Aggregation Problem Research Articles

HVAC system dynamic management in communities via an aggregation–disaggregation framework

The aggregation and disaggregation problem of Heating Ventilation and Air Conditioning (HVAC) systems in the building at the community-level have been considered in this work. The aim is to provide capacity reserves as a Virtual Power Plant (VPP) to support power system operation, improving grid reliability and efficiency in the face of increased use of renewable energy. Motivated by the layered operating structure of the power grid, an aggregation–disaggregation framework which operates at both slow and fast time-scales is proposed. At the slow time-scale, each building forecasts its HVAC system future electric power consumption information using predictions of disturbance and information of thermal zone via solving certain optimization problems. These values will be collected by a control center such as a community aggregator. Acting as a VPP, the control center determines each HVAC system future nominal electric power consumption by participating in the real-time electricity market or an independent power allocation plan. At the fast time-scale, a modified primal–dual gradient method based distributed control scheme is designed such that under real disturbances, the system can track the nominal consumption as closely as possible using only neighboring communications and local measurements. Finally, a numerical study demonstrates the effectiveness of the proposed framework.

MULTIVARIATE DISTRIBUTIONS WITH TIME AND CROSS-DEPENDENCE: AGGREGATION AND CAPITAL ALLOCATION

AbstractThis paper investigates risk aggregation and capital allocation problems for an insurance portfolio consisting of several lines of business. The class of multivariate INAR(1) processes is proposed to model different sources of dependence between the number of claims of the portfolio. The total capital required for the whole portfolio is evaluated under the TVaR risk measure, and the contribution of each line of business is derived under the TVaR-based allocation rule. We provide the risk aggregation and capital allocation formulas in the general case of continuous and strictly positive claim sizes and then in the case of mixed Erlang claim sizes. The impact of both time dependence and cross-dependence on the behavior of risk aggregation and capital allocation is numerically illustrated.

Co–Mn Mixed Metal Oxide Nanorods for On-Site Colorimetric Detection of SO32– in Food Samples

Excessive sulfite (SO32–) causes serious human health problems, making its detection highly valuable. In this work, a colorimetric sensor was presented for the detection of SO32– on the basis of Co–Mn mixed metal oxide (CoMn MMO) nanorods. CoMn MMOs composed of CoMn2O4 were uniformly dispersed among Co3O4 by calcining Co6(CO3)2(OH)8·H2O@CoMn2O4 nanorods in air, which had intrinsic oxidase-like activity. The synergistic introduction of CoMn2O4 promoted electron transfer in Co3O4, which not only prevented the aggregation problem but also increased the active sites, and the catalytic activity of the resulting CoMn MMO nanoenzyme was significantly enhanced. The obtained nanocomposites could catalyze the oxidation of the chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) to blue oxTMB with a quite low Michaelis–Menten constant (Km) of 0.042 mM and a quick Vmax of 0.85 × 10–8 M s–1 by generating a superoxide radical (·O2–). Based on the inhibitory effect of SO32– on the CoMn MMOs-TMB system, the proposed method allowed for SO32– detection with the linear concentration range from 1.56 to 15.58 μM and a limit of detection of 62.38 nM under optimal conditions. Combined with a smartphone chromaticity analysis app, we designed a portable and disposable agarose hydrogel test kit using the proposed system, which realized the on-site semiquantitative and visual detection of SO32– in food samples.

Peroxymonosulfate activation by immobilized CoFe2O4 network for the degradation of sulfamethoxazole

Heterogeneous activation of peroxymonosulfate (PMS) has been frequently proposed for the degradation of organic pollutants via the generation of reactive oxygen species (ROS). Spinel ferrite such as CoFe2O4 shows great advantage for PMS activation but suffers from aggregation problem and lack of microstructure. Herein, network-like CoFe2O4 immobilized on volcanic rock was synthesized for PMS activation for degrading sulfamethoxazole (SMX). The immobilized CoFe2O4 network exhibited good catalytic performance for SMX removal. Systematic investigations, including in situ ATR-FTIR and Raman spectroscopy analysis, chemical quenching experiments and electron paramagnetic resonance (EPR) tests, revealed that a redox cycle of Co2+/Co3+ induced the generation of ROS (SO4•−, OH• and 1O2) that participated in the degradation of SMX. Background constituents (e.g., inorganic ions and natural organic matters) exhibited limited influence on SMX removal. A continuous flow-through reaction in a fixed-bed column revealed the immobilized CoFe2O4 network could efficient degrade SMX with long-term durability, which therefore can be a promising catalyst for PMS activation for the degradation of SMX.

