Search Results (1,767)

In this article, we introduce a distribution that is an extension of the Power Maxwell (PM) distribution, which is based on the quotient of two independent random variables. These are the PM and a gamma distribution, respectively. In this way, the result is a model with greater kurtosis than the PM distribution. We study its probability density function and some properties, such as moments, asymmetry and kurtosis coefficient. An EM algorithm is proposed to estimate the parameters via the maximum likelihood method. A simulation study is carried out to study the asymptotic behaviour of our estimators. An application to a real dataset is also included. Full article

In recent years, artificial intelligence (AI) has increasingly utilized speech and video signals for emotion recognition, facial recognition, and depression detection, playing a crucial role in mental health assessment. However, the AI-driven research on detecting depression severity remains limited, and the existing models are often too large for lightweight deployment, restricting their real-time monitoring capabilities, especially in resource-constrained environments. To address these challenges, this study proposes a lightweight and accurate multimodal method for detecting depression severity, aiming to provide effective support for smart healthcare systems. Specifically, we design a multimodal detection network based on speech and video signals, enhancing the recognition of depression severity by optimizing the cross-modal fusion strategy. The model leverages Long Short-Term Memory (LSTM) networks to capture long-term dependencies in speech and visual sequences, effectively extracting dynamic features associated with depression. Considering the behavioral differences of respondents when interacting with human versus robotic interviewers, we train two separate sub-models and fuse their outputs using a Mixture of Experts (MOE) framework capable of modeling uncertainty, thereby suppressing the influence of low-confidence experts. In terms of the loss function, the traditional Mean Squared Error (MSE) is replaced with Negative Log-Likelihood (NLL) to better model prediction uncertainty and enhance robustness. The experimental results show that the improved AI model achieves an accuracy of 83.86% in depression severity recognition. The model’s floating-point operations per second (FLOPs) reached 0.468 GFLOPs, with a parameter size of only 0.52 MB, demonstrating its compact size and strong performance. These findings underscore the importance of emotion and facial recognition in AI applications for mental health, offering a promising solution for real-time depression monitoring in resource-limited environments. Full article

Background/Objectives: Primary obstructive megaureter (POM) is a rare congenital urological condition usually diagnosed prenatally or in early childhood. Conservative management is increasingly preferred due to a high potential for spontaneous resolution. However, reliable predictors of spontaneous resolution remain controversial, complicating clinical decision-making. This study aimed to identify the demographic, clinical, and imaging parameters predictive of spontaneous resolution in patients with primary obstructive megaureter. Methods: We retrospectively analyzed 21 pediatric patients diagnosed with primary obstructive megaureter, who were treated conservatively at the Maria Sklodowska Curie Emergency Clinical Hospital for Children from January 2015 to December 2024. Clinical parameters, imaging findings, and renal function were evaluated. Statistical analyses included univariate comparisons and multivariate logistic regression modeling. Results: Spontaneous resolution occurred in 12 (57%) patients, at a median age of 45.75 months. The only statistically significant predictor identified was the initial hydronephrosis grade (p = 0.046). Patients with mild-to-moderate dilation (Grades I–II) had a significantly higher resolution rate (11 of 15 cases) compared with those with severe dilation (1 of 6 cases). Ureteral diameter showed a trend toward predicting outcomes, with unresolved cases having larger median diameters (15 mm vs. 10.5 mm, p ≈ 0.08). Age at diagnosis, sex, bilateral involvement, and history of urinary infections did not significantly influence resolution rates. Conclusions: The initial severity of hydronephrosis significantly predicts spontaneous resolution in primary obstructive megaureter. Conservative management is particularly justified in mild-to-moderate cases, whereas patients with severe dilation may require early intervention due to lower resolution likelihood. Full article

Functional somatic symptoms (FSSs) in children—such as headaches, stomachaches, and muscle pain without clear medical explanations—pose a significant clinical challenge, often leading to repeated healthcare visits and impairments in daily functioning. While the role of parental psychological factors in shaping children’s FSSs has been suggested, empirical evidence remains limited and fragmented. This study addresses this gap by systematically examining the associations between parents’ reflective functioning, emotion regulation, alexithymia, and physical and mental health, and the frequency and severity of children’s FSSs. A total of 339 parents of children aged 6–12 completed surveys assessing their capacity to understand mental states, regulate emotions, and identify or describe feelings, as well as their self-reported physical and mental health. They also indicated whether their child experienced FSSs (e.g., headaches, stomachaches) more than once per week. Results revealed that parents of children with FSSs reported significantly lower levels of reflective functioning (lower certainty, higher uncertainty), higher alexithymic traits, and greater emotion regulation difficulties, alongside poorer physical and mental health indices. Logistic regression analyses demonstrated that emotion regulation difficulties and poorer mental health significantly increased the likelihood of a child exhibiting FSSs, while lower reflective functioning also emerged as a significant predictor. Furthermore, multiple linear regression indicated that emotion regulation challenges and poor mental health predicted greater severity of FSSs. These findings offer novel insights into how parents’ psychological and health characteristics can shape children’s somatic symptom expression, highlighting the need for family-focused interventions. By identifying and addressing parental emotional and cognitive difficulties, clinicians may be able to mitigate the intergenerational transmission of maladaptive stress responses, ultimately reducing the burden of FSSs in children. Full article

