Molecular Phylogeny, Species Delimitation and Taxonomic Revision of the Australian Spider Wasp Genus Heterodontonyx Haupt, 1935 (Hymenoptera: Pompilidae)

Heterodontonyx (Pompilidae: Pepsinae) is an understudied genus with Australasiandistribution and most species are endemic to Australia. There have recently been sometaxonomic changes involving the genera Heterodontonyx and Cryptocheilus based on molecular and morphological evidence, however, phylogenetic relationships within Heterodontonyx, species delimitation and formal revision have not been conducted. Here, we construct a Maximum Likelihood phylogeny estimate of Heterodontonyx based on ITS1, 5.8S, ITS2 region of 30 samples representing seven Heterodontonyx morphospecies. bPTP species delimitation approach was employed and ultimately the taxonomic revision of the genus was carried out. Phylogenetic analyses strongly support the monophyly of Heterodontonyx and the species delimitation method recognizes eight species among which one new species is identified.

Resonantly enhanced second-and third-harmonic generation in dielectric nonlinear metasurfaces

Nonlinear dielectric metasurfaces provide a promising approach to control and manipulate frequency conversion optical processes at the nanoscale,thus facilitating both advances in fundamental research and the development of new practical applications in photonics,lasing,and sensing.Here,we employ symmetry-broken metasurfaces made of centrosymmetric amorphous silicon for resonantly enhanced second-and third-order nonlinear optical response.Exploiting the rich physics of optical quasi-bound states in the continuum and guided mode resonances,we comprehensively study through rigorous numerical calculations the relative contribution of surface and bulk effects to second-harmonic generation(SHG)and the bulk contribution to third-harmonic generation(THG) from the meta-atoms.Next,we experimentally achieve optical resonances with high quality factors,which greatly boosts light-matter interaction,resulting in about 550 times SHG enhancement and nearly 5000-fold increase of THG.A good agreement between theoretical predictions and experimental measurements is observed.To gain deeper insights into the physics of the investigated nonlinear optical processes,we further numerically study the relation between nonlinear emission and the structural asymmetry of the metasurface and reveal that the generated harmonic signals arising from linear sharp resonances are highly dependent on the asymmetry of the meta-atoms.Our work suggests a fruitful strategy to enhance the harmonic generation and effectively control different orders of harmonics in all-dielectric metasurfaces,enabling the development of efficient active photonic nanodevices.

Ultrasensitive Indium Phosphide Nanomembrane Wearable Gas Sensors

Air quality is deteriorating due to continuing urbanization and industrialization.In particular,nitrogen dioxide(NO_(2))is a biologically and environmentally hazardous byproduct from fuel combustion that is ubiquitous in urban life.To address this issue,we report a high-performance flexible indium phosphide nanomembrane NO_(2)sensor for real-time air quality monitoring.An ultralow limit of detection of~200 ppt and a fast response have been achieved with this device by optimizing the film thickness and doping concentration during indium phosphide epitaxy.By varying the film thickness,a dynamic range of values for NO_(2)detection from parts per trillion(ppt)to parts per million(ppm)level have also been demonstrated under low bias voltage and at room temperature without additional light activation.Flexibility measurements show an adequately stable response after repeated bending.On-site testing of the sensor in a residential kitchen shows that NO_(2)concentration from the gas stove emission could exceed the NO_(2)Time Weighted Average limit,i.e.,200 ppb,highlighting the significance of real-time monitoring.Critically,the indium phosphide nanomembrane sensor element cost is estimated at<0.1 US$due to the miniatured size,nanoscale thickness,and ease of fabrication.With these superior performance characteristics,low cost,and real-world applicability,our indium phosphide nanomembrane sensors offer a promising solution for a variety of air quality monitoring applications.

