Creation Of Clusters Research Articles

Bridging Global Mandates and Local Realities: Intermediary clusters and interorganizational collaboration for international development

Addressing international sustainable development is an urgent and critical challenge. Existing research has focused on the role of individual intermediary organizations in supporting and shaping inclusive local markets to enhance development. However, it has overlooked how clusters of organizations act as intermediaries to build development capacity, and how they do so through local public institutions. This study focuses on the United Nations (UN) system and examines a specific transnational governance arrangement (Delivering as One) that envisioned the creation of local clusters of UN organizations to facilitate institutional capacity-building in developing countries. By comparing the evolution of the eight pilot UN arrangements, we distil a model of how intermediary clusters facilitate local capacity-building through a process of contextual bridging driven by interorganizational collaboration within the cluster. Our findings identify four mechanisms of contextual bridging. The first two – functional amalgamation of operations and development of a cohesive collective voice – strengthened internal collaboration among cluster members; subsequently, sharing authority with local stakeholders and the local calibration of interorganizational collaboration (IOC) allowed the entrenchment of IOC into the local context. Our work contributes to the literature on interorganizational collaboration and transnational governance in the face of global challenges.

Big data processing using hybrid Gaussian mixture model with salp swarm algorithm

The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.

Milk and dairy products market: creation of dairy clusters

In modern conditions, public health as a strategic resource is one of the most important indicators that determine the level and quality of life, and is ensured by saturating the market with high-quality food products with high consumer properties. The authors have studied the current state of production of milk and its processed products, which are in the second place in demand after bakery products. In each region of the republic there are processing enterprises of the dairy industry, but not all producers supply goods of high quality. Purpose - development of practical recommendations to provide the domestic market with high-grade and competitive dairy products on the basis of formation and development of dairy clusters. Methods - the system approach was used to determine the value chain from raw materials to finished products, statistical and comparative analysis - to study the dynamics, state, potential opportunities of the dairy subcomplex, economic entities, generalization method - to summarize the results of the study. Results - the main problems existing in the dairy industry of Kazakhstan are revealed: insufficiency of milk for food products, underutilization of production capacities, lack of large farms producing conditioned raw materials in the required volumes, high import dependence, purchase prices for raw milk. Conclusions - the authors recommend the formation of territorial dairy cluster structures. The implementation of this direction will allow to meet the needs not only of the domestic market, but also to achieve a high export potential of the dairy industry, to solve the issues of supply of necessary equipment, fodder for livestock, to increase the competitive advantages of dairy products, the level of utilization of the existing production potential, as well as to expand the raw material base. Of particular importance is the study of promising areas of the dairy industry, since milk and dairy products are necessarily present in the food basket of every family.

Clustering of agro-industrial complex of the Republic of Kazakhstan: prerequisites, distinguishing features, correlation matrix

The role of cluster formations in increasing the competitiveness of products and accelerating the innovative development of the agrarian economy of Kazakhstan is revealed. Theoretical and practical principles, prerequisites and methodological approaches to the creation of agro-industrial clusters are defined, their distinctive features are revealed. Purpose - economic interests of clustering in agro-industrial complex in the context of regions of the republic are considered. The selection of objects in 17 regions with the highest economic indicators of financial and economic activity was carried out. Methods – k-means - for preliminary reduction of data dimensionality by means of factor analysis; rapid cluster analysis - in order to combine economic entities into cluster components on the basis of similar characteristics. Results - primary data were collected, correlation matrix was constructed and the presence of generalized factors was assessed to reduce the dimensionality of the studied attributes. Multivariate statistical study of agricultural products production by regions for 2023 was carried out. The variant with 5 clusters, the order and map of location of 17 administrative-territorial units of regional significance were adopted, the dendrogram of intergroup linking of regions was developed. Conclusions - cluster analysis allows to distribute the totality of objects (regions) of the Republic of Kazakhstan into five cluster formations, connecting in them the enterprises having similar structure and production specialization. The identified groups can be used for rating assessment of agro-industrial cluster that promotes effective integration of resources and knowledge. It has been established that the application of the cluster approach contributes to increasing productivity and sustainability of the agricultural sector, ensuring long-term development and competitive advantages.

