Review on Polymer Materials for Solid Desiccant Cooling System Liu, Hongyan; Sundarrajan, Subramanian; Kumar, Ganesh Vijay ...
Macromolecular materials and engineering,
December 2023, 2023-12-00, 20231201, 2023-12-01, Letnik:
308, Številka:
12
Journal Article
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As an alternative form of vapor compression air conditioning devices, solid desiccant cooling (SDC) techniques have increasingly been explored recently. The overall performances of SDC primarily rely ...on the capability of dehumidification and regeneration of desiccant. A desiccant with a great uptake capability and excellent regeneration potential is preferred in an SDC system. Although traditional desiccants like silica gels and zeolites are able to absorb moisture at moderate levels, hygroscopic polymers show a superior ability in moisture sorption and desorption. Significant research has been conducted to investigate the hygroscopic polymers in SDC for household and industrial applications. Here, first, an introduction to SDC systems is presented, and then hygroscopic polymers from natural and synthetic origins are discussed. Synthetic polymers discussed are metal–organic frameworks (MOFs), covalent organic frameworks (COFs), covalent triazine frameworks (CTFs), amorphous porous organic polymers (POPs), polyelectrolytes, and polymer‐based composites. Their dehumidification behaviors in SDC systems, primarily desiccant‐coated heat exchanger (DCHE) systems, are compared and summarized. Binders employed in SDC systems are also summarized, as a proper binder enhances the overall performance of the desiccant system. It can be anticipated that hygroscopic polymers and binder materials would witness extensive applications in the future.
The summary of various hygroscopic polymers from natural and synthetic origins used in solid desiccant cooling (SDC) systems for household applications are presented. Synthetic polymers reviewed are metal–organic frameworks (MOFs), covalent organic frameworks (COFs), covalent triazine frameworks (CTFs), amorphous porous organic polymers (POPs), and so on. Their dehumidification behaviors and regeneration ability in desiccant‐coated heat exchanger are summarized.
Aggregating the Loose Threads Davey, Sonya; Ganesh, Vijay S.; Amato, Anthony A. ...
The New England journal of medicine,
07/2024, Letnik:
391, Številka:
1
Journal Article
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A 58-year-old woman presented to the emergency department with worsening dyspnea, dysphagia to liquids, and fatigue.
Endosomal sorting complex required for transport (ESCRT) complex proteins regulate biogenesis and release of extracellular vesicles (EVs), which enable cell-to-cell communication in the nervous ...system essential for development and adult function. We recently showed human loss-of-function (LOF) mutations in ESCRT-III member CHMP1A cause autosomal recessive microcephaly with pontocerebellar hypoplasia, but its mechanism was unclear. Here, we show Chmp1a is required for progenitor proliferation in mouse cortex and cerebellum and progenitor maintenance in human cerebral organoids. In Chmp1a null mice, this defect is associated with impaired sonic hedgehog (Shh) secretion and intraluminal vesicle (ILV) formation in multivesicular bodies (MVBs). Furthermore, we show CHMP1A is important for release of an EV subtype that contains AXL, RAB18, and TMED10 (ART) and SHH. Our findings show CHMP1A loss impairs secretion of SHH on ART-EVs, providing molecular mechanistic insights into the role of ESCRT proteins and EVs in the brain.
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•CHMP1A is required for progenitor proliferation in developing brain•Loss of CHMP1A decreases sonic hedgehog secretion•Loss of CHMP1A decreases intraluminal vesicles within multivesicular bodies•CHMP1A regulates SHH secretion on the extracellular vesicle subtype ART-EV
Extracellular vesicles (EVs) are essential for cell-to-cell communication in developing brain. Coulter et al. show that the human microcephaly gene CHMP1A is required for neuroprogenitor proliferation through regulation of vesicular secretion of the growth factor sonic hedgehog (SHH). CHMP1A specifically impairs SHH secretion on a distinctive EV subtype, ART-EV.
We introduce interpretable siamese neural networks (SNNs) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in special relativity, the ...transformation of electromagnetic fields, and the motion of particles in a central potential. In these examples, SNNs learn to identify data points belonging to the same event, field configuration, or trajectory of motion. We demonstrate that in the process of learning which data points belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities. Such SNNs are highly interpretable, which enables us to reveal the symmetry invariants and conserved quantities without prior knowledge.
A 56-year-old man receiving rituximab who had months of neurologic symptoms was found to have Jamestown Canyon virus in cerebrospinal fluid by clinical metagenomic sequencing. The patient died, and ...postmortem examination revealed extensive neuropathologic abnormalities. Deep sequencing enabled detailed characterization of viral genomes from the cerebrospinal fluid, cerebellum, and cerebral cortex.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Maintenance of DNA integrity is crucial for all cell types, but neurons are particularly sensitive to mutations in DNA repair genes, which lead to both abnormal development and neurodegeneration. We ...describe a previously unknown autosomal recessive disease characterized by microcephaly, early-onset, intractable seizures and developmental delay (denoted MCSZ). Using genome-wide linkage analysis in consanguineous families, we mapped the disease locus to chromosome 19q13.33 and identified multiple mutations in PNKP (polynucleotide kinase 3′-phosphatase) that result in severe neurological disease; in contrast, a splicing mutation is associated with more moderate symptoms. Unexpectedly, although the cells of individuals carrying this mutation are sensitive to radiation and other DNA-damaging agents, no such individual has yet developed cancer or immunodeficiency. Unlike other DNA repair defects that affect humans, PNKP mutations universally cause severe seizures. The neurological abnormalities in individuals with MCSZ may reflect a role for PNKP in several DNA repair pathways.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We use techniques from the fields of computer algebra and satisfiability checking to develop a new algorithm to search for complex Golay pairs. We implement this algorithm and use it to perform a ...complete search for complex Golay pairs of lengths up to 28. In doing so, we find that complex Golay pairs exist in the lengths 24 and 26 but do not exist in the lengths 23, 25, 27, and 28. This independently verifies work done by F. Fiedler in 2013 and confirms the 2002 conjecture of Craigen, Holzmann, and Kharaghani that complex Golay pairs of length 23 don't exist. Our algorithm is based on the recently proposed SAT+CAS paradigm of combining SAT solvers with computer algebra systems to efficiently search large spaces specified by both algebraic and logical constraints. The algorithm has two stages: first, a fine-tuned computer program uses functionality from computer algebra systems and numerical libraries to construct a list containing every sequence that could appear as the first sequence in a complex Golay pair up to equivalence. Second, a programmatic SAT solver constructs every sequence (if any) that pair off with the sequences constructed in the first stage to form a complex Golay pair. This extends work originally presented at the International Symposium on Symbolic and Algebraic Computation (ISSAC) in 2018; we discuss and implement several improvements to our algorithm that enabled us to improve the efficiency of the search and increase the maximum length we search from length 25 to 28.
This paper deals with evaluation of engine performance using preheated biofuel utilizing the waste heat generated from exhaust gases. The performance characteristics and emission parameters are ...compared with the trans-esterified biodiesel. It is understood that the preheated biofuel run in the engine was capable of producing appreciable brake thermal efficiency as that of biodiesel run CI engine. This effort simultaneously reduced the heat loss to the environment, thereby improving the exergy of the system. The neem biofuel was heated to 70°C and 80°C before injection by the exhaust gases. HC and CO emissions were reduced by 22.3% and 19.01% respectively. NOx emissions suffered an increase of 18.6%. The improvement in BTE was 5.5% compared to non-heated biofuel.
Machine learning and logical reasoning have been the two foundational pillars of Artificial Intelligence (AI) since its inception, and yet, until recently the interactions between these two fields ...have been relatively limited. Despite their individual success and largely independent development, there are new problems on the horizon that seem solvable only via a combination of ideas from these two fields of AI. These problems can be broadly characterized as follows: how can learning be used to make logical reasoning and synthesis/verification engines more efficient and powerful, and in the reverse direction, how can we use reasoning to improve the accuracy, generalizability, and trustworthiness of learning. In this perspective paper, we address the above-mentioned questions with an emphasis on certain paradigmatic trends at the intersection of learning and reasoning. Our intent here is not to be a comprehensive survey of all the ways in which learning and reasoning have been combined in the past. Rather we focus on certain recent paradigms where
corrective feedback loops
between learning and reasoning seem to play a particularly important role. Specifically, we observe the following three trends: first, the use of learning techniques (especially, reinforcement learning) in sequencing, selecting, and initializing proof rules in solvers/provers; second, combinations of inductive learning and deductive reasoning in the context of program synthesis and verification; and third, the use of solver layers in providing corrective feedback to machine learning models in order to help improve their accuracy, generalizability, and robustness with respect to partial specifications or domain knowledge. We believe that these paradigms are likely to have significant and dramatic impact on AI and its applications for a long time to come.
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