With the acceleration of urbanization process, the number of elevators in China has surged. Concurrently, the prevalence of older elevators has increased, leading to a rise in frequent malfunctions. In recent years, there has been a troubling frequency of elevator accidents resulting in casualties, which has had a negative social impact. The elevator braking system is crucial for ensuring the safe operation of the elevator, and brake failure is a significant contributor to elevator accidents. The failure modes of elevator brakes are complex and diverse, and the failure risk factors are mixed, correlated and unknown. Therefore, this paper is based on the Failure Mode and Effects Analysis (FMEA), focusing on the structural characteristics of the elevator brake to determine the equipment failure risk factors. Based on the accident prevention theory model (24Model) for comprehensive analysis of internal and external causes, this study identifies the comprehensive failure risk factors for elevator brakes. The study employs affiliation function to build the failure risk factor indicator system, the use of the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the hierarchical structure and internal relationship between the factors. Based on the research results, the factors contributing to the failure of elevator drum brakes can be identified and the interrelationships among these factors can be systematically elucidated. This analysis can serve as a valuable tool in pinpointing critical areas for routine elevator maintenance and upkeep, with the aim of minimizing the likelihood of drum brake malfunctions. Furthermore, the insights gained can inform the design and implementation of elevator monitoring and management systems, enabling a clearer focus on pertinent factors. Ultimately, this study furnishes a theoretical framework for the prevention and mitigation of such accidents. Full article

Demand–responsive transit (DRT) provides flexible, user-centric services and is gaining attention as a solution to modern transportation challenges. Establishing a minimum service level is crucial for its effectiveness, yet existing methods rely on supplier-centric indicators that fail to reflect user psychology and the flexible nature of DRT. To address this, this study applied a prospect theory from behavioral economics and used logistic regression analysis of stated preference survey data to determine minimum service levels based on user perceptions. To account for regional variations, we classified user groups based on primary transportation mode, travel purpose, and age, proposing dynamic minimum service levels tailored to each group. Additionally, using the maximum likelihood estimation method, we estimated value function parameters for the prospect theory, allowing us to analyze users’ loss aversion and sensitivity to DRT services. The findings indicated that users would accept higher fares for DRT than for conventional public transportation, provided it offers shorter travel times. Sensitivity to service levels varied across user groups, highlighting the need for differentiated policies. This study provides insights to optimize DRT operations, improve user satisfaction, and guide policies that reflect regional and demographic characteristics, enhancing the efficiency and effectiveness of DRT services. Full article

Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [¹¹C]DPA713, which targets the 18-kDa translocator protein (TSPO), offers the ability to quantify neuroinflammation in humans in vivo. The primary objectives of this study were to (1) assess whether uptake of [¹¹C]DPA713, a metric of neuroinflammation, is higher in the neuroforamina and spinal cord of patients with painful cervical radiculopathy compared with that in pain-free participants and (2) assess whether [¹¹C]DPA713 uptake is associated with clinical parameters, such as pain intensity. Methods: Dynamic 60 min [¹¹C]DPA713 PET/CT scans were acquired, and regions of interest were defined for neuroforamina and spinal cord. Resulting time-activity curves were fitted to a single-tissue compartment model using an image-derived input function, corrected for plasma-to-whole blood ratios and parent fractions, to obtain the volume of distribution (V_T) as the primary outcome measure. Secondary neuroinflammation metrics included 1T2k V_T without metabolite correction (1T2k_WB) and Logan V_T. Results: The results indicated elevated levels of 1T2k V_T at the neuroforamina (p < 0.04) but not at the spinal cord (p = 0.16). Neuroforamina and spinal cord 1T2k V_T lack associations with clinical parameters. Secondary neuroinflammatory metrics show associations with clinical parameters such as the likelihood of neuropathic pain. Conclusions: These findings enhance our understanding of painful cervical radiculopathy’s pathophysiology, emphasizing the neuroforamina levels of neuroinflammation as a potential therapeutic target. Full article

