In this paper, we consider a class of structured fractional minimization problems where the numerator part of the objective is the sum of a convex function and a Lipschitz differentiable (possibly) ...nonconvex function, while the denominator part is a convex function. By exploiting the structure of the problem, we propose a first-order algorithm, namely, a proximal-gradient-subgradient algorithm with backtracked extrapolation (PGSA_BE) for solving this type of optimization problem. It is worth pointing out that there are a few differences between our backtracked extrapolation and other popular extrapolations used in convex and nonconvex optimization. One of such differences is as follows: if the new iterate obtained from the extrapolated iteration satisfies a backtracking condition, then this new iterate will be replaced by the one generated from the non-extrapolated iteration. We show that any accumulation point of the sequence generated by PGSA_BE is a critical point of the problem regarded. In addition, by assuming that some auxiliary functions satisfy the Kurdyka-Łojasiewicz property, we are able to establish global convergence of the entire sequence, in the case where the denominator is locally Lipschitz differentiable, or its conjugate satisfies the calmness condition. Finally, we present some preliminary numerical results to illustrate the efficiency of PGSA_BE.
In this paper, we propose a model for parallel magnetic resonance imaging (pMRI) reconstruction, regularized by a carefully designed tight framelet system, that can lead to reconstructed images with ...much less artifacts in comparison to those from existing models. Our model is motivated from the observations that each receiver coil in a pMRI system is more sensitive to the specific object nearest to the coil, and all coil images are correlated. To exploit these observations, we first stack all coil images together as a 3-dimensional (3D) data matrix, and then design a 3D directional Haar tight framelet (3DHTF) to represent it. After analyzing sparse information of the coil images provided by the high-pass filters of the 3DHTF, we separate the high-pass filters into effective ones and ineffective ones, and we then devise a 3D directional Haar semi-tight framelet (3DHSTF) from the 3DHTF by replacing its ineffective filters with only one filter. This 3DHSTF is tailor-made for coil images, meanwhile, giving a significant saving in computation comparing to the 3DHTF. With the 3DHSTF, we propose an ℓ1-3DHSTF model for pMRI reconstruction. Numerical experiments for MRI phantom and in-vivo data sets are provided to demonstrate the superiority of our ℓ1-3DHSTF model in terms of the efficiency of reducing aliasing artifacts in the reconstructed images.
This paper considers the lower and upper bounds of eigenvalues of arrow-head matrices. We propose a parameterized decomposition of an arrowhead matrix which is a sum of a diagonal matrix and a ...special kind of arrowhead matrix whose eigenvalues can be computed explicitly. The eigenvalues of the arrowhead matrix are then estimated in terms of eigenvalues of the diagonal matrix and the special arrowhead matrix by using Weyl's theorem. Improved bounds of the eigenvalues are obtained by choosing a decomposition of the arrowhead matrix which can provide best bounds. Some applications of these results to hub matrices and wireless communications are discussed.
Although mining companies contribute positively to the social and economical components of sustainable development (SD) by generating employment and wealth, they still negatively contribute to the ...ecological component of SD. Therefore, mining companies are increasingly showing their inclination toward the adoption of green supply chain management (GSCM) in order to improve their ecological performance. With an extensive literature survey, various criteria and sub-criteria for improving the effectiveness of GSCM implementation are identified from the literature. Analytic hierarchy process (AHP) is used to evaluate the competitive priorities of these criteria, and interested organizations can use it as a procedural guidance for GSCM implementation. It has been found that mining companies have not given the “soft” factors of GSCM adequate attention. This study explores how the “appropriate implementation approach” and “continuous improvement” are the weaker areas of GSCM practice in the case of the Indian mining sector. Hence, mining industries need to focus on these weaker areas and bring necessary improvements to these areas in order to enhance their GSCM performance.
Harmful algal blooms (HABs) are serious ecological disasters in coastal areas, significantly influencing biogeochemical cycles driven by bacteria. The shifts in microbial communities during HABs have ...been widely investigated, but the assembly mechanisms of microbial communities during HABs are poorly understood. Here, using 16S rRNA gene amplicon sequencing, we analyzed the microbial communities during an early-spring diatom bloom, in order to investigate the dynamics of microbial assembly processes.
,
, and
were the main bacterial families during the bloom. The 30 most abundant operational taxonomic units (OTUs) segregated into 4 clusters according to specific bloom stages, exhibiting clear successional patterns during the bloom process. The succession of microbial communities correlated with changes in the dynamics of algal species. Based on the β-nearest taxon distance, we constructed a simulation model, which demonstrated that the assembly of microbial communities shifted from strong heterogenous selection in the early stage of the bloom to stochasticity in the middle stage and then to strong homogeneous selection in the late and after-bloom stages. These successions were driven mainly by chlorophyll
contents, which were affected mainly by
Moreover, functional prediction of microbial communities showed that microbial metabolic functions were significantly related to nitrogen metabolism. In summary, our results clearly suggested a dominant role of determinacy in microbial community assembly in HABs and will facilitate deeper understanding of the ecological processes shaping microbial communities during the algal bloom process.
Harmful algal blooms (HABs) significantly influence biogeochemical cycles driven by bacteria. The shifts in microbial communities during HABs have been studied intensively, but the assembly mechanisms of microbial communities during HABs are poorly understood, with limited investigation of the balance of deterministic and stochastic processes in shaping microbial communities in HABs. In this study, the dynamics and assembly of microbial communities in an early-spring diatom bloom process were investigated. Our data both confirm previously observed general microbial successional patterns and show new detailed mechanisms for microbial assembly in HABs. These results will facilitate deeper understanding of the ecological processes shaping microbial communities in HABs. In addition, predictions of metabolic potential in this study will facilitate understanding of the influence of HABs on nitrogen metabolism in marine environments.
How to address public health priorities after COVID-19 is becoming a critical task. To this end, we conducted wastewater surveillance for six leading pathogens, namely, SARS-CoV-2, norovirus, ...rotavirus, influenza A virus (IAV), enteroviruses and respiratory syncytial virus (RSV), in Nanchang city from January to April 2023. Metaviromic sequencing was conducted at the 1st, 4th, 7th, 9th, 12th and 14th weeks to reveal the dynamics of viral pathogens that were not covered by qPCR. Amplicon sequencing of the conserved region of norovirus GI and GII and the rotavirus and region encoding nonstructural protein of RSV was also conducted weekly. The results showed that after a rapid decrease in SARS-CoV-2 sewage concentrations occurred in January 2023, surges of norovirus, rotavirus, IAV and RSV started at the 6th, 7th, 8th and 11th weeks, respectively. The dynamics of the sewage concentrations of norovirus, rotavirus, IAV and RSV were consistent with the off-season resurgence of the above infectious diseases. Notably, peak sewage concentrations of norovirus GI, GII, rotavirus, IAV and RSV were found at the 6th, 3rd, 7th, 7th and 8th weeks, respectively. Astroviruses also resurge after the 7th week, as revealed by metaviromic data, suggesting that wastewater surveillance together with metaviromic data provides an essential early warning tool for revealing patterns of infectious disease resurgence.
Display omitted
•Wastewater surveillance reflected the unusual resurgence of infectious diseases.•Metaviromic data uncovered more pathogenic viruses but lack of enough resolution for genotyping.•Amplicon sequencing provided weekly dynamics of prevalent genotypes in the sewershed.
Vendor managed inventory has proven to be an effective tool for improving the supply chain performance by decreasing inventory-related costs and increasing customer service. It is quite evident from ...the literature that vendor managed inventory (VMI) has been successfully implemented in small and medium enterprises (SMEs). However, studies related to the implementation of VMI in Indian SMEs are very limited. Therefore, this study presents an empirical investigation of VMI practices in Indian SMEs using survey methodology. The paper evaluates the benefits, barriers, and effects of adopting VMI in Indian SMEs, and also investigates the IT tools and software used for VMI adoption. Furthermore, this study explores the dissimilarities among various sectors of SMEs adopting VMI. Based on the proposed methodology, it is found that organizational issues and unwillingness to share information are the major barriers. In terms of benefits, the major influencing variables are improved efficiency and improved channel relations.
Sensor selection in nonparametric decentralized detection is investigated. Kernel-based minimization framework with a weighted kernel is adopted, where the kernel weight parameters represent sensors' ...contributions to decision making. L 1 regularization on weight parameters is introduced into the risk function so that the resulting optimal decision rule contains a sparse vector of nonzero weight parameters. In this way, sensor selection is naturally performed because only sensors corresponding to nonzero weight parameters contribute to decision making. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to jointly perform weight selection (i.e., sensor selection) and optimize decision rules. Both algorithms are shown to converge to critical points for this non-convex optimization problem. Numerical results are provided to demonstrate the advantages and properties of the proposed sensor selection approach.
Wastewater-based epidemiology (WBE) is a promising tool for monitoring the spread of SARS-CoV-2 and other pathogens, providing a novel public health strategy to combat disease. In this study, we ...first analysed nationwide reports of infectious diseases and selected Salmonella, norovirus, and influenza A virus (IAV) as prioritized targets apart from SARS-CoV-2 for wastewater surveillance. Next, the decay rates of Salmonella, norovirus, and IAV in wastewater at various temperatures were established to obtain corrected pathogen concentrations in sewage. We then monitored the concentrations of these pathogens in wastewater treatment plant (WWTP) influents in three cities, establishing a prediction model to estimate the number of infected individuals based on the mass balance between total viral load in sewage and individual viral shedding. From October 2022 to March 2023, we conducted multipathogen wastewater surveillance (MPWS) in a WWTP serving one million people in Xi'an City, monitoring the concentration dynamics of SARS-CoV-2, Salmonella, norovirus, and IAV in sewage. The infection peaks of each pathogen were different, with Salmonella cases and sewage concentration declining from October to December 2022 and only occasionally detected thereafter. The SARS-CoV-2 concentration rapidly increased from December 5th, peaked on December 26th, and then quickly decreased until the end of the study. Norovirus and IAV were detected in wastewater from January to March 2023, peaking in February and March, respectively. We used the prediction models to estimate the rate of SARS-CoV-2 infection in Xi'an city, with nearly 90 % of the population infected in urban regions. There was no significant difference between the predicted and actual number of hospital admissions for IAV. We also accurately predicted the number of norovirus cases relative to the reported cases. Our findings highlight the importance of wastewater surveillance in addressing public health priorities, underscoring the need for a novel workflow that links the prediction results of populations with public health interventions and allocation of medical resources at the community level. This approach would prevent medical resource panic squeezes, reduce the severity and mortality of patients, and enhance overall public health outcomes.
We propose a variational model for restoration of images corrupted by multiplicative noise. The proposed model formulated in the logarithm transform domain of the desirable images consists of a data ...fitting term, a quadratic term, and a total variation regularizer. The data fitting term results directly from the presence of the multiplicative noise and the quadratic term reflects the statistics of the noise. We show that the proposed model is strictly convex under a mild condition. The solution of the model is then characterized in terms of the fixed-point of a nonlinear map described by the proximity operator of a function involved in the model. Based on the characterization, we present a fixed-point proximity algorithm for solving the model and analyze its convergence. Our numerical results indicate that the proposed model compares favorably to several existing state-of-the-art models with better results in terms of the peak signal-to-noise ratio of the denoised images and the CPU time consumed.
PDF
