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This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive limitations. While previous models of pedestrian behaviour have been either 'black-box' machine learning models or mechanistic models with explicit assumptions about cognitive factors, we combine both approaches. Specifically, we model mechanistically noisy human visual perception and assumed rewards in crossing, but we use reinforcement learning to learn bounded optimal behaviour policy. The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle. Notably, our findings suggest that behaviours previously framed as 'biases' in decision-making, such as speed-dependent gap acceptance, might instead be a product of rational adaptation to the constraints of visual perception. Our approach also permits fitting the parameters of cognitive constraints and rewards per individual, to better account for individual differences. To conclude, by leveraging both RL and mechanistic modelling, our model offers novel insights about pedestrian behaviour, and may provide a useful foundation for more accurate and scalable pedestrian models.

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation stra

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation stra

A smart grid (SG) is a modernized electrical grid utilizing new electronic information, communications, and control technologies. The smart grid enables the efficient use of energy resources, such as renewables like solar and wind, for environmental friendly energy production and consumption. Bi-directional communication makes possible intelligent metering, monitoring, management, demand response, and energy trading systems. Wireless communications are a potential option to enable communications in SGs but require research on the usability of different wireless solutions for various SG applications. The research of this thesis, firstly, focuses on a highly loaded public LTE network, in which typical cellular and SG traffic was transmitted simultaneously using the same base station and frequency bands, i.e., a so-called shared LTE network was evaluated. The SG traffic was modeled as three different demand response applications, automatic metering reading, and emergency messaging. Network simulations were conducted in a suburban scenario with different SG and typical traffic volumes. Secondly, a hybrid sensor-LTE network that combines IEEE 802.15.4 and LTE networks was compared against the shared LTE network. Thirdly, an ad hoc LTE method was proposed for smart meters to overcome issues relating to lack of base station connectivity. Fourthly, the applicability of a long-range wide area network (LoRaWAN) for delivering various amounts of traffic was researched. The results illustrated that shared LTE and hybrid sensor-LTE networks are suitable for delivering some amount of concurrent SG and typical cellular traffic. The ad hoc LTE method enables the delivery of demand response traffic using a multi-hop path from smart meters to the relay node connected to a base station. The reliability of LoRaWAN is insufficient for SG communications, but may be improved with some technology modifications., Tiivistelmä Älykäs sähköverkko on modernisoitu sähköverkko, joka hyödyntää sekä uudenlaista sähköistä tietoa että tietoliikenne- ja ohjaustekniikoita. Älykäs sähköverkko mahdollistaa energiaresurssien, kuten uusiutuvien energialähteiden (esimerkiksi aurinko ja tuuli), tehokkaan hyödyntämisen edesauttaen ympäristöystävällistä energian tuotantoa ja kulutusta. Kaksisuuntainen tiedonsiirto mahdollistaa älykkään mittauksen, monitoroinnin, hallinnan, kysyntäjouston ja energiakaupan sähköverkoissa. Langattomat tietoliikennetekniikat ovat potentiaalinen vaihtoehto tiedonsiirron toteuttamiseen älykkäissä sähköverkoissa, mutta vaativat tutkimusta eri langattomien ratkaisujen käyttökelpoisuudesta erilaisten älykkäiden sähköverkkojen sovellusten yhteydessä. Tämän väitöskirjan tutkimus, ensinnäkin, keskittyy erittäin kuormitettuun, julkiseen LTE-verkkoon, jossa tyypillisiä matkapuhelinliikenteitä ja älykkäiden sähköverkkojen dataa siirrettiin yhtäaikaisesti, käyttäen samaa tukiasemaa ja samoja taajuuskaistoja, toisin sanoen niin kutsuttu jaettu LTE-verkko evaluoitiin. Älykkäiden sähköverkkojen data mallinnettiin perustuen kolmeen eri kysyntäjoustosovellukseen, automaattiseen mittarinluentaan ja hätäviesteihin. Verkkosimulaatiot suoritettiin esikaupunkiskenaariossa, jossa siirrettiin eri määriä älykkäiden sähköverkkojen dataa ja tyypillisiä matkapuhelinliikenteitä. Toiseksi, hybridi-anturi-LTE-verkkoa, joka yhdistää IEEE 802.15.4- ja LTE-verkot, verrattiin jaettuun LTE-verkkoon. Kolmanneksi, ad hoc LTE -menetelmä kehitettiin älykkäitä sähkömittareita varten, jotta yhteysongelmat tukiasemaan saataisiin ratkaistua. Neljänneksi, tutkittiin pitkän kantaman laajaverkon (LoRaWAN) soveltuvuutta eri datamäärille. Tulokset osoittavat: Jaettu LTE-verkko ja hybridi-anturi-LTE-verkko ovat soveltuvia siirtämään tietyn määrän yhtäaikaista älykkäiden sähköverkkojen dataa ja tyypillistä matkapuhelinliikennettä. Ad hoc LTE -menetelmä mahdollistaa kysyntäjoustotiedon viestinnän käyttäen monihyppyr

