Search Results (438)

Fractal theory of particle size distribution (PSD) is a widely used approach in soil science. However, fractal studies on sandstone PSDs are scarce in sedimentology and geology. Taking littoral sandstones in the Carboniferous Donghetang Formation of the Hade Oilfield as an example, fractal dimensions of 115 fine sandstone and 150 silty sandstone PSDs are calculated and compared with particle size compositions and traditional statistical parameters in this paper. The results show that fractal dimension values in fine sandstones, 1.69–2.17 averaged at 1.99, are usually lower than that in silty sandstones, 2.12–2.73 averaged at 2.37. Fractal dimension and sandy content of littoral sandstones show a strong negative linear relationship. Significant logarithmic correlations are implied between fractal dimension and silty and clayey contents of littoral sandstones, which is different from linear relations in soil PSDs. The relationships between fractal dimension and mean, sorting, and skewness of silty sandstone PSDs are better than those of fine sandstones. Fractal dimension and kurtosis of silty sandstones and fine sandstones exhibit weak negative and positive linear relationships, respectively. Fractal dimension values in lower-shoreface facies, 2.05–2.47 averaged at 2.33, are generally higher than that in upper-shoreface facies, 1.79–2.30 averaged at 2.11. Fractal dimension values in bar and beach microfacies are commonly lower than those in trough microfacies. Combined with additional sedimentary information from various clastic deposits, the fractal dimension can serve as a new depositional environment indicator. Full article

This study examines the impact that fat-tailed distributions of the spread residuals have on the optimal orders for pairs trading of stocks and cryptocurrencies. Using daily data from selected pairs, the spread dynamics has been modeled through a mean-reverting Ornstein–Uhlenbeck process and investigates how deviations from normality affect strategy design and profitability. Specifically, we compared four fat-tailed distributions—Lévy stable, generalized hyperbolic, Johnson’s S_U, and non-centered Student’s t—and showed how they modify optimal entry and exit thresholds, and performance metrics. The main findings reveal that the proposed pairs trading strategy correctly captures some key stylized facts of residual spreads such as large jumps, skewness, and excess Kurtosis. Interestingly, we considered regime-switching behaviors to account for structural changes in market dynamics, providing empirical evidence that optimal trading rules are regime-dependent and significantly influenced by the residual distribution’s tail behavior. Unlike conventional approaches, we optimized the entry signal and link heavy tails not only to volatility clustering but also to the nonlinearity in switching regimes. These findings suggest the need to account for distributional properties and dynamic regimes when designing robust pairs trading strategies, providing a more realistic and effective framework of these strategies in highly volatile and non-normal markets. Full article

In this study, we introduce a novel distribution related to the gamma distribution, referred to as the generalized incomplete gamma distribution. This new family is defined through a stochastic representation involving a linear transformation of a random variable following a distribution derived from the upper incomplete gamma function. As a result, the proposed distribution exhibits a probability density function that effectively captures data exhibiting asymmetry and both mild and high levels of kurtosis, providing greater flexibility compared to the conventional gamma distribution. We analyze the probability density function and explore fundamental properties, including moments, skewness, and kurtosis coefficients. Parameter estimation is conducted via the maximum likelihood method, and a Monte Carlo simulation study is performed to assess the asymptotic properties of the maximum likelihood estimators. To illustrate the applicability of the proposed distribution, we present two case studies involving real-world datasets related to mineral concentration and the length of odontoblasts in guinea pigs, demonstrating that the proposed distribution provides a superior fit compared to the gamma, inverse Gaussian, and slash-type distributions. Full article

Objectives: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). Methods: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log₁₀, GLCM Entropy-log₁₀, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. Results: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (p = 0.0079), grading (p = 0.0130), SUVmax (p = 0.0017), SUVmean (p = 0.0011), CoV (p = 0.0037), HISTO Entropy-log₁₀ (p = 0.0039), GLCM Entropy-log₁₀ (p = 0.0044), and GLCM Dissimilarity (p = 0.0063). At multivariate analysis, only GLCM Entropy-log₁₀ was retained in the model (χ² = 7.7120, p = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log₁₀ >1.28 had a significantly better OS than patients with GLCM Entropy-log₁₀ ≤1.28 (χ² = 10.6063, p = 0.0011). Conclusions: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients. Full article

