Chemnitz University of Technology

We present a density-functional-based scheme for determining the necessary parameters of common nonorthogonal tight-binding (TB) models within the framework of the linear-combination-of-atomic-orbitals formalism using the local-density approximation (LDA). By only considering two-center integrals the Hamiltonian and overlap matrix elements are calculated out of suitable input densities and potentials rather than fitted to experimental data. We can derive analytical functions for the C-C, C-H, and H-H Hamiltonian and overlap matrix elements. The usual short-range repulsive potential appearing in most TB models is fitted to self-consistent calculations performed within the LDA. The calculation of forces is easy and allows an application of the method to molecular-dynamics simulations. Despite its extreme simplicity, the method is transferable to complex carbon and hydrocarbon systems. The determination of equilibrium geometries, total energies, and vibrational modes of carbon clusters, hydrocarbon molecules, and solid-state modifications of carbon yield results showing an overall good agreement with more sophisticated methods.

The fluorescence of individual nitrogen-vacancy defect centers in diamond was observed with room-temperature scanning confocal optical microscopy. The centers were photostable, showing no detectable change in their fluorescence emission spectrum as a function of time. Magnetic resonance on single centers at room temperature was shown to be feasible. The magnetic resonance spectra revealed marked changes in zero-field splitting parameters among different centers. These changes were attributed to strain-induced differences in the symmetry of the centers.

We mechanically exfoliate mono- and few-layers of the transition metal dichalcogenides molybdenum disulfide, molybdenum diselenide, and tungsten diselenide. The exact number of layers is unambiguously determined by atomic force microscopy and high-resolution Raman spectroscopy. Strong photoluminescence emission is caused by the transition from an indirect band gap semiconductor of bulk material to a direct band gap semiconductor in atomically thin form.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

In this meta-analysis, we give a comprehensive overview of the effects of meditation on psychological variables that can be extracted from empirical studies, concentrating on the effects of meditation on nonclinical groups of adult meditators. Mostly because of methodological problems, almost ¾ of an initially identified 595 studies had to be excluded. Most studies appear to have been conducted without sufficient theoretical background. To put the results into perspective, we briefly summarize the major theoretical approaches from both East and West. The 163 studies that allowed the calculation of effect sizes exhibited medium average effects (r = .28 for all studies and r = .27 for the n = 125 studies from reviewed journals), which cannot be explained by mere relaxation or cognitive restructuring effects. In general, results were strongest (medium to large) for changes in emotionality and relationship issues, less strong (about medium) for measures of attention, and weakest (small to medium) for more cognitive measures. However, specific findings varied across different approaches to meditation (transcendental meditation, mindfulness meditation, and other meditation techniques). Surprisingly, meditation experience only partially covaried with long-term impact on the variables examined. In general, the dependent variables used cover only some of the content areas about which predictions can be made from already existing theories about meditation; still, such predictions lack precision at present. We conclude that to arrive at a comprehensive understanding of why and how meditation works, emphasis should be placed on the development of more precise theories and measurement devices.

Two studies found positive relationships between the ability to manage emotions and the quality of social interactions, supporting the predictive and incremental validity of an ability measure of emotional intelligence, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). In a sample of 118 American college students (Study 1), higher scores on the managing emotions subscale of the MSCEIT were positively related to the quality of interactions with friends, evaluated separately by participants and two friends. In a diary study of social interaction with 103 German college students (Study 2), managing emotions scores were positively related to the perceived quality of interactions with opposite sex individuals. Scores on this subscale were also positively related to perceived success in impression management in social interactions with individuals of the opposite sex. In both studies, the main findings remained statistically significant after controlling for Big Five personality traits.

Variation in avian diversity in space and time is commonly used as a metric to assess environmental changes. Conventionally, such data were collected by expert observers, but passively collected acoustic data is rapidly emerging as an alternative survey technique. However, efficiently extracting accurate species richness data from large audio datasets has proven challenging. Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification. We developed a DNN, called BirdNET, capable of identifying 984 North American and European bird species by sound. Our task-specific model architecture was derived from the family of residual networks (ResNets), consisted of 157 layers with more than 27 million parameters, and was trained using extensive data pre-processing, augmentation, and mixup. We tested the model against three independent datasets: (a) 22,960 single-species recordings; (b) 286 h of fully annotated soundscape data collected by an array of autonomous recording units in a design analogous to what researchers might use to measure avian diversity in a field setting; and (c) 33,670 h of soundscape data from a single high-quality omnidirectional microphone deployed near four eBird hotspots frequented by expert birders. We found that domain-specific data augmentation is key to build models that are robust against high ambient noise levels and can cope with overlapping vocalizations. Task-specific model designs and training regimes for audio event recognition perform on-par with very complex architectures used in other domains (e.g., object detection in images). We also found that high temporal resolution of input spectrograms (short FFT window length) improves the classification performance for bird sounds. In summary, BirdNET achieved a mean average precision of 0.791 for single-species recordings, a F0.5 score of 0.414 for annotated soundscapes, and an average correlation of 0.251 with hotspot observation across 121 species and 4 years of audio data. By enabling the efficient extraction of the vocalizations of many hundreds of bird species from potentially vast amounts of audio data, BirdNET and similar tools have the potential to add tremendous value to existing and future passively collected audio datasets and may transform the field of avian ecology and conservation.

Effect sizes are the currency of psychological research. They quantify the results of a study to answer the research question and are used to calculate statistical power. The interpretation of effect sizes—when is an effect small, medium, or large?—has been guided by the recommendations Jacob Cohen gave in his pioneering writings starting in 1962: Either compare an effect with the effects found in past research or use certain conventional benchmarks. The present analysis shows that neither of these recommendations is currently applicable. From past publications without pre-registration, 900 effects were randomly drawn and compared with 93 effects from publications with pre-registration, revealing a large difference: Effects from the former (median r = .36) were much larger than effects from the latter (median r = .16). That is, certain biases, such as publication bias or questionable research practices, have caused a dramatic inflation in published effects, making it difficult to compare an actual effect with the real population effects (as these are unknown). In addition, there were very large differences in the mean effects between psychological sub-disciplines and between different study designs, making it impossible to apply any global benchmarks. Many more pre-registered studies are needed in the future to derive a reliable picture of real population effects.

Successful coping with technology is relevant for mastering daily life. Based on related conceptions, we propose affinity for technology interaction (ATI), defined as the tendency to actively engage in intensive technology interaction, as a key personal resource for coping with technology. We present the 9-item ATI scale, an economical unidimensional scale that assesses ATI as an interaction style rooted in the construct need for cognition (NFC). Results of multiple studies (n > 1500) showed that the scale achieves good to excellent reliability, exhibits expected moderate to high correlations with geekism, technology enthusiasm, NFC, self-reported success in technical problem-solving and technical system learning success, and also with usage of technical systems. Further, correlations of ATI with the Big Five personality dimensions were weak at most. Based on the results, the ATI scale appears to be a promising tool for research applications such as the characterization of user diversity in system usability tests and the construction of general models of user-technology interaction.

A simplified LCAO-DFT-LDA scheme for calculations of structure and electronic structure of large molecules, clusters, and solids is presented. Forces on the atoms are calculated in a semiempirical way considering the electronic states. The small computational effort of this treatment allows one to perform molecular dynamics (MD) simulations of molecules and clusters up to a few hundred atoms as well as corresponding simulations of condensed systems within the Born-Oppenheimer approximation. The accuracy of the method is illustrated by the results of calculations for a series of small molecules and clusters. © 1996 John Wiley & Sons, Inc.

We show that the computational complexity associated with the density-functional-based determination of infrared intensities and nonresonant Raman scattering activities is the same as that required for vibrational modes. Further, we use extremely large basis sets to determine the intrinsic accuracy for calculating such phenomena within the density-functional theory. We present benchmark calculations on ${\mathrm{CH}}_{4}$, ${\mathrm{H}}_{2}$O, ${\mathrm{C}}_{2}$${\mathrm{H}}_{2}$, ${\mathrm{C}}_{2}$${\mathrm{H}}_{4}$, and ${\mathrm{C}}_{2}$${\mathrm{H}}_{6}$ within both the local-density approximation (LDA) and the generalized gradient approximation (GGA). Tests of the reliability and numerical stability of the theoretical scheme are presented. We show that in order to obtain reliable results, appropriate polarization basis functions and well-converged wave functions are necessary. While most of the Raman spectra predicted by LDA agree very well with experimental data, some of the infrared intensities show substantial errors. The GGA functional overcomes most of these deficiencies, leading to an overall good agreement with experiment. \textcopyright{} 1996 The American Physical Society.

This study reports results from the first International Body Project (IBP-I), which surveyed 7,434 individuals in 10 major world regions about body weight ideals and body dissatisfaction. Participants completed the female Contour Drawing Figure Rating Scale (CDFRS) and self-reported their exposure to Western and local media. Results indicated there were significant cross-regional differences in the ideal female figure and body dissatisfaction, but effect sizes were small across high-socioeconomic-status (SES) sites. Within cultures, heavier bodies were preferred in low-SES sites compared to high-SES sites in Malaysia and South Africa (ds = 1.94-2.49) but not in Austria. Participant age, body mass index (BMI), and Western media exposure predicted body weight ideals. BMI and Western media exposure predicted body dissatisfaction among women. Our results show that body dissatisfaction and desire for thinness is commonplace in high-SES settings across world regions, highlighting the need for international attention to this problem.

