Scoring a forced-choice image-based assessment of personality: A comparison of machine learning, regression, and summative approaches

Recent years have seen rapid advancements in the way that personality is measured, resulting in a number of innovative predictive measures being proposed, including using features extracted from videos and social media profiles. In the context of selection, game- and image-based assessments of personality are emerging, which can overcome issues like social desirability bias, lack of engagement and low response rates that are associated with traditional self-report measures. Forced-choice formats, where respondents are asked to rank responses, can also mitigate issues such as acquiescence and social desirability bias. Previously, we reported on the development of a gamified forced-choice image-based assessment of the Big Five personality traits created for use in selection, using Lasso regression for the scoring algorithms. In this study, we compare the machine-learning-based Lasso approach to ordinary least squares regression, as well as the summative approach that is typical of forced-choice formats. We find that the Lasso approach performs best in terms of generalisability and convergent validity, although the other methods have greater discriminate validity. We recommend the use of predictive Lasso regression models for scoring forced-choice image-based measures of personality over the other approaches. Potential further studies are suggested

Studies on some free living protozoan from Salim Ali lake. Aurangabad

Protozoa are one-celled animals found worldwide in most habitats. Most species are free living, but all higher animals are infected with one or more species of protozoa. Infections range from asymptomatic to life threatening, depending on the species and strain of the parasite and the resistance of the host. Present study deals with the free living protozoa inhabiting fresh water bodies. The factor which influence their distribution and population in a given water bodies temperature, light, pH, chemical composition, acidity, and amount of food present in water and degree of adaptability of the individual protozoa to various environmental changes.&nbsp

Accurate <i>ab initio</i> ro-vibronic spectroscopy of the X²&#8719; CCN radical using explicitly correlated methods

Explicitly correlated CCSD(T)-F12b calculations have been carried out with systematic sequences of correlation consistent basis sets to determine accurate near-equilibrium potential energy surfaces for the X&lt;sup&gt;2&lt;/sup&gt;&#8719; and a&lt;sup&gt;4&lt;/sup&gt;&#931;&lt;sup&gt;−&lt;/sup&gt; electronic states of the CCN radical. After including contributions due to core correlation, scalar relativity, and higher order electron correlation effects, the latter utilizing large-scale multireference configuration interaction calculations, the resulting surfaces were employed in variational calculations of the ro-vibronic spectra. These calculations also included the use of accurate spin-orbit and dipole moment matrix elements. The resulting ro-vibronic transition energies, including the Renner-Teller sub-bands involving the bending mode, agree with the available experimental data to within 3 cm&lt;sup&gt;−1&lt;/sup&gt; in all cases. Full sets of spectroscopic constants are reported using the usual second-order perturbation theory expressions. Integrated absorption intensities are given for a number of selected vibronic band origins. A computational procedure similar to that used in the determination of the potential energy functions was also utilized to predict the formation enthalpy of CCN, &#916;H&lt;sub&gt;f&lt;/sub&gt;(0K) = 161.7 &#177; 0.5 kcal/mol

Synchrotron radiation photoemission spectroscopy of the oxygen modified CrCl3 surface

We investigate the experimentally challenging CrCl3 surface by photon energy dependent photoemission (PE). The core and valence electrons after cleavage of a single crystal, either in a ultra-high vacuum (UHV) or in air, are studied by keeping the samples at 150 degrees C, aiming at confirming the atomic composition with respect to the expected bulk atomic structure. A common spectroscopic denominator revealed by data is the presence of a stable, but only partially ordered Cl-O-Cr surface. The electronic core levels (Cl 2p, Cr 2p and 3p), the latter ones of cumbersome component determination, allowed us to quantify the electron charge transfer to the Cr atom as a net result of this modification and the increased exchange interaction between metal and ligand atoms. In particular, the analysis of multiplet components by the CMT4XPS code evidenced the charge transfer to be favored, and similarly the reduced crystal field due to the established polarization field. Though it is often claimed that a significant amount of Cl and Cr atomic vacancies has to be included, such a possibility can be excluded on the basis of the sign and the importance of the shift in the binding energy of core level electrons. The present methodological approach can be of great impact to quantify the structure of ordered sub-oxide phases occurring in mono or bi-layer Cr trihalides

Biomechanical modeling of human-robot accident scenarios: a computational assessment for heavy-payload-capacity robots

