Striking Accuracy Research Articles

A Performance Analysis of RNN Algorithms for Accurate Hypoglycemia Prediction in Type-1 Diabetes

Diabetes is a chronic disease originating from preeminent blood glucose levels where there is an impairment in the ability of the human body to produce or effectively use insulin, therefore resulting in possible complications to various organ systems. Hypoglycemia can be defined as a critical condition in diabetes that is characterized by very low levels of blood sugar and can emanate from any imbalance between insulin, glucose, and external factors such as medication or physical activity. The prediction of hypoglycemia is a very important task in diabetes management, which encompasses sophisticated technologies and deep-learning systems, such as RNNs, in performing analyses on patient-specific data. These must provide timely warnings to avoid hazardous blood sugar dips. This paper takes a close look at the performance of RNN in the case of accurate hypoglycemia prediction among patients with Type-1 Diabetes, considering a dataset from Shanghai T1DM. In this work, three different RNN architectures are considered for performance evaluation: Long-Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple RNN. The main goal was to compare their predictive performances in forecasting hypoglycemic events, which is an issue of utmost relevance when it comes to proactive diabetes management. The results show generally variable performances by the RNN models. Overall, GRU performed with striking accuracy in hypoglycemia predictions, while LSTM had high specificity. These findings underline that various metrics should be considered for the comprehensive evaluation of predictive models in the management of diabetes. It will give an idea about various RNN algorithms, strengths, and weaknesses to develop more effective and personalized strategies related to hypoglycemia prediction in Type-1 Diabetes.

Predictive analysis of breast cancer metastasis and identification of genetic markers using machine learning.

4 Background: The study devises a framework integrating machine learning (ML) paradigms with explainable artificial intelligence (XAI) to prognosticate the metastatic trajectory of breast cancer (BC) and delineate critical genomic indicators pertinent to metastasis. Methods: An examination was conducted on 98 initial BC specimens, which included 34 instances evolving into distant metastasis within a quintennial monitoring phase and 44 instances evincing no recurrence for a minimum pentad post-diagnosis. Genomic datasets underwent rigorous biostatistical scrutiny, followed by the implementation of an elastic net algorithm for feature discernment, thereby constraining the scope to a salient subset of genomic markers implicated in BC metastasis. An ensemble of advanced predictive models encompassing Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Adaptive Boosting (AdaBoost) was deployed. Model efficacy was gauged through metrics such as accuracy, F1 score, precision, recall, the Area Under the Receiver Operating Characteristic Curve (AUC), and the Brier score. To elucidate the reasoning behind the ML predictions and to navigate the opacity inherent in such models, a SHapley Additive exPlanations (SHAP) approach was invoked. Results: The predictive acumen of the LightGBM model was superior, evidenced by a striking accuracy of 96% and an AUC of 99.3%. Parallel to biostatistical assessments, SHAP analysis leveraging XAI illuminated that augmented expression levels of specific genes, namely TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (with statistical significance p ≤ 0.05), correlated with an escalated risk of BC metastasis. Conversely, diminished expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) was similarly associated with heightened metastatic susceptibility in BC. Conclusions: The insights garnered from this investigation may catalyze preventative strategies against BC progression and metastatic dissemination, thereby enhancing therapeutic efficacy through personalized intervention modalities for BC patients.

Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Accurately obtaining the aerodynamic parameters of a projectile is crucial for improving aircraft performance, optimizing design and control, and enhancing weapon strike accuracy. In this study, the Dung Beetle Optimization (DBO) algorithm was utilized to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To fully exploit the performance of the DBO algorithm and enhance identification accuracy, four improvement measures were proposed for the DBO algorithm. The enhanced DBO algorithm, known as the Improved Dung Beetle Optimization (IDBO) algorithm, was developed and validated using the CEC2017 test function. The proposed IDBO-KELM algorithm was applied to the aerodynamic parameter identification of projectiles. Comparative analysis with results from ELM, IDBO-ELM, and other models revealed that single ELM struggles to accurately identify aerodynamic parameters. While the identification results of IDBO-ELM were 3–5 orders of magnitude higher than those of ELM, a certain gap still existed between the identification results and the true values in the highly nonlinear transonic region. Notably, the IDBO-KELM model yielded the best identification results, with an overall improvement of 1–2 orders of magnitude compared to IDBO-ELM, effectively reducing errors in identification within the transonic region. Experimental findings demonstrate that the proposed algorithm combines the advantages of high accuracy and stability.

