Search Results (120)

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis—Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

This study focuses on estimating the unknown parameters and the reliability function of the inverted-Weibull distribution, using an improved adaptive progressive Type-II censoring scheme under a competing risks model. Both classical and Bayesian estimation approaches are explored to offer a thorough analysis. Under the classical approach, maximum likelihood estimators are obtained for the unknown parameters and the reliability function. Approximate confidence intervals are also constructed to assess the uncertainty in the estimates. From a Bayesian standpoint, symmetric Bayes estimates and highest posterior density credible intervals are computed using Markov Chain Monte Carlo sampling, assuming a symmetric squared error loss function. An extensive simulation study is carried out to assess how well the proposed methods perform under different experimental conditions, showing promising accuracy. To demonstrate the practical use of these methods, a real dataset is analyzed, consisting of the survival times of male mice aged 35 to 42 days after being exposed to 300 roentgens of X-ray radiation. The analysis demonstrated that the inverted Weibull distribution is well-suited for modeling the given dataset. Furthermore, the Bayesian estimation method, considering both point estimates and interval estimates, was found to be more effective than the classical approach in estimating the model parameters as well as the reliability function. Full article

Background: This study develops a decision-making framework for the identification and performance preservation of strategic products using a non-parametric analysis of items within the product portfolio. Methods: Data Envelopment Analysis (DEA) and the sensitivity analysis of Inverted Data Envelopment Analysis (IDEA) are adapted to explore a new application area in growth product management. A field study from the retail sector of a developing economy is conducted to evaluate the method’s practicality. Results: This study suggests that the power of suppliers, product shelf life, and the ratio of sales to inventory are important supply chain considerations in identifying strategic products accommodated in Slow-Moving Consumer Goods (SMCG) supply chains. Conclusions: The field study shows that sensitivity analysis, in the new application area, provides insights for the identification and performance preservation of strategic items in a product portfolio. Data-driven solutions tailored to the operational needs of the case company and its different product categories conclude this article.. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

The low fluorescence quantum efficiency of hydrophilic modified spiropyran in hydrogel matrices cannot be naturally improved during photoresponsive operation, which significantly limits their practical applications.In this study, a hybrid hydrogel system integrating metal plasmon resonance-enhanced fluorescence effects is designed through copolymerization of N,N′-bis(acryloyl)cystamine-modified Au nanoparticles (Au NPs), hydrophilic graft-modified spiropyran molecules, and N-isopropylacrylamide. This approach successfully achieves a spiropyran-based fluorescent hydrogel sensor with enhanced fluorescence intensity. Furthermore, an inverted pyramid-structured surface is engineered on the hydrogel using a template-assisted strategy, combining anti-reflection optical effects with plasmonic enhancement mechanisms. Molecular modification facilitated the integration of spiropyran and Au NPs into the hydrogel molecular chains, enhancing the dispersion of Au NPs within the hydrogel matrix and preventing fluorescence quenching from direct contact between Au NPs and spiropyran. Additionally, the anti-reflection effect of the hydrogel surface microstructure and the plasmon resonance effect of Au NPs were crucial in boosting the sensor’s fluorescence. Finally, the fluorescence intensity of the hydrogel increased by 10.2 times. In addition, under the action of excitation light, this sensor exhibited dual responsiveness of colorimetry and fluorescence, allowing for the sensing of heavy metal ions. The limit of detection for Zn²⁺ is as low as 0.803 μM, and the hydrogel exhibited more than 10 cycles of photo-isomerization and ion responsiveness. Full article

The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation (PNA) values generated from spectral indices to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model using the Monte Carlo Markov Chain (MCMC) technique. The initial management parameters, including sowing date, sowing rate, and nitrogen rate, are recalibrated based on the relationship between the remote sensing state variables and the simulated state variables. This integrated technique was tested on independent datasets acquired from three rice field tests at the experimental site in Deqing, China. The results showed that the data assimilation method achieved the most accurate LAI (R² = 0.939 and RMSE = 0.74) and PNA (R² = 0.926 and RMSE = 7.3 kg/ha) estimations compared with the spectral index method. Average differences (RE, %) between the inverted initialized parameters and the original input parameters for sowing date, seeding rate, and nitrogen amount were 1.33%, 4.75%, and 8.16%, respectively. The estimated yield was in good agreement with the measured yield (R² = 0.79 and RMSE = 661 kg/ha). The average root mean square deviation (RMSD) for the simulated values of yield was 745 kg/ha. Yield uncertainty from data assimilation between crop models and remote sensing was quantified. This study found that data assimilation of crop models and remote sensing data using the MCMC technique could improve the estimation of rice leaf area index (LAI), plant nitrogen accumulation (PNA), and yield. Data assimilation using the MCMC technique improves the prediction of LAI, PNA, and yield by solving the saturation effect of the normalized difference vegetation index (NDVI). This method proposed in this study can provide precise decision-making support for field management and anticipate regional yield fluctuations in advance. Full article

