Samsun İlinde Kurulabilecek Kompost Tesislerinin COCOSO ve WASPAS Yöntemiyle Optimallik Sıralaması

The compost product, which offers many benefits such as the evaluation of organic wastes, improvement of soil structure, neutralization of toxins and pH balance of the soil, has significant potential for the improvement of our country's lands. Considering the development of animal existence in our country, the production of compost product to be obtained from feces, which is the product of these animal beings, is an issue that needs to be emphasized. The choice of plant location, which must be determined for an investment to be made for the acquisition of this product emerges as a separate problem. For this reason, in this study, the order of optimality among the alternatives for compost plant installation is considered as a multi-criteria decision making problem. For this purpose, the criteria determined for 10 clusters with the potential of 35,829 animals that can produce compost in Samsun were weighted by the SWARA method. The optimal ranking of these 10 compost clusters was carried out using the COCOSO and WASPAS methods, by means of the criteria weights taken into consideration. According to the ranking results obtained from both methods, it was determined that the cluster number 27 was in the first rank, the cluster no 13 was in the second rank, and the cluster no 14 was in the third rank.

COPRAS ve MAIRCA Yöntemleriyle Çorum İlinde Kurulabilecek Kompost Tesislerinin Optimallik Sıralaması

The compost product, which is a biologically active substance, emerges as a result of microbial decomposition of organic materials under controlled conditions. This product, which is used for the improvement of soil structure and the development of agricultural products, also offers opportunities in terms of minimizing the damage caused by organic wastes to the environment. It is important to encourage efforts for compost production, especially in terms of both disposal and economic evaluation of wastes generated in animal production farms. Determining the most suitable location of a facility for the utilization of animal wastes as compost, which will be obtained from livestock enterprises scattered in different geographical areas, will be an essential study in terms of minimizing operating costs. For such a facility, it would be an appropriate approach to use multi-criteria decision making methods to choose among predetermined facility location alternatives. In this study, a total of 17 facility location alternatives with 83,163 cattle potential in Çorum province were ranked according to the criteria determined and weighted by means of SWARA method. The optimal ranking of 17 alternatives determined by K-Means clustering analysis was carried out by COPRAS and MAIRCA methods. According to the ranking results obtained from both methods, it was determined that cluster number 6 was in the first rank, cluster number 4 was in the second rank, and cluster number 3 was in the third rank.

EdClust: A heuristic sequence clustering method with higher sensitivity

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

Diversity of lateral line patterns and neuromast numbers in the genus Oryzias

ABSTRACT A remarkable diversity of lateral line patterns exists in adult teleost fishes, the basis of which is largely unknown. By analysing the lateral line patterns and organ numbers in 29 Oryzias species and strains we report a rapid diversification of the lateral line system within this genus. We show a strong dependence of lateral line elaboration (number of neuromasts per cluster, number of parallel lateral lines) on adult species body size irrespective of phylogenetic relationships. In addition, we report that the degree of elaboration of the anterior lateral line, posterior lateral line and caudal neuromast clusters is tightly linked within species, arguing for a globally coordinated mechanism controlling lateral line organ numbers and patterns. We provide evidence for a polygenic control over neuromast numbers and positioning in the genus Oryzias. Our data also indicate that the diversity in lateral lines can arise as a result of differences in patterning both during embryonic development and post-embryonically, where simpler embryonic patterns generate less complex adult patterns and organ numbers, arguing for a linkage between the two processes.

Unsupervised Learning Method for Events Identification in φ-OTDR

In this paper, an unsupervised-learning method for events-identification in φ-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, and noise-induced false vibration are clustered by the k-means algorithm. The equivalent classification accuracy of 99.4% has been obtained, compared with the actual classes of vibration-events in the experiment. With the cluster-number of 3, the maximal Calinski-Harabaz index and Silhouette coefficient are obtained as 2653 and 0.7206, respectively. It is found that our clustering method is effective for the events-identification of φ-OTDR without any prior labels, which provides an interesting application of unsupervised-learning in self-classification of vibration-events for φ-OTDR.

Mixed dark matter: matter power spectrum and halo mass function

We investigate and quantify the impact of mixed (cold and warm) dark matter models on large-scale structure observables. In this scenario, dark matter comes in two phases, a cold one (CDM) and a warm one (WDM): the presence of the latter causes a suppression in the matter power spectrum which is allowed by current constraints and may be detected in present-day and upcoming surveys. We run a large set of N-body simulations in order to build an efficient and accurate emulator to predict the aforementioned suppression with percent precision over a wide range of values for the WDM mass, Mwdm, and its fraction with respect to the totality of dark matter, fwdm. The suppression in the matter power spectrum is found to be independent of changes in the cosmological parameters at the 2% level for k≲ 10 h/Mpc and z≤ 3.5. In the same ranges, by applying a baryonification procedure on both ΛCDM and CWDM simulations to account for the effect of feedback, we find a similar level of agreement between the two scenarios. We examine the impact that such suppression has on weak lensing and angular galaxy clustering power spectra. Finally, we discuss the impact of mixed dark matter on the shape of the halo mass function and which analytical prescription yields the best agreement with simulations. We provide the reader with an application to galaxy cluster number counts.

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

EFFECT OF NUTRIENT SOLUTION ON GROWTH AND YIELD OF CHERRY TOMATO VARIETIES IN SOILLESS CULTURE

Nutrient solution and its nutritional compositions may have the effect on growth and fruit quality attributes of cherry tomato. To avoid the build-up of toxins, mineral deficiencies, nutrition abnormalities, or the spread of disease, producers should use optimum level of nutrient solution. Therefore, the present experiment was conducted to identify a suitable strength of nutrient solution for cherry tomato in hydroponic system. Treatment considered six levels of nutrient solution [viz., S1: ½ strength Rahman and Inden (2012), S2: ¾ strength Rahman and Inden (2012), S3: Full strength Rahman and Inden (2012), S4: ½ strength Hoagland and Arnon No. 2(1940), S5: ¾ strength Hoagland and Arnon No. 2 (1940) and S6: Full strength Hoagland and Arnon No. 2 (1940)] and two varieties [viz., V1: Local market cherry tomato (red), V2: Irelands cherry tomato (yellow)]. Growth and yield contributing characters, quality parameters, physiological traits and biochemical composition were analyzed. The maximum plant height, number of leaves per plant, first flowering, number of flowers per cluster, number of fruit per cluster, number of cluster per plant, average individual fruit weight and average cluster weight per plant were found in S3. Meanwhile, V2 performed better in respect of plant height, number of leaves per plant, first flowering, number of flowers per cluster, number of fruit per cluster, number of cluster per plant, average individual fruit weight and average cluster weight per plant. Therefore, cherry tomato cv. V2 can be cultured in hydroponic system with applying S3 (Full strength Rahman and Inden nutrient solution).

The Supersonic Project: SIGOs, A Proposed Progenitor to Globular Clusters, and Their Connections to Gravitational-wave Anisotropies

Supersonically induced gas objects (SIGOs), are structures with little to no dark-matter component predicted to exist in regions of the universe with large relative velocities between baryons and dark matter at the time of recombination. They have been suggested to be the progenitors of present-day globular clusters. Using simulations, SIGOs have been studied on small scales (around 2 Mpc) where these relative velocities are coherent. However, it is challenging to study SIGOs using simulations on large scales due to the varying relative velocities at scales larger than a few Mpc. Here, we study SIGO abundances semi-analytically: using perturbation theory, we predict the number density of SIGOs analytically, and compare these results to small-box numerical simulations. We use the agreement between the numerical and analytic calculations to extrapolate the large-scale variation of SIGO abundances over different stream velocities. As a result, we predict similar large-scale variations of objects with high gas densities before reionization that could possibly be observed by JWST. If indeed SIGOs are progenitors of globular clusters, then we expect a similar variation of globular cluster abundances over large scales. Significantly, we find that the expected number density of SIGOs is consistent with observed globular cluster number densities. As a proof-of-concept, and because globular clusters were proposed to be natural formation sites for gravitational wave sources from binary black-hole mergers, we show that SIGOs should imprint an anisotropy on the gravitational wave signal on the sky, consistent with their distribution.