Dynamics of manual impaction instruments during total hip arthroplasty.

Manual impaction, with a mallet and introducer, remains the standard method of installing cementless acetabular cups during total hip arthroplasty (THA). This study aims to quantify the accuracy and precision of manual impaction strikes during the seating of an acetabular component. This understanding aims to help improve impaction surgical techniques and inform the development of future technologies. Posterior approach THAs were carried out on three cadavers by an expert orthopaedic surgeon. An instrumented mallet and introducer were used to insert cementless acetabular cups. The motion of the mallet, relative to the introducer, was analyzed for a total of 110 strikes split into low-, medium-, and high-effort strikes. Three parameters were extracted from these data: strike vector, strike offset, and mallet face alignment. The force vector of the mallet strike, relative to the introducer axis, was misaligned by an average of 18.1°, resulting in an average wasted strike energy of 6.1%. Furthermore, the mean strike offset was 19.8 mm from the centre of the introducer axis and the mallet face, relative to the introducer strike face, was misaligned by a mean angle of 15.2° from the introducer strike face. The direction of the impact vector in manual impaction lacks both accuracy and precision. There is an opportunity to improve this through more advanced impaction instruments or surgical training.

How precision, strength and flexibility affect badminton skills

The importance of the research lies in developing flexibility exercises (positive and negative) that help improve the level of physical and skill performance, address some weak points, and develop factors that work to implement skills with a wider range of motion, accuracy, and high strength. This is what makes the game of badminton more developed and successful. Performing (dimensions) strokes And the forward projection) by not using the wrist and the correct movement of the striking arm. This makes the player’s performance ineffective, which requires her to practice flexibility and movement flow to address this weakness. The study aims to prepare exercises using flexibility (positive and negative) and to know the effect of flexibility exercises on some physical characteristics, accuracy and power of strikes (dimensions and forward projection،The researchers experimental method by designing two equal groups and on a sample of (10) female players. As for the field study procedures, flexibility exercises (positive and negative) were carried out over a period of (10) weeks and two units per week. The researchers concluded that Flexibility exercises (positive and negative) developed some physical characteristics and the accuracy and power of strokes, the data was analyzed using the spss version 26 application ., The results showed that (2) concentration has a significant impact on badminton skills ,with asignification mark of Push up test in 15 sec 2,01

Comparison Between Forehand and Backhand Stroke Techniques in the Accuracy of Strikes Among Extra-Curricular Table Tennis Students at State Senior High School 1 Sedayu

This research is a descriptive study. The method used is a survey. The study population consists of 12 students participating in table tennis extracurricular activities at SMA Negeri 1 Sedayu. Data collection techniques employed in this research include testing and measurement. The results of the study indicate that (1) Forehand stroke abilities in table tennis extracurricular activities at SMA Negeri 1 Sedayu fall into the "high" category. When calculated as a percentage, the category with a very high level is 33.3%, comprising 4 students, the "high" category is 41% with 5 students, the "moderate" category is 16.6% with 2 students, and the "low" category is 8.3% with 1 student. There are no students in the "very low" category. (2) Backhand stroke abilities in table tennis extracurricular activities at SMA Negeri 1 Sedayu are categorized as "moderate". When calculated as a percentage, the "very high" category is 16.6%, with 2 students, the "high" category is 16.6%, with 2 students, the "moderate" category is 50%, with 6 students, the "low" category is 16.6%, with 2 students, and there are no students in the "very low" category.

Intelligent Gloves for Assisting Individuals with Visual Impairment

This research endeavor revolved around the conceptualization, design, and evaluation of a technologically advanced smart glove tailored to aid individuals afflicted with visual impairments. The focal objective entailed integrating a diverse array of sensors and sophisticated features within the glove’s framework to effectively detect barriers and offer location-specific assistance. Rigorous experimentation and analysis were conducted to meticulously scrutinize the device’s performance and ascertain its efficacy. The experimental findings unequivocally substantiated the glove’s competence in identifying obstacles obstructing the user’s path, with a striking accuracy rate exceeding 95%. Notably, when the user engaged the yellow button while concurrently gesturing forward, the glove adeptly detected impediments situated within a onefoot proximity, promptly generating an auditory alert through an embedded speaker module. Similarly, activating the red button triggered the activation of the GPS sensor, enabling real-time determination of the user’s precise geographical coordinates. Subsequently, this invaluable location data was expeditiously disseminated via Line Notify, accompanied by a conveniently accessible Google Maps hyperlink. Moreover, the aforementioned coordinates were seamlessly displayed on an interconnected web server, thus facilitating immediate assistance from nearby individuals. In essence, the culmination of this research effort showcased the immense potential of the developed intelligent glove as a viable tool for ameliorating the challenges faced by individuals confronting visual impairments. The comprehensive evaluation outcomes provide a solid foundation for future enhancements and refinements aimed at elevating the device’s functionality and user experience to unprecedented heights.