Background/Objectives: Current approaches to monitoring obstructive sleep apnea (OSA) risk primarily focus on structural or functional abnormalities, often neglecting systemic metabolic and physiological factors. Resource-intensive methods, such as polysomnography (PSG), limit their routine applicability. This study aimed to evaluate composite nutritional-inflammatory indices derived from routine blood markers to identify feasible indices for OSA management and explore their association with OSA risk. Methods: Data from 9622 adults in the NHANES (2015–2020) and GWAS datasets were analyzed using logistic regression, restricted cubic splines, machine learning, and Mendelian randomization (MR). These techniques were employed to identify nutritional-inflammatory indices associated with OSA risk. Random forest modeling identified body mass index (BMI) and albumin (ALB) as key components of the advanced lung cancer inflammation index (ALI). Causal relationships between ALI components and OSA were validated using MR. Results: ALI was significantly associated with OSA, with individuals in the highest ALI tertile exhibiting a 59% higher likelihood of OSA (OR = 1.59, 95% CI: 1.38–1.84; p < 0.001). BMI and ALB were identified as key contributors to ALI and confirmed as causal risk factors for OSA (BMI: OR = 1.91, 95% CI: 1.80–2.02; ALB: OR = 1.11, 95% CI: 1.04–1.19). Age, gender, and the neutrophil-to-lymphocyte ratio (NLR) were also significant predictors. Conclusions: This study identifies ALI as a potential composite index for assessing OSA risk. Integrating statistical modeling, machine learning, and causal inference techniques highlights the utility of nutritional-inflammatory indices in improving OSA monitoring and management in clinical practice. Full article

Recently, remote sensing image scene classification (RSISC) has gained considerable interest from the research community. Numerous approaches have been developed to tackling this issue, with deep learning techniques standing out due to their great performance in RSISC. Nevertheless, there is a general consensus that deep learning techniques usually need a lot of labeled data to work best. Collecting sufficient labeled data usually necessitates substantial human labor and resource allocation. Hence, the significance of few-shot learning to RSISC has greatly increased. Thankfully, the recently proposed discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) method has introduced episodic training- and attention-based strategies to reduce the effect of background noise on the classification accuracy. Furthermore, DEADN4 uses deep global–local descriptors that extract both the overall features and detailed features, adjusts the loss function to distinguish between different classes better, and adds a term to make features within the same class closer together. This helps solve the problem of features within the same class being spread out and features between classes being too similar in remote sensing images. However, the DEADN4 method does not address the impact of large-scale variations in objects on RSISC. Therefore, we propose a two-stream deep nearest neighbor neural network (TSDN4) to resolve the aforementioned problem. Our framework consists of two streams: a global stream that assesses the likelihood of the whole image being associated with a particular class and a local stream that evaluates the probability of the most significant area corresponding to a particular class. The ultimate classification outcome is determined by putting together the results from both streams. Our method was evaluated across three distinct remote sensing image datasets to assess its effectiveness. To assess its performance, we compare our method with a range of advanced techniques, such as MatchingNet, RelationNet, MAML, Meta-SGD, DLA-MatchNet, DN4, DN4AM, and DEADN4, showcasing its encouraging results in addressing the challenges of few-shot RSISC. Full article

Online monitoring of partial discharge (PD) is a complex task traditionally requiring specialized expertise. However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. The proposed method employs a findpeaks algorithm based on Fast Fourier Transform (FFT) to extract frequency key features, denoted as f₁ and f₂, from the frequency spectrum. These features are used to estimate model parameters for each PD type, enabling the representation of their frequency density distributions in a 2D map (f₁, f₂) via Gaussian Mixture Models (GMMs). The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. For PD identification, log-likelihood and softmax functions are applied, achieving an evaluation accuracy of 96.68%. The model also demonstrates robust performance in identifying unknown PD data, with accuracy ranging from 78.10% to 95.11%. This approach enhances the distinction between PD types based on their frequency characteristics, providing a reliable tool for PD signal analysis and identification. Full article

Numerous epidemiological studies indicate that populations exposed to environmental toxicants such as heavy metals have a higher likelihood of developing Alzheimer’s disease (AD) compared to those unexposed, indicating a potential association between heavy metals exposure and AD. The aim of this review is to summarize contemporary mechanistic research exploring the associations of four important metals, arsenic (As), manganese (Mn), lead (Pb), and cadmium (Cd), with AD and possible pathways, processes, and molecular mechanisms on the basis of data from the most recent mechanistic studies. Primary research publications published during the last decade were identified via a search of the PubMed Database. A thorough literature search and final screening yielded 45 original research articles for this review. Of the 45 research articles, 6 pertain to As, 9 to Mn, 21 to Pb, and 9 to Cd exposures and AD pathobiology. Environmental exposure to these heavy metals induces a wide range of pathological processes that intersect with well-known mechanisms leading to AD, such as oxidative stress, mitochondrial dysfunction, protein aggregation, neuroinflammation, autophagy dysfunction, and tau hyperphosphorylation. While exposure to single metals shares some affected pathways, certain effects are unique to specific metals. For instance, Pb disrupts the blood–brain barrier (BBB) and mitochondrial functions and alters AD-related genes epigenetically. Cd triggers neuronal senescence via p53/p21/Rb. As disrupts nitric oxide (NO) signaling, cortical, and synaptic function. Mn causes glutamate excitotoxicity and dopamine neuron damage. Our review provides a deeper understanding of biological mechanisms showing how metals contribute to AD. Information regarding the potential metal-induced toxicity relevant to AD may help us develop effective therapeutic AD intervention, treatment, and prevention. Full article

