Search Results (438)

Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article

In magnetometer, accelerometer, and rate gyroscope (MARG) attitude and heading reference systems, accelerometers and magnetometers are susceptible to external acceleration and soft/hard magnetic anomalies, which reduce the attitude estimation accuracy. To address this problem, a sequential adaptive Kalman filter algorithm based on the geophysical field is proposed for anti-interference MARG attitude estimation. By establishing the linear system model based on the gravitational field and geomagnetic field, the singularity and coupling in other system models are avoided. Additionally, the sequential Sage–Husa adaptive strategy is employed to estimate the measurement noise parameters in real time by a specific force and magnetic vector, which suppresses the impact of external acceleration and the soft/hard magnetic anomalies. To verify the effectiveness and advancement of the proposed algorithm, a series of anti-interference experiments were designed. Experimental results show that, compared with the geophysical-field-based Kalman filter algorithm without an adaptive strategy, the proposed improved algorithm reduces the yaw maximum error by over 94% and inclination maximum error by over 21%, which improves the MARG attitude estimation robustness and makes this algorithm superior to the existing three adaptive strategies and two algorithms. Full article

Control charts are commonly practical as diagnostic tools in statistical applications to recognize probable changes in a process. Control charts find general use as diagnostic tools in statistics in the detection of probable shifts in a process. Among the variety of methods of detection of smaller shifts in processes, the cumulative sum (CUSUM) chart is the most useful in general use. The standard CUSUM chart is often based on the normal distribution, an assumption that does not often align with the quality characters of the majority of real processes. However, many real-world processes exhibit asymmetric and heavy-tailed behavior, which limits the performance of traditional symmetric control chart models. This study presents a new CUSUM control chart based on the inverse Maxwell (IM) distribution and terms it the IMCUSUM chart. The proposed chart’s performance is assessed based on run-length (RL) metrics, which comprise the RL average, the standard deviation of RL, and the median RL. Comparison with the existing IM exponentially weighted moving average (IMEWMA) chart is performed. The results reveal that the proposed IMCUSUM chart performs better compared with the existing IMEWMA chart, especially in the detection of small and moderate shifts in processes. The practical application of the proposed IMCUSUM chart is demonstrated with the application of the proposed and existing control charts in the survival analysis of the lifetimes of brake pads of cars. This real application example highlights the practical application of the proposed IMCUSUM chart in real processes. Full article

It is well known that early education and care lay the foundation for learning and wellbeing; however, resources available to support children with different life experiences can vary. For example, resources available to support early childhood educators working with young children from military families are particularly lacking. This is of concern, given that these children face a range of stressors in their daily lives. To address this gap, our interdisciplinary team used a co-creation framework to build a suite of free, online, psychosocial resources for the children and their parents, educators and support workers. To test the usability of the resources, we conducted an online survey with 83 Australian participants (parents, educators, and support workers) about their knowledge, skills and confidence in supporting these children and the children’s wellbeing. After the study, the participants were given access to the psychosocial resources for 6 to 12 months. Following this, an adapted survey was administered online (post-intervention) with 15 participants who had remained in the study during the COVID-19 pandemic. Quantitative data was analysed using cross-tabulation and descriptive statistics. Qualitative data was analysed using inductive thematic analysis. In our pre-intervention studies, 61% of parents and almost 26% of educators were only partially confident in understanding children’s responses to military-specific stressors. In contrast, in the current study, this number had fallen to under 7% (combined participant group), with perceived improvements noted in their views on the children’s wellbeing. These exploratory findings with a small sample size highlight the potential benefit of targeted programmes, professional development, and accessible resources for parents, educators, and support workers who assist children from military families. Full article

In this study, we introduced and analyzed the Slash–Log–Logistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to log–logistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distribution’s superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data. Full article

Following on from a circular economy in water, membrane technologies can play a role in resource recovery and high-quality water production but should also consider membrane industry circularity. Anaerobic membrane bioreactors (AnMBRs) are being used for advanced wastewater treatment, and their applications are growing due to advantages like lower sludge volume, better permeate quality, and the generation of biogas. High-Rejection (HR) AnMBRs retain a higher fraction of dissolved and particulate components to further promote resource recovery and obtain improved effluent quality. With the development of membrane technologies, end-of-life (EOL) membrane recycling is emerging for various applications. The feasibility of transforming EOL Reverse Osmosis (RO) membranes into ultrafiltration (UF)- and nanofiltration (NF)-like membranes and applying these membranes to submerged HR-AnMBR applications was evaluated. A small pilot AnMBR with granular biomass was operated with EOL RO membranes converted to submerged UF- and NF-like membranes and compared to commercial microfiltration (MF) membranes. UF- and NF-like plates were constructed, characterized, and introduced step-by-step into the AnMBR by the substitution of MF plates. A chemical oxygen demand (COD) removal study showed that while 77% removal of COD was possible with MF membranes, improved COD removal (i.e., 81.40% and 88.39%) was achieved using UF-like and NF-like membranes, respectively. Because of the higher retention of salts of the NF-like membrane, the salinity in the membrane bioreactor increased from 1300 to 1680 µS·cm⁻¹ but stabilized quickly and without a negative impact on system performance. Even without cleaning, minimal fouling and flux decline were observed for all tested configurations thanks to the use of granular biomass and low permeation flux. Permeate flux in the case of the NF-like membrane was slightly lower due to the required higher pressure. The present study demonstrated that the EOL-RO membranes may find applications in HR-AnMBRs to achieve superior permeate quality and move toward circular membrane processes. Full article