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing with increased productivity and flexibility. Workspaces are being transformed into fully shared spaces for performing tasks during human-robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The next technological epoch of Industry 5.0 has a heavy focus on human well-being, with humans and robots operating in synergy. However, the reluctance to adopt heavy-payload-capacity robots due to safety concerns is a major hurdle. Therefore, the importance of analyzing the level of injury after impact can never be neglected for the safety of workers and for designing a collaborative environment. In this study, quasi-static and dynamic analyses of accidental scenarios during HRC are performed for medium-and low-payload-capacity robots according to the conditions given in ISO TS 15066 to assess the threshold level of injury and pain, and is subsequently extended for high speeds and heavy payloads for collaborative robots. For this purpose, accidental scenarios are simulated in ANSYS using a 3D finite element model of an adult human index finger and hand, composed of cortical bone and soft tissue. Stresses and strains in the bone and tissue, and contact forces and energy transfer during impact are studied, and contact speed limit values are estimated. It is observed that heavy-payload-capacity robots must be restricted to 80% of the speed limit of low-payload-capacity robots. Biomechanical modeling of accident scenarios offers insights and, therefore, gives confidence in the adoption of heavy-payload robots in factories of the future. The analysis allows for prediction and assessment of different hypothetical accidental scenarios in HRC involving high speeds and heavy-payload-capacity robots

Test-Retest Reliability and Convergent Validity of Piezoelectric Force Plate Measures of Single-Leg Sit-to-Stand Performance in Trained Adults

The single-leg sit-to-stand (STS) test has emerged as a promising method of assessing lower-limb functional strength and asymmetry. However, the reliability of its performance parameters on a force plate has not been explored. This study examined the test-retest reliability and convergent validity of the single-leg STS test performed on a piezoelectric-based force plate in trained subjects. Thirty trained male adults (age: 21.4 ± 1.7 years) performed 3 separate single-leg STS days of testing to assess both intraday and interday reliability. Performance parameters included STS time, ground reaction force (GRF), and center of pressure (CoP) sway velocity. The relationship between single-leg STS parameters and unilateral countermovement jump (CMJ) variables was assessed for convergent validity. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) were calculated for reliability analyses, and convergent validity was assessed with Spearman's correlation coefficient (ρ). In the dominant leg, single-leg performance parameters showed moderate-to-excellent intraday reliability (ICC = 0.65-0.90, CV = 4.3-11.2%) and moderate interday reliability (ICC = 0.54-0.74, CV = 5.8-13.5%). In the nondominant leg, all single-leg STS performance parameters showed good intraday (ICC = 0.79-0.86, CV = 3.8-9.8%) and interday reliability (ICC = 0.75-0.82, CV = 4.6-9.7%). STS times in the dominant and nondominant legs were inversely related to unilateral CMJ velocity (ρ = -0.47 and -0.38, respectively). CoP sway velocity in the nondominant leg showed positive correlations with unilateral CMJ power and velocity (ρ = 0.38 and 0.54, respectively). In conclusion, the force plate-based single-leg STS test provides reliable measures of STS time, GRF, and CoP sway velocity in trained adults and could be used to assess lower-limb function and asymmetry.Terapia y Rehabilitació

Validity and Test–Retest Reliability of a Smartphone App for Measuring Rising Time, Velocity, Power, and Inter-Limb Asymmetry During Single-Leg Sit-to-Stand Test in Female-Trained Athletes

The aim was to determine the validity and test – retest reliability of the Sit to Stand App variables (rising time, vertical velocity, and power) for measuring single-leg sit-to-stand (STS) test compared to those derived from ground reaction force data. Twenty-seven female athletes performed the single-leg STS test over three consecutive sessions simultaneously recorded with a force plate and the smartphone app. Validity was assessed with Pearson’s correlation coefficient, intra-class correlation coefficient (ICC), and percentage error. Reliability was assessed with the ICC. Almost perfect correlations (r ≥ 0.95) and excellent agreement (ICC = 0.96–0.97) between the app and force plate measures with low percentage errors (≤7.3%) were found. The app showed good-to- excellent reliability (ICC = 0.88–0.92) with no differences in inter-limb asymmetry over three ses- sions. The app was highly valid and reliable for measuring single-leg STS performance. Practitioners can use this app to assess lower-limb performance in female athletes.Terapia y Rehabilitació

Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning

Current trends in manufacturing indicate that optimised decision making using new state-of-the-art machine learning (ML) technologies will be used. ML is a versatile technique that rapidly and accurately generates new insights from multifactorial data. The ML approach has been applied to a perovskite solar cell (PSC) database to elucidate trends in stability and forecast the stability of new configurations. A database consisting of 6038 entries of device characteristics, performance, and stability data was utilised, and a sequential minimal optimisation regression (SMOreg) model was employed to determine the most influential factors governing solar cell stability. When considering sub-sections of data, it was found that pin-device architectures provided the best model fittings with a training correlation efficiency of 0.963, compared to 0.699 for all device architectures. By establishing models for each PSC architecture, the analysis allows the identification of materials that can lead to improvements in stability. This paper also attempts to summarise some key challenges and trends in the current research methodologies