Subtraction Method Research Articles

Applications of Vedic Mathematics in Computer Science

The convergence of ancient wisdom and modern computation is an intriguing frontier explored in this research paper, which investigates the potential application of Vedic Mathematics in the domain of Computer Science. Vedic Mathematics, originating from ancient Indian texts known as the Vedas, offers a distinct approach to mathematical problem - solving that emphasizes mental agility, pattern recognition, and elegant algorithms. This paper delves into the historical context of Vedic Mathematics, tracing its origins and key principles, and highlights its relevance in contemporary Computer Science. The intersection of Vedic Mathematics and Computer Science is illuminated through the identification of shared principles, such as pattern recognition and optimization. The paper explores specific Vedic Mathematics techniques that have the potential to enhance computational efficiency, including the "Nikhilam" method for subtraction and the "Vertically and Crosswise" method for multiplication. Case studies are presented to demonstrate the practical application of Vedic Mathematics techniques in Computer Science, spanning areas such as data processing, algorithmic optimization, and error detection and correction. However, this integration is not without its challenges and ethical considerations. The paper addresses concerns about accuracy and reliability, particularly when integrating unconventional mathematical techniques into modern computational paradigms. Cultural sensitivity and proper acknowledgment of the origins of Vedic Mathematics are paramount, as is the need to ensure that the techniques are accessible to a diverse audience. The ethical implications of cross - cultural knowledge exchange are also explored, emphasizing the importance of collaboration and respect. Looking ahead, the paper outlines potential research directions and implications for education in Computer Science. The integration of Vedic Mathematics offers opportunities for developing novel algorithms, enhancing problem - solving skills, and fostering cross - cultural understanding. Collaborations between mathematicians and computer scientists are highlighted as a promising avenue for innovation in algorithmic design. In conclusion, the fusion of Vedic Mathematics and Computer Science represents a dynamic interplay of ancient traditions and modern advancements. As we navigate the complexities of integration, we embark on a journey that transcends disciplinary boundaries, fosters cultural appreciation, and holds the potential to reshape computational methodologies in the digital age.

A Comparison of Internal, Marginal, and Incisal Gaps in Zirconia Laminates Fabricated Using Subtractive Manufacturing and 3D Printing Methods

DLP printing is a new method for producing zirconia laminates that ensure clinically acceptable gaps in the internal, marginal, and incisal regions. A typical model of a central maxillary incisor was prepped by a dentist and scanned. The laminate was designed using CAD software version 2023. The laminates were fabricated using a milling machine (LSM group) and a DLP printer (LAM group) (N = 20). The gap was evaluated using the silicone replica method at designated measurement points. Statistical analyses were performed. The Shapiro–Wilk and Kolmogorov–Smirnov tests indicated a non-normal distribution, and the Mann–Whitney test was used. The LSM group had wider gaps than the LAM group except at point E (59.5 µm). The LAM group had wider gaps than the LSM group, except at points H (51.70 µm). No significant differences were observed between the LSM and LAM groups at any of the labiolingual measurement points. In the mesiodistal plane, a significant difference was observed between the two groups at point G, which was adjacent to the mesial side (p < 0.05). The results of this study indicate that DLP printing offers an innovative approach for producing zirconia laminates, as the incisal, internal, and marginal gaps are within clinically acceptable ranges compared with the AM method.

An improved multi-band spectral subtraction technique designed for cochlear implant users

In this paper, we propose a novel approach to reduce noise in cochlear implants by utilizing an enhanced multi-band spectral subtraction method. The main objective of this work is to optimize the subtraction factors used in the spectral subtraction process to improve the overall performance of the stimulation strategy in cochlear implants. These implants are designed to decompose incoming audio signals into multiple sub-bands for further processing. However, one of the persistent challenges in cochlear implant technology is maintaining speech intelligibility in noisy environments. To address this issue, our approach involves adapting the subtraction factors dynamically based on common noise patterns observed in real-world scenarios. To assess the effectiveness of this method, we conducted extensive simulations using noisy sentences corrupted by eight distinct types of environmental noise, including street noise, machinery, and crowd chatter. Objective evaluations were performed using established metrics for speech intelligibility and audio quality. The results demonstrate that our proposed method significantly enhances the signal-to-noise ratio, ensuring improved clarity of speech for users of cochlear implants. This approach is highly promising, as evidenced by the encouraging results obtained in our simulations. By enabling more precise signal processing, it contributes to better speech intelligibility and overall user satisfaction.

Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition

Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios.