Chromium–nickel austenitic stainless steels are widely used in various industries after their initial hardness and strength are increased. Apart from low-temperature thermal–chemical diffusion, the mechanical properties can be improved by surface cold working (SCW). A cheap and reliable form of static SCW is diamond burnishing (DB), which drastically improves the surface integrity (SI) and hence the operational behavior of the processed component. To be maximally effective, the DB parameters must be optimized according to a relevant criterion, depending on the desired effect. For high fatigue strength and/or high wear resistance, complex experimental tests are necessary, which require significant time and financial resources. This study presents a cost-effective optimization approach based on the DB process–SI–operating behavior correlations. Using these correlations, in addition to the correlations between appropriately selected SI characteristics, the proposed approach relies on the control of only three easy-to-measure roughness parameters, namely the arithmetic average roughness, skewness, and kurtosis, which, in turn, depend on the governing factors of the DB process. Full article

The investigation of sediment morphology and sediment regime is crucial for the initial stages of the hydromorphodynamic modeling of the Lower Danube basin. This helps in identifying significant habitats and potential obstacles that may disrupt the migration patterns of anadromous sturgeon species in the Lower Danube. This investigation involved the analysis of 10 samples, which were collected in equal quantities from specific places where hydrotechnical activities were conducted, specifically the Bala and Caleia branches. The sediment samples were analyzed to determine their morphological and structural characteristics through granulometric assessments. Additionally, three significant parameters, namely, the standard deviation, symmetry index (skewness index), and flattening index (kurtosis index), were used for further characterization. Full article

The transition stage to pre-primary school represents a key event, in which the support received from family and teachers is essential in facilitating the transition and in ensuring an effective adjustment to the school environment. Our study aimed to investigate the impact of mathematical prerequisites on cognitive development, compared to the impact of reading and writing prerequisites, from the perspective of parents and early childhood educators. Thus, we quantified the impact of reading–writing and math prerequisites on children’s transition from kindergarten to school, analyzing the challenges, opportunities, and possibilities that arise. The sample consisted of 685 parents and 188 teachers, using the preschool prerequisites screening standardized questionnaire developed by the company Cognitrom, a questionnaire-survey, and the focus group method. Initially, the fidelity of the research instrument was assessed by calculating Cronbach’s alpha coefficient. The data distribution was tested using the skewness and kurtosis coefficients. Subsequently, descriptive analyses were carried out in order to provide an overview of the data collected by performing a multiple linear regression analysis. In addition, the Phi coefficient and V Cramer’s V coefficient were used to analyze the association between the research variables. By corroborating the obtained results, we can state that, from the parents’ and early childhood teachers’ perspective, math prerequisites have a greater influence on children’s cognitive development in the transition process from kindergarten to school compared to reading–writing prerequisites, confirming the general hypothesis. Full article

In this study, we detailed the fabrication and characterization of photovoltaic structures based on PTB7:ICBA (1:1, wt.%) bulk-heterojunction on optical glass substrates by spin-coating. Some samples were deposited on a flat substrate, and others were placed on a patterned substrate obtained by nano-imprinting lithography; the induced effects were analyzed. We demonstrated that using a patterned substrate enhanced the maximum output power, primarily because the short-circuit current density increased. This can be considered a direct consequence of reduced optical reflection and improved optical absorption. The topological parameters evaluated by atomic force microscopy, namely, the root mean square, Skewness, and Kurtosis, had small values of around 2 nm and 1 nm, respectively. This proves that the mixture of a conductive polymer and a fullerene derivative creates a thin film network with a high flatness degree. The samples discussed in this paper were fabricated and characterized in air; we can admit that the results are encouraging, but further optimization is needed. Full article

