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Abstract The patch-clamp technique allows us to eavesdrop the gating behavior of individual ion channels with unprecedented temporal resolution. The signals arise from conformational changes of the channel protein as it makes rapid transitions between conducting and non-conducting states. However, unambiguous analysis of single-channel datasets is challenging given the inadvertently low signal-to-noise ratio as well as signal distortions caused by low-pass filtering. Ion channel kinetics are typically described using hidden Markov models (HMM), which allow conclusions on the inner workings of the protein. In this study, we present a Deep Learning approach for extracting models from single-channel recordings. Two-dimensional dwell-time histograms are computed from the idealized time series and are subsequently analyzed by two neural networks, that have been trained on simulated datasets, to determine the topology and the transition rates of the HMM. We show that this method is robust regarding noise and gating events beyond the corner frequency of the low-pass filter. In addition, we propose a method to evaluate the goodness of a predicted model by re-simulating the prediction. Finally, we tested the algorithm with data recorded on a patch-clamp setup. In principle, it meets the requirements for model extraction during an ongoing recording session in real-time.

Many studies in the chemical education literature report students' alternative conceptions in chemistry and the difficulty they present for future learning. In this paper, we review existing diagnostic tools used to uncover students' alternative conceptions in chemistry and suggest that there are two fundamental issues with such instruments, namely, the breadth of topics and concepts assessed and the reliance on forced choice responses. We argue that while existing instruments provide a way to assess students' overall understanding of chemical concepts, they cannot assess depth of understanding of any single concept, such as the particulate nature of matter--one of the central, organizing ideas in chemistry. Instead, we propose using qualitative approaches that utilize drawing tasks as an alternative diagnostic tool to uncover students' underlying struggles with fundamental chemistry concepts. Using this approach, we investigated college students' ability to balance chemical equations and draw appropriate particulate representations of those reactions. Emerging categories from students' particulate drawings were coded into several subcategories and revealed a number of underlying issues, such as lack of understanding of appropriate relationships between reacting species in solution, oxidation numbers, states and valences of species, the characteristics and nature of ions in solids and differences between ionic and covalent bonds. We describe these findings and consider the implications of qualitative-based diagnostics for instruction and science learning as a formative assessment tool. (Contains 2 tables and 13 figures.)

We report the findings of a large-scale (n = 1,337) qualitative descriptive analysis of U.S. high schools students' particulate representations of a chemical reaction, specifically, the combustion of methane. Data were collected as part of an end of course exam. Student representations were coded into 17 distinct subcategories under one of five broad themes: i) Particulate Representations with discrete atoms, ii) Inappropriate Particulate Representations, iii) Quasi-particulate Representations, iv) Non-particulate Representations, or v) Irrelevant Attempts. Only 35.1% of student responses showed representations with discrete atoms that appropriately matched the individual molecular formulas in the combustion reaction. Of student responses, 45% were representations with discrete atoms; however, these representations were not chemically appropriate, displaying either incorrect connections between atoms within individual molecules or inappropriate groupings of atoms into individual molecules. 5.9% of student responses were coded as quasi-particulate, and included representations that displayed some form of particles that did not show discrete atoms. 22.9% of student responses were coded as non-particulate. The remaining 5.5% of responses represented no real attempt to address the question. The various representations are illustrated and described to provide a map of the domain of students' alternative conceptions of chemical reactions. Interestingly, of the 65% of students who were able to balance the equation correctly, more than half were unable to show the appropriate particulate representation. The ability to represent a chemical reaction at the symbolic level does not guarantee the ability to represent the reaction at the particulate level. (Contains 7 tables and 1 figure.)

Reform-based curriculum materials have been suggested as a mechanism to make inquiry-based instruction more prevalent in secondary science classrooms, specifically when accompanied by comprehensive professional development (Loucks-Horsley, Hewson, Love, & Stiles, [1998]; Powell & Anderson, [2002]). This research examines the implementation of a reform-based high school chemistry curriculum in a large, urban school district. We explicitly consider the role of the teachers' knowledge and beliefs in their implementation of the reform-based chemistry curriculum, as well as school level factors. Qualitative and quantitative data were collected in the form of beliefs interviews and classroom observations from 27 high school chemistry teachers. Analysis of the data revealed that implementation of the curriculum was strongly influenced by the teachers' beliefs about teaching and learning, and the presence of a supportive network at their school sites.

Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque black-box models that lack transparency in decision-making. To address this issue, significant efforts have been made to develop explainable AI (XAI) systems that make outcomes comprehensible to users. Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction, and increase task performance. Evaluation research has emerged to define and measure the quality of these explanations, differentiating between formal evaluation methods and empirical approaches that utilize techniques from psychology and human–computer interaction. Despite the importance of empirical studies, evaluations remain underutilized, with literature reviews indicating a lack of rigorous evaluations from the user perspective. This review aims to guide researchers and practitioners in conducting effective empirical user-centered evaluations by analyzing several studies; categorizing their objectives, scope, and evaluation metrics; and offering an orientation map for research design and metric measurement.

This study presents the design, simulation, and comparative analysis of three advanced control strategies applied to a 3-Degree-of-Freedom (DoF) robot manipulator. The controllers investigated are a variant from the Computed Torque Control family, a Proportional–Derivative–Integral with fuzzy logic (PD-PI + fuzzy) controller, and a Model Predictive Control (MPC) scheme. The controller performance is evaluated through the tracking of predefined trajectories in the three-dimensional space. The results are analyzed through XYZ coordinate motion graphs and 3D trajectories. To quantify performance, three error indicators are employed: Residual Mean Square (RMS) with a value of 0.0720 for the Computed Torque Controller, Residual Standard Deviation (RSD), and Index of Agreement (IA). The results demonstrate that the proposed controllers achieve accurate trajectory tracking, with IA values close to unity, demonstrating a high degree of concordance between the desired and executed trajectories.

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots’ performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics.

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

The Swiss Group for Mass Spectrometry (SGMS) is a society dedicated to fostering collaboration among scientists across diverse mass spectrometry fields, ranging from gas-phase ion chemistry to life sciences. SGMS has a strong commitment to educating and supporting the next generation of mass spectrometrists pursuing MSc and PhD degrees. Our membership comprises about two hundred individuals, predominantly from Switzerland, representing academia, industry, and regulatory bodies.[1] SGMS is an Associate Member Society of the Swiss Chemical Society (SCS).

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Presents a brief overview of the important processes and techniques used industrially for forming thin films. (HM)

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.

Moisturization is a primary need in skin care. This study aimed to investigate whether the C12-C20 glucolipid emulsifier could provide minimalist water-in-oil emulsions (i.e., a minimum number of ingredients, in reasonable quantities) with a significant moisturizing effect across multiple geographies even if the emulsion structure needed to be adapted to meet local expectations. Four structures were tested containing a stabilizer and an oily phase that were adapted to address consumers’ skin feel expectations in each location. In vivo corneometry and transepidermal water loss (TEWL) measurements were carried out on volunteers with dry skin up to 24 h after application (leg or forearm; phototypes I to VI). The first investigation was completed in France comparing corneometry measurements to the control without the emulsifier and an untreated area. Studies were then performed in Brazil, India and Mauritius, combining corneometry and TEWL assessments. Significant increase in skin capacitance was observed in the four countries, compared to the untreated area, at 5/6/8 h after application and up to 24 h. The effect was also significant versus the control in the French study at 5 h. A significant decrease in TEWL compared to the untreated area was observed concurrently in Brazil, India and Mauritius.

X-ray absorption and X-ray free electron lasers are important tools to study chemical and structural dynamics, but spectral details like pre-edge features are inherently hard to detect. Here, the authors show that stochastic spectroscopy can yield similar spectral information to monochromatic spectroscopies, while increasing signal yield and reducing acquisition time.

