UC Berkeley的化工过程设计课程介绍

清华大学化学工程系正在进行课程体系调整,将化工设备设计和工艺设计两门课调整为一门课,由笔者讲授.本系教授刘铮博士1999年底在美国访问期间结识了UC Berkeley化工系的教师Fred Vorhis,对其开设的化工过程设计课程很感兴趣,并介绍给笔者.笔者与Fred Vorhis进行了较为广泛的交流并深入研究了其化工过程设计课程的教学大纲,发现有很多值得借鉴的地方.在征得Fred Vorhis同意后,将该门课程的教学大纲(Syllabus)的主要部分译出,并根据笔者与FredVorhis的交流讨论进行了补充,遂成此文,愿与同行切磋.

Particle Size Optimization of Thermochemical Salt Hydrates for High Energy Density Thermal Storage

Thermal energy storage(TES)solutions offer opportunities to reduce energy consumption,greenhouse gas emissions,and cost.Specifically,they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load,which are critical toward zero energy buildings.Thermochemical materials(TCMs)as a class of TES undergo a solid-gas reversible chemical reaction with water vapor to store and release energy with high storage capacities(600 kWh m^(-3))and negligible self-discharge that makes them uniquely suited as compact,stand-alone units for daily or seasonal storage.However,TCMs suffer from instabilities at the material(salt particles)and reactor level(packed beds of salt),resulting in poor multi-cycle efficiency and high-levelized cost of storage.In this study,a model is developed to predict the pulverization limit or Rcrit of various salt hydrates during thermal cycling.This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy-efficient manufacturing process(solid-state mixing)to make the composites.The model is experimentally validated on multiple TCM salt hydrates with different water content,and effect of Rcrit on hydration and dehydration kinetics is also investigated.

Aging impairs the osteocytic regulation of collagen integrity and bone quality

Poor bone quality is a major factor in skeletal fragility in elderly individuals.The molecular mechanisms that establish and maintain bone quality,independent of bone mass,are unknown but are thought to be primarily determined by osteocytes.We hypothesize that the age-related decline in bone quality results from the suppression of osteocyte perilacunar/canalicular remodeling(PLR),which maintains bone material properties.We examined bones from young and aged mice with osteocyte-intrinsic repression of TGFβsignaling(TβRII^(ocy−/−))that suppresses PLR.The control aged bone displayed decreased TGFβsignaling and PLR,but aging did not worsen the existing PLR suppression in male TβRII^(ocy−/−)bone.This relationship impacted the behavior of collagen material at the nanoscale and tissue scale in macromechanical tests.The effects of age on bone mass,density,and mineral material behavior were independent of osteocytic TGFβ.We determined that the decline in bone quality with age arises from the loss of osteocyte function and the loss of TGFβ-dependent maintenance of collagen integrity.

Real-Time Hybrid Simulation of Seismically Isolated Structures with Full-Scale Bearings and Large Computational Models

Hybrid simulation can be a cost effective approach for dynamic testing of structural components at full scale while capturing the system level response through interactions with a numerical model.The dynamic response of a seismically isolated structure depends on the combined characteristics of the ground motion,bearings,and superstructure.Therefore,dynamic full-scale system level tests of isolated structures under realistic dynamic loading conditions are desirable towards a holistic validation of this earthquake protection strategy.Moreover,bearing properties and their ultimate behavior have been shown to be highly dependent on rate-of-loading and scale size effects,especially under extreme loading conditions.Few laboratory facilities can test full-scale seismic isolation bearings under prescribed displacement and/or loading protocols.The adaptation of a full-scale bearing test machine for the implementation of real-time hybrid simulation is presented here with a focus on the challenges encountered in attaining reliable simulation results for large scale dynamic tests.These advanced real-time hybrid simulations of large and complex hybrid models with several thousands of degrees of freedom are some of the first to use high performance parallel computing to rapidly execute the numerical analyses.Challenges in the experimental setup included measured forces contaminated by delay and other systematic control errors in applying desired displacements.Friction and inertial forces generated by the large-scale loading apparatus can affect the accuracy of measured force feedbacks.Reliable results from real-time hybrid simulation requires implementation of compensation algorithms and correction of these various sources of errors.Overall,this research program confirms that real-time hybrid simulation is a viable testing method to experimentally assess the behavior of full-scale isolators while capturing interactions with the numerical models of the superstructure to evaluate system level and in-structure response.

Analytical evaluation and experimental validation on dynamic rocking behavior for shallow foundation considering structural response

The challenge in the practical application of rocking foundations is the estimation of its performance,particularly the rotation angle,during a strong earthquake.In this study,the dynamic rocking behavior for a shallow foundation considering structural response was evaluated through two analytical approaches:the conventional soil-foundation-structure interaction(SFSI)governing equation of a single-degree-of-freedom(SDOF)structure on a rocking shallow foundation,and the Housner rocking model(i.e.,a rocking rigid block on a rigid base).Both approaches were validated with dynamic centrifuge tests.The test models consisted of a soft soil deposit,a shallow rectangular foundation,and an SDOF structure dominated by a bending behavior.A total of 11 foundation-structure systems and six seismic waves,including recorded earthquake signals and sinusoidal waves,were utilized.The results showed that the conventional SFSI equation well predicted the maximum rotation during strong earthquakes.However,this method was less accurate regarding the rotational phase information and maximum rotation of the foundation during weak earthquakes.On the other hand,although the modified Housner′s rocking model required five parameters relevant to a soil-foundation-structure system,it overestimated the maximum rotation of the foundation when compared with the results from dynamic centrifuge tests.

Perspectives and clinical practices of optometrists in Saudi Arabia concerning myopia in children

AIM:To investigate the practice patterns of optometrists in Saudi Arabia regarding myopia management.METHODS:An internet-based survey was distributed to all practicing optometrists in Saudi Arabia(n=1886).The survey contained questions related to 1)demographics,2)knowledge about myopia and its associated complications,3)current clinical care,4)type and frequency of myopia treatment prescribed,and 5)potential barriers limiting treatment adoption.RESULTS:The completed surveys were collected from 171 optometrists(9.06%response rate,60%male).Knowledge regarding myopia-associated complications was prevalent but somewhat inaccurate among the respondents.Cycloplegic refraction at initial visit was used by 59%of the respondents.The cover test was the most reported binocular vision test(83%),and 38%of optometrists did not perform any ocular biometrics.Twothirds prescribed single-vision spectacles for children with myopia.Increased time spent outdoors was selected by 80%of the practitioners who prescribed myopia control treatment as the primary approach.Insufficient support and lack of clinical experience in providing myopia treatment were reported as the most important factors limiting the adoption of myopia management strategies.CONCLUSION:The current optometric practices in Saudi Arabia require further investigation.Optometrists appear to be somewhat aware of myopia and the associated risks.However,most evidence-based myopia treatments are not being locally adopted,primarily because of lack of support,lack of experience,and limited availability.

非线性嗅觉生理模型在一维序列识别中的应用

Water J Freeman教授在生理实验的基础上建立了一套基于嗅觉系统的神经网络模型。这套模型运用非线性动力学的方法较好的模拟了人体的嗅觉系统,并能够产生类似脑电的非周期背景信号。利用这个的基于嗅觉系统而建立的模型--K系列模型可以实现模式识别。本文首先从原理上介绍了K系列模型的拓扑结构和数学基础,然后通过实际的计算机数值模拟介绍了KII网络和KIII模型在实现一维序列识别时的方法和结果,简单说明了这个模型在模式识别上的特点。

静电梳齿激励差分检测式微型电场传感器

研制了一种基于静电梳齿激励、差分检测式微型电场传感器.利用相关检测原理对该传感器进行了测试,测试结果表明,在外电场强度为0.5×105～25×105V/m范围内,传感器的输出特性基本满足线性关系,灵敏度可达到0.53μV·m/kV.

Ski and SnoN, potent negative regulators of TGF-β signaling

Ski and the closely related SnoN were discovered as oncogenes by their ability to transform chicken embryo fibroblasts upon overexpression. While elevated expressions of Ski and SnoN have also been reported in many human cancer cells and tissues, consistent with their pro-oncogenic activity, emerging evidence also suggests a potential anti-oncogenic activity for both. In addition, Ski and SnoN have been implicated in regulation of cell differentiation, especially in the muscle and neuronal lineages. Multiple cellular partners of Ski and SnoN have been identifed in an effort to understand the molecular mechanisms underlying the complex roles of Ski and SnoN. In this review, we summarize recent findings on the biological functions of Ski and SnoN, their mechanisms of action and how their levels of expression are regulated.

