1960—2020年安阳市气候生产潜力变化与未来趋势

基于安阳市气象站点资料,运用Thornthwaite Memorial模型、Morlet小波分析以及Mann-Kendall检验等方法针对安阳市气候生产潜力时空演变特征进行分析。研究结果表明,近61年安阳市气温以0.19℃/10 a的倾向率呈增加趋势,年降水量以-4.64 mm/10 a的速率呈减少趋势,近61年安阳市气候生产潜力年平均为1037.95 g/(m^(2)·a),并以5.04 g/(m^(2)·a)/10 a的倾向率呈上升趋势。Morlet小波分析表明,近61年安阳市气候生产潜力存在28年左右的主周期变化;1978—2020年安阳市粮食单产显著提高,气候资源利用率波动增加,21世纪10年代平均气候资源利用率已达59.40%。安阳市气候生产潜力对降水变化更敏感,气候越暖湿,越有利于气候生产潜力的提高。R/S分析表明未来安阳市气候生产潜力将呈下降趋势。

利用长短期记忆网络LSTM对赤道太平洋海表面温度短期预报

海表面温度作为海洋中一个最重要的变量,对全球气候、海洋生态等有很大的影响,因此十分有必要对海表面温度(SST)进行预报。深度学习具备高效的数据处理能力,但目前利用深度学习对整个赤道太平洋的SST短期预报及预报技巧的研究仍较少。本文基于最优插值海表面温度(OISST)的日平均SST数据,利用长短期记忆(LSTM)网络构建了未来10天赤道太平洋(10°S~10°N,120°E~80°W)SST的逐日预报模型。LSTM预报模型利用1982~2010年的观测数据进行训练,2011~2020年的观测数据作为初值进行预报和检验评估。结果表明:赤道太平洋东部地区预报均方根误差(RMSE)大于中、西部,东部预报第1天RMSE为0.6℃左右,而中、西部均小于0.3℃。在不同的年际变化位相,预报RMSE在拉尼娜出现时期最大,正常年份次之,厄尔尼诺时期最小,RMSE在拉尼娜时期比在厄尔尼诺时期可达20%。预报偏差整体表现为东正、西负。相关预报技巧上,中部最好,可预报天数基本为10天以上,赤道冷舌附近可预报天数为4~7天,赤道西边部分地区可预报天数为3天。预报模型在赤道太平洋东部地区各月份预报技巧普遍低于西部地区,相比较而言各区域10、11月份预报技巧最低。总的来说,基于LSTM构建的SST预报模型能很好地捕捉到SST在时序上的演变特征,在不同案例中预报表现良好。同时该预报模型依靠数据驱动,能迅速且较好地预报未来10天以内的日平均SST的短期变化。

Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Treadmill exercise improves hippocampal neural plasticity and relieves cognitive deficits in a mouse model of epilepsy

Epilepsy frequently leads to cognitive dysfunction and approaches to treatment remain limited.Although regular exercise effectively improves learning and memory functions across multiple neurological diseases,its application in patients with epilepsy remains controversial.Here,we adopted a 14-day treadmill-exercise paradigm in a pilocarpine injection-induced mouse model of epilepsy.Cognitive assays confirmed the improvement of object and spatial memory after endurance training,and electrophysiological studies revealed the maintenance of hippocampal plasticity as a result of physical exercise.Investigations of the mechanisms underlying this effect revealed that exercise protected parvalbumin interneurons,probably via the suppression of neuroinflammation and improved integrity of blood-brain barrier.In summary,this work identified a previously unknown mechanism through which exercise improves cognitive rehabilitation in epilepsy.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

STAT3 ameliorates truncated tau-induced cognitive deficits

Proteolytic cleavage of tau by asparagine endopeptidase(AEP)creates tau-N368 fragments,which may drive the pathophysiology associated with synaptic dysfunction and memory deterioration in the brain of Alzheimer’s disease patients.Nonetheless,the molecular mechanisms of truncated tau-induced cognitive deficits remain unclear.Evidence suggests that signal transduction and activator of transcription-3(STAT3)is associated with modulating synaptic plasticity,cell apoptosis,and cognitive function.Using luciferase reporter assays,electrophoretic mobility shift assays,western blotting,and immunofluorescence,we found that human tau-N368 accumulation inhibited STAT3 activity by suppressing STAT3 translocation into the nucleus.Overexpression of STAT3 improved tau-N368-induced synaptic deficits and reduced neuronal loss,thereby improving the cognitive deficits in tau-N368 mice.Moreover,in tau-N368 mice,activation of STAT3 increased N-methyl-D-aspartic acid receptor levels,decreased Bcl-2 levels,reversed synaptic damage and neuronal loss,and thereby alleviated cognitive deficits caused by tau-N368.Taken together,STAT3 plays a critical role in truncated tau-related neuropathological changes.This indicates a new mechanism behind the effect of tau-N368 on synapses and memory deficits.STAT3 can be used as a new molecular target to treat tau-N368-induced protein pathology.

SIRT2 as a potential new therapeutic target for Alzheimer's disease

Alzheimer's disease is the most common cause of dementia globally with an increasing incidence over the years,bringing a heavy burden to individuals and society due to the lack of an effective treatment.In this context,sirtuin 2,the sirtuin with the highest expression in the brain,has emerged as a potential therapeutic target for neurodegenerative diseases.This review summarizes and discusses the complex roles of sirtuin 2 in different molecular mechanisms involved in Alzheimer's disease such as amyloid and tau pathology,microtubule stability,neuroinflammation,myelin formation,autophagy,and oxidative stress.The role of sirtuin 2 in all these processes highlights its potential implication in the etiology and development of Alzheimer's disease.However,its presence in different cell types and its enormous variety of substrates leads to apparently contra dictory conclusions when it comes to understanding its specific functions.Further studies in sirtuin 2 research with selective sirtuin2 modulators targeting specific sirtuin 2 substrates are necessary to clarify its specific functions under different conditions and to validate it as a novel pharmacological target.This will contribute to the development of new treatment strategies,not only for Alzheimer's disease but also for other neurodegenerative diseases.

Exercise-with-melatonin therapy improves sleep disorder and motor dysfunction in a rat model of ischemic stroke

Exercise-with-melatonin therapy has complementary and synergistic effects on spinal cord injury and Alzheimer's disease,but its effect on stroke is still poorly understood.In this study,we established a rat model of ischemic stroke by occluding the middle cerebral artery for 60 minutes.We treated the rats with exercise and melatonin therapy for 7 consecutive days.Results showed that exercise-with-melatonin therapy significantly prolonged sleep duration in the model rats,increased delta power values,and regularized delta power rhythm.Additionally,exercise-with-melatonin therapy improved coordination,endurance,and grip strength,as well as learning and memory abilities.At the same time,it led to higher hippocampal CA1 neuron activity and postsynaptic density thickness and lower expression of glutamate receptor 2 than did exercise or melatonin therapy alone.These findings suggest that exercise-withmelatonin therapy can alleviate sleep disorder and motor dysfunction by increasing glutamate receptor 2 protein expression and regulating hippocampal CA1 synaptic plasticity.

Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing

In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing applications.In particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor line.Chip manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line process.In this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device performance.After a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide platforms.Next,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode operation.Finally,the fabrication of photonic and photonic–electronic neuromorphic computing systems is reviewed.These systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern recognition.With large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.

A spatiotemporal deep learning method for excavation-induced wall deflections

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.

基于LSTM-文本分析的量化选股模型

随着国民生活水平的提高,越来越多的人投身于股票市场.为了科学有效地量化选股,通过将量化投资、深度学习及文本分析进行有机结合,来建立量化选股模型.首先,通过文本分析筛选出基本面利好的股票;然后,通过长短期记忆(long-short term memory,LSTM)选出预测准确度良好的股票;最后,预测所选出的股票在未来几天的股价趋势.在实证分析方面,通过本模型对部分股票进行运算,选取预测效果较好的股票:赢合科技.

Deer antler stem cell niche: An interesting perspective

In recent years,there has been considerable exploration into methods aimed at enhancing the regenerative capacity of transplanted and/or tissue-resident cells.Biomaterials,in particular,have garnered significant interest for their potential to serve as natural scaffolds for cells.In this editorial,we provide commentary on the study by Wang et al,in a recently published issue of World J Stem Cells,which investigates the use of a decellularized xenogeneic extracellular matrix(ECM)derived from antler stem cells for repairing osteochondral defects in rat knee joints.Our focus lies specifically on the crucial role of biological scaffolds as a strategy for augmenting stem cell potential and regenerative capabilities,thanks to the establishment of a favorable microenvironment(niche).Stem cell differen-tiation heavily depends on exposure to intrinsic properties of the ECM,including its chemical and protein composition,as well as the mechanical forces it can generate.Collectively,these physicochemical cues contribute to a bio-instructive signaling environment that offers tissue-specific guidance for achieving effective repair and regeneration.The interest in mechanobiology,often conceptualized as a form of“structural memory”,is steadily gaining more validation and momen-tum,especially in light of findings such as these.

National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the experts.The Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this meeting.This effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these patients.This coordinated effort can minimise delays and give patients a chance for curative treatment and effective palliation.The diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance cholangiopancreato-graphy.Brush cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for diagnosis.However,histopathologic confirmation is not always required before resection.Endoscopic ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for staging.The only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette classification.Selected patients with unresectable hCCA can be considered for liver transplantation.Adjuvant chemotherapy should be offered to patients with a high risk of recurrence.The use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary discussions.Patients with acute cholangitis can be drained with endoscopic or percutaneous biliary drainage.Palliative chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA.