У СИЛЕНИЕ МЕЖРЕГИОНАЛЬНЫХ ВЗАИМОДЕЙСТВИЙ КАК ИНСТРУМЕНТ СНИЖЕНИЯ ПРОСТРАНСТВЕННОЙ ПОЛЯРИЗАЦИИ (НА ПРИМЕРЕ РЕГИОНОВ СЕВЕРО-ЗАПАДНОГО ФЕДЕРАЛЬНОГО ОКРУГА)

This paper is devoted to the analysis of problems and prospects of strengthening interregional interactions. The strengthening of cooperative ties between regions has a beneficial effect not only on technological, but also on the socio- economic and spatial development of regions, thus, it can be considered as a tool to reduce spatial polarization. The article describes the regions of the Northwestern Federal District in the context of spatial polarization, analyzes the instrument of indus- trial and spatial policy – interregional clusters, analyzes the potential for the creation of new interregional clusters in the regions of the Northwestern Federal District based on the current industry specialization and, in general, to strengthen interactions between regions.

Making hospitals innovative: Macro-level policy to sustain micro-innovations in healthcare.

Successful innovation clusters are notoriously difficult to establish, and many attempts fail. How can we go about designing such systems reliably? We describe how ecosystems can be strengthened through grassroots bottom-up efforts that empower user and community innovation, as opposed to economic policies that dictate innovation. Specifically focusing on the healthcare industry, we advocate that community hospitals which constitute 90% of all hospitals in Canada are the ideal setting for such community innovation efforts. We investigated the distribution of innovation output from hospitals over the past 13years and found a decrease in predominance of major teaching hospitals, supporting the potential role for community hospitals in this space. We categorize different types of innovations and recommend institutional policies that can sustain bottom-up, micro-level efforts. Such policies could improve and enhance the development of micro-innovations and the creation of health innovation clusters.

Industrial recycling in El Salvador

Globally an increased awareness for the preservation of the planet is perceived. Different strategies are being held by countries in order to minimize problems such as pollution, access to vital resources, a responsible disposal of waste, and others. Part of this problem are the industries that generate pollution through the resources they exploit or directly through their waste. This study presents six different companies, conveniently selected in El Salvador, that include green initiatives into their business core in order to minimize the negative impact of their processes over the environment. They are well-known companies and stand out for the quality of their work. The research is qualitative, the methodology used to perform it was descriptive. The information collected from different sources guided the analysis of the theme in order to provide the guideline to the creation of the industrial recycling cluster in El Salvador. In order to propose the cluster, the researchers used Porter´s Diamond Model and the Five Forces Analysis. The results show that there are several advantages for the establishment of a cluster, such as, technology, training, internal resources within the companies, and others. On the other hand, the cluster will need more support from the government, communities and the citizens since most of the problems are due to the lack of regulations and the recycling culture in the population.

Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.

ORGANIZATIONAL FORMS OF IMPLEMENTATION OF INNOVATIVE ACTIVITIES OF AGRICULTURAL ENTERPRISES

Introduction. In modern conditions, it is difficult and very difficult for many economic entities in agriculture to oppose large enterprises, associations and transnational corporations in the market. Therefore, in order to strengthen their economic ties, to increase the competitiveness and efficiency of the produced products, each of these subjects tries to realize its potential, using the opportunities of cooperation and agro-industrial integration at all levels of management of the agrarian sector. Methods. The following methods were used to achieve the goal: theoretical generalization – when interpreting the category “cluster”; system approach – when determining the features of cluster structures compared to other types of enterprise associations; a combination of the historical and logical approach – in the study of the evolution of the formation of integration associations of economic entities, generalization of domestic and global experience of the functioning of cluster structures. Results. It is proved that despite some similarity between agroholdings and agroclusters, which consists mainly in the fact that they are mainly focused on the closed cycle of production of certain types of agro-industrial production, but the principles and mechanisms of their creation are fundamentally different in such features as: organizational legal form, principles of creation, production structure, status of agricultural organizations, competition and management. It has been established that in modern social and economic conditions, the creation and development of agricultural clusters in the country will allow: to increase the efficiency, investment attractiveness and innovative orientation of the functioning of agriculture, to ensure the reliability of the supply of the country and its regions with its own food and to create the necessary resources for its export supplies and processing products with high added value; to purposefully improve the processes of tactical and especially strategic planning and forecasting of the development of agriculture and state regulation of the market of agricultural products due to the use of a more effective organizational and economic mechanism of interaction between the state and agribusiness. Discussion. In the post-war period, the development of innovation clusters in order to intensify their activities requires solving the following problems: clusters need a guiding legislative document that would clearly define the role and place of clusters in the country's economy, the perspective of development and participation in relevant programs. This would immediately give impetus to many movements at the regional level, as well as in relations with international partners; clusters need significantly more financial support for already developed projects and programs for the development of the economy and innovative ecosystems, as well as for improving their own institutional capabilities; improving cooperation at the regional level. It is at this level that close cooperation on the development of clusters should take place at the level of authorities, regional development agencies, territorial communities, etc. After all, they are all interested in local business working better, development projects and investment attraction. Keywords: innovations, innovation cluster, agricultural enterprise, state, agricultural sector.

