Tracing Relationship between Cluster’s Performance and Transition to the Circular Economy

Clusters are defined as geographically close groups of organizations that work together to gain a competitive advantage. Clusters’ shared activities involve knowledge sharing, a common pool of resources, innovations, and cooperation. From a more advanced perspective, clusters can work in industrial symbiosis sharing resources, energy, water, and other products. Tendencies of recent research indicate the growing interest in shifting to an efficient use of resources and sustainable development through the circular economy (CE). Clusters can work as enablers of CE to achieve a competitive advantage. The purpose of this study is to trace the relationships between cluster performance and shifting to the CE indicators. Correlation analysis was used as a method to indicate the relationships between pairs of clusters’ performance and shifting to the CE indicators. The limitations of the research refer to the selection of the indicators as both concepts gain insights, although still debatable. The results show that 16 out of 25 cluster performance indicators were identified that have strong or moderate relationships among pairs while shifting to the CE indicators. These indicators are recommended to be included in observation, benchmarking, or evaluation of the clusters’ activities. They can be significant in monitoring the development of shifting to the CE or in combinations with other research areas.

Improvement Of Organizational And Economic Mechanisms Of Organization Of Vine Clusters

The article analyzes the role and importance of cluster structures in the development of the agricultural sector and foreign experience in organizing their activities. The authors also assessed the organizational mechanisms of agro-clusters and their role in improving the efficiency of cluster performance. As a result of the research, recommendations have been developed to improve the organizational and economic mechanisms of agro-clusters in Uzbekistan.

Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces

Deep clustering techniques combine representation learning with clustering objectives to improve their performance. Among existing deep clustering techniques, autoencoder-based methods are the most prevalent ones. While they achieve promising clustering results, they suffer from an inherent conflict between preserving details, as expressed by the reconstruction loss, and finding similar groups by ignoring details, as expressed by the clustering loss. This conflict leads to brittle training procedures, dependence on trade-off hyperparameters and less interpretable results. We propose our framework, ACe/DeC, that is compatible with Autoencoder Centroid based Deep Clustering methods and automatically learns a latent representation consisting of two separate spaces. The clustering space captures all cluster-specific information and the shared space explains general variation in the data. This separation resolves the above mentioned conflict and allows our method to learn both detailed reconstructions and cluster specific abstractions. We evaluate our framework with extensive experiments to show several benefits: (1) cluster performance – on various data sets we outperform relevant baselines; (2) no hyperparameter tuning – this improved performance is achieved without introducing new clustering specific hyperparameters; (3) interpretability – isolating the cluster specific information in a separate space is advantageous for data exploration and interpreting the clustering results; and (4) dimensionality of the embedded space – we automatically learn a low dimensional space for clustering. Our ACe/DeC framework isolates cluster information, increases stability and interpretability, while improving cluster performance.

ColFeatures: Automated data extraction and classification of bacterial colonies

Examining microbial colonies on agar plates have been at the core of microbiology for many decades. It is usually done manually, and therefore subject to bias besides requiring a considerable amount of time and effort. In order to optimize and standardize the identification of bacterial colonies growing on agar plates, we have developed an open access tool available on GitHub: ColFeatures. The software allows automated identification of bacterial colonies, extracts key morphological data and generate labels that ensure tracking of temporal development. We included machine learning algorithms that provide sorting of environmental isolates by using cluster methodologies. Furthermore, we show how cluster performance is evaluated using index scores (Silhouette, Calinski-Harabasz, Davies-Bouldin) to ensure the outcome of colony classification. As automation becomes more prominent in microbiology, tools as ColFeatures will assist identification of bacterial colonies on agar plates, bypassing human bias and complementing sequencing or mass spectrometry information that often comes attached with a considerable price tag.

