Original Data Research Articles

Levels of Cytokines in Leptospirosis Patients with Different Serovars and rfb Locus.

Leptospirosis has a wide spectrum of clinical manifestations ranging from mild to severe disease. The cytokine response is considered one of the key drivers for this varying manifestation. The different cytokine response observed in patients with leptospirosis could be due to the variation of infecting serovars. Since the rfb locus codes for the lipopolysaccharide synthesis of the bacterial cell wall, which also determines the serovar, this locus may play a role in driving a specific cytokine response in the host. We investigated 12 commonly used cytokine profiles in serum samples of culture, microscopic agglutination test (MAT), or polymerase chain reaction (PCR)-positive patients with leptospirosis. The sequences of the rfb locus in culture-positive samples were generated from whole genome sequencing and serovar status was drawn from original data published. Isolated cultures were subjected to whole genome sequencing using the PacBio RS II system, and the resulting data were used to determine the species. The recovered genomic data were annotated with the Rapid Annotation using Subsystem Technology (RAST) subsystem, and the rfb locus was extracted. The cytokine analysis was carried out using the Qiagen human ELISA kit. Eighteen samples were found to be positive by culture, while the other 7 samples were positive by PCR or MAT. Infections from Leptospira interrogans serovar Autumnalis (5), Pyrogens (3), Icterohaemorrhagiae (1) Leptospira borgpetersenii (all 7 samples clustered in same clonal group with serovar status not determined), Leptospira weilii (1 with serovar status not determined), and Leptospira kirschneri serovar Grippotyphosa (1) were included in the analysis. Three patients [infected with Leptospira interrogansserovar Autumnalis (2) and Pyrogens (1)] and 2 MAT-positive patients (highest titer against serovar Bratislava of L.interrognas) were reported to have severe clinical manifestations, while the rest had mild to moderate symptoms. Although the serum cytokine concentration of patients with severe clinical manifestation was comparatively higher, a statistically significant difference was observed only for interleukin (IL)-1β (P < 0.05). IL-10/tumor necrosis factor-alpha (TNF-α) ratio was high in patients with severe complications. In general, patients infected with L. interrogans showed higher concentration of cytokines compared to L. borgpetersenii.

Unsupervised Feature Selection With Flexible Optimal Graph.

In the unsupervised feature selection method based on spectral analysis, constructing a similarity matrix is a very important part. In existing methods, the linear low-dimensional projection used in the process of constructing the similarity matrix is too hard, it is very challenging to construct a reliable similarity matrix. To this end, we propose a method to construct a flexible optimal graph. Based on this, we propose an unsupervised feature selection method named unsupervised feature selection with flexible optimal graph and l2,1 -norm regularization (FOG-R). Unlike other methods that use linear projection to approximate the low-dimensional manifold of the original data when constructing a similarity matrix, FOG-R can learn a flexible optimal graph, and by combining flexible optimal graph learning and feature selection into a unified framework to get an adaptive similarity matrix. In addition, an iterative algorithm with a strict convergence proof is proposed to solve FOG-R. l2,1 -norm regularization will introduce an additional regularization parameter, which will cause parameter-tuning trouble. Therefore, we propose another unsupervised feature selection method, that is, unsupervised feature selection with a flexible optimal graph and l2,0 -norm constraint (FOG-C), which can avoid tuning additional parameters and obtain a more sparse projection matrix. Most critically, we propose an effective iterative algorithm that can solve FOG-C globally with strict convergence proof. Comparative experiments conducted on 12 public datasets show that FOG-R and FOG-C perform better than the other nine state-of-the-art unsupervised feature selection algorithms.

Psychological risk factors of somatic symptom disorder: A systematic review and meta-analysis of cross-sectional and longitudinal studies

