Pearson Correlation Coefficient Research Articles

Concurrent Validity and Reliability of In-Person and Supervised Remote STEADI Fall Risk Assessment in Community-Dwelling Older Adults.

Physical therapists play a vital role in preventing and managing falls in older adults. With advancements in digital health and technology, community fall prevention programs need to adopt valid and reliable telehealth-based assessments. The purpose of this study was to evaluate the validity and reliability of the telehealth-based timed up and go (TUG) test, 30-second chair stand test (30s-CST), and four-stage (4-stage) balance test as functional components of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk assessment. This cross-sectional study was conducted using a convenience sample of community-dwelling older adults. The TUG, 30s-CST, and 4-stage balance test were administered in random order in 1 session in the participant's own environment. Performance was scored concurrently by an in-person and synchronous telehealth rater. The video recordings of the performances were scored by an asynchronous telehealth rater on days 1 and 30 for inter- and intra-rater reliability. Additionally, participants performed the TUG test twice, using the distance measured by the participant and the distance measured by the in-person rater. To establish the validity of telehealth-based STEADI fall risk assessments, the Intraclass Correlation Coefficient (ICC), Pearson correlation coefficient, and 95% limits of agreement were derived. Inter- and intra-rater reliability were established by calculating ICC using a 2-way mixed model. Bland-Altman plots were created for nonsignificant proportional bias tests. Thirty community-dwelling older adults participated. Based on the STEADI algorithm, 13 participants were classified as having a moderate fall risk. A comparison of in-person and synchronous telehealth ratings showed excellent ICCs (0.97-0.99) and relationships (r=0.94-0.98). Bland-Altman plots were created for all tests except for the 30s-CST (t=-2.168, P =.04). All tests had good to excellent inter-rater reliability (ICC=0.84-1.00) and intra-rater reliability (0.77-1.00). No adverse events were reported. This study suggests that telehealth-administered functional tests in the STEADI fall risk assessment are valid and reliable when technology, environment, camera view, and angle are optimally managed.

Research on Real-Time Energy Consumption Prediction Method and Characteristics of Office Buildings Integrating Occupancy and Meteorological Data

A method based on Long Short-Term Memory (LSTM) networks is proposed to forecast hourly energy consumption. Using an office building in Shanghai as a case study, hourly data on occupancy, weather, and energy consumption were collected. Daily energy consumption was analyzed using single-link clustering, and days were classified into three types. The key input variables significantly influencing energy consumption, solar radiation, occupancy, and outdoor dry bulb temperature are identified by the Pearson correlation coefficient. By comparing five algorithms, it was found that the LSTM model performed the best. After considering the occupancy, the hourly MAPE was reduced from 11% to 9%. Accuracy improvements for each day type were noted as 1% for weekdays, 4% for Saturday, and 7% for Sunday. Further analysis indicated that the model started to predict the time (1:00) and commute time (7:00 and 17:00) with large errors. The model was optimized by varying the time step. For the times 1:00, 7:00, and 17:00, the best optimization of the model was achieved when the time step values were set to 6 h, 24 h, and 18 h with an MAPE of 3%, 6%, and 5%, respectively. As the model time step increased (≤2 weeks), the accuracy of the model decreased to 6%.

An Online Evaluation Method for Random Number Entropy Sources Based on Time-Frequency Feature Fusion

