Index Structure Research Articles

Atmospheric Turbulence Parameters Deduced from VerticalProfile of Cn2

The proposed framework can capture realistic spatiotemporal variations in atmospheric turbulence parameters based on local meteorological conditions, such as Fried's parameter r0, isoplanatic angles θ0, seeing ε, scintillation rate , and the wavefront coherence time τ0. Those parameters have been deduced from the refractive index structure parameter Two Iraqi locations were compared, specifically the middle and southern borders, and atmospheric turbulence parameters relevant to astronomical observation were measured, lines were drawn at latitudes 34.26° and 28.31°, respectively. The methodology included collecting meteorological data from both sites and performing numerical simulations based on atmospheric turbulence parameters. According to the criteria of air turbulence, the results showed that the middle of Iraq was better than the southern location for astronomical observation.

Size and vitality characteristics of bilberry (Vaccinium myrtillus L.) populations in the Central Bohemian region of the Czech Republic: a case study of non-timber forest products

Bilberry (Vaccinium myrtillus L.) is a key non-timber forest product that plays an important role in forest biodiversity and ecosystem function. This study evaluated the vitality levels of eight bilberry populations in the Central Bohemian region of the Czech Republic, examining whether their vitality depends on the habitat type. Morphometric analyses were conducted, measuring 13 static metric and 16 static allometric morphological parameters of bilberry shoots. Statistical methods, including correlation, factor analysis, and ANOVA, were employed to determine the vitality index and population vitality structure based on the algorithm developed by Y.A. Zlobin. The results indicated that bilberry plants exhibit significant morphological variability based on forest conditions, forming plants with specific habitus traits. High positive correlations were found between many morphological characteristics, which were grouped into five clusters, each comprising 2–4 parameters. Among these, phytomass, total leaf surface area, and plant height were identified as the key determinants of vitality levels. Bilberry populations in Central Bohemian forests were classified into three vitality categories: prosperous, balanced, and depressed. The study found that the most vigorous populations, with Q-index values between 0.3333 and 0.4375, were located in associations such as Pinetum (sylvestris) vaccinio (myrtilli)-oxalidosum (acetosellae), Pinetum (sylvestris) vaccinio (myrtilli), and Pineto (sylvestris)-Piceetum (abietis) vaccinio (myrtilli)-oxalidosum (acetosellae). These populations are also of high economic value due to their bioresource potential. The study underscores the importance of habitat type in influencing bilberry vitality and offers insights into future research on bilberry populations as a vital component of non-timber forest products.

Wind turbine blade health state assessment based on fuzzy evaluation

Abstract Due to the health state of wind turbine blade being affected by many factors and the mechanism being unclear, a fuzzy evaluation method using SCADA information is proposed. Firstly, the index structure of blade health state assessment is determined by correlation analysis of SCADA data. Secondly, entropy weight is used to allocate weights to all levels of parameters, and then the index data degradation function is optimized by the 3σ criterion. Thirdly, the ridge distribution function is utilized to establish the membership function of the health indexs. Finally, a mathematical model is created for the fuzzy comprehensive assessment of the blade health status. The described techniques is employed to assess the blade health state in the Qingdao wind farm. The “healthy” and “dangerous” states of the blade can be distinguished. The assessment result aligns with reality, verifying that the method in this paper is applicable to the blade health state assessment of wind farms.

Transmission characteristics of vortex light superposition in atmospheric turbulence disturbed by plane acoustic waves

Herein, we derive the expression for the atmospheric refractive index structure constant under the influence of planar acoustic wave perturbations under the influence of the acoustic field on the refractive index and energy of the atmosphere. Utilizing the low-frequency compensated power spectrum inversion technique, we simulate the refractive index power spectrum of atmospheric turbulence perturbed by a planar acoustic wave. Numerical analysis is conducted on the transmission characteristics of the vortex light superposition states in atmospheric turbulence perturbed by a plane acoustic wave under different acoustic wave transmission heights, acoustic pressure amplitudes, and frequencies. Results indicate that introducing an acoustic field induces fluctuations in the atmospheric refractive index structure constant, with a more pronounced impact on the refractive index than on energy. Compared with the sole consideration of the impact of the acoustic field on the atmospheric refractive index, incorporating its effect on atmospheric energy results in a decrease in the atmospheric refractive index structure constant. The impact of the acoustic field on atmospheric turbulence is directly proportional to both acoustic pressure amplitude and frequency. The influence of the acoustic field on the transmission properties of the vortex optical superposition state varies with different acoustic transmission distances. Consequently, the transmission characteristics of the vortex light superposition state can be actively modulated according to varying acoustic wave transmission distances. This study offers a theoretical basis for modulating the optical field transmission characteristics via acoustic fields.

Estimation of Subsurface Structure Using Euler Deconvolution Method of Magnetic Data at the Geothermal Area of Sonai Village and its Surroundings, Konawe Regency

It has been carried out research with the aim of determining the subsurface structure at the geothermal area of Sonai Village and its surroundings, Konawe Regency. The data used are magnetic data obtained through field measurements at 126 points in the N180oS direction. After the data were subjected to diurnal and IGRF corrections, a residual (local) magnetic field anomaly of around -150 nT to 90 nT was obtained. On the residual magnetic anomaly map which has been reduced to the Pole (RTP), the Euler Deconvolution (ED) method is applied to the Index Structure N=0 to estimate the subsurface structure in the form of the presence of minor faults, and it is known that there are 5 minor faults at a depth of around 9 to 38 meters. Information on the existence of these faults is then used in 2D modeling. Modeling results show that these minor faults cut through two rock layers, which are the layers composed of conglomerate rocks from the Alangga Formation and peridotites as bedrock from the Ultramafic Complex. One of the minor faults closest to the manifestation area (hot spring) is at coordinates around 4o1’12.412” S and 122o7’24.263” E to 4o1’15.532” S and 122o7’19.561” E with a distance of ±15 meters. The existence of these minor faults is thought to be the migration routes for heat flow or conduction to the surface at the geothermal area of Sonai Village and its surroundings.

BWBEV: A Bitwise Query Processing Algorithm for Approximate Prefix Search

We tackle the challenge of conducting an approximate prefix search within datasets of strings. We explore using a bit-parallelism technique to compute the edit distance between distinct strings and illustrate its adaptation for an approximate prefix search procedure referred to as BWBEV. This technique employs a unary representation of edit vectors alongside bitwise operations to efficiently update these vectors during the edit distance computation. We also show how to apply our new bit-parallelism technique strategy to online edit distance computation between strings without index structure. Our experiments with BWBEV applied to approximate prefix search for a query autocompletion task revealed a substantial acceleration of over 36% when contrasted against state-of-the-art methods.

GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.

Random Raman Lasing in Diode-Pumped Multi-Mode Graded-Index Fiber with Femtosecond Laser-Inscribed Random Refractive Index Structures of Various Shapes

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP01), as well as to select higher-order modes such as LP11 with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge.

Nonseparable panel models with index structure and correlated random effects

. To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution is established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation studies to assess finite-sample performance of the proposed estimator. We also use it to model dynamic earnings of women, and using our general identification results, we find a negative effect of the number of children not only on the selection into employment as usual, but also on the average earnings.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

EASL: Enhanced append-only skip list index for agile block data retrieval on blockchain

The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain. However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates a more innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub {https://github.com/jarednewell/EASL/}, to expedite future research and encourage the practical application of this effectual data index.

Validity and reliability of the Sustainable HEalthy Diet (SHED) index by comparison with EAT-Lancet diet, Mediterranean diet in Turkish adults.

Food consumption and diet are strongly associated with sustainability. The Sustainable HEalthy Diet index was developed to measure the nutritional, environmental, and sociocultural components of sustainable diets and healthy eating patterns. However, a methodological approach has yet to be proposed for Turkish adults. This study aimed to determine the validity and reliability of the SHED index in Turkish adults. Data were collected from 558 healthy adults using a web-based questionnaire. Internal consistency reliability was evaluated using Cronbach's alpha coefficient, and repeatability was evaluated using the test-retest method. Construct validity was investigated using the EAT-Lancet diet and the Mediterranean Diet Adherence Screener (MEDAS), and the adapted SHED index structures' accordance was evaluated with confirmatory factor analysis. Good reliability and repeatability were found (r = 0.758 and 0.795, respectively). A higher SHED index score was related to a greater intake of grains, fruits, and vegetables and a lower intake of meat, eggs, and dairy compared to EAT-Lancet diet food groups. A higher SHED index score was associated with a lower saturated fat and added sugar intake. While the SHED index was associated with greater adherence to the Mediterranean diet (r = 0.334, p < 0.001), it was negatively associated with non-alcoholic and diet non-alcoholic beverage consumption (r = -0.257 and -0.264, respectively; p < 0.001). The SHED index showed good validity and reliability in Turkish adults. Our results suggest that the SHED index can be used in epidemiological and intervention studies because it allows the measurement of diets in terms of health and sustainability to propose adaptations accordingly.

Research on the effect of thermal disturbance on temporal and spatial distributions of refractive index structure constant by moiré deflection tomography

Research on the effect of thermal disturbance on temporal and spatial distributions of refractive index structure constant by moiré deflection tomography

Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific and individualized regulatory mechanisms.

Energy transformation capacity is generally assumed to be a coherent individual trait driven by genetic and environmental factors. This predicts that some individuals should have high and others low mitochondrial oxidative phosphorylation (OxPhos) capacity across organ systems. Here, we test this assumption using multi-tissue molecular and enzymatic activities in mice and humans. Across up to 22 mouse tissues, neither mitochondrial OxPhos capacity nor mtDNA density were correlated between tissues (median r = -0.01-0.16), indicating that animals with high mitochondrial capacity in one tissue can have low capacity in other tissues. Similarly, the multi-tissue correlation structure of RNAseq-based indices of mitochondrial gene expression across 45 tissues from 948 women and men (GTEx) showed small to moderate coherence between only some tissues (regions of the same brain), but not between brain-body tissue pairs in the same person (median r = 0.01). Mitochondrial DNA copy number (mtDNAcn) also lacked coherence across organs and tissues. Mechanistically, tissue-specific differences in mitochondrial gene expression were attributable in part to i) tissue-specific activation of canonical energy sensing pathways including the transcriptional coactivator PGC-1 and the integrated stress response (ISR), and ii) proliferative activity across tissues. Finally, we identify subgroups of individuals with high mitochondrial gene expression in some tissues (e.g., heart) but low expression in others (e.g., skeletal muscle) who display different clinical phenotypic patterns. Taken together, these data raise the possibility that tissue-specific energy sensing pathways may contribute to the idiosyncratic mitochondrial distribution patterns associated with the inter-organ heterogeneity and phenotypic diversity among individuals.

Efficacy of arginine kinase as a promising RNAi target in Aphis gossypii genome as revealed through aphid bioassay on field-grown transgenic cotton plants

The cotton aphid Aphis gossypii is a major agricultural pest of cotton that causes substantial damage to the crop not only by sucking sap but also through virus transmission. Globally adopted traditional and contemporary approaches to control aphid infestation have certain limitations and are hazardous to human health. RNA interference (RNAi) technology has unfolded its potential as an effective crop protection strategy against various pests. In this study, we adopted plant-mediated RNAi strategy to enhance aphid resistance in cotton by targeting arginine kinase (AK), which is a crucial enzyme responsible for energy homeostasis in insects. We selected a 312bp dsRNA fragment containing eight siRNAs and showing optimum GC content, Hb index, and stable secondary structure based on computational prediction studies. The binary construct expressing dsRNA was used to transform local cotton variety MNH886 and four transgenic lines were obtained in the T1 generation. Out of the four T1 transgenic cotton lines, dsA-7 exhibited the highest aphid mortality (73.3%), whereas, dsA-1, dsA-3 and dsA-6 revealed 60%, 61%, and 66.6% aphid mortality, respectively, in comparison to 13.3% mortality in the mock control cotton line. Moreover, significant knockdown in mRNA expression of AK was observed in aphids fed dsA-7 which was 79%. In comparison, 54%, 47%, and 45% downregulation was recorded in aphids which fed on dsA-6, dsA-3, and dsA-1 transgenic cotton lines, respectively. These results revealed that plant-mediated downregulation of aphid RNA induced significant RNA interference in A. gossypii which resulted in considerable aphid mortality and led to plant protection against aphids.

Atomic-engineered gradient tunable solid-state metamaterials

Metamaterial has been captivated a popular notion, offering photonic functionalities beyond the capabilities of natural materials. Its desirable functionality primarily relies on well-controlled conditions such as structural resonance, dispersion, geometry, filling fraction, external actuation, etc. However, its fundamental building blocks-meta-atoms-still rely on naturally occurring substances. Here, we propose and validate the concept of gradient and reversible atomic-engineered metamaterials (GRAM), which represents a platform for continuously tunable solid metaphotonics by atomic manipulation. GRAM consists of an atomic heterogenous interface of amorphous host and noble metals at the bottom, and the top interface was designed to facilitate the reversible movement of foreign atoms. Continuous and reversible changes in GRAM's refractive index and atomic structures are observed in the presence of a thermal field. We achieve multiple optical states of GRAM at varying temperature and time and demonstrate GRAM-based tunable nanophotonic devices in the visible spectrum. Further, high-efficiency and programmable laser raster-scanning patterns can be locally controlled by adjusting power and speed, without any mask-assisted or complex nanofabrication. Our approach casts a distinct, multilevel, and reversible postfabrication recipe to modify a solid material's properties at the atomic scale, opening avenues for optical materials engineering, information storage, display, and encryption, as well as advanced thermal optics and photonics.

CSQUiD: an index and non-probability framework for constrained skyline query processing over uncertain data

Uncertainty of data, the degree to which data are inaccurate, imprecise, untrusted, and undetermined, is inherent in many contemporary database applications, and numerous research endeavours have been devoted to efficiently answer skyline queries over uncertain data. The literature discussed two different methods that could be used to handle the data uncertainty in which objects having continuous range values. The first method employs a probability-based approach, while the second assumes that the uncertain values are represented by their median values. Nevertheless, neither of these methods seem to be suitable for the modern high-dimensional uncertain databases due to the following reasons. The first method requires an intensive probability calculations while the second is impractical. Therefore, this work introduces an index, non-probability framework named Constrained Skyline Query processing on Uncertain Data (CSQUiD) aiming at reducing the computational time in processing constrained skyline queries over uncertain high-dimensional data. Given a collection of objects with uncertain data, the CSQUiD framework constructs the minimum bounding rectangles (MBRs) by employing the X-tree indexing structure. Instead of scanning the whole collection of objects, only objects within the dominant MBRs are analyzed in determining the final skylines. In addition, CSQUiD makes use of the Fuzzification approach where the exact value of each continuous range value of those dominant MBRs’ objects is identified. The proposed CSQUiD framework is validated using real and synthetic data sets through extensive experimentations. Based on the performance analysis conducted, by varying the sizes of the constrained query, the CSQUiD framework outperformed the most recent methods (CIS algorithm and SkyQUD-T framework) with an average improvement of 44.07% and 57.15% with regards to the number of pairwise comparisons, while the average improvement of CPU processing time over CIS and SkyQUD-T stood at 27.17% and 18.62%, respectively.

Distribution of fish species in the upper Po River Basin (NW Italy): a synthesis of 30 years of data

Freshwater fish is the most diverse group of vertebrates but, unfortunately, also one of the most threatened. While some well-known, charismatic species have been subject to important conservation efforts, many others have long been neglected. This paper aims to analyse the distribution over time of seven freshwater fish species and one lamprey in the upper Po River basin (NW Italy), an important biodiversity hotspot. Six of them are native species listed in Annex II of the Habitats Directive (Lampetra zanandreai, Protochondrostoma genei, Chondrostoma soetta, Telestes muticellus, Sabanejewia larvata, and Cottus gobio), while the other two are key invasive species (Silurus glanis and Misgurnus anguillicaudatus). Data from four regional fish population monitoring campaigns carried out between 1988 and 2019 were analysed. For each species and monitoring campaign, an average Representativeness Index, measuring abundance and population structure, and an Occurrence Frequency were calculated, and then assessed for changes over the time. Of the studied species, P. genei declined the most in the last 30 years, while C. soetta and S. larvata are in a very critical situation, with very few remaining populations. T. muticellus, C. gobio and L. zanandreai seem to have declined only slightly, but their vulnerability should not be underestimated. The two invasive species, on the other hand, show a substantial increase in occurrence as well as range. While some drivers for the decline in the native species vary according to their ecological and biological characteristics, others are more general and linked to the overall degradation of the river environments: habitat alterations, loss of connectivity, excessive water abstraction, pollution, and the presence of invasive alien species. The effects of climate change, such as the rise of inland water temperatures and the alteration of hydrological cycles, must also be taken into account. Diverse and far-reaching conservation efforts are needed to improve the fish habitat and thus also protect the unique biodiversity of this region.

Developing MATLAB graphical user interface for acquiring single star SCIDAR data

To enhance operational efficiency and meet experimental demands, we have developed a graphical user interface (GUI) using MATLAB for Acquiring Single Star SCIDAR Data, leveraging the software’s integrated GUI Development Environment (GUIDE) tool. This interface streamlines the preprocessing and numerical computation of the power spectrum of atmospheric speckles while providing real-time graphical representations of atmospheric parameters, including the vertical profile of the refractive index structure function Cn2(h). It also incorporates parameters related to adaptive optics and high angular resolution, such as seeing, enabling immediate and instantaneous visual assessment of observational conditions. Furthermore, the novelty of this GUI lies in the ease of acquiring and processing data from various atmospheric parameters. The Single Star SCIDAR (Scintillation Detection and Ranging) method relies on analyzing the scintillation of light from single stars to assess the turbulent characteristics of the atmosphere. This assessment is based on the description provided by Cn2(h) derived from minimizing an objective function determined using the power spectrum of atmospheric speckles from single stars. For this purpose, a minimization algorithm called active-set is used.

Evaluation of MSA mortar pore structure characteristics in water-saturated curing environment based on Talbot gradation theory

To investigate the impact of aggregate gradation on the pore structure of manufactured sand aggregate (MSA) mortar and its fractal characteristics under water-saturated curing conditions. The static pore structure characteristics of the pore structure of MSA mortar with 10 aggregate gradations were explored. The analysis is based on the Talbot gradation theory, the NMR technique, and fractal theory. The surface area and roughness of aggregate particles contribute to a reduction in the porosity of MSA mortar. The aggregate gradation is quantified using the Talbot index. The correlation characteristics and intrinsic relationships between Talbot index and different pore structure parameters, such as total porosity, pore gradation, pore fractal dimension and permeability of MSA mortar, are analyzed. Two pore division methods are employed to explore the interrelationships among pore types, fractal dimensions and Talbot index. Additionally, A correlation model between Talbot index and permeability is developed to investigate the intrinsic correlation of parameter variations from a microscopic perspective. It is found that aggregate gradation is optimal with a Talbot index of 0.4, leading to the highest number of micropores and the lowest number of larger pores, thereby enhancing resistance to seepage and dissolution. Furthermore, The contribution of pore size, content and type to permeability varies with different aggregate gradations. Overall, this study serves as a valuable guide for the design and durability evaluation of mortar concrete projects in water-saturated curing environments.