Linear Aggregates Research Articles

Method of reconfiguring industrial facilities using the apparatus of piecewise linear aggregates

A method has been developed for structural and functional reconfiguration of industrial facilities based on piecewise linear aggregates, with ranking elements according to the importance of fulfilling their target function. The method allows for the conjugation of elements of complex technical objects using the conjugation operator. An example of the application of the proposed method is given. Keywords: reconfiguration, industrial facility, system, piecewise linear aggregates, synthesis of elements. rikki2@yandex.ru, strange.y@mail.ru

Review article: A systematic review and future prospects of flood vulnerability indices

Abstract. Despite the increasing body of research on flood vulnerability, a review of the methods used in the construction of vulnerability indices is still missing. Here, we address this gap by providing a state-of-art account on flood vulnerability indices, highlighting worldwide trends and future research directions. A total of 95 peer-reviewed articles published between 2002–2019 were systematically analyzed. An exponential rise in research effort is demonstrated, with 80 % of the articles being published since 2015. The majority of these studies (62.1 %) focused on the neighborhood followed by the city scale (14.7 %). Min–max normalization (30.5 %), equal weighting (24.2 %), and linear aggregation (80.0 %) were the most common methods. With regard to the indicators used, a focus was given to socioeconomic aspects (e.g., population density, illiteracy rate, and gender), whilst components associated with the citizen's coping and adaptive capacity were slightly covered. Gaps in current research include a lack of sensitivity and uncertainty analyses (present in only 9.5 % and 3.2 % of papers, respectively), inadequate or inexistent validation of the results (present in 13.7 % of the studies), lack of transparency regarding the rationale for weighting and indicator selection, and use of static approaches, disregarding temporal dynamics. We discuss the challenges associated with these findings for the assessment of flood vulnerability and provide a research agenda for attending to these gaps. Overall, we argue that future research should be more theoretically grounded while, at the same time, considering validation and the dynamic aspects of vulnerability.

Scaling up uncertain predictions to higher levels of organisation tends to underestimate change

Abstract Uncertainty is an irreducible part of predictive science, causing us to over‐ or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system‐level properties (e.g. ecosystem function). Here we show that this process of scaling up uncertain predictions to higher levels of organisation has a surprising consequence: it tends to systematically underestimate the magnitude of system‐level change, an effect whose significance grows with the system's dimensionality. This stems from a geometric observation: in high dimensions there are more ways to be more different, than ways to be more similar. We focus on ecosystem‐level predictions generated from the combination of predictions at the species level. In this setting, the ecosystem's relevant dimensionality is a measure of its diversity (and not simply the number of species). We explain why dimensional effects do not play out when predicting change of a single linear aggregate property (e.g. total biomass), yet are revealed when predicting change of nonlinear properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi‐functional ecology. As an application we discuss the consequences of our theory for multiple‐stressor research. This empirical field focuses on interactions between stressors, defined as the error made by a prediction based on their observed individual effects. Our geometric approach can be visualised and explored with a web application (https://doi.org/10.5281/zenodo.4611133), and we provide pseudocode outlining how our theory can be applied. Our findings highlight and describe the counter‐intuitive effects of scaling up uncertain predictions, effects that can occur in any field of science where a reductionist approach is used to generate predictions. ​

DVLO Theory for the Understanding of Aβ1‐42 Amyloid Fiber Formation

Protein aggregation and amyloid fiber formation are early events in a plethora of human diseases, including neurodegenerative diseases like Alzheimer's disease (AD). Currently, the mechanism for the formation of amyloid fibers is not entirely understood. The general belief is that fiber formation results from protein misfolding, where the conversion of proteins to abnormal structures, like β-sheets, results in aggregation. However, atomic force microscopy data suggest that aggregation occurs in two steps: formation of colloids, known as nucleation units, and aggregation of said units to form fibers. We apply DLVO theory to elucidate Aβ1-42 aggregation, the formation of colloids or oligomers, and the linear aggregation of the latter to fibers. Specifically, we investigate (1) what restricts the size of the colloids and (2) what drives the linear aggregation of the colloids. We propose that as Aβ1-42 monomers aggregate, the charge of the colloid becomes overwhelmingly large, and attractive forces can no longer overcome repulsive forces, thereby limiting the size of the colloid. A cross α-sheet structure is also speculated to be generated during the initial aggregation process, yielding colloids with a strong dipole. Charge-dipole interaction is also thought to drive linear colloidal aggregation. Our result shows that as the Aβ1-42 monomers aggregate to form colloids, overall charge of the unit increases, eventually repelling approaching monomers and preventing colloids from growing further in size, with the charge of the colloid being 3.36×10-17 C at a radius of 5 nm. Charge-dipole interactions were found to increase with the size of the colloids, eventually driving linear colloidal aggregation. The fiber's axis is perpendicular to the colloid's dipole moment, and the chain eventually assumes a thermodynamically stable cross β-sheet structure. We conclude that increased charge of the colloid, as additional monomers are added, acts as the limiting factor restricting the size of the colloids. A transition from a cross α-sheet to a cross β-sheet structure was essential for the proposed linear colloidal aggregation model.

Assessment of Road Safety Performance for Southeast Asian Countries


 
 
 The aim of this research is to assess the road safety performance in Southeast Asian countries to measure to what extent the new vision "safe system" is considered in the national road safety strategies. For this, a road safety index RSAI(ESA) is developed to use as a tool of assessment. The theoretical framework is based on the main pillars of safe system principles: safer roads and mobility, safer vehicles, and safer road users. A set of indicators are selected based on specific criteria. Each indicator is weighted, normalised, then aggregated using a simple linear additive aggregation method to construct the RSAI(ESA). This index is used to assess the performance of road safety in Southeast Asian countries, and then to rank the countries accordingly. The results show that the countries which have high rate of road crashes have started to take action to solve road safety problems, while some countries need to take further steps to apply the recommendations of the DARS 2011-2020. The setting of the minimum vehicle safety standards regulated by the United Nations (UN) is the most critical element that all the countries in this region should consider in their road safety strategies.
 
 

Fully Bayesian aggregation

Can a group be an orthodox rational agent? This requires the group's aggregate preferences to follow expected utility (static rationality) and to evolve by Bayesian updating (dynamic rationality). Group rationality is possible, but the only preference aggregation rules which achieve it (and are minimally Paretian and continuous) are the linear-geometric rules, which combine individual values linearly and combine individual beliefs geometrically. Linear-geometric preference aggregation contrasts with classic linear-linear preference aggregation, which combines both values and beliefs linearly, but achieves only static rationality. Our characterisation of linear-geometric preference aggregation has two corollaries: a characterisation of linear aggregation of values (Harsanyi's Theorem) and a characterisation of geometric aggregation of beliefs.

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

Fabrication of nanorings from ultrathin layer of silver on zinc oxide-coated glass substrate

Fabrication of ring-like nanostructures of noble metal, particularly silver (Ag) nanostructures are challenging but long expected and suitable for various applications. Here, in this work, a strategy has been reported to fabricate Ag nanorings from ultrathin layer of the same. The edges of as-fabricated Ag nanorings were found continuous in nature rather than linear aggregation of adjacent nanoclusters as confirmed by high-resolution scanning electron microscope (SEM) micrographs. Interestingly, none of the Ag nanorings was found broken or cracked. Such clues led ones to a plausible hypothesis in demonstrating the fabrication mechanism. In this context, an in-depth morphological investigation and quantitative analysis have been carried out to understand the evolution of nanorings. SEM micrographs captured at the intermediate stage of Ag nanorings formation revealed that ultrathin layer of Ag turned into spherical nanoparticles before ending at the formation of such nanorings. High-resolution SEM micrographs confirmed that Ag nanoparticles were mostly spherical along with dark sheds at the center. A detailed morphological investigation of such Ag nanoparticles confirmed average estimated outer diameter of 376.74 ± 37.24 nm and average wall thickness of 102.89 ± 18.10 nm. Further estimation indicated Ag nanorings of 213.58 nm edge height which was verified in high-resolution SEM investigations. An attempt was taken to slice an individual Ag nanoparticle with SEM-aided focus ion beam (FIB) facility. A small deviation in heights was observed by high-resolution SEM micrographs of such SEM-aided FIB treated nanoparticles. Silver nanorings of different sizes and shapes (background) along with fractal features at the rim (inset) and plausible formation mechanism thereof (right).

DEA Cross-Efficiency Aggregation with Deviation Degree Based on Standardized Euclidean Distance

Data envelopment analysis (DEA) has been extended to cross-efficiency to provide better discrimination and ranking of decision-making units (DMUs). Current researches about cross-efficiency mainly focus on the non-uniqueness of optimal solution of linear programming and information aggregation. As a common distance metric, standardized Euclidean distance is introduced to define the discrimination power between two vectors and the deviation degree for measuring the difference between the individual preference and group ideal preference. Based on above definitions, an alternative method is presented to compare multiple optimal solutions, and further, a universal weighted cross-efficiency model considering both dynamic adjustment of weights and preference formulation is constructed for evaluation and ranking. Two numerical examples are given to illustrate the effectiveness of the comparison method for multiple optimal solutions and weights determination method of DMUs, respectively. At last, a practical application aimed at evaluating environmental treatment efficiency in western area of China is given. Comparative analysis shows that our model could be more moderate, flexible, and general than some available models and methods, which can extend the theoretical research of cross-efficiency evaluation.

