Applying the Shearlet-Based Complexity Measure for Analyzing Mass Transfer in Continuous-Flow Microchannels

Continuous-flow microchannels are widely employed for synthesizing various materials,including nanoparticles,polymers,and metal-organic frameworks(MOFs),to name a few.Microsystem technology allows precise control over reaction parameters,resulting in purer,more uniform,and structurally stable products due to more effective mass transfer manipulation.However,continuous-flow synthesis processes may be accompanied by the emergence of spatial convective structures initiating convective flows.On the one hand,convection can accelerate reactions by intensifying mass transfer.On the other hand,it may lead to non-uniformity in the final product or defects,especially in MOF microcrystal synthesis.The ability to distinguish regions of convective and diffusive mass transfer may be the key to performing higher-quality reactions and obtaining purer products.In this study,we investigate,for the first time,the possibility of using the information complexity measure as a criterion for assessing the intensity of mass transfer in microchannels,considering both spatial and temporal non-uniformities of liquid’s distributions resulting from convection formation.We calculate the complexity using shearlet transform based on a local approach.In contrast to existing methods for calculating complexity,the shearlet transform based approach provides a more detailed representation of local heterogeneities.Our analysis involves experimental images illustrating the mixing process of two non-reactive liquids in a Y-type continuous-flow microchannel under conditions of double-diffusive convection formation.The obtained complexity fields characterize the mixing process and structure formation,revealing variations in mass transfer intensity along the microchannel.We compare the results with cases of liquid mixing via a pure diffusive mechanism.Upon analysis,it was revealed that the complexity measure exhibits sensitivity to variations in the type of mass transfer,establishing its feasibility as an indirect criterion for assessing mass transfer intensity.The method presented can extend beyond flow analysis,finding application in the controlling of microstructures of various materials(porosity,for instance)or surface defects in metals,optical systems and other materials that hold significant relevance in materials science and engineering.

The Impact of Storyline Complexity on L2 Learners’Written Performance

Task-based Language Teaching(TBLT)research has provided ample evidence that cognitive complexity is an important aspect of task design that influences learner’s performance in terms of fluency,accuracy,and syntactic complexity.Despite the substantial number of empirical investigations into task complexity in journal articles,storyline complexity,one of the features of it,is scarcely investigated.Previous research mainly focused on the impact of storyline complexity on learners’oral performance,but the impact on learners’written performance is less investigated.Thus,this study aims at investigating the effects of narrative complexity of storyline on senior high school students’written performance,as displayed by its complexity,fluency,and accuracy.The present study has important pedagogical implications.That is,task design and assessment should make a distinction between different types of narrative tasks.For example,the task with single or dual storyline.Results on task complexity may contribute to informing the pedagogical choices made by teachers when prioritizing work with a specific linguistic dimension.

Exploring Biocomplexity in Cancer: A Comprehensive Review

Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide the self-organized structure. The living systems are open, dynamic structures performing random, stationary, stochastic, self-organizing processes. The self-organizing procedure is defined by the spatial-temporal fractal structure, which is self-similar both in space and time. The system’s complexity appears in its energetics, which tries the most efficient use of the available energies;for that, it organizes various well-connected networks. The controller of environmental relations is the Darwinian selection on a long-time scale. The energetics optimize the healthy processes tuned to the highest efficacy and minimal loss (minimalization of the entropy production). The organism is built up by morphogenetic rules and develops various networks from the genetic level to the organism. The networks have intensive crosstalk and form a balance in the Nash equilibrium, which is the homeostatic state in healthy conditions. Homeostasis may be described as a Nash equilibrium, which ensures energy distribution in a “democratic” way regarding the functions of the parts in the complete system. Cancer radically changes the network system in the organism. Cancer is a network disease. Deviation from healthy networking appears at every level, from genetic (molecular) to cells, tissues, organs, and organisms. The strong proliferation of malignant tissue is the origin of most of the life-threatening processes. The weak side of cancer development is the change of complex information networking in the system, being vulnerable to immune attacks. Cancer cells are masters of adaptation and evade immune surveillance. This hiding process can be broken by electromagnetic nonionizing radiation, for which the malignant structure has no adaptation strategy. Our objective is to review the different sides of living complexity and use the knowledge to fight against cancer.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

A Comparative Analysis of Syntactic Complexity in Applied Linguistics Abstracts Written by Chinese Novice Writers and Native English Advanced Writers

The rhetorical structure of abstracts has been a widely discussed topic, as it can greatly enhance the abstract writing skills of second-language writers. This study aims to provide guidance on the syntactic features that L2 learners can employ, as well as suggest which features they should focus on in English academic writing. To achieve this, all samples were analyzed for rhetorical moves using Hyland’s five-rhetorical move model. Additionally, all sentences were evaluated for syntactic complexity, considering measures such as global, clausal and phrasal complexity. The findings reveal that expert writers exhibit a more balanced use of syntactic complexity across moves, effectively fulfilling the rhetorical objectives of abstracts. On the other hand, MA students tend to rely excessively on embedded structures and dependent clauses in an attempt to increase complexity. The implications of these findings for academic writing research, pedagogy, and assessment are thoroughly discussed.

Analysis of the Role of Syntactic Complexity in Students’ Reading Comprehension: A Teacher’s Perspective

This study examines the role of the syntactic complexity of the text in the reading comprehension skills of students.Utilizing the qualitative method of research,this paper used structured interview questions as the main data-gathering instruments.English language teachers from Coral na Munti National High School were selected as the respondents of the study.Finding of the study suggests that the syntactic complexity of the text affects the reading comprehension of the students.Students found it challenging to understand the message that the author conveyed if he or she used a large number of phrases and clauses in one sentence.Furthermore,the complex sentence syntactic structure was deemed the most challenging for students to understand.To overcome said challenges in comprehending text,various reading intervention programs were utilized by teachers.These interventions include focused or targeted instruction and the implementation of the Project Dear,suggested by the Department of Education.These programs were proven to help students improve their comprehension as well as their knowledge in syntactical structure of sentences.This study underscores the importance of selecting appropriate reading materials and implementing suitable reading intervention programs to enhance students’comprehension skills.

An incommensurate fractional discrete macroeconomic system:Bifurcation,chaos,and complexity

This study proposes a novel fractional discrete-time macroeconomic system with incommensurate order.The dynamical behavior of the proposed macroeconomic model is investigated analytically and numerically.In particular,the zero equilibrium point stability is investigated to demonstrate that the discrete macroeconomic system exhibits chaotic behavior.Through using bifurcation diagrams,phase attractors,the maximum Lyapunov exponent and the 0–1 test,we verified that chaos exists in the new model with incommensurate fractional orders.Additionally,a complexity analysis is carried out utilizing the approximation entropy(ApEn)and C_(0)complexity to prove that chaos exists.Finally,the main findings of this study are presented using numerical simulations.

Complexity of Injective Homomorphisms to Small Tournaments, and of Injective Oriented Colourings

Several possible definitions of local injectivity for a homomorphism of an oriented graph G to an oriented graph H are considered. In each case, we determine the complexity of deciding whether there exists such a homomorphism when G is given and H is a fixed tournament on three or fewer vertices. Each possible definition leads to a locally-injective oriented colouring problem. A dichotomy theorem is proved in each case.

Low Complexity Detection Algorithms Based on ADMIN for Massive MIMO

This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the signal to interference plus noise ratio(SINR) for an uplink massive MIMO system.The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm,in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity.The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function(CDF) of SINR and a percentage,respectively.The symbols in higher SINR part are detected by MMSE.The interference of these symbols is then cancelled by successive interference cancellation(SIC).Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN.The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms.In addition,the computational complexity of ADMIN-T and ADMIN-P are significantly reduced.Furthermore,in the case of same number of transceiver antennas,the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

Economic complexity and environmental sustainability in eastern European economies:Evidence from novel Fourier approach