Traditional entropy source evaluation methods rely on statistical analysis and are hard to deploy on-chip or online. However, online detection of entropy source quality is necessary in some applications with high encryption levels. To address these issues, our experimental results demonstrate a significant negative correlation between minimum entropy values and prediction accuracy, with a Pearson correlation coefficient of −0.925 (p-value = 1.07 × 10−7). This finding offers a novel approach for assessing entropy source quality, achieving an accurate rate in predicting the next bit of a random sequence using neural networks. To further improve prediction capabilities, we also propose a novel deep learning architecture, Fast Fourier Transform-Attention Mechanism-Long Short-Term Memory Network (FFT-ATT-LSTM), that integrates a simplified soft attention mechanism with Fast Fourier Transform (FFT), enabling effective fusion of time-domain and frequency-domain features. The FFT-ATT-LSTM improves prediction accuracy by 4.46% and 8% over baseline networks when predicting random numbers. Additionally, FFT-ATT-LSTM maintains a compact parameter size of 33.90 KB, significantly smaller than Temporal Convolutional Networks (TCN) at 41.51 KB and Transformers at 61.51 KB, while retaining comparable prediction performance. This optimal balance between accuracy and resource efficiency makes FFT-ATT-LSTM suitable for online deployment, demonstrating considerable application potential.

Test–Retest Reliability and Concurrent Validity of FysioMeter C-Station Assessing Lower-Limb Muscle Strength via Isometric Mid-Thigh Pulls

The isometric mid-thigh pull (IMTP) is a reliable method to assess lower limb isometric muscle strength. A portable device (FysioMeter C-station) could serve as a suitable alternative. The reliability and concurrent validity of the FysioMeter C-station have not been evaluated for the IMTP unilateral test. The aims of this study were to (1) examine the between-session reliability of the unilateral IMTP test for the left and the right legs using the C-station, and (2) explore the concurrent validity of these measures when compared to gold standard force plates (AMTI). Twenty healthy, recreationally active males (age: 23 ± 4 years, height: 1.81 ± 0.60 m, weight: 79.6 ± 10.8 kg) participated and completed test sessions one week apart. The participants performed the following: (i) three bilateral IMTPs on dual force plates (AMTIBI); (ii) three unilateral IMTPs on each leg on a single force plate (AMTIRIGHT, AMTILEFT); and (iii) three unilateral IMTPs on each leg on the C-station (CSRIGHT, CSLEFT). The peak force was measured in all the testing modalities and an Intraclass Correlation Coefficient (ICC) and Pearson Correlation Coefficient (PCC) were used to evaluate the reliability and validity. The C-station showed good between-session reliability for CSLEFT (ICC = 0.84) and CSRIGHT (ICC = 0.85). A strong concurrent validity (PCC ≥ 0.82) was found for the C-station compared to the gold standard (AMTI). The C-station appears to be reliable for measuring unilateral IMTP in recreationally active males. Furthermore, strong concurrent validity of the C-station compared to the gold standard was demonstrated.

Comparative Study of Subclinical Hypothyroidism in Patients with and Without Metabolic Syndrome

Background: A group of metabolic abnormalities known as metabolic syndrome (MetS) greatly raises the risk of type 2 diabetes mellitus and cardiovascular illnesses. People with MetS frequently have subclinical hypothyroidism (SCH), which is defined by high levels of thyroid-stimulating hormone (TSH) with normal free thyroxine (T4). The research examined the relationship between thyroid function and metabolic parameters, as well as to assess the incidence of subclinical hypothyroidism in individuals with and without MetS. Methods: There were 960 participants in all, 480 of whom had a diagnosis of MetS and 480 of whom were healthy controls. Blood pressure and waist circumference were measured clinically. After an overnight fast, blood samples were taken utilising fully automated clinical chemistry and hormone analyzers to assay lipid profile, fasting blood glucose (FBG), and thyroid function tests (T3, T4, and TSH). The Pearson correlation coefficient and Student's t-test were used to analyse the data. Results: Statistically, there was a significant variation in the prevalence of subclinical hypothyroidism between the MetS group (16.3%) and the control group (5%; p < 0.001). In addition to having decreased HDL cholesterol, MetS patients also had higher FBG, blood pressure, triglycerides, and waist circumference. TSH levels and the elements of MetS, such as waist circumference (r = 0.41, p < 0.001), systolic blood pressure (r = 0.38, p < 0.001), FBG(r = 0.44, p < 0.001), triglycerides (r = 0.39, p < 0.001), and HDL cholesterol (r = -0.32, p < 0.001), were found to be significantly correlated with each other. Conclusion: The results of the study indicate that people with metabolic syndrome have a higher incidence of subclinical hypothyroidism, which may indicate a relationship between thyroid function and metabolic health. Regular thyroid function testing could help improve clinical outcomes by enabling early detection and management of SCH in MetS patients. Recommendations: When treating patients with metabolic syndrome, healthcare professionals should think about doing routine thyroid function tests. To manage thyroid dysfunction in patients with metabolic syndrome (MetS), further research is required to investigate the underlying mechanisms and create tailored therapies. Keywords: Metabolic Syndrome, Subclinical Hypothyroidism, Thyroid Function, Cardiovascular Risk, Metabolic Health

