has witnessed a paradigm shift with the emergence of online rental services dedicated to tripping. These platforms offer travelers a personalized and efficient approach to securing accommodations, transportation, and other travel essentials. By providing a diverse range of options tailored to individual preferences and budgets, they redefine the travel planning experience. With just a few clicks, travelers can explore and reserve accommodations that meet their specific needs, from cozy city apartments to serene countryside villas. Moreover, these platforms extend beyond accommodations to encompass various transportation options, catering to travelers' diverse needs. Additionally, they serve as a platform for hosts to showcase their properties, fostering a sense of community and cultural exchange among travelers. In this digital age, online rental services for tripping empower travelers to customize their journeys, making travel planning accessible, efficient, and enjoyable. Whether embarking on a solo adventure, planning a family vacation, or organizing a group retreat, these platforms facilitate the creation of memorable travel experiences.

Driver Drowsiness Detection using CNN

Abstract: Driver drowsiness is a significant factor contributing to road accidents worldwide, posing a major threat to public safety. This project presents a robust and efficient approach to detecting driver drowsiness using Convolutional Neural Networks (CNN). The proposed system aims to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by fatigue. The methodology involves capturing video frames of the driver's face using a camera installed in the vehicle. The CNN model is trained on a comprehensive dataset containing images of alert and drowsy states. Key facial landmarks and features, such as eye closure duration, yawning frequency, and head position, are extracted and analyzed to determine the driver's level of alertness. By leveraging the powerful feature extraction capabilities of CNNs, the system can accurately distinguish between alert and drowsy states with high precision. Experimental results demonstrate the effectiveness of the proposed system in real-world scenarios, achieving a high accuracy rate in detecting drowsiness. The system's performance is evaluated against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The deployment of this CNN-based drowsiness detection system can significantly reduce the risk of accidents, contributing to safer driving conditions and saving lives.

Accelerated mineralization of textile wastewater under 222 nm irradiation from Kr/Cl2 excilamp: an environmentally friendly and energy efficient approach

The textile dyeing and manufacturing industry is the major producer of significant amounts of wastewater that contain persistent substances such as azo dyes that require adequate remediation measures. Far ultraviolet at 222 nm light may provide an advantage for contaminants degradation as compared to conventional UV sources (254 nm). In this paper, the degradation of reactive black 5 (RB5) in artificial wastewater has been performed using a 222 nm Kr/Cl2 excimer source under direct photolysis and an advanced oxidation process using TiO2/H2O2. The solution pH, catalyst concentration, 222 nm intensity, initial concentration of dye, and addition of H2O2 influence the degradation rate constant. The molar absorption coefficient, quantum yield of RB5 at 222 nm and the electrical energy per order (EEO) from different treatment methods have been reported. RB5 shows 1.26 times higher molar absorption at 222 nm than at 254 nm. The EEO for excimer-222/H2O2 (∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 13 kWh/m3) is five times lower than that of the excimer-222/TiO2 process, which makes the process energy efficient. The degradation of wastewater has been carried out at three distinct pH values (2, 6, and 10), and the pH level of 10 exhibited the highest degree of degradation. The degradation rate in the alkaline medium is 8.27 and 2.05 times higher than in the acidic or ambient medium. Since textile effluent is highly alkaline, this result is significant, as no neutralization of the wastewater is required, and direct treatment is possible. A possible degradation pathway has been established based on Fourier transform infrared spectroscopy (FTIR) and high resolution mass spectroscopy (HRMS) analysis. The phytotoxicity of the treated wastewater has also been evaluated for its suitability for reuse in agriculture. The study reveals that the excimer-222/H2O2 treated wastewater significantly enhanced the germination percentage of Raphanus sativus seed (97%) compared to dye wastewater-grown seeds (75%). This work offers crucial information for future studies on the direct and indirect photolysis of azo dyes, as well as insight into the process of RB5 degradation under Kr/Cl2 excimer radiation.

Revolutionizing connectivity: Unleashing the power of 5G wireless networks enhanced by artificial intelligence for a smarter future

In the context of 5G wireless networks, this paper addresses a crucial concern—data loss in Analog-to-Digital Converters (ADCs) embedded within Multiple Input Multiple Output (MIMO) channels, a prevalent challenge in modern wireless communication systems. Despite the integration of ADCs in MIMO configurations to mitigate this issue, their implementation introduces significant complexities. In response to this, the paper introduces an innovative and efficient approach aimed at enhancing channel estimation in 5G MIMO systems. The proposed methodology relies on a heuristic-based optimization technique within a channel prediction framework. The framework uses feedback information collected through a Cascade Fusion Serial Network (CFSN) to analyze the receiver-side error ratio and estimate the channel coefficients of MIMO systems at the transmitter. An attention mechanism is incorporated into the model design through the use of a Convolutional Neural Networks - Gated Recurrent Units (CNN-GRU) and a Multi-Scaled Stacked Auto encoder. Parameters of Attention CNN-GRU, Stacked Auto encoder, and CFSN are refined using the Whale Optimization method. The optimization objective is to minimize key metrics, including Root Mean Squared Error (RMSE), Bit Error Rate (BER), and Mean Squared Error (MSE) associated with the estimated channel. Experimental evaluations underscore the efficacy of the proposed MIMO model in the context of 5G networks. The results demonstrate improved convergence rates, heightened prediction performance, and reduced computational costs compared to existing methods. This research contributes valuable insights into addressing data loss challenges specific to 5G MIMO systems, providing a pioneering solution that integrates heuristic optimization, advanced neural network architectures, and nature-inspired algorithms for parameter tuning.

