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I just finished reading Chip Brown's Good Morning Midnight: Life and Death in the Wild (New York: Riverhead Books, 2003), a book about SABR member Guy Waterman. That's right, one [...]

Black in the Deadball Era, Sherry Magee was called 'one of the greatest and most neglected of sluggers,' 'a born dynamiter with the bat,' and 'a genuine murderer of the [...]

Soil organic matter (SOM) represents one of the largest and most important reservoirs of organic carbon in terrestrial eco-systems. The quality of SOM (e.g., content of soil organic carbon, humic substances, humic acids, fulvic acids, and HA/FA ratio) is a key factor in determining soil fertility and productivity. The loss of SOM is currently considered a detrimental form of soil degradation. The study aims to evaluate the effect of mineral fertilising (NPK), farmyard manure, cattle slurry, and digestate applications in intensively used permanent grassland. The long-term field experiments were carried out at the locality Jevíčko, region of Boskovice Furrow Malá Haná (the Czech Republic). The effect of amending soil with different organic and mineral fertilisers was studied. Qualitative parameters of SOM, soil reaction, and the content of available nutrients (N, P, K, Ca, Mg) were followed. Results showed that the control (no organic input) and the NPK variants had the lowest SOM quality. Higher SOM quality was after the farmyard manure, Slurry, and Digetsate applications. The highest acidity (indicated by the lowest values of pH/KCl) was determined in the control, NPK, Slurry, and D variants. The most favourable soil reaction and available nutrient content were after the farmyard manure application. Humic substances spectral absorption in the UV-VIS and infrared spectral ranges showed that the highest absorbance and amount of aliphatic hydrophobic components were observed after the application of farmyard manure. The multi-criteria statistical evaluation, including principal component analysis and factor analysis, confirmed that amending soil with farmyard manure and slurry resulted in an increase in humic substances content and improved SOM quality.

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.

In this Special Issue, we embark on a journey into the exciting field of intelligent soft sensors, and take a deep dive into the groundbreaking advances and potential that these software algorithms have introduced in various fields [...]

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

Abstract Background According to the 2015 National Survey of the Nutritional Situation in Colombia the prevalence of stunting in children under 5 years of age was 10.8%. In terms of region, Bogotá, presented the highest prevalence rate (13%), a figure that exceeded national records. With the collaboration of local and national government, and nongovernmental it was decided to develop a pilot study involving a public health intervention with residents of Bogotá under 1 year of age with nutritional classification by anthropometry compatible with stunting risk or stunting. Methods Pre-experimental, before and after study that sought to determine the magnitude of the change in nutritional status through a 10 months public health nutrition intervention in children under one-year-old residing in 3 prioritized territories of Bogotá. Results The intervention comprised 1126 children living in the following territories in Bogotá: Kennedy, San Cristóbal, and Engativá. A total of 43.3% children presented delay in height for age, and 56.7% presented risk of short stature. In the final measurement, data were obtained from 686 children, identifying that 17% of the children progressed from stunting to a stunting risk and that 4.5% recovered their growth trajectory, achieving an adequate length for their age. Conclusion That children classified as at risk or stunting at the beginning of the intervention showed an increased probability of approaching or being in the appropriate growth trajectory according to the length-for-age indicator after the intervention.

Vegetable production may be the source of excessive residual nitrate that is prone to leaching to waters. To ascertain the risk of nitrate leaching in water collection area, the content of soil mineral nitrogen (Nmin = N-NO3− + N-NH4+) down to 120 cm depth was monitored in the years 2013–2016 on vegetable farms along lower Jizera river (in the Czech Republic). The risk of nitrate leaching below 30, 60, 90 and 120 cm during winter period was simulated with a simple model. The depths represent the limits of effective root depth and N depletion of groups of vegetables and field crops. The average autumn mineral nitrogen content in the fields, during experimental years, ranged from 101 kg to 134 kg N·ha−1 in the 0–120 cm soil layer, 85 to 92% of which was in the form of nitrate. The calculated leaching of nitrate from the topsoil (0–30 cm) and shallow subsoil (0–60 cm) ranged from 27 to 41%, and from 7 to 14% of autumn content, respectively. The risk of leaching below 60 cm and 90 cm was near to none during the experimental years due to the exceptionally low precipitation. High nitrate content in subsoil layers below 60 cm constitutes risk of leaching and water pollution due to shallow root systems of many vegetables and potatoes in seasons with normal weather and higher water percolation.

In the steel industry, the optimization of production processes has become increasingly important in recent years. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data from more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analysed. Information on the consumed energy in each step of the electric arc process is essential to increase the efficiency of the EAF. In the paper, four different modelling approaches for predicting electrical energy consumption during EAF operation are presented: linear regression, k-NN modelling, evolving and conventional fuzzy modelling. In the learning phase, from a set of more than ten regressors, only those that have the greatest impact on energy consumption were selected. The obtained models that can accurately predict the energy consumption are used to determine the optimal duration of the transformer profile during melting. The models can predict the optimal energy consumption by selecting pre-processed training data, where the main steps are to find and remove outlier batches with the highest energy consumption and identify the influencing variables that contribute most to the increased energy consumption. It should be emphasised that the electrical energy consumption was too high in most batches only because the melting time was unnecessarily prolonged. Using the proposed models, EAF operators can obtain information on the estimated energy consumption before batch processing depending on the scrap weight in each basket and the added additives, as well as information on the optimal melting time for a given EAF batch. All models were validated and compared using 30% of all data, with the fuzzy model in particular providing accurate prediction results. It is expected that the use of the developed models will lead to a reduction in energy consumption as well as an increase in EAF efficiency.

Recently, the social and commercial interest in location-based services (LBS) has been increasing significantly [...]

Presents letters to the editor on medical topics as of February 27, 2002. Risk factors for violent death in children; Evaluation of inhalational anthrax; Usefulness of positron emission tomography in evaluating dementia; Patient safety in trials of therapy for acute ischemic stroke.

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.