The occurrence frequency of regional atmospheric new aerosol particle formation and consecutive growth events (fNPF) were studied with respect to vegetation activity, aerosol properties, air pollutants and meteorological data in Budapest over the time interval from 2008 to 2018. The data set evaluated contained results of in situ measurements on the land surface that were mostly performed at the Budapest platform for Aerosol Research and Training Laboratory, of satellite-based products recorded by MODIS on Terra and of modelled vegetation emission-related properties from an advanced regional biogeochemical model. The annual mean relative occurrence frequencies were considerable (with an overall mean of 21 %), remained at a constant level (with an overall SD of 5 %) and did not exhibit tendentious change over the years. The shape of the distributions of monthly mean fNPF exhibited large variability from year to year, while the overall average distribution already possessed a characteristic pattern. The structure of the new particle formation (NPF) occurrence distributions was compared to those of environmental variables including concentrations of gas-phase H2SO4, SO2, O3, NO, NO2, CO, PM10 mass and NH3; particle numbers in the size fractions of 6–1000, 6–100 and 100–1000 nm; condensation sink; air temperature (T); relative humidity (RH); wind speed (WS); atmospheric pressure (P); global solar radiation (GRad); gross primary production (GPP) of vegetation; leaf area index (LAI); and stomatal conductance (SCT). There were no evident systematic similarities between fNPF on the one hand and all of the variables studied on the other hand, except for H2SO4 and perhaps NH3. The spring maximum in the NPF occurrence frequency distribution often overlapped with the time intervals of positive T anomaly in vegetated territories. The link between the potential heat stress exerted on plants in sultry summer intervals and the summer fNPF minimum could not be proven. The relevance of environmental variables was assessed by their ratios on NPF event days and on non-event days. The gas-phase H2SO4 concentration showed the largest monthly ratios, followed by O3. The WS, biogenic precursor gases and SO2 can generally favour NPF events, although their influence seemed to be constrained. An association between the fNPF and vegetation growth dynamics was clearly identified.

Template-based electrodeposition is a versatile technique for the fabrication of freestanding nanowires. However, nanowires of refractory metals, like niobium, have remained inaccessible via this technique due to preferential hydrogen evolution in aqueous solutions. In this work, we have developed a synthetic strategy for template-based electrodeposition of niobium nanowires using an ionic liquid. Optimal growth conditions have been determined using cyclic voltammetry. Structural characterization using transmission electron microscopy, electron diffraction, and energy dispersive spectroscopy showed that the niobium nanowires were polycrystalline with an average grain size of 14 nm. The use of ionic liquid-based electrolytes for nanowire deposition could be expanded to other metals improving material quality due to their high stability, good conductivity, and wide electrochemical window. Keywords: Niobium, Nanowire, Ionic liquid, Template-based electrodeposition

Mercury is a heavy metal of particular concern due to its adverse effects on human health and the environment. Recognizing this problem, the UN Minamata Convention on Mercury was recently adopted, where signatory countries agreed to reduce anthropogenic mercury emissions. To evaluate the effectiveness of the convention, quantitative knowledge on mercury emissions is crucial. So far, bottom-up approaches have successfully been applied to quantify mercury emission – especially for point sources. Distributed sources make up a large share of the emission; however, they are still poorly characterized. Here, we present a top-down approach to estimate mercury emissions based on atmospheric measurements in the city of Zurich, Switzerland. While monitoring the atmospheric mercury concentrations during inversion periods in Zurich, we were able to relate the concentration increase to the mercury emission strength of the city using a box model. By means of this boundary-layer budget approach, we succeeded in narrowing down the emissions of Zurich to range between 41±8 kg a−1 (upper bound) and 24±8 kg a−1 (lower bound). Thereby, we could quantify emissions from mixed, diffuse and point-like sources and derive an annual mercury per capita emission of 0.06 to 0.10 g a−1. The approach presented here has the potential to support authorities in setting up inventories and to validate emission estimations derived from the commonly applied bottom-up approaches. Furthermore, our method is applicable to other compounds and to a wide range of cities or other areas, where sources or sinks for mercury and other atmospheric pollutants are presumed.

The launch vehicle industry has long been considered a pioneering industry in systems engineering. Launch vehicles are large complex systems that require a methodical multi-disciplinary approach to design, build, and launch. Launch vehicles are used to deliver payloads—such as humans, robotic science missions, or national security payloads—to desired locations in space. Previous research has identified deficient or underperforming systems engineering as a leading contributor to launch vehicle failures. Launch vehicle failures can negatively affect national security, the economy, science, and society, thus highlighting the importance of understanding the factors that influence systems engineering in launch vehicle organizations in the United States. The purpose of this study was to identify and evaluate the relationships between organizational factors and systems engineering process performance. Structural equation modeling was used to develop a model of the relationships of these factors and test hypotheses. The results showed that organizational commitment, top management support, the perceived value of systems engineering, and systems engineering support significantly influence systems engineering process performance in the launch vehicle industry. Implications of this study for improving the performance of systems engineering in launch vehicle organizations are discussed.

Production systems are changing in many aspects on the way to a Factory of the Future, including the level of automation and communication between components. Besides all benefits, this evolution raises the amount, effect and type of anomalies and unforeseen behavior to a new level of complexity. Thus, new detection and mitigation concepts are required. Based on a use-case dealing with a distributed transportation system for production environments, this paper describes the different sources of possible anomalies with the same effect, anomaly detection methods and related mitigation techniques. Depending on the identified anomaly, the FoF should react accordingly, such as fleet or AGV reconfiguration, strong authentication and access control or a deletion of adversarial noises. In this paper, different types of mitigation actions are described that support the fleet in overcoming the effect of the anomaly or preventing them in the future. A concept to select the most appreciate mitigation method is presented, where the detection of the correct source of the anomaly is key. This paper shows how various techniques can work together to gain a holistic view on anomalies in the Factory of the Future for selecting the most appropriate mitigation technique.

Numerous projects and industrial and academic collaborations benefit from state-of-the-art facilities and expertise in analytical chemistry available at the Swiss Universities of Applied Sciences. This review summarizes areas of expertise in analytical sciences at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), the University of Applied Sciences and Arts Western Switzerland (HES-SO), and the Zurich University of Applied Sciences (ZHAW). We briefly discuss selected projects in different fields of analytical sciences

Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA documents were designed to be exclusively produced and interpreted by human domain experts. As a consequence, these documents often suffer from data consistency issues. This paper argues that due to the transitive perception of the causal relations, discordant and merged information cases are likely to occur. Thus, we propose to improve the consistency of FMEA documents as a step towards more efficient use of causal domain knowledge. In contrast to other work, this paper focuses on the consistency of causal relations expressed in the FMEA documents. To this end, based on an explicit scheme of types of inconsistencies derived from the causal perspective, novel methods to enhance the data quality in FMEA documents are presented. Data quality improvement will significantly improve downstream tasks, such as root cause analysis and automatic process control.

The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted. However, they are expensive in time and resources, and can slow down production. A promising way to overcome these drawbacks is process exploration through simulation, where the finite element method (FEM) is a well-established and robust simulation method. While FEM simulation can provide high-resolution results, it requires extensive computing resources to do so. In addition, the simulation design often depends on unknown process properties. To circumvent these drawbacks, we present a Gaussian Process surrogate model approach that accounts for real physical manufacturing process uncertainties and acts as a substitute for expensive FEM simulation, resulting in a fast and robust method that adequately depicts reality. We demonstrate that active learning can be easily applied with our surrogate model to improve computational resources. On top of that, we present a novel optimization method that treats aleatoric and epistemic uncertainties separately, allowing for greater flexibility in solving inverse problems. We evaluate our model using a typical manufacturing use case, the preforming of an Inconel 625 superalloy billet on a forging press.

The technical world of today fundamentally relies on structural analysis in the form of design and structural mechanic simulations. A traditional and robust simulation method is the physics-based finite element method (FEM) simulation. FEM simulations in structural mechanics are known to be very accurate; however, the higher the desired resolution, the more computational effort is required. Surrogate modeling provides a robust approach to address this drawback. Nonetheless, finding the right surrogate model and its hyperparameters for a specific use case is not a straightforward process. In this paper, we discuss and compare several classes of mesh-free surrogate models based on traditional and thriving machine learning (ML) and deep learning (DL) methods. We show that relatively simple algorithms (such as k-nearest neighbor regression) can be competitive in applications with low geometrical complexity and extrapolation requirements. With respect to tasks exhibiting higher geometric complexity, our results show that recent DL methods at the forefront of literature (such as physics-informed neural networks) are complicated to train and to parameterize and thus, require further research before they can be put to practical use. In contrast, we show that already well-researched DL methods, such as the multi-layer perceptron, are superior with respect to interpolation use cases and can be easily trained with available tools. With our work, we thus present a basis for the selection and practical implementation of surrogate models.