Fractional accumulative calibration-free odds (f-aCFO) design for delayed toxicity in phase I clinical trials.

The calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and practically useful but faces challenges when dealing with late-onset toxicity. The emergence of the time-to-event (TITE) method and fractional method leads to the development of TITE-CFO and fractional CFO (fCFO) designs to accumulate delayed toxicity. Nevertheless, existing CFO-type designs have untapped potential because they primarily consider dose information from the current position and its two neighboring positions. To incorporate information from all doses, we propose the accumulative CFO (aCFO) design by utilizing data at all dose levels similar to a tug-of-war game where players distant from the center also contribute their strength. This approach enhances full information utilization while still preserving the model-free and calibration-free characteristics. Extensive simulation studies demonstrate performance improvement over the original CFO design, emphasizing the advantages of incorporating information from a broader range of dose levels. Furthermore, we propose to incorporate late-onset outcomes into the TITE-aCFO and f-aCFO designs, with f-aCFO displaying superior performance over existing methods in both fixed and random simulation scenarios. In conclusion, the aCFO and f-aCFO designs can be considered robust, efficient, and user-friendly approaches for conducting phase I trials without or with late-onsite toxicity.

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

This paper presents an efficient design approach for microelectromechanical systems (MEMS) gyroscope considering the design constraint of MEMSCAP’s Silicon-on-Insulator Multi-User MEMS Processes (SOIMUMPs). A design for a decoupled mass MEMS resonant gyroscope with tuning electrodes is presented to minimize cross-axis coupling and optimize the gyroscope’s performance. The device features a size of 1.8 ×2.772 mm2 . A decoupled mass MEMS gyroscope has been designed using a novel mechanical beam configuration that optimizes the structural design to reduce unwanted interactions between drive and sense axes motions. Mode order has been achieved by obtaining the first two modes as drive and sense modes at 9946.3 Hz and 10 202.7 Hz, respectively. Parallel plate electrostatic tuning electrodes have been added to adjust and match the drive and sense modes at the resonant frequencies of 9946 Hz. This mode matching is crucial for optimizing gyroscope performance, accuracy, and sensitivity. The effects of temperature variation on the performance of the designed decoupled mass MEMS gyroscope have been investigated, particularly in terms of its mode-matched operation achieved using tuning electrodes. Finite element method (FEM) simulations were conducted, demonstrating that thermal stability was achieved, and mode-matched operation was maintained within the temperature range of −40 °C to 80 °C. The gyroscope exhibits enhanced performance compared to existing MEMS gyroscopes under mode-matched conditions, featuring a lower temperature coefficient of frequency (TCF) at 0.0415 Hz °C−1 in the drive mode and 0.0165 Hz °C−1 in the sense mode. FEM analysis for the fabrication process tolerances has been done, where MEMS gyroscope’s resonant frequencies, output capacitance, output displacement, and sense mode quality factor have shown negligible changes to fabrication process tolerances. Transient analysis was conducted using CoventorWare MEMS+ 3D schematic, which was integrated with MATLAB Simulink to measure the MEMS gyroscope’s output response, which corresponded with the varying input angular velocity signal.

Automatic Control of Polymer Dosage Using Floc Images in Sludge Dewatering Plant

This study introduces a novel method for estimating floc conditions in sludge dewatering plants by employing image analysis and automatic control of polymer dosage. While previous research has focused on drinking water treatment plants, few reports address polymer dosage optimization using image analysis in sludge dewatering plants. The challenge lies in the high sludge dry solids hindering individual floc recognition due to overlap. The study aims to estimate floc conditions by focusing on gap areas between flocs and implementing automatic polymer dosage control accordingly. The proposed method uses images from an Internet Protocol camera and semantic segmentation to identify the floc gap area. For validation of the estimation method, variations of over and under polymer dosage scenarios were investigated and compared with commonly used floc area methods. The findings indicate that the gap area estimation effectively reproduces the theory of polymer cohesion. Automatic polymer dosage control based on this method demonstrates stable operation in both scenarios. Notably, automatic control outperformed manual operation during continuous operation, resulting in a significant reduction in polymer dosage and a notable increase in heating efficiency compared to manual control. This study presents an efficient approach to optimize polymer dosage in sludge dewatering plants, utilizing image analysis for real-time monitoring and control. By focusing on the gap area between flocs, the method enhances accuracy in estimating floc conditions, thereby improving overall dewatering efficiency. The findings highlight the practical benefits of implementing automatic control systems in sludge treatment plants, potentially reducing costs and environmental impact.

CT-based radiomics analysis of different machine learning models for differentiating gnathic fibrous dysplasia and ossifying fibroma.

In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery. We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models' robustness and clinical value. We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy. Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.