Search

Based on current evidence and established critical thresholds for soil degradation indicators, it is concerning that over 60–70% of European soils are unhealthy due to unsustainable management and the impact of climate change. Despite European and national efforts to improve soil health, significant gaps remain. The proposal for a Soil Monitoring and Resilience Law, to be implemented by the European Union, seeks to establish a framework for soil monitoring and promote sustainable management practices to achieve healthy soils by 2050. This requires extensive data collection and soil monitoring systems to accurately estimate soil health across Europe, considering the diversity of soil types, climates, and land uses. To establish a framework for soil monitoring, we must understand the site-specific status of soil and the ranges of soil health indicators across specific pedoclimatic regions. In our study, we evaluated the soil status in agricultural areas in Denmark using soil health indicators and a site-specific benchmarking approach. We compiled nationally representative datasets, combining point and model-informed data of soil parameters such as organic carbon content, bulk density, pH, electrical conductivity, clay-to-soil organiccarbon ratio, water erosion, and nitrogen leaching. By categorizing Danish agricultural soils into monitoring units based on textural classes, landscape elements, and wetland types, we calculated benchmarks for these indicators, considering different cropping systems. Our approach provided detailed point-based results and a spatially explicit overview of the status of soil health indicators in Denmark. We identified areas where soil deviates from the benchmarks of different indicators. Such deviations might indicate soil functions operating outside the normal range, posing potential threats to soil health. This proposed framework could support the establishment of a baseline for assessing the directionality of future changes in soil health. Moreover, it is adaptable for implementation by other countries to support assessments of soil health. • Our proposed framework involves soil monitoring units as a basis for future soil health assessment. • We present a benchmarking approach to spatially assess the status of soil health indicators in Denmark. • The indicators distribution provides benchmarks to identify strengths and weaknesses in soil monitoring. • We propose an application of digital soil mapping products to derive a continuous view of the status of soil health indicators. [ABSTRACT FROM AUTHOR]

Regular physical activity reduces the risk of suffering obesity and high blood pressure, and slows down age-related loss of mobility and cognitive capabilities. However, 31% of the world population does not perform even the minimum recommend levels of physical activity to have a healthy life. On top of that, due to the COVID-19 Pandemic prevention measures involving isolation, lockdown, and working-from-home policies, adults have drastically reduced their physical activity by 30%, which further aggravates existing health conditions. In order to encourage exercising at home while still receiving proper instruction, this paper proposes a human-machine interface capable of supporting the motor learning of physical activities by providing training with constant practice of exercises and multimodal feedback. It consists of an interactive mixed-reality environment that does not require a human instructor or specialized facilities. As an application of the system, dance coaching was implemented. The information to be conveyed to the users are feet velocity and position trajectories, as well as the tempo of the desired motion. This is done by providing directional haptic feedback with wearable vibroactuators on the ankles of the user, visual feedback with a floor projection, and aural feedback with a metronome. In order to validate the proposed methodology, an experiment where ballroom dance is taught to 10 novice subjects was performed. Results show that when using the developed multimodal system, position and velocity trajectory errors are reduced by 60% and 37%, respectively, which demonstrates that users can understand and follow the multimodal feedback. After finishing the training and removing the system, users are still able to keep the position and velocity error at 61% and 42% lower than their initial performance, respectively. This fact suggests that subjects are able to retain the motor skills obtained during training. [ABSTRACT FROM AUTHOR]

Simple Summary: Cattle are the main reservoir of Shiga toxin-producing E. coli (STEC), foodborne pathogens that cause severe disease and outbreaks. However, not all STEC cause human illnesses or have the same virulence potential. Characterizing strains isolated worldwide allows insights into how strains spread and which isolates have a more significant risk potential. This study described STEC isolation rates from cattle in Chile and characterized 30 isolates. We obtained 93 STEC isolates from 56/446 (12.6%) fecal cattle samples. Then, 30 non-O157 STEC isolates were selected for complete characterization; we found isolates of 16 different sequence types and 17 serotypes. One isolate was resistant to tetracycline and carried resistance genes against the drug. Surveyed virulence genes (n = 31) were present from 13% to 100% of isolates, and one isolate carried 26/31 virulence genes. Most isolates (90%; 27/30) carried the stx2 gene, which is frequently linked to strains causing severe disease. A phylogenetic reconstruction demonstrated that isolates clustered based on serotypes, independent of their geographical origin (Central or Southern Chile). These results indicate that cattle in Chile carry a wide diversity of STEC potentially pathogenic for humans based on the presence of virulence genes. Cattle are the main reservoir of Shiga toxin-producing Escherichia coli (STEC), one of the world's most important foodborne pathogens. The pathogen causes severe human diseases and outbreaks. This study aimed to identify and characterize non-O157 STEC isolated from cattle feces from central and southern Chile. We analyzed 446 cattle fecal samples and isolated non-O157 STEC from 12.6% (56/446); a total of 93 different isolates were recovered. Most isolates displayed β-glucuronidase activity (96.8%; 90/93) and fermented sorbitol (86.0%; 80/93), whereas only 39.8% (37/93) were resistant to tellurite. A subgroup of 30 representative non-O157 STEC isolates was selected for whole-genome sequencing and bioinformatics analysis. In silico analysis showed that they grouped into 16 different sequence types and 17 serotypes; the serotypes most frequently identified were O116:H21 and O168:H8 (13% each). A single isolate of serotype O26:H11 was recovered. One isolate was resistant to tetracycline and carried resistance genes tet(A) and tet(R); no other isolate displayed antimicrobial resistance or carried antimicrobial resistance genes. The intimin gene (eae) was identified in 13.3% (4/30) of the genomes and 90% (27/30) carried the stx2 gene. A phylogenetic reconstruction demonstrated that the isolates clustered based on serotypes, independent of geographical origin. These results indicate that cattle in Chile carry a wide diversity of STEC potentially pathogenic for humans based on the presence of critical virulence genes. [ABSTRACT FROM AUTHOR]

