Impact of CSR perceptions on workers’ innovative behaviour: exploring the social exchange process and the role of perceived external prestige

The study aims to show how organisational corporate social responsibility (CSR) can influence workers’ attitudes, especially in terms of innovative behaviour (IB). A second aim is to explore the social exchange process that may underlie this relationship, by examining the mediating role of organisational trust (OT), affective commitment (AC) and happiness (HAP), and the moderating role of perceived external prestige (PEP). The authors employ structural equation modelling based on survey data obtained from 315 Portuguese individuals. The findings show that perceptions of CSR predict IB through a social exchange process which involves the mediating role of OT, AC and HAP and the moderating process of PEP. They suggest that managers should implement CSR practices because these can contribute towards fostering IB, but that they should also invest in communication and in the process of upgrading corporate image. This study enriches the existing knowledge about social exchange relationships in organisational contexts, and responds to the need to understand underlying mechanisms linking CSR with workers’ organisational outcomes, by analysing CSR practices from a holistic stakeholder perspective.info:eu-repo/semantics/publishedVersio

Ubiquitous model for wireless sensor networks monitoring

Wireless Sensor Networks (WSNs) belongs to a new technology trend where tiny and resource constrained devices are wirelessly interconnected and are able to interact with the surrounding environment by collecting data, such as temperature and humidity. Recently, due to the huge growth of mobile devices usage with Internet connection, smartphones are becoming the center of future ubiquitous wireless networks allowing users to access data network services, anytime and anywhere. According to the Internet of Things vision, interconnecting WSNs with smartphones and the Internet is a big challenge. Then, due to the heterogeneity of these devices new architectures are required. This dissertation focuses on the design and construction of a ubiquitous architecture for WSNs monitoring based on Web services, a relational database, and an Android mobile application. This architecture allows mobile users accessing real-time or historical data in a ubiquitous environment using smartphones. Besides that, a push notification system to alert mobile users when a sensor parameter overcomes a given threshold was created. The entire solution was evaluated and demonstrated using a laboratory WSN testbed, and is ready for use.As redes de sensores sem fios fazem parte de uma nova tendência tecnológica na qual pequenos dispositivos com recursos limitados comunicam entre si, sem fios, e interagem com o ambiente envolvente recolhendo uma grande diversidade de dados, tais como a temperatura e a humidade. Recentemente, devido ao enorme crescimento no uso de dispositivos móveis com ligação à Internet, os smartphones estão a tornar-se o centro das futuras redes sem fios ubíquas permitindo aos utilizadores aceder a dados, a qualquer hora e em qualquer lugar. De acordo com a visão da Internet of Things, interligar redes de sensores sem fios e smartphones usando a Internet é um grande desafio e novas arquitecturas são necessárias devido à heterogeneidade destes dispositivos. Esta dissertação centra-se na proposta e construção de uma arquitectura ubíqua para a monitorização de redes de sensores sem fios, baseada em serviços Web, apoiada numa base de dados relacional e uma aplicação móvel para o sistema operative Android. Esta arquitectura permite que os utilizadores móveis acedam a dados em tempo real e também a dados históricos, num ambiente móvel, usando smartphones. Além disso, foi desenvolvido um sistema de notificações push que alerta o utilizador quando um dado parâmetro de um sensor ultrapassa um limiar pré-definido. A solução construída foi testada e demonstrada utilizando uma testbed laboratorial e está pronta para utilização

Property Appraisal Platform

This document focuses on the internship in the company DeepNeuronic as part of the project ”Property Appraisal Platform”. This project’s main objective was to develop machine learning models capable of inferring real estate prices using machine learning models and a limited set of features capable of describing a property. In order to achieve the objective, the project was divided into two major phases. In the first phase the state of the art was studied and a dataset collection was put together with the aim of creating a comprehensive representation of the real estate market all across the globe. With this dataset collection available, a set of features was chosen according to their relevancy for the main problem. The second phase consisted of the major practical developments, such as the model creation and dataset improvements. With this in mind, the most relevant metrics were chosen and the models were evaluated in the chosen datasets, creating a set of baseline results to improve upon. Afterwards, multiple other experiments were done, tackling different areas of interest that could potentially improve upon the performance of the models. In total, four different models were evaluated and all the experiments improved upon the baseline results. As an highlight, in the last experiment we propose the transformation of the target label from the property price to the ”Coefficient of the price per square meter compared to the suburb average”. Using this new target label, the results obtained were considerably better. All of these experiments were redone in a new more complex dataset, with all of the experiments improving upon the baseline results obtained in this dataset, reinforcing the idea that these experiments can be used even in more complex datasets.Este documento foi criado no âmbito do estágio realizado na empresa DeepNeuronic como parte do projeto ”Plataforma de Avaliação de Propriedades”. O objetivo do mesmo foi desenvolver modelos de aprendizagem automática capazes de avaliar preços do mercado imobiliário usando modelos inteligentes e um conjunto limitado de características capazes de descrever uma propriedade. Para atingir este objetivo o projeto foi dividido em duas partes principais. Na primeira parte foi feito um estudo intensivo do estado da arte, e criada uma coleção de bancos de dados extensiva, representante do mercado imobiliário no mundo inteiro. Com esta coleção disponível, um conjunto de características foram escolhidas de acordo com a sua relevância para o problema em questão. A segunda fase consistiu nos desenvolvimentos práticos principais, envolvendo a criação de modelos e melhorias nos bancos de dados. Para isso foram escolhidas as métricas mais relevantes, e foram avaliados os modelos nos bancos de dados iniciais, criando assim um conjunto de resultados base. Seguidamente, múltiplas experiências foram feitas, abordando diferentes áreas de interesse que podiam potencialmente melhorar os resultados base. No total quatro modelos diferentes foram avaliados e as experiências realizadas todas melhoraram os resultados base obtidos. De especial relevância, na última experiência propomos a transformação do preço da propriedade para uma variável objetivo que pode ser descrita como o ”Coeficiente do preço por metro de área quadrado comparado à média do subúrbio”. Usando esta variável os resultados obtidos foram consideravelmente melhores, estas experiências foram refeitas em um novo banco de dados consideravelmente mais complexo, verificando-se também que todas estas experiências melhoram os resultados obtidos inicialmente, reforçando a ideia que estas experiências podem ser usadas mesmo em bancos de dados mais complexos

Towards an investigation on the determinants for effectiveness and efficiency of reverse logistics systems (RLS)

This article deals with the influence of economies of scale and postponement on the efficiency and effectiveness of reverse logistics systems (RLSs). In a global way, it aims to provide an understanding of RLSs to generate knowledge of practical and theoretical character. Starting from a generic model of circular flow of materials, the system studied is positioned between the final consumer and the traditional or direct supply chain. It is a qualitative approach over two case studies carried out in Portugal and Brazil dealing with scraptires. One deals with management system while the other deals with the reverse logistics system. As conclusions, the efficiency of RLSs is aided by economies of scale. Postponement has positive effects on efficiency by increasing the system capacity reducing logistics costs which indirectly leads to economies of scale, having a positive influence on the effectiveness of RLSs. According to what has been stated in this paper, the coordination between the direct and reverse flow is a typical case of a closed circuit

The impact of monetary policy and its surprises on bank’s risk-taking

The latest financial crisis accentuated the importance of understanding bank risk and its ties to financial stability. This paper looks to investigate the impact of monetary policy in the risk-taking behaviour of Euro Area banks, when taking unconventional monetary policy into account. Looking further into this relationship, the impact of unanticipated monetary policy shocks is also analysed. Using both fixed effects and a system GMM model, sufficient statistical evidence was found to claim that looser monetary policy leads to increased risk-taking behaviour from banks. This effect, however, is mitigated in case banks and/or the market originally anticipated an even looser stance by the central bank

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

UIDB/50006/2020 UIDB/04378/2020 Norma transitória DL 57/2016The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.publishersversionpublishe

The influence of consumption vocabulary on the encoding and retrieval of haptic information

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsSensory stimuli are often ambiguous, which makes it difficult for consumers to encode and retrieve them, and to construct their preferences. This project studies whether, in a prepurchase context, consumption vocabulary can help consumers to perceive what products are superior on haptic attributes. In an experiment with bed pillows, participants provided with evaluative criteria preferred the pillow with superior haptic attributes more often and to a larger extent than participants who had no evaluative criteria, which suggests the provision of criteria has a positive influence on preference construction. Improvements in memory for haptic attributes and disconnection from incongruent market information derived from that provision were not confirmed

Digital culture: the evolution of the cultural sector

Technology has been impacting all sectors widelyacross the economy, empowered by the new Information and Communication TechnologiestheDigital Culturewill contribute to the disappearance of limitations in cultural productionand consumption.In this research,we aim to assess the impacts of digitizationand Internet penetration in how culture is produced, consumed and distributed, focusing both the impacts in the ancientinstitutions andthe newly popularized spaces of domesticculture

Validação do modelo do integral térmico para a ervilha (Pisum sativum L.) para a indústria

Dissertação de mestrado em Agricultura Sustentável, apresentada na Escola Superior Agrária de Santarém, Instituto Politécnico de SantarémA cultura de ervilha assume grande importância na região do Vale do Tejo. A cultura é realizada em sistema de cultura intercalar, na época de outono-inverno, a anteceder a cultura principal de primavera. O escoamento da produção na grande maioria é para a indústria de congelação, por esta razão há uma elevada necessidade de garantir o escalonamento da colheita de forma a perfazer a capacidade diária industrial de um modo contínuo. Perante esta necessidade e exigência cada vez mais rigorosa por parte das indústrias, há a necessidade de recorrer a ferramentas mais precisas para a execução do planeamento de sementeiras e respectivas colheitas, como tal, neste trabalho procurou-se validar um modelo do integral térmico para duas das principais variedades de indústria de congelação, a “Boogie” e a “Naches”. Para o cálculo do integral térmico foi considerado a temperatura base de 4,5° C e procurou-se comparar a data estimada para a colheita tendo em conta as unidades de calor dos três anos anteriores a 2018, ou seja, 2014/2015, 2015/2016 e 2016/2017 com as temperaturas reais de 2017/2018. Tanto a “Boogie” como a “Naches” apresentaram um desvio relativo às datas de colheita inicialmente estimadas para com as datas reais de colheita que não é significativo, pelo que o modelo de integral térmico poderá ser usado para estimar a data de colheita nestas duas variedades. No entanto, quando o parâmetro da temperatura base foi alterado para 1 °C obteve-se melhores resultados no caso da “Boogie”. Para a variedade “Naches” a relação entre os valores observados e estimados da data de colheita foi mais próxima para uma temperatura base de 2°C.Green pea crop has great importance in the Vale do Tejo region. The crop is carried out in an intercropping system, in the autumn-winter season, before the main spring crop. The production flow is mostly for the freezing industry, for this reason there is a high need to ensure the harvesting of the crop in order to make up the daily industrial capacity in a continuous way. Faced with this need and increasingly stringent requirements of the industries, there is a need to use more precise tools for the execution of the planning of sowings and their crops, as such, in this work we have validate a model of the thermal integral for two of the main varieties of freezing industry, the "Boogie" and the "Naches". In order to calculate the thermal integral, was considered a base temperature of 4.5 ° C and an attempt was made to compare the estimated harvesting date, taking into account the heat units of the three years prior to 2018, years of 2014/2015, 2015/2016 and 2016/2017 with the actual temperatures of 2017/2018. Both "Boogie" and "Naches" showed a deviation relative to harvest dates initially estimated for actual harvest dates which is not significant, so the thermal integral model can be used to estimate the harvest date in these two varieties. However, when the base temperature parameter was changed to 1°C, better results were obtained in the case of Boogie. For the "Naches" variety the relationship between the observed and estimated values of the harvesting date was closer to a base temperature of 2°C.N/

Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach

UIDB/50006/2020 Norma transit?ria DL 57/2016 UIDP/04378/2020 LA/P/0140/2020Biofouling is the undesirable growth of micro-and macro-organisms on artificial waterimmersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand-and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.publishersversionpublishe