Exact Prediction Research Articles

A deep learning based automatic two-dimensional digital templating model for total knee arthroplasty

BackgroundPreoperative templating is an important step for total knee arthroplasty (TKA), facilitating hospital personnel in the anticipation and preparation of necessary surgical resources. Despite its importance, this process currently lacks automation. This study aimed to develop an artificial intelligence (AI) model to automate implant size prediction.MethodsA total of 13,281 (2938 anteroposterior, 10,343 lateral) knee radiographs obtained from the authors’ institute were utilized for model training, with 2302 (1034 anteroposterior, 1268 lateral) images set apart for validation and testing. The templating AI model integrates a pipeline composed of multiple steps for automated implant size estimation. To predict implant size, anterioposterior (AP) and lateral radiograph predictions were merged, selecting the smaller of the predicted sizes to prevent implant overhang. The model’s size predictions were validated with 81 real TKA data set apart from the training data, and its accuracy was compared to that of manual templating by an orthopedic specialist. Predictions matching the actual implanted sizes were labeled “exact” and those within one size, “accurate.” The influence of patient characteristics on the model’s prediction accuracy was also analyzed. The measurement time elapsed for implant sizing was recorded for both the AI model and the orthopedic specialist. Implant position predicted by the model was validated by comparing insert locations with postoperative images.ResultsCompared with data from 81 actual TKA procedures, the model provided exact predictions for 39.5% of femoral and 43.2% of tibial components. Allowing a one-size margin of error, 88.9% of predictions were deemed “accurate” for both components. Interobserver reliability (Cohen’s kappa) were 0.60 and 0.70 for femoral and tibial implants, respectively, both classified as “substantial.” The orthopedic specialist produced results accurate within one-size margin of error in 95.1% and 100% of cases for femoral and tibial components, respectively. Interobserver reliability between the orthopedic specialist and ground truth was 0.76 and 0.8 for femoral and tibial components, respectively. The measurement time per case was 48.7 s for the AI model, compared with 97.5 s for the orthopedic specialist. Compared with postoperative radiographs, predicted implant position had an error of less than 4 mm on average.ConclusionsAn AI-based templating tool for TKA was successfully developed, demonstrating satisfactory accuracy and efficiency. Its application could significantly reduce the clinical workload in TKA preparation.

Planning Accuracy and Stem Offset Assessment in Digital Two-Dimensional Versus Three-Dimensional Planning in Cementless Hip Arthroplasty: A Systematic Review and Meta-Analysis.

Background/Objectives: Total hip arthroplasty (THA) planning is crucial for restoring hip function and minimizing complications. The present systematic review and meta-analysis aimed to assess and compare the accuracy of 2D versus 3D preoperative planning in THA. Methods: The inclusion criteria were randomized controlled trials (RCTs) and observational studies (ROSs) published in English comparing the accuracy of 2D and 3D preoperative planning for total hip arthroplasty. We excluded review articles, registers, studies not written in English, studies that did not report the cup sizing accuracy or stem sizing accuracy or give a description of the preoperative planning method used, and non-comparative studies. In June 2024, following the PRISMA 2020 statement, a systematic review and a meta-analysis of the literature were conducted in PubMed, Scopus, and the Cochrane Library. The statistical analysis software Review Manager (RevMan) version 5.4 was used to perform the meta-analysis to compare the accuracy of 2D and 3D planning, and to assess the risk of bias, the ROBINS-I tool was used. Results: The analysis included 777 patients from six studies. The analysis showed that 3D planning offers superior precision compared to 2D planning, both for the cup (96.92% vs. 87.14%) and the stem (94.72% vs. 86.28%). The forest plots assessed a better trend for 3D planning in terms of exact size prediction and accuracy within ±1 size. Conclusions: The three-dimensional method was more precise and accurate than two-dimensional planning, both for the stem and the cup. It offered a detailed three-dimensional view of the patient's anatomy. The main limitation was the challenge in finding homogeneous data regarding biomechanical parameters, surgical approaches, and different planning systems for both three-dimensional and two-dimensional methods.

Forecasting material flows of modern logistics centres of railway freight stations exemplifi ed by Xi’an (the People’s Republic of China)

The purpose of the study is to create a more accurate material fl ow forecasting model of Xi’an freight railway station in China. The combined forecasting model is more validated for forecasting freight flows of regional logistics compared to three methods: grey forecasting, Markov chains, entropy weighting. Through the creation of the combined model, the grey forecasting method is combined with Markov chain correction, and the projected data is compared with the actual data to obtain higher accuracy of the forecasting model. A combined model using the grey forecasting method combined with Markov chain correction is created, with the forecast data compared with the actual data to obtain high accuracy of the forecasting model. The practical significance is that in the context of the present post-pandemic economic development, the logistics enterprises that do not operate in accordance with the modern logistics methods may be displaced by competitors. If the railway does not improve its logistics infrastructure, logistics equipment, railway logistics network platform, etc., it will lose out to other modes of transport. In order to meet the needs of logistics and improve the market competitiveness, the main indicator of a freight station is loading and logistics flow. Therefore, exact prediction of future changes in the logistics flow of a freight station can help to determine whether the station needs to be upgraded as a railway station or transformed into a certain type of a logistics centre.

On the predictability of possible storylines for forced complex systems

Abstract It is well-known that even for fairly simple deterministic nonlinear systems, exact prediction of future state is, on average, impossible beyond some small multiple of the Lyapunov time that quantifies the rate of separation of trajectories within an attractor. Nonetheless, it may be possible to find a physical measure that is the distribution of a trajectory within the attractor. In that sense, there can be a still weaker form of predictability. In this paper, we show that this can also fail but an even weaker form of predictability can appear for non-autonomous (i.e. forced) systems in the presence of tipping points. The {\em predictability of possible storylines} appears when one can interpret the frequencies of runs within an ensemble arriving at one of several possible future attractors (storylines) in a probabilistic manner.
As predictability is a major concern and a challenge in climate science, we illustrate this notion of predictability with two climate-related examples: a chaotic energy balance model and a global ocean model featuring a tipping point of the Atlantic Meridional Overturning Circulation.

Advancing projections of crown-of-thorns starfish to support management interventions

Outbreaks of corallivorous Crown of Thorns Starfish (Acanthaster spp.; CoTS) cause substantial coral mortality throughout the Indo-Pacific, particularly on the Great Barrier Reef (GBR). Refining CoTS population density modelling and understanding the disparities between real-world observations and model predictions is crucial for developing effective control strategies. Using a spatially explicit ecosystem model of the GBR, we compared CoTS density model predictions to observations and incorporated a new zone-specific mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predictions and observations: ∼81 % of categorical reef level CoTS densities matched or only differed by one category. However, underpredictions increased with higher observed densities. Zone-specific CoTS mortality reduced severe underpredictions from 7.1 % to 5.6 %, which is critical for managers as underpredictions indicate missing outbreaks where targeted culling is necessary, but also lead to underestimated coral loss attributed to CoTS outbreaks. Reef protection status affected prediction accuracy, highlighting the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the “initiation box”, a speculative area of primary outbreaks (i.e., initial abrupt population increases) on the GBR, also influenced accuracy, with exact predictions more likely outside. Accurately modelling initiation box dynamics is challenging due to limited empirical data on CoTS outbreaks, highlighting the need for focussed research on outbreak dynamics to enhance predictive accuracy. Spatial factors, such as region and shelf position, contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. Observations of CoTS can help refine model predictions, guide targeted control measures, and contribute to effective ecosystem management for the long-term resilience of the GBR and other reefs targeted by CoTS throughout the Indo-Pacific.

Sandwich-Like Complexation of Carbohydrates by Hydrogen Bonding and CH-π Interactions

Introduction: The design of carbohydrate-binding agents (artificial carbohydrate receptors) that enable selective and effective biomimetic recognition via noncovalent interactions is aimed either at a better understanding of natural recognition phenomena or at various potential applications in medicine and other fields. Although very interesting artificial receptors have been developed, the exact prediction of the receptor selectivity remains a challenge. Results and Methods: A molecular architecture based on a 1,3,5-substituted 2,4,6-triethylbenzene backbone bearing two aminopyridine- or aminopyrimidine-based recognition units and a purine moiety, which acts as both a hydrogen bonding site and a bridging component for the incorporation of additional substituents, has proved to be very useful for the development of effective carbohydrate-binding agents. This type of compounds has the ability to bind suitable carbohydrates through combined noncovalent interactions, where CH···π interactions can be formed on both faces of the carbohydrate substrate. The successful syntheses of the target compounds can be realized by the use of microwave irradiation and sealed tubes. The performed binding studies included 1H NMR spectroscopic titrations and measurements by isothermal titration calorimetry. Conclusion: The new compounds were developed as artificial receptors especially for carbohydrates with an all-equatorial substitution pattern and have the ability to predictably form strong 1:1 complexes with a suitable substrate. The use of the purine moiety in the construction of carbohydrate receptors with a 1,3,5-substituted 2,4,6-triethylbenzene backbone has proved to be a very promising approach. The possibilities for structural variation of this molecular architecture are manifold. As a result, a wide range of compounds can be synthesized to perform extensive studies on the relationships between structure and binding efficiency. Graphical Abstract Short Text: New compounds were developed as artificial receptors especially for carbohydrates with an all-equatorial substitution pattern and have the ability to predictably form 1:1 complexes with a suitable substrate. Key Topics: Artificial carbohydrate receptors; Combination of hydrogen bonding and CH۔۔۔π interactions; Molecular recognition

CW‐Seeded Parametric Combs with Quantum‐Limited Phase Noise

AbstractOptical frequency combs have revolutionized frequency metrology and spectroscopic measurements, enabling the most precise measurements of all physical quantities. However, precision frequency metrology heavily relies on mode‐locked laser combs, which are only directly available for a few selected near‐infrared wavelength ranges. Recently, a strong tendency emerged for combs in the mid‐infrared molecular fingerprint region. To this end, several methods for wavelength conversion have been proposed and demonstrated, which nevertheless cost a degradation of coherence properties. Here a first approach is presented toward measuring resulting phase noise fluctuations, exploiting the temporal resolution of the dispersive temporal interferometry (DTI) technique for experimentally unveiling the carrier‐envelope phase dynamics of a cw‐seeded femtosecond optical parametric amplifier (OPA). Particularly, it is experimentally demonstrated for the first time that unexpectedly low seed power levels suffice to exceed vacuum fluctuations by more than an order of magnitude, resulting in pulse‐to‐pulse phase fluctuations of 82 mrad at rather moderate cw seed levels of 8 mW. Additionally, a formula is provided that allows exact prediction of the experimentally observed noise levels. This study therefore provides new insight into the role of vacuum fluctuations in OPAs, which open up an avenue for coherent dual‐comb systems in the mid‐infrared and beyond.

Validation of mediCAD® software for fully digital preoperative planning of total hip arthroplasty: a retrospective study

IntroductionThe planning step that precedes a total hip arthroplasty (THA) procedure is crucial. Digital planning software programs are being increasingly used, although few studies have reported on the reliability of such tools. Furthermore, no studies have been conducted on the mediCAD® software, despite it being widely used in France. This led us to conduct a retrospective study to: (1) assess the accuracy of this planning software, (2) determine the intra- and inter-rater reliability, (3) determine how obesity affects the accuracy of planning. HypothesisTHA planning is accurate and reliable when using the mediCAD® software. Patients and methodsThis was a single center, retrospective study. One hundred one consecutive cases performed by a single experienced surgeon were planned retrospectively by two blinded surgeons on two separate occasions. The acetabular cup was cemented in 90 hips (89%), cementless in 11 hips (11%). A dual mobility cup was used in 21 hips (21%). The femoral stem was cemented in 60 hips (59%). The endpoint was the number of exact plans, defined as the same size as the actual implants. An acceptable match was defined as a difference of one size. The match was unacceptable if the planned and implanted size differed by more than 2 for the acetabular cup or by more than 1 size for the femoral stem. The intra-rater and inter-rater reliability were calculated using the intraclass correlation coefficient (ICC) with 95% confidence intervals (CI). ResultsExact agreement was found by the first rater for 15 planned acetabular cups (15%) and for 45 planned femoral stems (45%) relative to the implants used. The second rater reached exact agreement for 20 planned acetabular cups (20%) and 50 planned femoral stems (50%). The intra-rater reliability for the acetabular cup was average (ICC = 0.57; 95%CI [0.43–0.69]) and poor (ICC = 0.38 95%CI [0.20–054]) for the 1st and 2nd rater, respectively. The intra-rater reliability for the femoral stem was poor for the 1st rater (ICC = 0.47 95%CI [0.30–0.61]) and the 2nd rater (ICC = 0.45 95%CI [0.29–0.60]). The interobserver reliability was low for the planned acetabular cup (ICC = 0.39 95%CI [0.21–0.54]) and the planned femoral stem (ICC = 0.42 95%CI [0.24–0.57]). Overall, when combining the two raters, exact prediction of the acetabular cup was achieved in 31 hips (19%) in non-obese patients and in 7 hips (21%) in obese patients (p = 0.62). DiscussionThis study found acceptable reliability of the mediCAD® software. Experience level, radiograph magnification affected the planning outcome in this study, but obesity did not. We currently do not have the ability to incorporate a reliable radiological scale for two-dimensional templating. Some surgeons prefer using a CT scan, but this costs more than conventional radiographs and exposes the patient to more radiation. This study shows that the mediCAD® software can provide satisfactory output for the preoperative planning of THA. Level of evidenceIII; retrospective, diagnostic, comparative study