Solar tracking systems enable increased efficiency of a photovoltaic system through a continuous adjustment of its position with respect to the sun, thus increasing the generation of electrical energy. The integration of photovoltaic solar tracking systems in buildings and houses enables the energy needs of users in a broader way to be covered. This integration is facilitated through the existence of technologies such as access to the Internet through Wi-Fi, which allows developing systems to be encompassed within the domain of the "Internet of Things" (IoT). In this study, a first-of-its-kind "open-loop" solar tracker was designed and developed, which executes the tracking algorithm in the Firebase web service and allows the exchange of data with said service through a NodeMCU development board, which has an integrated Wi-Fi module. After an experimental campaign in Aguascalientes, central Mexico, gains in terms of collected energy on average were measured at 29.9% in May compared to an optimally tilted fixed photovoltaic system. This study opens the possibility of integrating power generation systems into the IoT domain, which, among other things, allows for constant monitoring of the behavior of the system. [ABSTRACT FROM AUTHOR]

In this work, a low-cost temperature sensor based on step-index optical fibers with a slanted surface at the distal is fabricated and tested. Distal ends of a series of optical fibers are mechanically polished to obtain slanted surfaces at 45°, 30°, and 22.5° and are tested in a sensor in the temperature range from 20 °C to 220 °C, exhibiting a spectral response to temperature with sensitivities of up to 21.5 pm/°C. [ABSTRACT FROM AUTHOR]

Electric motorcycles use a battery storage system and a controlled inverter (motor drive) to drive the wheels. Given that the sale of electric motorcycles is expected to grow significantly over the next decade, the aim of this research is to add a supercapacitor storage system to this simple network in order to extend the autonomy of the motorcycle. This paper analyzes and demonstrates the benefits of two different structures for adding a supercapacitor bank to a lithium-ion battery pack: a passive filter and a buffer for the battery. The advantages of each structure are presented and design guidelines are provided. In addition, we propose a synthetic inductor filter typology based on the two analyzed structures in order to increase the autonomy of the motorcycle. Simulations and experimental results using real data validate the analysis. This paper then uses real data extracted from an electric motorcycle to compare the four topologies: batteries only, the passive filter, the buffer, and the synthetic inductor semi-active hybrid storage system. The comparison shows that battery capacity is extended up to 7.8% with a smoother current profile. However, due to the losses from the added converter in cascade, the net capacity of the whole system is extended only up to 3.7%. Therefore, improvements in the dc/dc converter can further increase the entire system capacity. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems. [ABSTRACT FROM AUTHOR]

Archeological ceramic paste material typically consists of a mix of a clay matrix and various millimeter and sub-millimeter sized mineral inclusions. Micro X-ray fluorescence (XRF) is a standard compositional classification tool and in this work we propose and demonstrate an improved fluorescence map processing protocol where the mineral inclusions are automatically separated from the clay matrix to allow independent statistical analysis of the two parts. Application of this protocol allowed us to enhance the discrimination between different ceramic shards compared with the standard procedure of working with only the spatially averaged elemental concentrations. Using the new protocol, we performed an initial compositional classification of a set of 83 ceramic shards from the western slopes of the south central Andean region in the Arica y Parinacota region (Chile). Comparing the classifications obtained using the new versus the old (average concentrations only) protocols, we found that some samples were erroneously classified with the old protocol. From an archaeological perspective, a broad and heterogeneous regional sample set was used in this experimental study due to the fact that this was the first such analysis to be performed on ceramics from this region. This allowed a general overview to be obtained, however further work on more specific sample sets will be necessary to extract concrete archaeological conclusions. [ABSTRACT FROM AUTHOR]

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning. [ABSTRACT FROM AUTHOR]