Comparative analysis using UPLC and UV methods for concurrent quantification of therapeutic drugs for overactive bladder: Effective approach in green and white chemistry

This study evaluates the analysis of Mirabegron (MIR) and Solifenacin (SOL), widely used for treating overactive bladder (OAB), using two direct UPLC and spectrophotometric techniques. These methods were developed to be environmentally friendly, utilizing AGREE, GAPI, AGREEprep, ComplexGAPI, AMGS, ESA, HPLC-EAT, AMVI, and BAGI tools. Spectrophotometry, including the first derivative ratio spectrophotometric method (1DD), measures SOL and MIR at 224.8 nm and 258.6 nm, respectively. A method known as the extended ratio subtraction method (EXRSM) is used to resolve the overlap between the SOL and MIR. In this method, the binary mixture spectrum is divided by the divisor. A constant value is subtracted from the plateau region. MIR and SOL at λmax of 248.9 nm and 229.8 nm have a zero-order (D0) after being multiplied by a divisor. The MCR technique has successfully addressed the issue of spectral overlaps for SOL and MIR at 218.5 and 249.7 nm wavelengths. The second method, UPLC densitometry, separates pharmaceuticals using a UPLC C18 1.7 µm (2.1 × 50 mm) column with Ethanol: phosphate buffer pH 4.2 (35:65 v/v) mobile phase at a 0.6 ml/min flow rate, detecting drugs at 210 nm. Calibration curves for both techniques showed a correlation coefficient greater than 0.999 for SOL and MIR in the 1–65 µg mL−1, 1–60 µg mL−1, and 5–25 µg mL−1, respectively. The methods were validated according to International Conference on Harmonization (ICH) principles. These techniques offer cost-effective, eco-friendly alternatives to traditional methods, promoting sustainability in analytical chemistry.

An alternating minimization algorithm with trajectory for direct exoplanet detection. The AMAT algorithm

Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard point spread function (PSF) subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles and rely on signal-to-noise ratio maps to detect the planet. These steps do not interact or feed each other, leading to potential limitations in the accuracy and efficiency of exoplanet detection. We aim to develop a novel approach that iteratively finds the flux of the planet and the low-rank approximation of quasi-static signals in an attempt to improve upon current PSF subtraction techniques. In this study, we extend the standard L2 norm minimization paradigm to an L1 norm minimization framework in order to better account for noise statistics in the high contrast images. Then, we propose a new method, referred to as the alternating minimization algorithm with trajectory (AMAT), that makes more advanced use of estimating the low-rank approximation of the speckle field and the planet flux by alternating between them and utilizing both L1 and L2 norms. For the L1 norm minimization, we propose using L1 norm low-rank approximation (L1-LRA), a low-rank approximation computed using an exact block-cyclic coordinate descent method, while we use randomized singular value decomposition for the L2 norm minimization. Additionally, we enhance the visibility of the planet signal using a likelihood ratio as a post-processing step. Numerical experiments performed on a VLT/SPHERE-IRDIS dataset show the potential of AMAT to improve upon the existing approaches in terms of higher S/N, sensitivity limits (contrast curves), and receiver operating characteristic curves. Moreover, for a systematic comparison, we used datasets from the exoplanet data challenge to compare our algorithm with other algorithms in the challenge, and we find AMAT with a likelihood ratio map performs better than most algorithms tested on the exoplanet data challenge.

Eco-friendly four spectrophotometric approaches for the simultaneous determination of the recently FDA-approved combination, bupivacaine and meloxicam in pharmaceutical dosage forms

In this study, bupivacaine (BUP) and meloxicam (MLX) were simultaneously assayed in their co-formulated ampoules without interference using four affordable, sensitive, and eco-friendly spectrophotometric methods. The assay of MLX at 359.3 nm over the concentration range of 1.0–15.0 µg/mL was accomplished using a direct UV-spectrophotometric method (Method I) without interference from BUP. However, there was a significant overlap between the spectra of BUP and MLX, making it difficult to determine BUP directly from the UV spectrum. Therefore, various UV-based techniques, including second derivative spectrophotometry (Method II), ratio subtraction method (Method III), and absorption factor method (Method IV), were used to determine BUP over the concentration range of 5.0–80.0 µg/mL. The proposed methods could simultaneously determine the studied drugs with a challenging ratio of 33.3:1.0 (BUP: MLX), which increases the importance of the current study. The proposed methods were applied to estimate the studied drugs in commercial ampoules with high % recoveries and low %RSD values. The excellent eco-friendliness of the developed methods was demonstrated using GAPI and AGREE metrics. The developed methods were validated according to ICHQ2(R2) guidelines. The proposed methods can be better suited for the routine analysis of BUP and MLX in their fixed-dose combination with high selectivity.