Optimized coating adhesion and strength are the advantages of high-power impulse magnetron sputtering (HiPIMS) as an innovative physical vapor deposition (PVD) process. When depositing electrically non-conductive oxide ceramics as coatings with HiPIMS without dual magnetron sputtering (DMS) or mid-frequency (MF) sputtering, the growing coating leads to increasing electrical insulation of the anode. As a consequence, short circuits occur, and the process breaks down. This phenomenon is also known as the disappearing anode effect. In this study, a new approach involving adding electrically conductive carbide ceramics was tried to prevent the electrical insulation of the anode and thereby guarantee process stability. Yttria-stabilized zirconia (3YSZ) with 30 vol.% titanium carbide (TiC) targets are used in a non-reactive HiPIMS process. The main focus of this study is a parameter inquisition. Different HiPIMS parameters and their impact on the measured current at the substrate table are analyzed. This study shows the successful use of electrically conductive carbide ceramics in a non-conductive oxide as the target material. In addition, we discuss the observed high table currents with a low inert gas mix, where the process was not expected to be stable.

Describes a lecture demonstration illustrating Charles's Law with a balloon-balance thermometer. The technique allows a determination of the absolute minimum temperature to within an accuracy of 10 percent of the exact value: -273.15 degrees Celsius. (BT)

The aim of the research was to estimate the possibility of using mixed bacteria cultures consisting of Lactobacillus fermentum S8 and Staphylococcus carnosus ATCC 51365 in the meat curing process with a reduced amount of sodium nitrite and to study the effect of bacteria on residual nitrites and nitrates, nitrosyl pigments content, colour, pH, redox potential, microbiologic, and the sensory quality of a cooked meat product. The study was performed on heat treated three-model meat treatments in cans: (C) a control treatment with NaNO2 at 100 mg/kg, (M) a treatment with NaNO2 at 50 mg/kg and (SL) a treatment with NaNO2 at 50 mg/kg and L. fermentum S8 at about 107 cfu/g and S. carnosus ATCC 51365 at about of 107 cfu/g. The research was performed after production and after cold storage. It was shown that using a mixed bacteria culture for meat curing had an influence (p < 0.05) on reducing nitrite and nitrate levels and increasing the amount of nitrosyl pigments in the SL treatment compared to the M treatment. Applying mixed bacteria in curing meat with NaNO2 at 50 mg/kg allowed for obtaining a higher redness in the cooked meat product after production, storage and exposure to light than in the product cured with NaNO2 at 100 mg/kg, with similar sensory and microbiological quality in both products.

In this article, we propose a new scheme for a fuzzy logic controller, which includes acceleration as one of its linguistic variables, as opposed to other techniques and approaches that have been developed and reported in the literature. This method is used for controlling the tracking of the trajectory followed by the joints of a 2-DoF manipulator robot. To this end, a complete simulation environment is developed through the MatLab/Simulink® software. The dynamic model of the manipulator robot includes a vector that consists of the estimate of the friction forces present in the joints. Then, a controller based on fuzzy logic is designed and implemented for each joint. Finally, the performance of the developed system is assessed and then compared to the performance of a classic PID controller. The incorporation of the fuzzy variable acceleration significantly improved the system’s response.

We present an analysis of SO2 column densities derived from GOME-2 satellite measurements for the Kīlauea volcano (Hawai`i) for 2007–2012. During a period of enhanced degassing activity in March–November 2008, monthly mean SO2 emission rates and effective SO2 lifetimes are determined simultaneously from the observed downwind plume evolution and meteorological wind fields, without further model input. Kīlauea is particularly suited for quantitative investigations from satellite observations owing to the absence of interfering sources, the clearly defined downwind plumes caused by steady trade winds, and generally low cloud fractions. For March–November 2008, the effective SO2 lifetime is 1–2 days, and Kīlauea SO2 emission rates are 9–21 kt day−1, which is about 3 times higher than initially reported from ground-based monitoring systems.