A Novel Magnetic Coupling to Construct Spiral Deposition of Lithium Ions for Improving Anode Performance of Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) battery is one of the most prospective energy storage devices due to its high specific capacity, low cost and pollution-free reactant. However, the degradation of anode lithium metal and the formation of lithium dendrites seriously shorten the cycle life and reduce its safety. It’s a bad obstacle for the application of Li-S batteries. In this work, comparing and analyzing reported applications of the magnetic field simple parallel or perpendicular to the direction of the electric field, one central symmetric and curved magnetic field which is firstly coupled to Li-S batteries has unique advantage. With this magnetic field, lithium ions are subjected to centripetal Lorentz force (FB), and the trajectory of Li+ is transformed from linear aggregation deposition to rotational uniform deposition, inhibiting the formation of lithium dendrites. With 70 mT, the capacity attenuation rate is 0.14%, which is almost a quarter of that with 0 mT. The results of scanning electron microscope (SEM) images show that there are fewer cracks and bulges on the surface of anode with the magnetic field. It can be ascribed to the magnetohydrodynamics (MHD) effect, and the mechanism is also confirmed by the multi-physics field simulation. In summary, this study proves that the central symmetric and curved magnetic field develops a new possibility for mitigating lithium dendrites and improving performance of Li-S batteries.

Statistics of the stress, strain-rate and spin fields in viscoplastic polycrystals

This paper provides estimates for the stress, strain-rate and spin field statistics in viscoplastic polycrystals. They are obtained by combining the fully optimized second-order (FOSO) homogenization method with the self-consistent estimate for linear polycrystalline aggregates. The FOSO linearization method allows the consistent estimation of the field statistics in nonlinear composites and polycrystals directly from those in suitably optimized linear comparison composites (LCC). For the LCC, a novel generalized approach is used to extract the field statistics of the stress, the strain-rate and also of the continuum-spin fields (including the second moments). Then, the stress-field statistics are used to extract the plastic-spin statistics, which, together with the continuum-spin statistics, provide consistent estimates for the microstructural-spin statistics. Applications to power-law viscoplastic FCC polycrystals and ice-like HCP polycrystals are considered, and the overall and intragranular field fluctuations are computed and found to increase with nonlinearity exponent and grain anisotropy. In particular, the covariance tensors of the continuum-spin fluctuations within the grains of viscoplastic polycrystals, which are characterized here for the first time by means of nonlinear homogenization methods, are found to be quite significant, even when the corresponding averages of the continuum-spin in the grains are negligible, and thus have a strong influence on the corresponding microstructural-spin fluctuations.

Substituent-Mediated Transformation of Polynuclear Gold(I)-Sulfido Complexes—From Pentanuclear to Octadecanuclear Cluster-to-Cluster Transformation

Substituent-Mediated Transformation of Polynuclear Gold(I)-Sulfido Complexes—From Pentanuclear to Octadecanuclear Cluster-to-Cluster Transformation

DNA: Deeply Supervised Nonlinear Aggregation for Salient Object Detection.

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multiscale convolutional features in convolutional neural networks (CNNs). Many popular methods impose deep supervision to perform side-output predictions that are linearly aggregated for final saliency prediction. In this article, we theoretically and experimentally demonstrate that linear aggregation of side-output predictions is suboptimal, and it only makes limited use of the side-output information obtained by deep supervision. To solve this problem, we propose deeply supervised nonlinear aggregation (DNA) for better leveraging the complementary information of various side-outputs. Compared with existing methods, it: 1) aggregates side-output features rather than predictions and 2) adopts nonlinear instead of linear transformations. Experiments demonstrate that DNA can successfully break through the bottleneck of the current linear approaches. Specifically, the proposed saliency detector, a modified U-Net architecture with DNA, performs favorably against state-of-the-art methods on various datasets and evaluation metrics without bells and whistles.

Compressed Coded Distributed Computing

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme. Based on the compressed CDC technique, we then study a distributed training problem as one of its application. We characterize the communication load for this distributed training problem and show that it is asymptotically optimal.