This study investigates the relationship between digital transformation and the substantive innovation of single champion manufacturing enterprises (SCMEs). Using panel data from listed SCMEs between 2017 and 2022, we applied a double fixed-effects model to analyze the effects of digital transformation on substantive innovation performance. The findings indicate that digital transformation significantly enhances SCMEs’ innovation performance, exhibiting a positive linear relationship. However, as the degree of transformation increases, the effect gradually diminishes, following an inverted U-shaped pattern. Furthermore, we introduced a theoretical framework of enterprise niche resilience and examined the moderating roles of niche resource resilience and niche structural resilience in the relationship between digital transformation and innovation performance. The results show that factors such as human resource resilience, capital resource resilience, supply chain resilience, and shareholder governance resilience play critical roles in enhancing innovation capabilities and supporting the digital transformation process. Finally, from the perspectives of macro-, meso-, and microenterprise niche positioning, we further discussed the heterogeneity across different regions, industrial chains, and lifecycle stages. This research provides new insights into innovation theory, niche theory, and resilience theory, offering valuable practical implications for policymakers and SCME managers to respond to global risks and drive domestic industrial upgrades. Full article

To effectively address the dual challenges of insufficient motivation for digital transformation (DT) among small and medium-sized enterprises (SMEs) and low collaboration efficiency in the manufacturing supply chain within the context of the digital economy, this paper focuses on how government subsidy policies can promote the empowerment behavior of core manufacturing enterprises (CMEs) to resolve the DT difficulties of SMEs and drive the overall upgrade of the manufacturing industry. Based on evolutionary game theory, a three-party evolutionary game model involving the government, CMEs, and SMEs is constructed. The evolutionary stability strategies of the three parties under different scenarios are explored, and the evolutionary stability of system strategies under single-factor and two-factor interactions is analyzed through MATLAB simulations. The research results indicate that (1) the intensity of government subsidies shows an inverted U-shaped impact on their effectiveness. (2) The government subsidy to CMEs can not only directly incentivize empowerment but also indirectly promote the DT of SMEs. (3) CMEs converge to the empowerment strategy faster than SMEs, while SMEs are more sensitive to insufficient subsidies. (4) SMEs have weak self-transformation capabilities, and their willingness to undergo DT is significantly more influenced by external factors than by internal factors. The above findings can help to clarify the interactive relationships among the government, CMEs, and SMEs in the DT process and provide valuable suggestions from multiple perspectives to promote the SMEs’ DT. Full article

This article focuses on both classical and Bayesian inference of traffic intensity in a single-server Markovian queueing model considering balking. To reflect real-world situations, the article introduces the concept of balking, where customers opt not to join the queue due to the perceived waiting time. The essence of this article involves a comprehensive analysis of different loss functions, namely, the squared error loss function (SELF) and the precautionary loss function (PLF), on the accuracy of the Bayesian estimation. To evaluate the performance of the Bayesian method with various priors such as inverted beta, gamma, and Jeffreys distributions, an assessment is performed using the Markov Chain Monte Carlo (MCMC) simulation technique. The efficacy of the Bayesian estimators is assessed by comparing the mean squared errors (MSEs). Full article

Plant protease inhibitors are a ubiquitous feature of plant species and exert a substantial influence on plant stress responses. However, the KTI (Kunitz trypsin inhibitor) family responding to abiotic stress has not been fully characterized in Populus yunnanensis. In this study, we conducted a genome-wide study of the KTI family and analyzed their gene structure, gene duplication, conserved motifs, cis-acting elements, and response to stress treatment. A total of 29 KTIs were identified in the P. yunnanensis genome. Based on phylogenetic analysis, the PyKTIs were divided into four groups (1,2, 3, and 4). Promoter sequence analysis showed that the PyKTIs contain many cis-acting elements related to light, plant growth, hormone, and stress responses, indicating that PyKTIs are widely involved in various biological regulatory processes. RNA sequencing and real-time quantitative polymerase chain reaction analysis showed that KTI genes were differentially expressed under the inverted cutting stress of P. yunnanensis. Transcriptome analysis of P. yunnanensis leaves revealed that PyKTI16, PyKTI18, and PyKTI19 were highly upregulated after inverted cutting. Through the GEO query of Populus transcriptome data, KTI genes played a positive defense role in MeJa, drought, time series, and pathogen stress. This study provided comprehensive information for the KTI family in P. yunnanensis, which should be helpful for the functional characterization of P. yunnanensis KTI genes in the future. Full article

