Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid areas.We developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,China.We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in 2030.The results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,respectively.The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity factor.This suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation accuracy.The BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in 2030.In the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong District.Using exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological scenario.The results of this research can help to adjust urban planning and development policies.Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.

Patient-derived induced pluripotent stem cells with a MERTK mutation exhibit cell junction abnormalities and aberrant cellular differentiation potential

BACKGROUND Human induced pluripotent stem cell(hiPSC)technology is a valuable tool for generating patient-specific stem cells,facilitating disease modeling,and invest-igating disease mechanisms.However,iPSCs carrying specific mutations may limit their clinical applications due to certain inherent characteristics.AIM To investigate the impact of MERTK mutations on hiPSCs and determine whether hiPSC-derived extracellular vesicles(EVs)influence anomalous cell junction and differentiation potential.METHODS We employed a non-integrating reprogramming technique to generate peripheral blood-derived hiPSCs with and hiPSCs without a MERTK mutation.Chromo-somal karyotype analysis,flow cytometry,and immunofluorescent staining were utilized for hiPSC identification.Transcriptomics and proteomics were employed to elucidate the expression patterns associated with cell junction abnormalities and cellular differentiation potential.Additionally,EVs were isolated from the supernatant,and their RNA and protein cargos were examined to investigate the involvement of hiPSC-derived EVs in stem cell junction and differentiation.RESULTS The generated hiPSCs,both with and without a MERTK mutation,exhibited normal karyotype and expressed pluripotency markers;however,hiPSCs with a MERTK mutation demonstrated anomalous adhesion capability and differentiation potential,as confirmed by transcriptomic and proteomic profiling.Furthermore,hiPSC-derived EVs were involved in various biological processes,including cell junction and differentiation.CONCLUSION HiPSCs with a MERTK mutation displayed altered junction characteristics and aberrant differentiation potential.Furthermore,hiPSC-derived EVs played a regulatory role in various biological processes,including cell junction and differentiation.

Shi-pi-xiao-ji formula suppresses hepatocellular carcinoma by reducing cellular stiffness through upregulation of acetyl-coA acetyltransferase 1

BACKGROUND Previous studies have shown that the Shi-pi-xiao-ji(SPXJ)herbal decoction formula is effective in suppressing hepatocellular carcinoma(HCC),but the underlying mechanisms are not known.Therefore,this study investigated whether the antitumor effects of the SPXJ formula in treating HCC were mediated by acetyl-coA acetyltransferase 1(ACAT1)-regulated cellular stiffness.Through a series of experiments,we concluded that SPXJ inhibits the progression of HCC by upregulating the expression level of ACAT1,lowering the level of cholesterol in the cell membrane,and altering the cellular stiffness,which provides a new idea for the research of traditional Chinese medicine against HCC.AIM To investigate the anti-tumor effects of the SPXJ formula on the malignant progression of HCC.METHODS HCC cells were cultured in vitro with SPXJ-containing serum prepared by injecting SPXJ formula into wild-type mice.The apoptotic rate and proliferative,invasive,and migratory abilities of control and SPXJ-treated HCC cells were compared.Atomic force microscopy was used to determine the cell surface morphology and the Young’s modulus values of the control and SPXJ-treated HCC cells.Plasma membrane cholesterol levels in HCC cells were detected using the Amplex Red cholesterol detection kit.ACAT1 protein levels were estimated using western blotting.RESULTS Compared with the vehicle group,SPXJ serum considerably reduced proliferation of HCC cells,increased stiffness and apoptosis of HCC cells,inhibited migration and invasion of HCC cells,decreased plasma membrane cholesterol levels,and upregulated ACAT1 protein levels.However,treatment of HCC cells with the water-soluble cholesterol promoted proliferation,migration,and invasion of HCC cells as well as decreased cell stiffness and plasma membrane cholesterol levels,but did not alter the apoptotic rate and ACAT1 protein expression levels compared with the vehicle control.CONCLUSION SPXJ formula inhibited proliferation,invasion,and migration of HCC cells by decreasing plasma membrane cholesterol levels and altering cellular stiffness through upregulation of ACAT1 protein expression.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Simulation of Dynamic Recrystallization in 7075 Aluminum Alloy Using Cellular Automaton

The evolution of microstructure during hot deformation is key to achieving good mechanical properties in aluminum alloys.We have developed a cellular automaton(CA) based model to simulate the microstructural evolution in 7075 aluminum alloy during hot deformation.Isothermal compression tests were conducted to obtain material parameters for 7075 aluminum alloy,leading to the establishment of models for dislocation density,nucleation of recrystallized grains,and grain growth.Integrating these aspects with grain topological deformation,our CA model effectively predicts flow stress,dynamic recrystallization(DRX) volume fraction,and average grain size under diverse deformation conditions.A systematic comparison was made between electron back scattered diffraction(EBSD) maps and CA model simulated under different deformation temperatures(573 to 723 K),strain rates(0.001 to 1 s^(-1)),and strain amounts(30% to 70%).These analyses indicate that large strain,high temperature,and low strain rate facilitate dynamic recrystallization and grain refinement.The results from the CA model show good accuracy and predictive capability,with experimental error within 10%.

Modeling and Performance Analysis of UAV-Aided Millimeter Wave Cellular Networks with Stochastic Geometry

UAV-aided cellular networks,millimeter wave(mm-wave)communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G)and even 6G communications.By leveraging the power of stochastic geometry,this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network,where the terrestrial base stations(TBSs)and the UAV base stations(UBSs)coexist,and the UBSs are provided with mm-wave and multi-antenna techniques.By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point pro-´cess of type II(MPH-II),approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio(MISR)gain.The influence of some relevant parameters is discussed in detail,and some insights into the network deployment and optimization are revealed.Numerical results show that some trade-offs are worthy of being considered,such as the antenna array size,the altitude of the UAVs and the power control factor of the UBSs.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Full-Scale Isogeometric Topology Optimization of Cellular Structures Based on Kirchhoff-Love Shells

Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio.In this paper,a full-scale isogeometric topology optimization(ITO)method based on Kirchhoff-Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed.This method utilizes high-order continuous nonuniform rational B-splines(NURBS)as basis functions for Kirchhoff-Love shell elements.The geometric and analysis models of thin shells are unified by isogeometric analysis(IGA)to avoid geometric approximation error and improve computational accuracy.The topological configurations of thin-shell structures are described by constructing the effective density field on the controlmesh.Local volume constraints are imposed in the proximity of each control point to obtain bone-like cellular structures.To facilitate numerical implementation,the p-norm function is used to aggregate local volume constraints into an equivalent global constraint.Several numerical examples are provided to demonstrate the effectiveness of the proposed method.After simulation and comparative analysis,the results indicate that the cellular thin-shell structures optimized by the proposed method exhibit great load-carrying behavior and high damage robustness.

Cellular strategies to induce immune tolerance after liver transplantation:Clinical perspectives

Liver transplantation(LT)has become the most efficient treatment for pediatric and adult end-stage liver disease and the survival time after transplantation is becoming longer due to the development of surgical techniques and perioperative management.However,long-term side-effects of immunosuppressants,like infection,metabolic disorders and malignant tumor are gaining more attention.Immune tolerance is the status in which LT recipients no longer need to take any immunosuppressants,but the liver function and intrahepatic histology maintain normal.The approaches to achieve immune tolerance after transplantation include spontaneous,operational and induced tolerance.The first two means require no specific intervention but withdrawing immunosuppressant gradually during follow-up.No clinical factors or biomarkers so far could accurately predict who are suitable for immunosuppressant withdraw after transplantation.With the understanding to the underlying mechanisms of immune tolerance,many strategies have been developed to induce tolerance in LT recipients.Cellular strategy is one of the most promising methods for immune tolerance induction,including chimerism induced by hematopoietic stem cells and adoptive transfer of regulatory immune cells.The safety and efficacy of various cell products have been evaluated by prospective preclinical and clinical trials,while obstacles still exist before translating into clinical practice.Here,we will summarize the latest perspectives and concerns on the clinical application of cellular strategies in LT recipients.

Misdiagnosis of synovial sarcoma-cellular myofibroma with SRFRELA gene fusion:A case report

BACKGROUND Cellular myofibroma is a rare subtype of myofibroma that was first described in 2017.Its diagnosis is often challenging because of its relative rarity,lack of known genetic abnormalities,and expression of muscle markers that can be confused with sarcomas that have myogenic differentiation.Currently,scholars have limited knowledge of this disease,and published cases are few.Further accumulation of diagnostic and treatment experiences is required.CASE SUMMARY A 16-year-old girl experienced left upper limb swelling for 3 years.She sought medical attention at a local hospital 10 months ago,where magnetic resonance imaging revealed a 5-cm soft tissue mass.Needle biopsy performed at a local hospital resulted in the diagnosis of a spindle cell soft tissue sarcoma.The patient was referred to our hospital for limb salvage surgery with endoprosthetic replacement.She was initially diagnosed with a synovial sarcoma.Consequently,clinical management with chemotherapy was continued for the malignant sarcoma.Our pathology department also performed fluorescence in situ hybridization for result validation,which returned negative for SS18 gene breaks,indicating that it was not a synovial sarcoma.Next-generation sequencing was used to identify the SRF-RELA rearrangement.The final pathological diagnosis was a cellular/myofibroblastic neoplasm with an SRF-RELA gene fusion.The patient had initially received two courses of chemotherapy;however,chemotherapy was discontinued after the final diagnosis.CONCLUSION This case was misdiagnosed because of its rare occurrence,benign biological behavior,and pathological similarity to soft tissue sarcoma.

Global research trends and prospects of cellular metabolism in colorectal cancer

BACKGROUND An increasing number of studies have focused on the role of cellular metabolism in the development of colorectal cancer(CRC).However,no work is currently available to synthesize the field through bibliometrics.AIM To analyze the development in the field of“glucose metabolism”(GM),“amino acid metabolism”(AM),“lipid metabolism”(LM),and“nucleotide metabolism”(NM)in CRC by visualization.METHODS Articles within the abovementioned areas of GM,AM,LM and NM in CRC,which were published from January 1,1991,to December 31,2022,are retrieved from the Web of Science Core Collection and analyzed by CiteSpace 6.2.R4 and VOSviewer 1.6.19.RESULTS The field of LM in CRC presented the largest number of annual publications and the fastest increase in the last decade compared with the other three fields.Meanwhile,China and the United States were two of the most prominent contri-butors in these four areas.In addition,Gang Wang,Wei Jia,Maria Notar-nicola,and Cornelia Ulrich ranked first in publication numbers,while Jing-Yuan Fang,Senji Hirasawa,Wei Jia,and Charles Fuchs were the most cited authors on average in these four fields,respectively.“Gut microbiota”and“epithelial-mesenchymal transition”emerged as the newest burst words in GM,“gut microbiota”was the latest outburst word in AM,“metastasis”,“tumor microenvironment”,“fatty acid metabolism”,and“metabolic reprogramming”were the up-to-date outbreaking words in LM,while“epithelial-mesenchymal transition”and“apoptosis”were the most recently occurring words in NM.CONCLUSION Research in“cellular metabolism in CRC”is all the rage at the moment,and researchers are particularly interested in exploring the mechanism to explain the metabolic alterations in CRC.Targeting metabolic vulnerability appears to be a promising direction in CRC therapy.

Correlative factors of poor prognosis and abnormal cellular immune function in patients with Alzheimer’s disease

BACKGROUND Alzheimer’s disease(AD)is a serious disease causing human dementia and social problems.The quality of life and prognosis of AD patients have attracted much attention.The role of chronic immune inflammation in the pathogenesis of AD is becoming more and more important.AIM To study the relationship among cognitive dysfunction,abnormal cellular immune function,neuroimaging results and poor prognostic factors in patients.METHODS A retrospective analysis of 62 hospitalized patients clinical diagnosed with AD who were admitted to our hospital from November 2015 to November 2020.Collect cognitive dysfunction performance characteristics,laboratory test data and neuroimaging data from medical records within 24 h of admission,including Mini Mental State Examination Scale score,drawing clock test,blood T lymphocyte subsets,and neutrophils and lymphocyte ratio(NLR),disturbance of consciousness,extrapyramidal symptoms,electroencephalogram(EEG)and head nucleus magnetic spectroscopy(MRS)and other data.Multivariate logistic regression analysis was used to determine independent prog-nostic factors.the modified Rankin scale(mRS)was used to determine whether the prognosis was good.The correlation between drug treatment and prognostic mRS score was tested by the rank sum test.RESULTS Univariate analysis showed that abnormal cellular immune function,extrapyramidal symptoms,obvious disturbance of consciousness,abnormal EEG,increased NLR,abnormal MRS,and complicated pneumonia were related to the poor prognosis of AD patients.Multivariate logistic regression analysis showed that the decrease in the proportion of T lym-phocytes in the blood after abnormal cellular immune function(odd ratio:2.078,95%confidence interval:1.156-3.986,P<0.05)was an independent risk factor for predicting the poor prognosis of AD.The number of days of donepezil treatment to improve cognitive function was negatively correlated with mRS score(r=0.578,P<0.05).CONCLUSION The decrease in the proportion of T lymphocytes may have predictive value for the poor prognosis of AD.It is recommended that the proportion of T lymphocytes<55%is used as the cut-off threshold for predicting the poor prog-nosis of AD.The early and continuous drug treatment is associated with a good prognosis.

Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone:A Stochastic Geometry Approach

Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.

Cellular Senescence and SENEX Gene on the Peripheral CD4+CD25+ Treg Cells Enhancement in Elderly

Cellular senescence is a signal transduction process which maintained genomic stability and stopped mammalian cell growth. Furthermore, cellular senescence induces a protective response to a variety of DNA damage. However, this process is also associated with apoptosis, upregulated secretion of inflammatory cytokine, and promoted surrounding tissue damage. When cellular senescence accumulates to a certain extent, it triggers geriatric diseases, such as chronic inflammation, immune senescence-associated tumors and incontrollable infections. Cellular senescence gene SENEX, which was cloned in 2004, has been demonstrated to play a unique gatekeeper function in human endothelial cells when stress-induced pre-mature senescence and apoptosis occurr. The phenomenon that CD4+CD25+ Treg cells accumulated in the aged population has been well studied in recent years. Now Treg accumulation related to immune-pathology has attracted more interest. CD4+CD25+ Treg did not decline and age, but accumulated and suppressed immunoreaction. The enhanced Treg number and function may be associated with stress-induced premature senescence-mediated unique cellular senescence protection mechanisms, and SENEX may play a critical role in this process. In this article, we summarize the cellular senescence and SENEX gene in the accumulation and functional activity of CD4+CD25+ Treg in the elderly.

Gold Standard for Skin Cancer Treatment: Surgery (Mohs) or Microscopic Molecular-Cellular Therapy (Curaderm)?

Non-melanoma skin cancers or keratinocyte cancers such as basal cell carcinoma and squamous cell carcinoma make up approximately 80% and 20% respectively, of skin cancers with the 6 million people that are treated annually in the United States. 1 in 5 Americans and 2 in 3 Australians develop skin cancer by the age of 70 years and in Australia it is the most expensive, amassing $1.5 billion, to treat cancers. Non-melanoma skin cancers are often self-detected and are usually removed by various means in doctors’ surgeries. Mohs micrographic surgery is acclaimed to be the gold standard for the treatment of skin cancer. However, a novel microscopic molecular-cellular non-invasive topical therapy described in this article, challenges the status of Mohs procedure for being the acclaimed gold standard.

Cellular senescence throws new insights into patient classification and pharmacological interventions for clinical management of hepatocellular carcinoma

BACKGROUND Cellular senescence,a state of stable growth arrest,is intertwined with human cancers.However,characterization of cellular senescence-associated phenotypes in hepatocellular carcinoma(HCC)remains unexplored.AIM To address this issue,we delineated cellular senescence landscape across HCC.METHODS We enrolled two HCC datasets,TCGA-LIHC and International Cancer Genome Consortium(ICGC).Unsupervised clustering was executed to probe tumor heterogeneity based upon cellular senescence genes.Least absolute shrinkage and selection operator algorithm were utilized to define a cellular senescence-relevant scoring system.TRNP1 expression was measured in HCCs and normal tissues through immunohistochemistry,immunoblotting and quantitative real-time polymerase chain reaction.The influence of TMF-regulated nuclear protein(TRNP)1 on HCC senescence and growth was proven via a series of experiments.RESULTS TCGA-LIHC patients were classified as three cellular senescence subtypes,named C1–3.The robustness and reproducibility of these subtypes were proven in the ICGC cohort.C2 had the worst overall survival,C1 the next,and C3 the best.C2 presented the highest levels of immune checkpoints,abundance of immune cells,and immunogenetic indicators.Thus,C2 might possibly respond to immunotherapy.C2 had the lowest somatic mutation rate,while C1 presented the highest copy number variations.A cellular senescence-relevant gene signature was generated,which can predict patient survival,and chemo-or immunotherapeutic response.Experimentally,it was proven that TRNP1 presented the remarkable upregulation in HCCs.TRNP1 knockdown induced apoptosis and senescence of HCC cells and attenuated tumor growth.CONCLUSION These findings provide a systematic framework for assessing cellular senescence in HCC,which decode the tumor heterogeneity and tailor the pharmacological interventions to improve clinical management.

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Objective:Organoids are a powerful tool with broad application prospects in biomedicine.Notably,they provide alternatives to animal models for testing potential drugs before clinical trials.However,the number of passages for which organoids maintain cellular vitality ex vivo remains unclear.Methods:Herein,we constructed 55 gastric organoids from 35 individuals,serially passaged the organoids,and captured microscopic images for phenotypic evaluation.Senescence-associatedβ-galactosidase(SA-β-Gal),cell diameter in suspension,and gene expression reflecting cell cycle regulation were examined.The YOLOv3 object detection algorithm integrated with a convolutional block attention module(CBAM)was used to evaluate organoid vitality.Results:SA-β-Gal staining intensity;single-cell diameter;and expression of p15,p16,p21,CCNA2,CCNE2,and LMNB1 reflected the progression of aging in organoids during passaging.The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter,organoid number,and number×diameter,and the findings positively correlated with SA-β-Gal staining and single-cell diameter.Organoids derived from normal gastric mucosa had limited passaging ability(passages 1–5),before aging,whereas tumor organoids showed unlimited passaging potential for more than 45 passages(511 days)without showing clear senescence.Conclusions:Given the lack of indicators for evaluating organoid growth status,we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality.This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation

Due to the high charge transfer efficiency compared to that of non-porous materials,porous electrodes with larger surface area and thinner solid pore walls have been widely applied in the lithium-ion battery field.Since the capacity and charge-discharge efficiency of batteries are closely related to the microstructure of porous materials,a conceptually simple and computationally efficient cellular automata(CA)framework is proposed to reconstruct the porous electrode structure and simulate the reactiondiffusion process under the irregular solid-liquid boundary in this work.This framework is consisted of an electrode generating model and a reaction-diffusion model.Electrode structures with specific geometric properties,i.e.,porosity,surface area,size distribution,and eccentricity distribution can be constructed by the electrode generating model.The reaction-diffusion model is exemplified by solving the Fick's diffusion problem and simulating the cyclic voltammetry(CV)process.The discharging process in the lithium-ion battery are simulated through combining the above two CA models,and the simulation results are consistent with the well-known pseudo-two-dimensional(P2D)model.In addition,a set of electrodes with different microstructures are constructed and their reaction efficiencies are evaluated.The results indicate that there is an optimum combination of porosity and particle size for discharge efficiency.This framework is a promising one for studying the effect of electrode microstructure on battery performance due to its fully synchronous computation way,easy handled boundary conditions,and free of convergence concerns.

A robust symbol timing strategy for cellular systems

In cellular systems,establishing the initial symbol timing of potential preambles is the first step of a cell search.The envelope fluctuation of the downlink signal hinders the successful timing of conventional symbol timing methods.To solve this problem,a hybrid timing strategy is proposed with two novel detectors,namely the normalized replica-based detector and normalized differential detector.The strategy first detects all potential preambles via the normalized replica-based detector and then employs the normalized differential detector to verify the target preamble,which comes from the target cell and has the highest power.The strategy is unaffected by envelope fluctuation and has computational complexity comparable to that of conventional methods.Simu-lations and real-data tests show that the hybrid timing strategy is robust and practical for initial symbol timing.