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Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

Geographical indications (GIs) are expected to stimulate rural development by increasing the viability and resilience of farms in disadvantaged and remote areas. However, little quantitative evidence exists to support this expectation. This study fills this knowledge gap by quantitatively analyzing the effect of GI adoption on farm incomes in the EU olives and wine sectors.

The analysis uses data from the Farm Accountancy Data Network and EUROSTAT and an endogenous switching regression model to analyze the impact of GI adoption on farm incomes for specialized quality wine and olives producers in the year 2014.

The results show that GI adoption significantly improves farm incomes in both the olives and the wine sector.

The research uses data from the farm accountancy data network (FADN). This is seen as a limitation of the analysis. The research raises some concerns about the appropriateness of FADN for the assessment of farmers' involvement in food quality schemes and a reconsideration of FADN as a tool for farm performance analysis is advised.

This is one of few quantitative studies of the impact of geographical indications on farm performance. Furthermore, it gives insights into the mechanisms by which GI can affect farm incomes.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

This paper aims to describe the development of a knowledge management system (KMS) for the Nadir and Occultation for Mars Discovery (NOMAD) instrument on board the ESA/Roscosmos 2016 ExoMars Trace Gas Orbiter (TGO) spacecraft. The KMS collects knowledge acquired during the engineering process that involved over 30 project partners. In addition to the documentation and technical data (explicit knowledge), a dedicated effort was made to collect the gained experience (tacit knowledge) that is crucial for the operational phase of the TGO mission and also for future projects. The system is now in service and provides valuable information for the scientists and engineers working with NOMAD.

The NOMAD KMS was built around six areas: official documentation, technical specifications and test results, lessons learned, management data (proposals, deliverables, progress reports and minutes of meetings), picture files and movie files. Today, the KMS contains 110 GB of data spread over 11,000 documents and more than 13,000 media files. A computer-aided design (CAD) library contains a model of the full instrument as well as exported sub-parts in different formats. A context search engine for both documents and media files was implemented.

The conceived KMS design is basic, flexible and very robust. It can be adapted to future projects of a similar size.

The paper provides practical guidelines on how to retain the knowledge from a larger aerospace project. The KMS tool presented here works offline, requires no maintenance and conforms to data protection standards.

This paper shows how knowledge management requirements for space missions can be fulfilled. The paper demonstrates how to transform the large collection of project data into a useful tool and how to address usability aspects.

Humour and, in particular, jokes have received little serious academic scrutiny in the entrepreneurship literature to date. To address this, the purpose of this paper is to examine publicly available jokes about entrepreneurs to establish what such jokes tell us about how humour, particularly entrepreneur jokes shapes public perceptions of entrepreneurial identity. This is important because humour may be an integral part of an individual's entrepreneurial identity. The authors thus contribute to understandings of the complex nature of entrepreneurial identity and how public perceptions of humour influence such by encapsulating negative public perception of entrepreneurs which may act as a de-legitimisation mechanism.

From a representative sample of entrepreneur jokes located on the web using netnographic techniques, the authors apply a multi-disciplinary framework to analyse the material and its messages to establish how such jokes shape public perceptions.

The findings suggest that jokes convey a pejorative message about how entrepreneurs are perceived by the public with the content and message of the jokes being negative and derogatory. Common themes contained in the punchlines include – criminality, greed, dishonesty, hubris, stupidity, misfortune, ridicule and deviousness – all of which may de-legitimise generic entrepreneurial identity. In the process, the authors uncovered liminal aspects of joke telling and consumption in that the perception of jokes about entrepreneurs relate to the time and context in which the joke is told given that situational cleverness is a key facet of such jokes. In addition, the authors discuss variations across jokes.

The authors discuss learning outcomes for future research and potential future studies into humour in an entrepreneurial context.

This study places humour and joking on the research stage, making an incremental contribution. The authors add to the literature on the use of entrepreneurial humour and in particular in relation to how jokes influence public perception of entrepreneurs. From the data collected, the authors develop some fresh insights into the variation and range of entrepreneurship related jokes accessible online.

Repetitive transcranial magnetic stimulation (rTMS) has been proven to be efficacious in the treatment of Major Depressive Disorder (MDD). We previously examined the effectiveness of rTMS for MDD in an applied clinical setting, AwakeningsKC Clinical Neuroscience Institute (CNI) and found high remission rates for patients diagnosed with MDD following rTMS treatment. An unexpected relationship with body composition and rTMS unit was discovered. This sub-study extends the previous investigation through a focused analysis of the effects of body composition on response to rTMS in the treatment of MDD. We utilized data collected from a retrospective review of medical records for patients diagnosed with MDD undergoing rTMS therapy at AwakeningsKC CNI. Patient Health Questionnaire 9 (PHQ-9) scores, time to remission status and body mass index (BMI) at baseline were considered while referencing two different rTMS instruments (MagVenture; NeuroStar). We found 23 (9%) of 247 participants met criteria for obese status (BMI?30) with an average baseline PHQ-9 score of 22±4, classified as “severe depression”. Obesity status was differentially impacted by the rTMS instrument used for treatment. Patients with obesity showed a shorter time to remission (mean 2.7±0.27 vs. mean 3.4±0.3 weeks) and proportionately greater remission rate (100% vs. 71%) when treated using the MagVenture relative to the NeuroStar instrument. Clinical response to rTMS therapy for MDD appears to be guided by individual factors including body composition and rTMS parameters such as the unit used for treatment. Further study of these influences could aid in the optimization of clinical response to rTMS.

Frequently the universities' Placement Service is based on the student's hard profile at the expense of soft traits. On the other side, the “person–organization fit” axiom suggests firms are looking for profiles with specific soft skills to face the increasing level of environmental turbulence. This research aims to understand if high-resilience students also have high academic achievements and how the three components of resilience (emotional intelligence, positive thinking, planfulness) can have different impact on individual performances.

The research was conducted on students enrolled on different courses of studies and years in an Economics and Law faculty. A questionnaire was administered during the first exam session (ante-Covid) and the second and third exam sessions (post-Covid). This questionnaire consists of 84 questions related to planfulness, emotional intelligence and positive thinking, whose combination can be considered a measure of resilience. In fact, the Principal Component Analysis (PCA) was carried to identify these three new variables (the components) based on the 84 initial ones. Finally, an ordered logit model was implemented to verify whether, and in what direction, planfulness, emotional intelligence, positive thinking and Covid 19 (the independent variables) affected the students' performance (the dependent one).

While planfulness positively affected academic performance, emotional intelligence affected it negatively. The impact of positive thinking and Covid was not significant, and thus what emerged from the preliminary analysis of the grades is not confirmed.

This is a case study of a university experience that is paying great care in preparing students to satisfy the firms' work demands. To confirm and refine results the sample will be expanded to other faculties and other life/soft skills will be investigated.

This soft trait approach—that studies how various measures of soft skills are related to course grades—has a two-fold significance by crafting universities' placement activities and facilitating firms' onboarding.

This is a case study of a university experience; a university that is paying great attention to preparing students ready to satisfy the firms' work demands but also citizens capable of supporting the growth of their nation and society in general.

The research can be considered a first step towards the inclusion of the formal evaluation of the students' life skills in their academic path, creating a link with their achievements.

Among the growing interest towards market segmentation and targeted marketing, the current study adopted a scientometric approach to examine the literature on wine selection and preferences. The current review specifically attempts to shed light on the research that explores the determinants of wine preferences at multiple levels of analysis.

CiteSpace was used to compute a Document Co-Citation Analysis (DCA) on a sample of 114,048 eligible references obtained from 2,846 publications downloaded from Scopus on 24 May 2021.

An optimized network of 1,505 nodes and 4,616 links was generated. Within the network, impactful publications on the topic and thematic domains of research were identified. Specifically, two thematic macro-areas were identified through a qualitative analysis of papers included in the 7 major clusters. The first one - “Methods of Wine Making” - included clusters #0, #3, #5, #6 and #18. The second one - “Consumers' Attitudes and Preferences Towards Wine” - included clusters #1 and #2. The first thematic macro-area included more technical aspects referring to the process of wine making, while the second thematic macro-area focused more on the factors influencing individuals' preferences and attitudes towards wine. To reflect the aims of the current paper, publications giving light to the “Consumers' Attitudes and Preferences Towards Wine” macro-area were analyzed in detail.

The resulting insights may help wine makers and wine sellers optimize their work in relation to market segments and to the factors influencing individuals' purchasing behaviors.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.