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the Commotions model - a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control - for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles., Human-Robot Interaction, Learning & Autonomous Control

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features., Funding Agencies|European research project "SHAPE-IT - Supporting the Interaction of Humans and Automated Vehicles: Preparing for the Environment of Tomorrow"; European Union [860410]

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation stra

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation stra

Context: Successfully addressing stakeholder concerns that are related to software system development and operation is crucial to achieving development goals. The importance of using a systematic approach to addressing these concerns throughout the software development life cycle is growing as more and more systems are employed to handle critical tasks. Objective: The goal of this study is to provide an overview of addressing concerns across the software development life cycle. Method: A systematic mapping study was conducted using a pre-defined protocol. Four digital databases were searched for research literature and primary studies were selected after a three round selection process conducted by multiple researchers. Results: The extracted data are processed and the results are reported from different viewpoints. The results are also analyzed against our research goals. Conclusion: We show that there is a considerable variation in the use of terminologies and addressing concerns in different phases of the software development life cycle., Comment: 18th International Conference on Evaluation and Assessment in Software Engineering (EASE), London, United Kingdom

Understanding the interaction between different road users is critical for road safety and automated vehicles (AVs). Existing mathematical models on this topic have been proposed based mostly on either cognitive or machine learning (ML) approaches. However, current cognitive models are incapable of simulating road user trajectories in general scenarios, and ML models lack a focus on the mechanisms generating the behavior and take a high-level perspective which can cause failures to capture important human-like behaviors. Here, we develop a model of human pedestrian crossing decisions based on computational rationality, an approach using deep reinforcement learning (RL) to learn boundedly optimal behavior policies given human constraints, in our case a model of the limited human visual system. We show that the proposed combined cognitive-RL model captures human-like patterns of gap acceptance and crossing initiation time. Interestingly, our model's decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a "bias" in human gap acceptance behavior. However, our results suggest that this is instead a rational adaption to human perceptual limitations. Moreover, we demonstrate an approach to accounting for individual differences in computational rationality models, by conditioning the RL policy on the parameters of the human constraints. Our results demonstrate the feasibility of generating more human-like road user behavior by combining RL with cognitive models., Comment: 6 pages, 5 figures,1 table, manuscript created for consideration at IEEE IV 2023 conference

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the \emph{Commotions} model -- a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control -- for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles., Comment: 6 pages, 2 figures

Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is a need to complement naturalistic datasets of road user interaction with targeted, controlled study data. This paper describes a dataset collected in a simulator study conducted in the project COMMOTIONS, addressing urban driving interactions, in a state of the art moving base driving simulator. The study focused on two types of near-crash situations that can arise in urban driving interactions, and also collected data on human driver gap acceptance across a range of controlled gap sequences., Comment: 5 pages, 8 figures, 6 tables, data techincal description paper, Open Science Foundation - https://osf.io/eazg5

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features., Comment: 8 pages, 7 figures, 9 tables. Accepted in 2023 IEEE Intelligent Vehicles Symposium (IV). DOI: 10.1109/IV55152.2023.10186616

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

One of the objectives of software engineering education is to make students to learn essential teamwork skills. This is done by having the students work in groups for course assignments. Student team composition plays a vital role in this, as it significantly affects learning outcomes, what is learned, and how. The study presented in this paper aims to better understand the student team composition in software engineering education and investigate the factors affecting it in the international software engineering education context. Those factors should be taken into consideration by software engineering teachers when they design group work assignments in their courses. In this paper, the initial findings of the ongoing Action research study are presented. The results give some identified principles that should be considered when designing student team composition in software engineering courses.

Goods moved on board container ships constitute 70 percent of all world cargo by economic value, which makes container ships vital things in the broader agglomeration of contemporary global capitalism. These vessels are in themselves also containers of other forms of value: they on occasion store, move, and disperse noneconomic forms of social worth. Building on the insights of critical logistics studies and coupling them with anthropological insights on value, the article proposes an ethnographic “life-cycle” approach to the study of container ships that broadens maritime anthropology to encompass contemporary forms of seaborne capitalism. With the container vessel functioning as a connecting device between different “sited” fieldwork experiences in shipbuilding, shipping, and shipbreaking, such a collaborative effort can bring the larger system of maritime transportation into focus. Furthermore, when viewed through the value-within-life-cycle prism, the container ship may present itself not as an object that has a singular form but rather one that is made up of a multitude of ships that come in and out of being. By describing it as a gestalt—a dynamic material assemblage that is more than the sum of its parts—attention is paid here to how highly dependent the container ship is on geographical and social context, on ever-shifting layers of value attributed to it, and on the multifarious meaning-making among the workers laboring around it.

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

BACKGROUND: This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age ≥50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after ≥2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. METHODS: Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing ≥2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. RESULTS: Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. CONCLUSIONS: Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination. Clinical Trials Registration. NCT02723773.

Mature test automation is key for achieving high product quality at speed. However, many organizations still have immature test automation practices that impede them to reap expected benefits. Not all attempts to improve test automation practices are successful, usually caused by ineffective assessment and improvement. This dissertation aims to develop the guidelines for test automation maturity assessment and improvement by synthesizing existing guidelines from various sources, demonstrating the adoption of developed guidelines, and evaluating the effectiveness of the developed guidelines with practitioners. In this dissertation, we synthesized a taxonomy of test automation best practices from 18 test maturity models. We also reviewed 26 academic literature and 55 grey literature sources on the same topic, and found that the current literature includes the guidelines that are not covered in test maturity models, like new technical best practices and advice (e.g., improvement approaches, technical techniques, and experience-based heuristics) on conducting certain best practices. Using synthesized test automation best practices from test maturity models as the base, we developed a test automation maturity survey and distributed it in the current industry to explore the state of practice of test automation maturity. Based on 151 survey responses coming from above 100 organizations in 25 countries, several observations were made to demonstrate the adoption of test automation best practices proposed by existing test maturity models, e.g., there is a lack of guidelines on designing and executing automated tests and the right metrics to measure test automation performance in general. We observed that high levels of test automation maturity (assessed by test automation best practices from test maturity models) can lead to high product quality at speed in modern software developments, using empirical evidence from an experience study (that examined the experience o, Tiivistelmä Kypsä testiautomaatio on avainasemassa korkean tuotelaadun saavuttamisessa ilman että tarvitsee tinkiä tuotekehityksen nopeudesta. Usein organisaatioiden vielä kehittymättömät testiautomaatiokäytännöt estävät niitä saamasta automaatiolta odotettuja hyötyjä. Yritykset parantaa testiautomaatiokäytäntöjä eivät aina onnistu, mikä johtuu yleensä puutteellisesta testiautomaation kypsyydenarvioinnista ja -parantamisesta. Tämän väitöskirjan tavoitteena on kehittää ohjeistusta testiautomaation kypsyyden arviointiin ja parantamiseen syntetisoimalla olemassa olevia ohjeita eri lähteistä, tutkimalla kehitettyjen ohjeiden omaksumista ja arvioimalla kehitettyjen ohjeiden tehokkuutta yhdessä ohjelmistoteollisuuden toimijoiden kanssa. Väitöskirjassa syntetisoitiin testiautomaation hyvien käytäntöjen taksonomia 18 testikypsyysmallista. Kirjallisuuskatsauksessa tutkittiin 26 akateemista ja 55 harmaankirjallisuuden lähdettä. Havaittiin, että nykyinen kirjallisuus sisältää ohjeita, joita ei ole testikypsyysmalleissa, kuten uusia teknisiä hyviä käytäntöjä ja neuvoja (esim. teknisiä menetelmiä ja kokemuspohjaista heuristiikkaa) tiettyjen hyvien käytäntöjen toteuttamiseksi. Kirjallisuudesta syntetisoitujen, kypsyysmalleihin pohjautuvien hyvien käytäntöjen avulla kehitettiin kyselylomake ohjelmistoteollisuuden testiautomaation kypsyyden tilan mittaamiseen. Yli 100 organisaatiosta ja 25 maasta saadun 151 kyselyvastauksen perusteella tehtiin havaintoja testiautomaation hyvien käytäntöjen omaksumisesta. Puutteita havaittiin testiautomaation suunnittelun ja käytön ohjeistuksessa sekä hyötyjen mittaamisen käytänteissä. Tulokset osoittavat, että testiautomaation korkea kypsyysaste voi johtaa tuotteiden korkeaan laatuun hidastamatta tuotekehityksen nopeutta nykyaikaisessa ohjelmistokehityksessä. Havainnot perustuvat sekä empiiriseen kokemustutkimukseen, jossa tutkittiin testiautomaation kypsyyden parantamisessa onnistuneen DevOps-tiimin kokemuksia että kvantitatiiviseen tutkimukseen

Mature test automation is key for achieving software quality at speed. In this paper, we present a multivocal literature review with the objective to survey and synthesize the guidelines given in the literature for improving test automation maturity. We selected and reviewed 81 primary studies, consisting of 26 academic literature and 55 grey literature sources. From primary studies, we extracted 26 test automation best practices (e.g., Define an effective test automation strategy, Set up good test environments, and Develop high-quality test scripts) and collected many pieces of advice (e.g., in forms of implementation/improvement approaches, technical techniques, concepts, and experience-based heuristics) on how to conduct these best practices. We made main observations: (1) There are only six best practices whose positive effect on maturity improvement have been evaluated by academic studies using formal empirical methods; (2) several technical related best practices in this MLR were not presented in test maturity models; (3) some best practices can be linked to success factors and maturity impediments proposed by other scholars; (4) most pieces of advice on how to conduct proposed best practices were identified from experience studies and their effectiveness need to be further evaluated with cross-site empirical evidence using formal empirical methods; (5) in the literature, some advice on how to conduct certain best practices are conflicting, and some advice on how to conduct certain best practices still need further qualitative analysis.

The popularity of continuous integration (CI) is increasing as a result of market pressure to release product features or updates frequently. The ability of CI to deliver quality at speed depends on reliable test automation. In this paper, we present an empirical study to observe the effect of test automation maturity (assessed by standard best practices in the literature) on product quality, test automation effort, and release cycle in the CI context of open source projects. We run our test automation maturity survey and got responses from 37 open source java projects. We also mined software repositories of the same projects. The main results of regression analysis reveal that, higher levels of test automation maturity are positively associated with higher product quality (p-value=0.000624) and shorter release cycle (p-value=0.01891); There is no statistically significant evidence of increased test automation effort due to higher levels of test automation maturity and product quality. Thus, we conclude that, a potential benefit of improving test automation maturity (using standard best practices) is product quality improvement and release cycle acceleration in the CI context of open source projects. We encourage future research to extend our findings by adding more datasets with different programming languages and CI tools, closed source projects, and large-scale industrial projects. Our recommendation to practitioners (in the similar CI context) is to utilize standard best practices to improve test automation maturity.

Software engineering (SE) profession requires various technical and non-technical skills. The skills required are influenced by the changes in the field, industry, and global trends. The changes are also reflected in the recruitment. To understand the current situation and expectations from jobseekers, this study investigates how language, intercultural, and communication skills are presented in the job ads applicable to junior software engineers. The study is based on job ads published in the Vacancies job seeking service maintained by Public Employment and Business Service in Finland. Data contained 166 job ads of which 60% were published in Finnish and 40% in English. Data analysis was based on content and thematic analysis. Based on the job ads, language, both Finnish and English, and communication skills were largely presented and required from junior software engineers, but intercultural skills and multiculturalism were almost missing. The job ads described a multifaceted, multidisciplinary communication environment where junior software engineers work. Moreover, Finnish SE labor markets have needs for fluent Finnish and English speakers; Finnish SE working environment is almost bilingual in practice. Intercultural aspects of communication were not generally visible. The peculiarities of intercultural communication are either not understood or not been aware of in designing the job ads. Moreover, the language of the ad indicates the needs and awareness. The job ads written in English highlight English skills, present Finnish skills mainly as an advantage, and emphasize communication and intercultural skills.

Mature test automation is key for achieving software quality at speed. In this paper, we present a multivocal literature review with the objective to survey and synthesize the guidelines given in the literature for improving test automation maturity. We selected and reviewed 81 primary studies, consisting of 26 academic literature and 55 grey literature sources. From primary studies, we extracted 26 test automation best practices (e.g., Define an effective test automation strategy, Set up good test environments, Develop high-quality test scripts) and collected many pieces of advice (e.g., in forms of implementation/improvement approaches, technical techniques, concepts, experience-based heuristics) on how to conduct these best practices. We made main observations: (1) There are only 6 best practices whose positive effect on maturity improvement have been evaluated by academic studies using formal empirical methods; (2) Several technical related best practices in this MLR were not presented in test maturity models; (3) Some best practices can be linked to success factors and maturity impediments proposed by other scholars; (4) Most pieces of advice on how to conduct proposed best practices were identified from experience studies and their effectiveness need to be further evaluated with cross-site empirical evidence using formal empirical methods; (5) In the literature, some advice on how to conduct certain best practices are conflicting, and some advice on how to conduct certain best practices still need further qualitative analysis., Comment: Journal of Software: Testing, Verification and Reliability (2022)

There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks., Comment: Accepted for publication in IEEE Robotics & Automation Magazine. In press

The popularity of continuous integration (CI) is increasing as a result of market pressure to release product features or updates frequently. The ability of CI to deliver quality at speed depends on reliable test automation. In this paper, we present an empirical study to observe the effect of test automation maturity (assessed by standard best practices in the literature) on product quality, test automation effort, and release cycle in the CI context of open source projects. We run our test automation maturity survey and got responses from 37 open source java projects. We also mined software repositories of the same projects. The main results of regression analysis reveal that, higher levels of test automation maturity are positively associated with higher product quality (p-value=0.000624) and shorter release cycle (p-value=0.01891); There is no statistically significant evidence of increased test automation effort due to higher levels of test automation maturity and product quality. Thus, we conclude that, a potential benefit of improving test automation maturity (using standard best practices) is product quality improvement and release cycle acceleration in the CI context of open source projects. We encourage future research to extend our findings by adding more datasets with different programming languages and CI tools, closed source projects, and large-scale industrial projects. Our recommendation to practitioners (in the similar CI context) is to utilize standard best practices to improve test automation maturity.

Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focus on quantitative error metrics like the root mean square error (RMSE) to learn and report their models' capabilities. This focus on quantitative error metrics tends to ignore the more important behavioral aspect of the models, raising the question of whether these models really predict human-like behavior. Thus, we propose to analyze the output of machine-learned models much like we would analyze human data in conventional behavioral research. We introduce quantitative metrics to demonstrate presence of three different behavioral phenomena in a naturalistic highway driving dataset: 1) The kinematics-dependence of who passes a merging point first 2) Lane change by an on-highway vehicle to accommodate an on-ramp vehicle 3) Lane changes by vehicles on the highway to avoid lead vehicle conflicts. Then, we analyze the behavior of three machine-learned models using the same metrics. Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior. Additionally, the collision aversion analysis during lane changes showed that the models struggled to capture the physical aspect of human driving: leaving adequate gap between the vehicles. Thus, our analysis highlighted the inadequacy of simple quantitative metrics and the need to take a broader behavioral perspective when analyzing machine-learned models of human driving predictions., Comment: This work has been accepted for publication in the IEEE Transactions on Intelligent Transportation Systems journal on 13th March 2023

Objective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background: Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results: The drivers’ probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion: Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application: Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles., Human-Robot Interaction

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

Efficacy against herpes zoster and immune responses to the adjuvanted recombinant zoster vaccine plateaued at high levels between 5.1 and 7.1 years (mean) post-vaccination, suggesting that its clinical benefit in older adults is sustained for at least 7 years post-vaccination. Background This ongoing follow-up study evaluated the persistence of efficacy and immune responses for 6 additional years in adults vaccinated with the glycoprotein E (gE)-based adjuvanted recombinant zoster vaccine (RZV) at age >= 50 years in 2 pivotal efficacy trials (ZOE-50 and ZOE-70). The present interim analysis was performed after >= 2 additional years of follow-up (between 5.1 and 7.1 years [mean] post-vaccination) and includes partial data for year (Y) 8 post-vaccination. Methods Annual assessments were performed for efficacy against herpes zoster (HZ) from Y6 post-vaccination and for anti-gE antibody concentrations and gE-specific CD4[2+] T-cell (expressing >= 2 of 4 assessed activation markers) frequencies from Y5 post-vaccination. Results Of 7413 participants enrolled for the long-term efficacy assessment, 7277 (mean age at vaccination, 67.2 years), 813, and 108 were included in the cohorts evaluating efficacy, humoral immune responses, and cell-mediated immune responses, respectively. Efficacy of RZV against HZ through this interim analysis was 84.0% (95% confidence interval [CI], 75.9-89.8) from the start of this follow-up study and 90.9% (95% CI, 88.2-93.2) from vaccination in ZOE-50/70. Annual vaccine efficacy estimates were >84% for each year since vaccination and remained stable through this interim analysis. Anti-gE antibody geometric mean concentrations and median frequencies of gE-specific CD4[2+] T cells reached a plateau at approximately 6-fold above pre-vaccination levels. Conclusions Efficacy against HZ and immune responses to RZV remained high, suggesting that the clinical benefit of RZV in older adults is sustained for at least 7 years post-vaccination., Zoster-049 Study Groupopen access