A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen

Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry.Discrete fracture networks(DFNs)are increasingly used in engineering analyses to spatially model fractures at various scales.The reliability of coal DFNs largely depends on the confidence in the input cleat statistics.Estimates of these parameters can be made from image-based three-dimensional(3D)characterization of coal cleats using X-ray micro-computed tomography(m CT).One key step in this process,after cleat extraction,is the separation of individual cleats,without which the cleats are a connected network and statistics for different cleat sets cannot be measured.In this paper,a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray m CT images.Kernels(filters)representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images.The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin,Queensland,Australia.It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation.Bedding-parallel fractures are also identified and separated,which has his-torically been challenging to delineate and rarely reported.A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling.Finally,variability and heterogeneity with respect to the core axis are investigated.Significant heterogeneity is observed and suggests that the representative elementary volume(REV)of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.

Below-Moho Earthquakes of Tibet,Himalaya,the Indian Foreland,and Worldwide:How,Where and Why?

We seek to understand lithospheric rheology by mapping continental earthquake depths relative to Moho depth,across the entire India/Asia convergent orogen,and eventually worldwide.Such mapping has particular value in geothermometry,and potentially in identifying regions of delamination.How:We are extending our Sn/Lg method beyond amplitude ratios of regional seismic phases measured on arrays(array Sn/Lg method,Wang and Klemperer,2021)to include frequency proxies for earthquake depth relative to Moho(Wang&Klemperer,2024a,b;Harris et al.,2024).

High-Q resonant terahertz metasurfaces

In photonics, the quest for high-quality (high Q) resonances driven by the physics of bound states in the continuum (BIC)1,2has motivated researchers to explore innovative avenues for realizing groundbreaking applications in lasing3, sensing4and nonlinear photonics5. A conventional strategy to harness the properties of BICs involves breaking the symmetry of resonators in a uniform lattice, allowing uncoupled modes to interact with free space that opens a leaky channel in the form of socalled (quasi) q BIC6modes.

Drishti Paint 3.2:a new open-source tool for both 2D and 3D segmentation

X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.

How Group Leaders Build Stable Community Buying Groups:A Perspective Based on the Differential Mode of Association

Since 2016,community group buying has grown significantly in China,largely driven by its efficient logistics,supply chains,low prices,and convenience.This model has been further popularized during the COVID-19 pandemic due to its effectiveness in meeting daily needs while minimizing human-to-human contact.A key component of this business model is the“group leaders”-influential individuals within a community responsible for managing group buying activities,which include order collection,supplier liaison,and goods distribution.Their primary task is to form and sustain a reliable community group buying consortium,a task that demands excellent organizational and interpersonal skills.This paper examines this phenomenon using the lens of the differential mode of association,a theoretical model explaining interpersonal relationships in traditional Chinese society.The research indicates that group leaders,through regular interaction with consumers,are able to alter their social network position,increase their influence,understand consumer needs,provide satisfying services,and enhance trust,thereby transforming consumers into loyal group buying participants.This transformation not only brings stability to group buying activities but also reinforces the community influence of group leaders,thus fostering the growth of community group buying.

A randomized controlled trial of Promoting Physical Activity in Regional and Remote Cancer Survivors(PPARCS)

Background:Physical activity(PA)is important for cancer survivors.Trials of remotely delivered interventions are needed to assist in reaching under-served non-metropolitan cancer survivors.The objective of this study was to ascertain whether wearable technology,coupled with health coaching was effective in increasing PA in breast and colorectal cancer survivors living in regional and remote areas in Australia.Methods:Cancer survivors from 5 states were randomized to intervention and control arms.Intervention participants were given a Fitbit Charge 2TMand received up to 6 telephone health coaching sessions.Control participants received PA print materials.Accelerometer assessments at baseline and 12 weeks measured moderate-to-vigorous PA(MVPA),light PA,and sedentary behavior.Results:Eighty-seven participants were recruited(age=63±11 years;74(85%)female).There was a significant net improvement in MVPA of 49.8 min/week,favoring the intervention group(95%confidence interval(95%CI):13.6-86.1,p=0.007).There was also a net increase in MVPA bouts of 39.5 min/week(95%CI:11.9-67.1,p=0.005),favoring the intervention group.Both groups improved light PA and sedentary behavior,but there were no between-group differences.Conclusion:This’s the first study to demonstrate that,when compared to standard practice(i.e.,PA education),a wearable technology intervention coupled with distance-based health coaching,improves MVPA in non-metropolitan cancer survivors.The results display promise for the use of scalable interventions using smart wearable technology in conjunction with phone-based health coaching to foster increased PA in geographically disadvantaged cancer survivors.

The Rise of Asian Defense Diplomacy:Convergence or Divergence in Sino-Australian Security Relations?

The rise of multilateral defense diplomacy in Asia is an important new phenomenon.In addition to the newly established ADMM+ process and the Shangri-La Dialogue(SLD) ,ASEAN defense ministers,ASEAN chiefs of defense and ASEAN chiefs of intelligence are now all meeting on a regular basis.Such gatherings were almost unthinkable as recently as a decade ago.This paper seeks to interpret these developments and their implications for the Sino-Australian security relationship.It argues that while these recent developments appear to reflect a deepening of Asian security cooperation,they are actually a manifestation of an intensifying institutional competition,namely between the ASEAN-centered ADMM and the Western-backed SLD.This,in turn,is symptomatic of a broader institutional contestation——reflecting underlying strategic competition——which is becoming a distinctive feature of Asia's emerging security ‘architecture.’ The paper goes on to argue that China and Australia find themselves on opposite sides of this competition and,particularly in the area of defense diplomacy,are likely to become increasingly important players in fuelling it.The paper concludes by observing why such a dynamic is potentially problematic and offers a modest set of policy recommendations designed to assist Beijing and Canberra in addressing and alleviating their competitive tendencies.

The current status of nitrogen fertiliser use efficiency and future research directions for the Australian cotton industry

Fifty years of sustained investment in research and development has left the Australian cotton industry well placed to manage nitrogen（N） fertiliser. The average production in the Australian cotton industry today is greater than two tonnes of lint per hectare due to improved plant genetics and crop management. However, this average yield is well below the yield that would be expected from the amount of N fertiliser used. It is clear from the recent studies that across all growing regions, conversion of fertiliser N into lint is not uniformly occurring at application rates greater than 200-240 kg·hm;of N. This indicates that factors other than N availability are limiting yield, and that the observed nitrogen fertiliser use efficiency（NFUE） values may be caused by subsoil constraints such as sodicity and compaction. There is a need to investigate the impact of subsoil constraints on yield and NFUE.Gains in NFUE will be made through improved N fertiliser application timing, better targeting the amount of fertiliser applied for the expected yield, and improved soil N management. There is also a need to improve the ability and confidence of growers to estimate the contribution of soil N mineralisation to the crop N budget. Many Australian studies including data that could theoretically be collated in a meta-analysis suggest relative NFUE values as a function of irrigation technique; however, with the extensive list of uncontrolled variables and few studies using non-furrow irrigation, this would be a poor substitute for a single field-based study directly measuring their efficacies. In irrigated cotton, a re-examination of optimal NFUE is due because of the availability of new varieties and the potential management and long-term soil resilience implications of the continued removal of mineralised soil N suggested by high NFUE values. NFUE critical limits still need to be derived for dryland systems.

FAIR Geoscientific Samples and Data Need International Collaboration

1 Introduction The primary goal of the Deep-time Digital Earth project is to develop an open collaboration and data sharing platform that enables the transition of deep-time geoscientific research to a Big Data driven paradigm.Such an open platform will require the ability to effectively and efficiently access and integrate a wide variety of digital Earth data.

Donald V.Helmberger,the master mentor:Testimonials from former international students

Don trained many Ph.D.students over the〜5 decades of his career,at least 45 were advised or co-advised according to a Caltech account.A large number were international students including a high percentage of Chinese students.The contributors to this article graduated over a time span of 26 years,and we are now distributed in 6 countries across 14 time zones,yet we represent only a small fraction of his former students and postdocs.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.

Machine Learning‑Enhanced Flexible Mechanical Sensing

To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly needed.Nowadays,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software.Significant research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology.Meanwhile,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors.Machine learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing data.In this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented.Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion identification.Finally,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed.These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

A machine learning approach for accelerated design of magnesium alloys. Part A:Alloy data and property space

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.

Probabilistic time series forecasting with deep non-linear state space models

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series observations.It is a widespread challenge in various tasks,such as risk management and decision making.To investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general framework.Variants of SSM achieve considerable success in many fields,such as engineering and statistics.However,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and noisy.Therefore,it is very difficult to determinate an SSM for classical statistical approaches.In this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series data.We fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series data.In particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear functions.After that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive definitive.Therefore,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural networks.In this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic processes.Experiments in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

A review of rural transformation studies:Definition,measurement,and indicators

The paper reviews studies of rural transformation in three dimensions-definition,measurement,and indicators-and summarises the findings of rural transformation research.The scope of rural transformation includes four elements-productivity,rurality,inclusiveness,and sustainability.Current concepts of the dimensions of rural transformation and their associated indicators are insufficient for policy decision-making,as they lack objectivity,feasibility,accountability,comprehensiveness,and comparability.Future research to develop new measures to assess rural transformation in developing countries is valuable.Furthermore,there is potential to explore the topic in some directions:urbanisation strategy,public intervention(i.e.,institution,policy,and investment),gender inclusiveness,market creation,and international trade.

Third-harmonic generation and imaging with resonant Si membrane metasurface

Dielectric metasurfaces play an increasingly important role in enhancing optical nonlinear generations owing to their ability to support strong light-matter interactions based on Mie-type multipolar resonances.Compared to metasurfaces composed of the periodic arrangement of nanoparticles,inverse,so-called,membrane metasurfaces offer unique possibilities for supporting multipolar resonances,while maintaining small unit cell size,large mode volume and high field enhancement for enhancing nonlinear frequency conversion.Here,we theoretically and experimentally investigate the formation of bound states in the continuum(BICs)from silicon dimer-hole membrane metasurfaces.We demonstrate that our BIC-formed resonance features a strong and tailorable electric near-field confinement inside the silicon membrane films.Furthermore,we show that by tuning the gap between the holes,one can open a leaky channel to transform these regular BICs into quasi-BICs,which can be excited directly under normal plane wave incidence.To prove the capabilities of such metasurfaces,we demonstrate the conversion of an infrared image to the visible range,based on the Third-harmonic generation(THG)process with the resonant membrane metasurfaces.Our results suggest a new paradigm for realising efficient nonlinear photonics metadevices and hold promise for extending the applications of nonlinear structuring surfaces to new types of all-optical near-infrared imaging technologies.

Nitrogen cold plasma treatment stabilizes Cu^(0)/Cu^(+) electrocatalysts to enhance CO_(2) to C2 conversion

Cu-based materials are ideal catalysts for CO_(2) electrocatalytic reduction reaction(CO_(2)RR) into multicarbon products.However,such reactions require stringent conditions on local environments of catalyst surfaces,which currently are the global pressing challenges.Here,a stabilized activation of Cu^(0)/Cu^(+)-onAg interface by N_(2) cold plasma treatment was developed for improving Faradaic efficiency(FE) of CO_(2)RR into C2 products.The resultant Ag@Cu-CuN_x exhibits a C2 FE of 72% with a partial current density of-14.9 mA cm^(-2) at-1.0 V vs.RHE(reversible hydrogen electrode).Combining density functional theory(DFT) and experimental investigations,we unveiled that Cu^(0)/Cu^(+) species can be co ntrollably tu ned by the incorporation of nitrogen to form CuN_x on Ag surface,i.e.,Ag@Cu-CuN_x.This strategy enhances ^(*)CO intermediates generation and accelerates C-C coupling both thermodynamically and kinetically.The intermediates O^(*)C^(*)CO,^(*)COOH,and ^(*)CO were detected by in-situ attenuated total internal reflection surface enhanced infrared absorption spectroscopy(ATR-SEIRAS).The uncovered CO_(2)RR-into-C2 products were carried out along CO_(2)→^(*)COOH→^(*)CO→O^(*)C^(*)CO→^(*)C_(2)H_(3)O→^(*)C_(2)H_(4)O→ C_(2)H_(5)OH(or ^(*)C_(2)H_(3)O→^(*)O+C_(2)H_(4)) paths over Ag@Cu-CuN_x electrocatalyst.This work provides a new approach to design Cu-based electrocatalysts with high-efficiency,mild condition,and stable CO_(2)RR to C2 products.