Index Structure Research Articles

In-silico analysis of ribosome inactivating protein (RIP) of the Cucurbitaceae family.

Ribosome-inactivating proteins (RIPs) are highly active N-glycosidases that depurinate both bacterial and eukaryotic rRNAs, halting protein synthesis during translation. Found in a diverse spectrum of plant species and tissues, RIPs possess antifungal, antibacterial, antiviral, and insecticidal properties linked to plant defense. In this study, we investigated the physiochemical properties of RIP peptides from the Cucurbitaceae family through bioinformatics approaches. Molecular weight, isoelectric point, aliphatic index, extinction coefficient, and secondary structures were analyzed, revealing their hydrophobic nature. The novelty of this work lies in the comprehensive examination of RIPs from the Cucurbitaceae family and their potential therapeutic applications. The study also elucidated the binding interactions of Cucurbitaceae RIPs with key biological targets, including Interleukin-6 (IL-6). Strong hydrogen bond interactions between RIPs and these targets suggest potential for innovative insilico drug design and therapeutic applications, particularly in cancer treatment. Comprehensive analysis of bond lengths using Ligpolt + software provides insights for optimizing molecular interactions, offering a valuable tool for drug design and structural biology studies.

Comparative analysis of C n2 estimation methods for sonic anemometer data

Wind speed and sonic temperature measured with ultrasonic anemometers are often utilized to estimate the refractive index structure parameter C n 2, a vital parameter for optical propagation. In this work, we compare four methods to estimate C n 2 from C T 2, using the same temporal sonic temperature data streams for two separated sonic anemometers on a homogenous path. Values of C n 2 obtained with these four methods using field trial data are compared to those from a commercial scintillometer and from the differential image motion method using a grid of light sources positioned at the end of a common path. In addition to the comparison between the methods, we also consider appropriate error bars for C n 2 based on sonic temperature considering only the errors from having a finite number of turbulent samples. The Bayesian and power spectral methods were found to give adequate estimates for strong turbulence levels but consistently overestimated the C n 2 for weak turbulence. The nearest neighbors and structure function methods performed well under all turbulence strengths tested.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Analysis of e-government and digital society indicators over the years: a comparative study of the EU member states

Purpose Benchmarking e-government and digital society developments using relevant indicators provides crucial insights into what aspects to consider while building a resilient digital society in which digital public services are delivered effectively and sustainably. The purpose of this paper is to analyse selected indices and indicators over the years and provide findings and recommendations on what indicators contribute most to the development. Design/methodology/approach A mixed research approach was used to conduct the research and collect, analyse and interpret data. A qualitative analysis involving the search, decomposition and comparison approaches to identify e-government and digital society reports, indices, rankings and indicators was followed by a quantitative analysis comprising of regression and cluster analyses. Findings The findings revealed that changes in the mix of indicators used by e-government and digital society indices can be attributed to advances in ICT and channels through which people communicate and receive information. The authors found that digital and telecommunication infrastructures and the quality of their parameters such as broadband have the biggest influence on progress of the e-government and digital societies developments and contribute most to clustering of the EU member states into groups. Originality/value The paper provides insights into how the structures of related indices changed over the years and how different indicators contribute to benchmarking of e-government and digital society developments by means of their weights. It provides governments with recommendations on which indicators to focus most.

Reference optical turbulence characteristics at the Large Solar Vacuum Telescope site

Abstract Large ground-based solar telescopes are equipped with adaptive optics systems to correct wavefront distortions induced in the turbulent atmosphere. The design of the adaptive optics system strongly depends on the vertical profiles of the optical turbulence. In particular, the characteristics of the optical turbulence determine the design of tomographic adaptive optics systems, which provide image correction within a wide field of view. In the article, a new method to estimate reference optical turbulence characteristics from Era-5 reanalysis assimilated data is presented. This method is based on the dependence of the air refractive index structure constant $C_n^2$ on the vertical shears of wind speed as well as the outer scale of turbulence L0. The L0 parameter is estimated by minimization of the dispersion between the modeled and measured values of the refractive index structure constant $C_n^2$ within the surface layer. For the first time, parametrization coefficients and reference profiles of optical turbulence averaged for the period 1940–2022 are calculated for the Large Solar Vacuum Telescope (LSVT) site. The calculated optical turbulence profiles are representative; these profiles correspond to typical changes of the measured values of the Fried parameter, the isoplanatic angle, and the outer scale of turbulence at the LSVT site. The model turbulence profiles are verified taking into account the Shack–Hartmann wavefront sensor measurements at the LSVT. The higher accuracy of estimation of the optical turbulence characteristics makes it possible to refine parameters relevant to the LSVT adaptive optics system. The obtained results can be used in order to develop high-resolution solar adaptive optics technologies as applied to ground-based telescopes including those using the principles of atmospheric tomography.

Randomness of optical turbulence generated by Rayleigh–Bénard convection using intensity statistics

The experimental study of optical turbulence proves difficult due to challenges in generating controllable conditions in a laboratory environment. Confined water tanks that produce Rayleigh–Bénard (RB) convection are one method to generate optical turbulence using a controllable temperature gradient. It is of utmost concern to quantify the properties of the optical turbulence generated for characterization of other optical applications such as imaging, sensing, or communications. In this experimental study a Gaussian beam is propagated through a RB water tank where two intensity measurements are made at the receiver’s pupil and focal plane. The pupil and focal plane results include quantification of the intensity fluctuation distribution, scintillation distribution, and refractive index structure constant at various values of the temperature gradient. The angle of arrival fluctuations is also calculated at the focal plane to obtain a second estimate of C n 2. The pupil plane estimate for C n 2 using scintillation index and focal plane angle of arrival fluctuations is compared to preliminary predictions of C n 2 as a function of RB temperature gradient showing C n 2∼ΔT4/3. The outcomes of the study confirm that the RB process produces intensity fluctuations that follow gamma–gamma and log-normal probability density functions. Estimates of the refractive index structure constant C n 2 produce the same trends with different magnitudes when measured from the pupil and focal plane.

Long-term measurement and characterization of boundary layer optical turbulence

The United States Naval Academy long-term scintillation measurement campaign was a multi-year effort to characterize optical turbulence in the near-maritime atmospheric boundary layer (ABL). At its core, the field experiment consists of in situ measurements of bulk atmospheric and oceanographic parameters, as well as path-averaged measurements of the refractive index structure parameter, C n 2, collected using a large-aperture scintillometer. The field experiment ran from January 1st, 2020, through September 26th, 2023, representing the most comprehensive collection of optical turbulence measurements in the near-maritime ABL to date. Long-term measurements enable researchers to evaluate existing theory and develop new models applicable to this environment. The present study characterizes some of the physical relationships that affect optical turbulence. This characterization focuses on the relationship between local optical turbulence and select atmospheric and oceanographic parameters. The impact of temperature gradients on the extent of optical turbulence was analyzed, along with its interactions with relative humidity and wind speed. The diurnal and seasonal variations in measured C n 2 were examined, with comparisons drawn against other environments. Further information and the full dataset are publicly available through the optical turbulence benchmark repository [Jellen et al., GitHub, 2023].

Confirmatory Factor Analysis of Sleep Quality among Grandparents Raising Grandchildren

This study examined the factor structure of the Pittsburgh Sleep Quality Index (PSQI) and the sleep quality of custodial grandparents during the Coronavirus pandemic (COVID-19). A convenience sample of custodial grandparents (N=102) between the ages of 31 and 78 completed the Pittsburgh Sleep Quality Index (PSQI) tool.

A provably lightweight and secure DSSE scheme, with a constant storage cost for a smart device client.

Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryption (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme outperforms existing ones, reducing server-side search costs significantly.

Backbone Index and GNN Models for Skyline Path Query Evaluation over Multi-cost Road Networks

Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use an unreasonable amount of space and time to build, making them impractical for processing large graphs. In this paper, we propose a novel index structure, backbone index , and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We present efficient approaches to find approximate solutions to SPQs by utilizing the backbone index structure. Furthermore, considering making good use of historical query and query results, we propose two models, S kyline P ath G raph N eural N etwork (SP-GNN) and T ransfer SP-GNN (TSP-GNN), to support effective SPQ processing. Our extensive experiments on real-world large road networks show that the backbone index can support finding meaningful approximate SPQ solutions efficiently. The backbone index can be constructed in a reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs. The results on the SP-GNN and TSP-GNN models also show that both models can help get approximate SPQ answers efficiently.