Language Dominance in the Input and Use Patterns of Cameroon’s Special Bilingual Students

This paper examines dominance in the language input and use patterns of students of Cameroon’s Special Bilingual Education Programme (SBEP). The participants were 373 bilingual students from 10 schools set in French-speaking Cameroon, who submitted self-ratings via an adapted version of the Bilingual Language Profile questionnaire by Birdsong, Gertken and Amengual (2012). The data were analysed using the subtraction method to determine dominance. The results showed a consistent pattern of OL1-dominance for all the three domains considered under language input (viz. listening, reading, and watch time), but a more contrasted pattern for language use, as the respondents were found to be French-dominant (or slightly OL1-dominant to balanced) in the outer circle of relationships (circle of friends and school environment) and OL1-dominant in the inner circle (self-talk and family circle). The findings of this study suggest that individual language use is more significantly impacted by societal dominance than language input is, most likely because the latter is less constrained by relationships and offers a greater possibility of choice. They equally suggest that there is a directionality in the influence of societal dominance, with a decreasing effect from the public to the private sphere. Lastly, the study underscores the powerful negative impact the lack of official recognition can have on the maintenance of local ethnic languages in a multilingual context.

Speech enhancement by using novel multiband spectral subtraction method along with a reduction of the cross spectral component

Speech enhancement by using novel multiband spectral subtraction method along with a reduction of the cross spectral component

Subtractive genomics and comparative metabolic pathways profiling revealed novel drug targets in Ureaplasma urealyticum.

Ureaplasma urealyticum is a commensal organism found in the human lower genitourinary tract, which can cause urogenital infections and complications in susceptible individuals. The emergence of antibiotic resistance, coupled with the absence of vaccines, underscores the necessity for new drug targets to effectively treat U. urealyticum infections. We employed a subtractive genomics approach combined with comparative metabolic pathway analysis to identify novel drug targets against U. urealyticum infection. The complete proteomes of 13 Ureaplasma strains were analyzed using various subtractive genomics methods to systematically identify unique proteins. Subsequently, the shortlisted proteins were selected for further structure-based studies. Our subtractive genomics analysis successfully narrowed down the proteomes of the 13 Ureaplasma strains to two target proteins, B5ZC96 and B5ZAH8. After further in-depth analyses, the results suggested that these two proteins may serve as novel therapeutic targets against U. urealyticum infection. The identification of B5ZC96 and B5ZAH8 as novel drug targets marked a significant advancement toward developing new therapeutic strategies against U. urealyticum infections. These proteins could serve as foundational elements for the development of lead drug candidates aimed at inhibiting their function, thereby mitigating the risk of drug-resistant infections. The potential to target these proteins without inducing side effects, owing to their specificity to U. urealyticum, positions them as promising candidates for further research and development. This study establishes a framework for targeted therapy against U. urealyticum, which could be particularly beneficial in the context of escalating antibiotic resistance.

Resource allocation optimization of device-to-device communication using machine learning algorithms

Device-to-device communication is envisioning next-generation wireless communication. The utility of the device-to-device communication model encourages emerging communication systems such as 5G and the Internet of Things. The allocation of resources and channel interference are major challenges in device-to-device communication. This paper proposes machine-learning-based algorithms for resource allocation and optimization of device-to-device communication. The proposed machine learning algorithm is a cascaded support vector machine. The cascaded support vector machine mapped the parameters of CUEs and DUEs. We create an iterative algorithm to achieve low-power, energy-efficient resource allocation with mode selection by formulating a novel optimization problem to maximize energy efficiency using the subtractive form method to solve a fractional objective function. We obtain data samples from a suboptimal algorithm to train the cascaded algorithm and verify the trained algorithm. Our numerical results show that the proposed cascaded machine learning-based transmission algorithm's accuracy reaches about 88%–95% despite its simple structure due to the limitation in computing power. The analysis of results suggests that the proposed algorithm is more efficient than SVM, DSVM, and the reinforcement learning (RL) algorithm.

A geometrically scalable method for manufacturing high quality factor mechanical resonators.

We present what we believe to be a novel, geometrically scalable manufacturing method for creating compact, low-resonance frequency, and high quality factor fused silica resonators. These resonators are intended to be used in inertial sensors for measuring external disturbances of sensitive physics experiments. The novel method uses direct bonding and chemical-mechanical polishing (CMP) in order to overcome the limitations of current subtractive manufacturing methods, which face prohibitive cost and complexity as material removal increases, inherently restricting the design flexibility of the resonator. We demonstrate a prototype with a test mass of only 3 g that reaches a quality factor of Q = 118 000 ± 400 at a resonance frequency of below 20 Hz. This advancement is particularly significant for future gravitational wave observatories, such as the Einstein Telescope.

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.

X-ray coronary angiography background subtraction by adaptive weighted total variation regularized online RPCA

Objective.X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections.Approach.The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images.Main results.To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively.Significance.The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

Online tree-structure-constrained RPCA for background subtraction of X-ray coronary angiography images

Background and objectiveBackground subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging. MethodsThe current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames. ResultsThe proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure. ConclusionsThe proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions.