ObjectiveCurrent diagnostic concepts of somatic symptom disorder (SSD) in DSM-5 and bodily distress disorder (BDD) in ICD-11 require certain psychological criteria, but researchers have called for further specification. Therefore, in a first step, this systematic review and meta-analysis aimed to summarize the current evidence on psychological factors associated with SSD/BDD and/or disorder-relevant clinical outcomes such as symptom severity and impairment. MethodsPsychological factors were systematically searched using Pubmed, Cochrane Library and Psycinfo via EBSCO. Studies providing original data in English or German, after 2009 were included. Cross-sectional, cohort and case-control studies investigating at least one psychological factor in individuals with SSD/BDD in the context of disorder-relevant outcomes were included. ResultsForty-three eligible studies (n = 3760 patients) in SSD (none in BDD) provided data on at least one psychological factor, 37 in case-control format, 10 cross-sectional and 5 longitudinal. Meta-analyses of the case-control studies found patients with SSD to be more impaired by depression (SMD = 1.80), anxiety (SMD = 1.55), health anxiety (SMD = 1.31) and alexithymia (SMD = 1.39), compared to healthy controls. Longitudinal results are scarce, mixed, and require refining, individual studies suggest self-concept of bodily weakness, anxiety and depression to be predictive for persistent SSD and physical functioning. ConclusionThis review provides a detailed overview of the current evidence of psychological factors in relation to SSD/BDD. Future studies on SSD and BDD should include under-studied psychological factors, such as negative affect, fear avoidance, or emotion regulation. More longitudinal studies are needed to assess the predictive value of these factors.

Ulnar-Sided Wrist Pain: A Diagnostic Evaluation Guide From 30-Plus Years of Experience.

While multiple ulnar-sided wrist pain (USWP) diagnostic evaluation guides have been presented, none have included original clinical data or statistical analysis. The purpose of this study is to provide a diagnostic evaluation guide derived from original clinical data and analysis to help clinicians arrive at a differential diagnosis for USWP. Using a computer search of patients presenting with sprains, instability, and laxity of the wrist, 385 patient charts were identified. Patient demographics, mechanism of injury, subjective complaints, physical findings, and diagnostic test findings were reviewed. Statistical analysis was performed to determine sensitivity and specificity of diagnostic methods on their ability to identify lunotriquetral ligament tears, triangular fibrocartilage complex (TFCC) tears, and ulnar impaction syndrome. Diagnostic arthroscopy was used as the reference standard. Ninety-three patients, comprising 101 cases of USWP, were included in the study. The onset of injury was traumatic in 83 out of 101 cases with motor vehicle accidents (N=46) being the most common, followed by overuse (N=18), and a fall onto an outstretched hand (N=16). The ulnocarpal tenderness test exhibited sensitivity/specificity of 72%/33%; lunotriquetral ligament laxity test of 42%/62%; bone scan of 80%/33%; radiocarpal arthrogram of 90%/98% for TFCC tears and 50%/91% for lunotriquetral ligament tears; midcarpal arthrogram of 82%/86% for lunotriquetral ligament tears.The mean ulnar variance on standard posteroanterior view radiograph was 0.95 mm, increasing to 2.67 mm on gripping posteroanterior view. Physicians should suspect a lunotriquetral ligament and/or TFCC tear with the acute onset of USWP following a loaded dorsiflexed mechanism of injury. Ulnocarpal tenderness tests and pre-operative ulnar variance measures are effective for increasing suspicion of USW pathology. Bone scans are helpful in diagnosing ulnar impaction syndrome in conjunction with radiographic findings. A combination of midcarpal arthrogram for lunotriquetral ligament tears and radiocarpal arthrogram for TFCC tears should be employed.

Sustainable and smart: Exploring the nexus between blockchain and sustainability in inter-organisational collaboration

This exploratory study examines the nexus between blockchain and collaboration towards sustainability, which has been largely understudied despite advances in understanding blockchain technology. The urgency of sustainability objectives presents an opportunity to explore blockchain’s potential as a collaboration enabler. The study investigates the opportunities and challenges associated with blockchain’s use in this context to understand the relevance and conditions for its adoption. The research draws on original survey data and insights from expert interviews, making two significant contributions: (i) expanding on the limited literature on stakeholder perceptions of blockchain, and (ii) providing a framework outlining the conditions under which blockchain may support stakeholders initiate, manage and evaluate collaborative activities. The study’s findings are illustrated with reference to the challenges of broadening and deepening collaboration towards sustainability and can inform decision-making processes related to the adoption of blockchain technologies.

Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment

Partial discharge (PD) characteristics are very important for the diagnosis of damaged transformation equipment. If the power transmission and transformation equipment fails, it will cause large economic losses, and thus prevention is better than treatment. One of the effective methods for high-voltage insulation degradation detection is to observe the phenomenon of partial discharge of equipment, which is the earliest characteristic. Prevention can be carried out in advance, and trend observation is better than periodic inspection. Long-term observation can effectively reduce the probability of misjudgment. This project intends to develop a high-speed data-acquisition device to acquire the original discharge waveform data of partial discharge for noise suppression. In order to improve the diagnostic efficiency and accuracy of the diagnostic system, it is necessary to suppress the noise of the measurement data, so as to carry out the fault identification of the discharge type. Through this method, the actual operating data of the system can be recorded as the original discharge waveform, in order to understand the original discharge waveform conditions of the power transmission and transformation equipment in multiple partial discharge measurements, and then after noise suppression, it can be regarded as a type of partial discharge identification.

Modal decomposition integrated model for ultra-supercritical coal-fired power plant reheater tube temperature multi-step prediction

Overheating of reheater tubes in ultra-supercritical coal-fired power plants can affect the efficiency and safety of power generation (PG). To avoid reheater tubes overheating and bursting, this study proposes a modal decomposition integrated model (MDIM) for multi-step prediction of the reheater tube temperature to help managers adopt appropriate measures based on the predicted temperature changes. Considering the non-smooth and non-linear nature of the original temperature data, this study applies complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the temperature data to remove noise and extract non-linear features effectively. In this study, the residual network18-convolutional block attention module (ResNet18-CBAM), transformer, gate recurrent unit (GRU), and temporal convolutional network (TCN) are applied to predict each component with different degrees of complexity after decomposition. The results of each component are integrated by the multilayer perceptron (MLP). The proposed MDIM is evaluated with various metrics. For single-step prediction, mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are 0.037, 0.0062, and 0.066, respectively. In the case of 48-step prediction, the corresponding values are 1.97, 0.33, and 2.4. Therefore, the proposed MDIM achieves outstanding results in both single-step and multi-step prediction.

Survey of Medical Applications of Federated Learning.

Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain. We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security. In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns. FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

Principle of signal-to-noise ratio direct measurement

The sensitivity of radio receivers and other systems used in all fields of practical applications of electronics is carried out at certain signal-to-noise ratio value. Examples of systems, for which this ratio is one of the important qualitative indicators, are given. The aim of the investigation is to solve the actual problem of signal-to-noise ratio measurement in a wide range of frequencies, simplify instruments and improve their technical and metrological parameters. A principle of signal-to-noise ratio measurement has been proposed, its theoretical foundation has been given. Precise relationship between the signal-to-noise ratio and the parameters of noise amplitude, phase and frequency modulation has been established, thus modulation meters can measure signal-to-noise ratio. There are three options for implementing the developed measurement principle. All of them use one existing industrial modulation meter and provide direct signal-to-noise ratio. For two options there are no restrictions on the origin and type of noise, it may be arbitrary. A pilot test of the measurement principle was conducted, and three implementation options were actually tested. Their discrepancies with the original data of the signal and noise generator lie within error limits of the instrumentation of the former. Mathematical modeling of the principle of signal-to-noise ratio measurement in Mathcad software package was also carried out. Recommendations on the use of the principle are given. The fundamental advantages of the developed principle consist of simple implementation by a single industrial instrument and solving the problem of metrological ensuring of signal-to-noise measuring instruments using the existing state verification plans. The main technical and metrological characteristics obtained by using the principle are determined by parameters of existing modulation meters and are proven to be high, the frequency range is till 26,5 GHz and the measurement error is as low as 8 %.

Optimizing road sign detection using the segment anything model for background pixel exclusion

Road sign recognition plays a great role in automatic driving. At present, for the task of road sign recognition, the structure related to CNN is mainly used to classify the detected road signs. However, due to the complexity of road conditions and weather, the detected images often contain complex background pixels, which affects the accuracy of classification. Due to the release of segment anything model, there is a powerful tool for eliminating background pixels quickly and conveniently. We optimize the data to retain only the road sign part of the images, and directly let the model train the image without back- ground pixels to realize the recognition task. This paper constructs two CNN classification models with the same structure through comparative experiments. One model uses the segmented data set for training, and the other model uses the original data set for training. The performance of the two models is evaluated and compared to verify the optimization effect brought by the segmented data set. It is found that segment anything model can accurately cut most of the images in the data set. And the performance of the model trained by the segmented data set is better than that of the model using the original data set. Moreover, the optimized model can achieve high accuracy in less training times.

Dying whilst on probation: a scoping review of mortality amongst those under community justice supervision.

