Precision Index Research Articles

Optimization of teacher evaluation indicator system based on fuzzy-DEMATEL-BP

The establishment of a reasonable teacher evaluation indicator system has always been a research hotspot in teacher evaluation. Simplifying and essential indicators are the foundation for maintaining the speed and accuracy of teacher evaluation. Therefore, optimizing indicators in a convincing and interpretable manner becomes highly important. Due to the complex causal relationships and fuzzy uncertainties among teacher evaluation indicators, this paper proposes a method that combines the Triangular Fuzzy Decision-making Trial and Evaluation Laboratory Model (Fuzzy-DEMATEL) with Backpropagation Neural Network (BP) to optimize the complexity of assessment systems and identify key indicators, thereby establishing a precise and rational teacher evaluation index system. DEMATEL allows us to simplify and analyze the complex causal relationships among assessment indicators, mapping them into a causal relationship diagram. Fuzzy logic effectively handles the fuzzy and uncertain relationships among the indicators, converting fuzzy information into computable forms. The BP neural network is a data training model that, from an objective data perspective, compensates for subjective errors, thereby optimizing our indicator results. In addition, we conducted empirical and comparative research using relevant data from the TIMSS 2019 dataset, and found that this method can reduce the original indicator quantity by approximately 28 %–30 %, compared to methods such as Multi-Criteria Decision Making (MCDM), the results are superior and the indicators are more accurate.

Regional brain activity and neural network changes in cognitive-motor dual-task interference: A functional near-infrared spectroscopy study

Previous neuroimaging studies have reported dual-task interference (DTi) and deterioration of task performance in a cognitive-motor dual task (DT) compared to that in a single task (ST). Greater frontoparietal activity is a neural signature of DTi; nonetheless, the underlying mechanism of cortical network in DTi still remains unclear. This study aimed to investigate the regional brain activity and neural network changes during DTi induced by highly demanding cognitive-motor DT. Thirty-four right-handed healthy young adults performed the spiral-drawing task. They underwent a paced auditory serial addition test (PASAT) simultaneously or independently while their cortical activity was measured using functional near-infrared spectroscopy. Motor performance was determined using the balanced integration score (BIS), a balanced index of drawing speed and precision. The cognitive task of the PASAT was administered with two difficulty levels defined by 1 s (PASAT-1 s) and 2 s (PASAT-2 s) intervals, allowing for the serial addition of numbers. Cognitive performance was determined using the percentage of correct responses. These motor and cognitive performances were significantly reduced during DT, which combined a drawing and a cognitive task at either difficulty level, compared to those in the corresponding ST conditions. The DT conditions were also characterized by significantly increased activity in the right dorsolateral prefrontal cortex (DLPFC) compared to that in the ST conditions. Multivariate Granger causality (GC) analysis of cortical activity in the selected frontoparietal regions of interest further revealed selective top-down causal connectivity from the right DLPFC to the right inferior parietal cortex during DTs. Furthermore, changes in the frontoparietal GC connectivity strength between the PASAT-2 s DT and ST conditions significantly correlated negatively with changes in the percentage of correct responses. Therefore, DTi can occur even in cognitively proficient young adults, and the right DLPFC and frontoparietal network being crucial neural mechanisms underlying DTi. These findings provide new insights into DTi and its underlying neural mechanisms and have implications for the clinical utility of cognitive-motor DTs applied to clinical populations with cognitive decline, such as those with psychiatric and brain disorders.

Multilayer all-dielectric metasurfaces expanding color gamut

Abstract Structural color, arising from the interaction between nanostructures and light, has experienced rapid development in recent years. However, high-order Mie resonances in dielectric materials often induce unnecessary sub-peaks, particularly at shorter wavelengths, reducing the vibrancy of colors. To address this, we have developed a multilayer dielectric metasurface based on silicon-rich silicon nitride (SRN), achieving expanded color gamut through precise refractive index matching and suppression of high-order resonances. This strategy introduces more design dimensions and can reduce the complexity of material deposition. It enables the generation of vibrant colors in a 3 × 3 array, with a resolution of approximately 25,400 dpi, demonstrating its potential applications in displays.

