Performance testing of lidar receivers

In addition to the considerations about the different types of noise sources, dynamic range, and linearity of a lidar receiver, one requires information about the pulse shape retaining capabilities of the receiver. For this purpose, relatively precise information about the height resolution as well as the recovery time of the receiver, due both to large transients and to fast changes in the received signal, is required. As more and more analog receivers using fast analog to digital converters and transient recorders will be used in the future lidar systems, methods to test these devices are essential. The method proposed for this purpose is shown. Tests were carried out using LCW-10, LT-20, and FTVR-2 as optical parts of the optical pulse generator circuits. A commercial optical receiver, LNOR, and a transient recorder, VK 220-4, were parts of the receiver system

Reasoning with concept diagrams about antipatterns

Ontologies are notoriously hard to define, express and reason about. Many tools have been developed to ease the debugging and the reasoning process with ontologies, however they often lack accessibility and formalisation. A visual representation language, concept diagrams, was developed for expressing and reasoning about ontologies in an accessible way. Indeed, empirical studies show that concept diagrams are cognitively more accessible to users in ontology debugging tasks. In this paper we answer the question of “ How can concept diagrams be used to reason about inconsistencies and incoherence of ontologies?”. We do so by formalising a set of inference rules for concept diagrams that enables stepwise verification of the inconsistency and/or incoherence of a set of ontology axioms. The design of inference rules is driven by empirical evidence that concise (merged) diagrams are easier to comprehend for users than a set of lower level diagrams that offer a one-to-one translation of OWL ontology axioms into concept diagrams. We prove that our inference rules are sound, and exemplify how they can be used to reason about inconsistencies and incoherence. Finally, we indicate how our rules can serve as a foundation for new rules required when representing ontologies in diverse new domains

Icon: A diagrammatic theorem prover for ontologies

Concept diagrams form a visual language that is aimed at non-experts for the specification of ontologies and reason- ing about them. Empirical evidence suggests that they are more accessible to ontology users than symbolic notations typically used for ontologies (e.g., DL, OWL). Here, we re- port on iCon, a theorem prover for concept diagrams that al- lows reasoning about ontologies diagrammatically. The input to iCon is a theorem that needs proving to establish how an entailment, in an ontology that needs debugging, is caused by a minimal set of axioms. Such a minimal set of axioms is called an entailment justification. Carrying out inference in iCon provides a diagrammatic proof (i.e., explanation) that shows how the axioms in an entailment justification give rise to the entailment under investigation. iCon proofs are for- mally verified and guaranteed to be correct.Zohre

Deductive reasoning about expressive statements using external graphical representations

Research in psychology on reasoning has often been restricted to relatively inexpressive statements involving quantifiers. This is limited to situations that typically do not arise in practical settings, such as ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants’ performance when reasoning with two notations. The first used topology to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topological- spatial representations were more effective than topological representations. Unlike topological-spatial representations, reasoning with topological representations was harder when involving multiple quantifiers and binary relations than single quantifiers and unary relations. These findings are compared to those for sentential reasoning tasks

Promising Developments in Bio-Based Products as Alternatives to Conventional Plastics to Enable Circular Economy in Ukraine

Transforming the plastic industry toward producing more sustainable alternatives than conventional plastics, as an essential enabler of the bio-based circular economy (CE), requires reinforcing initiatives to drive solutions from the lab to the market. In this regard, startups and ideation and innovation events can potentially play significant roles in consolidating efforts and investments by academia and industry to foster bio-based and biodegradable plastic-related developments. This study aimed to present the current trends and challenges of bioplastics and bio-based materials as sustainable alternatives for plastics. On this basis, having conducted a systematic literature review, the seminal research themes of the bio-based materials and bioplastics literature were unfolded and discussed. Then, the most recent developments of bio-based sustainable products in Ukraine, as alternatives to petroleum-based plastics, that have gained publicity through local startup programs and hackathons were presented. The findings shed light on the potential of the bio-based sector to facilitate the CE transition through (i) rendering innovative solutions most of which have been less noticed in academia before; (ii) enhancing academic debate and bridging the gap between developers, scholars, and practitioners within the plastic industry toward creating circularity across the supply chain; (iii) identifying the main challenges and future perspectives for further investigations in the future

Deep Learning Model Based on ResNet-50 for Beef Quality Classification

Food quality measurement is one of the most essential topics in agriculture and industrial fields. To classify healthy food using computer visual inspection, a new architecture was proposed to classify beef images to specify the rancid and healthy ones. In traditional measurements, the specialists are not able to classify such images, due to the huge number of beef images required to build a deep learning model. In the present study, different images of beef including healthy and rancid cases were collected according to the analysis done by the Laboratory of Food Technology, Faculty of Agriculture, Kafrelsheikh University in January of 2020. The texture analysis of the beef surface of the enrolled images makes it difficult to distinguish between the rancid and healthy images. Moreover, a deep learning approach based on ResNet-50 was presented as a promising classifier to grade and classify the beef images. In this work, a limited number of images were used to present the research problem of image resource limitation; eight healthy images and ten rancid beef images. This number of images is not sufficient to be retrained using deep learning approaches. Thus, Generative Adversarial Network (GAN) was proposed to augment the enrolled images to produce one hundred eighty images. The results obtained based on ResNet-50 classification achieve accuracy of 96.03%, 91.67%, and 88.89% in the training, testing, and validation phases, respectively. Furthermore, a comparison of the current model (ResNet-50) with the classical and deep learning architecture is made to demonstrate the efficiency of ResNet-50, in image classification

Effect of copolymer composition on particle morphology and release behavior in vitro using progesterone

This study was aimed at improving dissolution rate and sustained release of progesterone by varying copolymer composition and polymer: drug ratio of PLGA. Drug-loaded particles were prepared using electrohydrodynamic atomization. The effects of polymer: drug ratio and copolymer composition on particle properties and in vitro drug-release profile were investigated. The physical form of the generated particles was determined via X-ray powder diffraction (XRPD) and Fourier transform infrared spectroscopy (FTIR). Drug release in vitro was found to be dependent on copolymer composition, where the release rate increased with decreased lactide content of PLGA. Particles produced with solutions of copolymer (75:25) had elongated shapes. In general, the obtained results indicated that the prepared microparticles were ideal carriers for oral administration of progesterone offering great potential to improve the dissolution rate of drugs that suffer from low aqueous solubility

Accessible reasoning with diagrams: From cognition to automation

High-tech systems are ubiquitous and often safety and se- curity critical: reasoning about their correctness is paramount. Thus, precise modelling and formal reasoning are necessary in order to convey knowledge unambiguously and accurately. Whilst mathematical mod- elling adds great rigour, it is opaque to many stakeholders which leads to errors in data handling, delays in product release, for example. This is a major motivation for the development of diagrammatic approaches to formalisation and reasoning about models of knowledge. In this paper, we present an interactive theorem prover, called iCon, for a highly expressive diagrammatic logic that is capable of modelling OWL 2 ontologies and, thus, has practical relevance. Significantly, this work is the first to design diagrammatic inference rules using insights into what humans find accessible. Specifically, we conducted an experiment about relative cognitive benefits of primitive (small step) and derived (big step) inferences, and use the results to guide the implementation of inference rules in iCon