The electrochemical performance of MnO 2 nanorods prepared by a precipitation reaction was investigated in 0.5 mol/L Li 2 SO 4, Na 2 SO 4, and K 2 SO 4 aqueous electrolyte solutions. Results show that at the slow scan rates, the nanorods show the largest capacitance (201 F/g) in Li 2 SO 4 electrolyte since the reversible intercalation/deintercalation of Li + in the solid phase produces an additional capacitance besides the capacitance based on the absorption/desorption reaction. At fast scan rates they show the largest capacitance in the K 2 SO 4 electrolyte due to the smallest hydration radius of K +, highest ionic conductivity, and lowest equivalent series resistance (ESR). An asymmetric activated carbon (AC)/K 2 SO 4 /MnO 2 supercapacitor could be cycled reversibly between 0 and 1.8 V with an energy density of 17 Wh/kg at 2 kW/kg, much higher than those of the AC/K 2 SO 4 /AC supercapacitor and AC/Li 2 SO 4 /LiMn 2 O 4 hybrid supercapacitor. Moreover, this supercapacitor exhibits excellent cycling behavior with no more than 6% capacitance loss after 23 000 cycles at 10C rate even when the dissolved oxygen is not removed.

In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a hands-on tutorial illustrating how a priori and post hoc power analyses for the most frequently used two-level models are conducted. We describe how a population model for the power analysis can be specified by using standardized input parameters and how the power analysis is implemented in SIMR, a very flexible power estimation method based on Monte Carlo simulation. Finally, we provide case-sensitive rules of thumb for deriving sufficient sample sizes as well as minimum detectable effect sizes that yield a power ≥ .80 for the effects and input parameters most frequently analyzed by psychologists. For medium variance components, the results indicate that with lower level (L1) sample sizes up to 30 and higher level (L2) sample sizes up to 200, medium and large fixed effects can be detected. However, small L2 direct- or cross-level interaction effects cannot be detected with up to 200 clusters. The tutorial and guidelines should be of help to researchers dealing with multilevel study designs such as individuals clustered within groups or repeated measurements clustered within individuals. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In this tutorial review we describe the recent progress on catalytic microtubular engines fabricated by rolled-up nanotech on polymers. We summarize the technical aspects of the technology and the basic principles that cause the catalytic microengines to self-propel in fuel solutions. The control over speed, directionality and interactions of the microengines to perform tasks such as cargo transportation is also discussed. We compare this technology to other fabrication techniques of catalytic micro-/nanomotors and outline challenges and opportunities for such engines in future studies. Since rolled-up nanotech on polymers can easily integrate almost any type of inorganic material, huge potential and advanced performance such as high speed, cargo delivery, motion control, and dynamic assembly are foreseen--ultimately promising a practical way to construct versatile and intelligent catalytic tubular microrobots.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Why do people listen to music? Over the past several decades, scholars have proposed numerous functions that listening to music might fulfill. However, different theoretical approaches, different methods, and different samples have left a heterogeneous picture regarding the number and nature of musical functions. Moreover, there remains no agreement about the underlying dimensions of these functions. Part one of the paper reviews the research contributions that have explicitly referred to musical functions. It is concluded that a comprehensive investigation addressing the basic dimensions underlying the plethora of functions of music listening is warranted. Part two of the paper presents an empirical investigation of hundreds of functions that could be extracted from the reviewed contributions. These functions were distilled to 129 non-redundant functions that were then rated by 834 respondents. Principal component analysis suggested three distinct underlying dimensions: People listen to music to regulate arousal and mood, to achieve self-awareness, and as an expression of social relatedness. The first and second dimensions were judged to be much more important than the third-a result that contrasts with the idea that music has evolved primarily as a means for social cohesion and communication. The implications of these results are discussed in light of theories on the origin and the functionality of music listening and also for the application of musical stimuli in all areas of psychology and for research in music cognition.

Hierarchical MoS2/polyaniline nanowires, integrating MoS2 nanosheets with conductive polyaniline, serve as prominent anode materials for Li-ion batteries, presenting high capacity and good cyclability. The polyaniline-hybrid structure and hierarchical features significantly promote the Li-storage performance as compared with the bare MoS2, indicating new opportunities for developing electrode nanomaterials. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

We present a novel chemical database for gas-phase astrochemistry. Named the KInetic Database for Astrochemistry
\n(KIDA), this database consists of gas-phase reactions with rate coefficients and uncertainties that will be vetted
\nto the greatest extent possible. Submissions of measured and calculated rate coefficients are welcome, and will
\nbe studied by experts before inclusion into the database. Besides providing kinetic information for the interstellar
\nmedium, KIDA is planned to contain such data for planetary atmospheres and for circumstellar envelopes. Each
\nyear, a subset of the reactions in the database (kida.uva) will be provided as a network for the simulation of the
\nchemistry of dense interstellar clouds with temperatures between 10 K and 300 K. We also provide a code, named
\nNahoon, to study the time-dependent gas-phase chemistry of zero-dimensional and one-dimensional interstellar
\nsources.