The objective of this thesis is to develop and productize a cooling element for bioleaching reactor-specific product series for a corporation operating in the minerals processing and metals refining industry. The thesis consists of the theory of product development process of new reactor cooling element, and empirical case study. The purpose of the new cooling element series is to provide innovative, robust, and cost-efficient option for bioleaching specific reactors series besides the previously developed products. With a properly productized new cooling element, the company has a robust and price-competitive cooling element to offer and as a result, put the team in an efficient position when receiving offer requests. Having product series specified cooling element saves design costs and time in project implementation and as a result, lowers the costs of the overall reactor. This thesis uses qualitative research methods. Research sources are collected from the literature and through interviews with the research team. The product development was performed using the case company-specific product development method. The empirical case study consists of opportunity checking, concept development, product development, product launch, and data analysis of the cooling element. The product development project is executed for only bioleaching cooling elements for the reactor sizes from 5m to 15m. The product development focused on cost-efficiency and robustness. The benefits of the new cooling element were collected only in a short time frame with study groups' limited points of view. This study helps the company product engineering department to develop an innovative and cost-efficient reactor cooling element. After this study, the product engineering department can further develop and test the new cooling element to minimize the uncertainties that could affect the project’s technical and financial performance. The study also provides the possibility for and further researc, Tämän diplomityön tavoitteena on kehittää ja tuotteistaa jäähdytyselementti bioliuotusreaktorikohtaisille tuotesarjoille mineraalien ja metallien jalostusteollisuudessa toimivalle yritykselle. Diplomityö koostuu uuden reaktorijäähdytyselementin tuotekehitysprosessin teoriasta ja empiirisestä tutkimuksesta. Uuden jäähdytyselementtisarjan tarkoituksena on tarjota innovatiivinen, kestävä ja kustannustehokas vaihtoehto bioliuotusreaktorikohtaisille tuotesarjoille aiemmin kehitettyjen ratkaisujen ohelle. Uuden asianmukaisesti tuotteistetun jäähdytyselementin avulla yrityksellä on kestävä ja hintakilpailukykyinen ratkaisu, jonka avulla yritys on kilpailukykyisessä asemassa vastaanottaessaan tarjouksia. Tuotesarjakohtaisesti suunniteltu jäähdytyselementti säästää suunnittelukustannuksia ja aikaa projektin toteutuksessa ja alentaa reaktorin kokonaiskustannuksia. Tässä diplomityössä käytetään kvalitatiivisia tutkimusmenetelmiä. Tutkimustietoa kerättiin kirjallisuudesta ja haastattelemalla tutkimusryhmiä. Tuotekehitys tehtiin yrityskohtaisten tuotekehitysmenetelmien avulla. Empiirinen tutkimus koostuu kehityskohteiden tunnistamisesta, konseptoinnista, tuotekehityksestä, tuotteen lanseerauksesta ja jäähdytyselementin data-analyysistä. Tuotekehitysprojekti toteutettiin ainoastaan halkaisijaltaan 5–15 m bioliuotusreaktorien jäähdytyselementeille. Tuotekehityksessä keskityttiin kustannustehokkuuteen ja kestävyyteen. Uuden jäähdytyselementin hyödyt mitattiin vain lyhyellä aikavälillä ja rajoitetuilla tutkimusryhmien näkökulmilla. Tämä tutkimus auttaa yrityksen tuotesuunnitteluosastoa kehittämään innovatiivisia ja kustannustehokkaita reaktorijäähdytyselementtejä. Tämän tutkimuksen jälkeen tuotesuunnitteluosasto pystyy jatkokehittämään ja testaamaan uutta jäähdytyselementtiä minimoimakseen epävarmuustekijät, jotka voivat vaikuttaa projektin tekniseen ja taloudelliseen suorituskykyyn. Tutkimus tarjoaa myös mahdollisuuden selvittää kehitetyn tekniikan käyttöä myös lämmityselementeissä