Data suggest mortality rates of those under community justice services such as probation or parole have been increasing year on year. Little is known about why and how these individuals are dying. This scoping review explores the causes and contributing factors of mortality in those under community justice supervision. Studies published between 2011 and 2021 were identified across CINAHL, Embase, Global Health, Ovid Medline and PsycINFO. Articles were included if they presented original data on either mortality rates among those under community justice supervision or risk factors associated with the mortality of those under community justice supervision. Searches identified 101 unique articles of which 13 were included in the review. Articles were representative of five countries. All articles were either retrospective reviews or retrospective cohort studies. The studies fell into the categories of all-cause mortality, self-inflicted deaths or drug-related deaths. Mortality rates of those under community justice supervision were found to be consistently higher than mortality rates for the general population regardless of cause of death. Factors identified as affecting mortality included history of drug use, history of self-harm and previous imprisonment including length of time in custody and experience of hospitalisation or solitary confinement while in custody.

Reassessing the Role and the Benefits of Junior Ministers in Coalition Governments

Junior ministers are very common in coalition governments. Existing research argues that parties assign junior ministers to satisfy the office goals of coalition partners or as a mechanism to manage delegation costs. This article aims to reassess this argument. Using interviews, personal calendars, coalition agreements and an original data set on junior ministers in Israel, it finds that junior ministers are engaged in policymaking either on general issues under the ministry’s jurisdiction or issues that are salient to their party. Although rival junior ministers have the capacity and the incentive to monitor the minister, they do so only on the margins, either because they do not need to, or because they focus on safeguarding their policymaking autonomy. Finally, all junior ministers assist the minister and represent the ministry.

Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis

BackgroundSynthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects’ metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts.MethodsThree synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3).ResultsThree pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric.ConclusionThe initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

Study on the aging status of insulators based on hyperspectral imaging technology.

The acidic environment is one of the main factors leading to the aging of silicone rubber (SiR) insulators. Aging can reduce the surface hydrophobicity and pollution flashover resistance of insulators, threatening the safe and stable operation of the power grid. Therefore, evaluating the aging state of insulators is essential to prevent flashover accidents on the transmission line. This paper is based on an optical hyperspectral imaging (HSI) technology for pixel-level assessment of insulator aging status. Firstly, the SiR samples were artificially aged in three typical acidic solutions with different concentrations of HNO3, H2SO4, and HCl, and six aging grades of SiR samples were prepared. The HSI of SiR at each aging grade was extracted using a hyperspectral imager. To reduce the calculation complexity and eliminate the interference of useless information in the band, this paper proposes a joint random forest- principal component analysis (RF-PCA) dimensionality reduction method to reduce the original 256-dimensional hyperspectral data to 7 dimensions. Finally, to capture local features in hyperspectral images more effectively and retain the most significant information of the spectral lines, a convolutional neural network (CNN) was used to build a classification model for pixel-level assessment of the SiR's aging state of and visual prediction of insulators' defects. The research method in this paper provides an important guarantee for the timely detection of safety hazards in the power grid.

Complemented subspace-based weighted collaborative representation model for imbalanced learning

Collaborative representation-based classifiers (CRCs) have demonstrated remarkable classification performance in various pattern recognition fields. However, their success heavily relies on a balanced class distribution. More radically, significantly skewed class distributions may severely affect the effectiveness of the CRCs. To address this limitation, in this study, a complemented subspace-based regularization technique is integrated into the CRC framework for tackling imbalanced classification tasks. To enhance recognition accuracy for minority classes, a class weight learning algorithm is presented, in which the weight of each class is adaptively obtained according to the prior distribution information of the original data. Furthermore, the proposed model provides an efficient closed-form solution, ensuring computational efficiency comparable to CRCs. Extensive experiments on diverse imbalanced datasets are carried out to substantiate the superiority of our method over numerous advanced imbalanced learning algorithms.

Thromboembolism in peripartum cardiomyopathy: a systematic review.

Women with peripartum cardiomyopathy (PPCM) are at an increased risk of arterial and venous thromboembolic events. The review summarizes the evidence on the incidence of thromboembolic complications in women with PPCM, diagnostic approaches, related outcomes, and effects of therapies that have been used. English articles were retrieved from Web of Science and PubMed using search terms to capture studies related to PPCM (or postpartum cardiomyopathy) and all combinations of thrombosis- and embolism-related keywords. A total of 347 articles from PubMed and 85 from Web of Science were obtained, and after removing duplicates, 327 articles were screened for original data and classified into four domains: epidemiology, risk factors, diagnosis, and therapy of thromboembolism in PPCM. Ultimately, 30 articles were included. Data were synthesized in summary tables for each domain. Studies in the United States and Europe reported varying incidence for thromboembolism in PPCM, up to 14% in 6 months. Risk factors include elevated levels of coagulation factors, decreased protein C and S activity, decreased fibrinolysis, and a low left ventricular ejection fraction (LVEF). Cesarean delivery and post-operative status were correlated with a higher incidence of thromboembolic complications. Diagnosis relied mostly on ultrasonography and magnetic resonance and depended on the suspected location of thrombus. Anticoagulation has been used mostly for PPCM patients with a reduced LVEF, with the duration varying across guidelines and healthcare systems. Unfractionated heparin and low molecular weight heparin (LMWH) were considered safe choices during pregnancy, while warfarin and novel oral anticoagulants (NOACs) were used postpartum. The association of bromocriptine with risk of thromboembolic complications remains debated. There are important gaps in our understanding of the epidemiology, risk stratification, and optimal secondary prevention of thromboembolism in PPCM. Larger prospective studies with detailed phenotyping are required.

[Optimization Design of Interbody Fusion Cage withDifferent Bone Densities Printed in 3D].

Based on the original data of cervical spine, the models of cervical C6 and C7 segments were reconstructed through medical image processing and reverse modeling operations, then the models were assembled to obtain the basic data of interbody fusion cage. According to the basic data, the structures of rectangular porous, gradient porous and octahedral porous interbody fusion cages were established respectively. Maximum force on the adult male neck was applied to the fusion device, and the stress, strain and maximum deformation of the fusion device were solved by finite element analysis. The elastic modulus decrease of the design, and the rectangular porosity structures with different porosity were analyzed and optimized. The results showed that the elastic modulus of the fusion cage with three structures decreased in varying degrees, and the porosity of the interbody fusion cage with rectangular structure was about 60%, which was the most decreased elastic modulus.

Relevance variable selection variational auto-encoder network for quality-related nonlinear process monitoring

Quality-related process monitoring is essential for revealing changes in product quality and ensuring industrial safety. Therefore, it is crucial to distinguish enough quality-related features within the data. To learn the nonlinear characteristics and obtain quality-related features in process data, a novel process monitoring method named relevance variable selection variational auto-encoder (RVS-VAE) is proposed. Firstly, to enhance the strong quality-related variables and learn the time correlation, the normalized original data is weighted by mutual information and augmented into data matrices. Secondly, the RVS strategy is proposed to select the most quality-related variables from the latent features. These quality-related features are further utilized to monitor the process. What is more, when a fault is detected, relevance variable relative contribution (RVRC) method is presented to locate the fault variables. Finally, performance of the proposed RVS-VAE method is evaluated through comparative experiments on tennessee eastman (TE) process.

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit

BackgroundAlarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many – mostly unnecessary – alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. ObjectiveWe present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. MethodsPolicy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models – for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. ResultsARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. ConclusionARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike – with organisational, economic, and clinical benefits for the implementing hospital.

Application Of The Least Square Method For Website Based Shrimp Sales Prediction

UD Arif is a company engaged in the business of buying and selling shrimp, this company is located on Jalan Soreang No.127 Pitue, Desa Pitue, Kec. Ma'rang, Kab.Pangkep. The problem that is often experienced at UD Arif is the amount of shrimp availability that does not match customer demand because shrimp quickly deteriorate and sales fluctuate due to market demand, such as the size of the shrimp that must be stocked, the quality of the shrimp, and too much stock on the market resulting in Shrimp sales decline Therefore the aim of this study is to produce an application that can make UD Arif's youth easier in predicting shrimp sales. So the method used in this study is the least squares method, which is a method that can handle data that experiences ups and downs, where it is influenced by seasons and trends. The result of this research is to produce a website that can predict shrimp sales. These results are known by means of correlation, namely looking for a relationship between the original data and the predicted data calculated using Excel. From this correlation method, it is known that with 12 months of test data, it is possible to have a vannamei shrimp test with a MAPE result of 3.7%.