A comprehensive investigation on alleviating oxidative stress and inflammation in hyperglycaemic conditions through in vitro experiments and computational analysis

Protein glycation, hyper-inflammatory reactions, and oxidative stress play a crucial role in the pathophysiology of numerous diseases. The current work evaluated the protective ability of ethyl alcohol extract of leaves from holy basil (Ocimum sanctum Linn) against inflammation, oxidative stress, glycation and advanced glycation endproducts formation.Various in vitro assays assessed prementioned properties of holy basil. In addition, molecular docking was conducted. The highest hydrogen peroxide reduction activity (72.7 %) and maximum percentage of DPPH scavenging (71.3 %) depicted its vigorous antioxidant abilities. Furthermore, it showed the most excellent protection against proteinase activity (67.247 %), prevention of denaturation of egg albumin (65.29 %), and BSA (bovine serum albumin) (68.87 %) with 600 µg/ml. Percent aggregation index (57.528 %), browning intensity (56.61 %), and amyloid structure (57.0 %) were all reduced significantly using 600 μg/ml of extract. Additionally, the antimicrobial potential was also confirmed. According to a molecular docking study, active leaf extract ingredients were found to bind with superoxide dismutase, catalase, and carbonic anhydrase. As a conclusion, O. sanctum has a variety of health-promoting properties that may reduce the severity of many diseases in diabetic patients. However, in order to ascertain the mechanisms of action of the components of its leaves in disease prevention, more thorough research based on pharmacological aspects is needed.

Comparison of the imaginary parts of the atmospheric refractive index structure parameter and aerosol flux based on different measurement methods

Abstract. The complexity of aerosol particle properties and the diversity of characterizations make aerosol vertical transport flux measurements and analysis difficult. Although there are different methods, such as aerosol particle number concentration flux and aerosol mass flux based on the eddy covariance principle as well as aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle, there is a lack of mutual validation among the different methods. In this paper, a comparison of aerosol mass flux measurements based on the eddy covariance principle and aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is carried out. The key idea of aerosol mass flux measurements based on the light-propagated large-aperture scintillation principle is the determination of the imaginary part of the atmospheric equivalent refractive index structure parameter (AERISP). In this paper, we first compare the AERISPs measured by two different methods and then compare the aerosol mass vertical transport fluxes obtained by different methods. The experiments were conducted on the campus of the University of Science and Technology of China (USTC). A light propagation experiment was carried out between two tall buildings to obtain the imaginary and real parts of the AERISPs for the whole path, which in turn can be used to obtain the aerosol vertical transport flux. An updated visibility meter is installed on the meteorological tower in the middle of the light path, which is utilized to obtain the single-point visibility, which is then converted to the imaginary part of the atmospheric equivalent refractive index (AERI). The imaginary parts of the AERISP were obtained via spectral analysis with visibility data. The results show that the imaginary parts of the AERISPs obtained by different methods are in good agreement. The imaginary part of the AERI measured by the visibility meter and the vertical wind speed obtained by the ultrasonic anemometer were used for covariance calculations to obtain the aerosol vertical transport flux. The trends in aerosol vertical transport fluxes obtained by the different methods are consistent, and there are differences in some details, which may be caused by the inhomogeneity in the vertical transport of aerosol fluxes. The experimental results also showed that urban green land is a sink area for aerosol particles.

Space-efficient and high-performance inline deduplication for emerging hybrid storage system with Libra＋

In the face of the ever-increasing data volume of data-intensive applications, deduplication, as a promising solution to reduce space consumption, has attracted increasing attention in modern storage systems. Specifically, in modern SSD caching-based hybrid storage systems, inline deduplication achieves data reduction on the fly before storing data in the SSDs, thereby enhancing performance and extending their lifespan. However, with the emergence of new storage media featuring fast access speed and high endurance, hybrid storage systems have evolved by incorporating these new storage devices in place of SSDs at the front end of the primary storage. In this work, we revisit the concept of inline deduplication in the context of emerging hybrid storage and propose a new inline deduplication system designed for emerging hybrid storage, named Libra＋. Libra＋ contains three components: First, we propose two cooperative index structures for memory and cache. Second, we design a selective deduplication approach to perform targeted deduplication. Third, we introduce a lightweight dirty list that replaces the conventional dirty list in inline deduplication, reducing time overhead and space costs. Experimental results show that Libra＋ can achieve simultaneous performance improvement and space cost reduction.