Phosphate invert glasses (PIGs) have been attracting attention as materials for bone repair. PIGs have a high flexibility in chemical composition because they are composed of orthophosphate and pyrophosphate and can easily incorporate various ions in their glass networks. In our previous work, incorporation of niobium (Nb) into melt-quench-derived PIGs was effective in terms of controlling their ion release, and Nb ions promoted the activity of osteoblast-like cells. In the present work, a liquid-phase method was used for synthesizing Nb-containing PIGs, as this method allows us to prepare a glass precursor solution at room temperature, which can be attributed to improved glass-shape design. Nb-containing PIGs were successfully prepared, and their ion release behavior was controlled by changing the Nb content in the PIGs. The functions of Nb varied according to its content. For example, in the case of PIGs containing a larger amount of Nb, Nb acted as both the network modifier and former while also inducing the formation of chain-like structures. These glasses possessed a gradual ion release in a tris-HCl buffer solution. Cotton-wool-like structured scaffolds were fabricated using the synthesized Nb-containing glass using a wet-spinning method. Because the scaffolds possess excellent flexibility and controllable ion release, they are good candidates for new biomaterials. Full article

Background: A limited number of studies have investigated the role of environmental chemicals in the etiology of mild cognitive impairment (MCI). We performed a cross-sectional study of the association between exposure to selected trace elements and the biomarkers of cognitive decline. Methods: During 2019–2021, we recruited 128 newly diagnosed patients with MCI from two Neurology Clinics in Northern Italy, i.e., Modena and Reggio Emilia. At baseline, we measured serum and cerebrospinal fluid (CSF) concentrations of cadmium, copper, iron, manganese, and zinc using inductively coupled plasma mass spectrometry. With immuno-enzymatic assays, we estimated concentrations of β-amyloid 1-40, β-amyloid 1-42, Total Tau and phosphorylated Tau181 proteins, neurofilament light chain (NfL), and the mini-mental state examination (MMSE) to assess cognitive status. We used spline regression to explore the shape of the association between exposure and each endpoint, adjusted for age at diagnosis, educational attainment, MMSE, and sex. Results: In analyses between the serum and CSF concentrations of trace metals, we found monotonic positive correlations between copper and zinc, while an inverse association was observed for cadmium. Serum cadmium concentrations were inversely associated with amyloid ratio and positively associated with Tau proteins. Serum iron concentrations showed the opposite trend, while copper, manganese, and zinc displayed heterogeneous non-linear associations with amyloid ratio and Tau biomarkers. Regarding CSF exposure biomarkers, only cadmium consistently showed an inverse association with amyloid ratio, while iron was positively associated with Tau. Cadmium concentrations in CSF were not appreciably associated with serum NfL levels, while we observed an inverted U-shaped association with CSF NfL, similar to that observed for copper. In CSF, zinc was the only trace element positively associated with NfL at high concentrations. Conclusions: In this cross-sectional study, high serum cadmium concentrations were associated with selected biomarkers of cognitive impairment. Findings for the other trace elements were difficult to interpret, showing complex and inconsistent associations with the neurodegenerative endpoints examined. Full article

Osteoporosis, a common metabolic bone disorder, leads to increased fracture risk and significant morbidity, particularly in postmenopausal women and the elderly. Traditional treatments often fail to fully restore bone health and may cause side effects, prompting the exploration of regenerative therapies. Adipose-derived stem cells (ADSCs) offer potential for osteoporosis treatment, but their natural inclination toward adipogenic rather than osteogenic differentiation poses a challenge. This study investigates a novel approach combining differentiation inducers (DIs), three-dimensional (3D) hydrogel scaffolds, and photobiomodulation (PBM) to promote osteogenic differentiation of immortalised ADSCs. A dextran-based 3D hydrogel matrix, supplemented with a DI cocktail of dexamethasone, β-glycerophosphate disodium, and ascorbic acid, was used to foster osteogenesis. PBM was applied using near-infrared (825 nm), green (525 nm), and combined wavelengths at fluences of 3 J/cm², 5 J/cm², and 7 J/cm² to enhance osteogenic potential. Flow cytometry identified osteoblast-specific markers, while inverted light microscopy evaluated cellular morphology. Reactive oxygen species assays measured oxidative stress, and quantitative polymerase chain reaction (qPCR) revealed upregulated gene expression linked to osteogenesis. The findings demonstrate that integrating DIs, 3D hydrogels, and PBM effectively drives osteogenic differentiation in immortalised ADSCs. The PBM enhanced osteogenic marker expression, induced morphological changes, and upregulated gene activity, presenting a promising framework for bone regeneration. Future research should assess the stability and functionality of these differentiated cells and explore their applicability in preclinical models of bone injury or degeneration. This integrative approach demonstrated specific efficacy in promoting the osteogenic differentiation of ADSCs, highlighting its potential application in developing targeted treatments for osteoporosis. Full article

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data. Full article

In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage. Full article