Molecule‑Level Multiscale Design of Nonflammable Gel Polymer Electrolyte to Build Stable SEI/CEI for Lithium Metal Battery

The risk of flammability is an unavoidable issue for gel polymer electrolytes(GPEs).Usually,flameretardant solvents are necessary to be used,but most of them would react with anode/cathode easily and cause serious interfacial instability,which is a big challenge for design and application of nonflammable GPEs.Here,a nonflammable GPE(SGPE)is developed by in situ polymerizing trifluoroethyl methacrylate(TFMA)monomers with flame-retardant triethyl phosphate(TEP)solvents and LiTFSI–LiDFOB dual lithium salts.TEP is strongly anchored to PTFMA matrix via polarity interaction between-P=O and-CH_(2)CF_(3).It reduces free TEP molecules,which obviously mitigates interfacial reactions,and enhances flame-retardant performance of TEP surprisingly.Anchored TEP molecules are also inhibited in solvation of Li^(+),leading to anion-dominated solvation sheath,which creates inorganic-rich solid electrolyte interface/cathode electrolyte interface layers.Such coordination structure changes Li^(+)transport from sluggish vehicular to fast structural transport,raising ionic conductivity to 1.03 mS cm^(-1) and transfer number to 0.41 at 30℃.The Li|SGPE|Li cell presents highly reversible Li stripping/plating performance for over 1000 h at 0.1 mA cm^(−2),and 4.2 V LiCoO_(2)|SGPE|Li battery delivers high average specific capacity>120 mAh g^(−1) over 200 cycles.This study paves a new way to make nonflammable GPE that is compatible with Li metal anode.

Charting the course to solid-state dual-ion batteries

An electrolyte destined for use in a dual-ion battery(DIB)must be stable at the inherently high potential required for anion intercalation in the graphite electrode,while also protecting the Al current collector from anodic dissolution.A higher salt concentration is needed in the electrolyte,in comparison to typical battery electrolytes,to maximize energy density,while ensuring acceptable ionic conductivity and operational safety.In recent years,studies have demonstrated that highly concentrated organic electrolytes,ionic liquids,gel polymer electrolytes(GPEs),ionogels,and water-in-salt electrolytes can potentially be used in DIBs.GPEs can help reduce the use of solvents and thus lead to a substantial change in the Coulombic efficiency,energy density,and long-term cycle life of DIBs.Furthermore,GPEs are suited to manufacture compact DIB designs without separators by virtue of their mechanical strength and electrical performance.In this review,we highlight the latest advances in the application of different electrolytes in DIBs,with particular emphasis on GPEs.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

Time trends and outcomes of gastrostomy placement in a Swedish national cohort over two decades

BACKGROUND Percutaneous endoscopic gastrostomy(PEG)and laparoscopically inserted gastrostomy have become the gold standard for adult patients and children,respectively,requiring long-term enteral nutrition support.Procedure-related mortality is a rare event,often reported to be zero in smaller studies.National data on 30-d mortality and long-term survival rates after gastrostomy placement are scarce in the literature.AIM To study the use of gastrostomies in Sweden from 1998-2019 and to analyze procedure-related mortality and short-term(<30 d)and long-term survival.METHODS In this retrospective,population-based cohort study,individuals that had received a gastrostomy between 1998-2019 in Sweden were included.Individuals were identified in the Swedish National Patient Register,and survival analysis was possible by cross-referencing the Swedish Death Register.The cohort was divided into three age groups:Children(0-18 years);adults(19-64 years);and elderly(≥65 years).Kaplan-Meier with log-rank test and Cox regression were used for survival analysis.RESULTS In total 48682 individuals(52%males,average age 60.9±25.3 years)were identified.The cohort consisted of 12.0%children,29.5%adults,and 58.5%elderly.An increased use of gastrostomies was observed during the study period,from 13.7/100000 to 22.3/100000 individuals(P<0.001).The use of PEG more than doubled(about 800 to 1800/year),with a corresponding decrease in open gastrostomy(about 700 to 340/year).Laparoscopic gastrostomy increased more than ten-fold(about 20 to 240/year).Overall,PEG,open gastrostomy,and laparoscopic gastrostomy constituted 70.0%(n=34060),23.3%(n=11336),and 4.9%(n=2404),respectively.Procedure-related mortality was 0.1%(n=44)overall(PEG:0.05%,open:0.24%,laparoscopic:0.04%).The overall 30-d mortality rate was 10.0%(PEG:9.8%,open:12.4%,laparoscopic:1.7%)and decreased from 11.6%in 1998-2009 vs 8.5%in 2010-2019(P<0.001).One-year and ten-year survival rates for children,adults,and elderly were 93.7%,67.5%,and 42.1%and 79.9%,39.2%,and 6.8%,respectively.The most common causes of death were malignancies and cardiovascular and respiratory diseases.CONCLUSION The annual use of gastrostomies in Sweden increased during the study period,with a shift towards more minimally invasive procedures.Although procedure-related death was rare,the overall 30-d mortality rate was high(10%).To overcome this,we believe that patient selection should be improved.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

Association of accelerometer-measured sleep duration and different intensities of physical activity with incident type 2 diabetes in a population-based cohort study

Purpose:The aim of the current study was to investigate the association of accelerometer-measured sleep duration and different intensities of physical activity(PA)with the risk of incident type 2 diabetes in a population-based prospective cohort study.Methods:Altogether,88,000 participants(mean age=62.2±7.9 years,mean±SD)were included from the UK Biobank.Sleep duration(short:<6 h/day;normal:6-8 h/day;long:>8 h/day)and PA of different intensities were measured using a wrist-won accelerometer over a 7-day period between 2013 and 2015.PA was classified according to the median or World Health Organization-recommendation:total volume of PA(high,low),moderate-to-vigorous PA(MVPA)(recommended,not recommended),and light-intensity PA(high,low).Incidence of type 2diabetes was ascertained using hospital records or death registries.Results:During a median follow-up of 7.0 years,1615 incident type 2 diabetes cases were documented.Compared with normal sleep duration,short(hazard ratio(HR)=1.21,95%confidence interval(95%CI):1.03-1.41)but not long sleep duration(HR=1.01,95%CI:0.89-1.15)was associated with excessive type 2 diabetes risk.This increased risk among short sleepers seems to be protected against by PA.Compared with normal sleepers with high or recommended PA,short sleepers with low volume of PA(HR=1.81,95%CI:1.46-2.25),not recommended(below the World Health Organization-recommended level of)MVPA(HR=1.92,95%CI:1.55-2.36),or low light-intensity PA(HR=1.49,95%CI:1.13-1.90)had a higher risk of type 2 diabetes,while short sleepers with a high volume of PA(HR=1.14,95%CI:0.88-1.49),recommended MVPA(HR=1.02,95%CI:0.71-1.48),or high light-intensity PA(HR=1.14,95%CI:0.92-1.41)did not.Conclusion:Accelerometer-measured short but not long sleep duration was associated with a higher risk of incident type 2 diabetes.A higher level of PA,regardless of intensity,potentially ameliorates this excessive risk.

Potential of molecular chaperones for treating Alzheimer’s disease

Alzheimer’s disease(AD)is the most prevalent form of dementia,i.e.,progressive memory loss and profound cognitive dysfunction,resulting in a considerable societal burden.At the neuropathological level,the brains of AD patients exhibit amyloid-β(Aβ)plaques,neurofibrillary tangles,and neuroinflammation(Sala Frigerio and De Strooper,2016).

Enhancing Li-S battery performance via functional polymer binders for polysulfide inhibition

The commercialization of lithium-sulfur(Li-S)batteries faces several challenges,including poor conductivity,unexpected volume expansion,and continuous sulfur loss from the cathode due to redox shuttling.In this study,we introduce a novel polymer via a simple cross-linking between poly(ether-thioureas)(PETU)and poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)as a bifunctio nal binder for Li-S batteries(devotes as"PPTU").Compared to polyvinylidene fluoride(PVDF),as-prepared PPTU exhibits significantly higher electrical conductivity,facilitating electrochemical reactions.Additionally,PPTU demonstrates effective adsorption of lithium polysulfides,leading to improved cycling stability by suppressing the shuttling effect.We investigate this behavior by monitoring morphological changes at the cell interface using synchrotron X-ray tomography.Cells with PPTU binders exhibit remarkable rate performance,desired reversibility,and excellent cycling stability even under stringent bending and twisting conditions.Our work represents promising progress in functional polymer binder development for Li-S batteries.

Bayesian model averaging(BMA)for nuclear data evaluation

To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.

Influence of O-O formation pathways and charge transfer mediator on lipid bilayer membrane-like photoanodes for water oxidation

Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipathic Ru(bda)-based catalysts(bda=2,2'-bipyrdine-6,6'-dicarbonoxyl acid)and aliphatic chain decorated electrode surfaces,forming lipid bilayer membrane(LBM)-like structures.The Ru(bda)catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O-O formation mechanisms.However,compared to the slow charge transfer process,the O-O formation pathways did not determine the PEC water oxidation efficiency of the dyesensitized photoanodes,and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs.Instead,charge transfer plays a decisive role in the PEC water oxidation rate.When an indolo[3,2-b]carbazole derivative was introduced between the Ru(bda)catalysts and aliphatic chain-modified photosensitizer in LBM films,serving as a charge transfer mediator for the tyrosine-histidine pair in PSⅡ,the PEC water oxidation performance of the corresponding photoanodes was dramatically enhanced.

Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

Multi-stage formation of the Feragen ophiolite,Norway:Implication from petrology and geochemistry of peridotites and chromitites and its potential for prospecting

The ultramafic massif of Feragen,which belongs to the eastern ophiolitic belt of Norway,has abundant amounts of chromite ores.Recent studies have revealed a complex melt evolution in a supra-subduction zone(SSZ)environment.This study presents new whole-rock major element,trace element,and platinum-group element chemistry to evaluate their petrogenesis and tectonic evolution.Harzburgites have high CaO,Al_(2)O_(3),TiO_(2),MgO,and REE contents corresponding to abyssal peridotites,whereas dunites have low CaO,Al_(2)O_(3),TiO_(2),MgO,and REE contents corresponding to SSZ peridotites.The Cr^(#)and TiO_(2) of chromian spinels in the harzburgites suggest as much as about 15%–20%melting and the dunites are more depleted with>40%melting.The harzburgites and the dunites and high-Cr chromitites represent,respectively,the products of low-degree partial melting in a back-arc setting,and the products of melt-rock interaction in a SSZ environment.The calculated fO_(2) values for dunites and high-Cr chromitites(-0.17–+0.23 and+2.78–+5.65,respectively and generally above the FMQ buffer)are also consistent with the interaction between back-arc ophiolites with oxidized boninitic melts in a SSZ setting.

Assessing cutter-rock interaction during TBM tunnelling in granite:Large-scale standing rotary cutting tests and 3D DEM simulations

The widespread utilisation of tunnel boring machines(TBMs)in underground construction engineering requires a detailed investigation of the cutter-rock interaction.In this paper,we conduct a series of largescale standing rotary cutting tests on granite in conjunction with high-fidelity numerical simulations based on a particle-type discrete element method(DEM)to explore the effects of key cutting parameters on the TBM cutter performance and the distribution of cutter-rock contact stresses.The assessment results of cutter performance obtained from the cutting tests and numerical simulations reveal similar dependencies on the key cutting parameters.More specifically,the normal and rolling forces exhibit a positive correlation with penetration but are slightly influenced by the cutting radius.In contrast,the side force decreases as the cutting radius increases.Additionally,the side force shows a positive relationship with the penetration for smaller cutting radii but tends to become negative as the cutting radius increases.The cutter's relative effectiveness in rock breaking is significantly impacted by the penetration but shows little dependency on the cutting radius.Consequently,an optimal penetration is identified,leading to a low boreability index and specific energy.A combined Hertz-Weibull function is developed to fit the cutter-rock contact stress distribution obtained in DEM simulations,whereby an improved CSM(Colorado School of Mines)model is proposed by replacing the original monotonic cutting force distribution with this combined Hertz-Weibull model.The proposed model outperforms the original CSM model as demonstrated by a comparison of the estimated cutting forces with those from the tests/simulations.The findings from this work that advance our understanding of TBM cutter performance have important implications for improving the efficiency and reliability of TBM tunnelling in granite.

Extracellular Vesicles from Mesenchymal Stromal Cells (imEVs) Improve Cold Preservation of Isolated Mitochondria

Mitochondrial organelle transplantation (MOT) is an innovative strategy for the treatment of mitochondrial dysfunction such as cardiac ischemic reperfusion injuries, Parkinson’s diseases, brain and spinal cord injuries, and amyotrophic lateral sclerosis (ALS). However, one of the major challenges for widespread usage is a methodology for preservation of isolated mitochondria. Extracellular vesicles (EVs) are phospholipid bilayer-enclosed vesicles released from cells. EVs carry a cargo of proteins, nucleic acids, lipids, metabolites, and even organelles such as mitochondria. Purpose: To test if EVs enhance the stability of isolated mitochondria. Methods: We mixed isolated mitochondria of fibroblasts with EVs of mesenchymal stromal cells (imEVs) (9:1 in volume) and stored the mixture at 2°C - 6°C for different time periods. We measured morphology, mitochondrial membrane potential (MMP) and mitochondrial ATP content at 0, 2, 5 days. Key findings: After 2 days of storage, the mito-chondria without imEVs lost approximate 70% MMP (RFU: 1822 ± 68), compared to the fresh mitochondria (RFU: 5458 ± 52) (p 0.05). In agreement with MMP, mitochondria without imEVs lost significant mitochondrial ATP content (p 0.05), after 2 days of cold storage, compared to fresh mitochondria. Microscopy showed that imEVs promoted aggregation of isolated mitochondria. Summary: The preliminary data showed that imEVs enhanced the stability of isolated mitochondria in cold storage.

Inbreeding and genetic load in a pair of sibling grouse species:Tetrastes sewersowi and T.bonasia

Genetic load and inbreeding are recognized as important factors to be considered in conservation programs.Elevated levels of both can increase the risk of population extinction by negatively impacting fitness-related characters in many species of plants and animals,including humans(inbreeding depression).Genomic tech-niques are increasingly used in measuring and understanding genetic load and inbreeding and their importance in evolution and conservation.We used whole genome resequencing data from two sibling grouse species in subarctic Eurasia to quantify both.We found a large range of inbreeding measured as FROH(fraction of runs of homozygosity)in individuals from different populations of Chinese Grouse(Tetrastes sewerzowi)and Hazel Grouse(T.bonasia).FROH estimated from genome-wide runs of homozygosity(ROH)ranged from 0.02 to 0.24 among Chinese Grouse populations and from 0.01 to 0.44 in Hazel Grouse.Individuals from a population of Chinese Grouse residing in the Qilian mountains and from the European populations of Hazel Grouse(including samples from Sweden,Germany and Northeast Poland)were the most inbred(FROH ranged from 0.10 to 0.23 and 0.11 to 0.44,respectively).These levels are comparable to other highly inbred populations of birds.Hazel Grouse from northern China and Chinese Grouse residing in the Qinghai-Tibetan Plateau showed relatively lower inbreeding levels.Comparisons of the ratio between deleterious missense mutations and synonymous mutations revealed higher levels in Chinese Grouse as compared to Hazel Grouse.These results are possibly explained by higher fixation rates,mutational melt down,in the range-restricted Chinese Grouse compared to the wide-ranging Hazel Grouse.However,when we compared the relatively more severe class of loss-of-function muta-tions,Hazel Grouse had slightly higher levels than Chinese Grouse,a result which may indicate that purifying selection(purging)has been more efficient in Chinese Grouse on this class of mutations.

Population genomic data reveal low genetic diversity,divergence and local adaptation among threatened Reeves's Pheasant(Syrmaticus reevesii)

Population genomic data could provide valuable information for conservation efforts;however,limited studies have been conducted to investigate the genetic status of threatened pheasants.Reeves’s Pheasant(Syrmaticus reevesii)is facing population decline,attributed to increases in habitat loss.There is a knowledge gap in understanding the genomic status and genetic basis underlying the local adaptation of this threatened bird.Here,we used population genomic data to assess population structure,genetic diversity,inbreeding patterns,and genetic divergence.Furthermore,we identified candidate genes linked with adaptation across the current distribution of Reeves’s Pheasant.The present study assembled the first de novo genome sequence of Reeves’s Pheasant and annotated 19,458 genes.We also sequenced 30 individuals from three populations(Dabie Mountain,Shennongjia,Qinling Mountain)and found that there was clear population structure among those populations.By comparing with other threatened species,we found that Reeves’s Pheasants have low genetic diversity.Runs of homozygosity suggest that the Shennongjia population has experienced serious inbreeding.The demographic history results indicated that three populations experienced several declines during the glacial period.Local adaptative analysis among the populations identified 241 candidate genes under directional selection.They are involved in a large variety of processes,including the immune response and pigmentation.Our results suggest that the three populations should be considered as three different conservation units.The current study provides genetic evidence for conserving the threatened Reeves’s Pheasant and provides genomic resources for global biodiversity management.

Heuristic Estimation of the Vacuum Energy Density of the Universe: Part II-Analysis Based on Frequency Domain Electromagnetic Radiation

In Part I of this paper, an inequality satisfied by the vacuum energy density of the universe was derived using an indirect and heuristic procedure. The derivation is based on a proposed thought experiment, according to which an electron is accelerated to a constant and relativistic speed at a distance L from a perfectly conducting plane. The charge of the electron was represented by a spherical charge distribution located within the Compton wavelength of the electron. Subsequently, the electron is incident on the perfect conductor giving rise to transition radiation. The energy associated with the transition radiation depends on the parameter L. It was shown that an inequality satisfied by the vacuum energy density will emerge when the length L is pushed to cosmological dimensions and the product of the radiated energy, and the time duration of emission is constrained by Heisenberg’s uncertainty principle. In this paper, a similar analysis is conducted with a chain of electrons oscillating sinusoidally and located above a conducting plane. In the thought experiment presented in this paper, the behavior of the energy radiated by the chain of oscillating electrons is studied in the frequency domain as a function of the length L of the chain. It is shown that when the length L is pushed to cosmological dimensions and the energy radiated within a single burst of duration of half a period of oscillation is constrained by the fact that electromagnetic energy consists of photons, an inequality satisfied by the vacuum energy density emerges as a result. The derived inequality is given by where is the vacuum energy density. This result is consistent with the measured value of the vacuum energy density, which is 5.38 × 10-10 J/m. The result obtained here is in better agreement with experimental data than the one obtained in Part I of this paper with time domain radiation.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.