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Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.
Solar chimney power plants (SCPPs) are encouraging sustainable energy sources due to their low cost, abundance, low maintenance, and eco-friendliness. However, despite significant efforts to optimize SCPP design, their efficiency and power generation capabilities remain limited. Researchers have explored modifications in plant geometry and hybridization to improve efficiency. Despite extensive work in this area, commercialization of SCPPs has not yet been achieved. Most of the research is numerical and may differ from real-world practical use. The number of experimental studies is also relatively small. To facilitate commercialization, further investigation with practical and feasible dimensions is required. This comprehensive review paper aims to provide an in-depth analysis of experimental approaches and advancements in the field of SCPPs. The paper begins with an introduction, highlighting the background, significance, and objectives of the review. It provides an overview of the plants, discussing their principles and operation as innovative renewable energy systems. The historical development and evolution of solar chimneys are explored, shedding light on their progression over time. Case studies of operational hybrid SCPPs are examined to showcase real-world applications and performance. The paper also addresses environmental impacts and sustainability considerations associated with SCPPs. Furthermore, recommendations for future research and development in this field are provided to guide researchers and industry professionals. This study focuses on the possibility of commercialization of both standalone and hybrid SCPPs.
The structure of psychopathy is conceptualized differently in various models and no consensus has yet been reached. This study aimed to further clarify psychopathy’s content and structure by validating the German Comprehensive Assessment of Psychopathic Personality—Self-Report (CAPP-SR). For this purpose, we used a sample of n = 458 participants. The CAPP’s six factors were replicated in an exploratory factor analysis. Further confirmatory analysis revealed recommendations for optimizing the CAPP. Thus, both an optimized version and the original CAPP-SR were examined. The construct validity of both versions was then examined using a different self-rating instrument for psychopathy, the Triarchic Psychopathy Measure. The implications of the findings and further research directions are discussed.
The Gezi Park protests in Istanbul (Türkiye) gained worldwide attention in 2013. Both men and women took part in the protests, which were heavily cracked down on by the government. The present study examined 273 Turkish women’s attitudes and motivations for taking part in the protests. The results show that the following variables had a significant impact on protest participation: lifestyle threats posed by religious values/norms and by the government; feelings of marginalization as a woman; political dissatisfaction; gender discrimination; and affiliations with feminism. Regarding the impact of attitudes on women’s political participation and discrimination, this study provides insights into the state of research on gender discrimination and feministic identity.
Previous research suggests that noise sensitivity is related to inefficient auditory processing that might increase the mental load of noise and affect noise evaluation. This assumption was tested in an experiment using a dual-task paradigm with a visual primary task and an auditory secondary task. Results showed that participants' noise sensitivity was positively correlated with mental effort. Furthermore, mental effort mediated the effect of noise sensitivity on loudness and unpleasantness ratings. The results thus support the idea that noise sensitivity is related to increased mental effort and difficulties in filtering auditory information and that situational factors should be considered.
Urban air pollution has become a pressing challenge in recent times, demanding innovative solutions. This review delves into the potential of Solar Chimney Power Plants (SCPPs) as a sustainable approach to mitigating air pollution. The idea of mitigation of pollution may be an added advantage to the use of SCPPs in practice. Recent advancements, such as the integration of photocatalytic reactors (PCRs) for the elimination of greenhouse gases (GHGs), emphasizing the importance of addressing non-CO2 GHGs like CH4 and N2O are analyzed. The novelty of this review is that it not only focuses on the shifting and removal of particulate matter but also on the removal of greenhouse gases. Numerous case studies, ranging from filter-equipped SCPPs to Solar-Assisted Large-Scale Cleaning Systems (SALSCSs), are reviewed, providing a comprehensive understanding of their design, performance, and potential benefits. This review serves as a guide for researchers and policymakers, emphasizing the need for multifaceted approaches to address the intricate nexus of air pollution, renewable energy generation, and climate change mitigation.
The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.
This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (hi = 1.85 to 0.1 m), chimney inlet diameter (dch = 10.16 to 55.88 m), and chimney outlet diameter (do = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of hi = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5dch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.
