Concept Of Aggregation Research Articles

Consistent effects of science and scientist characteristics on public trust across political regimes

Recent years have seen an increased research interest in the determinants of public trust in science. While some argue that democracy should be the political regime most conducive to science, recent debates about salient scientific findings revealed considerable cracks in the public perception of science. We argue that existing cross-national work on trust in science is incomplete because it uses an aggregate concept of “science”. People in different political environments likely have different conceptions of what science is, which can have consequences for perceptions and trust. To remedy this shortcoming, we present results from a preregistered survey experiment in ten countries (N = 8441), which covers a broad spectrum of political regimes and tests how science and scientists’ characteristics influence public trust. We find that, against expectations, female scientists and scientists engaging in public activism are both perceived as more trustworthy. High-impact research is trusted more than low-impact research, and it does not matter whether a scientist is a co-national. Overall, our experiment reveals few differences across political regimes. Additional survey results show that respondents’ education and exposure to science have similar relationships with trust across autocratic and democratic countries. A striking difference we find is that while political orientation has little impact in autocratic countries, it is strongly related to trust across democracies as perceptions of science become increasingly politicized.

Different paths to improving together: a taxonomy of buyer-supplier collaborations for sustainability in food supply networks

Purpose Extant research shows collaboration among supply chain (SC) partners can address the significant environmental impacts of industrial food systems, but can be risky and resource-intensive. Past studies have predominantly treated buyer–supplier sustainability collaborations as a single aggregate concept missing the theoretical richness. This study aims to provide a more nuanced understanding of these collaborations for jointly improving sustainable food supply networks. Design/methodology/approach A multiple case study was conducted covering 8 SCs in the Dutch food processing industry, using data from 27 interviewees and extensive secondary material. Findings By applying the dynamic relational view, this study identifies three types of buyer–supplier collaboration, reflecting three paths of sustainable value creation: (1) the bilateral path, featuring equal participation and extensive collaboration, yielding substantial environmental and economic benefits; (2) the buyer-driven path, where the buyer leads the collaboration to address sustainability issues that are raised by stakeholders, relying on supplier expertise to improve SC traceability and sustainability reputation, albeit at higher costs; (3) the supplier-driven path, where the supplier leads the collaboration for incremental environmental and economic improvements. Practical implications The taxonomy results provide practical guidelines to assist managers in selecting the most suitable collaboration type for their specific sustainability goals and more effectively address sustainability challenges. Originality/value The three identified types of collaboration form a novel taxonomy for improving sustainability in food supply networks, representing different paths for SC partners to achieve progressively more substantial sustainability improvements. This taxonomy challenges the perspective that adopting sustainability invariably leads to increased costs by providing evidence of simultaneous economic and environmental improvements.

Comparative secured transactions law: how and to what extent do global standards affect the reform of Japanese inventory financing law

Abstract Since March 2021 Japan has been undertaking official legislative discussions at the Committee of Secured Transactions Law Reform under the Legislative Council of the Ministry of Justice Japan to draft a statute of secured transactions of personal property. These discussions are now in their final stages. Enhancing the reliability and feasibility of security devices for inventory financing is the core purpose of the proposed legislation. As a result of policy-making decisions, the conventional Japanese security device of ownership transfer for security purposes will not be replaced by a new regime which reflects the modern principles originating from the UCC9 and illustrated in the UNCITRAL Model Law on Secured Transactions. However, the significance of such global standards in the context of Japanese law has been two-fold. First, they illustrate what conventional Japanese law achieves through its own alternative rules. Second, the Council Committee is likely to propose several changes to refresh our conventional security device by adding new principles which have traditionally been of less concern to Japanese practitioners. In this article, the author substantiates the influence of these global standards on Japanese legislation. This article focuses on the functional approach taken by the ownership transfer type security interest, the rules relevant to publicity of security interests as modified by the first-to-register-or-possession rule as suggested by global standards, the concept of aggregate of inventory as collateral as modified by registration to cover perfection in security interests in goods joining the aggregate after a debtor's dishonest disposition, and the assignment of accounts receivables accompanied with ownership transfer of inventory which may be viewed as functionally close to security interests in proceeds of inventory.

Investigating how parental participation shapes the educational attainment of Indian children

Although greater participation from parents is preferred, it does not always translate into better outcomes. Examining the link between parental involvement in the home and academic achievement is the goal of this study. Data were collected from 337 students at Chandigarh University (CU) in India. The researcher gathered the primary dataset from two nations during the academic year. How this study develops the concept of feature aggregation significantly increased the age group of students' prediction accuracy. Compared to students from more distant or indirect profiles of family participation, students from households with aggressive parenting approaches frequently underperformed. Even after the impacts of context or background variables have been considered, parental participation patterns continue to affect learning outcomes at the level of each kid and the school. Schools should consider parental engagement when creating family knowledge and teaching programs since it is crucial for student success. At schools with more open family ideologies, there is less intra school diversity in student academic success.