A cross-domain-based channel selection method for motor imagery.

Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental resultsvalidated that CDCS is effective in selecting important channels.

The relationship between intrinsic capacity and functional ability in older adults

IntroductionIntrinsic Capacity in integrated geriatric care emphasizes the importance of a thorough functional assessment. Monitoring the intrinsic capacity of older individuals provides standardized and reliable information to prevent early disability. This study assessed the relationship between intrinsic capacity and functional ability in older adults.MethodThis cross-sectional study involved 210 older individuals aged 60 and above referred to Rasoul Akram Hospital. Data collection included ADL and IADL scales, the Snellen chart, the Hearing Handicap Inventory, the hand dynamometer, the TUG test, the AMT test, and the GDS. Data analysis was conducted using SPSS software v.26 via independent t-tests, ANOVA, Pearson correlation coefficient, and multiple regression analysis.ResultsThe participants were 70.13 ± 7.04 years. 57.6% were older females and 42.4% were older males. Age showed a significant and inverse correlation with both ADL (P < 0.001, r = -0.23) and IADL (P < 0.001, r = -0.39). The adjusted coefficient of determination (R²) for the five domains of intrinsic capacity (sensory, cognitive, locomotion, psychological, vitality) as well as age and BMI was 0.16 for ADL and 0.32 for IADL.ConclusionAge and TUG could only weakly explain ADL changes, while vision, hand grip strength, cognition, and age had moderate predictive ability of IADL. Therefore, by assessing these predictors, we can predict disability before it occurs and make necessary interventions.

Investigating how Artificial Intelligence AI Systems Powered by Emotional Recognition Technologies can Assist in Conflict Resolution by Understanding Human Emotions and Behavior

This study looks at the potential of emotional detection technology using artificial intelligence to assist university administration in Pakistan in resolving conflicts. Sixty deputy directors from institutions within provinces of Punjab and Khyber Pakhtunkhwa (KPK) took part in the quantitative study. A self-completion survey consisting of Likert scale items was employed to collect data. The survey touched on issues such as the effectiveness of communication, empathy, accuracy, and ethical matters related to artificial intelligence. Through regression analysis (Beta coefficients ranging from 0.59 to 0.76), post-hoc analysis (ANOVA F-values ranging from 4.78 to 7.12), and correlation analysis (Pearson correlation coefficients ranging from 0.62 to 0.82), 60 individuals constituted the sample. Based on the findings, AI not only identifies geographical and experiential differences, but also significantly improves communication, accuracy, and empathy in conflict resolution. Ethics, privacy, and prejudice were also discussed in the study. It highlights the ethical implications of AI while presenting evidence that it could be beneficial in resolving disputes.

Validation of the Singing Voice Handicap Index in Greek Singers: Normal and Voice-Disordered Participants.

The Singing Voice Handicap Index (SVHI) was culturally adapted and validated in Greek to examine the impacts of voice problems on a singer's everyday life. The translated version was administered to 120 singers in total, along with the translated version of the Voice Handicap Index (VHI), a sort voice history questionnaire, two Self-Rating Dysphonia Severity Scales (SRDSSs), and two visual analog scales. A week after the original completion of the Greek version of SVHI, a second copy of the SVHI was administered to 50% of the participants. The results showed high test-retest reliability (Pearson correlation coefficient (PCC): 0.859, P=0.000) and excellent internal consistency (Cronbach's Alpha=0.960). A significant difference between healthy and voice disordered singers emerged (18.01±13.213, 43.26±20.196; K-W: P=0.000; ROC: Area=0.863, Std.= 0.041, P=0.000). Also, high concurrent (Spearman's rho 0.55, P= 0.000) and face (Spearman's rho: SRDSS1 0.481, P=0.000; SRDSS2 0.504, P =0.000) validity scores were observed. Regarding the relation of the demographic variables or habits that could relate to voice disorders, only the years of experience (PCC: -0.240, P=0.008) and singing lessons (Spearman's rho: -0.286, P=0.002) had a strong correlation with SVHI. In conclusion, the Greek version of the SVHI questionnaire was validated successfully as a reliable tool with proper internal consistency. It will serve as a suitable and valuable instrument for self-evaluation of a vocal difficulty's effects on a singer's day-to-day life.

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.

The Use of Atmospheric Reanalysis Data for the Estimation of Solar Irradiation Considering the Effect of Atmospheric Aerosols over Brazil

In recent years, several studies have evaluated the potential of renewable energy sources in response to climate change and high energy demand. Due to its equatorial location and significant solar and wind potential, Brazil has incorporated alternative sources into its energy matrix, driven by more efficient and economical technologies for solar energy. However, the availability of observed data is still limited, and many studies rely on satellite estimates or extrapolations of in situ observations from other regions, compromising the efficiency of new technologies. This study uses NASA MERRA-2 reanalysis data to evaluate the influence of aerosols and cloudiness on the estimate of solar irradiance in Brazil. INMET stations were chosen in regions representative of the Brazilian climate and geography, with more than 12 years of observational data. MERRA-2 includes aerosol fields that interact with the model’s radiation fields, with a spatial resolution of 0.5° and hourly temporal resolution. Variables used include shortwave radiation fluxes and aerosol optical depth. Statistical indices used in performance analysis include mean bias, mean squared error, and Pearson correlation coefficient. The stations’ diurnal solar irradiance cycles were compared with MERRA-2 reanalysis data, considering different scenarios of aerosol and cloudiness effects. The reanalysis data represented the Bauru and Santa Maria stations well, while others, such as Barreiras and Goiânia, showed underestimation. Monthly cycling was also analyzed, highlighting seasonality, with greater amplitude in Santa Maria and lower in Caicó. In some locations, such as Campo Grande, the influence of aerosols is more significant, especially during the dry months, when forest fires, mainly in the Amazon region, increase the aerosol optical depth. The results show that reanalysis estimates can be used to evaluate the temporal variability of solar irradiation in regions without observational data. In conclusion, the study was able to evaluate the temporal variability of solar irradiation in Brazil using MERRA-2 atmospheric reanalysis data, demonstrating that, although there are differences with observational data, reanalysis estimates are useful in areas without observed data, with values correlation values above 0.8 and reaching values close to 0.95. However, although small, the differences observed between measured and estimated solar irradiation are generally caused by the inability of models to adequately represent the fraction of clouds and aerosols in the atmosphere.

Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury

IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p &amp;gt; 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p &amp;gt; 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.

Sentiment Analysis and Rating Video Game Dimensions via NLP

Online gaming platforms generate vast amounts of user comments, which serve as valuable information for potential users and purchasers. Extracting meaningful guiding information from this data is crucial. As a Natural Language Processing technique (referred to as NLP), sentiment analysis technology demonstrates a high efficiency in accurately discerning the emotional tones within the comments, thereby objectively evaluating the advantages and disadvantages of gaming products based on user comments. In this paper, the comments of two games are analyzed, Grand Theft Auto V (referred to as GTAV) and Cyberpunk 2077 (referred to as 2077) on the Steam platform. Based on the self-built sentiment dictionary, keywords representing different sentiments were extracted by applying two different sentiment analysis models which are VADER and TEXTBLOB. Then we apply two models to classify the sentiments that the keywords express. Finally, the ratings of five dimensions (community, gameplay, storyline, sound and graphic) were obtained by transforming the sentiment analyze score, and the differences in the training results of different models were compared. The result analysis shows the performance difference between the VADER model and TEXTBLOB model through analyzing the standardized scores and the Pearson correlation coefficient. And it was particularly noted that the VADER model is better at capturing emotional changes in game reviews, while the TEXTBLOB model may not fully represent the users emotional inclination towards various aspects related to the game. The results reveal significant differences in model performance, providing insights into their effectiveness for gaming comment analysis.

Analysis of MRI-based vertebral bone quality (VBQ) scores in patients with degenerative lumbar scoliosis.

Degenerative lumbar scoliosis (DLS) often causes various issues such as neural symptoms and osteoporosis (OP). Vertebral bone quality (VBQ) is a crucial factor that surgeons take into consider prior to surgery. However, little attention has been given to the use of VBQ in evaluating vertebral bone mass in DLS patients. The aim of this study was to investigate the ability of MRI-based VBQ score to evaluate bone mass in patients with DLS and its ability to predict OP. A total of 130 patients were enrolled and divided into the DLS and non-DLS groups. The VBQ scores of the L1-L4 vertebral bodies were measured in noncontrast T1WI. Dual X-ray absorptiometry (DXA) was used to assess osteoporotic status. Additionally, patients with DLS were divided into two groups: with vertebral rotatory subluxation (VRS) and without VRS. The VBQ scores, CT values, and DXA indicators were compared between the DLS and non-DLS groups, and between VRS and non-VRS groups. VBQ was compared with the CT value, bone density and T-score using the Pearson correlation coefficient. VBQ was obtained according to the non-DLS group and compared with the accuracy of diagnosing osteoporosis with DXA. The DLS group was significantly different from the non-DLS group in terms of the VBQ score (3.53 vs. 2.98), CT value (109.81 vs. 134.60), T score (-1.19 vs. -1.72) and bone mineral density (BMD) value (1.04 vs. 0.94). The VRS group presented a larger Cobb angle, lower CT value and higher VBQ score. The AUC of the VBQ score in the prediction of osteoporosis was 0.804, with a sensitivity of 68.4% and a specificity of 85.7%. VBQ can reduce interference from degeneration and deformities and can be used as a complementary method for vertebral quality assessment.

Novel Real-Time Fluoroscopic Assessment Method of Transcatheter Heart Valve Expansion Following Balloon-Expandable TAVR.

Transcatheter heart valve (THV) underexpansion after transcatheter aortic valve replacement may be associated with worse outcomes. THV expansion can be assessed fluoroscopically using a pigtail for calibration; however, the accuracy of this technique specific to transcatheter aortic valve replacement is unknown. We assessed the accuracy and reproducibility of a novel fluoroscopic method to assess THV expansion using the THV commissural post for calibration. Patients who underwent transcatheter aortic valve replacement with a SAPIEN 3 (S3) THV had documented 3 cusp and cusp overlap views, and post-implant computed tomography was identified. THV expansion was fluoroscopically assessed in the 3 cusp and cusp overlap views using the S3 commissural post height and the pigtail as a reference for calibration. The correlation between the 2 methods and computed tomography was evaluated. Forty patients were included. On the bench, the commissural post height measured 3.3, 3.5, 4.0, and 4.5 mm for the 20 mm S3, 23 mm S3, 26 mm S3, and 29 mm S3, respectively. The Pearson correlation coefficient (r) with computed tomography for measuring the inflow, mid-portion, and outflow THV diameter was 0.98, 0.97, and 0.98 for the commissural post height method and 0.82, 0.81, and 0.78 for the pigtail method, respectively. Unlike the pigtail method, the correlation between the commissural post height method and computed tomography remained strong across all THV sizes and in both the 3 cusp and cusp overlap views. The commissural post height method is a novel real-time fluoroscopic tool that has the potential to assess THV expansion and guide further optimization after balloon-expandable transcatheter aortic valve replacement.

A group scheduling algorithm for massive heterogeneous data in the “dual carbon” digital intelligence monitoring center considering time-varying characteristics and priorities

In view of the fact that the data of the “double carbon” digital intelligent monitoring center has the characteristics of constantly changing with time and that there are key tasks with high real-time requirements in the massive heterogeneous data, a “double carbon” that takes into account time-varying characteristics and priorities is proposed. The massive heterogeneous data grouping scheduling algorithm of the digital monitoring center schedules data according to the urgency of the task. The functional data analysis (FDA) method is used to convert the massive multi-source heterogeneous data of the “double carbon” digital intelligence monitoring center into continuous functions to solve the problem of frequency inconsistency and unify the data format; through the CNN–LSTM based on the Attention mechanism. The model extracts time-varying features from the data that eliminates heterogeneous characteristics, and implements data grouping in the “dual carbon” digital intelligence monitoring center; by setting differentiated priorities for different groups of data, it combines the data scheduling demand estimation model and delayed response time (RTT) factor and congestion factor, calculate the data priority-oriented data scheduling link similarity (DPLS), allocate the data to be scheduled to the scheduling link with the highest DPLS value for transmission, and realize the “double carbon” digital intelligence monitoring center data group scheduling. Experimental results show that this algorithm can unify heterogeneous data to the form of functional expression and improve data consistency. The absolute value of the Pearson correlation coefficient of data grouping reaches 0.953, and the grouping effect is good. High-priority data can be scheduled to the best transmission link to improve the efficiency and reliability of data transmission and realize the optimal allocation of resources.

RELATIONSHIP BETWEEN CONVENTIONAL SINGLE LEG STANCE BALANCE ASSESSMENT AND SENSOR-BASED TEST USING ELECTRONIC WOBBLE BOARD

Assessing balance is essential for determining the likelihood of falling, particularly in older populations and rehabilitative situations. Traditional methods of assessing balance, such as the single leg stance, have been shown to be beneficial. However, these methods do not give the same level of information as more modern instruments. This study examines the accuracy and reliability of a sensor-based balance evaluation tool known as the Fitness Balance Board (FIBOD). FIBOD is a unique instrument that is used for both balance training and correct assessment, besides existing balance evaluation methods. While conducting the study, individuals with good health were recruited and a series of trials were conducted under different ‘test and trial’ settings to assess the efficacy of SLS in comparison to FIBOD. The Overall Stability Index (OSI) of FIBOD has a mean value of 3.66±2.54, which suggests that it is highly sensitivity. The Medio-Lateral Stability Index (MLSI) and Anterior-Posterior Stability Index (APSI) had average values of 2.71±2.32 and 2.21±1.53, respectively. In addition, the angular velocity of the sway was measured at 3.60±2.89 degrees per second. The Pearson correlation coefficient of -0.814 between OSI and SLS in closed-eye situation indicates a significant and substantial negative association. The confidence interval ranging from -0.922 to -0.589 demonstrated a strong connection.

Performance of the automated digital cell image analyzer UIMD PBIA in white blood cell classification: a comparative study with sysmex DI-60.

This study aimed to evaluate the performance of PBIA (UIMD, Seoul, Republic of Korea), an automated digital morphology analyzer using deep learning, for white blood cell (WBC) classification in peripheral blood smears and compare it with the widely used DI-60 (Sysmex, Kobe, Japan). A total of 461 slides were analyzed using PBIA and DI-60. For each instrument, pre-classification performance was evaluated on the basis of post-classification results verified by users. Pre- and post-classification results were compared with manual WBC differentials, and the ability to identify abnormal cells was assessed. The pre-classification performance of PBIA was better than that of DI-60 for most cell classes. PBIA had an accuracy of 90.0 % and Cohen's kappa of 0.934, higher than DI-60 (45.5 % accuracy and 0.629 kappa) across all cell classes. The pre-classification performance of both instruments decreased when abnormal cells were observed in manual counts, but PBIA still performed better. PBIA also appeared to show better correlation with manual WBC differential counts, particularly in pre-classification (Pearson's correlation coefficient: 0.696-0.944 vs. 0.230-0.882 for neutrophils, lymphocytes, monocytes, eosinophils, basophils, and blasts), although the mean differences varied by cell class. For abnormal cells identified in manual counts, PBIA exhibited more false positives for blasts (30.5 vs. 2.3 %), while DI-60 hada higher rate of false negatives (42.1 vs. 6.1 %). Both instruments exhibited high false negative rates for atypical lymphocytes. PBIA demonstrated better performance than DI-60, highlighting its clinical utility. Further multicenter studies are required for full validation.

Cardiac myosin binding protein C correlate with cardiac troponin I during an exercise training program in patients with HFrEF.

Cardiac myosin binding protein C (cMyC) is an emerging new biomarker of myocardial injury rising earlier and cleared faster than cardiac troponins. It has discriminatory power similar to high-sensitive troponins in diagnosing myocardial infarction in patients presenting with chest pain. It is also associated with outcome in patients with acute heart failure. It is currently unclear how it relates to cardiac troponins in patients with chronic heart failure undergoing exercise training. This is a post hoc analysis of symptomatic heart failure patients in the multicentre randomized SMARTEX trial. Patients were randomized to one of three arms: high-intensity interval training, moderate continuous training and recommendation of regular exercise serving as control group (CG) for 12weeks. As the training load in the two intervention arms was similar, these patients were merged and constituted the intervention group (IG). Clinical data and measurements were obtained at baseline and at 12weeks. In 205 patients, serum was available for cMyC testing and in 196 patients, serum was available for hs-cTni testing. Due to non-normal distribution, cMyC and hs-cTnI measurements were log-transformed. A Bland-Altman plot was employed to evaluate the agreement of cMyC with hs-cTnI measurements. Lastly, a linear regression model was applied. No significant differences were observed in the change of cMyC levels between the groups throughout the intervention period (∆ cMyC IG: -0.5 [IQR: -3.4; 2.1] vs. ∆ cMyC CG: -0.7 [IQR: -2.7; 2.6]). The change in log hs-cTnI was significantly correlated with the change in log cMyC during the 12-week intervention period, with a Pearson correlation coefficient of R=0.52 (95% CI 0.37-0.66, P<0.001). For every 10% increase in cMyC levels, hs-cTnI levels rose by approximately 5%. Changes in levels of the novel biomarker cMyC were significantly associated with hs-cTnI serum levels in patients with symptomatic chronic HFrEF during a structured 12-week exercise training programme. This may indicate that cMyC has a role as a future marker of subclinical myocardial damage.

Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning

Multiple linear regression models were trained to predict the degree of substitution (DS) of cellulose acetate based on raw infrared (IR) spectroscopic data. A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from 1H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.069 in DS on test data, demonstrating higher accuracy compared to the manual evaluation based on peak integration. Limiting the model to physically relevant areas unexpectedly showed the peak to be the strongest predictor of DS. By applying a n-best feature selection algorithm based on the F-statistic of the Pearson correlation coefficient, several relevant areas were identified and the optimized model achieved an improved MAE of 0.052. Predicting the DS of other cellulose acetate data sets yielded similar accuracy, demonstrating that the developed models are robust and suitable for efficient and accurate routine evaluations. The model solely trained on cellulose acetate was further able to predict the DS of other cellulose esters with an accuracy of in DS and model architectures for a more general analysis of cellulose esters were proposed.