Real Time Analysis of Self-Driving Vehicles using Computer Vision

Computer vision technique plays a mighty role in predicting various steering angles, obstacles on the road, traffic signals, pedestrians and is capable of regulating a vehicle which is autonomous and can be controlled without human intervention. This technique brightens up the probabilities of immersing it into autonomous vehicle for its control and safety while driving. This paper demonstrates the usage of computer vision technique for the enhancement in control of self-driving vehicles. The image dataset is stockpiled via virtual environment i.e. usage of Udacity simulator where the center, left and right side of the vehicle on road is captured and this gathered dataset is employed to train the model. The model training is performed with the employment of convolutional neural network and NVidia model to extract the specificities of images while training. The trained and extracted model is inculcated again to the virtual Udacity simulator to test the accuracy of the trained model while driving the vehicle autonomously in the virtual environment. The accuracy of the trained model can be predicted by minimizing the loss percentage while training, this will be reflected once the vehicle is autonomously operated in the virtual environment. The assessment comprises of condensed research synthesis and evaluation. The conspicuous challenges of utilizing computer vision is the scarcity of real world datasets which ensures that there will be overreliance on the data induced from the virtual environments. Inclusion of real-world datasets in the future will give the researchers an upper hand in predicting the exact prediction model for autonomous vehicles

Implementation of exact thermal analysis in extended Oxley’s predictive machining theory

Finding the exact solution to the generated temperatures that are more closely aligned with the actual nature of the process is important while conducting analytical modeling of the chip formation process in metal cutting. The extended Oxley’s predictive machining theory is implemented with the exact thermal analysis approach. This study presents temperature distributions near the shear plane on the workpiece side along with an exact temperature prediction for orthogonal cutting using the Komanduri-Hou model for the shear plane heat source. An explicit solution is also derived to obtain the tool-chip interface temperatures. The intricate formulae utilized in the original research study of Komanduri-Hou are solved using MATLAB code. The approach used here makes it easier to use the exact solution in future research for various categories of materials. The temperature distribution plots help to clarify the metal-cutting procedure and understand the different heat zones. The outcomes of shear angle, forces, stresses, strains, strain rates, and average temperatures are consistent with those reported in earlier studies.

A Koopman–Takens Theorem: Linear Least Squares Prediction of Nonlinear Time Series

The least squares linear filter, also called the Wiener filter, is a popular tool to predict the next element(s) of time series by linear combination of time-delayed observations. We consider observation sequences of deterministic dynamics, and ask: Which pairs of observation function and dynamics are predictable? If one allows for nonlinear mappings of time-delayed observations, then Takens’ well-known theorem implies that a set of pairs, large in a specific topological sense, exists for which an exact prediction is possible. We show that a similar statement applies for the linear least squares filter in the infinite-delay limit, by considering the forecast problem for invertible measure-preserving maps and the Koopman operator on square-integrable functions.

A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

Background/ObjectivesCanonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to ACMG criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes is poorly understood. Methods168 rare CSSVs in unselected blood-expressed genes were identified by genome sequencing in 112 individuals, and their impact on splicing was interrogated manually in RNA sequencing (RNA-seq) data. Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. ResultsThere was no evidence of a frameshift nor of reduced expression consistent with nonsense-mediated decay for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame indels, 3.6% resulted in full exon skipping (in-frame), and 0.6% resulted in full intron inclusion (in-frame). The predicted impact on splicing using (i) SpliceAI, (ii) MaxEntScan, and (iii) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63% and 61% of CSSVs, respectively. ConclusionApproximately 1 in 4 rare CSSVs may not cause LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.

Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.

Optimization of Battery Application for Wind Energy Storage

The largest disadvantage of wind energy is the lack of exact predictability and its fluctuations, which are causing problems in the power system operation. The consequence is that even though other energy sources can be saved by using wind energy, the short-term power fluctuation has to be secured by a conventional plant. A possible solution can be the storage of wind energy. In this paper battery systems are analyzed as a backup for a 400 MW offshore wind farm with help of special developed calculation models. The goal is to reach small difference between forecasted and actual wind power and to increase the reliabilty of delivered wind energy using battery systems. For the determinations the properties of the battery system are considered and integrated into the calculation model. The overall target is to reduce the reserve energy requirement of the offshore wind farms.

