Assisted / autonomous vs. human driving assessment on the DiM driving simulator using objective / subjective characterization

Weill Cornell Medical College of Cornell University

This study assessed the impact of a new critical care nursing orientation model implemented at a Midwestern university-affiliated medical center using several variables including satisfaction, retention, turnover, vacancy, preparedness to manage patient care assignments, and length and cost of orientation. The model included a pathway for experienced critical care nurses, experienced non–critical care nurses, and graduate nurses. It used a blended learning approach with AACN’s Essentials of Critical Care Orientation (ECCO), the Pulmonary Artery Catheter Education Project (PACEP), class and simulation sessions, precepted clinical experiences, video training, and demonstration and testing. The Basic Knowledge Assessment Test (BKAT) was used to identify participants’ learning needs. Data were collected from 173 participants and their preceptors and managers at 3 months and several follow-up intervals during the first year of implementation.Simulation experiences and pocket guides were rated as the most useful teaching methods by participants. Managers, staff educators, and preceptors reported satisfaction with the orientation program and on-unit components, and they rated participants as being well prepared to manage patient care assignments. ICU retention rates rose from 91.2% to 93.7% over the year, and retention rates of newly hired nurses ranged from 93.8% to 100%. ICU turnover rates decreased from 8.77% to 6.29% and ICU position vacancy rates decreased from 14.3% to 4.8%. The overall orientaton length for all groups remained unchanged. The study’s results indicated that the new model for critical care nursing orientation helped increase satisfaction and retention rates and decrease turnover and position vacancy rates.Lead author Linda Morris, PhD, APN, said the new model was such a new way of doing things that it was a perfect setup for studying outcomes. She proposed to administration that the project be considered not only as a new program, but also as a research project. She noted, “Critical care educators across the country have struggled for years to provide a meaningful and effective orientation program for critical care nurses. Our main goal was to see if the new program achieved our intended outcome: competent nurses who have the ability to think critically. And we wanted to identify any secondary outcomes it might achieve.”She said the proposal came at a good time politically. “The hospital was preparing for Magnet and, in the midst of a relatively high vacancy rate, was preparing major hiring initiatives to open 2 new critical care service lines,” she noted.Morris explained that the new orientation model was distinctly different from the old model. “The previous program was what we called a ‘traditional approach.’ It consisted of classroom instruction and clinical time with a preceptor.” This burdened the preceptors who had nurses of varying levels of experience and spent much of their time teaching theory—such as significance of waveforms, cardiac rhythm interpretation, and basic skills such as starting IVs and nasogastric tubes. The new model replaced the traditional approach with 4 primary components: online learning with ECCO (standardizing content), clinical time with the preceptor (application of content), case studies (critical thinking), and use of the high-fidelity simulator (critical thinking in real-time).Morris said that, among other findings, the study results demonstrated improvements in the preparation level for managing patient care assignments. “Managers saw the most dramatic improvement in the new grads. They were amazed to find them ‘competent beginners’ directly out of orientation.”The study demonstrated that the new orientation model increased satisfaction and retention rates and decreased turnover and position vacancy rates. Several teaching strategies were integrated into the program including class and simulation sessions, precepted clinical experiences, video training, and return demonstration and testing. Morris said one of the keys to their success was that they tried to focus on assessing the critical thinking ability of the orientees individually, no matter their level of experience. She noted, “It’s impossible to do that in a classroom format. Not everyone has access to a high-fidelity simulator, but the more life-like experiences that can be provided, the more learning takes place.”The study showed that using a variety of instructional methods both to provide educational content and to integrate critical thinking concepts was beneficial. Morris concluded, “The ability to think critically under pressure is a key quality of a critical care nurse, and it is possible to teach critical thinking by using simulations and case studies.”This feature briefly describes the personal journey and background story of the EBR article’s lead investigators, discussing the circumstances that led them to undertake the line of inquiry represented in the research article featured in this issue.The idea for lead investigator Linda Morris’s research study was born out of frustration. “As a clinical nurse specialist and educator for many years, I have felt the frustration of trying to provide a meaningful critical care orientation program for nurses with a wide variation in experience,” she said.She noted, “Before we began, there was much dissatisfaction with the orientation program; preceptors were frazzled, managers were anxious because turnover was high, the experienced orientees were frustrated at having to sit through classes they probably didn’t need, and the new grads were completely overwhelmed.“In creating the program, she was pleased to discover how many resources were available. “In addition to staff educators, there were education specialists and curriculum designers to help write the curriculum and the teaching aids.”She found coinvestigators among colleagues who were already invested in the program as managers and educators—and as a result they “were happy to be part of the research study.”Morris offered this advice to future researchers: “Knowledge is power, and research is the key to the kingdom.” She said communication is important because “nurses speak the language of patient care, advocacy, and patient safety. Our administrators speak the language of outcomes, cost-benefit, and patient satisfaction. In order to improve clinical care at the bedside, we must start speaking the language of our administrators to help them support us and understand the possibilities.”Finally, she noted, “Research is rarely an individual activity. It is best when there is a diverse group that has members with different skills and different ways of thinking.”

The possible transition to fully autonomous cars represents a paradigm shift, which is likely to have a profound impact on driving experience and automobile technology acceptance. Using an online questionnaire, Rodel et al. [7] have found that measures for User Acceptance (UA) and User Experience (UX) decline with increasing autonomy level. In this study, we investigate the differences in UA and UX for vehicles with different levels of automation in a more immersive context. We used a simple driving simulator setup in a virtual reality environment (using an Oculus Rift headset). We designed three tasks which each represented a different level of automation and asked participants (N = 17) to fill out the Car Technology Acceptance Model (CTAM) questionnaire after using each autonomy level. The immersion of the simulator setup was assessed with a standardized questionnaire. In contrast to Rodel et al. [7] results do not show a general decline in UA and UX with increasing autonomy, but suggest that Performance Expectancy, Perceived Safety and Social Influence are significantly higher for the fully automated condition than for no automation. The scores for immersion ranging about the average of benchmark evaluations indicate that the users felt quite immersed, but that there is still room for improving the VR setup.

An autonomous driving system requires the safety and availability of automated driving. For example, an autonomous driving system with automation level 3 requires the functions to request the driver to take over driving and to sustain safe automated driving until the driver accepts the request if a hardware failure occurs. However, there is a demand to continue automated driving if the system maintains sufficient performance for automated driving after the failure occurs. Therefore, we propose a graceful degradation design process to improve the automated driving continuation rate by defining degradation functions against performance limitation and hardware failure. The process integrates and extends ISO/PAS 21448 and ISO26262 and carries out these tasks in the order of system-level, ECU-level, and microcontroller-level degradation design. Furthermore, we propose a framework to calculate worst-case mode switch time (WCMST), which means the time duration from failure detection to degradation processing, by utilizing degradation design results. To evaluate the proposed process and framework, we applied them to the prototype system with automation level 3. The evaluation results showed that the designed system can sustain automated driving against 86.1% of performance degradation factors and that the framework can improve the calculation accuracy of WCMST by 35.3%.

A Key Issue in Product Life Cycle: Disassembly

According to Sheridan and Verplank's classification of levels of automation, taxiing on an airport still has a very low automation level. Key element for increased automation in taxi guidance is a precise and reliable navigation capability. As a Ground Based Augmentation System (GBAS) can fulfill the stringent requirements for a precision approach, it can play an important role in providing the required navigation performance during taxiing as well. This paper will explain the use of GBAS for taxi guidance. It presents, how the required navigation performance for automatic taxiing can be provided using GBAS. Besides the highly precision position service provided via Differential GPS, the Terminal Area Path (TAP) messages will be used to provide the aircraft with accurate and reliable information about the taxi route. Taxi trials with DLR's A320 research aircraft ATRA (Advanced Technology Research Aircraft) and the GBAS ground station at airport Braunschweig-Wolfsburg (EDVE) showed that over the whole maneuvering area the received VHF signal level was adequate for transmitting such a “Ground-TAP”. These tests were performed with an antenna on the aircraft located at a height of 8ft, which is lower than the actual required coverage minimum of 12ft defined in ICAO Annex 10. Together with a redefined voice communication structure and a list of predefined routes the use of GBAS for taxi routing functionalities of an Advanced Surface Movement Guidance & Control System (A-SMGCS) is feasible. Due to the independency from an on-board database the combination of such a message broadcast and the GNSS based positioning can be the key enabler for a future auto taxiing functionality.

This paper describes human-in-the-loop research at NASA Ames Research Center on service provider-based automated separation assurance for the Next Generation Air Transportation System (NextGen). Key human/automation integration aspects such as levels of automation and roles and responsibilities of automated separation management are investigated in the Airspace Operations Laboratory. A trilogy of part-task studies was designed to examine efficiency, safety, workload impact and acceptability of central aspects of the concept. Findings from a 2007 study on strategic trajectory-based automated separation assurance with data link-equipped aircraft are discussed in detail. Preliminary results on mixed operations with data link and conventional aircraft gathered in spring 2008 are included. The experiment design for investigating tactical safety assurance and off-nominal situations in summer 2008 is outlined. This research was funded by the Separation Assurance element of NASA’s Next Generation Air Transportation System – Airspace Project.

