Empirical Fit to Inelastic Electron-Deuteron and Electron-Neutron Resonance Region Transverse Cross Sections

An empirical fit is described to measurements of inclusive inelastic electron-deuteron cross sections in the kinematic r ange of four-momentum transfer $0 \le Q^2<10$ GeV$^2$ and final state invariant mass $1.1<W<3.2$ GeV. The deuteron fit relies on a fit of the ratio $R_p$ of longitudinal to transverse cross sections for the proton, and the assumption $R_p=R_n$. The underlying fit parameters describe the average cross section for proton and neutron, with a plane-wave impulse approximation used to fit to the deuteron data. An additional term is used to fill in the dip between the quasi-elastic peak and the $\Delta(1232)$ resonance. The mean deviation of data from the fit is 3%, with less than 4% of the data points deviating from the fit by more than 10%.Comment: 16 pages, 5 figures, submitted to Phys. Rev. C. Text clarified in response to referee comment

Judging Covers

Cover versions form a loose but identifiable category of tracks and performances. We distinguish four kinds of covers and argue that they mark important differences in the modes of evaluation that are possible or appropriate for each: mimic covers, which aim merely to echo the canonical track; rendition covers, which change the sound of the canonical track; transformative covers, which diverge so much as to instantiate a distinct, albeit derivative song; and referential covers, which not only instantiate a distinct song, but for which the new song is in part about the original song. In order to allow for the very possibility of transformative and referential covers, we argue that a cover is characterized by relation to a canonical track rather than merely by being a new instance of a song that had been recorded previousl

The Silent Listener

Hip-hop has changed into something new; today\u27s fan is different from those of old. Those on the vanguard of hip-hop were very much a part of the culture by virtue of living what many of the rappers talked about, but the new demographic of hip-hop is once removed from the subject matter. That is to say that most consumers of hip-hop music cannot relate to much of what is portrayed in the songs, which has made it less important for artists to convey a pertinent message to their audience. Nicholas P. Christy III is a freshman from New York City. His major is currently Undeclared, although he is interested in studying SMAD/Communications. He is a music enthusiast and manages a Hip-Hop production team based in Queens, NY

Pedogenic characteristics of soil in Melur block, Madurai district, Tamil Nadu in India: A case study

Soil is an important source of human life and agricultural production. Studying on the pedon and its site characteristics pave the way for understanding the nature of soils and its utility. A study on pedological characterization of soils in Melur block, Madurai District (Tamil Nadu), was carried out during 2019-2020 using grid sampling with village map/cadastral maps. Soil mapping unit-based soil samples were collected in Chunampoor, Thuvarangulam, Poonjuthi and Veppapadupu and pedons were characterized as per the standard procedure. The results showed that soils were moderately deep to very deep in nature, ranging from 2.5 YR  3/6 to 10YR 4/6. The soil texture varied from sandy clay loam to sandy clay with weak to moderate sub-angular blocky structure. The consistency of soil varied from slightly hard to very hard when dry, very friable to firm when moist, slightly sticky to very sticky and slightly plastic to very plastic in wet condition. The crops viz., paddy, sugarcane, banana, groundnut and vegetables were very suitable for such type of soil of the Madurai district

Weighted Residual Target Proximity Kernel Pursuit Regression based Students Admission Prediction for Higher Education

&nbsp;Education plays a significant role in providing individuals with the knowledge, skills, and tools needed for personal as well as academic growth. Due to the increasing number of higher education graduates, student admissions process is essential for selecting qualified candidates for admission in a universities or colleges. An admissions system with suitable and reliable criteria is important to select students who performing well academically as well as other activities at institutions. Therefore, each university or college needs to use the best possible techniques for analyzing the history of a student's academic performance and other extracurricular activities before admitting them. Education Data Mining (EDM) involves the application of data mining techniques to large educational databases with the aim of discovering useful information. &nbsp;Several machine learning techniques have been developed in this area, but there are issues related to time efficiency and errors in prediction of admissibility for higher education. To address the aforementioned challenge, a novel technique named Weighted Residual Target Proximity Kernel Pursuit Regression (WRTPKPR) has been developed. This technique aims for the accurate prediction of graduate admissions with minimal error by mapping the course to the students based on their interests and CGPA secured. The proposed WRTPKPR technique includes three major phases namely data acquisition, preprocessing, and feature selection for accurate predictive analytics.Top of Form&nbsp; The WRTPKPR technique initiates by collecting information from the dataset during the data acquisition phase. &nbsp;Following data acquisition, the WRTPKPR technique undergoes data preprocessing to transform the input data into a suitable format for accurately predicting whether the student is admissible or not. &nbsp;Two key processes are conducted in the data preprocessing phase, namely, missing data imputation and outlier data detection. In the initial step, the Horvitz–Thompson Weighted imputation method is applied to generate missing data points based on other known data points in the dataset. In the second step, an outlier detection method based on the maximum normalized residual test is employed to identify data points that significantly deviate from the rest of the data point in the dataset. &nbsp;With the preprocessed dataset, the target feature selection process is conducted by applying Kernel Cook's Proximity Projection Pursuit Regression. Based on the selected target features, accurate admission predictions are made for higher education graduates with minimal time consumption. Experimental evaluation considers factors such as admission prediction accuracy, precision, recall, F1-score and admission prediction time. The results demonstrate that the proposed WRTPKPR technique achieves efficient performance outcomes, including higher accuracy, precision, with minimized time