Attempting to measure the intensity of opposing feelings in elections: A polarization approach to Catalonia’s independence case

This paper addresses the Catalonian political process for independence. Based on the outcome of the 2017 Catalonian Regional Election, it firstly innovates by building an independence-feeling indicator for each municipality. Subsequently, we resort to the concept of polarization to assess the level of inherent conflict. Besides, we evaluate to what extent polarization can be explained by different variables. Then, in the second part of the paper, we redo the analysis but, to handle the potential aggregation problem that arises with geographically reference data (MAUP problem), we compute for each municipality two (antiindependence-feeling and proindependence-feeling) indicators rather than one. In doing so, we avoid merging votes that offset each other and, by applying the polarization index, we not only test for (inter-) polarization but also (intra-) polarization (within municipalities). The results show important differences. More specifically, although polarization across municipalities is relatively small, there is a high level of polarization within them. Moreover, while population density, Catalan proficiency and place of birth are instrumental when explaining polarization if a single indicator is computed, in the case of two indicators unemployment rate, age and wealth also emerge as remarkable variables, while among the three previous ones the importance of population density decreases.

From Digital Health to Digital Well-being: Systematic Scoping Review.

BackgroundDigital health refers to the proper use of technology for improving the health and well-being of people and enhancing the care of patients through the intelligent processing of clinical and genetic data. Despite increasing interest in well-being in both health care and technology, there is no clear understanding of what constitutes well-being, which leads to uncertainty in how to create well-being through digital health. In an effort to clarify this uncertainty, Brey developed a framework to define problems in technology for well-being using the following four categories: epistemological problem, scope problem, specification problem, and aggregation problem.ObjectiveThis systematic scoping review aims to gain insights into how to define and address well-being in digital health.MethodsWe followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Papers were identified from 6 databases and included if they addressed the design or evaluation of digital health and reported the enhancement of patient well-being as their purpose. These papers were divided into design and evaluation papers. We studied how the 4 problems in technology for well-being are considered per paper.ResultsA total of 117 studies were eligible for analysis (n=46, 39.3% design papers and n=71, 60.7% evaluation papers). For the epistemological problem, the thematic analysis resulted in various definitions of well-being, which were grouped into the following seven values: healthy body, functional me, healthy mind, happy me, social me, self-managing me, and external conditions. Design papers mostly considered well-being as healthy body and self-managing me, whereas evaluation papers considered the values of healthy mind and happy me. Users were rarely involved in defining well-being. For the scope problem, patients with chronic care needs were commonly considered as the main users. Design papers also regularly involved other users, such as caregivers and relatives. These users were often not involved in evaluation papers. For the specification problem, most design and evaluation papers focused on the provision of care support through a digital platform. Design papers used numerous design methods, whereas evaluation papers mostly considered pre-post measurements and randomized controlled trials. For the aggregation problem, value conflicts were rarely described.ConclusionsCurrent practice has found pragmatic ways of circumventing or dealing with the problems of digital health for well-being. Major differences exist between the design and evaluation of digital health, particularly regarding their conceptualization of well-being and the types of users studied. In addition, we found that current methodologies for designing and evaluating digital health can be improved. For optimal digital health for well-being, multidisciplinary collaborations that move beyond the common dichotomy of design and evaluation are needed.

Problems of Red Blood Cell Aggregation and Deformation Assessed by Laser Tweezers, Diffuse Light Scattering and Laser Diffractometry

This study aims to highlight the problems with implementing optical techniques (laser tweezers, diffuse light scattering and laser diffractometry) in clinical hemorheological practice. We provide the feasibility of these techniques to assess microrheological effects of various molecular mechanisms affecting RBC aggregation and deformability. In particular, we show that they allow assessment of changes in RBC aggregation in whole blood samples both on the level of single cells and on the level of large ensembles of cells. Application of these methods allows for studying the mechanisms of RBC aggregation because they are sensitive to changes in the medium which surrounds the RBC (i.e., blood plasma, serum or model solutions of blood plasma proteins) and to changes in the cellular properties of RBCs (i.e., effects on the cell membrane due to glycoprotein inhibition).