The research focuses on the emerging domain of Big Data and the Internet of Things in the context of architectural design and operation. The profession of architecture relies on the use of data in almost all stages of the building cycle. However, this data is often utilised in a trivial manner, without clearly addressing how the data is utilised, when it is utilised, the value of such utilisation and the impact the data has on the design operations and the overall building. Data in architecture mainly serves as a medium of communication to generate a design. Data can only be as good as the technology available at the time it is gathered. Nevertheless, the role of data has changed with the advancement of digital data technologies such as Big Data and the Internet of Things. Digital data is now a driver for businesses and operations in other industries. The investigation of contemporary data utilisation in architecture design reveals that data is not utilised as a driver for the design in most cases and, when it is utilised as a driver, it is not exploited and is not explicitly addressed as part of the business. A knowledge gap in architecture in addressing the utilisation of data and addressing digital data as a driver in design operations is identified. This identification is supplemented by observing that data-driven operations provide the potential for better and more efficient design and business. To fill this knowledge gap and to build a foundation for data utilisation in architecture, this thesis proposes a Data-Driven Operational Framework for architecture, which is the main output of this research and its main contribution to knowledge. The Data-Driven Operational Framework reveals and explains the required components and operations for employing a data-driven design approach in architectural processes and business. In order to develop such a framework, an investigation of current architectural cases that utilise digital data was completed, which is a crucial part of the research. However, it was not possible to investigate these cases without having a thorough understanding of the state-of-the-art data technologies and an understanding of the existing taxonomy of data and the existing taxonomy of value in architectural operations. To build this taxonomy of data, a literature review investigating the terms data, digital data operations, Big Data and the Internet of Things was conducted. To build the taxonomy of value, a literature review of values, value creation and valuation methods in architecture was performed. Also, this value investigation led to the development of a Digital Value Equaliser, which is a conceptual representation that supports the analysis of values in architectural design cases. The case studies were analysed following the coding techniques of Grounded Theory Methodology. The coding procedures were followed systematically and continuously until data saturation was reached. Reaching data saturation led to the development of the Data-Driven Operational Framework for architecture. The Data-Driven Operational Framework has two theoretical applications, the Data-Driven Levels in architectural operations framework and the Data-Driven Impact on the AEC framework. These two theoretical frameworks are the findings of the second part of the research and add to the research contribution. The Data-Driven Levels framework reveals the different automation levels in utilising data in architectural operations. This framework classifies data operations in architecture into six levels according to how automated they are and the degree of human involvement in each operation. The Data-Driven Impact framework shows the anticipated impact of employing data-driven operations on the existing business and cultural models in architecture, engineering and construction (AEC). This shows the required business and cultural changes in operating an architecture business. The Impact framework supports architects to identify what measures and changes are needed to benefit from the use of data-driven operations in their practices and business.

In Tanzania, inadequate infrastructures and shortages of trauma-response training exacerbate trauma-related fatalities. McGill University's Centre for Global Surgery introduced the Trauma and Disaster Team Response course (TDTR) to address these challenges. This study assesses the impact of simulation-based TDTR training on care providers' knowledge/skills and healthcare processes to enhance patient outcomes. The study used a pre-post-interventional design. TDTR, led by Tanzanian instructors at Muhimbili Orthopedic Institute from August 16-18, 2023, involved 22 participants in blended online and in-person approaches with simulated skills sessions. Validated tools assessed participants' knowledge/skills and teamwork pre/post-interventions, alongside feedback surveys. Outcome measures included evaluating 24-h emergency department patient arrival-to-care time pre-/post-TDTR interventions, analyzed using parametric and non-parametric tests based on data distributions. Participants' self-assessment skills significantly improved (median increase from 34 to 58, p<0.001), along with teamwork (median increase from 44.5 to 87.5, p<0.003). While 99% of participants expressed satisfaction with TDTR meeting their expectations, 97% were interested in teaching future sessions. The six-month post-intervention arrival-to-care time significantly decreased from 29 to 13min, indicating a 55.17% improvement (p<0.004). The intervention led to fewer ward admissions (35.26% from 51.67%) and more directed to operating theaters (29.83% from 16.85%), suggesting improved patient management (p<0.018). The study confirmed surgical skills training effectiveness in Tanzanian settings, highlighting TDTR's role in improving teamwork and healthcare processes that enhanced patient outcomes. To sustain progress and empower independent trauma educators, ongoing refresher sessions and expanding TDTR across low- and middle-income countries are recommended to align with global surgery goals.