Composite Measures for Assessing Multidimensional Social Exclusion in Later Life: Conceptual and Methodological Challenges

Although there are a number of approaches to constructing a measure of multidimensional social exclusion in later life, theoretical and methodological challenges exist around the aggregation and weighting of constituent indicators. This is in addition to a reliance on secondary data sources that were not designed to collect information on social exclusion. In this paper, we address these challenges by comparing a range of existing and novel approaches to constructing a composite measure and assess their performance in explaining social exclusion in later life. We focus on three widely used approaches (sum-of-scores with an applied threshold; principal component analysis; normalisation with linear aggregation), and three novel supervised machine-learning based approaches (least absolute shrinkage and selection operator; classification and regression tree; random forest). Using an older age social exclusion conceptual framework, these approaches are applied empirically with data from Wave 1 of The Irish Longitudinal Study on Ageing (TILDA). The performances of the approaches are assessed using variables that are causally related to social exclusion.

A Model for Demand Planning in Supply Chains with Congestion Effects

This paper is concerned with demand planning for internal supply chains consisting of workstations, production facilities, warehouses, and transportation links. We address the issue of how to help a supplier firmly accept orders and subsequently plan to fulfill demand. We first formulate a linear aggregate planning model for demand management that incorporates elements of order promising, recipe run constraints, and capacity limitations. Using several scenarios, we discuss the use of the model in demand planning and capacity planning to help a supplier firmly respond to requests for quotations. We extend the model to incorporate congestion effects at assembly and blending nodes using clearing functions; the resulting model is nonlinear. We develop and test two algorithms to solve the nonlinear model: one based on inner approximation and the other on outer approximation.

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes. DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCA MethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance. ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.

Delocalized exciton formation in C60 linear molecular aggregates.

Organic semiconducting materials containing C60 molecules are efficient acceptors for planar perovskite solar cells. In this work, we theoretically investigate the optical and excitonic properties of C60 linear molecular aggregates (composed of 1 to 7 C60 molecules) via the real-time-propagation rt-TDDFT technique. In the case of a single C60 molecule, the photoabsorption peaks are dominated by localized molecular excitons. We furthermore demonstrate that, in the case of linear molecular aggregates, the photoabsorption peaks are contributed by localized molecular excitons, charge transfer excitons, and Wannier-like delocalized excitons. This result is different to the accepted theory that only localized molecular excitons or charge transfer excitons can be produced in organic semiconducting materials. This work provides additional insights into the exciton formation in C60 molecular aggregates and may help in the rational design of efficient solar cells.

Synthesis and characterization of bis(amine)palladium(II) carboxylate complexes as precursors of palladium nanoparticles.

The synthesis and characterization of the adducts of n-alkyl amine and palladium n-alkyl carboxylate, [Pd(R2NH2)2(R1COO)2] (R1 = 1, 7, and 11; R2 = 8, 12, and 16), as precursors for the synthesis of palladium nanoparticles (PdNPs) was carried out via differential scanning calorimetry, FT-IR, Raman and UV-Vis spectroscopy, NMR spectroscopy (1H, 13C pulsed field gradient spin-echo (PGSE), and 13C CP-MAS), and powder X-ray diffraction. Pd n-alkyl carboxylates were obtained by a ligand exchange reaction from palladium acetate and the appropriate aliphatic carboxylic acid. It is proposed that carboxyl moieties in the presence of amine ligands are bound to palladium ions via monodentate bonding as opposed to bridging bidentate coordination of pure palladium carboxylate which exists in the form of polymer aggregates. All the studied palladium carboxylate/amine complexes form bilayer lamellar structures and exhibit first-order melting transitions. The evidence presented in this study shows that the phase behavior of bivalent metal carboxylates is mainly controlled by the type of coordination of carboxylate head groups. For n-alkyl carboxylates, linear chain type aggregates replace the trimeric units of Pd acetate. In solution, in the presence of amine, palladium salt aggregates disintegrate and the Pd complex is isolated and stabilized by amine molecules. Using bis(amine) palladium carboxylate adducts as precursors, palladium nanoparticles were fabricated. During high temperature thermolysis, the bis(amine) Pd carboxylate complex decomposes to form small sized Pd nanoparticles. Combining NMR techniques with FTIR spectroscopy, it was possible to follow an original stabilization mechanism. PdNPs are stabilized by weakly interacting long chain aliphatic amide and carboxylic acid derived from the palladium precursor.

Fully Bayesian Aggregation

Can a group be an orthodox rational agent? This requires the group's aggregate preferences to follow expected utility (static rationality) and to evolve by Bayesian updating (dynamic rationality). Group rationality is possible, but the only preference aggregation rules which achieve it (and are minimally Paretian and continuous) are the linear-geometric rules, which combine individual values linearly and combine individual beliefs geometrically. Linear-geometric preference aggregation contrasts with classic linear-linear preference aggregation, which combines both values and beliefs linearly, but achieves only static rationality. Our characterisation of linear-geometric preference aggregation has two corollaries: a characterisation of linear aggregation of values (Harsanyi's Theorem) and a characterisation of geometric aggregation of beliefs.