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<p>While fresh vegetables (FVs) consumption is essential for public health, some high-profile outbreaks that cause severe illnesses are related to their consumption. To illustrate, the Center for Disease Control (CDC) and Prevention has estimated 48 million cases of foodborne illnesses in the U.S. per year; of them, about 46% are associated with FVs. The economic impact of food safety issues, estimated at $51 billion annually, is due to medical costs, productivity losses, and loss of consumer trust (Hoffman et al., 2021). </p> <p>The proliferation of risk mitigation methods (GAP, HACCP), food safety policies (FSMA), and information (labels, media, government) out in the market today, suggests that the way consumers understand food safety might be different from what policymakers, researchers, and retailers try to communicate. In addition, consumers' heterogeneous perceptions and beliefs can make communication with policymakers, researchers, and industry stakeholders ineffective when assessing food safety risks. </p> <p>Given the high demand for FVs and the communication mismatch with consumers, it is crucial to understand how consumers value food safety when purchasing FVs. This study clustered FVs consumers based on their food safety concerns. First, a Principal Component Analysis (PCA) identified the most relevant food safety dimensions. Later, using the food safety dimensions, this study segmented FVs consumers based on their food safety concerns. Finally, through a Multinomial Probit model (MNP), this study provided the main factors driving cluster membership.</p> <p>Our results suggest the existence of four segments of FVs consumers: “Worriers” (45% of our sample), who highly valued all the food safety characteristics when buying FVs. “Labelers” (20.3% of our sample) mainly valued attributes related to nutritional and environmental characteristics. “Pretty Vegetables” (17.3% of our sample) searched for the best and safest produce possible. Lastly, “DIYers” (17.3% of our sample) valued the least variables related to convenience in FVs. Finally, some of the main drivers of cluster membership were related to demographics, consumption, information sources, and perceptions about food safety of FVs consumers. These results can help policymakers, researchers, and retailers communicate food safety information more efficiently among different segments of consumers.</p>

[pt] Atualmente, a indústria cimenteira tem usado o coprocessamento no intuito de atender a aspectos econômicos e de sustentabilidade por meio da utilização de resíduos industriais como matérias-primas e/ou combustíveis não convencionais. Isto pode gerar, através da introdução de maior variabilidade de elementos menores, consequências nas reações de clinquerização com geração de modificações morfológicas dos cristais e nas propriedades que influenciam estas reações (tensão superficial, viscosidade). Em função da maior utilização do coprocessamento, as análises mineralógica e microestrutural se tornaram ainda mais significativas para apoio a formação do diagnóstico do processo incluindo o grau de reatividade do clínquer e por sua vez, previsões do desempenho do cimento. No entanto, o número de variáveis de controle do forno de clinquer é tão elevado que se propôs o emprego da metodologia estatística chamada Análise de Componentes Principais (PCAPrincipal Analysis Components em inglês) para escolher as de maior representatividade. Os resultados da caracterização mineralógica do clínquer coprocessado com o resíduo CSS50 utilizando o Método de Rietveld/Difração de Raios-X dos quatro principais constituintes do clínquer foram próximos aos valores teóricos (potenciais de Bogue). A fase predominante do C3S foi monoclínica, típica de clínqueres industriais, e quanto ao C3A, a fase predominante foi cúbica. O diagnóstico apresentado pela caracterização microestrutural apresentou clínquer com alta reatividade. Quanto ao cimento coprocessado com o resíduo CSS50, foram realizados os ensaios físico-mecânicos: tempo de pega e resistência a compressão, conforme ABNT NBR 16607 e NBR 7215. Os ensaios de tempo de pega e resistência à compressão do cimento atenderam plenamente a norma ABNT NBR 16697. / [en] Currently, the cement industry uses the coprocessing of industries residues as raw materials or alternative fuel for attending economics and the sustainability aspects. This procedure can introduce a variety of minor elements that can affect the clinkerization reactions by producing morphological changes of the clinker crystals and changing the surface tension and viscosity. Thus, mineralogic and microstrutural characterizations are necessary to understand the clinker reactivity and its effect on the Portland cement properties. Nonetheless, the number of clinquer kiln control variables is enormous, and it is proposed to use the Principal Component Analysis (PCA) to choose the most important ones. The X-ray diffraction characterization of the clinker showed that the four major constituents are consistent with the theorical values (Bogue potentials). The C3S phase was monoclinic, which is usual for industrial clinkers, and the C3A phase was usually cubic. These results suggest that the formation of a high reactivity clinker. The physico-mechanical characterizations setting time and compressive strength of the Portland cement were conducted according to NBR 16607 and NBR 7215 ABNT norms. The Portland Cement results agreed with the NBR 16697 ABNT norm.

One of banks core businesses today is to, in various ways, lend capital to the market and in return receive interest rate. But giving out credit comes with great risk and, therefore, precautions need to be taken. It is impossible to forecast exactly which obligor (borrower) that will default on its exposure. However, with well functioning risk management, institutions can lower the severity of their loss. In this study, we consider using a multi-factor model to calculate concentration risk for Swedish credit portfolios, which is a type of credit risk that is usually caused by high concentration of credit exposures distributed over few industrial sectors. In its existing form, the multi-factor model uses fixed sector correlations with predetermined sectors as input. Instead, we propose to use a data-driven approach based on data from the Stockholm stock exchange. In a simulation study, we find that the distributions of total credit loss are somewhat different under the original approach than under our proposed approach. This suggests that further research is needed to investigate whether the two approaches are interchangeable.

Ögonkontakt-sensorer skapar möjligheten att tolka användarens uppmärksamhet, vilketkan användas av system på en mängd olika vis. Dessa inkluderar att skapa nya möjligheterför människa-dator-interaktion och mäta mönster i uppmärksamhet hos individer.I den här uppsatsen gör vi ett försök till att konstruera en ögonkontakt-sensor med hjälpav en Raspberry Pi, med målet att göra den praktisk i verkliga scenarion. För att fastställaatt den är praktisk satte vi upp ett antal kriterier baserat på tidigare användning avögonkontakt-sensorer. För att möta dessa kriterier valde vi att använda en maskininlärningsmetodför att träna en klassificerare med bilder för att lära systemet att upptäcka omen användare har ögonkontakt eller ej. Vårt mål var att undersöka hur god prestanda vikunde uppnå gällande precision, hastighet och avstånd. Efter att ha testat kombinationerav fyra olika metoder för feature extraction kunde vi fastslå att den bästa övergripandeprecisionen uppnåddes genom att använda LDA-komprimering på pixeldatan från varjebild, medan PCA-komprimering var bäst när input-bilderna liknande de från träningen.När vi undersökte systemets hastighet fann vi att nedskalning av bilder hade en stor effektpå hastigheten, men detta sänkte också både precision och maximalt avstånd. Vi lyckadesminska den negativa effekten som en minskad skala hos en bild hade på precisionen, mendet maximala avståndet som sensorn fungerade på var fortfarande relativ till skalan och iförlängningen hastigheten. / Eye contact detection sensors have the possibility of inferring user attention, which can beutilized by a system in a multitude of different ways, including supporting human-computerinteraction and measuring human attention patterns. In this thesis we attempt to builda versatile eye contact sensor using a Raspberry Pi that is suited for real world practicalusage. In order to ensure practicality, we constructed a set of criteria for the system basedon previous implementations. To meet these criteria, we opted to use an appearance-basedmachine learning method where we train a classifier with training images in order to inferif users look at the camera or not. Our aim was to investigate how well we could detecteye contacts on the Raspberry Pi in terms of accuracy, speed and range. After extensivetesting on combinations of four different feature extraction methods, we found that LinearDiscriminant Analysis compression of pixel data provided the best overall accuracy, butPrincipal Component Analysis compression performed the best when tested on imagesfrom the same dataset as the training data. When investigating the speed of the system,we found that down-scaling input images had a huge effect on the speed, but also loweredthe accuracy and range. While we managed to mitigate the effects the scale had on theaccuracy, the range of the system is still relative to the scale of input images and byextension speed.

Due to the unpredictability of the stock market,forecasting stock prices is a challenging task. In this project,we will investigate the performance of the machine learningalgorithm LSTM for stock market prediction. The algorithmwill be based only on historical numerical data and technicalindicators for IBM and FORD. Furthermore, the denoising anddimension reduction algorithm, PCA, is applied to the stockdata, to examine if the performance of forecasting the stockprice is greater than the initial model. A second method, transferlearning, is applied by training the model on the IBM datasetand then applying it on the FORD dataset, and vice versa, toevaluate if the results will improve. The results show that whenthe PCA algorithm is applied to the dataset separately, and incombination with transfer learning, the performance is greater incomparison to the initial model. Moreover, the transfer learningmodel is inconsistent as the performance is worse for FORD inrespect to the initial model, but better for IBM. Thus, concerningthe results when forecasting stock prices using related tools, it issuggested to use trial and error to identify which of the modelsthat performs the optimally. / Att förutse aktiekurser är en utmanande uppgift. Detta beror på aktiemarknadens oförutsägbarhet. Därför kommer vi i detta projekt att undersöka prestandan för maskininlärnings algoritmen LSTMs prognosförmåga för aktie priser. Algoritmen baseras endast på historisk numerisk data och tekniska indikatorer for företagen IBM och FORD. Vidare tillämpas brus minskande och dimension reducerande algorithmen, PCA, på aktiedata för att undersöka om prestandan för att förutse aktie priser är bättre än den ursprungliga modellen. En andra metod, transfer learning, tillämpas genom att träna modellen på IBM data och sedan använda den på FORD data, och vice versa, för att utvärdera om resultaten kommer att förbättras. Resultaten visar, när PCA-algoritmen tillämpas på aktiedata separat, och i kombination med transfer learning är prestandan bättre jämfört med bas modellen. Vidare kan vi inte dra slutsatser om transfer learning då prestandan är sämre för FORD med avseende på bas modellen, men bättre för IBM. I hänsyn till resultaten så föreslås det att man tillämpar modellerna för att identifiera vilken som är mest optimal när man arbetar i ett relaterat ämnesområde. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Effects of the environmental degradation in the last century rose awareness on the need to manage natural resources in a more sustainable manner. The role of the private sector in greenhouse gases emissions and resources use is significant and hence encompasses huge potential to mitigate the environmental impact of human activities. Environmental Management Systems (EMS) has been regarded as an effective way to manage the environmental impact of enterprises. Factors impacting the performance of EMS in French enterprises are investigated. Three aspects of EMS are studied: managerial processes, policy-making process and tools used to monitor environmental impact. Influence of sectorial activity on EMS is studied. In the first phase, a qualitative research approach is used, and twenty-one French enterprises from different sectors are interviewed. Two types of employees are surveyed: employees in charge of the environmental policy within the enterprise, and employees not related to environmental policies. In the second phase, exploratory factor analysis is employed to identify practices leading to best environmental performance. Results from the exploratory analysis reveal that firms with the highest environmental performance tend to have a long-term agenda and consult external stakeholders, namely their customers, to elaborate their environmental policy. Regarding communication channels to educate employees on sustainable practices, it is found that executives are often more informed on sustainable practices and are not responsive to same channels. Results from the qualitative analysis show lack of financial support is still the first obstacle to EMS implementation, and business strategy is rarely aligned with the environmental strategy of the company. Therefore, there is a strong need to create a compelling business case for EMS. / Effekterna av miljöförstöring under förra seklet ökade medvetenheten om behovet av att hantera resurser på ett mer hållbart sätt. Den privata sektorns roll i utsläpp av växthusgaser och resursanvändning är betydande och omfattar därför en enorm potential för att påverka miljöns hållbarhet. Environmental Management Systems (EMS) har betraktats som ett effektivt sätt att hantera företagens miljöpåverkan. Faktorer som påverkar resultatet av EMS i franska företag undersöks. Tre aspekter av EMS studeras: ledningsprocesser, beslutsfattande och verktyg som används för att övervaka miljöpåverkan. Påverkan av sektoraktivitet på EMS studeras. I den första fasen används en kvalitativ forskningsmetod och 21 franska företag från olika sektorer intervjuas. Två typer av anställda undersöks: anställda som ansvarar för miljöpolitiken inom företaget och anställda som inte är relaterade till miljöpolitiken. I den andra fasen används undersökande faktoranalys för att identifiera metoder som leder till bästa miljöprestanda. Resultat från den undersökande analysen visar att företag med högsta miljöprestanda tenderar att ha en långsiktig agenda och konsultera externa intressenter, nämligen deras kunder, för att utarbeta sin miljöpolicy. Beträffande kommunikationskanaler för att utbilda anställda om hållbar praxis, konstateras att chefer ofta är mer informerade om hållbara metoder och inte svarar på samma kanaler. Resultat från den kvalitativa analysen visar att bristen på ekonomiskt stöd fortfarande är det första hinderet för implementering av EMS, och affärsstrategi är sällan i linje med företagets miljöstrategi. Därför finns det ett starkt behov av att skapa ett övertygande affärsfall för EMS.

The objective of this work was to employ artificial neural networks (NN) to solve inverse problems in different engineering fields, overcoming various obstacles in applying NN to different problems and benefiting from the experience of solving different types of inverse problems. The inverse problems investigated are: 1) damage detection in structures, 2) detection of an anomaly in a light-diffusive medium, such as human tissue using optical imaging, 3) structural optimization of fiber optic sensor design. All of these problems require solving highly complex inverse problems and the treatments benefit from employing neural networks which have strength in generalization, pattern recognition, and fault tolerance. Moreover, the neural networks for the three problems are similar, and a method found suitable for solving one type of problem can be applied for solving other types of problems. Solution of inverse problems using neural networks consists of two parts. The first is repeatedly solving the direct problem, obtaining the response of a system for known parameters and constructing the set of the solutions to be used as training sets for NN. The next step is training neural networks so that the trained neural networks can produce a set of parameters of interest for the response of the system. Mainly feed-forward backpropagation NN were used in this work. One of the obstacles in applying artificial neural networks is the need for solving the direct problem repeatedly and generating a large enough number of training sets. To reduce the time required in solving the direct problems of structural dynamics and photon transport in opaque tissue, the finite element method was used. To solve transient problems, which include some of the problems addressed here, and are computationally intensive, the modal superposition and the modal acceleration methods were employed. The need for generating a large enough number of training sets required by NN was fulfilled by automatically generating the training sets using a script program in the MATLAB environment. This program automatically generated finite element models with different parameters, and the program also included scripts that combined the whole solution processes in different engineering packages for the direct problem and the inverse problem using neural networks. Another obstacle in applying artificial neural networks in solving inverse problems is that the dimension and the size of the training sets required for the NN can be too large to use NN effectively with the available computational resources. To overcome this obstacle, Principal Component Analysis is used to reduce the dimension of the inputs for the NN without excessively impairing the integrity of the data. Orthogonal Arrays were also used to select a smaller number of training sets that can efficiently represent the given system. / Ph. D.