The analysis of lifetime data under censoring schemes plays a vital role in reliability studies and survival analysis, where complete information is often difficult to obtain. This work focuses on the estimation of the parameters of the recently proposed generalized Kavya–Manoharan exponential (GKME) distribution under progressive Type-I interval censoring, a censoring scheme that frequently arises in medical and industrial life-testing experiments. Estimation procedures are developed under both classical and Bayesian paradigms, providing a comprehensive framework for inference. In the Bayesian setting, parameter estimation is carried out using Markov Chain Monte Carlo (MCMC) techniques under two distinct loss functions: the squared error loss function (SELF) and the general entropy loss function (GELF). For interval estimation, asymptotic confidence intervals as well as highest posterior density (HPD) credible intervals are constructed. The performance of the proposed estimators is systematically evaluated through a Monte Carlo simulation study in terms of mean squared error (MSE) and the average lengths of the interval estimates. The practical usefulness of the developed methodology is further demonstrated through the analysis of a real dataset on survival times of guinea pigs exposed to virulent tubercle bacilli. The findings indicate that the proposed methods provide flexible and efficient tools for analyzing progressively interval-censored lifetime data. Full article

Accurate modeling of industrial and biomedical data is often challenging due to skewness, heavy tails, and complex variability, which traditional probability distributions fail to capture. To address this, we propose the Induced Ailamujia Lifetime Distribution (IALD), a flexible generalization of the Ailamujia distribution developed via an induced transformation. The IALD accommodates diverse dataset characteristics through a wide range of probability density and hazard rate shapes. Several key statistical properties are derived, including moments, reliability measures, quantile and generating functions, probability weighted moments, and entropy measures. Model parameters are estimated using six classical methods, with their performance assessed through simulation. The practical utility of the IALD is demonstrated using two real datasets from biomedical and industrial fields, where it consistently outperforms existing lifetime models. These results confirm the IALD as a powerful and promising tool for reliability, engineering, and biomedical data analysis. Full article

Male infertility is a global health concern, and many cases are idiopathic in nature. The development and differentiation of germ cells (Gcs) are supported by Sertoli cells (Scs). Differentiated Scs support the development of Gcs into sperm, and hence, male fertility. We previously reported on a developmental switch in Scs around 12 days of age onwards in rats. During the process of the differentiation of Scs, the differential expression of mitophagy-related genes and its role in male fertility are poorly understood. To address this gap, we evaluated the microarray dataset GSE48795 to identify 12 mitophagy-related hub genes, including B-Cell Leukemia/Lymphoma 2 (Bcl2) and FBJ murine osteosarcoma viral oncogene homolog (Fos). We identify Neuron-derived orphan receptor 1 (Nor1) as a potential mitophagy-related gene of interest due to its strong regulatory association with two hub genes, Bcl2 and Fos, which were differentially expressed during Sc maturation. To validate this finding, we generated a transgenic rat model with the Sc-specific knockdown of Nor1 during puberty. A functional analysis showed impaired spermatogenesis with reduced fertility in these transgenic rats. Our findings suggest that Nor1 may be an important mitophagy-related gene regulating the function of Scs and thereby regulating male fertility. Full article

The integration of sustainability competences into education presents significant challenges, particularly in embedding Education for Sustainable Development (ESD) into contextually relevant learning experiences. This study presents the design and validation of the Art Nouveau Path, a Mobile Augmented Reality Game (MARG) developed within the EduCITY ecosystem to foster competences, such as sustainability values, systems thinking, and future literacy. Grounded in the GreenComp framework and employing a Design-based Research (DBR) approach, the intervention was validated with 33 in-service teachers through a simulation-based workshop and a curricular review and complemented by a diagnostic questionnaire was administered to 221 students. This questionnaire (S1-PRE) provided the baseline data on sustainability awareness, digital readiness, and heritage-related learning interest. The teachers confirmed the MARG’s curricular adequacy value and interdisciplinary potential, while the students’ diagnostics revealed mixed conceptions; although 73.30% considered sustainability competences important, only 61.10% expressed interest in learning more about them. Also, 72.40% showed interest in learning about sustainability through local Art Nouveau heritage, and 79.60% considered the theme attractive, indicating potential for emotional and cognitive engagement. The Art Nouveau Path provides an exploratory and replicable model of curriculum-integrated ESD, connecting cultural heritage with competence-based learning for the operationalization of the GreenComp framework in support of SDG 4.7. Full article

