Standard Algorithm Research Articles

Development of Surgical Safety Algorithms for Various Nosologies through the Development of Practice-Oriented Training Programs by Surgeons

Surgical safety issues are becoming particularly relevant today. The vector of development of this strategically important area is seen by us in the organization of effective training of surgeons in the implementation of surgical safety algorithms for various nosologies developed by expert groups of the Russian Society of Surgeons. The article describes the main stages of organizing the educational process for teaching surgeons a standardized algorithm for safely performing laparoscopic cholecystectomy in the process of mastering the practice-oriented program "Moscow School of Surgery".

Solving the homogeneous Bethe-Salpeter equation with a quantum annealer

The homogeneous Bethe-Salpeter equation (hBSE), describing a bound system in a genuinely relativistic quantum-field theory framework, was solved for the first time by using a D-Wave quantum annealer. After applying standard techniques of discretization, the hBSE, in ladder approximation, can be formally transformed in a generalized eigenvalue problem (GEVP), with two square matrices: one symmetric and the other nonsymmetric. The latter matrix poses the challenge of obtaining a suitable formal approach for investigating the GEVP by means of a quantum annealer, i.e., to recast it as a quadratic unconstrained binary optimization problem. A broad numerical analysis of the proposed algorithms, applied to matrices of dimension up to 64, was carried out by using both the simulated-annealing package and the D-Wave . The numerical results very nicely compare with those obtained with standard classical algorithms, and also show interesting scalability features. Published by the American Physical Society 2024

Implementation of a standardized epidural top-up algorithm for inadequate labor epidural analgesia: a single-center retrospective study

Background We hypothesized that implementation of a labor epidural management algorithm would increase the labor epidural catheter replacement rate at our hospital. Methods Our institutional review board approved this study and waived the requirement for informed consent. Patients who had labor epidural analgesia and delivered vaginally or had replacement of an epidural catheter prior to vaginal delivery from August 1, 2022 to December 31, 2022 and from August 1, 2023 to December 31, 2023 were included in the study. Study investigators entered data from the electronic medical record into REDCap. Results A total of 530 and 740 patients received labor epidural analgesia and met inclusion criteria before and after implementation of the algorithm, respectively. Patients who received labor epidural analgesia after implementation of the protocol had an absolute increase of 1.0% in the catheter replacement rate, which was not statistically significant (P = 0.34). A multivariate logistic regression found that the number or rescue analgesia boluses (odds ratio 2.68; 95% confidence interval 2.092, 3.434; P < 0.001) and operator level of training (odds ratio 0.41; 95% confidence interval 0.226, 0.743; P = 0.003) were associated with catheter replacement. Conclusion After implementation of a labor epidural catheter management algorithm, patients had an increase in labor epidural catheter replacement that was not statistically significant.

A holistic approach to software fault prediction with dynamic classification

Software Fault Prediction is a critical domain in machine learning aimed at pre-emptively identifying and mitigating software faults. This study addresses challenges related to imbalanced datasets and feature selection, significantly enhancing the effectiveness of fault prediction models. We mitigate class imbalance in the Unified Dataset using the Random-Over Sampling technique, resulting in superior accuracy for minority-class predictions. Additionally, we employ the innovative Ant-Colony Optimization algorithm (ACO) for feature selection, extracting pertinent features to amplify model performance. Recognizing the limitations of individual machine learning models, we introduce the Dynamic Classifier, a ground-breaking ensemble that combines predictions from multiple algorithms, elevating fault prediction precision. Model parameters are fine-tuned using the Grid-Search Method, achieving an accuracy of 94.129% and superior overall performance compared to random forest, decision tree and other standard machine learning algorithms. The core contribution of this study lies in the comparative analysis, pitting our Dynamic Classifier against Standard Algorithms using diverse performance metrics. The results unequivocally establish the Dynamic Classifier as a frontrunner, highlighting its prowess in fault prediction. In conclusion, this research introduces a comprehensive and innovative approach to software fault prediction. It pioneers the resolution of class imbalance, employs cutting-edge feature selection, and introduces dynamic ensemble classifiers. The proposed methodology, showcasing a significant advancement in performance over existing methods, illuminates the path toward developing more accurate and efficient fault prediction models.

Lenacapavir to prevent HIV infection: current prices versus estimated costs of production.

