Space Analysis Research Articles

Articulatory characteristics and vowel space analysis of Mandarin Chinese non-low vowels in Korean-speaking learners

Vowels are a fundamental component of Mandarin Chinese syllables. Accurate pronunciation of Mandarin Chinese relies on the distinct articulation of different vowels, especially the visually similar “ü” and “u”. Korean-speaking learners of Chinese Mandarin as a second language commonly exhibit non-standard pronunciation or errors in articulating “ü”, leading to confusion, particularly in non-low vowels centered around this sound. Using Praat to analyze the Formant values of non-low vowel sounds from six participants, a comparison between Korean L2 Chinese speakers and native Chinese speakers revealed vowel spaces differentiated through F2 values for /u/ and /o/, while /ɣ/ and /o/ are distinguished by F1 values. The sound similar to Korean “ㅟ” differs from /y/ in that the former is a gliding sound transitioning from low to high. The distinction between /y/ and /u/, /o/, /ɣ/ was found to be unclear, with /y/ often merging into sounds like “iu” and “io”. This aligns with historical vowel changes in Chinese “ü”, noted in previous linguistic studies, showing variations like “iu” and “io” over different periods. Korean L2 Chinese speakers tend to substitute /ɯ/ for /u/ and “ㅟ” (/ɯ/+/i/) for /y/, avoiding rounded lip movements in pronunciation, thus reducing effort and the need for additional phonemes. Furthermore, the F3 value for Korean L2 Chinese /y/ is notably lower, suggesting that the correct articulation should closely resemble the tongue position of /i/, combined with rounded lips.

Functional principal component analysis for incomplete space–time data

Environmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space–time data, when data may be affected by complex missing data structures. We formalize the problem in the framework of functional data analysis, proposing an innovative method of functional principal component analysis (fPCA) for incomplete space–time data. The functional nature of the proposed method permits to borrow information from measurements observed at nearby spatio-temporal locations. The resulting functional principal components are smooth fields over the considered spatio-temporal domain, and can lead to interesting insights in the spatio-temporal dynamic of the phenomenon under study. Moreover, they can be used to provide a reconstruction of the missing entries, also under severe missing data patterns. The proposed model combines a weighted rank-one approximation of the data matrix with a roughness penalty. We show that the estimation problem can be solved using a majorize–minimization approach, and provide a numerically efficient algorithm for its solution. Thanks to a discretization based on finite elements in space and B-splines in time, the proposed method can handle multidimensional spatial domains with complex shapes, such as water bodies with complicated shorelines, or curved spatial regions with complex orography. As shown by simulation studies, the proposed space–time fPCA is superior to alternative techniques for Principal Component Analysis with missing data. We further highlight the potentiality of the proposed method for environmental problems, by applying space–time fPCA to the study of the lake water surface temperature (LWST) of Lake Victoria, in Central Africa, starting from satellite measurements with large gaps. LWST is considered one of the fundamental indicators of how climate change is affecting the environment, and is recognized as an essential climate variable.

Multiple ESIPT and triplet formation pathways in photoexcited 2-mercapto-5-methyl-1,3-benzenedicarboxaldehyde

Simultaneous dynamical events happening upon photoexcitation of 2-mercapto-5-methyl-1,3-benzenedicarboxaldehyde (MMB) are studied using quantum wavepacket simulations. Several crossings among S1, S2, S3, T3, and T4 states are identified using the potential energy profile analyses in vibrational coordinate space. Simulations in the singlet manifold show nonadiabatic population transfer to higher states (S2 and S3) within a few tens of femtoseconds upon initiating the wavepacket on S1. Such population exchange opens up multiple excited-state intramolecular proton transfer (ESIPT) pathways involving all three singlet states, i.e., S1, S2 and S3. The singlet-triplet dynamics using spin-orbit coupling constants reveal that the triplet generation via S1 to T3/T4 is a minor deactivation pathway and can compete with the proton transfer process.

Seasonality, Latitudinal Dependence, and Structural Evolution of Arctic Cyclones

Abstract Arctic cyclones (ACs) are an important component of the Arctic climate system. While previous studies focused on case studies or samples of intense ACs, an AC tracking algorithm is applied here to ERA5 to provide more than 9300 tracks. This large sample enables evaluations of seasonality, latitudinal dependence, and the structural evolution of ACs using storm-centered composite analysis and phase space analysis. The structures of ACs of different genesis regions, polar versus midlatitude, are also examined and compared. The results show that ACs typically have an asymmetric horizontal structure with cold air to the west and warm air to the east of the cyclone center. Cyclone asymmetry decreases, and the circulation becomes more barotropic in higher latitudes. ACs of polar origin are more symmetric than ACs of midlatitude origin and dominate the cyclone occurrences over the Arctic Ocean. Regarding seasonality, winter ACs are more intense and have a stronger horizontal asymmetry, and the cyclonic circulation extends higher into the stratosphere than summer ACs. In contrast, summer ACs have stronger warm anomalies in the lower stratosphere associated with subsidence above the cyclone center, and the cyclonic circulation typically does not extend beyond 50 hPa. The latitudinal and seasonal variations of AC structure are consistent with the latitudinal and seasonal differences in environmental baroclinicity. Additionally, our analyses show that the structural evolution of ACs is characterized by reduced vertical tilt and asymmetry, weakened temperature contrast between west and east sectors in troposphere, and reduced updraft strength in the later stage of the AC life cycle. Significance Statement Arctic cyclones play a crucial role in climate projections and risk assessments for Arctic infrastructure, transportation, and other human activities. This study aims to enhance our understanding of Arctic cyclone characteristics, including their origin, structure, seasonality, and evolution. We discovered that Arctic cyclones exhibit various structures in different environments. Their degrees of symmetry, vertical tilts, temperature contrasts, and updraft strengths vary with season, latitude, and life cycle stage.

A family of robust chaotic S-unimodal maps based on the Gaussian function

This research paper introduces a family of one-dimensional S-unimodal maps based on the Gaussian function, designed to exhibit robust chaos across a wide range of parameters. These maps are developed to display robust chaos by avoiding multiple fixed points that are primarily responsible for the coexisting attractors in 1D maps. The parameter space analysis reveals that chaotic behaviour is sustained across the entire parameter space, except for a very narrow region. The study employs a comprehensive computational approach, including quantitative measures such as sample entropy, Lyapunov exponent, and invariant measures. The uniformly higher values of sample entropy, uniform positive values of the Lyapunov exponent, and the existence of invariant measures in a region of parameter space confirm the presence of robust chaos in these maps. Such a promising class of robust chaotic maps may be potentially used in diverse fields such as chaos-based cryptography, pseudo-random number generation, communication systems, and more.

Investigating socio-spatial differentiation for metro travelers using smart card data: Older people vs. others

As population aging has been an issue worldwide, the mobility of older people have attracted the attention of scholars from urban planning, transport geography, and social science. However, few have investigated socio-spatial differentiation among mobility groups, considering their daily needs and activity spaces. To fill this research gap, we conducted a comparative analysis of socio-spatial differentiation, based on individual activity spaces. We used smart card data from Kunming, China, to identify selected individuals’ residential locations and travel patterns, and evaluate their accessed activity space. We performed a disaggregated analysis of the individual activity space, and then aggregated the activity counts on each grid. This study found that the residential locations of older metro travelers are significantly different from those of other metro travelers. In addition, socio-spatial differentiation was found to exist due to different daily requirements. The results were confirmed in three LASSO models with built environment variables. These findings are useful in urban and transportation planning to improve elder-friendly services.

Transferability of Machine Learning Models for Predicting Raman Spectra.

Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of experimental spectra, and the advent of machine learning techniques makes it possible to predict Raman spectra while achieving a good balance between efficiency and accuracy. However, the transferability of machine learning models across different molecules remains poorly understood. This work proposed a new strategy whereby machine learning-based polarizability models were trained on similar but smaller alkane molecules to predict spectra of larger alkanes, avoiding extensive first-principles calculations on certain systems. Results showed that the developed polarizability model for alkanes with a maximum of nine carbon atoms can exhibit high accuracy in the predictions of polarizabilities and Raman spectra for the n-undecane molecule (11 carbon atoms), validating its reasonable extrapolation capability. Additionally, a descriptor space analysis method was further introduced to evaluate the transferability, demonstrating potentials for accurate and efficient Raman predictions of large molecules using limited training data labeled for smaller molecules.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces.

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.

Spatial analysis of urban green space and its utilization rate for the flood-prone region Assam, India

Spatial analysis of urban green space and its utilization rate for the flood-prone region Assam, India

Effect of Electrostatic Field Assisted Thawing on the Quality of Previously Frozen Beef Striploins

The objective of this study was to evaluate the impact of applying electrostatic field (EF)–assisted thawing on the quality attributes of previously frozen beef striploin. Beef striploins from both sides of 12 USDA Choice carcasses were halved, frozen at −40°C, and thawed under 4 EF voltage treatments: 0 kV (control), 2.5 kV, 5 kV, and 10 kV. After reaching the internal temperature of −1°C, striploins were weighed for yield calculation, swabbed for microbial analysis, fabricated into steaks, and assigned to either 0- or 14-d aging period and retail displayed for 0 or 7 d. Subjective and objective color measurements were taken during the retail display. Upon completion of retail display, Warner-Bratzler shear force (WBSF), cook loss, sarcomere length, troponin-T degradation, muscle fiber spacing, lipid oxidation, antioxidant capacity, pH, and proximate analysis were performed. All EF treatments increased purge loss compared to the control (P<0.05) and did not improve thawing speed, with samples from 10 kV actually taking the longest to thaw (P<0.05). The 2.5 kV and 5 kV samples aged 14 d showed less discoloration than those from 0 kV and 10 kV, and 5 kV samples aged 14 d had higher a* than those from the other treatments (P<0.05). Samples thawed under 10 kV showed a reduction in WBSF compared to the control (P<0.05), but there was no impact of EF on aerobic plate count, sarcomere length, troponin-T degradation,relative fat %, crude protein %, moisture %, purge protein concentration, pH, lipid oxidation, or antioxidant capacity for either the hydrophilic (water soluble) and lipophilic (lipid soluble) portion of the samples (P>0.05). Overall, our study determined that there was no economic benefit to apply EF during thawing regarding yield and purge loss. However, the application of EF may improve tenderness and extend shelf life of beef during retail display.

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold’s cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold’s cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.

Quantum image encryption algorithm based on four-dimensional chaos

Background: Quantum image processing is rapidly developing in the field of quantum computing, and it can be successfully implemented on the Noisy Intermediate-Scale Quantum (NISQ) device. Quantum image encryption holds a pivotal position in this domain. However, the encryption process often encounters security vulnerabilities and entails complex computational complexities, thereby consuming substantial quantum resources. To address this, the present study proposes a quantum image encryption algorithm based on four-dimensional chaos.Methods: The classical image is first encoded into quantum information using the Generalized Quantum Image Representation (GQIR) method. Subsequently, the trajectory of the four-dimensional chaotic system is randomized, and multi-dimensional chaotic keys are generated to initially encrypt the pixel values of the image. Then, the Arnold transformation is applied to randomly encrypt the pixel positions, resulting in the encrypted image. During the decryption process, the inverse process of encryption is employed to restore the original image.Results: We simulated this process in the Python environment, and the information entropy analysis experiment showed that the information entropy of the three encrypted images reached above 7.999, so the system has good encryption. At the same time, the correlation of the pixel distribution after the encryption algorithm is weak, which proves that the control parameters of the chaotic system can effectively reduce the correlation between pixels in the image. In the final key space analysis, the key space issued by our encryption can reach $10140\gg 2128$.Conclusion: Our method is resistant to destructive attacks and can produce scrambled images with higher encryption and usability. This algorithm solves the problems of general encryption algorithms such as periodicity, small key space, and vulnerability to statistical analysis, and proposes a reliable and effective encryption scheme. By making full use of the characteristics of Arnold transformation permutation, ergodicity and the randomness of the four-dimensional chaotic system, the encryption algorithm uses the larger key space provided by the four-dimensional Lorenz system.

Assessing Narrative Space: From Setting to Narrative Environments

Abstract This essay addresses the insufficient attention paid in narrative theory to the reformulated concept of space resulting from the spatial turn in the analysis of narrative space. To address this lack of attention, the essay proposes the concept of “narrative environments” as a new lens for conceiving of narrative space that distinguishes it from setting. Narrative environments are the formal narrative expression of space: the combined social contextualization and historicization and environmental imaginings that have historically produced that space and are called forth in the narrative to build narrative space and its interactions with other narrative components. These are distinguished from “setting” in that the latter has been interpreted as textually self-sufficient, not relying on social and cultural knowledge. The article reviews recent debates on narrative space and their branching into the fields of critical geography and ecocriticism in order to bring the reenvisioned concept of space resulting from the spatial turn into the discussion. The functioning of this new perspective on space is illustrated through the analysis of island and ocean space and its narrative effects in J. M. Coetzee's Foe.

Dark radiation from the primordial thermal bath in momentum space

Motivated by the stunning projections for future CMB surveys, we evaluate the amount of dark radiation produced in the early Universe by two-body decays or binary scatterings with thermal bath particles via a rigorous analysis in momentum space. We track the evolution of the dark radiation phase space distribution, and we use the asymptotic solution to evaluate the amount of additional relativistic energy density parameterized in terms of an effective number of additional neutrino species ΔN eff. Our approach allows for studying light particles that never reach equilibrium across cosmic history, and to scrutinize the physics of the decoupling when they thermalize instead. We incorporate quantum statistical effects for all the particles involved in the production processes, and we account for the energy exchanged between the visible and invisible sectors. Non-instantaneous decoupling is responsible for spectral distortions in the final distributions, and we quantify how they translate into the corresponding value for ΔN eff. Finally, we undertake a comprehensive comparison between our exact results and approximated methods commonly employed in the existing literature. Remarkably, we find that the difference can be larger than the experimental sensitivity of future observations, justifying the need for a rigorous analysis in momentum space.

Changes in filtration and capacitance properties of highly porous reservoir in underground gas storage: CT-based and geomechanical modeling

The paper presents the results of comprehensive studies of filtration and capacitance properties of highly porous reservoir rocks of the aquifer of an underground gas storage facility. The geomechanical part of the research included studying the dependence of rock permeability on the stress-strain state in the vicinity of the wells, and physical modeling of the implementation of the method of increasing the permeability of the wellbore zone - the method of directional unloading of the reservoir. The digital part of the research included computed tomography (CT)-based computer analysis of the internal structure, pore space characteristics, and filtration properties before and after the tests. According to the results of physical modeling of deformation and filtration processes, it is found that the permeability of rocks before fracture depends on the stress-strain state insignificantly, and this influence is reversible. However, when downhole pressure reaches 7-8 MPa, macrocracks in the rock begin to grow, accompanied by irreversible permeability increase. Porosity, geodesic tortuosity and permeability values were obtained based on digital studies and numerical modeling. A weak degree of transversal anisotropy of the filtration properties of rocks was detected. Based on the analysis of pore size distribution, pressure field and flow velocities, high homogeneity and connectivity of the rock pore space is shown. The absence of pronounced changes in pore space characteristics and pore permeability after non-uniform triaxial loading rocks was shown. On the basis of geometrical analysis of pore space, the reasons for weak permeability anisotropy were identified. The filtration-capacitance properties obtained from the digital analysis showed very good agreement with the results of field and laboratory measurements. The physical modeling has confirmed the efficiency of application of the directional unloading method for the reservoir under study. The necessary parameters of its application were calculated: bottomhole geometry, stage of operation, stresses and pressure drawdown value.

Intraspecific variability of leaf form and function across habitat types.

Trait-based ecology has already revealed main independent axes of trait variation defining trait spaces that summarize plant adaptive strategies, but often ignoring intraspecific trait variability (ITV). By using empirical ITV-level data for two independent dimensions of leaf form and function and 167 species across five habitat types (coastal dunes, forests, grasslands, heathlands, wetlands) in the Italian peninsula, we found that ITV: (i) rotated the axes of trait variation that define the trait space; (ii) increased the variance explained by these axes and (iii) affected the functional structure of the target trait space. However, the magnitude of these effects was rather small and depended on the trait and habitat type. Our results reinforce the idea that ITV is context-dependent, calling for careful extrapolations of ITV patterns across traits and spatial scales. Importantly, our study provides a framework that can be used to start integrating ITV into trait space analyses.

Dynamics of winch-integrated cable-driven parallel robots and acceleration space analysis

Dynamics of winch-integrated cable-driven parallel robots and acceleration space analysis

Optimizing echo state networks for continuous gesture recognition in mobile devices: A comparative study

Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.

Flavour-changing top quark decays in the alternative left-right model

We examine flavour-changing neutral-current decays of the top quark, t→qγ, t→qZ, t→qH, and t→qg (with q=u,c), in the Alternative Left-Right Model, a left right-symmetric model featuring exotic quarks and light bosons. These decays have a very small probability of occurring within the Standard Model, but they can be enhanced in this model through the presence of the exotic states. While associated signals may be detected directly at the LHC, rare decays have the advantage of offering means to probe new particles indirectly, through loop-contributions. We perform a comprehensive analysis of the model's parameter space to demonstrate the possible existence of enhancements in the corresponding branching ratios, of 106 for the branching ratios B(t→uZ) and B(t→uH), and in the range of 102−104 for the other decays, relative to the Standard Model. We subsequently determine the preferred parameter space regions of the model in terms of potential of being reached in the near future.

Machine learning-based optimization of a multi-step ion exchange chromatography for ternary protein separation

Ion-exchange chromatography is an essential but complicated step in the biopharmaceutical downstream process, with multiple factors affecting the separation efficiency. Model-based optimization can help expedite process developments with limited time and resource investments. To address the mechanistic model's high computational complexity, a machine learning (ML)-based optimization framework was introduced. Specifically, Gaussian Process Regression models were utilized as substitutes for the mechanistic model in calculating the constraints and objective function. To further reduce the required sampling, feasibility and optimization stages were incorporated into the framework, with a penalty introduced at each stage to guide the search. This ML-based framework was applied to a case study to separate a protein mixture. Compared to the results obtained through genetic algorithm, this approach enhanced productivity by 50.1 % and reduced computation time by 70.8 % simultaneously. The effects of peak cutting criteria on the optimal results were examined, followed by a detailed analysis of design space.