Behavioral diversity and biomechanical determinants of the outcome of a fish predator–prey interaction

AbstractPredator–prey interactions are ubiquitous and under strong selection because of the consequences experienced by both predator and prey if they lose the interaction. Biomechanics and behavior play important roles in the outcome of these interactions, but many studies focus on the prey, restrict the range of behaviors considered, and the role of prey boldness in the outcome is not understood. We used high‐speed video to test for effects of multiple measures of performance and kinematics of both the predator and prey, and boldness of prey on the outcome of interactions between Pike Cichlids (Crenicichla sp.) and Guppies (Poecilia reticulata). We found high variation in the behaviors employed during the predator–prey interactions, including in suction versus raptorial feeding, strike accuracy, and guppy responsiveness. We also found that predators moving relatively slower and prey moving relatively faster were more successful at consuming the prey and evading the predator, respectively. Prey that reacted farther from the predator was more likely to escape predation, but boldness of the prey did not affect the interaction. Our work suggests that a high level of variation in predator–prey interactions is widespread, even when strike and escape behaviors are stereotyped. We also showed that what both the predator and the prey do during an interaction are important in determining the outcome.

A deep learning-based automated image analysis for histological evaluation of broiler pectoral muscle

Global market demand for chicken breast muscle with high yield and quality, together with the high incidence rate of breast muscle abnormalities in recent years highlights the need for tools that can provide a rapid and precise evaluation of breast muscle development and morphology. In this study, we used a novel deep learning-based automated image analysis workflow combining Fiji (ImageJ) with Cellpose and MorphoLibJ plugins to generate an automated diameter and cross-sectional area quantification for broiler breast muscle. We compared data of myofiber diameter from 14-day-old broiler chicks, generated either by manual analysis or by automated analysis. Comparison between manual and automated analysis methods exhibited a striking accuracy rate of up to 99.91%. Moreover, the automated analysis method was much faster. When the automated analysis method was implemented on 84 breast muscle cross-section images it characterized 59,128 myofibers within 4.2 h, while manual analysis of 27 breast muscle cross-section images enabled analysis of 17,333 myofibers in 54 h. The automated image analysis method was also more productive, producing data sets of both diameter and cross-sectional area at an 80-fold higher rate than the manual analysis (26,279 vs. 321 data sets per hour, respectively). In order to demonstrate the ability of this automated image analysis tool to detect differences in breast muscle histomorphology, we applied it on cross sections from chicks of control and in ovo feeding group, injected with a methionine source [2-hydroxy-4-(methylthio) butanoic calcium salt (HMTBa)], known to effect skeletal muscle histomorphology. Analysis was performed on 19,807 myofibers from the control group and 21,755 myofibers from the HMTBa group and was completed in less than 1 h. The clear advantages of this automated image analysis workflow characterized by high precision, high speed, and high productiveness demonstrate its potential to be implemented as a reproducible and readily adaptable research or diagnostic tool for chicken breast muscle development and morphology.

Discovering and Interpreting Biased Concepts in Online Communities

Language carries implicit human biases, functioning both as a reflection and a perpetuation of stereotypes that people carry with them. Recently, ML-based NLP methods such as word embeddings have been shown to learn such language biases with striking accuracy. This capability of word embeddings has been successfully exploited as a tool to quantify and study human biases. However, previous studies only consider a predefined set of biased concepts to attest (e.g., whether gender is more or less associated with particular jobs), or just discover biased words without helping to understand their meaning at the conceptual level. As such, these approaches can be either unable to find biased concepts that have not been defined in advance, or the biases they find are difficult to interpret and study. This could make existing approaches unsuitable to discover and interpret biases in online communities, as such communities may carry different biases than those in mainstream culture. This paper improves upon, extends, and evaluates our previous data-driven method to automatically discover and help interpret biased concepts encoded in word embeddings. We apply this approach to study the biased concepts present in the language used in online communities and experimentally show the validity and stability of our method

Epaxial and hypaxial co-contraction: a mechanism for modulating strike pressure and accuracy during suction feeding in channel catfish.

