LinkedIn Blunders: A Mixed Method Study of College Students’ Profiles

Although a significant need exists for college students to market their job skills effectively to potential employers, no prior research systematically analyzed the quality of information included in college students’ LinkedIn profiles. This study used a marketing framework to evaluate the effectiveness of information in LinkedIn profiles posted by current and former community college students. The mixed method study analyzed 340 publicly available LinkedIn profiles for students who reported attending 89 community colleges in the United States. The results suggest many college students may not understand how to use a LinkedIn profile to market their skills to potential employers. Key sections were often left blank and profiles failed to communicate students’ unique value proposition. Content analysis revealed 75% of profiles contained an experience section with poor to below average descriptions. Comparative analysis found profiles for unemployed individuals and those seeking a new position were significantly worse than profiles for their employed counterparts. Additionally, LinkedIn profiles for students from large community colleges had significantly more writing errors than profiles for students attending medium-size community colleges. After discussing implications of the research, recommendations based on the study’s results are suggested for career service staff, educators, and students

Experimental data of cathodes manufactured in a convective dryer at the pilot-plant scale, and charge and discharge capacities of half-coin lithium-ion cells.

Megtec Systems pilot-plant scale continuous convective coater. The data was generated as part of an experimental design involving the following coating-drying process variables and ranges: comma bar gap, 80-140 µm; web speed, 0.5-1.5 m/min; coating ratio, 110-150%; drying temperature, 85-110 °C and drying air speed, 5-15 m/s. The manufacturing data include pre-calendered coating thickness, mass loading dry and wet, pre-calendered porosity, spatial autocorrelation and join counting (SAJC) -score for carbon and for fluorine, cell thickness, coating weight and porosity of 15 different electrode coatings and 45 half-coin cells. The electrochemical data was obtained at 25 °C in a Maccor 4000 series battery cycler and consists of charge and discharge capacities at C/20, C/5, C/2, 1C, 2C, 5C and 10C C-rates. Discharge gravimetric and volumetric capacities, rate performance (at 5C:0.2C) and first cycle loss data is also reported. Details of the experimental design and a comprehensive analysis of the data can be found in the co-submitted manuscript (Román-Ramírez et al., 2021). Additional collected data not used in Román-Ramírez et al. (2021) is reported in the present manuscript and include visual observations of coating defects, rheological properties of the electrode slurries (solid content, viscosity, coating shear rate and viscosity at coating shear rate), room temperature and room humidity during the coatings and first cycle loss of the coin cells. Raw and analyzed data is made available. The reported data can be used to extend the analysis reported in Román-Ramírez et al. (2021), and for the comparison of relevant data obtained at different manufacturing scales. [Abstract copyright: © 2021 The Author(s). Published by Elsevier Inc.

Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics

The large number of parameters involved in each step of Li-ion electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final product. Optimization of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint. Data-driven models offer a solution for manufacturing optimization problems and underpin future aspirations for manufacturing volumes. This study combines machine-learning approaches with the experimental data to build data-driven models for predicting final battery performance. The models capture the interdependencies between the key parameters of electrode manufacturing, its structural features, and the electrical performance characteristics of the associated Li-ion cells. The methodology here is based on a set of designed experiments conducted in a controlled environment, altering electrode coating control parameters of comma bar gap, line speed and coating ratio, obtaining the electrode structural properties of active material mass loading, thickness, and porosity, extracting the manufactured half-cell characteristics at various cycling conditions, and finally building models for interconnectivity studies and predictions. Investigating and quantifying performance predictability through a systems' view of the manufacturing process is the main novelty of this paper. Comparisons between different machine-learning models, analysis of models’ performance with a limited number of inputs, analysis of robustness to measurement noise and data-size are other contributions of this study. The results suggest that, given manufacturing parameters, the coated electrode properties and cell characteristics can be predicted with about 5% and 3% errors respectively. The presented concepts are believed to link the manufacturing at lab-scale to the pilot-line scale and support smart, optimised, and clean production of electrodes for high-quality Li-ion batteries

Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems. However, it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process. Complicated production processes involving mechanical, chemical, and electrical operations makes the predictability of the manufacturing process a challenge, hence the process is optimised through trial and error rather systematic simulation. To establish an in-depth understanding of the interconnected processes and manufacturing parameters, this paper combines data-mining techniques and real production to offer a method for the systematic analysis, understanding and improving the Li-ion battery electrode manufacturing chain. The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only, it covers both of the cathode and anode case studies. Furthermore, it is based on real manufacturing data, proposes a systematic design of experiment method for generating high quality and representative data, and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode. Through this study, machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters. Moreover, the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models. The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain

Seniority number in spin-adapted spaces and compactness of configuration interaction wave functions