This paper explores a progressive-stress accelerated life test under progressive type-II censoring with binomial random removal. It assumes a cumulative exposure model in which the lifetimes of test units follow a Marshall–Olkin length-biased exponential distribution. The study derives maximum likelihood and Bayes estimates of the model parameters and constructs Bayes estimates of the unknown parameters under various loss functions. In addition, this study provides approximate, credible, and bootstrapping confidence intervals for the estimators. Moreover, it evaluates three optimal test methods to determine the most effective censoring approach based on various optimality criteria. A real-life dataset is analyzed to demonstrate the proposed procedures and simulation studies used to compare two different designs of the progressive-stress test. Full article

Area-wide coordinated urban regeneration is a strategic approach to upgrading urban functions, enhancing the allocation efficiency of land resources, and enhancing the overall urban environment from a project portfolio perspective. However, implementing area-wide coordinated urban regeneration faces significant challenges, including project delays, terminations, and difficulties in achieving investment returns. These challenges are particularly acute in smaller Chinese cities. While most previous research has paid attention to large Chinese cities, they usually neglect the risks associated with urban regeneration from an area-wide project portfolio perspective. To address this gap, this research develops a comprehensive list of risk indicators for area-side coordinated urban regeneration based on project portfolio management theory. Stakeholder opinions on the likelihood and impact of these risk indicators were collected by a questionnaire survey. A risk evaluation method, integrating the C-OWA operator and grey cluster analysis, was proposed to assess these risks. Risk management and control strategies were then proposed based on different risk levels. A case study of the coordinated urban regeneration of Yancheng’s Chaoyang area was conducted to evaluate comprehensive risk levels and provide tailored recommendations for risk control. This study offers practical guidance for urban planners and policymakers to improve decision-making in small cities and contributes new insights into risk management in the field of urban development. Full article

This study develops a new method for generating families of distributions based on the alpha power transformation and the trigonometric function, which enables enormous versatility in the resulting sub-models and enhances the ability to more accurately characterize tail shapes. This proposed family of distributions is characterized by a single parameter, which exhibits considerable flexibility in capturing asymmetric datasets, making it a valuable alternative to some families of distributions that require additional parameters to achieve similar levels of flexibility. The sine alpha power generated family is introduced using the proposed method, and some of its members and properties are discussed. A particular member, the sine alpha power-Weibull (SAP-W), is investigated in depth. Graphical representations of the new distribution display monotone and non-monotone forms, whereas the hazard rate function takes a reversed J shape, J shape, bathtub, increasing, and decreasing shapes. Various characteristics of SAP-W distribution are derived, including moments, rényi entropies, and order statistics. Parameters of SAP-W are estimated using the maximum likelihood technique, and the effectiveness of these estimators is examined via Monte Carlo simulations. The superiority and potentiality of the proposed approach are demonstrated by analyzing three real-life engineering applications. The SAP-W outperforms several competing models, showing its flexibility. Additionally, a novel-log location-scale regression model is presented using SAP-W. The regression model’s significance is illustrated through its application to real data. Full article

This article introduces a new continuous lifetime distribution within the Gamma family, called the induced Xgamma distribution, and explores its various statistical properties. The proposed distribution’s estimation and prediction are investigated using Bayesian and non-Bayesian approaches under progressively Type-II censored data. The maximum likelihood and maximum product spacing methods are applied for the non-Bayesian approach, and some of their performances are evaluated. In the Bayesian framework, the numerical approximation technique utilizing the Metropolis–Hastings algorithm within the Markov chain Monte Carlo is employed under different loss functions, including the squared error loss, general entropy, and LINEX loss. Interval estimation methods, such as asymptotic confidence intervals, log-normal asymptotic confidence intervals, and highest posterior density intervals, are also developed. A comprehensive numerical study using Monte Carlo simulations is conducted to evaluate the performance of the proposed point and interval estimation methods through progressive Type-II censored data. Furthermore, the applicability and effectiveness of the proposed distribution are demonstrated through three real-world datasets from the fields of medicine and engineering. Full article