Relations of the spaces Ap (Ω) and C p (∂Ω)

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Springer.Let Ω be a Jordan domain in C, J an open arc of ∂Ω and φ : D → Ω a Riemann map from the open unit disk D onto Ω. Under certain assumptions on φ we prove that if a holomorphic function f ∈ H(Ω) extends continuously on Ω ∪ J and p ∈ {1, 2, . . . } ∪ {∞}, then the following equivalence holds: the derivatives f (l) , 1 ≤ l ≤ p, l ∈ N, extend continuously on Ω ∪ J if and only if the function f|J has continuous derivatives on J with respect to the position of orders l, 1 ≤ l ≤ p, l ∈ N. Moreover, we show that for the relevant function spaces, the topology induced by the l−derivatives on Ω, 0 ≤ l ≤ p, l ∈ N, coincides with the topology induced by the same derivatives taken with respect to the position on J

The relation between sustainability performance and stock market returns: An Empirical analysis of the Dow Jones Sustainability Index Europe

This paper investigates the relation between corporate financial performance (CFP) and corporate sustainability performance (CSP). This is done by first analyzing a sample of European stocks that were added to or deleted from the Dow Jones Sustainability Europe Index (DJSI Europe) over the period 2009–2013, and second by analyzing a sample of European stocks that were recognized as industry group leaders in CSP by the DJSI Europe over the same period. The impacts are measured in terms of (abnormal) stock returns. For the first analysis no strong evidence could be found that the announcement of the inclusion and exclusion events has any significant impact on stock return. However, on the day of change (CD) and in the period following CD, index inclusion (exclusion) stocks experience a significant but temporary increase (decrease) in stock return. These results seem to support Harris and Eitan’s (1986) price pressure hypothesis, which postulates that event announcement does not carry information and any shift in demand and hence the corresponding price change is temporary. From the second analysis, on industry group leaders, it can be concluded that the market rewards firms with high CSP. In the period after the day of change, industry group leader stocks experience a permanent and significant positive growth in stock returns. This conclusion can be supported by the resource based perspective, which posits that firms capable of investing heavily in CSP have greater underlying resources which in turn should produce higher financial performance (Alexander and Bucholz, 1978; Waddock and Graves, 1997; Clarkson et al., 2006)

Real Time Sentiment Change Detection of Twitter Data Streams

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime

Microbiology and atmospheric processes: Biological, physical and chemical characterization of aerosol particles

The interest in bioaerosols has traditionally been linked to health hazards for humans, animals and plants. However, several components of bioaerosols exhibit physical properties of great significance for cloud processes, such as ice nucleation and cloud condensation. To gain a better understanding of their influence on climate, it is therefore important to determine the composition, concentration, seasonal fluctuation, regional diversity and evolution of bioaerosols. In this paper, we will review briefly the existing techniques for detection, quantification, physical and chemical analysis of biological particles, attempting to bridge physical, chemical and biological methods for analysis of biological particles and integrate them with aerosol sampling techniques. We will also explore some emerging spectroscopy techniques for bulk and single-particle analysis that have potential for in-situ physical and chemical analysis. Lastly, we will outline open questions and further desired capabilities (e. g., in-situ, sensitive, both broad and selective, on-line, time-resolved, rapid, versatile, cost-effective techniques) required prior to comprehensive understanding of chemical and physical characterization of bioaerosols

Detection of Fake Generated Scientific Abstracts

The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated. Thus, researchers have been working on developing effective systems to identify machine-generated text. In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence and explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text. We analyze the models' performance and address several research questions that rise during the analysis of the results. By conducting this research, we shed light on the capabilities and limitations of Artificial Intelligence generated text

The use of a combined bipedicled axial perforator based fasciocutaneous flap for the treatment of a traumatic diabetic foot wound: a case report

The axial and perforator vascularised fasciocutaneous flaps are reliable and effective treatment methods for covering lower limb post-traumatic, septic, Charcot, and diabetic foot wounds. The authors describe the unique utilisation of a hybrid flap as an axial-perforator flap combination for the treatment of a traumatic diabetic foot wound

Heterogeneous ice nucleation activity of bacteria: new laboratory experiments at simulated cloud conditions

The ice nucleation activities of five different <i>Pseudomonas syringae</i>, <i>Pseudomonas viridiflava</i> and <i>Erwinia herbicola</i> bacterial species and of Snomax™ were investigated in the temperature range between −5 and −15°C. Water suspensions of these bacteria were directly sprayed into the cloud chamber of the AIDA facility of Forschungszentrum Karlsruhe at a temperature of −5.7°C. At this temperature, about 1% of the Snomax™ cells induced immersion freezing of the spray droplets before the droplets evaporated in the cloud chamber. The living cells didn't induce any detectable immersion freezing in the spray droplets at −5.7°C. After evaporation of the spray droplets the bacterial cells remained as aerosol particles in the cloud chamber and were exposed to typical cloud formation conditions in experiments with expansion cooling to about −11°C. During these experiments, the bacterial cells first acted as cloud condensation nuclei to form cloud droplets. Then, only a minor fraction of the cells acted as heterogeneous ice nuclei either in the condensation or the immersion mode. The results indicate that the bacteria investigated in the present study are mainly ice active in the temperature range between −7 and −11°C with an ice nucleation (IN) active fraction of the order of 10<sup>−4</sup>. In agreement to previous literature results, the ice nucleation efficiency of Snomax™ cells was much larger with an IN active fraction of 0.2 at temperatures around −8°C

EMT Factors and Metabolic Pathways in Cancer.

The epithelial-mesenchymal transition (EMT) represents a biological program during which epithelial cells lose their cell identity and acquire a mesenchymal phenotype. EMT is normally observed during organismal development, wound healing and tissue fibrosis. However, this process can be hijacked by cancer cells and is often associated with resistance to apoptosis, acquisition of tissue invasiveness, cancer stem cell characteristics, and cancer treatment resistance. It is becoming evident that EMT is a complex, multifactorial spectrum, often involving episodic, transient or partial events. Multiple factors have been causally implicated in EMT including transcription factors (e.g., SNAIL, TWIST, ZEB), epigenetic modifications, microRNAs (e.g., miR-200 family) and more recently, long non-coding RNAs. However, the relevance of metabolic pathways in EMT is only recently being recognized. Importantly, alterations in key metabolic pathways affect cancer development and progression. In this review, we report the roles of key EMT factors and describe their interactions and interconnectedness. We introduce metabolic pathways that are involved in EMT, including glycolysis, the TCA cycle, lipid and amino acid metabolism, and characterize the relationship between EMT factors and cancer metabolism. Finally, we present therapeutic opportunities involving EMT, with particular focus on cancer metabolic pathways

A Role for Noncoding Variation in Schizophrenia

A large portion of common variant loci associated with genetic risk for schizophrenia reside within noncoding sequence of unknown function. Here, we demonstrate promoter and enhancer enrichment in schizophrenia variants associated with expression quantitative trait loci (eQTL). The enrichment is greater when functional annotations derived from the human brain are used relative to peripheral tissues. Regulatory trait concordance analysis ranked genes within schizophrenia genome-wide significant loci for a potential functional role, based on colocalization of a risk SNP, eQTL, and regulatory element sequence. We identified potential physical interactions of noncontiguous proximal and distal regulatory elements. This was verified in prefrontal cortex and -induced pluripotent stem cell-derived neurons for the L-type calcium channel (CACNA1C) risk locus. Our findings point to a functional link between schizophrenia-associated noncoding SNPs and 3D genome architecture associated with chromosomal loopings and transcriptional regulation in the brain