Search

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios.

Physical and morphological characteristics of Greek wheat landrace and cultivar seeds were evaluated, aiming to assess their bread- and pasta-making quality. Furthermore, the plant agromorphological traits of wheat landraces and cultivars were measured and correlated with seed physical properties. Fifteen Triticum spp. accessions, out of which four cultivars (two Triticum aestivum and two T. durum), nine landraces (two T. durum, five T. aestivum, one T. dicoccum and one T. polonicum) and two T. durum of unknown status were studied. Seventeen morphological plant and seed traits were measured based on UPOV descriptors. Ear emergence was earlier in cultivars than in landraces. The plant weight of the landraces was, in some cases, almost twice the cultivars’. Seed firmness (62.96–194.85N) was positively correlated with thousand kernel weight and volume (0.840 and 0.791, P < 0.05, respectively). Based on the agromorphological traits, cluster analysis clearly separated the species and the cultivars from the landraces, and the unknown status accessions were grouped with the cultivars. Cluster analysis with all measured traits set the wheat accessions in the following distinct groups: (1) T. durum cultivars including the unknown status accessions, (2) T. aestivum cultivars, and (3) T. aestivum and T. durum landraces. T. polonicum and T. dicoccum each formed a separate group. Seed physical properties of the analyzed Greek landraces indicated their suitability for bread and/or pasta making.

Landraces are identified for their genetic diversity and heritage value. Geographically isolated areas are gradually recognized for their agricultural diversity and importance in conservation. One of the consequences of agricultural intensification is an increase in crop uniformity and hence a reduction in the landraces’ genetic resources. Messinia, located in Southwest Peloponnese, Greece, is characterized by a diverse terrain and smallholdings. The geomorphological character of the area and farmers’ selections have led to new landraces (local varieties) with unique traits and specific adaptations. A total of 110 villages/settlements were visited between 2013–2016, aiming to explore the existing wealth of landraces and the degree of genetic erosion. The genetic material collected and the testimonies extracted from local communities were compared to data from past expeditions, gene banks, and portal databases. Of the 427 collected samples, the majority belonged to annual vegetable or pulse species, indicating the genetic diversity of the groups, which was likely related to their culinary value. Perennial crops are priced both as commercial and staple crops, with olive trees dominating the agricultural landscape. Genetic erosion and production decline were noticed for cereals. It is concluded that socio-cultural and agricultural trends have a strong influence on the survival of landraces. Without exhausting the agricultural wealth of the region, the present study suggests that Messinia is an agrobiodiversity hotspot that includes neglected crops.

Reducing emissions from fluorinated gases (F-gases) is considered one of the key conditions for the European Union (EU) to be able to meet the requirements of the European Green Deal, primary objectives of which are the reduction in greenhouse gas emissions by at least 55% by 2030 compared to 1990 and the gradual transition to net climate neutrality by 2050. To this end, a quota system for hydrofluorocarbons (HFCs) is already in place, introduced through the updated F-Gas Regulation (517/2014). The aim of this paper is to review an innovative scheme currently under development that strongly promotes the Recovery, Recycling and Reuse (3R) of F-gases extracted from the heating, ventilation, air conditioning and refrigeration (HVAC-R) equipment of the building sector, thus offering a sustainable alternative source for refrigerant supplies. Therefore, the basic operating principles of the so-called “3R ECOSYSTEM” are outlined and especially its three main components, which are: the self-certification/self-declaration platform, the F-gas identification and recycling units (prototypes) and the Retradeables marketplace. In addition, an overview of both the demonstration phase and the scheduled replication phase of the 3R ECOSYSTEM is provided, including the estimated impact on carbon dioxide (CO2) savings due to the progressive uptake of the circular use of existing F-gases across Europe.

The behaviour of steel structures is affected by two nonlinearities—the geometric and material nonlinearity—and by the unavoidable presence of imperfections. To evaluate the ultimate capacity of a structure, these effects should be taken into consideration during the design process, either explicitly in the analysis or implicitly through the verification checks. In this context, Eurocode 3 provides several design approaches of different complexity and accuracy. The advantages and disadvantages of these approaches are discussed. Five different methods in conformity with the Eurocode provisions are applied for the design of four moment resisting steel frames of varying slenderness. The influence of nonlinearities and imperfections in respect to the slenderness of the structure is illustrated. The examined methods are compared in terms of the predicted ultimate capacity and their efficiency is assessed against the most accurate between them, i.e., an advanced geometrically and materially nonlinear analysis. It is shown that considerable differences arise between the methods. Nevertheless, except for the commonly used 2nd order analysis followed by cross-section verifications, the remaining methods are mostly on the safe side.

