Petrology,geochronology,and geophysical characterization of Mesoproterozoic rocks in central Illinois,USA

The Precambrian basement rocks of the Eastern Granite-Rhyolite Province(EGRP)in central Illinois(midcontinent region of North America)exhibit a complex history of early volcanism,granite emplacement,and intrusion of mafic rocks.A comprehensive suite of dedicated petrographic analyses,geophysical logs,and drill core from four basement-penetrating wells,two-dimensional and three-dimensional seismic reflection data,and U-Pb age data from the Illinois Basin–Decatur Project(IBDP)and Illinois Carbon Capture Storage(ICCS)Project site provide new constraints for interpreting the Precambrian basement of the Illinois Basin.These new data reveal the basement to be compositionally and structurally complex,having typical EGRP felsic volcanic rocks intruded by the first reported gabbro in the Precambrian basement in Illinois.Zircons(n?29)from rhyolite give a U-Pb weighted mean average age of 14679 Ma.Zircons(n?3)from a gabbro dike that intrudes the rhyolite yield a concordia age of 107312 Ma,which corresponds to Grenville-age extension and represents the first Grenville-age rock in Illinois and in the EGRP.A high-resolution three-dimensional seismic reflection volume,coincident with the four wells,provides a context for interpreting the petrological data and implies a high degree of heterogeneity for basement rocks at the IBDP–ICCS site,as also shown by the drill cores.The occurrence of Grenville-age gabbro is related to a prominent bowl-like structure observed on local two-dimensional seismic reflection profiles and the three-dimensional volume that is interpreted as a deep-seated mafic sill complex.Furthermore,heterogeneities such as the brecciated EGRP rhyolite and later gabbro intrusion observed in the basement lithology at the IBDP–ICCS may reflect previously unknown distal elements of the 1.1 Ga Midcontinent Rift in the EGRP and more likely Grenville-age extension.

Neighborhood deprivation and availability of culturally specific African-American and Latino fruits and vegetables in five small central Illinois cities

Introduction: Although individual-level dietary behavior among racial/ethnic minority groups in the US is influenced by cultural food preferences and socioeconomic position, few studies of the food store environment have simultaneously examined both factors. The objective of this cross-sectional study was to investigate the availability of culturally specific fruits and vegetables for African Americans and Latinos by levels of neighborhood deprivation. The 5 small central Illinois cities selected for the study have exhibited increasing numbers of both racial/ ethnic groups in the last decade. Methods: A validated audit tool was used to survey 118 food stores in 2008. Census 2000 block group data was used to create a neighborhood deprivation index (categorized as low, medium, and high) based on socioeconomic characteristics using principal component analysis. Statistical analyses were performed in SPSS version 17.0 to determine whether the availability of culturally specific fruits and vegetables (n = 31) varied by neighborhood levels of deprivation and store type. Results: Fewer than 50% of neighborhoods carried culturally specific fruits and vegetables, with the lowest availability found in low deprivation neighborhoods (p < 0.05). Culturally specific fruits and vegetables were most often found in neighborhoods with medium levels of deprivation, and in grocery stores (p < 0.05). Latino fruits and vegetables were less likely to be found across neighborhoods or in stores, compared to African-American fruits and vegetables. Conclusions: The limited availability of culturally specific fruits and vegetables for African Americans and Latinos highlights potential environmental challenges with adherence to daily dietary guidelines for fruit and vegetable consumption in these groups.

Motivated by a mandate: a university-clinic partnership to develop a perinatal depression registry at a community based hospital in the Midwest

Disparities in maternal mental health outcomes persist despite the myriad of existing evidence based treatments and recent public health prevention policy efforts. Integrated health care delivery models such as Collaborative Care and patient medical home models have the potential to reduce health disparities in clinic settings. These evidence-based approaches require multidisciplinary teams for successful implementation and to provide quality care to improve specified patient outcomes. However, strategies for successful collaboration and steps for critical reflection are often overlooked in clinical and health services research. Furthermore, a shared vision of social justice is essential in the process of building and sustaining patient-centered care models, but is often understated. The purpose of this paper is to describe the development and implementation of a social justice-informed hospital-based perinatal depression registry to address maternal health disparities. Our partnership is informed by community-based participatory research (CBPR) principles for carrying out health services research. We describe the steps for building a sustainable university-hospital collaboration between traditional and non-traditional researchers using principles from CBPR in a clinic setting.

Breakthrough of Carbon-Ash Recalcitrance in Hydrochar via Molten Carbonate:Engineering Mineral-Rich Biowaste Toward Sustainable Platform Carbon Materials

The function-led design of porous hydrochar from mineral-rich biowaste for environmental applications inevitably suffers from carbon-ash recalcitrance.However,a method to alter the original carbon skeleton with ash remains elusive and hinders the availability of hydrochar.Herein,we propose a facile strategy for breaking the rigid structure of carbon-ash coupled hydrochar using phase-tunable molten carbonates.A case system was designed in which livestock manure and NaHCO3 were used to prepare the activated hydrochar,and NH3 served as the target contaminant.Due to the redox effect,we found that organic fractions significantly advanced the melting temperature of Na2CO3 below 800℃.The Na species steadily broke the carbon-ash interaction as the thermal intensity increased and transformed inorganic constituents to facilitate ash dissolution,rebuilding the hydrochar skeleton with abundant hierarchical channels and active defect edges.The surface polarity and mesopore distribution collectively governed the five cycles NH3 adsorption attenuation process.Manure hydrochar delivered favorable potential for application with a maximum overall adsorption capacity of 100.49 mg·g^(-1).Integrated spectroscopic characterization and theoretical computations revealed that incorporating NH3 on the carbon surface could transfer electrons to chemisorbed oxygen,which promoted the oxidation of pyridine-N during adsorption.This work offers deep insight into the structure function correlation of hydrochar and inspires a more rational design of engineered hydrochar from high-ash biowaste.

Multi-disciplinary Pathways to Computing:A Scalable and Col aborative Approach to Capitalize on the Demand for Computer Science Education

The number of students demanding computer science(CS)education is rapidly rising,and while faculty sizes are also growing,the traditional pipeline consisting of a CS major,a CS master’s,and then a move to industry or a Ph.D.program is simply not scalable.To address this problem,the Department of Computing at the University of Illinois has introduced a multidisciplinary approach to computing,which is a scalable and collaborative approach to capitalize on the tremendous demand for computer science education.The key component of the approach is the blended major,also referred to as“CS+X”,where CS denotes computer science and X denotes a non-computing field.These CS+X blended degrees enable win-win partnerships among multiple subject areas,distributing the educational responsibilities while growing the entire university.To meet the demand from non-CS majors,another pathway that is offered is a graduate certificate program in addition to the traditional minor program.To accommodate the large number of students,scalable teaching tools,such as automatic graders,have also been developed.

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Who could and should give exercise prescription:Physicians,exercise and health scientists,fitness trainers,or ChatGPT?

Benefits of exercise for personal health,as well as preventing and treating chronic diseases,have been well documented during the past several decades.1 Exercise prescription is a specialized field that involves assessing an individual’s health status,fitness level,and specific goals to create a tailored exercise program based often on the FITT-VP principle.

Circumscription of the East Asia clade (Apiaceae subfamily Apioideae) and the taxonomic placements of several problematic genera

The East Asia(or Physospermopsis) clade was recognized in previous molecular phylogenetic investigations into the higher-level relationships of Apiaceae subfamily Apioideae. The composition of this clade, the phylogenetic relationships among its constituent taxa, and the placement of species previously determined to be problematic have yet to be resolved. Herein, nr DNA ITS sequences were obtained for150 accessions of Apioideae, representing species whose distributions are in East Asia or genera having one or more species included within the East Asia clade. These data, along with published ITS sequences from other Apioideae(for 3678 accessions altogether), were subjected to maximum likelihood and Bayesian inference analyses. The results show that the East Asia clade contains representatives of 11currently recognized genera: Hansenia, Hymenolaena, Keraymonia, Sinolimprichtia, Acronema, Hymenidium, Physospermopsis, Pimpinella, Sinocarum, Tongoloa, and Trachydium. However, the latter seven genera have members falling outside of the East Asia clade, including the generic types of all except Tongoloa. Within the clade, the species comprising these seven genera are widely intermingled, greatly increasing confusion among relationships than previously realized. The problematic species Physospermopsis cuneata is confirmed as falling within the East Asia clade, whereas P. rubrinervis allies with the generic type in tribe Pleurospermeae. Physospermopsis kingdon-wardii is confirmed as a member of the genus Physospermopsis, whereas the generic attributions of P. cuneata and Tongoloa stewardii remain unclear. Two species of Sinocarum(S. filicinum and S. wolffianum) are transferred into the genus Meeboldia. This is the most comprehensive molecular phylogenetic investigation of the East Asia clade to date, and while the results increase systematic understanding of the clade, they also highlight the need for further studies of one of the most taxonomically intractable groups in Apioideae.

Global prevalence and gender inequalities in at least 60 min of self-reported moderate-to-vigorous physical activity 1 or more days per week:An analysis with 707,616 adolescents

Background:Guidelines recommend that adolescents should accumulate an average of 60 min per day of moderate-to-vigorous physical activity(MVPA).However,using only this cut-off could hide important information.For instance,from a population-level point of view,increasing physical activity for those with no or low physical activity could provide more health benefits than increasing physical activity for those with intermediate levels.Also,including a more sensitive cut-point of≥1 days per week could be an additional strategy for identifying those with low access/opportunities for physical activity practice.Thus,the current study aims to estimate the prevalence of≥60 min of MVPA≥1 days per week among adolescents globally,and to describe any relevant gender inequalities.Methods:We used representative datasets from 146 countries/territories collected between 2003 and 2019.MVPA was self-reported.Participants were grouped into younger(≤14 years old)and older(>14 years old)adolescents.Crude Poisson regression models were used to identify the relative differences in≥60 min of MVPA≥1 days per week between boys and girls,and random-effects meta-analysis models were used to identify the pooled estimates.Analyses were stratified by country and region.Results:Approximately 80%of both younger and older adolescents reported≥60 min of MVPA≥1 days per week.This prevalence was≥94%in Europe and Central Asia and North America,while the estimates for the other regions were<77%.The prevalence of≥60 min of MVPA≥1 days per week was higher among boys than girls,with the largest differences occurring among the oldest adolescents(Prevalence ratio_(≤14y)=1.04(95%confidence interval(95%CI)):1.03-1.04)vs.Prevalence ratio_(>14y)=1.09(95%CI:1.08-1.10)).Conclusion:Approximately 8 out of 10 adolescents reported accumulating≥60 min of MVPA≥1 days per week,with notable differences between regions.Gender differences were observed in several countries,especially among the oldest adolescents.Priorities for physical activity promotion among adolescents should include increasing access/opportunities for physical activity among those who do not achieve≥60 min of MVPA≥1 days per week and reducing gender inequalities.

考虑过阻尼模态参与的改进复模态叠加法

为处理非比例阻尼系统可能出现的过阻尼模态,提出了一种改进的复模态叠加强迫振动分析方法,建议无论模态是否过阻尼,复模态振动分析时应成对使用复模态.以一个连接任意布置外部阻尼器的悬臂梁典型非比例阻尼系统为例,其第1阶模态会随阻尼系数的增加而过阻尼.在厘清复模态响应与实际动力响应关系的基础上阐述了采用改进复模态叠加法获得时域响应、传递函数和方差的完整理论方法.将方程分为实部和虚部,使得原复数域运动方程成为基于实数矩阵的增广状态空间方程,从而克服了采用复数矩阵计算时变响应的困难.此外,针对基于白噪声滤波的外部激励,提出了一种高效方差响应评估方法,降低了计算资源消耗.结果表明,相较于假设模态法,复模态叠加法的结果更接近于有限元结果.基于遍历法研究最优阻尼器参数和位置时,采用多个小阻尼阻尼器优于单个大阻尼阻尼器.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.

Nonlinear optical imaging by detection with optical parametric amplification(invited paper)

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields.However,due to the use of photomultiplier tubes(PMTs),the wide application of nonlinear optical imaging is limited by the incapability of imaging under am-bient light.In this paper,we propose and demonstrate a new optical imaging detection method based on optical parametric amplification(OPA).As a nonlinear optical process,OPA in-trinsically rejects ambient light photons by coherence gating.Periodical poled lithium niobate(PPLN)crystals are used in this study as the media for OPA.Compared to bulk nonlinear optical crystals,PPLN crystals support the generation of OPA signal with lower pump power.Therefore,this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers,which facilitate high-speed optical signal detection,such as in spec-troscopy and imaging.A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation(SHG)and coherent anti-Stokes Raman scattering(CARS)microscopy imaging,and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions.With OPA detection,ambient-light-on SHG and CARS imaging becomes possible,and achieves a similar result as PMT detection under strictly dark environments.These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex.light ing conditions.

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

Ground movement patterns and shallow foundation performance in Iskenderun coastline during the 2023 Kahramanmaras earthquake sequence

This study focuses on the field reconnaissance efforts for investigating ground deformation behavior and building foundation performance in Iskenderun.Many structures experienced significant damage or collapsed due to strong ground shaking in the coastal Iskenderun district of Hatay during the 2023 Kahramanmaras earthquake sequence.Many buildings were also impacted by ground failure due to liquefaction of deposits in the area.Preliminary information regarding the general subsurface profile and post-earthquake data collected during the reconnaissance was used to perform simplified liquefaction and lateral spreading analyses.The empirical methods provide first order estimations of settlements and lateral spreading but are not sufficient when the nature of the structure-soil-structure interaction and earthquake sequence is considered.Measurements of vertical displacements and building tilt,liquefaction manifestations,and structural and foundation parameters potentially affecting foundation settlements including foundation geometry,building contact pressure,and building elastic period were used to evaluate seismic response of the ground and the structures through empirical procedures.Most of the buildings exhibited poor to mediocre foundation performance due to liquefaction-prone subsurface profiles,especially on the coastline.Ongoing field and numerical research will reveal the cause of significant ground failure in the area as well as refined estimates for the seismic deformations.

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.

The PcERF5 promotes anthocyanin biosynthesis in red-fleshed pear(Pyrus communis)through both activating and interacting with PcMYB transcription factors

As there is a strong interest in red-skinned pears,the molecular mechanism of anthocyanin regulation in red-skinned pears has been widely investigated;however,little is known about the molecular mechanism of anthocyanin regulation in red-fleshed pears due to limited availability of such germplasm,primarily found in European pears(Pyrus communis).In this study,based on transcriptomic analysis in red-fleshed and white-fleshed pears,we identified an ethylene response factor(ERF)from P.communis,PcERF5,of which expression level in fruit flesh was significantly correlated with anthocyanin content.We then verified the function of PcERF5 in regulating anthocyanin accumulation by genetic transformation in both pear skin and apple calli.PcERF5 regulated anthocyanin biosynthesis by different regulatory pathways.On the one hand,PcERF5 can activate the transcription of flavonoid biosynthetic genes(PcDFR,PcANS and PcUFGT)and two key transcription factors encoding genes PcMYB10 and PcMYB114.On the other hand,PcERF5 interacted with PcMYB10 to form the ERF5-MYB10 protein complex that enhanced the transcriptional activation of PcERF5 on its target genes.Our results suggested that PcERF5 functioned as a transcriptional activator in regulating anthocyanin biosynthesis,which provides new insights into the regulatory mechanism of anthocyanin biosynthesis.This new knowledge will provide guidance for molecular breeding of red-fleshed pear.

群体断层激活及负面效应涌现:熙宁变法缘何从志同道合走向四分五裂?

群体断层构念与决定组织变革成败的高管团队多重人际交互及其演进表征十分契合。但群体断层因为什么而激活?激活了的群体断层是如何影响高管团队的群体过程及组织绩效产出?这些问题亟需实证性的探索和检验。本研究基于《王安石年谱长编》,辅以《宋史》及后人相关专题研究,采用质性研究中的扎根理论方法,以王安石越次入对宋神宗到王安石第二次罢相为观察窗口期,对此期间与他存在密切交互关系的52个关键人物的交互过程、交互结果及成因进行了编码分析。结果发现:(1)群体断层激活及负面效应涌现能完美解释熙宁变法的演进过程及最终的失败;(2)关键岗位人员王安石人际交互风格的不同构面是激活熙宁变法中不同群体断层的主要变量;(3)王安石与直接上级宋神宗、本位群体变法新晋和他位群体同朝老臣之间交互的聚散性,决定了活化的群体断层效应正负及交互演进表征,进而决定了熙宁变法的失败。本研究为群体断层激活变量的识别及不同群体断层在同一时空下的叠加效应提供了新的洞见,同时为组织变革及高管团队多样性的管理提供了实证资鉴和理论指导。

TopSTO:a 115-line code for topology optimization of structures under stationary stochastic dynamic loading

The use of topology optimization in structural design under dynamic excitation is becoming more prevalent in the literature.While many such applications utilize frequency or time domain formulations,relatively few consider stochastic dynamic excitations.This paper presents an efficient and compact code called TopSTO for structural topology optimization considering stationary stochastic dynamic loading using a method derived from random vibration theory.The theory,described in conjunction with the implementation in the provided code,is illustrated for a seismically excited building.This work demonstrates the efficiency of the approach in terms of both the computational resources and minimal amount of code required.This code is intended to serve as a baseline for understanding the theory and implementation of this topology optimization approach and as a foundation for additional applications and developments.

建筑拆除废弃物再生骨料颗粒破碎与压缩特性

使用回收的建筑拆除废弃物作为无黏结骨料是路面基层/底基层施工的资源节约型、经济型和生态友好型替代方法之一。单个粗集料颗粒的细观强度和压碎特性会对此类再生无黏结集料的宏观强度和变形行为产生重大影响。本文通过单颗粒压碎试验和侧限压缩试验系统地研究了不同粒径和成分的再生骨料的压碎强度和压缩变形特性。试验结果表明,单颗粒破碎强度具有显著的尺寸效应,对于同种类型的再生骨料,破碎应力峰值随颗粒尺寸的增大而减小。此外,采用双曲线函数可以很好地描述侧限压缩过程的应力-应变关系。在相同的垂直应力水平下,随着粒径的增大,垂直应变和屈服应力值分别逐渐增大和减小。最后,采用颗粒破碎指标B_(r)量化了不同垂直应力水平下各粒度范围的颗粒破碎情况。根据破碎指标Br的预测公式的极限值,可得出各粒度范围的极限破碎状态。