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

SyAet med denna studie var a< förstå hur konsumenter i Sverige med olika könsroller och åldrarupplever elektroniska (e-) handelswebbsidor som =llförlitliga baserat på den visuella designen.SyAet besvarades genom a< undersöka färg- och typografins påverkan på upplevd =llförlitlighet före-handelswebbsidor, u=från konsumentens perspek=v.År 2021 används e-handeltjänster alltmer för a< köpa produkter. Företag kan numera nå ut =ll fleroch större målgrupper genom e-handel då företagets geografiska belägenhet inte längre äravgörande för kunden. EAersom e-handelsmarknaden växer och konkurrensen blir allt hårdare, ärdet vik=gt a< företagen är medvetna om dess visuella presenta=on på webbsidan för a< fåkonsumenternas =llit. Resultat av =digare studier visar a< färg och typografiska val är vik=ga faktorerför huruvida en webbsida upplevs =llförlitlig. Tidigare forskning visar även på hur olika kön och åldrarförhåller sig =ll internet och hur olika deras användningsmönster ter sig vad gäller teknologi.Denna studie uUördes med hjälp av en visuell innehållsanalys och en enkätundersökning. Resultatenfrån den visuella innehållsanalysen gav verktyg =ll a< skapa fik=va webbsidor med olika färg ochtypsni<, som vidare användes i enkätundersökningen. Totalt tolv e-handelswebbsidor skapades =llenkätundersökningen för a< utreda vilken färg samt typsni< som upplevs vara mest =llförlitlig.Studien indikerade jämna resultat mellan åldersgrupper samt könsroller vad gäller upplevelse av=llförlitlighet u=från färg- och typografival på e-handelswebbsidor. Resultatet visade tendenser =lla< den orangea samt gula webbdesignen upplevdes minst =llförlitliga enligt respondenterna i dennastudie. Något mer =llförlitliga upplevdes färgerna grön, blå, röd och rosa. Studien indikerade a

This introduction proposes an anthropology of global cargo circulation by placing the maritime shipping industry at the center of global capitalism. With container economies we refer to the maritime global circulation of cargo that is sustained by an undervalued labor force, dependent upon unstable logistics infrastructures and driven by speculative capital. Container economies, we argue, are produced by adding, moving, and destroying value through the maritime supply chain. In this introduction, we reflect upon the implications of containerization and its wider consequences for logistics labor. We argue that maritime logistics and labor is best understood by taking into account their wider networks of dependency expressed through kinship relations, ethnicity and coexisting regimes of value.

Several time synchronous ad hoc networks may operate in the same geographical area. Although network time synchronization within an individual network has been widely discussed, internetwork time synchronization relying on time sources the networks carry has not been addressed. Therefore, this paper proposes an internetwork time synchronization procedure that allows to use one network as a time source and share its time, like a global navigation satellite system time, to other networks. The internetwork time synchronization procedure runs in gateway nodes that could be a member in several networks, and it collaborates with the individual network time synchronization algorithms that operate at the network level. Consequently, the paper proposes a generic, robust hybrid network time synchronization protocol and analyses its performance using a sensible metric called a mutual consensus. It is shown that the common time is achieved with a limited variance in the networks that operate in the master-slave, distributed or hybrid mode. Provided numerical examples illustrate the behavior of the algorithm and confirm the theoretical findings.

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

Introduction: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle's intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator. Method: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band – SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured. Results: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, cross

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Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on the concept of sensorimotor control, we develop a modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects these principles to the decision theory and is applied to determine whether such an approach can reproduce the following phenomena: When two pedestrians travel on crossing paths, (a) their interaction is sensitive to initial asymmetries, and (b) based on which, they rapidly resolve collision conflict by adapting their behaviors. When a pedestrian crosses the road while facing an approaching car, (c) either road user yields to the other to resolve their conflict, akin to the pedestrian interaction, and (d) the outcome reveals a specific situational kinematics, associated with the nature of vehicle acceleration. We show that these phenomena emerge naturally from our modeling framework when the model can evolve its parameters as a consequence of the situations. We believe that the modeling framework and phenomenon-centered analysis offer promising tools to understand road user interactions. We conclude with a discussion on how the model can be instrumental in studying the safety-critical situations when including other variables in road-user interactions., Comment: 10 pages, 7 figures

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

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Autonomous vehicles should be able to predict the future states of its environment and respond appropriately. Specifically, predicting the behavior of surrounding human drivers is vital for such platforms to share the same road with humans. Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles. This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We are motivated by improving the prediction accuracy when a surrounding vehicle performs lane change and highway merging maneuvers. We propose a novel pooling strategy to capture the inter-dependencies between the neighbor vehicles. Depending solely on Euclidean trajectory representation, the existing pooling strategies do not model the context information of the maneuvers intended by a surrounding vehicle. In contrast, our pooling mechanism employs polar trajectory representation, vehicles orientation and radial velocity. This results in an implicitly maneuver-aware pooling operation. We incorporated the proposed pooling mechanism into a generative encoder-decoder model, and evaluated our method on the public NGSIM dataset. The results of maneuver-based trajectory predictions demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches. Our "Pooling Toolbox" code is available at https://github.com/m-hasan-n/pooling., Comment: Preprint (under review IROS'21). arXiv admin note: text overlap with arXiv:2104.11180

