Search Results (1,951)

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between deaf individuals and the hearing population. However, achieving subject-independent SLR remains a significant challenge due to variations in signing styles, hand shapes, and movement patterns among users. Traditional Markov Chain-based models struggle with generalizing across different signers, often leading to reduced recognition accuracy and increased uncertainty. These limitations arise from the inability of conventional models to effectively capture diverse gesture dynamics while maintaining robustness to inter-user variability. To address these challenges, this study proposes an adaptive SLR framework that integrates Markov Chains with a Niching Genetic Algorithm (NGA). The NGA optimizes the transition probabilities and structural parameters of the Markov Chain model, enabling it to learn diverse signing patterns while avoiding premature convergence to suboptimal solutions. In the proposed SLR framework, GA is employed to determine the optimal transition probabilities for the Markov Chain components operating across multiple signing contexts. To enhance the diversity of the initial population and improve the model’s adaptability to signer variations, a niche model is integrated using a Context-Based Clearing (CBC) technique. This approach mitigates premature convergence by promoting genetic diversity, ensuring that the population maintains a wide range of potential solutions. By minimizing gene association within chromosomes, the CBC technique enhances the model’s ability to learn diverse gesture transitions and movement dynamics across different users. This optimization process enables the Markov Chain to better generalize subject-independent sign language recognition, leading to improved classification accuracy, robustness against signer variability, and reduced misclassification rates. Experimental evaluations demonstrate a significant improvement in recognition performance, reduced error rates, and enhanced generalization across unseen signers, validating the effectiveness of the proposed approach. Full article

Objectives: This cross-sectional, descriptive correlational research investigated the relationship between stress, academic motivation, and subjective vitality among nursing students. Methods: Participants were recruited through a non-probability purposive sampling approach. An anonymous online survey was conducted with 188 first- to fourth-year study nursing students, assessing their perceived academic stress using the Perceptions of Academic Stress Scale, academic motivation using the Student Academic Motivation Scale (SAMS-21), and subjective vitality using the Subjective Vitality Scale. Descriptive statistics, bivariate correlational analysis, and multivariate analysis were employed in this study. Results: The results indicate that students experience moderate stress levels during exam sessions, with higher stress associated with workload and examinations. Academic motivation was characterized by high extrinsic motivation, which identified regulation and intrinsic motivation to know. A significant difference in a form of extrinsic motivation—introjected regulation—was found between student groups, with a tendency for this motivation to decrease over the years of study. No statistically significant relationship was found between students’ academic stress and subjective vitality. Conclusions: Academic stress related to workload and exams is determined by both demographic factors, such as age and year of study, and psychological factors, including academic self-perception and amotivation, which highlight the multifaceted nature of the stress experienced by nursing students. Students’ subjective vitality is related to intrinsic motivation—to know and achieve—and to all extrinsic motivation. External forms of regulation, especially introjected regulation, are significantly related to students’ subjective vitality. Full article

In this paper, time series of length T are seen as frequency distributions. Each distribution is defined with respect to a statistical variable having T observed values. A methodological system based on Gini’s approach is put forward, so the statistical model through which time series are handled is a frequency distribution studied inside a linear system. In addition to the starting frequency distributions that are observed, other frequency distributions are treated. Thus, marginal distributions based on the notion of proportionality are introduced together with joint distributions. Both distributions are statistical models. A fundamental invariance property related to marginal distributions is made explicit in this research work, so one can focus on collections of marginal frequency distributions, identifying multiple frequency distributions. For this reason, the latter is studied via a tensor. As frequency distributions are practical realizations of nonparametric probability distributions over $\mathbb{R}$, one passes from frequency distributions to discrete random variables. In this paper, a mathematical model that generates time series is put forward. It is a stochastic process based on subjective previsions of random variables. A subdivision of the exchangeability of variables of a statistical nature is shown, so a reinterpretation of principal component analysis that is based on the notion of proportionality also characterizes this research work. Full article