Recent technological development, decreasing costs coupled with an increasing availability of unmanned aerial vehicles (UAVs) have made UAV-based condition monitoring and diagnosis of power equipment attractive. Traditional labor-intensive manual interventions have been steadily replaced by solutions where the level of automation is gradually increasing. At the same time, a tendency to scale up inspection missions to cover entire overhead line segments, as opposed to spot investigations, has been emerging. Along this line of trends, a system addressing power line monitoring with a focus on revealing faults invisible to the naked eye is presented in this work. In order to meet safety constraints and at the same time cover a certain distance to be financially appealing (typically around 100 km per day), flight missions are recommended to keep at least 15-20 meters from the infrastructure and at the same time be executed at a velocity of at least 5 m/s. Under these challenging dynamic conditions, research project VOLTAIR relies on commercial ultraviolet and infrared imaging devices while aiming at a drastic reduction of time-consuming on-site visits through the use of UAVs and automatic data analysis.

Many advances have been made in stereo-vision-based tube measurement. This approach is characterised by its accuracy, level of automation, non-contact nature, reliability, simplicity of operation and speed. Many studies have indicated that multi-stereo-vision technology can solve the occlusion problem and be used to efficiently and accurately measure complicated tubes. Increasing demand for fast and accurate quality control of tubes has significantly improved the confidence of users of this technology. The purpose of this paper is to review the research papers published in the tube measurement based on stereo-vision research area. Following a detailed introduction, this paper first discusses the measurement problem and requirements and then reviews the current state of academic research on the key techniques, including three-dimensional (3D) reconstruction, parameter calculation and accuracy verification. This is followed by a summary and conclusion. This paper’s aim is to help interested researchers find the suitable and accurate 3D reconstruction method of different kinds of tubes in the literature and set up a tube measurement system quickly.

This report with recommendations is the result of an expert panel meeting on PAT applications in food industry that was organized by the M3C Section of the European Society of Biochemical Engineering Science (ESBES) at the 10th ESBES Symposium. The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for wider applications of PAT in food industry. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided in this report. It concludes with a number of recommendations to facilitate further developments and a wider application of PAT in food industry. Process Analytical Technologies (PAT) [1] (European Medicines Agency EMA-FDA pilot program for parallel assessment of Quality by Design applications; Document EMA/172347/2011) have been extensively discussed in literature, particularly with respect to (bio)pharmaceutical process modelling, monitoring and control [2]. Table 1 provides an historic overview of the PAT development in the context of food applications. Although successful applications within food industry are increasingly being reported [3, 4], the session on PAT in food industries at the ESBES-IFIBiop 2014 in Lille highlighted significant challenges and opportunities for further development in this area. This position paper briefly reviews the current state-of-the-art, industrial needs and opportunities as well as scientific challenges to be addressed in order to extend the use of PAT methodology in the food industries. Currently, quality and safety control are still based mainly on discontinuous analysis with traditional analytical methods in the lab or, at best, at-line measurements. This is no longer sufficient to fulfill the needs of the food industry. Due to higher safety and quality standards and demands and high throughput of production facilities, the number of samples to be analyzed is increasing. Rapid analysis methods and PAT are required to address these needs along the complete production chain leading to a better understanding and control of raw materials, intermediate products in the production process as well as the final products to be packaged and delivered. The goal is to achieve real time analysis in order: • to avoid usage of any out-of-specification raw material and to detect adulteration, substitution, tampering and counterfeiting leading to non-authentic products; • to be able to intervene and stop/change processes in order to secure the target quality; • to assess the final quality to avoid out-of-specification products being packaged and shipped, thus leading to undesirable customer dissatisfaction and costs associated with the resolution of complaints. Over a hundred years ago (on May 22nd, 1913) the first patent on a PAT device ("Pfeiffenanalysator" for measuring the ratio of H2 and N2 gas for ammonia synthesis) was granted to Paul Gmelin from Badische Anilin- und Soda-Fabrik, BASF (Patentschrift Nr. 281157, Klasse 42/. Gruppe 4). Since then, PAT found broad application in chemical industry, which is dominated by highly automated, continuous processes. Today in chemical processes, such as the synthesis of isocyanates, e.g. hexamethylendiisocyanate (HDI), typically 60-130 PAT measurements are collected. In contrast, in sectors like (bio)pharmaceutical or food industry, the application of PAT is significantly less frequent today, especially, in terms of on-line analytics, where the measurement takes place in or close to the production step (or unit operation). One reason for this is the reduced degree of automation of the processes in these industries, which are dominated by unit operations, single production steps consecutively executed after each other, resulting in production batches. Analytics of a production batch takes place mostly off-line in analytical laboratories during hold-up times of the process intermediate between the different unit operations. Regulatory requirements represent an equally important driver for extensive online analytics. With the 2004 FDA's Quality by Design initiative, regulatory agencies demand systematic risk based process development and understanding of the processing space. This also allows for flexible process adjustments within the explored space for producing the desired product quality. In order to achieve the required understanding, more extensive analytical data is required, ideally obtained in-time on raw materials, process intermediates and ideally also on product quality attributes. In food industry, where typically high product titers are obtained, product analytics is sometimes possible by in-line measurements, such as spectroscopy (section 4). In biopharmaceutical processes, product titers are usually very low, the products are proteins with high molecular weight, and quality attributes are challenging to analyse. In these situations, online sampling with automated sample preparation and analytics may open up a solution [5]. But, identifying and implementing analytical methods, which are fast enough for delivering results on product quality attributes (e.g. protein glycosylation) in time, during the unit operation for allowing process modulation, remains a challenge. The overall typical goals in industry are high safety, high and stable product quality, high yield, low consumption of resources (materials, energy, room, time, and people), reduced influence of variability in raw materials as well as an increased shelf life of products which holds also in food processes. However, compared to the chemical and biopharmaceutical industries, food industry has to deal with certain characteristics, which renders PAT a formidable challenge in this context. The characteristic features in food industry are: • Raw materials are not pure substances: they are complex combinations of pure substances with varying compositions • Raw materials are soft, variable size, fragile and slippery • Physical properties of raw materials depend heavily on temperature, pressure, moisture and harvesting, and storage conditions • Raw materials undergo usually a phase transformation during processing and hence change their physical properties during processing • Micro processes (physical, (bio)-chemical, microbiological) are frequently not known • Highly perishable products. Here the challenge is to produce good quality, which maintains for a long time (shelf life) • High demand for hygiene These characteristics lead to a challenge for the application of sensors in the food industry. For important quality and process variables, such as sensory assessment, micro flora or spoilage, reliable and robust sensors are not yet available. Moreover, the sensors which are available are typically used only in isolated applications and frequently provide insufficient reliability. They are often not integrated in a common data management infrastructure. The materials of construction do not always consider suitability of contact with food and the solutions developed within advanced research projects frequently demand high care and maintenance. One reason why PAT is not as common as in other areas is that in food industry knowledge from various subject areas is necessary (such as physics, chemistry, biology, mathematics, informatics, engineering, nutritional science). For unit operations mathematical models are available in principle, but frequently they are too complicated to adapt. One of the biggest challenges in food industries is the dynamic nature of the processes. Changes in geometry, porosity, microstructure, solubility as well as mass and energy transfer coefficients must be addressed. Mostly gradients of temperature and moisture have to be considered as well as changes in kinetic parameters during process run. End product qualities like color, smell and 'brokens' also strongly influence the process. Therefore, food processes can be considered to be significantly more complex then chemical and bio-pharmaceutical processes. To achieve more stable processes PAT applications are necessary to check the quality of the raw and processed materials and their relationship to each other. If the quality is changing, then control actions resulting in online parameter changes are required in order to maintain constant product quality. Measurement systems are required, which guarantee that the process is in accordance with recipe and formulation. Furthermore, disturbances must be compensated for using control actions to reduce process variability and to conform to food regulations. As Glassey [6] argues, PAT methodology can aid in product design and testing as well as in ensuring full compliance with the HACCP and ISO 22000:2005 requirements during processing. As demonstrated below, PAT methodologies have the potential to aid the identification of critical control points and their critical limits, their effective monitoring and control, but also effective communication with suppliers and customers. Documentation which requires measurements of important variables is especially important so that traceability can be guaranteed. Here PAT has to deliver the corresponding measurement systems. Clearly a high demand for PAT in food industry is evident as is the need for further developments in the science and technology that help address the specific challenges posed by the characteristics of the food industry highlighted above. The complexity of raw materials that are typically soft and easily damageable is arguably one of the major scientific and technological challenges in food industry. During storage and processing the quality of such raw materials can decline due to oxidation processes, pressure and temperature effects. In addition, the visual impression of the final product (i.e. its appearance) is much more important than in other industries due to the fact that this will influence the purchase decision of the consumer. During the whole processing from raw material to the storage of the final product, hygiene is of utmost importance. Therefore non-invasive sensor systems are required in these applications. Measurement systems based on optical principles are particularly suitable from this perspective. Such measurement systems, including near infrared (NIR), Raman and fluorescence spectroscopy as well as computer-assisted image-based systems, potentially have a number of advantages in food process supervision and automation. Spectroscopic methods and imaging devices are well suited for PAT purposes because they are fast, non-destructive, provide multiple chemical information, allowing remote in-process analysis via fiber optics or instruments mounted directly on-line. Fluorescence spectroscopy is the most sensitive spectroscopic technique. Recently, many applications have been developed using fluorescence techniques [3, 7, 8]. Raw materials, the supervision of processing as well as product quality and the contamination of the equipment can be monitored by fluorescence. For example, Everard et al. [3] presented a method for detection of fecal contamination on spinach leaves. They coupled three hyperspectral imaging (HSI) configurations with two multivariate image analysis techniques and compared fluorescence imaging in the visible region with ultra violet and violet excitation sources, and reflectance imaging in the visible to near-infrared regions. They showed, that both fluorescence configurations had 100% detection rates for fecal contamination up to 1:10 dilution level and violet HSI had 99% and 87% detection rates for 1:20 and 1:30 levels, respectively. Everard et al. emphasized that on-line detection of fecal contamination on leaves has the potential to reduce the cases of food borne illnesses and their associated costs. A similar approach is presented by Lee et al. [7] where bovine faeces on Romaine lettuce and baby spinach leaves were investigated. They pointed out that two-band ratios using bands at 665.6 nm and 680.0 nm for lettuce and at 660.8 nm and 680.0 nm for spinach effectively differentiated all contamination spots applied. Grote et al. [8] described a fluorescence measurement technique to monitor a sourdough fermentation process. For the prediction of pH value and acidity during rye sourdough fermentations they applied partial least squares regression and principal component regression models for prediction and compared them with an evaluation where principal component analysis was combined with artificial neural networks. Depending on process operation and evaluation technique the average percentage root mean square errors of prediction for pH values were between 2.5 and 5.1%. For the prediction of the acidity level, the best results were between 6.0 and 8.1%. Liu et al. [9] used the Hoffman reaction to convert acrylamide to a compound which shows strong fluorescence emission at 480 nm. They showed good correlation of acrylamide in the range of 0.015 μg/mL to 20 μg/mL. Using this technique the food security will be increased. A fluorescence imaging device to detect deli residues on deli slicers were used by Beck et al. [10] processing four cheeses and four processed meats. The authors suggested that a fluorescence imaging device can be applied for routine use even in delicatessens. Fig. 1 clearly shows that the application of fluorescence for the monitoring of food processes increased from less than 10 before 2003 to more than 60 a year since 2013. This figure includes all papers from a search containing the key words "Food" and "Fluorescence". Although most of these papers describe laboratory applications rather than industrial PAT applications, they indicate potential future applications in this technology in food industry. Number of published papers obtained from the Scopus database searching for the words "Food" and "Image analysis" or "Raman" or "Near infrared spectroscopy" or "Fluorescence spectroscopy". Raman spectroscopy can only be applied if no fluorescence occurs in the corresponding excitation range. He et al. [11] applied the surface enhanced Raman scattering spectroscopy to detect banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. They concluded, that Raman spectroscopy and chemometric evaluation techniques can be used to identify banned food additives to ensure food safety. Ilaslan et al. [12] presented a method based on Raman spectroscopy to provide a rapid method for evaluating the quantitative analysis of glucose, fructose, and sucrose in soft drinks. Wang et al. [13] applied a Raman spectrometer as a process analyzer to monitor the wine fermentation. They demonstrated that sugar, ethanol and glycerol can be measured on-line with high correlation (higher than 0.98) to the HPLC reference measurements. Nache et al. [14] investigated Raman spectra from pork meat to monitor the early postmortem lactate accumulation and pH decline. They suggested that the locally weighted regression applied to the standard normal variate (SNV) normalized Raman spectra provide one of the most accurate and robust models with a cross-validated coefficient of determination (r2cv) of 0.97 for pH and lactate, a cross-validated root mean square error (RMSECV) of 4.5 mmol/kg for the lactate prediction and 0.06 pH-units for the pH prediction. These results demonstrate significant potential of combining chemometrics and Raman spectroscopy for on-line meat quality control applications. Fig. 1 shows the number of papers recorded in Scopus (search words "Food" and "Raman"). Compared to fluorescence, the number of papers describing Raman spectroscopy is twice as high in recent years with a significant rate of increase, demonstrating the increasing interest in this method for food process monitoring. Computer vision systems enable one of the main aspects of consumer preference – the appearance of a product – to be inherently considered. Therefore, computer vision systems for the supervision of food processing also gained importance over the years. An overview of several examples is given by Sun [15]. Especially the supervision of food drying processes is discussed by Aghbashlo at al. [16]. They pointed out that there is a large unexploited potential in the image data captured during various food processing operations. More informative feature extraction algorithms or novel pattern recognition procedures have to be developed. Paquet-Durand et al. [4] described a system for the supervision of the baking process. They demonstrated, by using the Viola–Jones-algorithm as well as neural networks, that the pastry can be identified and the volume increase as well as color development can be monitored. To detect defective apples Zhang et al. [17] used a computer vision system, which was combined with an automatic lightness correction system. For 160 samples they showed a 95% overall detection accuracy. For the evaluation they used a weighted relevance vector machine classifier. Fig. 1 shows the number of papers over time from Scopus searching for "Food" and "Image analysis". Here the applications start earlier compared to the spectroscopic methods, the number of applications per year is higher than in the case of spectroscopic applications; except in the year 2014, where more reports of Raman applications were published. Moreover imaging systems using visible and NIR region are available to perform quality checks based on the spectroscopic information derived from each point in the image. One example is the on-line analysis of the widely varying fat distribution in salmon for sorting purposes [18]. Near infrared (NIR) is a well-established method for rapid analysis of food raw materials and products [18], either on-/in-line, at-line or off-line. In the lab or at-line multiple components such as fat, protein, moisture and many more, can be analyzed without any sample preparation in all kinds of liquid, solid and semi-solid samples. Raw materials can be verified for identity and further characterized regarding composition already in the goods reception. The composition, freshness and adulteration of edible oils [19], e.g. olive oil [20], can be analyzed rapidly before a truck is unloaded. Many other sample types like meat, grains, flour, dairy products [21] and others can be analyzed. Even mixtures like vitamin premixes can be analyzed to make sure that the correct material was delivered and can be used in production. There are several approaches and technologies for PAT available, ranging from simple filter based devices over dispersive diode array spectrometers to Fourier-Transform (FT)-NIR instruments. Samples can be analyzed in-line by fiber optic probes or by contactless systems (Fig. 2). Smaller instruments can be directly attached to pipes, chutes or in other installations. A) NIR reflection probe in a fluid bed dryer. (B) Probe head for contactless measurement of grain and other solids. Applications cover a broad range from a simple monitoring of moisture content of a product on a conveyor belt to more complex control situations. In dairy industry there are demands to control milk powder production by monitoring the feed of the spray tower and the powder after fluid bed drying. Other important production steps to control are the standardization of milk, cream, whey and concentrates [22]. In frying processes with big volumes of oil an in-line monitoring of the oil quality with regards to acid value, anisidine value and the content of polar and polymerized components [23] are of particular interest. Another example of high volume processes where monitoring of protein, moisture and ash is important is the milling of grain to flour and the production of cereals of any kind. Finally there are more and more fermentation processes controlled by in-line NIR to optimize the conditions and follow the feeding and consumption cycles during biomass buildup [24]. In 2013 roughly 120 papers were published, however the recent increase is not as steep as in the case of Raman spectroscopy (Fig. 1). The economic benefits due to PAT can be attributed to higher product quality and yield as well as decreased product variation. This will reduce the overall production costs significantly and increase competitiveness. Furthermore, the knowledge and understanding of the process will increase. However, the level of automation in industrial food processes is significantly lower compared to the chemical and pharmaceutical processes. This is partly due to the complexity of food processes. On the other hand, the lack of reliable sensor systems to determine important process parameters and variables contributed to this in the past. Sensors represent a fundamentalpart of all automation systems, although they only represent a part of the requirements for wider application of PAT in food industry. The areas of further development and recommendations enabling more extensive use of the PAT within this industry also include: • Further industrial case studies and wider dissemination of the positive impact of reliable sensor technology in raw material and product quality as well as process monitoring. • Operational and maintenance requirements of sensor technologies will remain a significant aspect of future sensor technology uptake in this industry. • Data management and analysis software will play a significant role in extending the PAT application within the food industry. Platform solutions, preferably supporting wireless data transfer, at competitive pricing levels are required. • Cost benefit analysis studies are particularly important given the business drivers of this industry. • The impact of PAT is likely to be more pronounced in the new processes, although it is important to continue to encourage the application of PAT approaches within established food processes to enhance the process economics and compliance with the standards ensuring safe food supply chains. • PAT can significantly contribute to the stringent traceability and documentation requirements, although issues of compatibility and data standardization will impact upon data management systems employed by the companies within the supply chain. The authors declare no financial or commercial conflict of interest.

Research Data Infrastructures (RDIs) in Earth System Science must balance FAIR-compliant data management, operational requirements, and the practical needs of researchers operating heterogeneous sensor networks at scale. These design goals are not always fully aligned and may even conflict in operational environments. At the EGU General Assembly 2025, we introduced a modular digital ecosystem for time series data management designed to address these challenges. One year later, we report on the transition from prototype deployment to sustained operational use and reflect on how user feedback and operational constraints shaped the system’s evolution.The ecosystem has since been deployed as a production infrastructure at the Helmholtz Centre for Environmental Research - UFZ, where it currently supports approximately 20 research projects. The system manages around three billion observations from diverse sensor networks, with temporal resolutions of up to 5 seconds. This operational setting exposed challenges that were not fully apparent at the design and implementation stages, particularly regarding the scalability of data integration workflows, robustness under continuous load, and the interaction between metadata management, data ingestion, and automated quality control.The ecosystem comprises three modular components: the Sensor Management System – SMS [1] for standardized metadata registration, the time.IO [2] platform for storage, transfer, and visualization of time series data, and the System for Automated Quality Control – SaQC [3] for automated data analysis and quality assurance. While the modular design enabled reuse and interoperability, early operational phases revealed scaling bottlenecks that led to service outages, necessitating substantial refinements of ingestion pipelines, deployment strategies, and monitoring mechanisms.User-centric development also played a central role in stabilizing and extending the infrastructure. Continuous feedback from active projects influenced interface design, automation levels, and operational workflows, highlighting the importance of iterative co-design in bridging the gap between conceptual design goals and sustainable, user-accepted operation. We summarize key lessons learned from one year of operational use and discuss implications for building and operating sustainable, interoperable RDIs that effectively support Earth system science across disciplines and scales.[1] Lorenz, C., Brinckmann, N., Bumberger, J., Hanisch, M., Kuhnert, T., Loup, U., Moorthy, R., Obersteiner, F., Schäfer, D., Schnicke, T. (2025). Sensor Management System (SMS): Open-source software for FAIR sensor metadata management in Earth system sciences. SoftwareX (submitted), https://arxiv.org/abs/2512.17280[2] Bumberger, J., Abbrent, M., Brinckmann N., Hemmen, J., Kunkel, R., Lorenz, C., Lünenschloß, P., Palm, B., Schnicke, T., Schulz, C., van der Schaaf, H., and Schäfer, D. (2025). Digital Ecosystem for FAIR Time Series Data Management in Environmental System Science. SoftwareX, 102038, https://doi.org/10.1016/j.softx.2025.102038[3] Schmidt, L., Schäfer, D., Geller, J., Lünenschloss, P., Palm, B., Rinke, K., Rebmann, C., Rode, M., &amp; Bumberger, J. (2023). System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science. Environmental Modelling &amp; Software, 105809. https://doi.org/10.1016/j.envsoft.2023.105809