Most fish species use concentric epaxial and hypaxial contractions to suction feed, whereby both muscle groups produce cranial expansion and negative intraoral pressures. In contrast, channel catfish (Ictalurus punctatus) suction feed with little to no cranial elevation and epaxial shortening, generating suction power primarily with hypaxial shortening and pectoral girdle retraction. We hypothesized that channel catfish (1) actively anchor the head via isometric contraction of the epaxials and (2) vary feeding performance by modulating the absolute and relative outputs of the co-contracting muscles. We used a combination of electromyography, intraoral pressure recordings and specimen manipulation, and developed a new dual-lever model to explore this idea. We detected epaxial and hypaxial co-contraction prior to suction force development in all strikes. Our model revealed that the differential between the co-contracting muscles may be used to modulate suction pressure and strike accuracy.

Multikink scattering in the ϕ6 model revisited

Antikink-kink ($\overline{\mathrm{K}}\mathrm{K}$) collisions in the ${\ensuremath{\phi}}^{6}$ model exhibit resonant scattering although the ${\ensuremath{\phi}}^{6}$ kinks do not support any bound states to which energy could be transferred. In P. Dorey et al. [Kink-Antikink Collisions in the ${\ensuremath{\phi}}^{6}$ Model, Phys. Rev. Lett. 107, 091602 (2011)] it was conjectured that, instead, energy is transferred to a collective bound mode of the full $\overline{\mathrm{K}}\mathrm{K}$ configuration. Here we present further strong evidence for this conjecture. Further, we construct a collective coordinate model (CCM) for $\overline{\mathrm{K}}\mathrm{K}$ scattering based on this collective bound mode trapped between the $\overline{\mathrm{K}}\mathrm{K}$ pair which allows us to reproduce the full dynamics of $\overline{\mathrm{K}}\mathrm{K}$ scattering with striking accuracy. We also study kink-antikink ($\mathrm{K}\overline{\mathrm{K}}$) scattering and its description by a CCM. In this case a significant role of radiation is discovered.

Review of Research Methods for Hypersonic Vehicle Reentry Trajectory Planning

Hypersonic vehicle has many advantages, such as wide range of maneuver, strong penetration ability, high strike accuracy, and so on. Reentry trajectory planning is one of the key technologies to support hypersonic vehicle systems. It is necessary to plan feasible or optimal trajectory under the process constraints such as heat flux, dynamic pressure, overload, and terminal constraints such as altitude and velocity. At present, traditional methods are difficult to meet the task requirements of trajectory planning and online trajectory generation under complex conditions with multiple constraints. As an artificial intelligence method, reinforcement learning has strong robustness and the characteristics of "offline training and online deployment", which can make up for the shortcomings of traditional methods and show great potential in trajectory planning. This paper introduces the current research status of traditional trajectory planning methods and reinforcement learning methods, and proposes that the reentry trajectory planning methods will be intelligent in the future.

Solving Poisson Equations by the MN-Curve Approach

In this paper, we adopt the choice theory of the shape parameters contained in the smooth radial basis functions to solve Poisson equations. Luh’s choice theory, based on harmonic analysis, is mathematically complicated and applies only to function interpolation. Here, we aim at presenting an easily accessible approach to solving differential equations with the choice theory which proves to be very successful, not only by its easy accessibility but also by its striking accuracy and efficiency. Our emphases are on the highly reliable prediction of the optimal value of the shape parameter and the extremely small approximation errors of the numerical solutions to the differential equations. We hope that our approach can be accepted by both mathematicians and non-mathematicians.

Design and accuracy test of polar moment of inertia measuring equipment for projectile and rocket

The moment of inertia is an important mass characteristic parameter. It has a direct impact on the trajectory, flight stability, strike accuracy, and service life of the projectile. To meet the measurement of the polar moment of inertia of large projectiles and arrows, this paper uses the vibration compound pendulum method to measure the polar moment of inertia of large projectiles and arrows, deeply analyzes the measurement principle, and designs a polar moment of inertia test equipment. Through the measurement of standard samples, the measurement accuracy of the equipment is tested, and the causes of error are analyzed. The experimental results show that the maximum relative error is less than 0.5 %, and the relative uncertainty is 0.813 %, which meets the requirements of expected technical indicators.

Model characteristics of competitive activity of female MMA mixed martial arts fighters of different weight classes

Purpose: to determine model characteristics of the competitive activity of female MMA mixed martial arts athletes of different weight classes. Material & Method: the study involved 150 top female mixed martial arts MMA fighters. Participants were divided into 3 groups according to the weight class of 50 athletes each: Strawweight (48 kg to 52 kg), Flyweight (52 kg to 56 kg), and Bantamweight (56 kg to 61 kg). The following methods were used: analysis of scientific and methodological information and Internet sources; generalization of best practices; analysis of protocols and videos of competitive activities; methods of mathematical statistics. Results: female athletes of each weight class have their characteristics in terms of competitive activity indicators. Thus, the finish of the fight with a striking differential, attempts, and accuracy of takedowns is significantly better (p<0.05-0.01) in women`s strawweight fighters. Strawweight female fighters perform a large number of sig strikes landed, which have high accuracy, due to which they win the fight, and this is explained by a large arsenal of technical and tactical actions. Women’s flyweight fighters need more time to finish the fight early. They also have the least number of sig strikes landed during the fight, which are performed with little accuracy, and this is due to the rational distribution of strength in a fight. Women’s bantamweight fighters conduct a large number of knockdowns landed and submissions during the entire fight, this can be explained by the high level of development of special endurance. It was found that the representatives of all weight classes perform mostly distance strikes landed and their sig head strikes landed are the best ones. Conclusions: applying methods of pedagogical observation and mathematical statistics allowed us to determine the structure and model characteristics of modern competitive activities of elite female mixed martial arts MMA athletes for each weight class. The model characteristics obtained during the study can be used to plan the training process of qualified female athletes and to solve the issues of managing their preparation for competitions.

HUBUNGAN DAYA LEDAK OTOT TUNGKAI DAN KECEPATAN REAKSI TERHADAP KETEPATAN ANGKATAN KAKI PADA ATLET GULAT PONTIANAK

AbstrakThe problem in this study is whether or not there is a relationship between muscle explosive power and speed on the accuracy of foot strikes in wrestling and aims to determine the relationship between one variable and another variable. The method used in this research is to find a quantitative research method with a correlational form. The population and sample in this study found 46 people. The sampling technique in this study used a purposive sampling technique, and 10 samples were used. As well as data collection techniques using tests and measurements for leg muscle explosive power using a vertical jump test, on reaction speed using a full body test, while the accuracy of leg lift using a leg lift test. Based on the results of the study, the correlation coefficient which shows the magnitude of rcount partially for muscle explosive power is 0.889 for reaction speed of 0.935, while simultaneously obtained is 0.889. This result is bigger than rtable, which is 0.632. From table 4.2 for leg muscle explosive power, it was obtained that 3 people 30% at intervals <21, and 7 people 70% at intervals 21-30, while for the other intervals there were none because the average athlete was at intervals <21 and 21-30 . From table 4.3 obtained 2 people 20% at intervals 0.301-0.400, 7 people 70% at intervals 0.401-0.500, 1 person 10% at intervals >501. Therefore, there is a positive relationship between muscle explosive power and speed of the relationship with the Pontianak athlete workforceKeywords: Muscle explosive power, reaction speed, leg lift accuracy

Performance Alteration Induced by Weight Cutting in Mixed Martial Arts-A Biomechanical Pilot Investigation.

Currently, there are pros and cons of research results related to weight cutting in combat sports, resulting in inconclusive results regarding the effects of weight-cut on athletes’ performance, and biomechanical investigations are hardly seen. Therefore, this pilot study tried to fill the gap by initiating an exploration in real-life competitions. It is our hope to add biomechanical insights (advantages/disadvantages) that would discern the impact of weight cutting on competitive performance and help to structure hypotheses in future research. The method consisted of 3D motion capture, EMG measurement and biomechanical modeling. Through the synchronized data, striking power, striking accuracy and reaction time were quantitatively determined. Pre- and post-test design was used to test common strikes before weight cutting and 24 h after weigh-in. Seven male athletes from local clubs were tested during regional competitions. Results were characterized by using descriptive statistics (means and standard deviations) and T-tests were performed to contrast differences between the pre- and post-tests. This pilot study has revealed that there is actually weight-regain instead of weight-loss. The weight-regain would speed up the perceptional and total reaction, slow down the limbs’ movement, worsen the striking accuracy and, possibly, decrease the strike power. The preliminary results are inconclusive regarding the competitive advantages/disadvantages induced by weight cutting. Further biomechanical studies are needed to deal with the controversial subject more objectively and scientifically.