Despite improvements in treatment and oral pre-exposure prophylaxis (PrEP) access, 1.3 million people acquired HIV in 2022. Six-monthly lenacapavir PrEP could benefit tens of millions of people at high risk of infection. However, prices are currently up to $44 819 per person per year (pppy). We projected minimum lenacapavir pricing based on generic mass production and a Cost-Plus (Cost+) model. Current active pharmaceutical ingredient (API) and key starting materials (KSMs) costs were obtained from export databases. The routes of synthesis (ROS) were analysed to project a cost of goods (COGs). Formulation, vials and profit margin costs were included using standardized algorithms and Cost+ pricing. We estimated prices with scale-up to supply 1 million then 10 million treatment-years, comparing this with national list prices. The lenacapavir API is currently exported from India for $64 480/kg on 1 kg scale. Based on the ROS and KSMs, API COGs of $25 000/kg and $10 000/kg are achievable for a committed demand of 1 million (2 million tonnes/annum of API) and 10 million treatment-years, respectively. Including formulation steps, injectable lenacapavir could be mass produced for approximately $94 pppy for 1 million and $41 for 10 million treatment-years, if voluntary licences are in place and competition between generic suppliers substantially improves. Greater scale-up with improvements in manufacturers' ROS could reduce prices further. Currently lenacapavir costs $25 395-44 819 pppy. Lenacapavir could be mass produced for <$100 pppy at launch. Voluntary licensing and multiple suppliers are required to achieve these low prices. This mechanism is already in place for other antiretrovirals. To date, Gilead has not agreed lenacapavir voluntary licences with the Medicines Patent Pool.

NCLWO: Newton’s cooling law-based weighted oversampling algorithm for imbalanced datasets with feature noise

Imbalanced datasets pose challenges to standard classification algorithms. Although oversampling techniques can balance the number of samples across different classes, the difficulties of imbalanced classification is not solely imbalanced data itself but other factors, such as small disjuncts and overlapping regions, especially in the presence of noise. Traditional oversampling techniques are not effectively address these intricacies. To this end, we propose a novel oversampling method called Newton’s Cooling Law-Based Weighted Oversampling (NCLWO). The proposed method initially calculates the weight of the minority class based on density and closeness factors to identify hard-to-learn samples, assigning them higher heat. Subsequently, Newton’s Cooling Law is applied to each minority class sample by using it as the center and expanding the sampling region outward, gradually decreasing the heat until reaching a balanced state. Finally, majority class samples within the sampling region are translated to eliminate overlapping areas, and a weighted oversampling approach is employed to synthesize informative minority class samples. The experimental study, carried out on a set of benchmark datasets, confirm that the proposed method not only outperforms state-of-the-art oversampling approaches but also shows greater robustness in the presence of feature noise.

An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials

Due to the similar characteristics of chaotic systems and cryptography, public key encryption algorithms based on chaotic systems are worth in-depth research and have high value for the future. Chebyshev polynomials have good properties and are often used in the design of public key algorithms. This paper improves the Bose Multi-Chaotic Public Key Cryptographic Algorithm (BMPKC) by applying Chebyshev polynomials. The proposed algorithm (CMPKC-) introduces the selective coefficient based on the properties of Chebyshev polynomials, allowing the special functions that need to be negotiated in the original system to be freely and randomly chosen as Chebyshev polynomials, and can also be expanded to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes shortcomings of the Bose algorithm by iteratively iterating the selected Chebyshev polynomials based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and software testing results indicate that this algorithm has strong robustness against brute force attacks, achieving a higher attack time for breaking the private key compared to the CEPKC, BMPKC, and CMPKC algorithms. Compared to the CMPKC algorithm, our proposal algorithm achieves better performance in the encryption and decryption phases. Furthermore, we combine this Multi-Chaotic System Key Exchange Protocol with the Advanced Encryption Standard (AES) algorithm, while providing a demonstration, offering more possibilities for practical applications of this system.