An Efficient Approach to Solve the Constrained OWA Aggregation Problem

Constrained ordered weighted averaging (OWA) aggregation attempts to solve the OWA optimization problem subject to multiple constraints. The problem is nonlinear in nature due to the reordered variables of arguments in the objective function, and the solution approach via mixed integer linear programming is quite complex even in the problem with one restriction of which coefficients are all one. Recently, this has been relaxed to allow a constraint with variable coefficients but the solution approach is still abstruse. In this paper, we present a new intuitive method to constructing a problem with auxiliary symmetric constraints to convert it into linear programming problem. The side effect is that we encounter many small sub-problems to be solved. Interestingly, however, we discover that they share common symmetric features in the extreme points of the feasible region of each sub-problem. Consequently, we show that the structure of extreme points and the reordering process of input arguments peculiar to the OWA operator lead to a closed optimal solution to the constrained OWA optimization problem. Further, we extend our findings to the OWA optimization problem constrained by a range of order-preserving constraints and present the closed optimal solutions.

Correlations between states and events in the simulation of dependability using Markov processes

The paper examines the correlations between states and events that are used in the construction of process diagrams that describe the dependability of items. Based on the constructed state and event diagram, input data is generated and the mathematical method is selected that is implemented in accordance with the problem at hand. The distinctive features and advantages of the matrix method are presented. Aim. To improve the simulation methods by clarifying the correlation between states and events and using matrix methods of calculation. Methods. The examined causal relationships between states and events allowed establishing correlations between them, i.e., an event can be the cause of a state change, then a state change is a consequence; a state can be the cause of an event, then an event is a consequence of a state. Under this approach, an event can cause a state change, while at the same time an event is a consequence of a state. The situation with states is similar. A state can be the cause of an event, while at the same time a state is the consequence of an event. It is also noted that a single state may cause a number of events, while an event can also cause a number of states. Examples of such correlations are given. It is noted that the duration of a state can be constant, random or zero. The examined correlations between states and events enable a substantiated construction of a diagram of states and transitions. A substantiated construction of a diagram of states and transitions results from a conceptual model, in which all states and events are given a physical and technical interpretation that transforms into a formal state-transition diagram. A special attention is given to the matrix methods that have a number of advantages, i.e., compactness and simplicity of converting the input characteristics into output characteristics, availability of standard software, use of verification procedures, feasibility of implementation using standard computer-based tools. The input data is also generated in matrix form. The paper indicates the characteristics of a state-transition diagram that can be calculated from the input data. Note is made of the use of methods based on semi-Markov processes. The author points out that, while using matrix methods, cycles should be generated. A relevant matter associated with the large number of states and the consequent problem of aggregation of states is touched upon. Two approaches to the aggregation of states are set forth that allow keeping the system’s output characteristics unchanged. Results. A proposal is formulated for the construction of a dependability model involving a number of stages, i.e., definition of the goal of simulation with the indication of the used dependability indicators, description of the conceptual model, construction of a substantiated state-transition diagram, selection of the mathematical method, calculations, discussing the findings, conclusions and suggestions based on the performed simulation. Discussion and conclusions. A dependability model should take into consideration the causal relationships between states and events that are established based on the physical, as well as the engineering and technical features of the item. Taking these relationships into account, a state diagram is generated that enables initial data compilation. The matrix method is efficient and has a number of useful features. The above considerations are methodological in their nature. They can be helpful for generating dependability models of technical systems and studying the dependability theory in educational institutions.

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.

MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images

In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, which may yield poorly robust contextual information. To address this problem, we propose a novel multi-level context refinement network (MLCRNet) that aggregates three levels of contextual information effectively and efficiently in an adaptive manner. First, we designed a local-level context aggregation module to capture local information around each pixel. Second, we integrate multiple levels of context, namely, local-level, image-level, and semantic-level, to aggregate contextual information from a comprehensive perspective dynamically. Third, we propose an efficient multi-level context transform (EMCT) module to address feature redundancy and to improve the efficiency of our multi-level contexts. Finally, based on the EMCT module and feature pyramid network (FPN) framework, we propose a multi-level context feature refinement (MLCR) module to enhance feature representation by leveraging multi-level contextual information. Extensive empirical evidence demonstrates that our MLCRNet achieves state-of-the-art performance on the ISPRS Potsdam and Vaihingen datasets.