ECONOMIC AND LEGAL ASPECTS OF PROVIDING INNOVATIVE DEVELOPMENT OF THE ECONOMY IN THE CONDITIONS OF INCREASED INFLUENCE OF FACTORS OF A MILITARY NATURE: THE EXPERIENCE OF UKRAINE AND THE COUNTRIES OF CENTRAL AND EASTERN EUROPE

The purpose of the article is to analyze the state of innovative development of the economy of Ukraine and the countries of Central and Eastern Europe and to identify the problems of its provision in the conditions of martial law. The results of the study prove that factors of a military nature have an ambiguous effect on stimulating the innovative development of the country's economy. It has been established that economic evolution through innovation is a prerequisite for strengthening its competitiveness in countering modern challenges and threats. The results of the empirical study made it possible to find out that during the large-scale aggression of Russia on the territory of Ukraine in 2022, a decrease in the level of innovative economic development was also observed in the countries of Central and Eastern Europe. It was established that in 2023, the countries of the analyzed group managed to increase the indicators of innovative activity of business entities and the parameters of the innovative development of the economy. Important problems in ensuring economic progress through innovative activity have been identified: (1) shortcomings of the legislation regulating the innovation sphere; (2) lack of formation of a unified innovation infrastructure; (3) significant tax burden; (4) lack of an effective system of taxation and provision of tax benefits and preferences; (5) lack of effective investment mechanisms in the field of innovation; (6) low level of technological capacity of production facilities; (7) imperfection of the education system; (8) low level of financing of research activities; (9) focus on the export of raw materials and dependence on high-tech imports. In order to ensure sustainable innovative development of the country's economy, it is necessary to create appropriate conditions for the development of innovative potential, activate the creation of cross-border innovation clusters, and strengthen the country's investment attractiveness.

The spatial imaginaries of creative clusters: Examining the role of R&D programmes in creative placemaking

Creative clusters – geographic concentrations of creative industries sector activity, its skilled individuals, organisations and institutions – have attracted significant investment globally, becoming an important driver of economic growth. In this paper, we frame investment in creative clusters as a mechanism for creative placemaking. Creative placemaking occupies a dual role as a driver of economic development via arts-led ‘regeneration’ of high streets and flagship infrastructural projects like cultural quarters, yet also as a sustained stewarding of creative places through social engagement and community-centred decision-making about cultural projects. This distinction is typically framed as ‘top-down’ versus ‘bottom-up’ creative placemaking. We use the Bristol+Bath Creative R+D (BBCRD) programme as a case study to show how this distinction becomes less black and white. BBCRD created a ‘twin city’ spatial imaginary distinct from the geography of existing cultural or placemaking policy remits. The novel contribution of this paper is in how it evidences the intersecting scales of creative placemaking and unpacks the effectiveness of multi-city regionality for creative clustering, using fine-grained empirical data on the impact of top-down placemaking initiatives for the existing creative ecology of a place. This type of data and analysis is largely missing from literature on both clustering and placemaking. Given the continued international replication of the clusters model, recently renewed via ‘supercluster’ and ‘creative corridor’ discourses, we propose that an ecological understanding − that takes place specificity, relationality and scale into consideration – is pressing, and offers a route for complementarity between top-down and bottom-up creative placemaking.