A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

Modeling the Effect of Random Environmental Perturbation on Data Precision in Niger Delta Crude Oil Production

Decision-makers most times rely on data precision to support their decision-making processes. There is also a strong belief, however, that data quality or data precision problems are widespread in practice and that reliance on poor data quality or wrong data sets can lead to devastating consequences on crude oil production and financing. This study applies the Runge-Kutta numerical method of order 45 (ordinary differential equation of order 45) to examine the effect of data precision index on the Niger Delta crude oil production due to the variation of the initial condition. The study has found out that the initial condition four (4), (IC 4) with a data precision index value of 0.0118 ranked best in the first crude oil well, the initial condition (IC) 15 with a data precision index value of 0.0021 ranked best in the second crude oil well, the initial condition (IC) 17 with a data precision index value of 0.0021 ranked best in the third crude oil well and the initial condition (IC) 20 with a data precision index value of 0.0234 ranked best in the fourth crude oil well. The study has observed that each crude oil data is associated with a best-fit-value of the data precision index.

The impact of sleep quality on visual working memory varied with the duration of maintenance.

Sleep quality can affect the performance of visual working memory. However, the effect of sleep quality on the maintenance stage, which is the key to maintain the quality and efficiency of visual working memory representation, remains unclear. This study is the first to explore the effect of sleep quality on the maintenance of visual working memory information. 60 healthy college students completed the Pittsburgh Sleep Quality Index (PSQI) and color recall task of visual working memory. A mixed experimental design of sleep quality (high or low) and delay duration (1, 4, or 6 s) was used to assess the effect of sleep quality on the maintenance phase of visual working memory. The main effects of sleep quality were significant on visual working memory quantity, precision and offset indexes. Among the quantity index, the interaction between sleep quality and delay duration was also significant. This suggests that prolonging the delay time in the maintenance phase leads to difficulty in maintaining attention to the task for those with lower sleep quality, which results in poorer working memory quantitative representations. Increases in the delay duration of the maintenance phase in visual working memory intensify the impact of sleep quality on task performance. Our study provides evidence to reveal the relationship between sleep quality and visual working memory and offers recommendations for improving sleep quality and cognitive functioning in individuals.

Precise refractive index measurement of fused silica optics

Abstract We have developed an experimental platform that non-destructively and precisely measures the Refractive Index (RI) and dispersion of ultra-polished fused silica optics. Using Total Internal Reflection Digital Holographic Microscopy (TIRDHM), we exploit the phase change of reflected light in Total Internal Reflection (TIR) mode. This phase change depends on the incident angle at the TIR interface and the refractive indices of the involved media. We have optimized a combination of higher TIR phase sensitivity, considerable penetration depth, and minimized phase measurement inaccuracies through simulations to design our experiment. Key features include a custom-made precision Right-Angle Prism (RAP) of Astrositall material, a seamless interface with fused silica optics on TIR interface through optical contact, and single-shot measurement. We have demonstrated the accuracy of measuring fused silica optics through proof of concept and experimental results. Our measurements on two different samples show accuracy better than ±3 × 10−4 compared to those obtained using a commercially available critical angle Refractometer (Metricon). Importantly, the setup offers the advantage of spatially mapping the refractive index, unlike point measurements by available Refractometers.

Machine learning methods for adult OSAHS risk prediction

BackgroundObstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS.Materials and methodsClinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938.ConclusionWe established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

FAT1 as a tumor mutation burden specific gene affects the immunotherapy effect in head and neck squamous cell cancer

BackgroundResponse to immunotherapy is the main challenge of head and neck squamous cancer (HNSCC) treatment. Previous studies have indicated that tumor mutational burden (TMB) is associated with prognosis, but it is not always a precise index. Hence, investigating specific genetic mutations and tumor microenvironment (TME) changes in TMB-high patients is essential for precision therapy of HNSCC. MethodsA total of 33 HNSCC patients were enrolled in this study. We calculated the TMB score based on next-generation sequencing (NGS) sequencing and grouped these patients based on TMB score. Then, we examined the immune microenvironment of HNSCC using assessments of the bulk transcriptome and the single-cell RNA sequence (scRNA-seq) focusing on the molecular nature of TMB and mutations in HNSCC from our cohort. The association of the mutation pattern and TMB was analyzed in The Cancer Genome Atlas (TCGA) and validated by our cohort. Results33 HNSCC patients were divided into three groups (TMB-low, -medium, and -high) based on TMB score. In the result of 520-gene panel sequencing data, we found that FAT1 and LRP1B mutations were highly prevalent in TMB-high patients. FAT1 mutations are associated with resistance to immunotherapy in HNSCC patients. This involves many metabolism-related pathways like RERE, AIRE, HOMER1, etc. In the scRNA-seq data, regulatory T cells (Tregs), monocytes, and DCs were found mainly enriched in TMB-high samples. ConclusionOur analysis unraveled the FAT1 gene as an assistant predictor when we use TMB as a biomarker of drug resistance in HNSCC. Tregs, monocytes, and dendritic cells (DCs) were found mainly enriched in TMB-high samples.