One Approach to Solve Some Problems of Management Under Uncertainty

We consider the approaches to making decisions for control problems in nonstandard situations with a lack of the previous experience and incomplete knowledge of the considered problem. In such cases we usually cannot do without expert evaluations which lead to the process of group decision-making, and it becomes necessary to solve a problem of alternatives aggregation. It has been proposed to solve such problems by means of fuzzy sets. The approach is based on the coordination index and the similarity of finite collections of fuzzy sets and takes into account the specific character of the fuzzy aggregation operator. The approach is discussed and its algorithm is presented. An example of the application of the proposed method is given.

Effect of pyrolysis carbon black and carbon nanotubes on properties of natural rubber conductive composites

AbstractCarbon based materials with excellent conductivity such as conductive carbon black, graphene, and carbon nanotubes (CNTs) are often used in the preparation of conductive polymer composites. Pyrolysis carbon black, as a recycled material of waste rubber, can be used as a substitute for other carbon black in some fields, which can greatly reduce the production cost and promote reuse of used rubber. In this article, pyrolysis carbon black is mixed with CNT and added to natural rubber by mechanical blending method to get the composite material. The synergistic effect of pyrolysis carbon black and CNT, 3D network structure and formation of conductive network are investigated, and the properties of composites are analyzed. The results show that the 3D “grape cluster” network structure formed by the synergistic action of the pyrolysis carbon black and CNT, which led to the improvement of the curing properties and mechanical properties of the composites, increase the Payne effect, decrease the dispersion of the filler, and significant reduction the volume resistivity of the composites. With the increasing of CNT content, the variation of the properties of the composites changed and stabilized. In general, when 40 phr pyrolysis carbon black and 4 phr CNT are mixed into rubber matrix, the composite has the best comprehensive performance, and the volume resistivity is 9.25E5. Our results provide new thoughts in preparing natural rubber conductive composites. By adding highly CNT and inexpensive pyrolysis carbon black, the problem of easy aggregation of a single filler is overcome, which is more economical than adding two highly conductive fillers, and the process is simpler and time‐saving compared to the modified solution. More studies need to be done in order to reveal the effects of pyrolysis carbon black in practical conductive composites.

A philosopher's guide to multidimensional equality

AbstractTake a distribution involving two or more dimensions—say, apples and oranges. If Smith fares better on one dimension, while Jones fares better on another, how do we compare them? Rawlsians call this the indexing problem: how do we identify the worst‐off group overall? Proponents of the capabilities approach call it the aggregation problem: how do we weight the various dimensions? This essay examines possible solutions, including interpersonal comparisons of utility, the envy test, the egalitarian equivalent approach, undominated diversity, and the solidarity test. Although this essay does not solve the problem, it does identify the crucial question: should we equalize each person's bundle of resources or capabilities, the extent to which each person's bundle fits her preferences, or standing in the community? Since the question is normative—what should we equalize?—philosophers have much to contribute to the debate, and the payoff could be considerable: both policy decisions and other egalitarian debates could hinge on the answer.

Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.

Swelling-Resistant, Crosslinked Polyvinyl Alcohol Membranes with High ZIF-8 Nanofiller Loadings as Effective Solid Electrolytes for Alkaline Fuel Cells.

The present work investigates the direct mixing of aqueous zeolitic imidazolate framework-8 (ZIF-8) suspension into a polyvinyl alcohol (PVA) and crosslinked with glutaraldehyde (GA) to form swelling-resistant, mechanically robust and conductivity retentive composite membranes. This drying-free nanofiller incorporation method enhances the homogeneous ZIF-8 distributions in the PVA/ZIF-8/GA composites to overcome the nanofiller aggregation problem in the mixed matrix membranes. Various ZIF-8 concentrations (25.4, 40.5 and 45.4 wt.%) are used to study the suitability of the resulting GA-crosslinked composites for direct alkaline methanol fuel cell (DAMFC). Surface morphological analysis confirmed homogeneous ZIF-8 particle distribution in the GA-crosslinked composites with a defect- and crack-free structure. The increased ionic conductivity (21% higher than the ZIF-free base material) and suppressed alcohol permeability (94% lower from the base material) of PVA/40.5%ZIF-8/GA resulted in the highest selectivity among the prepared composites. In addition, the GA-crosslinked composites’ selectivity increased to 1.5–2 times that of those without crosslink. Moreover, the ZIF-8 nanofillers improved the mechanical strength and alkaline stability of the composites. This was due to the negligible volume swelling ratio (<1.4%) of high (>40%) ZIF-8-loaded composites. After 168 h of alkaline treatment, the PVA/40.5%ZIF-8/GA composite had almost negligible ionic conductivity loss (0.19%) compared with the initial material. The maximum power density (Pmax) of PVA/40.5%ZIF-8/GA composite was 190.5 mW cm−2 at 60 °C, an increase of 181% from the PVA/GA membrane. Moreover, the Pmax of PVA/40.5%ZIF-8/GA was 10% higher than that without GA crosslinking. These swelling-resistant and stable solid electrolytes are promising in alkaline fuel cell applications.

Three-Dimensional MXenes for Supercapacitors: A Review.

Supercapacitors have the characteristics of high power density and long cycle life, but the low energy density limits their further development. The 2D transitional metal carbides/nitrides (MXenes) show great application prospects in the field of supercapacitors due to their superior volumetric capacitance, metallic-like conductivity, tunable surface terminations, and structural advantages. However, like other 2D materials, MXenes suffer from the inevitable problem of nanosheet restacking and aggregation, which reduces the overall active surface sites and blocks the accessibility of the electrolyte ions. The transformation of 2D MXene nanosheets into 3D architectures is proven effective to overcome the restacking problem. The review briefly summarizes the preparation strategies of 3D MXene materials, including template-assisted method, framework-assisted method, chemical assembly method, foaming method, and other methods with the discussion centered on the performances of 3D MXenes in supercapacitors. Finally, an outlook on the current progress and opportunities is given to highlight the increasing popularity of 3D MXenes in supercapacitors.

Private Multi-Group Aggregation

We study the differentially private multi-group aggregation (PMGA) problem. This setting involves a single server and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> users. Each user belongs to one of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> distinct groups and holds a discrete value. The goal is to design schemes that allow the server to find the aggregate (sum) of the values in each group (with high accuracy) under communication and local differential privacy constraints. The privacy constraint guarantees that the user’s group remains private. This is motivated by applications where a user’s group can reveal sensitive information, such as his religious and political beliefs, health condition, or race. We propose a novel scheme, dubbed Query and Aggregate (Q&A) for PMGA. The novelty of Q&A is that it is an interactive aggregation scheme. In Q&A, each user is assigned a random query matrix, to which he sends the server an answer based on his group and value. We characterize the Q&A scheme’s performance in terms of accuracy (MSE), privacy, and communication. We compare Q&A to the Randomized Group (RG) scheme, which is non-interactive and adapts existing randomized response schemes to the PMGA setting. We observe that typically Q&A outperforms RG, in terms of privacy vs. utility, in the high privacy regime.

A group decision‐making algorithm considering interaction and feedback mechanisms for dynamic supplier selection under q‐ rung orthopair fuzzy information

Supplier selection is vital for enterprises to operate stably and achieve and sustain a competitive advantage. However, from the initial establishment to the gradual development and maturity of the enterprise, the supplier selection criteria change dynamically, and decision-makers are hardly in agreement in each stage, which creates challenges for enterprises when choosing suitable suppliers. As such, this paper proposes a multicriteria group decision-making method based on an interaction and feedback mechanism (IFM) and q-rung orthopair fuzzy sets theory. We introduce an IFM to achieve consensus among different decision-makers. We then develop the q-rung orthopair fuzzy weighted partitioned Bonferroni mean (q-ROFWPBM) operator to address the aggregation problem of dynamic multicriteria. A group decision-making algorithm combining the IFM and q-ROFWPBM operator is proposed to analyze the Hongxing Erke supplier selection. The results show that the proposed method can not only account for large differences of opinion among decision-makers during group decision-making but also consider the dynamic changes of supplier selection criteria in different stages of enterprise development and help enterprises choose suppliers suitable for their own development characteristics.

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.