Development of an intelligent evolution algorithm for open pit mines’ long-term production scheduling using the concept of block aggregation

Development of an intelligent evolution algorithm for open pit mines’ long-term production scheduling using the concept of block aggregation

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Productivity improvement tools used in the Industry-An Overview

Productivity is generally considered to be the efficient utilization of organizational resources and is measured in terms of the efficiency of a worker, company or nation.[1] Productivity is one of the widely used manufacturing performance measure and is essential for driving and managing production improvements[2]. Productivity relates to the efficiency of production factors in the production process. The higher the productivity, the lower the input, and consequently the lower the product cost [3]. The full benefits of productivity improvement measures are realized when productivity is examined from two perspectives: operational efficiency (output/input) of an individual worker or a business unit as well as performance (effectiveness) with regard to end user or customer satisfaction [1]. Productivity is an aggregated concept of overall efficiency/effectiveness of the organization concerned. Today most firms are interested in a strategic approach for improving productivity and quality in their organizations. Tools are playing vital role to increase productivity in the Industry. Key Words: Productivity, tpm, lean, value engineering, JIT

Scalable Multi-Robot Task Allocation Using Graph Deep Reinforcement Learning with Graph Normalization

Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage.

Aggregation of random elements over bounded lattices

Aggregation functions are widely used to fuse information from different sources in a unique value. In many cases, the aggregated information is related to some experimental measure or random sampling of a population. In this direction, it is reasonable to consider aggregation of random elements. In this paper, the concept of aggregation functions of random elements over bounded lattices, which are measurable functions from a probability space to a bounded lattice, is presented. In particular, starting from a partially ordered set, a measurable space is constructed. Random elements are considered to be measurable functions from a probability space to the measurable space. The concept of aggregation of random elements over bounded lattices is defined by generalizing the monotonicity and the boundary conditions in terms of stochastic orders. Several types, such as the induced, random and degenerated aggregations of random elements over bounded lattices are defined and some coherence properties are studied. Particular examples regarding the aggregation of random variables, random graphs and random semi-positive matrices are provided.

Perspectives on the misconception of levitating soil aggregates

Abstract. Soil aggregation is an important process in nearly all soils across the globe. Aggregates develop over time through a series of abiotic and biotic processes and interactions, including plant growth and decay, microbial activity, plant and microbial exudation, bioturbation, and physicochemical stabilization processes, and are greatly influenced by soil management practices. Together, and through feedback with organic matter and primary soil particles, these processes form dynamic soil aggregates and pore spaces, which jointly constitute a soil's structure and contribute to overall soil functioning. Nevertheless, the concept of soil aggregates is hotly debated, leading to confusion about their function or relevancy to soil processes. We argue here that the opposition to the concept of soil aggregation likely stems from the fact that the methods for the characterization of soil aggregates have largely been developed in the context of arable soils, where tillage promotes the formation of distinct soil aggregates that are easily visible in the topsoil. We propose that the widespread use of conceptual figures showing detached and isolated aggregates can be misleading and has contributed to the skepticism towards soil aggregates. However, the fact that we do not always see discrete aggregates within soils in situ does not mean that aggregates do not exist or are not relevant to the study of soil processes. Given that, by definition, soil aggregates consist of any group of soil particles that cohere more strongly to each other than neighboring particles, aggregates may, but do not necessarily need to be, bordered by pore space. Here, we illustrate how aggregates can form and dissipate within the context of undisturbed, intact soils, highlighting the point that aggregates do not necessarily need to have a discrete physical boundary and can exist seamlessly embedded in the soil. We hope that our contribution helps the debate on soil aggregates and supports the foundation of a shared understanding on the characterization and function of soil structure.