An Adjustable Pneumatic Planter with Reduced Source Vibration for Better Precision in Field Seeding.

The growing demand for agricultural output and limited resources encourage precision applications to generate higher-order output by utilizing minimal inputs of seed, fertilizer, land, and water. An electronically operated planter was developed, considering problems like ground-wheel skidding, field vibration, and the lack of ease in field adjustments of ground-wheel-driven seed-metering plates. The seed-metering plate of each unit of the developed planter is individually driven by a brushless direct current (BLDC) motor, and a BLDC motor-based aspirator is attached for pneumatic suction of seeds. The revolutions per minute (RPM) of the seed-metering plate are controlled by a microcontroller as per the received data relating to RPM from the ground wheel and the current RPM of the seed-metering plate. A feedback loop with proportional integral derivative (PID) control is responsible for reducing the error. Additionally, each row unit is attached to a parallelogram-based depth control system that can provide depth between 0 and 100 mm. The suction pressure in each unit is regulated as per seed type using the RPM control knob of an individual BLDC motor-based aspirator. The row-to-row spacing can be changed from 350 mm to any desired spacing. The cotton variety selected for the study was RCH 659, and the crucial parameters like orifice size, vacuum pressure, and forward speed were optimized in the laboratory with the adoption of a central composite rotatable design. An orifice diameter of 2.947 mm with vacuum pressure of 3.961 kPa and forward speed of 4.261 km/h was found optimal. A quality feed index of 93% with a precision index of 8.01% was observed from laboratory tests under optimized conditions. Quality feed index and precision index values of 88.8 and 12.75%, respectively, were obtained from field tests under optimized conditions.

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician’s workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.

Detection of pneumonia in chest X‐ray image based on separable convolutional neural network

SummaryPneumonia disease progress rapidly leading to missed diagnosis and diagnosis error. However, it is significant to be diagnosed accurately as well as adopted timely treatments, particularly for children and elderly people over 65. In the first place, we propose a novel method for data augmentation to expand non‐case samples in order to solve the problem of imbalance of the raw data. Then we have designed a neural network based on separable convolution layers, using deep learning to classify between Pneumonia cases and normal cases from chest X‐ray images. Finally, well‐known performance measures are utilized to evaluate our model, obtaining the following results: Accuracy (0.97), AUC (0.99), Sensitivity (0.98), Specificity (0.98), Precision (0.95), F1‐Score (0.97), and Youden Index (0.90). Simultaneously, we highlighted the proposed neural network recognition area via Class Activation Method for the purpose of providing an explainable diagnosis. Compared with the‐state‐of‐art models, our model performed better, and it may be regarded as an alternative in countries or regions lack of the relevant equipment and professional radiologists.

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on brain MRI images. Materials and methods: The BraTs2023 dataset was used in this study. The image dataset was converted from three dimensions to two dimensions and then subjected to pre-processing via four image enhancement techniques, including contrast-limited adaptive histogram equalization (CLAHE), gamma correction (GC), non-local mean filter (NLMF), and median and Wiener filter (MWF). Subsequently, it was evaluated for structural similarity index (SSIM) and mean squared error. The deep learning segmentation model was created using the U-Net architecture and assessed for dice similarity coefficient (DSC), accuracy, precision, recall, F1-score, and Jaccard index for tumor segmentation. Results: The performance of enhanced image results for CLAHE, GC, NLMF, and MWF techniques shows SSIM values of 0.912, 0.905, 0.999, and 0.911, respectively. The dice similarity coefficient (DSC) for segmentation without image enhancement was 0.817. The DSC of segmentation for CLAHE, GC, NLMF, and MWF techniques were 0.818, 0.812, 0.820, and 0.797, respectively. Conclusion: The enhanced image technique could affect the performance of tumor segmentation. by the enhanced image for use in a trained model may increase or decrease performance depending on the chosen image enhancement technique and the parameters determined by each method.

Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods

Brain-computer interface (BCI) is an emerging technology which provides a road to control communication and external devices. Electroencephalogram (EEG)-based motor imagery (MI) tasks recognition has important research significance for stroke, disability and others in BCI fields. However, enhancing the classification performance for decoding MI-related EEG signals presents a significant challenge, primarily due to the variability across different subjects and the presence of irrelevant channels. To address this issue, a novel hybrid structure is developed in this study to classify the MI tasks via deep separable convolution network (DSCNN) and bidirectional long short-term memory (BLSTM). First, the collected time-series EEG signals are initially processed into a matrix grid. Subsequently, data segments formed using a sliding window strategy are inputted into proposed DSCNN model for feature extraction (FE) across various dimensions. And, the spatial-temporal features extracted are then fed into the BLSTM network, which further refines vital time-series features to identify five distinct types of MI-related tasks. Ultimately, the evaluation results of our method demonstrate that the developed model achieves a 98.09% accuracy rate on the EEGMMIDB physiological datasets over a 4-second period for MI tasks by adopting full channels, outperforming other existing studies. Besides, the results of the five evaluation indexes of Recall, Precision, Test-auc, and F1-score also achieve 97.76%, 97.98%, 98.63% and 97.86%, respectively. Moreover, a Gradient-class Activation Mapping (GRAD-CAM) visualization technique is adopted to select the vital EEG channels and reduce the irrelevant information. As a result, we also obtained a satisfying outcome of 94.52% accuracy with 36 channels selected using the Grad-CAM approach. Our study not only provides an optimal trade-off between recognition rate and number of channels with half the number of channels reduced, but also it can also advances practical application research in the field of BCI rehabilitation medicine, effectively.

Deep learning-assisted automatic quality assessment of concrete surfaces with cracks and bugholes

Cracks and bugholes are common factors affecting the quality of concrete surface, thus automatic detection of apparent defects is essential for concrete surface quality assessment. This study proposes an automatic quality assessment method based on deep learning for concrete surfaces with different scales of cracks and bugholes. In particular, a modified cascade mask region-based convolutional neural network (MC Mask R-CNN) with ResNet and feature pyramid network (FPN) is presented. Furthermore, a novel quantitative analysis algorithm for instance segmentation and size quantification of cracks and bugholes is proposed. Multi-scale features extracted by ResNet can be integrated using FPN. Closed operations and fixed elliptical kernels are adopted to reduce inaccuracies associated with crack discontinuities. Datasets collected at construction sites are used for training and testing of the network after data augmentation. Comparison work was carried out to evaluate the proposed model, and the box mean average precision (APbbox) and the mask mean average precision (APmask) index can reach up to 0.588 and 0.553. The overall quantization errors of cracks and bugholes were within acceptable limits. Validation experiments were conducted to illustrate the validity of the quality assessment method.

Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the Graph Convolution Network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman’s 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the Graph Convolution Network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with an accuracy equal to 92.68%. Therefore, utilizing this network, the types of arrhythmia were recognized and classified. Also, the new network proposed by the Graph Convolution Network-Weighted Mutual Information (GCN-WMI) was more accurate than those conducted in other studies on the same data set (Chapman). Based on the obtained results, the structure proposed in this study increased the accuracy of cardiac arrhythmia diagnosis and classification on the Chapman data set. Achieving such accuracy for arrhythmia diagnosis is a great achievement in clinical sciences.

Assessing the patient's affective perception of their psychotherapist: validation of the in-Session Patient Affective Reactions Questionnaire.

Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to tailor therapeutic interventions and improve treatment outcomes. This study aims to evaluate the factor structure, reliability, and validity of the in-Session Patient Affective Reactions Questionnaire (SPARQ), a concise self-report measure designed for practical application in real-world psychotherapy settings. Validation data was gathered from (N = 700) adult patients in individual psychotherapy. These patients completed the SPARQ in conjunction with additional measures capturing sociodemographic details, characteristics of therapeutic interventions, individual personality traits, mental health symptom severity, elements of the therapeutic relationship, and session outcomes. This comprehensive approach was employed to assess the construct and criterion-related validity of the SPARQ. The SPARQ has a two-factor structure: Positive Affect (k = 4, ω total = .87) and Negative Affect (k = 4, ω total = .75). Bifactor confirmatory factor analysis (CFA) yielded the following fit indices: X2[df] = 2.53, CFI = .99; TLI = .98; RMSEA = .05; and SRMR = .02. Multi-group CFAs demonstrated measurement invariance (i) across patients who attended psychotherapy sessions in person versus in remote mode, and (ii) across patients with and without psychiatric diagnoses confirmed metric invariance. Furthermore, the SPARQ showed meaningful correlations with concurrently administered measures. The SPARQ proves to be a valuable instrument in clinical, training, and research contexts, adept at capturing patients' session-level affective responses towards their therapist and perceptions of the therapeutic alliance. Comprehensive descriptive statistics and a range of score precision indices have been reported, intended to serve as benchmarks for future research.

Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development.

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

Efficient Algorithm for Proportional Lumpability and Its Application to Selfish Mining in Public Blockchains

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices.