Crop Agnostic Monitoring Driven by Deep Learning

Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a single platform. This creates a significant gap between available technology and farmer requirements. We propose a novel field agnostic monitoring technique that is able to operate on two different robots, in arable farmland or a glasshouse (horticultural setting). Instance segmentation forms the backbone of this approach from which object location and class, object area, and yield information can be obtained. In arable farmland, our segmentation network is able to estimate crop and weed at a species level and in a glasshouse we are able to estimate the sweet pepper and their ripeness. For yield information, we introduce a novel matching criterion that removes the pixel-wise constraints of previous versions. This approach is able to accurately estimate the number of fruit (sweet pepper) in a glasshouse with a normalized absolute error of 4.7% and an R2 of 0.901 with the visual ground truth. When applied to cluttered arable farmland scenes it improves on the prior approach by 50%. Finally, a qualitative analysis shows the validity of this agnostic monitoring algorithm by supplying decision enabling information to the farmer such as the impact of a low level weeding intervention scheme.

Aging Across the Life Course: Research Collections Available from the National Archive of Computerized Data on Aging

The creation and maintenance of sustainable data archives can be challenging, but it offers clear advantages. Properly curated data can be used by multiple researchers, testing a variety of hypotheses, and increasing the return on investment to the expensive process of data collection. Having an internally managed archival system also provides greater control and autonomy in the equitable distribution of data resources. This process ensures all researchers will have full use of the data for original research, teaching, and new directions once the data leaves the control of the local investigator’s control. This poster reviews the advantages of having a local strategy geared toward the preservation and sharing of gerontological research data. Using the National Archive of Computerized Data on Aging (NACDA) as a working example, the poster offers an overview of collections at NACDA. Using our metadata tools and variable search database, NACDA can identify studies in its collections that examine aspects of aging and health among adults during their lifecourse. Many of the studies are longitudinal or repeat measure cross-sectional studies. We are also able to identify studies that focus on aging that are not maintained by NACDA but which are available to interested researchers. Using a strategy of archival preservation combined with a strong focus on productive research Innovation in Aging, NACDA has amassed data and metadata covering a wide array of studies worldwide that address the aging lifecourse. Because our collections are multinational, we share these data at no cost to interested users worldwide

SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the contextually salient multi-variate motif (CS-motif) discovery problem and then propose a salient multi-variate motif (SMM) algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.

A New Paradigm for Ensuring Digital Drug Care Quality Monitoring Based on Ontology Tools

Systematizing and conceptualizing of the components of the patient’s diagnosis at definite Clinical Situations are the important steps in constructing the medical electronic platforms to monitor the drug treatment quality and to realize the process of risk-management of drug care. The risk-management is a hard and expensive process for the non-profit hospitals. The Information technologies have a high potential for solving these problems. The conceptual schemes to construct the multimodal medical electronic platform were discussed. The Information Space of Clinical Practice, Information Field for formulating the detailed patient’s diagnosis at the definite Clinical Situations, Information Environment for the components of the detailed patient’s diagnosis were described.

Cross-Activation of Two Nitrogenase Gene Clusters by CnfR1 or CnfR2 in the Cyanobacterium Anabaena variabilis

Cyanobacterial nitrogen fixation is important in the global nitrogen cycle, in oceanic productivity, and in many plant and fungal symbioses. While the proteins that mediate nitrogen fixation have been well characterized, the regulation of this complex and expensive process is poorly understood in cyanobacteria.

Development of Near Gold-Colored TixZr1-xN Nano Thin Films by Arc Ion Plating

A gold plating technique with wet chemical solution is widely used to make different metallic colors for various industrial applications. However, it results in environmental pollution due to the generation of contaminated water, and it is also an expensive process. As an alternative approach, it is often desirable to use the Physical Vapor Deposition coating method that does not generate such pollution, and to use cheaper material to imitate gold colors. In this letter, target materials consisting of Ti–Zr alloy are employed to realize tens of nanometers thickness metallic thin film that can produce a metallic color that is close to that of natural gold. TiZrN thin films with a thickness ranging (2,000 to 4,000) nm are formed on a substrate using Arc Ion Plating. The results showed that the thin films exhibited an adhesion force of 50 N or more and a hardness of 1,500 Hv or more under a −100 V bias condition, and various gold colors could be realized by changing the ratio of Ti and Zr element of the alloy target.

Biogas Reforming as a Precursor for Integrated Algae Biorefineries: Simulation and Techno-Economic Analysis

Biogas is a significant by-product produced in algae processing and may be used for many different applications, not only as a renewable energy carrier but also as a chemical intermediate in integrated algae-based biorefineries. In this work, the reforming of biogas to H2/CO2 mixtures (referred to as SynFeed) as feed for the direct hydrogenation of CO2 to methanol is investigated. Two conventional processes, namely steam methane and autothermal reforming, with upstream CO2 separation from raw biogas are compared to novel concepts of direct biogas bi- and tri-reforming. In addition, downstream CO2 separation from SynFeed using the commercial Selexol process to produce pure H2 and CO2 is considered. The results show that upstream CO2 separation with subsequent steam methane reforming is the most economic process, costing 142.48 €/tSynFeed, and taking into consideration the revenue from excess hydrogen. Bi-reforming is the most expensive process, with a cost of 413.44 €/tSynFeed, due to the high demand of raw biogas input. Overall, SynFeed from biogas is more economical than SynFeed from CO2 capture and water electrolysis (464 €/tSynFeed), but is slightly more expensive than using natural gas as an input (107 €/SynFeed). Carbon capture using Selexol comes with costs of 22.58–27.19 €/tCO2, where approximately 50% of the costs are derived from the final CO2 compression.

Production of tomato seedlings using seeds pelleted with Natrum muriaticum and submitted to saline stress

The development and yield of plants is directly related to the effects of salinity. There are several reports in scientific studies of significant reduction in the growth and production of tomato in soils with high electrical conductivity. The correction or recovery of salinized soils, although technically possible, is a slow and very expensive process, making it necessary to develop new technologies. The objective of this study is to evaluate the production of tomato (Solanum lycopersicon L.) seedlings using seeds pelleted with a homeopathic preparation of Natrum muriaticum (Nat-m) submitted to saline stress. The treatments consisted of the pelletization of tomato seeds with six dynamizations of Nat-m (3cH, 5cH, 7cH, 9cH, 11cH and 13cH). Coated or uncoated seeds (controls) were placed in phenolic foam blocks, kept in plastic trays previously moistened with 50 mM NaCl saline solution (2.922 g.L-1 of NaCl, electrical conductivity = 4.5 dSm-1) and nutrient solution (0.15 dS.m-1) at half the ionic strength, containing 4, 1, 2, 1, 0.5 and 0.5 mmol.L-1 of N, P, K, Ca, Mg and S, and 17.5, 9.5, 10.5, 2, 0.45 and 0.35 mmol.L-1 of Fe, Mn, Zn, Cu and Mo, respectively. The variables evaluated were germination percentage, germination speed index, shoot length, leaf area, number of leaves, root volume, root dry matter, and shoot dry matter. The treatments pelleted seeds/talc Nat-m 5cH and pelleted seeds/talc Nat-m 7cH increased all variables evaluated. They differed statistically from the controls, with a positive response for the development of tomato seedlings under disequilibrium conditions.

Automatic Fruit Detection and Couting System Using Neural Network

In the field of agriculture, Identification and counting the number of fruits from the image helps the farmers in crop estimation. At present manual counting of fruits present in many places. The current practice of yield estimation based on the manual counting of fruits has many drawbacks as it is time consuming and expensive process. while considering the progress of fruit detection, estimating proper and accurate fruit counts from images in real-world scenarios such as orchards is still a challenging problem. The focus of this paper is on the web application of fruit yield estimation. This web application helps the farmers to count the number of fruits easily. This system provides an automated and efficient fruit counting system using computer vision techniques. This paper provides the progress towards in-field fruit counting using neural network object detection methods. So this process is done by recognizing each fruit in the image and taking the count. In the neural network, we have used YOLO architecture for recognizing the fruits.

Repurposing Drugs to Treat Heart and Brain Illness

Drug development is a complicated, slow and expensive process with high failure rates. One strategy to mitigate these factors is to recycle existing drugs with viable safety profiles and have gained Food and Drug Administration approval following extensive clinical trials. Cardiovascular and neurodegenerative diseases are difficult to treat, and there exist few effective therapeutics, necessitating the development of new, more efficacious drugs. Recent scientific studies have led to a mechanistic understanding of heart and brain disease progression, which has led researchers to assess myriad drugs for their potential as pharmacological treatments for these ailments. The focus of this review is to survey strategies for the selection of drug repurposing candidates and provide representative case studies where drug repurposing strategies were used to discover therapeutics for cardiovascular and neurodegenerative diseases, with a focus on anti-inflammatory processes where new drug alternatives are needed.