Data analytics in ensemble learning for effective crop yield prediction

In agricultural research, Crop Yield Prediction (CYP) offers the best decision-making to assist farmers in agricultural yield forecasting efficiently. Most of the existing studies have not considered the exhaustive exploration of data analytics techniques in Machine Learning (ML) for CYP due to which the existing models have not given the optimal results. The main objective of this study is to investigate the effectiveness of data analytics in Ensemble Learning (EL) techniques for more reliable and high-performance CYP models. This article proposes an expert system model, Blended Expert System for Crop Yield Prediction (BESCYP), designed to predict the precise crop yields for specific agricultural lands in the Assam state of India. The proposed BESCYP employs Expectation–Maximization (EM) algorithm to treat missing values, the Isolation Forest (IF) technique to analyze outliers, the Genetic Algorithm (GA) to perform feature selection, Robust Scaling (RS) technique to perform data normalization and the Extra Tree (ET) for classification that overcome the variance and overfitting problem commonly associated with standard ML algorithms. The evaluation of the proposed BESCYP model has been performed using accuracy, precision, recall, and F-1 score on a dataset obtained from International Crops Research Institute for Semi-Arid Tropics (ICRISAT). The proposed model is compared against different standard ML algorithms, EL algorithms and various existing models available in the literature, and the experimental results show that the proposed BESCYP model outperforms other models.

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.

Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.

Arthroscopic Debridement and Resection of a Bennett Lesion in a Collegiate Baseball Pitcher

Background: Overhead throwing athletes are predisposed to ossification along the superior to inferior posterior glenoid rim, termed thrower's exostosis or a Bennett lesion. These lesions can result in posterior shoulder pain during throwing and decreased shoulder range of motion, and they can be associated with posterior labral tears and undersurface rotator cuff tears. In this video technique, we describe the arthroscopic debridement and resection of a symptomatic unstable Bennett lesion in a collegiate baseball pitcher who had unsuccessful nonoperative treatment. Indications: Currently, there is no standard treatment algorithm for Bennett lesions. Arthroscopic intervention is typically indicated in overhead throwers who have unsuccessful nonoperative protocols, including stretching of the posterior capsule, strengthening of the rotator cuff, and injections. Technique Description: In the lateral decubitus position, standard posterior viewing and anterior working arthroscopic portals are created followed by diagnostic arthroscopy. A high anterior accessory portal is created to view the posterior labrum and evaluate for tears. The location of the Bennett lesion is determined using a switching stick or probe. In this case, an accessory posterior viewing portal and capsulotomy are created under spinal needle visualization and a 70° scope is utilized for an improved view. Through the capsulotomy, a motorized shaver and radiofrequency wand is used to work along the posterior inferior glenoid neck to expose the lesion. Once the lesion is fully demarked, a hooded bur is utilized to debride the entirety of the lesion back to the smooth bony surface of the glenoid neck. After resection, the capsule is left open to avoid overtightening the posterior capsule in overhead throwing athletes. Results: Arthroscopic debridement and resection of a symptomatic Bennett lesion in a collegiate baseball pitcher allowed the patient to return to pain-free pitching at the same level of collegiate play the following season. Discussion/Conclusion: An unstable Bennett lesion can be a source of pain in the overhead throwing athlete. If nonoperative treatment modalities fail to resolve symptoms, arthroscopic debridement and excision of this lesion utilizing a posterior capsulotomy and accessory posterior viewing portal as described in this video technique is a safe and effective surgical option. Patient Consent Disclosure Statement: The author(s) attests that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.

Centralized multi-robot logistic system: An approach using the island model genetic algorithm as task scheduler

The practicality allied with technological and logistical advances led customers increasingly to join e-commerce. This phenomenon was even more expanded during the COVID-19 pandemic. Because of the growing demand for logistic services, warehouses must adapt and seek faster and more efficient processes. A means to achieve that is to use mobile robots in transportation-related tasks. However, their effectiveness is only achieved if the system has an efficient work distributor. This research developed a centralized coordination architecture using the island model genetic algorithm for multi-robot logistic task allocation. The coordination receives, allocates, and monitors tasks performed by robots with different payload capacities and average speeds. The architecture was developed upon the robotic operating system. Thus, any robot with a robotic operating system-based internal system can work under the coordination of this architecture. On the results, the task scheduler, developed in previous work, had the evaluation complemented. The scheduler based on the island model genetic algorithm allocates more tasks than the standard genetic algorithm when using the same heuristic. Regarding coordination architecture, the system successfully managed a group of robots in a simulated environment. It also detected and notified failures during task execution. Therefore, this system provides a complete task scheduling, allocation, and monitoring solution.