Assessment of Bullet Impact and Failure Mechanism of Concrete Containing Polythene Aggregates

The time from the initial stage of existence on Earth, the war continued. In this concern, India’s DRDO (Defence Research and Development Organization) and OFB (Ordnance Factory Board) worked appreciatively to develop new protective gear against assault weapons. The main focus of bullet or blast proof structures is to safeguard the life of soldiers, their accessories and gears, along with confidential and classified acquittance, firearm and ammunition, and sensitive systems. Defence structures like bunkers and tranches were designed in such discipline to keep them active round-the-clock with heavy armoury and made functional near to Line of Control (LOC) and Line of Actual Control (LAC) which oppose the effect of highly intensive blast waves. In such a manner, field testing with different Indian assault rifles i.e. INSAS (Indian Small Arms System), SIG 716i (US origin), and INSAS LMG (Indian Small Arms System-Light Machine Gun) was made from different ranges starting from 50mts to 300 mts extreme. Such structures were designed to protect the safety of soldiers from the bump of bullet effect. Structures designed are very strong and durable with opposition to high lateral load and visible surfacing. Construction material used for building plays a foremost role. The investigation is based on unique and advanced ideas in designing the defence structures. The investigation elaborates on the difference between conventional designing and the special designing of defence structures, as the advanced concept of polythene aggregates performs better than ordinary concrete.

A common weights multiplicative aggregation approach for composite indicators: the case of Global City Competitiveness Index

We propose a mathematical programming approach to assess a set of entities against a composite index. The weights used to construct the composite index are derived via optimization process. Specifically, we adopt the multiplicative method for the aggregation of the components (sub-indicators) of the evaluated index, which reduces the effect of compensation between the components. Also, we model the problem of identifying a common weighting scheme for the evaluated entities as a multiple objective programming (MOP) problem, with the performance of each entity being a distinct objective-criterion. This allows for comparisons and inferences between the assessed entities as a common ground is established. We stick to the concept of multiplicative aggregation by scalarizing the MOP problem using the weighted product method. Utilizing the multiplicative aggregation method in our modelling approach results in a nonlinear model that is scale invariant. However, using logarithms we show that is straightforwardly converted to a linear equivalent model, which is translation invariant. We examine whether the derived weighting scheme is unique, and in case there are alternative optimal solutions, we employ a max–min achievement function to identify a unique solution. We demonstrate our approach using synthetic datasets and compare it to other multiplicative approaches in the literature. We show that the proposed approach has neglective computational requirements and discriminates to a greater extent than those approaches with which we are compared, while at the same time provides a similar ranking. Similarities and dissimilarities in the results are justified. Since our approach is translation invariant, it enables us to apply it to cases where the data needs translation. We further illustrate our approach for such a case using the Global City Competitiveness Index, developed by Citigroup's Economist Intelligence Unit (EIU), which evaluates the competitiveness of 120 major cities worldwide.

Computable aggregations of random variables

Aggregation theory is devoted to the fusing of several values into a unique output that summarizes the given information. Typically, the aggregation process is formalized in terms of an increasing mathematical function that maps the input values to the result, fulfilling some boundary conditions. However, this formalization can be too restrictive for some scenarios. In some cases, the inputs can be seen as observations of random variables, the aggregation result being also a random variable. In others, the aggregation process can be identified as a program that performs the aggregation rather than a mathematical function. In this direction, the concepts of aggregation of random variables and computable aggregation have been defined in the literature. This paper is devoted to the definition of computable aggregation of random variables, which are computer programs, not functions, that aggregate random variables, not numbers. Special attention is given to different possible alternatives to modelize random variables and monotonicity. The implementation of some examples is also provided.

Aggregation, Transmission, and Toxicity of the Microtubule-Associated Protein Tau: A Complex Comprehension.

The microtubule-associated protein tau is an intrinsically disordered protein containing a few short and transient secondary structures. Tau physiologically associates with microtubules (MTs) for its stabilization and detaches from MTs to regulate its dynamics. Under pathological conditions, tau is abnormally modified, detaches from MTs, and forms protein aggregates in neuronal and glial cells. Tau protein aggregates can be found in a number of devastating neurodegenerative diseases known as "tauopathies", such as Alzheimer's disease (AD), frontotemporal dementia (FTD), corticobasal degeneration (CBD), etc. However, it is still unclear how the tau protein is compacted into ordered protein aggregates, and the toxicity of the aggregates is still debated. Fortunately, there has been considerable progress in the study of tau in recent years, particularly in the understanding of the intercellular transmission of pathological tau species, the structure of tau aggregates, and the conformational change events in the tau polymerization process. In this review, we summarize the concepts of tau protein aggregation and discuss the views on tau protein transmission and toxicity.