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

In the last several years, a variety of approaches and tools have been developed for giving access to open educational resources (OER) related to food safety, security, and food standards, as well to various targeted audiences (e.g., farmers, agronomists). The aim of this paper is to present a technology infrastructure currently in demonstration mode at Michigan State University that will collect various agricultural education content (training descriptions, open educational content, competencies, and standards) and provide them through various interfaces, based on the needs of the targeted audience. The overall architecture of the infrastructure for the food safety sector and the needed components are presented, as well as the main usage scenarios that explain how the infrastructure can enhance existing platforms and services in the area of food safety and food security.

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios. [ABSTRACT FROM AUTHOR]

Even though prefabricated steel Bailey bridges have been used for more than 80 years, limited studies of their structural features have been conducted, most of which do not consider their response in operational conditions. This study aimed at determining the modal parameters of a 30.48 m length Triple-Single (TS) Bailey bridge based on traffic-induced vibrations and comparing them with numerical results. Low-cost improvised accelerometers recorded and logged the actual response time histories, while a three-dimensional (3D) numerical model was developed to carry out the relevant dynamic analyses. The identification of modal parameters was based on the Operational Modal Analysis (OMA) process and the Frequency Domain Decomposition (FDD) method. Numerical analysis results are in accordance with the operational dynamic response of the Triple -Single Bailey bridge, confirming that the numerical model can effectively be used for extended dynamic analysis. In addition, the analysis of raw time histories through the OMA process indicates that the response is affected by the connections’ condition, in particular, the eventual looseness of bolts and pins. At least five eigenfrequencies were estimated and matched with relevant mode shapes.

The main objective of this study was to investigate the performance of faba bean landraces originating from different regions of Greece under both organic and conventional farming systems focusing mainly on yield, biological nitrogen fixation (BNF), and competitiveness to weeds. Faba bean exhibited a high ability to fix nitrogen from the atmosphere, as indicated by the percentage of N2 derived from the atmosphere which exceeded 75% in all evaluated varieties, and the total amount of biologically fixed N up to full anthesis, which fluctuated from 118.5 to 193.9 kg ha−1 in the various cropping systems and cultivars. The weed density was appreciably higher in the organic plots, without significant differences among the faba bean cultivars, while wild mustard and corn poppy were the most competitive weeds. The application of inorganic starter fertiliser in the conventionally-treated plots had no negative effect on biologically-fixed nitrogen by faba bean plants, while the herbicide pendimethalin had no negative impact on the nodulation process. Protein concentrations in faba bean cultivars fluctuated from 27.3% to 31.4%. The evaluated landraces could be utilised in breeding programmes due to their earliness, and their high performance in terms of protein content, BNF ability, and productivity.

Landraces are identified for their genetic diversity and heritage value. Geographically isolated areas are gradually recognized for their agricultural diversity and importance in conservation. One of the consequences of agricultural intensification is an increase in crop uniformity and hence a reduction in the landraces' genetic resources. Messinia, located in Southwest Peloponnese, Greece, is characterized by a diverse terrain and smallholdings. The geomorphological character of the area and farmers' selections have led to new landraces (local varieties) with unique traits and specific adaptations. A total of 110 villages/settlements were visited between 2013–2016, aiming to explore the existing wealth of landraces and the degree of genetic erosion. The genetic material collected and the testimonies extracted from local communities were compared to data from past expeditions, gene banks, and portal databases. Of the 427 collected samples, the majority belonged to annual vegetable or pulse species, indicating the genetic diversity of the groups, which was likely related to their culinary value. Perennial crops are priced both as commercial and staple crops, with olive trees dominating the agricultural landscape. Genetic erosion and production decline were noticed for cereals. It is concluded that socio-cultural and agricultural trends have a strong influence on the survival of landraces. Without exhausting the agricultural wealth of the region, the present study suggests that Messinia is an agrobiodiversity hotspot that includes neglected crops. [ABSTRACT FROM AUTHOR]

Genetic erosion of landraces is increasing worldwide, however there are still regions rich in landrace biodiversity, such as islands and mountainous isolated areas. Defining the reasons of landrace abandonment in these areas, as well as collecting and preserving landraces, is of outmost importance. In this context, the Agricultural University of Athens organized missions in 53 villages of Arcadia, a prefecture rich in floral biodiversity and variable climatic conditions and topography. The aim was to collect samples of the on-farm (in-situ) conserved annual crop landraces and record the location of perennial crop local varieties. Since traditional knowledge and practices have been playing a vital role in the survival of landraces and local varieties, information was obtained through personal interviews with locals using semi-structured questionnaires. Even though the number of accessions collected from Arcadia has been reduced (141 samples) compared to previous collections and genetic erosion is advancing dramatically for cereals and pulses, a significant number of landraces is still cultivated by the locals. The reasons of landrace abandonment were other sources of occupation than agriculture, such as public service jobs, mechanization, and commercialization of agriculture. Gastronomic and agro-tourism along with European Union trademarks and policies can support locals in landrace/local varieties in-situ conservation.

