| 1. |
- Angelopoulou, Michailia, et al.
(författare)
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Directly immersible silicon photonic probes : Application to rapid SARS-CoV-2 serological testing br
- 2022
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Ingår i: Biosensors & bioelectronics. - : Elsevier BV. - 0956-5663 .- 1873-4235. ; 215
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Tidskriftsartikel (refereegranskat)abstract
- Silicon photonic probes based on broad-band Mach-Zehnder interferometry are explored for the first time as directly immersible immunosensors alleviating the need for microfluidics and pumps. Each probe includes two U- shaped waveguides allowing light in- and out-coupling from the same chip side through a bifurcated fiber and a mechanical coupler. At the opposite chip side, two Mach-Zehnder interferometers (MZI) are located enabling real-time monitoring of binding reactions by immersion of this chip side into a sample. The sensing arm windows of the two MZIs have different length resulting in two distinct peaks in the Fourier domain, the phase shift of which can be monitored independently through Fast Fourier Transform of the output spectrum. The photonic probes analytical potential was demonstrated through detection of antibodies against SARS-CoV-2 in human serum samples. For this, one MZI was functionalized with the Receptor Binding Domain (RBD) of SARS-CoV-2 Spike 1 protein, and the other with bovine serum albumin to serve as reference. The biofunctionalized probes were immersed for 10 min in human serum sample and then for 5 min in goat anti-human IgG Fc specific antibody solution. Using a humanized rat antibody against SARS-CoV-2 RBD, a detection limit of 20 ng/mL was determined. Analysis of human serum samples indicated that the proposed sensor discriminated completely non- infected/non-vaccinated from vaccinated individuals, and the antibodies levels determined correlated well with those determined in the same samples by ELISA. These results demonstrated the potential of the proposed sensor to serve as an efficient tool for expeditious point-of-care testing
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| 2. |
- Brütt, Katharina, et al.
(författare)
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Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs
- 2023
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Ingår i: Proceedings of the National Academy of Sciences - PNAS. - : National Academy of Sciences. - 1091-6490 .- 0027-8424. ; 120:23
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Tidskriftsartikel (refereegranskat)abstract
- Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.
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| 3. |
- Rousopoulou, Vaia, et al.
(författare)
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Cognitive analytics platform with AI solutions for anomaly detection
- 2022
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Ingår i: Computers in industry (Print). - : Elsevier BV. - 0166-3615 .- 1872-6194. ; 134, s. 103555-103555
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Tidskriftsartikel (refereegranskat)abstract
- This work presents a cognitive analytics platform for anomaly detection which is capable to handle, analyze and exploit resourcefully machine data from a shop-floor of factory, so as to support the emerging and growing needs of manufacturing industry. The introduced system contributes to industrial digitization and creation of smart factories by providing a generic platform which is a complete solution supporting standards-based factory connectivity, data management, various AI models training and comparisons, live predictions and real-time visualizations. The proposed system is built on a micro-service architecture, in order to be extendable and adaptive over time, and contains three core modules, the Data Acquisition, the Knowledge Management and the Predictive maintenance, which contribute to machine failure prediction and activate predictive maintenance procedures, to efficient production schemes and decision making, to monitor anomalies and handle unforeseen conditions, to predict future behaviours on time series etc. The proposed platform utilizes continuous re-training mechanisms enabling a self learning approach for the delivery of AI solutions, usable also for various production data, guaranteeing the quality of results without continuous monitoring and human-resources allocation for AI models’ retraining. This cognitive platform is supported by machine learning techniques and deep learning architectures in order to achieve the desired performance in the management of factory processes and needs. All the information generated by the proposed platform is provided to the end user through a user interface that utilizes advanced visualization techniques.
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