Chain-based machine learning for full PVT data prediction

Building machine learning (ML) models based on pressure-volume-temperature (PVT) data is of paramount importance to capture trends and predict fluid behavior in a very heterogeneous and highly nonlinear thermodynamic system. PVT samples stored in an oil company database are often not complete and might be missing properties; both black oil and compositional. Before delving into building optimized fluid models, it is required to have a clean and structured PVT database complete with all the required properties. We present multiple novel algorithms developed to accurately predict a complete set of black oil and compositional properties within a PVT database. The proposed methodology consists of predicting properties in series, starting from a minimal set of data (black oil and compositional) and obtaining a complete set of data for all PVT samples. The order through which this is completed relies on benefiting from the existing data to predict missing data starting from the highest correlating data to the lowest. We also honored the physical nature of correlations between properties, and consequently, ranked properties for prediction as this leads to less error propagation. In addition, we have implemented data clustering prior to training ML models. Clustering is used to categorize the fluid samples into families based on the collective behavior of their different features, and hence, improve the quality of the PVT samples' properties prediction using machine learning. Several options are tested where clustering is performed using black oil properties only, compositional properties, or a combined set of black oil and compositional properties and the clustering scenario leading to the least prediction error is adopted. We have trained ML models to generically predict all black oil and compositional properties resulting from laboratory experiments, including molecular weights (MW) and mole fractions of heavy fractions (any set of heavy fractions) from the mole fractions of all the commonly available components up to C7+ and molecular weight of C7+. The massive data set used in this paper enabled comprehensive testing of the developed algorithms and provided striking accuracy of the predicted PVT properties; especially the compositional ones. Despite all the significant efforts shown in the literature concerning predicting PVT properties, the missing link is a systematic methodology to complete PVT samples' properties in a consistent manner. Furthermore, the focus in the literature is mainly on forecasting black oil properties; compositional properties are scarcely considered. Algorithms developed in this paper address these two limitations and are tested using a uniquely large data set available for onshore and offshore fields and reservoirs in Abu Dhabi. No previous algorithms, to the best of our knowledge, are tested on such a large data set. • Systematic, chain-based, methodology to complete PVT samples' compositional and black oil properties in a consistent manner. • Clustering to categorize fluid samples based on their thermodynamic behavior and reveal deep insights of these categories. • Application of the proposed algorithms for compositional splitting was a powerful illustration of the proposed algorithms. • Algorithms are tested using a uniquely large data set available for onshore and offshore fields and reservoirs in Abu Dhabi.

On the Unexpected Accuracy of the M06L Functional in the Calculation of 1JFC Spin-Spin Coupling Constants.

One-bond spin-spin coupling constants (SSCCs) between F and C are computed with density functional theory (DFT). Surprisingly, M06L stands out for its striking accuracy, outperforming any other investigated functional, including PBE0, otherwise considered one of the most reliable for couplings involving F. Although the computation of nuclear magnetic resonance (NMR) parameters involving F is known to be a challenging task, even with a rather small basis set as pcJ-1, M06L provides results with a MAD = 11.7 Hz, whereas the average deviation gets as much as 5 times larger for PBE0 (MAD = 60.0 Hz). In the context of SSCCs on the order of 300 Hz, this is particularly remarkable. We find that the accuracy of M06L/pcJ-1 in predicting 1JFC constants does not stem from a well-suited exchange or correlation part of the functional. Instead, it is believed to arise from a fortuitous cancellation of errors, as revealed by investigating the convergence of the basis set. Our findings also indicate that 1JFC constants are highly dependent on the amount of exact exchange included in the expression of the functional, with large fractions being critically important to achieving satisfactory results. Studying the effects of the geometry on 1JFC, we find that optimizing the geometry at the level of theory used to calculate SSCCs generally improves the quality of the results, although the combination of a M06-2X/aug-cc-pVTZ geometry with M06L/pcJ-1 1JFC constants best reproduces the experimental data for organofluorine compounds (with the exception of fluoroalkenes).