Life testing of products often requires extended observation periods. To shorten the duration of these tests, products can be subjected to more extreme conditions than those encountered in normal use; an approach known as accelerated life testing (ALT) is considered. This study investigates the estimation of unknown parameters and the acceleration factor for the modified Fréchet-Lomax exponential distribution (MFLED), utilizing Type II progressively first-failure censored (PFFC) samples obtained under the framework of constant-stress partially accelerated life testing (CSPALT). Maximum likelihood (ML) estimation is employed to obtain point estimates for the model parameters and the acceleration factor, while the Fisher information matrix is used to construct asymptotic confidence intervals (ACIs) for these estimates. To improve the precision of inference, two parametric bootstrap methods are also implemented. In the Bayesian context, a method for eliciting prior hyperparameters is proposed, and Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) method. These estimates are evaluated under both symmetric and asymmetric loss functions, and the corresponding credible intervals (CRIs) are computed. A comprehensive simulation study is conducted to compare the performance of ML, bootstrap, and Bayesian estimators in terms of mean squared error and coverage probabilities of confidence intervals. Finally, real-world failure time data of light-emitting diodes (LEDs) are analyzed to demonstrate the applicability and efficiency of the proposed methods in practical reliability studies, highlighting their value in modeling the lifetime behavior of electronic components. Full article

This study presents a comprehensive analysis of the M/Hypo_K/1/m queuing model to evaluate the performance of marshaling yards in freight rail classification systems. The model effectively captures the complex, multi-phase nature of service and repair processes by incorporating hypo-exponential probability distributions. The marshaling yard is modeled as a finite-capacity, single-server queue subject to potential server failures, reflecting real-world disruptions. Two complementary methodological frameworks are employed: a mathematical model based on continuous-time Markov chains (CTMCs) and a simulation model constructed using Colored Petri Nets (CPNs). In the analytical approach, both service time and repair time follow hypo-exponential distributions, which are used to approximate the gamma distribution. The simulation model built in CPN Tools allows for dynamic visualization and performance evaluation. In the CPN model, we applied a gamma distribution, which allowed us to evaluate the accuracy of the approximation implemented in the analytical model. The result indicated that utilization of the marshaling yard in primary shunting was approximately 23.81%, and with secondary shunting, 22.53%. The study output proves that the hypo-exponential distribution is able to approximate the gamma distribution. This dual-framework approach, combining analytics with simulation, provides a deeper understanding of system behavior, supporting data-driven decisions for capacity planning, failure mitigation, and operational optimization in freight rail networks. Full article

In many complex applications, both data heterogeneity and homogeneity are present simultaneously. Overlooking either aspect can lead to misleading statistical inferences. Moreover, the increasing prevalence of complex, non-Euclidean data calls for more sophisticated modeling techniques. To address these challenges, we propose a density data response additive model, where the response variable is represented by a distributional density function. In this framework, individual effect curves are assumed to be homogeneous within groups but heterogeneous across groups, while covariates that explain variation share common additive bivariate functions. We begin by applying a transformation to map density functions into a linear space. To estimate the unknown subject-specific functions and the additive bivariate components, we adopt a B-spline series approximation method. Latent group structures are uncovered using a hierarchical agglomerative clustering algorithm, which allows our method to recover the true underlying groupings with high probability. To further improve estimation efficiency, we develop refined spline-backfitted local linear estimators for both the grouped structures and the additive bivariate functions in the post-grouping model. We also establish the asymptotic properties of the proposed estimators, including their convergence rates, asymptotic distributions, and post-grouping oracle efficiency. The effectiveness of our method is demonstrated through extensive simulation studies and real-world data analysis, both of which show promising and robust performance. Full article

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

Background: While a few studies suggest that depigmentation tends to develop more frequently in patients with malignant melanoma (MM), the association between vitiligo and MM has been sparsely investigated in the setting of controlled studies. Methods: A population-based case–control study compared 14,632 patients with vitiligo with 71,580 control subjects matched by age, sex, and ethnicity regarding the prevalence of preexisting MM. Logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CIs) of developing vitiligo both in univariate and multivariate models, adjusting for demographic variables and comorbidities. The OR was also stratified by age, sex, ethnicity, and ultraorthodox status. Results: The prevalence of preexisting MM was statistically comparable between individuals with vitiligo and controls (0.30% vs. 0.35%, respectively). In the general study population, a history of MM was not significantly associated with an elevated likelihood of developing vitiligo (multivariate OR, 1.03; CI 95%, 0.76–1.40). Among the Arab population, however, preexisting MM was associated with a sixfold-increased likelihood of subsequent vitiligo (univariate OR, 6.55; 95% CI, 1.46–29.27). Patients with vitiligo and comorbid MM were older at the onset of vitiligo, had a higher burden of comorbid conditions, and showed an overrepresentation of Jewish ancestry. Conclusions: A history of MM does not increase the probability of vitiligo in the general Israeli population, except among the Arab minority, who show a sixfold-elevated odds of vitiligo after MM. Further investigation is essential to gain deeper insights into this relationship. Full article