Modern poultry production demands scalable and non-invasive methods to monitor animal welfare, particularly as broiler strains are increasingly bred for rapid growth, often at the expense of mobility and health. This study evaluates two advanced computer vision techniques—Optical Flow and the Unrest Index—to assess movement patterns in broiler chickens. Three commercial broiler strains (Hybro^®, Cobb^®, and Ross^®) were housed in controlled environments and continuously monitored using ceiling-mounted video systems. Chicken movements were detected and tracked using a YOLO model, with centroid data informing both the Unrest Index and distance walked metrics. Optical Flow velocity metrics (mean, variance, skewness, and kurtosis) were extracted using the Farnebäck algorithm. Pearson correlation analyses revealed strong associations between Optical Flow variables and traditional movement indicators, with average velocity showing the strongest correlation to walked distance and the Unrest Index. Among the evaluated strains, Cobb^® demonstrated the strongest correlation between Optical Flow variance and the Unrest Index, indicating a distinct movement profile. The equipment’s movement and the camera’s slight instability had a minimal effect on the Optical Flow measurement. Still, its strong correlation with the Unrest Index and walking distance accredits it as an effective method for high-resolution behavioral monitoring. This study supports the integration of Optical Flow and Unrest Index technologies into precision livestock systems, offering a foundation for predictive welfare management at scale. Full article

The objective of this work was to study distortion of the 3D surface textures measurement using the stylus tip technique. This distortion was simulated using a dilation procedure. Topographies of many machined surfaces of various characters measured using a white light interferometer were analysed. The growth in the tip of the radius of the stylus produced larger changes in surface texture. Mechanical filtration by a stylus tip caused a reduction in the amplitude parameters, hybrid parameters, and average radius of the peak curvature. Variations in skewness (Ssk) and kurtosis (Sku) depended on the type of surface. The depth and density of the furrows had a tendency to decrease. The measurement results were more distorted for a higher roughness amplitude and a smaller main wavelength. Full article

This paper examines the essential role of intervehicle spacing on two-lane rural roads, highlighting its significance for traffic safety and management. Recent technological advancements have enabled the precise positioning of vehicles on highways through video recordings and image processing techniques. However, these systems are less applicable to rural roads due to the absence of extensive sensor networks. This study bridges this gap by proposing a simulation-based model to evaluate the probability density of intervehicle spacing under varying traffic conditions. The simulation model integrates macroscopic traffic flow theories with microscopic car following models, simulating intervehicle spacings over a considerable highway segment. Calibration and validation were conducted using data from a two-lane road in Northern Italy. The simulation results identify key characteristics of spacing distribution, including positive skewness (i.e., a longer tail toward higher values), high kurtosis (a peaked distribution with frequent extreme values), non-zero minimum values, and autocorrelation at high traffic densities (indicative of platooning behavior). The Pearson type III distribution was determined to be the most suitable fit for the experimental data. Thus, future research should focus on parameter estimation for the Pearson type III distribution to further understand intervehicle spacing under varying traffic conditions and to expand applications to various road types and traffic scenarios. Full article