The acceptance of generalized distributions has significantly improved over the past two decades. In this paper, we introduce a new generalized distribution: Topp–Leone odd Weibull flexible-G family of distributions (FoD). The new FoD is a combination of two FOD; the Topp–Leone-G and odd Weibull-flexible-G families. The proposed FoD possesses more flexibility compared to the two individual FoD when considered separately. Some selected statistical properties of this new model are derived. Three special cases from the proposed family are considered. The new model exhibits symmetry and long or short tails, and it also addresses various levels of kurtosis. Monte Carlo simulation studies were conducted to verify the consistency of the maximum likelihood estimators. Two real data examples were used as illustrations on the flexibility of the new model in comparison to other competing models. The developed model proved to perform better than all the selected competing models. Full article

As part of this study, we design a reliability acceptance sampling plan under the assumption that the lifetime of a product follows the Topp–Leone generated Weibull (TLGW) distribution, a model that exhibits structural symmetry in its hazard rate behavior and distributional form. The fundamental procedures for constructing such a plan are described. We compute and tabulate the minimum sample sizes required for given risk criteria using both binomial and Poisson models for the number of failures. We also provide the operating characteristic (OC) values for the proposed sampling plans, and determine the minimum ratios of true mean life to specified mean life needed to satisfy a given producer’s risk. The role of symmetry in the TLGW distribution is highlighted in its balanced tail properties and shape characteristics, which influence the performance of the acceptance sampling plan. Finally, we illustrate the applicability of the proposed plan with real-world data. Full article

Industrial systems often rely on specialized redundant systems to enhance reliability and prevent unexpected failures. This study introduces a novel three-parameter model, the Poisson–Weibull distribution (PWD), and discovers its various key properties. The primary focus of the study is to develop stress–strength (SS) model based on this newly developed distribution. Parameter estimation for both the PWD and SS models is carried out using maximum likelihood estimation (MLE) and Bayesian estimation techniques. Given the complexity of the proposed distribution, numerical approximation techniques are employed to obtain reliable parameter estimates. A comprehensive simulation study employing the Monte Carlo simulation (MCS) and Markov Chain Monte Carlo (MCMC) examines the behavior of the PWD and SS model parameters under various scenarios. The development of the SS model enhances understanding of the PWD’s dynamics while providing practical insights into its real-life applications and limitations. The effectiveness of the proposed distribution and the SS reliability measure is established through applications to real-life data sets. Full article

Background: The limited aqueous solubility of BCS Class II drugs, exemplified by itraconazole (ITR), continues to hinder their bioavailability and therapeutic performance following oral administration. The present study investigated the development of amorphous solid dispersions (ASDs) of ITR via continuous manufacturing technologies, such as hot melt extrusion (HME) and spray drying (SD), to improve drug release. Methods: Polymer selection was guided by Hansen solubility parameter (HSP) analysis, film casting, and molecular modeling, leading to the identification of aminoalkyl methacrylate copolymer type A (Eudragit^® EPO), polyvinyl caprolactam–polyvinyl acetate–polyethylene glycol graft copolymer (Soluplus^®), and hypromellose acetate succinate HG (AQOAT^® AS-HG) as suitable carriers. ASDs were prepared at drug-to-polymer ratios of 1:1, 1:2, and 2:1. Comprehensive characterization was performed using ATR-FTIR, NMR, DSC, PXRD, SEM, PLM, and contact angle analysis. Results: HME demonstrated higher process efficiency, solvent-free operation, and superior dissolution enhancement compared to SD. Optimized HME-based ASDs were formulated into tablets. The ITR–Eudragit^® EPO formulation achieved 95.88% drug release within 2 h (Weibull model, R² > 0.99), while Soluplus^® and AQOAT^® AS-HG systems achieved complete release, best described by the Peppas–Sahlin model. Molecular modeling confirmed favorable drug–polymer interactions, correlating with the formation of stable complex and enhanced release performance. Conclusions: HME-based continuous manufacturing provides a scalable and robust strategy for improving the oral delivery of poorly water-soluble drugs. Integrating predictive modeling with experimental screening enables the rational design of ASD formulations with optimized dissolution behavior, offering potential for improved therapeutic outcomes in BCS Class II drug delivery. Full article

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has brought to light unexpected complications beyond respiratory illness, including effects on kidney function and a potential link to kidney stone disease (KSD). This review proposes a novel framework connecting COVID-19-induced epigenetic reprogramming to disruptions in mitochondrial sulfur metabolism and the pathogenesis of kidney stones. We examine how SARS-CoV-2 interferes with host methylation processes, leading to elevated homocysteine (Hcy) levels and impairment of the trans-sulfuration pathway mechanisms particularly relevant in metabolic disorders such as homocystinuria. These epigenetic and metabolic alterations may promote specific kidney stone subtypes through disrupted sulfur and oxalate handling. Additionally, we explore the role of COVID-19-associated gut dysbiosis in increasing oxalate production and driving calcium oxalate stone formation. Together, these pathways may accelerate the transition from acute kidney injury (AKI) to chronic KSD, linking viral methylation interference, sulfur amino acid imbalance, mitochondrial dysfunction, and microbiota changes. Unlike earlier reviews that address these mechanisms separately, this work offers an integrated hypothesis to explain post-viral renal lithogenesis and highlights the potential of targeting sulfur metabolism and redox pathways as therapeutic strategies for KSD triggered or aggravated by viral infections such as COVID-19. Full article