Landak Industrial Park (LIP) Development Strategy

Landak Industrial Park (KIL; Kawasan Industri Landak) is a national strategic project for Industrial Estates in West Kalimantan and is included in the List of 14 Priority Industrial Estate Developments Outside Java Island in the 2020-2024 National Medium-Term Development Plan (RPJMN; Rencana Pembangunan Jangka Menengah Nasional). The construction of KIL is experiencing delays, and to prevent the project from stalling, the main sources of industrial growth are new investment and increased utility from the current capacity. This study’s approach is both qualitative and quantitative. The analysis technique used is SWOT analysis using Internal Strategic Factor Analysis Summary (IFAS) and External Strategic Factor Analysis Summary (EFAS), developed using spatial analysis techniques from the concept of economic aggregation, which then generates value propositions and makes them the basis of a marketing strategy that includes market segmentation (segmenting), setting market targets (targeting), and creating a unique position (positioning). The result is from the SWOT analysis. KIL is in the turnaround quadrant, a position with a strategy of maximizing opportunities because it has major internal weaknesses. The geographical reach of other economic groups or business clusters that are quite close together is a trigger for the development of KIL. Scenarios from the KIL development strategy are expected to support strategic business decisions.

Landak Industrial Park (LIP) Development Strategy

Landak Industrial Park (KIL; Kawasan Industri Landak) is a national strategic project for Industrial Estates in West Kalimantan and is included in the List of 14 Priority Industrial Estate Developments Outside Java Island in the 2020-2024 National Medium-Term Development Plan (RPJMN; Rencana Pembangunan Jangka Menengah Nasional). The construction of KIL is experiencing delays, and to prevent the project from stalling, the main sources of industrial growth are new investment and increased utility from the current capacity. This study’s approach is both qualitative and quantitative. The analysis technique used is SWOT analysis using Internal Strategic Factor Analysis Summary (IFAS) and External Strategic Factor Analysis Summary (EFAS), developed using spatial analysis techniques from the concept of economic aggregation, which then generates value propositions and makes them the basis of a marketing strategy that includes market segmentation (segmenting), setting market targets (targeting), and creating a unique position (positioning). The result is from the SWOT analysis. KIL is in the turnaround quadrant, a position with a strategy of maximizing opportunities because it has major internal weaknesses. The geographical reach of other economic groups or business clusters that are quite close together is a trigger for the development of KIL. Scenarios from the KIL development strategy are expected to support strategic business decisions.

A Lightweight Real-Time System for Object Detection in Enterprise Information Systems for Frequency-Based Feature Separation

In the domain of target detection in mobile and embedded devices, neural network model inference speed is a crucial metric. This paper introduces YOLO-FLNet, a lightweight algorithm for detecting people in open scenes. The model utilizes the DFEM structure to capture and process high-frequency and low-frequency information in the feature map. Additionally, the VoV-DFEM structure, based on the concept of one-shot aggregation, enhances feature aggregation from different scales and frequencies in the backbone network. To validate its performance, experiments were conducted using publicly available datasets on a computer with dedicated GPUs. As a result, compared to YOLOv7-tiny, YOLO-FLNet achieved a 0.3% mAP@0.5 improvement, reduced parameter size by 52.9%, and increased inference speed by 30.2%. These characteristics make it valuable for person detection in engineering domains, providing theoretical guidance for lightweight models in edge computing.

Optical channel aggregation based on modulation format conversion by coherent spectral superposition with electro-optic modulators

Spectrally efficient modulation formats become essential for optical network scaling as the demand for routed data streams exceeds the present wavelength-division multiplexing systems’ throughput. However, achieving high spectral efficiency at high data rates requires complex and bandwidth-intensive electronics. In this study, we propose an all-optical aggregation scheme that combines multiple low spectral efficiency optical wavelength channels from an optical frequency comb based transmitter into fewer channels with higher spectral efficiency. Our method utilizes coherent spectral superposition and optical vector summation, eliminating the need for optical nonlinearities and relying on linear signal processing with an electro-optic modulator. By adjusting the phase of the radio frequency signal driving the modulator, we can easily achieve the required optical phase tuning for vector addition in the I-Q plane. Through experimental demonstrations, we show that the proposed approach enables the generation of 10 GBd PAM-4 and 10 GBd quadrature phase shift keying (QPSK) signals by aggregating two 10 GBd binary phase shift keying signals. Similarly, we aggregate two 10 GBd QPSK signals into one 10 GBd quadrature amplitude modulation-16 (QAM-16) signal. The experiments employ both conventional and sinc-shaped Nyquist signals. Our in-line, all-optical aggregation concept significantly enhances operational capacity while reducing complexity. It offers a promising solution for realizing flexible integrated optical transmitters for advanced modulation format signals using lower-quality electronics. Additionally, it aligns with the requirements of future dynamically reconfigurable optical networks that leverage spectral traffic aggregation. Given its reliance on linear signal processing with an electro-optic modulator, the integration of the method into any integrated photonic platform is straightforward.

PPDA-FAF: Maintaining Data Security and Privacy in Green IoT-Based Agriculture

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.

FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem; however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy.