Time-resolved Rayleigh scattering measurements of methane clusters for laser-cluster fusion experiments

We present a time-resolved analysis of Rayleigh scattering measurements to determine the average size of methane clusters and find the optimum timing for laser-cluster fusion experiments. We measure Rayleigh scattering and determine the average size of methane clusters varying the backing pressure (P0) from 11 bar to 69 bar. Regarding the onset of clustering, we estimate that the average size of methane clusters at the onset of clustering is Nc0≅20 at 11 bar. According to our measurements, the average cluster radius r follows the power law of r∝P01.86. Our ion time-of-flight measurements indicate that we have produced energetic deuterium ions with kT = 52±2 keV after laser-cluster interaction using CD4 gas at 50 bar. We find that this ion temperature agrees with the predicted temperature from CD4 clusters at 50 bar with r = 14 nm assuming the Coulomb explosion model.

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.

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).

Web Usage Mining Menggunakan Algoritma Clastering K-Mean

Algoritma K-means merupakan salah satu algoritma yang digunakan untuk metode clustering dalam data mining. Algoritma ini hanya bisa digunakan untuk mengolah data bertipe numerik menjadi pengetahuan. Metode ini cocok digunakan untuk mengolah data log access file server web untuk bidang web usage mining. Dari sekian banyak data di log access pengunjung dapat diambil pengetahuannya setelah diolah oleh algoritma K-mean. Penelitian ini dilakukan untuk mengetahui kluster dari waktu yang digunakan oleh pengguna untuk mengakses website pada sebuah instansti. Setelah melakukan try and error dalam menetapkan jumlah k dan nilai centroid awal,maka diperoleh 4 kluster. Dengan penggunaan distance measure yaitu squared Euclidean distance. Dengan average cluster distance sama dengan 207,286. Nilai Davies boudin index untuk klaster k sama dengan 4 adalah 0,076.

Low-Cost, Computer Vision-Based, Prebloom Cluster Count Prediction in Vineyards

Traditional methods for estimating the number of grape clusters in a vineyard generally involve manually counting the number of clusters per vine in a subset of the vineyard and scaling by the total number of vines; a technique that can be laborious, costly, and with an accuracy that depends on the size of the sample. We demonstrate that traditional cluster counting has a high variance in yield estimate accuracy and is highly sensitive to the particular counter and choice of the subset of counted vines. We propose a simple computer vision-based method for improving the reliability of these yield estimates using cheap and easily accessible hardware for growers. This method detects, tracks, and counts clusters and shoots in videos collected using a smartphone camera that is driven or walked through the vineyard at night. With a random selection of calibration data, this method achieved an average cluster count error of 4.9% across two growing seasons and two cultivars by detecting and counting clusters. Traditional methods yielded an average cluster count error of 7.9% across the same dataset. Moreover, the proposed method yielded a maximum error of 12.6% while the traditional method yielded a maximum error of 23.5%. The proposed method can be deployed before flowering, while the canopy is sparse, which improves maximum visibility of clusters and shoots, generalizability across different cultivars and growing seasons, and earlier yield estimates compared to prior work in the area.

Nucleation Controlled by Non-Fickian Fractional Diffusion

Kinetic models of aggregation and dissolution of clusters in disordered heterogeneous materials based on subdiffusive equations containing fractional derivatives are studied. Using the generalized fractional Fick law and fractional Fokker–Planck equation for impurity diffusion with localization, we consider modifications of the classical models of Ham, Aaron–Kotler, and Lifshitz–Slezov for nucleation and decomposition of solid solutions. The asymptotic time dependencies of supersaturation degree, average cluster size, and other characteristics at the stages of subdiffusion-limited nucleation and coalescence are calculated and analyzed.