Background and Objective: A lifestyle-associated disease, diabetes mellitus, mandates compliance with established policies by physicians and patients to achieve optimal glycemic control. Collaborative care from health care providers and patients is essential for effective management, which slows disease progression, improves quality of life, and reduces medication costs. This study assessed the effectiveness of pharmacist-led educational initiatives provided to patients on clinical outcomes and direct treatment costs for those with diabetes in two public hospitals in Malaysia. Methods: Four hundred type 2 diabetes patients included in this study were randomly allocated to two corresponding groups. The control group, consisting of 200 patients (100 from each hospital), received standard treatment using the Malaysian Clinical Practice Guideline 2015, while the intervention group, also comprising 200 patients (100 from each hospital), received pharmacist-led care through Diabetic Medication Therapy Adherence Clinics (DMTACs), including pharmacist-provided education, alongside conventional treatment. The patients were equally selected from both hospitals based on the sample size calculation. The pharmacists provided educational interventions emphasizing dietary adjustments, lifestyle modifications, the significance of physical activity, and appropriate medication storage. Among these, 143 control patients and 156 intervention patients completed this one-year study, which comprised an initial and two follow-up visits. Clinical outcomes and treatment expenses were evaluated, and a data analysis was performed utilizing version 24 SPSS. Descriptive statistics were presented as the mean ± standard deviation, including normality assessed using the skewness, kurtosis, and Kolmogorov–Smirnov test. Independent t-tests were applied for hypothesis testing when the data showed normal distribution. Paired t-tests were used for cost assessments. Results: After the research, the group receiving intervention had a much higher decrease in HbA1c levels relative to the control group (3.59% versus 2.17%; p < 0.001). The intervention group had considerable decreases in systolic blood pressure (9.29 mmHg) and similarly in diastolic blood pressure (7.58 mmHg; with p < 0.005). Additionally, the levels of cholesterol in the intervention group improved significantly (0.13 mmol/L; p < 0.001). Moreover, treatment expenses for the pharmacist-led intervention group showed a substantial reduction (p < 0.001). By the second follow-up, the additional cost per patient since baseline was MYR 236.07 (Malaysian Ringgit), approximately 53.45 USD, in the control group, compared to only MYR 47.33 per patient, approximately 10.72 USD, in the intervention group with pharmacist involvement. Only medication costs were considered, and all unnecessary medications were discontinued as patient clinical outcomes improved sufficiently with pharmacist intervention, allowing for management through lifestyle changes alone. Counseling costs were not included since the pharmacists providing education were already employed in these hospitals, and no additional pharmacists were appointed for this purpose. Conclusion: Pharmacist-led interventions led to a significant improvement in HbA1c levels. While medication expenses increased in both groups from the initial follow-up, the control group exhibited a significantly greater increase in costs and HbA1c levels than the intervention group. Full article

We present a new one-parameter discrete Muth (DsMuth) distribution, a flexible probability mass function designed for modeling count data, particularly over-dispersed data. The proposed distribution is derived through the survival discretization approach. Several of the proposed distribution’s characteristics and reliability measures are investigated, including the mean, variance, skewness, kurtosis, probability-generating function, moments, moment-generating function, mean residual life, quantile function, and entropy. Different estimation approaches, including maximum likelihood, moments, and proportion, are explored to identify unknown distribution parameters. The performance of these estimators is assessed through simulations under different parameter settings and sample sizes. Additionally, a real dataset is used to emphasize the significance of the proposed distribution compared to other available discrete probability distributions. Full article

The morphological characteristics of catchments are key controls on how flow is routed through catchments and the spatial and temporal dynamics of floods, therefore influencing the shape of hydrographs at any location. Here, we developed a hydro-morphic catchment classification to understand the extent to which various catchment characteristics act as controls on flood behaviour. The catchment characteristics include: size (as measured by gauge position in catchment and valley confinement at the gauge site), shape (elongation ratio and form factor), topography (catchment relief and longitudinal slope), and drainage network structure (drainage density). A total of 2452 high flow (near bankfull) and overbank flood hydrographs from rivers in 17 coastal catchments of New South Wales (NSW), Australia were used. Cluster analysis on hydrograph shape metrics of kurtosis, skewness, and rate-of-rise was performed to identify classes of hydrographs and their median shape. Three statistically distinct clusters were delineated for both high flows and overbank floods, and categorised as flashy, intermediate, and broad. Topographic characteristics of catchments (i.e., relief and longitudinal slope) were commonly among the dominant controls for all high flow and overbank flood hydrographs, excluding broad overbank floods. Drainage network structure (i.e., drainage density) also controlled flashy and intermediate high flows, and intermediate and broad overbank floods, while catchment size (i.e., gauge position in the network) influenced broad high flows. Catchment shape (i.e., elongation ratio) influenced broad overbank floods, and is a dominant control on flashy high flows, and intermediate and broad overbank floods. Overall, topographic controls were more useful for differentiating the hydrological behaviour of high flows relative to overbank floods. Understanding the relative control of different catchment morphometric characteristics on flow and flood behaviour can be used to identify the aspects of flood behaviour that are set by imposed controls and cannot therefore be realistically manipulated in management. A hydro-morphic classification can also be used in the design and calibration of hydrological models, tailoring their use to hydro-morphic catchment class. Full article

This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures. Full article