The purpose of this research is the determination of a prediction model that could predict 50m and 100m n swimming results on surface. The sample of this research consisted of 9 n swimmers of competitive level. All participants swam 50 and 100 meters on the water surface with monofin and a snorkel with maximum intensity. In two next sessions, force of the leg extensors and ankle extensors were measured in the workout gym with a dynamometer. The results of this study concluded in a potential statistical significance of regression analysis for 50m fin swimming with snorkel on the surface of the water. Relative force values of ankle and leg extensors are better predictors of 50m fin swimming. No similar results were found for 100m fin swimming. These findings probably suggest that in shorter distance of 50m fin swimming, force variables of extensor muscles play a greater role in comparison to longer distance of 100m n swimming. The measurement of force of legs in n swimming is of particular interest because legs are mostly responsible for the propulsion in fin swimming and should further be studied with other force protocols, different exercises and larger sample.

Due to the continuous development of technology and the introduction of Information and Communication Technologies (ICT) in modern life, it becomes necessary to introduce new tools in the educational process. The integration of new ICTs depends on the objectives and the teaching approach adopted by the teachers. This research project aims to study the new role of ICTs in educational practice that has been developed by the teachers of the University of Patras. Based on a quantitative study, this research tries to identify the role of ICT, educating students through new technologies to acquire skills they will require when they want to enter the labor market. The results of the survey show that for most surveyed teachers, ICT is a research tool as well as an important pedagogical tool for explaining basic concepts and promoting the learning environment. L’Integrazione delle tecnologie digitali nell’istruzione terziaria per preparare gli studenti al mercato del lavoro. Oggi, a causa del continuo sviluppo delle tecnologie e dell’introduzione delle Nuove Tecnologie dell’Informazione e della Comunicazione (TIC) nella vita moderna, è diventato necessario introdurre nuovi strumenti nel processo educativo. L’integrazione di nuove TIC dipende dagli obiettivi e dagli approcci didattici adottati dagli insegnanti. Il progetto di ricerca che si presenta nel contributo mira a studiare il nuovo ruolo sviluppato dagli insegnanti dell’Università di Patrasso, nell’uso delle TIC nella pratica educativa. Sulla base di uno studio quantitativo, questa ricerca si propone di identificare il ruolo delle TIC, educando gli studenti, attraverso l’implementazione di nuove tecnologie, al fine di acquisire nuove competenze utili per l’inserimento professionale. Dai risultati dello studio condotto si evince che per la maggior parte degli insegnanti che hanno partecipato a questo progetto le TIC sono uno strumento di ricerca e un importante strumento pedagogico per spiegare concetti di base e promuovere l’ambiente di apprendimento.

Reducing emissions from fluorinated gases (F-gases) is considered one of the key conditions for the European Union (EU) to be able to meet the requirements of the European Green Deal, primary objectives of which are the reduction in greenhouse gas emissions by at least 55% by 2030 compared to 1990 and the gradual transition to net climate neutrality by 2050. To this end, a quota system for hydrofluorocarbons (HFCs) is already in place, introduced through the updated F-Gas Regulation (517/2014). The aim of this paper is to review an innovative scheme currently under development that strongly promotes the Recovery, Recycling and Reuse (3R) of F-gases extracted from the heating, ventilation, air conditioning and refrigeration (HVAC-R) equipment of the building sector, thus offering a sustainable alternative source for refrigerant supplies. Therefore, the basic operating principles of the so-called "3R ECOSYSTEM" are outlined and especially its three main components, which are: the self-certification/self-declaration platform, the F-gas identification and recycling units (prototypes) and the Retradeables marketplace. In addition, an overview of both the demonstration phase and the scheduled replication phase of the 3R ECOSYSTEM is provided, including the estimated impact on carbon dioxide (CO2) savings due to the progressive uptake of the circular use of existing F-gases across Europe. [ABSTRACT FROM AUTHOR]

The images of the Sentinel-2 constellation can help the verification process of farmers' declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study's geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows. [ABSTRACT FROM AUTHOR]

This article aims to assist professors in introducing concepts of self, philosophy, religions, the universe, existential dilemmas, etc., in their basic international business classes. Using active learning and five-member student teams, a student organized and administered conference adds a very useful dimension of knowledge sacrificing only one teaching day. The article, capitalizing on more than 25 years of international business teaching and conference experiences, provides "how-to" suggestions to organize similar events. (Contains 5 notes.)

The article presents the successful experience of teaching the basic International Business (IB) undergraduate class, in an interactive way and in classes of more than 200 students. The approach follows modified routines taught in AACSB workshops for much smaller audiences and capitalizes on 5-member student teams. The format has been used for three years in a row and has produced extremely good results, including significant class interest, very high outcome assessments, annual CD-ROMs on a variety of topics, a very active student association, student-organized conferences, a career center, student-to-business interaction and undertaking of social causes.