The original Modbus specification for RS-485 and RS-232 buses supported broadcast transmission. As the protocol evolved into Modbus-TCP, to use the TCP transport, this useful feature was lost, likely due to the point-to-point nature of TCP connections. Later proposals did not restore the broadcast transmission capability, although they used UDP as transport and UDP, by itself, would have supported it. Moreover, they did not address the inherent lack of reliable delivery of UDP, leaving datagram loss detection and recovery to the application layer. This paper describes a novel redesign of Modbus-UDP that addresses the aforementioned shortcomings. It achieves a mean round-trip time of only 38% with respect to Modbus-TCP and seamlessly supports a previously published protocol based on Modbus broadcast. In addition, the built-in retransmission of Modbus-UDP reacts more efficiently than the equivalent Modbus-TCP mechanism, exhibiting 50% of its round-trip standard deviation when subject to a 1% two-way IP datagram loss probability. Combined with the lower overhead of UDP versus TCP, this makes the redesigned Modbus-UDP protocol better suited for a variety of Industrial Internet of Things systems with limited computing and communication resources. Full article

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39x${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

Alzheimer’s disease (AD) is the most common cause of cognitive decline. Among the various susceptibility genes, the gene of apolipoprotein E (APOE) is probably the most important. It may be present in three allelic forms, termed ε2, ε3 and ε4, and the most common genotype is the ε3/ε3. Recently, it has been observed that subjects with the ε4/ε4 genotype may show near-full penetrance of AD biology (pathology and biomarkers), leading to the suggestion that ε4 homozygosity may represent a distinct genetic type of AD. The aim of the present study was to investigate the role of ε4 homozygosity or heterozygosity in the presence or absence of the AD biomarker profile in patients with cognitive disorders in the Greek population. A total of 274 patients were included in the study. They underwent APOE genotyping and cerebrospinal fluid (CSF) biomarker profiling. The presence of ε4 was associated with a lower age of symptom onset and decreased amyloid biomarkers (irrespective to AD or non-AD profiles), and predicted the presence of an AD profile by a positive predictive value approaching 100%. In conclusion, the ε4 allele has a significant effect on the risk and clinical parameters of cognitive impairment and AD in the Greek population, while the ε4/ε4 genotype may be highly indicative of the (co)existence of AD in cognitively impaired patients. Full article

Genomic instability markers are important hallmarks of aging, as previously evidenced within the European study of biomarkers of human aging, MARK-AGE; however, establishing the specific metabolic determinants of vascular aging is challenging. The objective of the present study was to evaluate the impact of the susceptibility to oxidation of serum LDL particles (LDLox) and the plasma metabolization products of nitric oxide (NOx) on relevant genomic instability markers. The analysis was performed on a MARK-AGE cohort of 1326 subjects (635 men and 691 women, 35–75 years old) randomly recruited from the general population. The Inverse Probability of Treatment Weighting causal inference algorithm was implemented in order to assess the potential causal relationship between the LDLox and NOx octile-based thresholds and three genomic instability markers measured in mononuclear leukocytes: the percentage of telomeres shorter than 3 kb, the initial DNA integrity, and the DNA damage after irradiation with 3.8 Gy. The results showed statistically significant telomere shortening for LDLox, while NOx yielded a significant impact on DNA integrity. Overall, the effect on the genomic instability markers was higher than for the confirmed vascular aging determinants, such as low HDL cholesterol levels, indicating a meaningful impact even for small changes in LDLox and NOx values. Full article