The volume of freight transport on roads has grown steadily over the years and is expected to increase significantly over the next decade. Truck driver coaching systems have proved a key element in boosting efficiency, profitability and safety of the haulage sector whilst contributing to infrastructure asset management objectives. Notwithstanding new, commercial driver coaching systems have shown up recently in the market, still they operate as "black boxes", whilst their functionality and automation level is very limited. Given the enormous numbers of truck fleets currently operating worldwide, the necessity for an adaptable driver coaching system, capable of providing real-time functionality is evident. This study reports the design characteristics and first results from PEGASUS research project aiming at developing a self-trained, unified, driver-coaching system based on geolocation, IoT (Internet of Things) and BI (Business Intelligence) techniques. The system by design aims at reducing operational costs for the truck fleet, improving road safety, as well as contributing to green transportation. In effect, the system has been designed to produce user-friendly, two-way advice to lorry drivers tailored to the road conditions (e.g., geometry, road markings, neighboring vehicles) and operational (e.g., weather, traffic) conditions in real time and in a dynamic manner.

This paper reviews the importance of industry–university relationships and the strategic considerations within the context of these partnerships in university research centers. We have identified several factors that are particularly important to industry in building relationships with university centers. These include: acquiring skills, knowledge and gaining access to university facilities; organizational cultures that are more organic; flexible university policies for intellectual property rights, patents and licenses; and the presence of champions. Firms also have explicit collaborative strategies for partnering with universities where firms can be segmented into three distinct clusters: Collegial Players, Aggressive Players and Targeted Players. Collegial Players are often large firms working with universities and university‐sponsored consortia on topics of interest that have long‐term value rather than promise immediate commercial opportunities. Aggressive Players are both large and small firms who employ university relationships specifically to develop and commercialize a wide range of marketable products and services. Targeted Players are often smaller firms, largely interested in using university relationships to address specific issues central to their business. We conclude by discussing key implications for both industry and universities.