MAGI3 Suppresses Glioma Cell Proliferation via Upregulation of PTEN Expression

Objective To investigate the role and molecular mechanism of membrane-associated guanylate kinase inverted 3 （MAGI3） in glioma cell proliferation. Methods The expression levels of MAGI3 and PTEN were assessed in glioma samples by Western blotting. MAGI3 was stably transfected into C6 glioma cells to obtain C6-MAGI3 cells. Then, the proliferation, the expression levels of MAGI3 and PTEN, and Akt phosphorylation were evaluated in C6 and C6-MAGI3 cells. Xenograft tumor models were established by subcutaneous injection of C6 and C6-MAGI3 cells into nude mice, and the growth rates of xenografts in the mice were compared. The potential role of MAGI3 expression in PI3K/Akt signaling activation was further investigated by examining the correlation between MAGI3 expression and the expression of PI3K/Akt signaling downstream target genes in a glioma dataset using gene set enrichment analysis （GSEA）. Results Expression levels of MAGI3 and PTEN were significantly downregulated in gliomas. Overexpression of MAGI3 in the glioma C6 cell line upregulated PTEN protein expression, inhibited the phosphorylation of Akt, and suppressed cell proliferation. MAGI3 overexpression also inhibited the growth of C6 glioma tumor xenografts in nude mice. Analysis based on the GEO database confirmed the negative correlation between activation of PI3K/Akt pathway and MAGI3 mRNA levels in human glioma samples. Conclusion The loss of MAGI3 expression in downregulation of PTEN expression, leading potential glioma suppressor. glioma may enhance the proliferation of glioma cells via to the activation of the PI3K/Akt pathway. MAGI3 is a

遗产,从古代到现代:祖产、垃圾与奖励

从过去与现在、文化与人类学的关系维度中看待与研究遗产,遗产将呈现出动态性、易变性与转换性特质,透过祖产、奖励与垃圾理论的实践,遗产的这一动态转换与变迁过程得以进一步体现,但同样在这些过程中所显现出的遗产社会化、固态化与网络化现象与问题则值得反思与探讨。

Distribution of Eye Diseases in Kasungu District, Malawi, Central Africa—A Retrospective Cross-Sectional Study

Analysis of eye diseases of patients at Kasungu District Hospital in Malawi was made. Malawi is one of the poorest countries in the world and the health system faces a lot challenges in terms of resources. The study was, therefore, done to understand the burden and distribution of eye diseases in this resource-limited setting. A retrospective study was conducted by extracting data from data registers in the outpatient eye department for the period of May 2015 to June 2016. The data of the reported eye diseases analyzed with variables such as patient gender, eye disease type, patient age and times of the year. There was no association between eye diseases and gender nor with times of the year. However, it was noted that the commonest type of eye disease was conjunctivitis. And, there was strong association of some disease type with age, for example, conjunctivitis was common in young age group while cataract was common in the elderly. It was shown in this study that many of the eye diseases endemic in Africa do generally occur in this selected district as well. However, the analysis presents the possibility of reducing the incidences of many diseases by preventive measures and access to health facilities on time.

利用卫星影像获得汶川地震之同震地表变形来探讨其断层几何模型(英文)

On 12th May 2008, a MW7.9 earthquake occurred on the Longmenshan fault in the mountains to the west of Sichuan Province, which shook many Asian cities and killed at least 69000 people. The surface ruptures strike NE and extend up to 270 km along the boundary between the eastern Tibetan Plateau and the Sichuan basin. It was difficult to complete the detailed mapping of surface ruptures and to measure fault slip in the field because well-defined features to correlate across the fault were rare in mountainous area. Near surface deformation is in fact composed of contributions from internal faulting and folding. Using the COSI-Corr software, we obtain coseismic ground displacements deduced from sub-pixel correlation of SPOT5 and ENVISAT and also from D-InSAR on ENVISAT. The results can provide continuous surface displacements. In the south, the results show significant shortening, generally <10 meters, and two parallel ruptures, while in the north, they show strike slip dominant and only a single rupture. Based on our results, we simulate the fault geometry in different locations. To realize the long-term fault kinematics a comparison is further needed between coseismic deformation and long-term geomorphic deformation recorded in the landforms.

Eguchi–Hanson Singularities in U(2)-Invariant Ricci Flow

We show that a Ricci flow in four dimensions can develop singularities modeled on the Eguchi–Hanson space.In particular,we prove that starting from a class of asymptotically cylindrical U(2)-invariant initial metrics on T S^(2),a Type II singularity modeled on the Eguchi–Hanson space develops in finite time.Furthermore,we show that for these Ricci flows the only possible blow-up limits are(i)the Eguchi–Hanson space,(ii)the flat R4/Z2 orbifold,(iii)the 4d Bryant soliton quotiented by Z2,and(iv)the shrinking cylinder R×RP^(3).As a byproduct of our work,we also prove the existence of a new family of Type II singularities caused by the collapse of a two-sphere of self-intersection|k|≥3.

Tensorial tomographic Fourier ptychography with applications to muscle tissue imaging

We report tensorial tomographic Fourier ptychography(T^(2)oFu),a nonscanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in 3D.Built upon Fourier ptychography,a quantitative phase imaging technique,T^(2)oFu additionally highlights the vectorial nature of light.The imaging setup consists of a standard microscope equipped with an LED matrix,a polarization generator,and a polarization-sensitive camera.Permittivity tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three dimensions.We demonstrate T^(2)oFu’s efficiency through volumetric reconstructions of refractive index,birefringence,and orientation for various validation samples,as well as tissue samples from muscle fibers and diseased heart tissue.Our reconstructions of healthy muscle fibers reveal their 3D fine-filament structures with consistent orientations.Additionally,we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.

RNA-Seq Analysis of Developing Nasturtium Seeds （Tropaeolum majus）： Identification and Characterization of an Additional Galactosyltransferase Involved in Xyloglucan Biosynthesis

A deep-sequencing approach was pursued utilizing 454 and Illumina sequencing methods to discover new genes involved in xyloglucan biosynthesis, cDNA sequences were generated from developing nasturtium （Tropaeolum majus） seeds, which produce large amounts of non-fucosylated xyloglucan as a seed storage polymer. In addition to known xyloglucan biosynthetic genes, a previously uncharacterized putative xyloglucan galactosyltransferase was iden- tified. Analysis of an Arabidopsis thaliana mutant line defective in the corresponding ortholog （AT5G62220） revealed that this gene shows no redundancy with the previously characterized xyloglucan galactosyltransferase, MUR3, but is required for galactosyl-substitution of xyloglucan at a different position. The gene was termed XLT2 for Xyloglucan L-side chain galactosylTransferase position 2. It represents an enzyme in the same subclade of glycosyltransferase family 47 as MUR3. A double mutant defective in both MUR3 （mur3.1） and XLT2 led to an Arabidopsis plant with xyloglucan that consists essentially of only xylosylated glucosyl units, with no further substitutions.

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy.

Construction of ground-state preserving sparse lattice models for predictive materials simulations

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states.However,despite recent advances,the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation,since this property is not guaranteed by default.In this paper,we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data.The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters.The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes,i.e.,Li_(2x)Fe_(2(1−x))O_(2) and Li_(2x)Ti_(2(1−x))O_(2),for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging.We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction,but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement.This method provides a general tool for building robust,compressed and constrained physical models with predictive power.

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

Validating neural networks for spectroscopic classification on a universal synthetic dataset

To aid the development of machine learning models for automated spectroscopic data classification,we created a universal synthetic dataset for the validation of their performance.The dataset mimics the characteristic appearance of experimental measurements from techniques such as X-ray diffraction,nuclear magnetic resonance,and Raman spectroscopy among others.We applied eight neural network architectures to classify artificial spectra,evaluating their ability to handle common experimental artifacts.While all models achieved over 98%accuracy on the synthetic dataset,misclassifications occurred when spectra had overlapping peaks or intensities.We found that non-linear activation functions,specifically ReLU in the fully-connected layers,were crucial for distinguishing between these classes,while adding more sophisticated components,such as residual blocks or normalization layers,provided no performance benefit.Based on these findings,we summarize key design principles for neural networks in spectroscopic data classification and publicly share all scripts used in this study.