Background: The International Frontal Sinus Anatomy Classification (IFAC) is a consensus created to simplify the classification of cells affecting frontal sinus drainage. Our study aims to determine the prevalence of the frontal cell variants using the IFAC and to identify their association with the development of FS in the Korean population. Methods: A total of 1060 computed tomography scans of paranasal sinuses (PNS CT) were reviewed. Patient demographics were recorded, and the presentation of types of IFAC cells and presence of frontal sinusitis (FS) were documented. Results: The mean age of the subjects’ scans is 49.8 ± 17, ranging from 16 to 94 years old. The frequency of cells presents from most common to least common are agger nasi cells (ANCs) at 97.1%, suprabullar cells (SBCs) at 73.8%, supraagger cells (SACs) at 38.1%, supraorbital ethmoid cells (SOECs) at 23.3%, frontal septal cells (FSCs) at 19.2%, suprabullar frontal cells (SBFCs) at 16.3% and supraagger frontal cells (SAFCs) at 10.1%. A total of 183 (17.7%) frontal sinuses had an infection, of which the majority were male 67.2%. The presence of SAFCs and/or SBFCs is significantly associated with the development of FS with OR_SAFC = 1.646 and OR_SBFC = 4.483, respectively. Conclusion: The presence of SAFCs and SBFCs statistically increased the probability of developing FS. Full article

Crossover trials are specifically designed to evaluate treatment effects within individual participants through within-subject comparisons. In a standard AB/BA crossover trial, participants are randomly allocated to one of two treatment sequences: either the AB sequence (where patients receive treatment A first and then cross over to treatment B after a washout period) or the BA sequence (where patients receive B first and then cross over to A after a washout period). Asymptotic and approximate unconditional test procedures, based on two Wald-type statistics, the likelihood ratio statistic, and the score test statistic for the odds ratio (OR), are developed to evaluate the equality of treatment effects in this trial design. Additionally, confidence intervals for OR are constructed, accompanied by an approximate sample size calculation methodology to control the interval width at a pre-specified precision. Empirical analyses demonstrate that asymptotic test procedures exhibit robust performance in moderate to large sample sizes, though they occasionally yield unsatisfactory type I error rates when the sample size is small. In such cases, approximate unconditional test procedures emerge as a rigorous alternative. All proposed confidence intervals achieve satisfactory coverage probabilities, and the approximate sample size estimation method demonstrates high accuracy, as evidenced by empirical coverage probabilities aligning closely with pre-specified confidence levels under estimated sample sizes. To validate practical utility, two real examples are used to illustrate the proposed methodologies. Full article

The impact of processing time, temperature, and sample on solution ratio parameters, along with pulsing microwave pretreatment, was assessed in the osmotic dehydration of waxy skin highbush blueberries. Fresh blueberries were pre-treated with 20% microwave power for 90 s before being subjected to osmotic dehydration for 8 h in a 60 °Brix sucrose solution, with three different sample to solution ratios (1:4, 1:7, and 1:10). Changes in water loss, solid gain, total anthocyanin content, total phenolic content, and total soluble solid content during osmotic dehydration, as well as color and texture changes, were investigated at four temperature levels (room temperature, 60 °C, 65 °C, and 70 °C). The highest rate of reduction in the total soluble solid content in the osmotic solution was observed during the initial hours (0–4 h) of the process. The most effective combination for reducing the total soluble content of the osmotic agent involved the microwave-pretreatment of the blueberries at 70 °C, using a sample to solution ratio of 1:4, resulting in a decrease of 11.98%, compared to 7.83% for non-pretreated samples. The solid gain was found to be affected by the sample to solution ratio × temperature × pretreatment at a 1% probability level (p ≤ 0.01). The temperature, osmotic solution ratio, and microwave pretreatment interacted together to affect the quality parameters of the osmotically dehydrated blueberries, including total anthocyanin content, total phenolic content, and color. Higher temperatures, along with microwave pretreatment, showed the worst effects on the quality characteristics mentioned. Microwave pretreatment did not change the texture significantly in comparison with non-pretreated blueberry samples. The enhancing effect of microwave pretreatment and higher temperatures on the efficiency of the osmotic dehydration process was obvious. An optimized microwave pretreatment can reduce both the required processing time and temperature for the osmotic dehydration of waxy skinned blueberries, which in turn can lead to the higher quality preservation of processed blueberries and lower energy consumption. This could be especially useful for the large-scale processing of waxy skinned berries. Full article