Predicting future behavior of the surrounding vehicles is crucial for self-driving platforms to safely navigate through other traffic. This is critical when making decisions like crossing an unsignalized intersection. We address the problem of vehicle motion prediction in a challenging roundabout environment by learning from human driver data. We extend existing recurrent encoder-decoder models to be advantageously combined with anchor trajectories to predict vehicle behaviors on a roundabout. Drivers' intentions are encoded by a set of maneuvers that correspond to semantic driving concepts. Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout. The proposed model can output a multi-modal distribution over the predicted future trajectories based on the maneuver-specific anchors. We evaluate our model using the public RounD dataset and the experiment results show the effectiveness of the proposed maneuver-based anchor regression in improving prediction accuracy, reducing the average RMSE to 28% less than the best baseline. Our code is available at https://github.com/m-hasan-n/roundabout., Comment: Under Review IROS 2021

There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error metrics, for example average distance between model-generated and observed trajectories. Such metrics permit relative comparison of models, but do not provide clearly interpretable information on how close to human behavior the models actually come, for example in terms of higher-level behavior phenomena that are known to be present in human driving. We study highway driving as an example scenario, and introduce metrics to quantitatively demonstrate the presence, in a naturalistic dataset, of two familiar behavioral phenomena: (1) The kinematics-dependent contest, between on-highway and on-ramp vehicles, of who passes the merging point first. (2) Courtesy lane changes away from the outermost lane, to leave space for a merging vehicle. Applying the exact same metrics to the output of a state-of-the-art machine-learned model, we show that the model is capable of reproducing the former phenomenon, but not the latter. We argue that this type of behavioral analysis provides information that is not available from conventional model-fitting metrics, and that it may be useful to analyze (and possibly fit) models also based on these types of behavioral criteria., Comment: This paper has been submitted to 24th IEEE International Conference on Intelligent Transportation - ITSC2021, September 19-22, 2021 Indianapolis, IN, United States

SyAet med denna studie var a< förstå hur konsumenter i Sverige med olika könsroller och åldrarupplever elektroniska (e-) handelswebbsidor som =llförlitliga baserat på den visuella designen.SyAet besvarades genom a< undersöka färg- och typografins påverkan på upplevd =llförlitlighet före-handelswebbsidor, u=från konsumentens perspek=v.År 2021 används e-handeltjänster alltmer för a< köpa produkter. Företag kan numera nå ut =ll fleroch större målgrupper genom e-handel då företagets geografiska belägenhet inte längre äravgörande för kunden. EAersom e-handelsmarknaden växer och konkurrensen blir allt hårdare, ärdet vik=gt a< företagen är medvetna om dess visuella presenta=on på webbsidan för a< fåkonsumenternas =llit. Resultat av =digare studier visar a< färg och typografiska val är vik=ga faktorerför huruvida en webbsida upplevs =llförlitlig. Tidigare forskning visar även på hur olika kön och åldrarförhåller sig =ll internet och hur olika deras användningsmönster ter sig vad gäller teknologi.Denna studie uUördes med hjälp av en visuell innehållsanalys och en enkätundersökning. Resultatenfrån den visuella innehållsanalysen gav verktyg =ll a< skapa fik=va webbsidor med olika färg ochtypsni<, som vidare användes i enkätundersökningen. Totalt tolv e-handelswebbsidor skapades =llenkätundersökningen för a< utreda vilken färg samt typsni< som upplevs vara mest =llförlitlig.Studien indikerade jämna resultat mellan åldersgrupper samt könsroller vad gäller upplevelse av=llförlitlighet u=från färg- och typografival på e-handelswebbsidor. Resultatet visade tendenser =lla< den orangea samt gula webbdesignen upplevdes minst =llförlitliga enligt respondenterna i dennastudie. Något mer =llförlitliga upplevdes färgerna grön, blå, röd och rosa. Studien indikerade a

Permafrost thaw in peatlands is one of the most widespread and worrying consequences of climate warming in the sub-Arctic area. To predict future climate feedbacks, it is important to study the history of permafrost aggradation and thaw. Plant macrofossil analysis with radiocarbon dating has been widely used in detecting past permafrost dynamics in peatlands, however, due to a lack of permafrost-specific plant indicator species, determining the exact timing of permafrost aggradation remains a challenge. In this study, we investigated if oribatid mites can be used to determine Holocene permafrost aggradation and degradation in Canadian mires. Based on analyses of subfossil oribatid mite assemblages of Holocene peat profiles from two mires in the Hudson Bay Lowlands area, our results suggest that two species, Carabodes labyrinthicus and Neoribates aurantiacus, are useful bioindicators, which can be used in palaeoecological studies determining permafrost histories. Moreover, our results show that subfossil oribatid mite remains can reveal periods of permafrost, which cannot be determined with certainty based on plant macrofossils alone.