Weather Forecasting Using Artificial Neural Network (ANN): A Review

Extreme weather occurrences provide issues that necessitate the development of technology capable of accurate analysis and exact prediction in order to successfully reduce their effects. Artificial neural networks (ANNs) have appeared in the last few years as a promising weather forecasting technology because of their capacity to manage intricate and nonlinear weather factors. The use of ANNs in weather element prediction has shown considerable improvements in forecasting precision and accuracy. Factors influencing an ANN model's efficacy include input Data Type and Volume for Training, Hidden Layer Neuron Count, network architecture, Activation Functions, and training algorithms. In this paper, we will present a thorough review of the applications of Artificial Neural Networks (ANNs) in weather forecasting, specifically focusing on temperature and rainfall prediction over the past 15 years. To enhance reader comprehension, The work of numerous researchers in this topic is methodically analyzed and compared in in tabular format. The authors aim to facilitate future research decisions by offering an organized review, aiding in the determination of suitable input features, transfer functions, and training algorithms for accurate temperature and rainfall predictions.

Investigation on Preserving Privacy of Electronic Medical Record using Split Learning

Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare.

Outlier Management and its Impact on Diabetes Prediction: A Voting Ensemble Study

The chronic metabolic disorder known as diabetes mellitus, which is defined by hyperglycemia, poses a significant threat to the health of people all over the world. The categorization is broken down into two primary categories: Type 1 and Type 2, with each category having its own unique causes and approaches to treatment. It is very necessary for the effective management of illnesses to have both the prompt detection and the exact prediction of outcomes. The applications of machine learning and data mining are becoming increasingly important as tools in this setting. The current research study analyses the usage of machine learning models, specifically Voting Ensembles, for the goal of predicting diabetes. Specifically, the researchers were interested in how accurate these models were. Using GridSearchCV, the Voting Ensemble, which consists of LightGBM, XGBoost, and AdaBoost, is fine-tuned to manage outliers. This may be done with or without the Interquartile Range (IQR) pre-processing. The results of a comparative analysis of performance, which is carried out, illustrate the benefits that are linked with outlier management. According to the findings, the Voting Ensemble model, when paired with IQR pre-processing, possesses greater accuracy, precision, and AUC score, which makes it more acceptable for predicting diabetes. Despite this, the strategy that does not use the IQR continues to be a workable and reasonable alternative. The current study emphasizes both the significance of outlier management within the area of healthcare analytics and the effect of data preparation procedures on the accuracy of prediction models. Both of these topics are brought up because of the relevance of the current work.

Pineline: Industrialization of high-energy theory predictions

We present a collection of tools efficiently automating the computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors with the exact NNLO predictions in a PDF fit. Program summaryProgram Title:pinelineCPC Library link to program files:https://doi.org/10.17632/dyvns7gnwy.1Developer's repository link:https://nnpdf.github.io/pineline/Licensing provisions: GPLv3Programming language: Python, RustNature of the problem: The computation of theoretical quantities in particle physics often involves computationally-intensive tasks such as the calculation of differential cross sections in a systematic and reproducible way. Different groups often use different conventions and choices which makes tasks such as the fitting of physical parameters or quantities very computationally challenging and hard to reliably reproduce.Solution method: We create a pipeline of tools such that a user can define an observable and a theory framework and obtain a final object, containing all relevant theoretical information. Such objects can be then used in a variety of interchangeable ways (fitting, analysis, experimental comparisons).

Application of Time Series for Casting Using Machine Learn Method

Time series forecasting plays a vital role in optimizing processes through many industries, and the casting for the domain is no exception. Casting processes suddenly require exact predictions to shine product quality, low waste, and optimize resource allocations. This paper discovers the application of machine learning methods for time series forecasting. It starts with an exact review of existing research, enjoying the critical role of correct forecasting in this field. The research leverages device learning techniques, adding autoregressive integrated walking average (ARIMA), Long Short-Term Memory (LSTM) networks and XGBoost, to model & guess time series data related to casting processes. A different and unique dataset is collected, again processed and used for experimentation. The results shows the ability and control of these machine learning methods in forecasting many possibilities of casting processes. Through a serious talk of the results and their real implications, these paper implies to the increasing body of memory in the space of time series forecasting for casting. The discovery offer insights into a feasibility of using machine learning to shows casting operations, improve product quality and optimize resource use. This research no only shows the potential of machine learning in the casting field but also shows as a foundation for further Discovery and application of advanced forecasting techniques in this domain.

Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools

BackgroundSingle-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.ResultsThe minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with sim 50% of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only sim 11% of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5–10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions.ConclusionsIn general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.