Between the City and Images:An Analysis of Mainstream Media’s Paths of Constructing the Cultural Memory of a City:Taking Chengdu Radio and Television’s“Hi Chengdu”as an Example

Mainstream media play a crucial role in constructing the cultural memory of a city.This study used 319 short videos released by“Hi Chengdu,”a new media product of Chengdu Radio and Television,as samples.Based on the grounded theory,a research framework encompassing“content,technology,and discourse”was established to explore the paths through which mainstream media construct the cultural memory.Regarding content,this paper emphasized temporal and spatial contexts and urban spaces,delving deep into the themes of the cultural memory and vehicles for it.In terms of technology,this paper discussed the practice of leveraging audio/visual-mode discourse to stitch together the impressions of a city and evoke emotional resonance to create a“flow”of memory.As for discourse,this paper looked at the performance of a communication ritual to frame concepts and shape urban identity.It is essential to break free from conventional thinking and leverage local culture as the primary driving force to further boost a city’s productivity,in order to excel in cultural communication.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

New‑Generation Ferroelectric AlScN Materials

Ferroelectrics have great potential in the field of nonvolatile memory due to programmable polarization states by external electric field in nonvolatile manner.However,complementary metal oxide semiconductor compatibility and uniformity of ferroelectric performance after size scaling have always been two thorny issues hindering practical application of ferroelectric memory devices.The emerging ferroelectricity of wurtzite structure nitride offers opportunities to circumvent the dilemma.This review covers the mechanism of ferroelectricity and domain dynamics in ferroelectric AlScN films.The performance optimization of AlScN films grown by different techniques is summarized and their applications for memories and emerging in-memory computing are illustrated.Finally,the challenges and perspectives regarding the commercial avenue of ferroelectric AlScN are discussed.

Microstructure and martensitic transformation in quaternary NiTiHfV alloy

The effect of age hardening on the microstructure,martensitic transformation behavior,and shape memory properties of the(Ni_(50)Ti_(30)Hf_(20))_(95)V_(5)alloy was investigated by scanning electron microscopy,transmission electron microscopy,X-ray diffraction,differential scanning calorimetry,microhardness,and bending tests.The results demonstrate a significant influence of V addition on the microstructure of the alloy.V addition leads to the formation of a(Ni,V)_(2)(Ti,Hf)-type Laves phase,which coexists with B19'martensite at room temperature.Aging at 550℃results in precipitation hardening due to the formation of nano-scale orthorhombic H-phase,with the peak hardness observed after 3 h of aging.The alloy at peak hardness state exhibits higher transformation strain and lower unrecovered strain compared to the solution-treated sample.The aged sample achieves a maximum transformation strain of 1.56%under 500 MPa.

Research on Performance Optimization of Spark Distributed Computing Platform

Spark,a distributed computing platform,has rapidly developed in the field of big data.Its in-memory computing feature reduces disk read overhead and shortens data processing time,making it have broad application prospects in large-scale computing applications such as machine learning and image processing.However,the performance of the Spark platform still needs to be improved.When a large number of tasks are processed simultaneously,Spark’s cache replacementmechanismcannot identify high-value data partitions,resulting inmemory resources not being fully utilized and affecting the performance of the Spark platform.To address the problem that Spark’s default cache replacement algorithm cannot accurately evaluate high-value data partitions,firstly the weight influence factors of data partitions are modeled and evaluated.Then,based on this weighted model,a cache replacement algorithm based on dynamic weighted data value is proposed,which takes into account hit rate and data difference.Better integration and usage strategies are implemented based on LRU(LeastRecentlyUsed).Theweight update algorithm updates the weight value when the data partition information changes,accurately measuring the importance of the partition in the current job;the cache removal algorithm clears partitions without useful values in the cache to releasememory resources;the weight replacement algorithm combines partition weights and partition information to replace RDD partitions when memory remaining space is insufficient.Finally,by setting up a Spark cluster environment,the algorithm proposed in this paper is experimentally verified.Experiments have shown that this algorithmcan effectively improve cache hit rate,enhance the performance of the platform,and reduce job execution time by 7.61%compared to existing improved algorithms.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor

Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sparking significant advancements in electronic devices that utilize 2D TMDs.Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance.This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor.It delves into the impacts of miniaturization,including the reduction of channel length,gate length,source/drain contact length,and dielectric thickness on transistor operation and performance.In addition,this review provides a detailed analysis of performance parameters such as source/drain contact resistance,subthreshold swing,hysteresis loop,carrier mobility,on/off ratio,and the development of p-type and single logic transistors.This review details the two logical expressions of the single 2D-TMD logic transistor,including current and voltage.It also emphasizes the role of 2D TMD-based transistors as memory devices,focusing on enhancing memory operation speed,endurance,data retention,and extinction ratio,as well as reducing energy consumption in memory devices functioning as artificial synapses.This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications.It underscores the anticipated challenges,opportunities,and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.