IRRIGATION PRODUCTION FUNCTIONS WITH WATER-CAPITAL SUBSTITUTION

The dynamics of biomass growth implies that the yield of irrigated crops depends, in addition to the total amount of water applied, on irrigation scheduling during the growing period. Advanced irrigation technologies relax constraints on irrigation rates and timing, allowing to better adjust irrigation scheduling to the varying needs of the plants along the growing period. Irrigation production functions, then, should include capital (or expenditures on irrigation equipment) in addition to aggregate water. We derive such functions and study their water-capital substitution properties. Implications for water demand and adoption of irrigation technologies are investigated. An empirical application confirms these properties.Resource /Energy Economics and Policy,

Using clustering algorithms to examine the association between working memory training trajectories and therapeutic outcomes among psychiatric and healthy populations

Working memory (WM) training has gained interest due to its potential to enhance cognitive functioning and reduce symptoms of mental disorders. Nevertheless, inconsistent results suggest that individual differences may have an impact on training efficacy. This study examined whether individual differences in training performance can predict therapeutic outcomes of WM training, measured as changes in anxiety and depression symptoms in sub-clinical and healthy populations. The study also investigated the association between cognitive abilities at baseline and different training improvement trajectories. Ninety-six participants (50 females, mean age = 27.67, SD = 8.84) were trained using the same WM training task (duration ranged between 7 to 15 sessions). An algorithm was then used to cluster them based on their learning trajectories. We found three main WM training trajectories, which in turn were related to changes in anxiety symptoms following the training. Additionally, executive function abilities at baseline predicted training trajectories. These findings highlight the potential for using clustering algorithms to reveal the benefits of cognitive training to alleviate maladaptive psychological symptoms

Extending in vitro digestion models to specific human populations: Perspectives, practical tools and bio-relevant information

[EN] Background In vitro digestion models show great promise in facilitating the rationale design of foods. This paper provides a look into the current state of the art and outlines possible future paths for developments of digestion models recreating the diverse physiological conditions of specific groups of the human population. Scope and approach Based on a collective effort of experts, this paper outlines considerations and parameters needed for development of new in vitro digestion models, e.g. gastric pH, enzymatic activities, gastric emptying rate and more. These and other parameters are detrimental to the adequate development of in vitro models that enable deeper insight into matters of food luminal breakdown as well as nutrient and nutraceutical bioaccessibility. Subsequently, we present an overview of some new and emerging in vitro digestion models mirroring the gastro-intestinal conditions of infants, the elderly and patients of cystic fibrosis or gastric bypass surgery. Key findings and conclusions This paper calls for synchronization, harmonization and validation of potential developments in in vitro digestion models that would greatly facilitate manufacturing of foods tailored or even personalized, to a certain extent, to various strata of the human population.Shani-Levi, C.; Alvito, P.; Andrés Grau, AM.; Assunção, R.; Barbera, R.; Blanquet-Diot, S.; Bourlieu, C.... (2017). Extending in vitro digestion models to specific human populations: Perspectives, practical tools and bio-relevant information. Trends in Food Science & Technology. 60:52-63. https://doi.org/10.1016/j.tifs.2016.10.017S52636

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

The harmonized INFOGEST in vitro digestion method: From knowledge to action

Within the active field of in vitro digestion in food research, the COST Action INFOGEST aimed to harmonize in vitro protocols simulating human digestion on the basis of physiologically inferred conditions. A harmonized static in vitro digestion (IVD) method was recently published as a primary output from this network. To validate this protocol, inter-laboratory trials were conducted within the INFOGEST network. A first study was performed using skim milk powder (SMP) as a model food and served to compare the different in-house digestion protocols used among the INFOGEST members. In a second inter-laboratory study applying the harmonized protocol, the degree of consistency in protein hydrolysis was investigated. Analysis of the hydrolyzed proteins, after the gastric and intestinal phases, showed that caseins were mainly hydrolyzed during the gastric phase, whereas β-lactoglobulin was, as previously shown, resistant to pepsin. Moreover, generation of free amino acids occurred mainly during the intestinal phase.The study also showed that a few critical steps were responsible for the remaining inter-laboratory variability. The largest deviations arose from the determination of pepsin activity. Therefore, this step was further clarified, harmonized, and implemented in a third inter-laboratory study.The present work gives an overview of all three inter-laboratory studies, showing that the IVD INFOGEST method has led to an increased consistency that enables a better comparability of in vitro digestion studies in the future

Understanding Three-Dimensional Images: the Recognition of Abdominal Anatomy from Computer Axial Tomograms (CAT)

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 1981. Simultaneously published in the technical report series.Understanding 3-D images is conceived as the automatic extraction of the 3-D geometry of objects presented therein. This particular approach is employed in analyzing Computed Axial Tomograms (CAT) of the human abdomen. A collection of CAT scans is searched as a 3-D image and matched against a detailed geometrical model of the abdominal anatomy. Detected organ boundaries serve to construct an instance of the model that reflects the anatomical variations of a particular patient as revealed by the scan. The importance of this work is gained from the major role that CAT images played in diagnostic medicine during the last decade. No less important is their growing application in industry, especially for nondestructive inspection of manufactured products. Because manual examination of CAT scans is tedious and time consuming, automatic analysis is a promising means for easing the growing burden on radiology departments, and for increased efficiency in industry. In the medical application field, several efforts have developed systems that employ data-directed approaches by boundary-following and region-growing techniques for semiautomatic detection of organ boundaries. These approaches, however, tend not to work for organs like kidneys, which can have obscured boundaries. In contrast, a model-directed approach, as employed here, overcomes these difficulties by capitalizing on two important characteristics of the problem. First, the digital image is a direct representation of a 3-D domain (i.e., it samples the density of corresponding locations in a [limited] 3-D domain) that is unlike computer vision, in which images are samples of the 2-D projection of the domain (cf. analysis of outdoor scenes or chest radiology). Second, organs in CAT images can be described by geometrical and relational types of knowledge that are usually repeatable from patient to patient. This knowledge is used in a model-directed approach in several ways: for example, knowledge of the gross anatomy and the location of easy-to-find organs (e.g., the spinal column) and help in locating hard-to-find organs (e.g., the kidney). . Key technical contributions of the thesis are the following: • A decentralized organization of knowledge for image understanding by specializing search strategies to the various expected types of boundaries in the image; • A demonstration of a technique for organization and application of 3-D geometrical knowledge to the analysis of 3-D images, based on the use of Generalized-Cylinders; and • A promotion of the technique of Generalized-Cylinders to a degree never tried before, by the use of parametric cubic uniform B-splines. The system is suitable for fast execution, since the hierarchical model is inherently parallel. Other difficulties that result from the huge amount of redundant information presented in the data are managed by concentrating on local searches in the vicinity of expected organ locations, in small subimages, and in a pyramid-of-resolutions data organization. Expected benefits of the system (in the medical environment) are, first, the 3-D display of organ shapes; second, improved medical diagnosis based on the revealed anatomy; third, treatment planning; and fourth, improved image quality by real-time analysis and control of the scanning process. In industry, this system can be adopted as a basis for the automatic inspection of manufactured parts in a production line

Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach

Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless system operation grow. Model-based systems engineering, an approach based on conceptual models, copes with this challenge. Models help construct formal system representations, visualize them, understand the design, simulate the system, and discover design flaws early on. Modeling tools can benefit tremendously from querying capabilities that enable gaining deep insights into system aspects that direct model observations do not reveal. Querying mechanisms can unveil and explain cause-and-effect phenomena, identify central components, and estimate impacts or risks associated with changes. Being connected networks of system elements, models can be effectively represented as graphs, to which queries are applied. Capitalizing on established graph-theoretic algorithms to solve a large variety of problems can elevate the modeling experience to new levels. To utilize this rich set of capabilities, one must convert the model into a graph and store it in a graph database with no significant loss of information. Applying the appropriate algorithms and translating the query response back to the original intelligible and meaningful diagrammatic and textual model representation is most valuable. We present and demonstrate a querying approach of converting Object-Process Methodology (OPM) ISO 19450 models into graphs, storing them in a Neo4J graph database, and performing queries that answer complex questions on various system aspects, providing key insights into the modeled system or phenomenon and helping to improve the system design

PACS and Electronic Health Records

Electronic Health Record (EHR) is a major component of the health informatics domain. An important part of the EHR is the medical images obtained over a patient’s lifetime and stored in diverse PACS. The vision presented in this paper is that future medical information systems will convert data from various medical sources – including diverse modalities, PACS, HIS, CIS, RIS, and proprietary systems – to HL7 standard XML documents. Then, the various documents are indexed and compiled to EHRs, upon which complex queries can be posed. We describe the conversion of data retrieved from PACS systems through DICOM to HL7 standard XML documents. This enables the EHR system to answer queries such as “Get all chest images of patients at the age of 20-30, that have blood type ‘A ’ and are allergic to pine trees”, which a single PACS cannot answer. The integration of data from multiple sources makes our approach capable of delivering such answers. It enables the correlation of medical, demographic, clinical, and even genetic information. In addition, by fully indexing all the tagged data in DICOM objects, it becomes possible to offer access to huge amounts of valuable data, which can be better exploited in the specific radiology domain