This work extends the concept of seniority number, which has been widely used for classifying N-electron Slater determinants, to wave functions of N electrons and spin S, as well as to N-electron spin-adapted Hilbert spaces. We propose a spin-free formulation of the seniority number operator and perform a study on the behavior of the expectation values of this operator under transformations of the molecular basis sets. This study leads to propose a quantitative evaluation for the convergence of the expansions of the wave functions in terms of Slater determinants. The non-invariant character of the seniority number operator expectation value of a wave function with respect to a unitary transformation of the molecular orbital basis set, allows us to search for a change of basis which minimizes that expectation value. The results found in the description of wave functions of selected atoms and molecules show that the expansions expressed in these bases exhibit a more rapid convergence than those formulated in the canonical molecular orbital bases and even in the natural orbital ones.Fil: Alcoba, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Torre, Alicia. Universidad del País Vasco. Facultad de Ciencia y Tecnología. Departamento de Química Física; España;Fil: Lain, Luis. Universidad del País Vasco. Facultad de Ciencia y Tecnología. Departamento de Química Física; España;Fil: Massaccesi, Gustavo Ernesto. Universidad de Buenos Aires. Ciclo Básico Común; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaFil: Oña, Ofelia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

Spontaneous cytokine production in children according to biological characteristics and environmental exposures.

BACKGROUND: Environmental factors are likely to have profound effects on the development of host immune responses, with serious implications for infectious diseases and inflammatory disorders such as asthma. OBJECTIVE: This study was designed to investigate the effects of environmental exposures on the cytokine profile of children. METHODS: The study involved measurement of T helper (Th) 1 (interferon-gamma), 2 [interleukin (IL)-5 and IL-13], and the regulatory cytokine IL-10 in unstimulated peripheral blood leukocytes from 1,376 children 4-11 years of age living in a poor urban area of the tropics. We also assessed the impact of environmental exposures in addition to biological characteristics recorded at the time of blood collection and earlier in childhood (0-3 years before blood collection). RESULTS: The proportion of children producing IL-10 was greater among those without access to drinking water [p < 0.05, chi-square test, odds ratio (OR) = 1.67]. The proportion of children producing IL-5 and IL-10 (OR = 10.76) was significantly greater in households that had never had a sewage system (p < 0.05, trend test). CONCLUSIONS: These data provide evidence for the profound effects of environmental exposures in early life as well as immune homeostasis in later childhood. Decreased hygiene (lack of access to clean drinking water and sanitation) in the first 3 years of life is associated with higher spontaneous IL-10 production up to 8 years later in life

Configuration interaction wave functions: A seniority number approach

This work deals with the configuration interaction method when an N-electron Hamiltonian is projected on Slater determinants which are classified according to their seniority number values. We study the spin features of the wave functions and the size of the matrices required to formulate states of any spin symmetry within this treatment. Correlation energies associated with the wave functions arising from the seniority-based configuration interaction procedure are determined for three types of molecular orbital basis: canonical molecular orbitals, natural orbitals, and the orbitals resulting from minimizing the expectation value of the N-electron seniority number operator. The performance of these bases is analyzed by means of numerical results obtained from selected N-electron systems of several spin symmetries. The comparison of the results highlights the efficiency of the molecular orbital basis which minimizes the mean value of the seniority number for a state, yielding energy values closer to those provided by the full configuration interaction procedure.Fil: Alcoba, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Torre, Alicia. Universidad del Pais Vasco; EspañaFil: Lain, Luis . Universidad del Pais Vasco; EspañaFil: Massaccesi, Gustavo Ernesto. Universidad de Buenos Aires. Ciclo Básico Común; ArgentinaFil: Oña, Ofelia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

MIC evaluation and testing for the Yucca Mountain repository

The U.S. Department of Energy is engaged in a suitability study for a potential deep geological repository at Yucca Mountain (YM), Nevada, for the containment and storage of high-level nuclear waste. There is growing awareness that biotic factors could affect the integrity of the repository directly through microbially induced corrosion (MIC) of waste package (WP) materials and other repository elements. A program to determine the degree that microorganisms, especially bacteria, influence the corrosion of waste package materials has therefore been undertaken. These studies include testing candidate waste package materials for their susceptibility to MIC, and also seek to determine rates of biocorrosion under varying environmental conditions, as well as predict rates of waste package corrosion over the long term. Previous characterization of bacterial isolates derived from YM geologic material showed that many possessed biochemical activities associated with MIC, 2. Various Yucca Mountain microbes demonstrated the abilities to oxidize iron, reduce sulfate to sulfide, produce acids, and generate exopolysaccharides (or `slime`). Table 1 summarizes previously characterized YM organisms and their associated relevant activities. A subset of the characterized YM bacteria were spread on WP alloy coupons in systems designed to collect polarization resistance (Rp) data for corrosion rate calculations, and to determine cathodic and anodic potentiodynamic polarization to assess corrosion mechanisms. Coupons inoculated with bacteria were compared to those that remained sterile, to determine the bacterial contribution to overall corrosion rates