Efficient mass manufacturing of high-density, ultra-low-loss Si3N4 photonic integrated circuits

Silicon nitride (Si3N4) photonic integrated circuits (PICs) offer significant advantages over traditional silicon photonics, including low loss and superior power handling at optical communication wavelength bands. To facilitate high-density integration and effective nonlinearity, the use of thick, stoichiometric Si3N4 films is crucial. However, when using low-pressure chemical vapor deposition (LPCVD) to achieve high optical material transparency, Si3N4 films exhibit large tensile stress on the order of GPa, leading to wafer cracking that challenges mass production. Methods for crack prevention are therefore essential. The photonic Damascene process has addressed this issue, attaining record low-loss Si3N4 PICs, but it lacks control of the waveguide height, leading to large random variations of waveguide dispersion and unpredictable spectrum responses of critical functional devices such as optical couplers. Conversely, subtractive processes achieve better dimension control but rely on techniques unsuitable for large-scale production. To date, an outstanding challenge is to attain both lithographic precision and ultra-low loss in high-confinement Si3N4 PICs that are compatible with large-scale foundry manufacturing. Here, we present a single-step deposited, DUV-based subtractive method for producing wafer-scale ultra-low-loss Si3N4 PICs that harmonize these necessities. By employing deep etching of densely distributed, interconnected trenches into the substrate, we effectively mitigate the tensile stress in the Si3N4 layer, enabling direct deposition of thick films without cracking and substantially prolonged storage duration. A secondary ion mass spectrometry (SIMS) analysis reveals that these deep trenches simultaneously serve as gettering centers for metal impurities, in particular copper, thereby reducing the absorption loss in Si3N4 waveguides. Lastly, we identify ultraviolet (UV)-radiation-induced damage that can be remedied through a rapid thermal annealing. Collectively, we develop ultra-low-loss Si3N4 microresonators and 0.5-m-long spiral waveguides with losses down to 1.4 dB/m at 1550 nm with high production yield. This work addresses the long-standing challenges toward scalable and cost-effective production of tightly confined, low-loss Si3N4 PICs as used for quantum photonics, large-scale linear and nonlinear photonics, photonic computing, and narrow-linewidth lasers.

Application of total carbon analysis for carbon dioxide fixation in cementitious materials

This study focuses on the accurate evaluation of carbon dioxide capture in cementitious materials, a critical factor in reducing the carbon footprint of the concrete industry. Currently, there is no standardized method accurately quantifying fixed CO2 in carbonated cementitious materials. Additionally, since cementitious materials may contain organic carbon (TOC), which can lead to an overestimation of total inorganic carbon (TIC), separating and quantifying TIC and TOC is essential but remains challenging. We introduce a novel, straightforward approach for separately quantifying TIC and TOC based on total carbon measurement. Experiments were conducted using carbonated cement paste and a mixture containing polypropylene powder at various mass ratios. The optimal dissolution method for measuring TIC via acid solution and subsequent pretreatment was validated using X-ray diffraction analysis. This study proposes a subtraction method for determining TIC and TOC content in cementitious materials, providing a practical solution for environmental assessments in the concrete industry.

Intaglio surface of CNC milled versus 3D printed maxillary complete denture bases – An in vitro investigation of the accuracy of seven systems

ObjectivesThe accuracy of the intaglio surface of maxillary complete dentures produced with a variety of digital manufacturing systems have been comprehensively investigated, however, not for multiple systems in the same study. This in vitro study endeavors to measure and compare the accuracy of the intaglio surface of maxillary complete denture bases constructed with different photopolymerization-based 3D printing and CNC milling systems. Materials and MethodsTwo subtractive manufacturing machines and five additive manufacturing machines were used to manufacture a maxillary denture bases (n = 10) for analysis. The samples were stored in artificial saliva at 37 °C for 7 days to mimic intraoral use before they were air dried for 5 min, and then scanned with an E3 dental laboratory scanner to generate STL files. A 3D metrology software program was used to analyze the data against the original CAD design. Statistical differences were determined with 1-way analysis of variance (ANOVA) and the Kruskal Wallis test (α=0.05). Color deviation heat maps were used to interpret areas of clinical significance. ResultsThe results indicated that the subtractive manufacturing method delivers the truest and most precise intaglio surface of maxillary complete denture bases. The additive manufacturing technique delivers less true and less precise results with inconsistency observed between the different systems as opposed to the subtractive manufacturing groups which showed no significant differences. ConclusionsWhen manufacturing a complete maxillary denture base subtractive manufacturing will deliver the most consistent and true results.