LESSONS LEARNED FROM CROSS-VALIDATION OF FIBER OPTICS AND PRODUCTION LOGGING CLUSTER PERFORMANCE ASSESSMENT IN THE UNCONVENTIONAL WELLS

The well design with long lateral section and multistage frac completion has been proven effective for development of the unconventional reservoirs. Top-tier well production in unconventional reservoir can be achieved by optimizing hydraulic completion and stimulation design, which necessitates an understanding of flow behavior and hydrocarbon contribution allocation. Historically, conventional production logging (PL) surveys were scarcely used in unconventional reservoirs due to limited and often expensive conveyance options, as well as complicated and non-unique inflow interpretations caused by intricate and changing multi-phase flow behavior (Prakash et al., 2008). The assessment of the cluster performance gradually shifted towards distributed acoustic (DAS) and temperature (DTS) sensing methods using fiber optics cable, which continuously gained popularity in the industry. Fiber optics measurements were anticipated to generate production profiles along the lateral with sub-cluster resolution to assist with optimal completions design selection. Encapsulation of the fiber in the carbon rod provided alternative conveyance method for retrievable DFO measurements, which gained popularity due to cost-efficiency and operational convenience (Gardner et al., 2015). Recent utilization of micro-sensor technology in PL tools, (Abbassi et al, 2018, Donovan et al, 2019) allowed dramatic reduction of the size and the weight of the PL toolstring without compromising wellbore coverage by sensor array. Such ultra-compact PL toolstring could utilize the carbon rod as a taxi and provide mutually beneficial and innovative surveillance combination to evaluate production profile in the unconventional reservoirs. Array holdup and velocity measurements across wellbore from PL would reveal more details regarding multi-phase flow behavior, which could be used for cross-validation and constraining of production inflow interpretation based on DFO measurements. This paper summarizes the lessons learned, key observations and best practices from the unique 4 well program, where such innovative combination was tested in gas rich Duvernay shale reservoir.

Varying VAWT Cluster Configuration and the Effect on Individual Rotor and Overall Cluster Performance

The effect of separation distance between turbines on overall cluster performance were simulated using computational fluid dynamics software and we found that at a distance equivalent to two rotors, there was an improvement of +8.06% in the average performance of the cluster compared to a single, isolated turbine. A very small improvement in performance was noted at the equivalent distance of 12 rotor diameters. The performances of three individual turbines in pyramid- and inverted pyramid-shaped vertical axis wind turbine clustered farm configurations with varying oblique angles at a fixed spacing of two equivalent rotor diameters were also investigated. The design experiment involves the simulation of test cases with oblique angles from 15° to 165° at an interval of 15° and the turbines were allowed to rotate through 18 full rotations. The results show that the left and right turbines increase in performance as the angle with respect to the streamline axis increases, with the exception of the 165° angle. The center turbine, meanwhile, attained its maximum performance at a 45° oblique angle. The maximum cluster performance was found to be in the configuration where the turbines were oriented in a line (i.e., side by side) and perpendicular to the free-stream wind velocity, exhibiting an overall performance improvement of 9.78% compared to the isolated turbine. Other array configurations show improvements ranging from 6.58% to 9.57% compared to the isolated turbine, except in the extreme cases of 15° and 165°, where a decrease in the cluster performance was noted due to blockage induced by the left and right turbines, and the center turbines, respectively.

ANALYSIS THE CLUSTER PERFORMANCE OF REAL DATASET USING SPSS TOOL WITH K-MEANS APPROACH VIA PCA

Partitioning problems are handled by the idea of cluster and this technique which plays the essential work in mining of data from the given dataset. The K-Means cluster is well accepted theory to apply on huge datasets, but has some drawbacks. The factual dataset is taken from the repository of data used for clustering. Furthermore, as getting the outcome of this procedure is essential to resolve the limitations and quality enhanced of cluster by apply the Principal Component Analysis (PCA) on the dataset. In paper we have demonstrate the results by experimental for factual datasets with dissimilarities. We have worked to validate the experimental significant for the clusters metric and component size minimized for different dataset during the processing on SPSS tool on the basis of eigenvalues. In this research paper we also discussed the comparative analysis of distance between initial centroid of wine and disease of heart dataset at the level of cluster k=2 and k=3.