Convolutional neural networks-based multi-sensor data fusion analysis for transmission lines

Abstract With the development of the power system, accurate assessment and fault diagnosis of the health status of transmission lines have become increasingly important. However, the standard data fusion algorithms do not fully exploit the correlation and weight disparity among multiple sensors. This study proposes a technique for analyzing transmission lines using a convolutional neural network-based approach to address this issue. By fusing data collected from multiple sensors in different directions, it can more accurately capture key information about transmission lines and conduct comprehensive analysis to evaluate their health status.

762. A PROPOSAL FOR CERVICAL ANASTOMOTIC LEAKAGE CLASSIFICATION AFTER ESOPHAGECTOMY

Abstract Background Anastomotic leak (AL), a potently life-threatening complication after esophagectomy, has a wide spectrum of presentation. There are also several treatment modalities but uncertainty about its clinical applicability remains, being mostly guided by personal experience and available resources. Regarding cervical anastomosis, literature is especially scarce. Although classically it was managed conservatively with cervical wound drainage, 60-70% have an intrathoracic manifestation, which is not only associated with worst clinical severity, as it appears more difficult to manage. In this study, we propose a new classification system for cervical esophago-gastric leak, based on its imaging presentation, with a standardized therapeutic approach. Methods Cervical AL classification and therapeutic protocol were developed through retrospective evaluation of a prospective database, including all patients submitted to esophagectomy with gastric conduit reconstruction and cervical anastomosis since the 1st January 2018 to the 31st March 2024, in an upper gastro-intestinal surgery unit, at a tertiary cancer center. AL manifestation was classified according to imaging appearance on CT scan, reviewed by a dedicated radiologist. Protocol was developed considering treatment patterns according to each manifestation and respective success rate. Results During this period, 253 esophagectomies were performed, 171 with cervical anastomosis and thus included to analysis. AL rate was 21.6%. Four types of AL manifestation were identified: A – exclusive cervical manifestation (32.4%); B – mediastinal drainage without pleural involvement (8.1%); C – apical pleural collection (8.1%); D – pleural involvement (51.2%). Based on these patterns, a standardized therapeutic algorithm was developed (figure 1): conservative treatment with cervical wound drainage for A, endoscopic therapy for B and C (first-line endoluminal esosponge®, stent as option) and for type D, in addition to endoscopic therapy, pleural drainage and empyema decortication in refractory cases. Conclusion Although well stablished and easy to apply, the Esophagectomy Complications Consensus Group (ECCG) classification for esophagectomy AL differentiates its severity, but does not guide its treatment. In this study, we identified four types of cervical AL manifestation, which we consider relevant to select the appropriate treatment modality. This protocol was recently adopted in our center and we pretend to evaluate its results in a short-term.

Back pain: diagnostic and therapeutic algorithms in outpatient practice

Back pain is a common reason for seeking primary care from older and younger people. Early spinal imaging by a therapist is indicated to eliminate potentially dangerous causes of pain. Independent magnetic resonance imaging or computed tomography examination at the patient’s initiative in the absence of “red flags” back pain in most cases does not come close to identifying the obvious cause of pain syndrome, and it creates the preconditions for the formation of an exaggerated picture of the disease in the patient himself, driving him into the “trap” of chronic pain. Attention of the polyclinic doctor to the clinical symptoms detailed in the article, following the standard algorithms of diagnosis will allow timely suspicion of “red flags” back pain, avoid hyperdiagnosis of the cause, and minimize the risk of its chronization. The algorithm for treatment of acute and chronic episodes of pain should include informing the patient about the favorable outcome in the vast majority of cases, optimizing the physical activity of the patient, the use of complex drug therapy, the basis of which are nonsteroidal anti-inflammatory drugs (predominantly selective), muscle relaxants and other pathogenetically based medicines.

Algorithm-based Management of Infantile Hemangiomas: Reducing Sequelae and Surgical Interventions

Background: In Japan, oral propranolol (PPL) and pulsed dye laser are available for infantile hemangioma (IH) treatment without patient cost-sharing. However, no standardized algorithm exists to guide treatment selection that balances efficacy, potential side effects, and aesthetic risks. This study presents a comprehensive approach utilizing a treatment algorithm and aesthetic risk scoring system. Methods: This retrospective study analyzed outcomes from 156 patients with IHs. Oral PPL was used in IH patients with functional issues, whereas the rest underwent an aesthetic risk assessment that categorized them into low-, moderate-, or high-risk groups to guide treatment choices. Final treatment decisions depended on parental preference. The outcomes of algorithm-compliant and noncompliant patients were compared statistically. Results: The risk score's interrater reliability was 0.973 (95% confidence interval 0.933–0.992), with a mean intrarater reliability of 0.968 ± 0.027 and a mean evaluation time of 14.1 ± 5.0 seconds per case. Among the 156 patients, 88% pursued the algorithm-recommended treatment, whereas 12% opted for different approaches. Algorithm-compliant patients experienced significantly fewer sequelae than did noncompliant patients (5% versus 33%, P &lt; 0.001). Compared with noncompliant patients, algorithm-compliant patients tended to require shorter treatment durations (17.9 versus 25.4 mo, P = 0.08) and fewer laser sessions (5.8 versus 7.2, P = 0.30), with a younger age at resolution (21.3 versus 29.0 mo, P = 0.08). Conclusions: Aesthetic concerns can be crucial for patients with IHs. This study introduces a comprehensive IH management algorithm to reduce the sequelae requiring surgical interventions and improve patients’ quality of life.

Outpatient Management of Fever and Neutropenia in Low-risk Children with Solid Tumors: A Quality Improvement Initiative.

Management of febrile neutropenia in pediatric oncology usually requires inpatient parenteral antibiotics after initial evaluation, but some patients at lower risk of sepsis could be safely managed outpatient. We describe a quality improvement project to increase outpatient management of fever and neutropenia. We designed a standardized algorithm for children with a solid tumor diagnosis and low risk for bacteremia. The aim was to achieve outpatient management for at least 80% of eligible patients within 20 months of project initiation. We used plan-do-study-act cycles to improve algorithm compliance, including optimizing medical record decision support, developing targeted educational materials and outreach, and restructuring outpatient processes to allow for close follow-up. We surveyed patients (age ≥12 y) and parents/caregivers to assess the impact of outpatient management. The initiative led to 71% (n = 34) of eligible patients being managed as outpatients. Six percent (n = 2) of patients developed bacteremia, resulting in hospital admission. Fifteen of 26 parents/caregivers and five of 11 patients approached completed the survey. For the preferred setting of febrile neutropenia management, 83% of patients preferred to be home versus 40% of parents/caregivers. No patient expressed any of the three highest ratings in the question exploring fear regarding outpatient febrile neutropenia management versus 67% of parents/caregivers. Some children with a solid tumor diagnosis at low risk for bacteremia are safely managed for febrile neutropenia as outpatients. Targeted efforts to engage parents/caregivers early in this practice change are necessary for success.

A Structured Lightweight Encryption Architecture for Data Protection in IoT

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks.

A CNNbased energy management strategy for a Hybrid energy storage system in electric vehicles

When approaching a large-scale performance, the choice, size, and administration of an Energy Storage System (ESS) for an Electric Vehicle (EV) are crucial. As the peak-to-average power demand ratio is relatively large, particularly for an urban ride that is frequently marked by rapid deceleration as well as acceleration, the complementary characteristics of the battery and Ultra-Capacitor (UC)render this arrangement a viable Hybrid energy storage system (HESS) for EV. Enhanced dynamic responsiveness, increased miles per charge, and extended battery life provided by the HESS increase the Electric Vehicle (EV’s) effectiveness. The primary objective of the study that has been suggested is to create a smart Energy Management Strategy (EMS) for EVs. A battery package and a properly sized Ultra-Capacitor (UC) together give the required high power along with energy density. This research proposes a CNN-based power management technique to aid in efficient EMS. Additionally, by adjusting the Convolution Neural Network (CNN) classifier’s weights through Improved Honey Badger Optimization (IHBO), the adaptive approach of the Standard HBO Algorithm, the performance of the classifier is improved. In MATLAB, the suggested CNN-based model for BESS EM is simulated and experimental assessment and analysis are done in terms of converter current and battery SoC. By contrasting the suggested approach against several standardized models, the performance analysis of the proposed work is assessed to validate its performance.

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure.In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results.In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels.