Dry Powder Microfeeding System for Solid Freeform Fabrication

Second generation SFF techniques allow both composition and shape to be downloaded directly from a computer file so that 3D functionally graded materials (FGM) can be assembled. Methods for multi-material deposition are also needed in combinatorial research, colour management and pharmaceutical dosing. In this work, computer-controlled microfeeding systems using ultrasonic vibration of a capillary were built. A wide range of stable flow rate control and switching control were achieved in the acoustic vibration system, and uniform powder doses were obtained in the ultrasonic system. The experimental results show that the nozzle diameter, transmission fluid depth, waveforms, voltage amplitude, frequency and oscillation duration all influence the dose mass. Among these factors, the nozzle diameter, voltage amplitude and oscillation duration can be used to control the dose mass. Raster printing of patterns with various resolution and dot size are demonstrated.Mechanical Engineerin

A plane linkage and its tessellation for deployable structure

Deployable structures are widely used in space applications such as solar arrays and antennas. Recently, inspired by origami, more deployable structures have been developed. This paper outlined a novel design scheme for deployable structures by taking a plane linkage as an origami unit with a large deployable ratio. The mountain and valley (M-V) crease assignment and kinematics of the plane linkage were analyzed. Physical interference in the folding progress was discovered geometrically and resolved by the split-vertex technique. Finally, tessellation of the derived pattern was successfully used to create a large-deployable-ratio structure, which was found to exhibit considerable potential in future space applications

Microporous carbons for gas storage

Available from British Library Document Supply Centre-DSC:DXN013215 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Role of endolysosomes and inter-organellar signaling in brain disease

Endosomes and lysosomes (endolysosomes) are membrane bounded organelles that play a key role in cell survival and cell death. These acidic intracellular organelles are the principal sites for intracellular hydrolytic activity required for the maintenance of cellular homeostasis. Endolysosomes are involved in the degradation of plasma membrane components, extracellular macromolecules as well as intracellular macromolecules and cellular fragments. Understanding the physiological significance and pathological relevance of endolysosomes is now complicated by relatively recent findings of physical and functional interactions between endolysosomes with other intracellular organelles including endoplasmic reticulum, mitochondria, plasma membranes, and peroxisomes. Indeed, evidence clearly indicates that endolysosome dysfunction and inter-organellar signaling occurs in different neurodegenerative diseases including Alzheimer’s disease (AD), HIV-1 associated neurocognitive disease (HAND), Parkinson’s disease (PD) as well as various forms of brain cancer such as glioblastoma multiforme (GBM). These findings open new areas of cell biology research focusing on understanding the physiological actions and pathophysiological consequences of inter-organellar communication. Here, we will review findings of others and us that endolysosome de-acidification and dysfunction coupled with impaired inter-organellar signaling is involved in the pathogenesis of AD, HAND, PD, and GBM. A more comprehensive appreciation of cell biology and inter-organellar signaling could lead to the development of new drugs to prevent or cure these diseases

Optimizacija prehospitalnih strategija upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou pomoću algortima za duboko učenje

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the pre-hospital first aid efficiency. At the Department of Emergency, Huizhou Third People’s Hospital as the research subject, the common pre-hospital first aid procedure for infectious diseases was identified. The Petri net was used to model and determine the execution time of each link of the pre-hospital first aid process. The isomorphic Markov chain was used to optimize the pre-hospital first aid procedure for infectious diseases. In terms of the emergency path, deep learning was combined with the reinforcement learning model to construct the reinforcement learning model for ambulance path planning. Isomorphic Markov chain analysis revealed that the patient status when returning to the hospital, the time needed for the ambulance to come to designated location, and the on-site treatment were the main problems in the first aid process, and the time needed for the pre-hospital first aid process was reduced by 25.17% after optimization. In conclusion, Petri net and isomorphic Markov chain can optimize the pre-hospital first aid management strategies for patients with infectious diseases, and the use of deep learning algorithm can effectively plan the emergency path, achieving intelligent and informationalized pre-hospital transfer, which provides a basis for reducing the suffering, mortality, and disability rate of patients with infectious diseases.Cilj istraživanja bio je optimizirati strategiju prehospitalnog upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou, Kina, za koju se očekuje da pruži osnovu za prevenciju i kontrolu epidemije, da spasi živote te da poveća učinkovitost prehospitalne prve pomoći. Istraživanje je provedeno na Hitnom odjelu Treće narodne bolnice u gradu Huizhou, gdje je utvrđen opći prehospitalni postupak prve pomoći za zarazne bolesti. Petrijeva mreža je primijenjena kako bi se modeliralo i odredilo vrijeme izvršenja svake karike u procesu prehospitalne prve pomoći. Izomorfni Markovljev lanac primijenjen je za optimizaciju prehospitalnog postupka prve pomoći za zarazne bolesti. Za putanju hitnosti, duboko učenje je kombinirano s modelom pojačanog učenja kako bi se konstruirao model osnaživanja učenja za planiranje putanje vozila hitne pomoći. Analiza Markovljeva lanca pokazala je da su status bolesnika na povratku u bolnicu, vrijeme potrebno da vozilo hitne pomoći dođe na određenu lokaciju i skrb na mjestu događaja glavni problemi u procesu prve pomoći te da je vrijeme potrebno za prehospitalni proces prve pomoći smanjeno za 25,17% nakon optimizacije. Zaključeno je da Petrijeva mreža i izomorfni Markovljev lanac mogu optimizirati strategije upravljanja prehospitalnom prvom pomoći za bolesnike sa zaraznim bolestima te da primjena algoritma dubokog učenja može učinkovito planirati putanju tima hitne pomoći, čime se postiže pametan i informatizirani prehospitalni prijevoz, što čini osnovu za smanjenje patnje, smrtnosti i stope invalidnosti za bolesnike sa zaraznim bolestima