Synthesis and characterization of black hydrogenated TiO2-rGO nanocomposites with enhanced photocatalytic activity

In this research, TiO2-GO composites were prepared by combination process included sol-gel for synthesizing TiO2 nanoparticles and afterward loaded GO on TiO2 (NPs) with hydrothermal treatment in ethanol/water medium. The black TiO2–H nanoparticles and TiO2-rGO-H nanocomposites were prepared through simple thermal hydrogen treatment at 600 °C for 3 h. The synthesized nanoparticles and the composite catalysts were characterized by XRD, FESEM, FTIR analyzes and the BET (Brunauer–Emmett–Teller) surface area measurement. The results revealed that synthesized TiO2 nanopowder was almost anatase phase with high crystallinity when coupled with GO leads to a decrease in the size of the crystallites by creation of smaller TiO2 clusters in result of enhanced dispersion of TiO2 on GO sheets. However, adding up GO has slightly improved optical properties of the photocatalyst samples. Compared with TiO2-GO composites, the high temperature (600 °C) hydrogenation produced anatase/rutile black TiO2-rGO nanocomposites enhanced hydrogen production rate 9 orders of magnitude higher than those observed for TiO2 powders, reached values of more than 368 μ mol g− 1 h – 1. A significant reduction in the energy band gap of 2.72 eV (DRS results) and also decreasing the electron-hole recombination according to PL spectra results has been recorded for TiO2-rGO-H nanocomposite. In addition, the black TiO2 nanoparticles and TiO2-rGO nanocomposites possessed much more concentration of oxygen vacancies and carrier concentration according to Raman analysis.

The Donetsk Region's coal industry: Activation of innovation processes

Subject. This article discusses the issues of managing the coal sector of the economy. Objectives. The article aims to form a mechanism for the implementation of innovation processes in the coal mining industry, taking into account the economic interests of the Donetsk People's Republic. Methods. For the study, we used systems and comparative analyses. Results. The article proposes an organizational and economic mechanism that provides an optimal combination of traditional goals and long-term objectives to intensify innovation processes in the coal industry, the main directions of development of which are the creation of clusters and technical re-equipment of enterprises. Conclusions. In the Donetsk region, measures have been taken to attract investment and create favorable conditions for business.

A topological data analysis approach for detecting data poisoning attacks against machine learning based network intrusion detection systems

Data poisoning attacks pose a significant security risk to network security software that utilizes machine learning (ML) for network intrusion detection. As network traffic continues to surge, ML becomes indispensable in detecting and characterizing malicious actors attempting to infiltrate computer networks. However, conventional ML assumes a benign environment, leaving room for adversaries to violate this assumption during the training phase. Detecting data poisoning attacks proves to be a challenging task, as attackers employ subtle alterations in the training data to create backdoors, trojans or triggers. Traditional techniques for addressing data poisoning attacks often focus only on enhancing ML model robustness rather than detecting poisoned data, necessitating the development of novel, more effective approaches. Hence, there is an urgent need to develop new methods for identifying poisoned data, ensuring the security of ML. We introduce a novel approach that harnesses the power of topological data analysis and unsupervised learning, enabling the early identification of poisoned data before training an ML model for network intrusion detection. Leveraging our approach, the extraction of topological features and subsequent application of clustering techniques leads to the creation of new clusters exclusively composed of poisoned data for removal prior to ML model training.

Attendees' experiences of anime conventions in the United States: a netnography study

PurposeGuided by stimulus-organism-response (SOR) theory, this study analyzed the user-generated content (UGC) produced by attendees from six anime conventions in the USA.Design/methodology/approachA total of 739 online reviews and 1,932 photos were collected from the social platforms of six large anime conventions in the USA (Yelp and Facebook), and the study employed thematic analysis and image analysis to analyze the collected UGCs.FindingsThe findings revealed eight main themes (i.e. ambient and space, customers, service and products, sign and symbol, social density, emotional status, motivation, and behavior intention) and 32 subthemes across the three dimensions of SOR theory. Leveraging the power of cutting-edge image analysis, the image labels obtained from the analysis contributed to the creation of network clusters. The result of the image analysis also continued consistently with the thematic analysis result, which reflected SOR theory.Research limitations/implicationsTheoretically, the study applied SOR theory and blended thematic and image analyses to gain a comprehensive understanding of anime convention attendees’ experience and categorized the attendees’ emotional status as positive or negative to reflect their overall evaluation. Practically, this study highlighted some complaints from attendees and provided suggestions for operators. However, the study focused only on large anime conventions in the USA; future studies should compare attendees’ experiences with small and large conventions or anime conventions worldwide.Originality/valueThe study utilized UGCs to understand the key patterns essential to attendees during anime conventions in the USA and applied SOR theory to its investigation.

Impact of environmental, energy and business efficiency on [formula omitted] emissions: A composite indicator for emerging Asia-Pacific markets

Environmental, energy, business and development dynamics have received strong research interest. However, previous studies have mainly explored these topics separately. Conversely, we study these aspects jointly, constructing a composite indicator and assessing resulting impact on several SDGs domains. The aim of the analysis is to discover the importance of latent aspects in explaning the environmental, energy and business efficiency for specific countries. Estimating this multidimensional efficiency is relevant for the identification of the conditions for the achievement of CO2 containment and sustainable development progress. We make use of the World Bank's World Development Indicators (WB WDI) for selected emerging countries from the Asia-Pacific region ranging from lower-middle-income to upper-middle-income economies, following the WB Group country classifications by income level. We build a composite indicator for eliciting diverse dimensions related to the environment, energy policy and economic factors. The first steps of the indicators analysis are achieved by the combination of the DEA and Tobit analyses. Therefore, we examine the environmental, energy and business efficiency according to the economic development stage of the analysed countries, making use of an entropy index. The index’ robustness is tested at both stages – first, exploring the Tobit analysis, therefore, by inspecting an additional Asia-Pacific country having a different income with respect to the benchmark. The results suggest the creation of country clusters. Above all, China outstands in terms of environmental efficiency and R&D, as a result of a broader energy development strategic plan in the last few decades.

An Effective Clustering Based Privacy Preserving Model Against Feature Attacks

The rise in healthcare-related illnesses has generated a substantial amount of patient data, making the safeguarding of patient data privacy imperative. Existing privacy protection methods face challenges, including longer execution times, compromised data quality, and increased information loss as data dimensions expand. Effective attribute selection is vital to enhance preservation methods. Our research introduces a privacy-preserving clustering approach that addresses these concerns through two stages: feature selection and anonymization. The first stage selects relevant features using symmetrical uncertainty (SU) and eliminates duplicates with Kendall’s Tau Correlation Coefficient. The Utility Preserved Anonymization (UPA) algorithm is employed in the second phase to achieve privacy preservation. Additionally, our approach reduces data dimensionality to simplify cluster creation for anonymization. Experimental analysis on real-time data demonstrates the strategy’s effectiveness, with outstanding sensitivity (97.85%) and accuracy (95%), efficiently eliminating unnecessary features and simplifying clustering complexity.

Enhancing security in wireless sensor networks: A fusion of deep learning and energy-efficient routing

The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications.

Sc-Modified C3N4 Nanotubes for High-Capacity Hydrogen Storage: A Theoretical Prediction.

Utilizing hydrogen as a viable substitute for fossil fuels requires the exploration of hydrogen storage materials with high capacity, high quality, and effective reversibility at room temperature. In this study, the stability and capacity for hydrogen storage in the Sc-modified C3N4 nanotube are thoroughly examined through the application of density functional theory (DFT). Our finding indicates that a strong coupling between the Sc-3d orbitals and N-2p orbitals stabilizes the Sc-modified C3N4 nanotube at a high temperature (500 K), and the high migration barrier (5.10 eV) between adjacent Sc atoms prevents the creation of metal clusters. Particularly, it has been found that each Sc-modified C3N4 nanotube is capable of adsorbing up to nine H2 molecules, and the gravimetric hydrogen storage density is calculated to be 7.29 wt%. It reveals an average adsorption energy of -0.20 eV, with an estimated average desorption temperature of 258 K. This shows that a Sc-modified C3N4 nanotube can store hydrogen at low temperatures and harness it at room temperature, which will reduce energy consumption and protect the system from high desorption temperatures. Moreover, charge donation and reverse transfer from the Sc-3d orbital to the H-1s orbital suggest the presence of the Kubas effect between the Sc-modified C3N4 nanotube and H2 molecules. We draw the conclusion that a Sc-modified C3N4 nanotube exhibits exceptional potential as a stable and efficient hydrogen storage substrate.

Industrial cluster and knowledge creation: a bibliometric analysis and literature review

The interaction between the development of industrial clusters and knowledge generation has become a fundamental element in driving innovation systems. Examining industrial clusters in knowledge creation terms is interesting for policymakers, investors, businesses and academia. This article provides a thematic and intellectual overview of the existing scientific literature on industrial clusters, their evolution and their relation with knowledge creation. The aim is to identify the scientific literature foundations in this area, different intellectual, conceptual and social structures, and trends in the research field. The study is a bibliometric analysis, whose results show that researchers’ topics of interest have considerably evolved over the last four decades. Initially, most studies focus on policies for efficient clusters management and later, starting in the 2010s, they evolve towards the importance of the networks of resources and knowledge that clusters provide, and how to promote learning and innovation as a key activity for economic development.