Transient Modeling of a Thermosiphon based Air Conditioner with Compact Thermal Storage: Modeling and Validation

The Roving Comforter (RoCo) is an innovative personal thermal management technology currently being developed at the University of Maryland as part of Advanced Research Projects Agency – Energy (ARPA-e) project. Among several system configurations of RoCo, the paper focuses on a miniature battery-powered vapor compression cycle (VCC) system fitted on an autonomous robotic platform. The heat rejected from the condenser during its operation is stored in a compact phase change material (PCM) based heat storage device. The PCM can store only a limited amount of heat until it melts completely. The PCM heat exchanger needs to be designed so that it captures the heat generated during the VCC operation and then gets frozen with minimal energy usage during the non-operation mode. A thermosiphon mechanism is envisioned for this recharging of PCM material during the non-operation mode. It operates through the same refrigerant circuit by bypassing certain components like the compressor. Thus the circuit operates as a VCC during the day and as thermosiphon during the night. The VCC needs to have a high coefficient of performance (COP) while the thermosiphon needs to reject heat at highest possible rate. Hence transient modeling of thermosiphon is desired. The prototype developed has a COP of 2.85 and needs roughly 8 hours to recharge the thermosiphon. These trends have been captured on a transient model for the VCC operation while the development of thermosiphon modeling is being carried out. Comparison of modeling results with the experimental data have been provided to estimate the error in the model. Several cases of RoCo thermosiphon are then simulated using the model for the optimum design fulfilling requirements for both VCC and thermosiphon

Computer Simulation of Bioprocess

Bioprocess optimization is important in order to make the bioproduction process more efficient and economic. The conventional optimization methods are costly and less efficient. On the other hand, modeling and computer simulation can reveal the mechanisms behind the phenomenon to some extent, to assist the deep analysis and efficient optimization of bioprocesses. In this chapter, modeling and computer simulation of microbial growth and metabolism kinetics, bioreactor dynamics, bioreactor feedback control will be made to show the application methods and the usefulness of modeling and computer simulation methods in optimization of the bioprocess technology

Improved power by collapsing rare and common variants based on a data-adaptive forward selection strategy

Genome-wide association studies have been used successfully to detect associations between common genetic variants and complex diseases, but common single-nucleotide polymorphisms (SNPs) detected by these studies explain only 5–10% of disease heritability. Alternatively, the common disease/rare variants hypothesis suggests that complex diseases are often caused by multiple rare variants with moderate to high effects. Under this hypothesis, the analysis of the cumulative effect of rare variants may thus help us discover the missing genetic variations. Collapsing all rare variants across a functional region is currently a popular method to find rare variants that may have a causal effect on certain diseases. However, the power of tests based on collapsing methods is often impaired by misclassification of functional variants. We develop a data-adaptive forward selection procedure that selectively chooses only variants that improve the association signal between functional regions and the disease risk. We apply our strategy to the Genetic Analysis Workshop 17 unrelated individuals data with quantitative traits. The type I error rate and the power of different collapsing functions are evaluated. The substantially higher power of the proposed strategy was demonstrated. The new method provides a useful strategy for the association study of sequencing data by taking advantage of the selection of rare variants

Efficacy mechanisms research progress of the active components in the characteristic woody edible oils

Woody edible oils are a type of vegetable oil. Woody edible oils like olive oil have greater quantities of unsaturated fatty acids (UFAs), particularly essential FAs, as well as vitamin E, phytosterols, and other nutrients that are becoming more vital in human health. As a result, finding high-quality woody oil resource plants is critical to ensuring enough edible oil supply. As six novel woody crops, Paeonia suffruticosa, Plukenetia volubilis, Acer truncatum, Olea europaea, Camellia sinensis, and Camellia oleifera are characterized by high oil production, widespread cultivation, adaptability, and various active ingredients. The six woody crop oils contain UFAs (e.g., α-linolenic acid, oleic acid, and linoleic acid), vitamin E, polyphenols, phytosterols, and so forth. The presence of these active ingredients confers anti-inflammatory, antioxidant, cholesterol and lipid metabolism regulating, blood lipid lowering, immune boosting, memory improving, intestinal flora regulating, and other properties to the oils, which are beneficial to body health. This article examined in depth the seed resources, FA composition, active component kinds, active ingredient efficacy mechanism, and physiological impacts of these six novel woody crop oils. These developments lay a solid platform for further study and development of these woody oil crops.This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2021C02002), Zhejiang Provincial Natural Sciences Foundation of China under Grant (No. LZ22C200006), Top young talents of the ten thousand talents program of Zhejiang Province (ZJWR0308016), Key R&D projects in Zhejiang Province (2023C04010), and Zhejiang Basic Public Welfare Research Project (LGN21C200006). Agusti Romero acknowledges financial support from the CERCA Program from the Generalitat of Catalonia. We would like to thank all contributors of the current study for their concepts, ideas, contribution, and provision.info:eu-repo/semantics/publishedVersio

Towards clinical AI fairness: A translational perspective

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness

Electronic correlations and flattened band in magnetic Weyl semimetal candidate Co3Sn2S2

The interplay between electronic correlations and topological protection may offer a rich avenue for discovering emergent quantum phenomena in condensed matter. However, electronic correlations have so far been little investigated in Weyl semimetals (WSMs) by experiments. Here, we report a combined optical spectroscopy and theoretical calculation study on the strength of electronic correlations in a kagome magnet Co3Sn2S2 and the influence of electronic correlations on its WSM state expected within a single-particle picture. The electronic kinetic energy estimated from our optical data is about half of that obtained from single-particle ab initio calculations, which indicates intermediate-strength electronic correlations in this system. Furthermore, comparing the energy ratios between the interband-transition peaks at high energies in the experimental and single-particle-ab-initio-calculation derived optical conductivity spectra with the electronic bandwidth renormalization factors obtained by many-body calculations enables us to estimate the Coulomb-interaction strength (U ~ 4 eV) of electronic correlations in Co3Sn2S2. Our many-body calculations with U ~ 4 eV show that a WSM state, which is characterized by bulk Weyl cones and surface Fermi arcs, survives in this correlated electron system. Besides, a sharp experimental optical conductivity peak at low energy, which is absent in the single-particle-ab-initio-calculation-derived optical conductivity spectrum but is consistent with the optical conductivity peaks obtained by many-body calculations, indicates that an electronic band connecting the two Weyl cones is flattened by electronic correlations and emerges near the Fermi energy in Co3Sn2S2. Our work paves the way for exploring flat-band-generated quantum phenomena in WSMs

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead