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Organizational commitments to equality change how people view women’s and men’s professional success
(2024)
To address women’s underrepresentation in high-status positions, many organizations have committed to gender equality. But is women’s professional success viewed less positively when organizations commit to women’s advancement? Do equality commitments have positive effects on evaluations of successful men? We fielded a survey experiment with a national probability sample in Germany (N = 3229) that varied employees’ gender and their organization’s commitment to equality. Respondents read about a recently promoted employee and rated how decisive of a role they thought intelligence and effort played in getting the employee promoted from 1 “Not at all decisive” to 7 “Very decisive” and the fairness of the promotion from 1 “Very unfair” to 7 “Very fair.” When organizations committed to women’s advancement rather than uniform performance standards, people believed intelligence and effort were less decisive in women’s promotions, but that intelligence was more decisive in men’s promotions. People viewed women’s promotions as least fair and men’s as most fair in organizations committed to women’s advancement. However, women’s promotions were still viewed more positively than men’s in all conditions and on all outcomes, suggesting people believed that organizations had double standards for success that required women to be smarter and work harder to be promoted, especially in organizations that did not make equality commitments.
“Ick bin een Berlina”
(2024)
Background: Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents. Some studies suggest that people prefer robots to speak in the user’s dialect, while others indicate a preference for different dialects.
Methods: Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (Mage = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence.
Results: We found a positive relationship between participants’ self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants’ dialect proficiency, dialect performance and their assessment of robot’s competence for the standard German-speaking robot. Participants’ age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot’s competence positively predicted its trustworthiness.
Discussion: Our results inform the design of social robots and emphasize the importance of device control in online experiments.
Fast Holocene slip and localized strain along the Liquiñe-Ofqui strike-slip fault system, Chile
(2021)
In active tectonic settings dominated by strike-slip kinematics, slip partitioning across subparallel faults is a common feature; therefore, assessing the degree of partitioning and strain localization is paramount for seismic hazard assessments. Here, we estimate a slip rate of 18.8 +/- 2.0 mm/year over the past 9.0 +/- 0.1 ka for a single strand of the Liquirie-Ofqui Fault System, which straddles the Main Cordillera in Southern Chile. This Holocene rate accounts for similar to 82% of the trench-parallel component of oblique plate convergence and is similar to million-year estimates integrated over the entire fault system. Our results imply that strain localizes on a single fault at millennial time scale but over longer time scales strain localization is not sustained. The fast millennial slip rate in the absence of historical Mw> 6.5 earthquakes along the Liquine-Ofqui Fault System implies either a component of aseismic slip or Mw similar to 7 earthquakes involving multi-trace ruptures and > 150-year repeat times. Our results have implications for the understanding of strike-slip fault system dynamics within volcanic arcs and seismic hazard assessments.
After initial detection of target archival DNA of a 116-year-old syntype specimen of the smooth lantern shark, Etmopterus pusillus, in a single-stranded DNA library, we shotgun-sequenced additional 9 million reads from this same DNA library. Sequencing reads were used for extracting mitochondrial sequence information for analyses of mitochondrial DNA characteristics and reconstruction of the mitochondrial genome. The archival DNA is highly fragmented. A total of 4599 mitochondrial reads were available for the genome reconstruction using an iterative mapping approach. The resulting genome sequence has 12 times coverage and a length of 16 741 bp. All 37 vertebrate mitochondrial loci plus the control region were identified and annotated. The mitochondrial NADH2 gene was subsequently used to place the syntype haplotype in a network comprising multiple E. pusillus samples from various distant localities as well as sequences from a morphological similar species, the shortfin smooth lantern shark Etmopterus joungi. Results confirm the almost global distribution of E. pusillus and suggest E. joungi to be a junior synonym of E. pusillus. As mitochondrial DNA often represents the only available reference information in non-model organisms, this study illustrates the importance of mitochondrial DNA from an aged, wet collection type specimen for taxonomy.
Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra.
Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins.
Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge.
Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments.
We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task.
Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
Inorganic perovskite solar cells show excellent thermal stability, but the reported power conversion efficiencies are still lower than for organic-inorganic perovskites. This is mainly caused by lower open-circuit voltages (V(OC)s). Herein, the reasons for the low V-OC in inorganic CsPbI2Br perovskite solar cells are investigated. Intensity-dependent photoluminescence measurements for different layer stacks reveal that n-i-p and p-i-n CsPbI2Br solar cells exhibit a strong mismatch between quasi-Fermi level splitting (QFLS) and V-OC. Specifically, the CsPbI2Br p-i-n perovskite solar cell has a QFLS-e center dot V-OC mismatch of 179 meV, compared with 11 meV for a reference cell with an organic-inorganic perovskite of similar bandgap. On the other hand, this study shows that the CsPbI2Br films with a bandgap of 1.9 eV have a very low defect density, resulting in an efficiency potential of 20.3% with a MeO-2PACz hole-transporting layer and 20.8% on compact TiO2. Using ultraviolet photoelectron spectroscopy measurements, energy level misalignment is identified as a possible reason for the QFLS-e center dot V-OC mismatch and strategies for overcoming this V-OC limitation are discussed. This work highlights the need to control the interfacial energetics in inorganic perovskite solar cells, but also gives promise for high efficiencies once this issue is resolved.
A detailed investigation of the energy levels of perylene-3,4,9,10-tetracarboxylic tetraethylester as a representative compound for the whole family of perylene esters was performed. It was revealed via electrochemical measurements that one oxidation and two reductions take place. The bandgaps determined via the electrochemical approach are in good agreement with the optical bandgap obtained from the absorption spectra via a Tauc plot. In addition, absorption spectra in dependence of the electrochemical potential were the basis for extensive quantum-chemical calculations of the neutral, monoanionic, and dianionic molecules. For this purpose, calculations based on density functional theory were compared with post-Hartree-Fock methods and the CAM-B3LYP functional proved to be the most reliable choice for the calculation of absorption spectra. Furthermore, spectral features found experimentally could be reproduced with vibronic calculations and allowed to understand their origins. In particular, the two lowest energy absorption bands of the anion are not caused by absorption of two distinct electronic states, which might have been expected from vertical excitation calculations, but both states exhibit a strong vibronic progression resulting in contributions to both bands.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.