Globally,economies have become complex and new technologies have transformed and facilitated the modernization of economies.In the previous literature,economic complexity approach has become one of the popular tools in the development and innovation studies of economic geography.Researchers have found that green technology and eco-innovation approaches should be used to decisively reduce the effects of carbon emissions on the environment.However,debates about the impact of economic complexity on environment remain unsettled since some emerging production technologies have far-reaching pollution effects.This study explored the impacts of economic complexity on environmental sustainability in Turkey using the novel Fourier-based approaches,namely:Fourier Augmented Dickey-Fuller(FADF)and Fourier Autoregressive-Distributed Lag(FARDL)models.The Fourier-based approaches indicated that all variables(economic complexity index(ECI),GDP,energy consumption,and CO_(2)emission(CO_(2)E))are cointegrated in the long run.Additionally,the FARDL model implied that(i)in the long run,the effect of ECI(as a proxy for economic complexity),GDP(as a proxy for economic growth),and energy consumption on CO_(2)E(as a proxy for environmental quality)are important;(ii)economic complexity decreases environmental degradation in Turkey;and(iii)economic growth and energy consumption negatively affect environmental quality.The results also showed that economic complexity could be used as a policy tool to tackle environmental degradation.The findings also revealed that the fossil fuelbased economy will continue to expand and undermine Turkey’s efforts to meet its net zero emission target by 2053.Therefore,policy-makers should take actions and establish diversified economic,environmental,and energy strategies.For policy insights,the Turkish governments can use the combination of tax exemptions and technical support systems to support knowledge creation and the diffusion of environmentally friendly technologies The governments can also impose strict environmental regulations on the knowledge development phases.

A novel complex-high-order graph convolutional network paradigm:ChyGCN

In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.

Discovery of nano organo-clay complex pore-fractures in shale and its scientific significance:A case study of Cretaceous Qingshankou Formation shale,Songliao Basin,NE China

A new pore type,nano-scale organo-clay complex pore-fracture was first discovered based on argon ion polishing-field emission scanning electron microscopy,energy dispersive spectroscopy and three-dimensional reconstruction by focused ion-scanning electron in combination with analysis of TOC,R_(o)values,X-ray diffraction etc.in the Cretaceous Qingshankou Formation shale in the Songliao Basin,NE China.Such pore characteristics and evolution study show that:(1)Organo-clay complex pore-fractures are developed in the shale matrix and in the form of spongy and reticular aggregates.Different from circular or oval organic pores discovered in other shales,a single organo-clay complex pore is square,rectangular,rhombic or slaty,with the pore diameter generally less than 200 nm.(2)With thermal maturity increasing,the elements(C,Si,Al,O,Mg,Fe,etc.)in organo-clay complex change accordingly,showing that organic matter shrinkage due to hydrocarbon generation and clay mineral transformation both affect organo-clay complex pore-fracture formation.(3)At high thermal maturity,the Qingshankou Formation shale is dominated by nano-scale organo-clay complex pore-fractures with the percentage reaching more than 70%of total pore space.The spatial connectivity of organo-clay complex pore-fractures is significantly better than that of organic pores.It is suggested that organo-complex pore-fractures are the main pore space of laminar shale at high thermal maturity and are the main oil and gas accumulation space in the core area of continental shale oil.The discovery of nano-scale organo-clay complex pore-fractures changes the conventional view that inorganic pores are the main reservoir space and has scientific significance for the study of shale oil formation and accumulation laws.

The Interplay of Task Complexity(±Here-and-Now)and Students’Proficiency in the Written Narratives in English as a Foreign Language

Task-Based Language Teaching has witnessed growing interest in the impact of task complexity on Second language(L2)learners’linguistic performance.Within this field,the influence of resource-directing and resource-dispersing features of cognitive task complexity has attracted much attention.Research on task complexity focuses on the influence of different task features on language learners’production in terms of linguistic complexity,accuracy,fluency and lexis.Within this field,a line of investigation that has attracted much attention is the influence of resource-directing and resource-dispersing features of cognitive task complexity.The review is helpful for better understanding of task complexity and allows to draw some preliminary pedagogical implications that may be useful for task-based syllabus design.

Three-dimensional magnetic reconnection in complex multiple X-point configurations in an ancient solar-lunar terrestrial system

Magnetic reconnection processes in three-dimensional(3D)complex field configurations have been investigated in different magneto-plasma systems in space,laboratory,and astrophysical systems.Two-dimensional(2D)features of magnetic reconnection have been well developed and applied successfully to systems with symmetrical property,such as toroidal fusion plasmas and laboratory experiments with an axial symmetry.But in asymmetric systems,the 3D features are inevitably different from those in the 2D case.Magnetic reconnection structures in multiple celestial body systems,particularly star-planet-Moon systems,bring fresh insights to the understanding of the 3D geometry of reconnection.Thus,we take magnetic reconnection in an ancient solar-lunar terrestrial magneto-plasma system as an example by using its crucial parameters approximately estimated already and also some specific applications in pathways for energy and matter transports among Earth,ancient Moon,and the interplanetary magnetic field(IMF).Then,magnetic reconnection of the ancient lunar-terrestrial magnetospheres with the IMF is investigated numerically in this work.In a 3D simulation for the Earth-Moon-IMF system,topological features of complex magnetic reconnection configurations and dynamical characteristics of magnetic reconnection processes are studied.It is found that a coupled lunar-terrestrial magnetosphere is formed,and under various IMF orientations,multiple X-points emerge at distinct locations,showing three typical magnetic reconnection structures in such a geometry,i.e.,the X-line,the triple current sheets,and the A-B null pairs.The results can conduce to further understanding of reconnection physics in 3D for plasmas in complex magnetic configurations,and also a possible mechanism for energy and matters transport in evolutions of similar astrophysical systems.

Synthesis and Physico-Chemical Characterizations of Novel Hydrazone Ligands and Their Metal Complexes against Hormone-Dependent and Independent Cancers

This work deals with the synthesis and physicochemical characterizations of a new group of novel retinoidal ligands and their metal complexes. Their in vitro anti-proliferative activities have shown that ligand L1 is effective against human breast cancer BT-20 and MCF-7 cell lines. At the same time, compound L2 exerts its effect on human prostate cancer PC-3 and human breast cancer MDA-MB-231 and MCF-7 cell lines respectively. The retinoid ligands exert their pleiotropic action toward retinoic acid receptors (RARs) than their metal complexes but all compounds exhibit concentration-dependent.

Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

Magma Mixing Genesis of the Mafic Enclaves and Related Granitoids in the Kan Granite-Gneiss Complex of Central Côte d’Ivoire: Evidence from Geology, Petrology and Geochemistry

The mafic enclaves from Paleoproterozoic domain are considered to be the results of large-scale crust-mantle interaction and magma mixing. In this paper, petrography, mineralogy and geochemistry were jointly used to determine the origin of the mafic enclaves and their relationship with the host granitoids of the Kan granite-gneiss complex. This study also provides new information on crust-mantle interactions. The mafic enclaves of the Kan vary in shape and size and have intermediate chemical compositions. The diagrams used show a number of similarities in the major elements (and often in the trace elements) between the mafic enclaves and the host granitoids. Geochemical show that the Kan rock are metaluminous, enriched in silica, medium to high-K calc-alkaline I-type granite. The similarities reflect a mixing of basic and acid magma. Mafic enclaves have a typical magmatic structure, which is characterized by magma mixing. The genesis of these rocks is associated with the context of subduction. They result from the mixing of a mafic magma originating from the mantle and linked to subduction, and a granitic magma (type I granite) that arises from the partial melting of the crust.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Interference in Complex CDMA-OFDM/OQAM for Better Performance at Low SNR

This article is about orthogonal frequency-division multiplexing with quadrature amplitude modulation combined with code division multiplexing access for complex data transmission. It aims to present a method which uses two interfering subsets in order to improve the performance of the transmission scheme. The idea is to spread in a coherent manner some data amongst two different codes belonging to the two different subsets involved in complex orthogonal frequency-division multiplexing with quadrature amplitude modulation and code division multiplexing access. This will improve the useful signal level at the receiving side and therefore improve the decoding process especially at low signal to noise ratio. However, this procedure implies some interference with other codes therefore creating a certain noise which is noticeable at high signal to noise ratio.