A Neural Network Method for Inversion of Turbulence Strength

Accurate inversion of atmospheric turbulence strength is a challenging problem in modern turbulence research due to its practical significance. Inspired by transfer learning, we propose a new neural network method consisting of convolution and pooling modules for the atmospheric turbulence strength inversion problem. Its input is the intensity image of the beam and its output is the refractive index structure constant characterizing the atmospheric turbulence strength. We evaluate the inversion performance of the neural network at different beams. Meanwhile, to enhance the generalisation of the network, we mix data sets from different turbulence environments to construct new data sets. Additionally, the inverted atmospheric turbulence strength is used as a priori information to help identify turbulent targets. Experimental results demonstrate the effectiveness of our proposed method.

Ecological Status of Treehopper Caresa tuarina in District Hyderabad, Sindh Pakistan

The treehopper, also known as Caresa tuarina, is an intriguing creature with more than a hundred recorded species. Treehoppers are a very diverse group of insects, that play a crucial role in the ecosystem, yet many species remain uncovered in terms of their ecological status. Objectives: To investigate the ecological status of C. taurina and propose conservation strategies to ensure its preservation in the district Hyderabad, Sindh, Pakistan. Methods: Field survey was carried out in different areas of Hyderabad to investigate the habitat and population of C. tuarina. For preservation of C. taurina specie, naphthalene balls and 70% alcohol were used. Environmental factors including temperature, humidity, plant cover, air pressure, soil type, and UV index and habitat structure were noted at each sample location. Results: The collected treehoppers were small size insects, measuring around 8 mm in length. Agricultural areas and riparian zones were shown to be important habitats for C. taurina populations. Different physical characteristics included soil type was lay and whose terrain was plane land; there was a full moon, 34°C in the mean temperature; 27% Mean humidity; 21.22 psig of air pressure; and a moderate UV index. Urbanization, intensified agriculture, pesticide usage, and changes in habitat appropriateness brought on by climate change are the main causes of habitat degradation for populations of C. taurina. Conclusions: It is concluded that population of treehoppers (C. tuarina) mostly found in agriculture and riparian zones. They are facing habitat degradation as a result of urbanization, intensive agriculture, pesticide use, and changes in habitat appropriateness brought on by climate change.

IQual: a new computer program for qualitative phase analysis with efficient search–match capability

iQual is a new computer program developed for qualitative phase analysis using powder diffraction data. It is based on a new database named Crystal_db, derived from the latest Crystallography Open Database. Crystal_db has been created with organized functional modules and optimized index structures, greatly enhancing the database's search efficiency. The iQual program offers users a wide range of querying capabilities and convenient visualization features. In addition to providing an efficient interactive interface, the program combines with the optimized database to enable efficient searching for and finding of the data entries that best match the experimental data for qualitative analysis of X-ray diffraction patterns. The highlight of this search–match method lies in an effective two-step approach that makes full use of the database structure. A preliminary screening is adopted, based on the five strongest diffraction peaks of each candidate phase, prior to scoring the candidate phases. Matrix operations in both screening and scoring processes enhance the program's efficiency.

CALOTRITON: a convective boundary layer height estimation algorithm from ultra-high-frequency (UHF) wind profiler data

Abstract. Long time series of observations of atmospheric dynamics and composition are collected at the French Pyrenean Platform for Observation of the Atmosphere (P2OA). Planetary boundary layer depth is a key variable of the climate system, but it remains difficult to estimate and analyse statistically. In order to obtain reliable estimates of the convective boundary layer height (Zi) and to allow long-term series analyses, a new restitution algorithm, named CALOTRITON, has been developed. It is based on the observations of an ultra-high-frequency (UHF) radar wind profiler (RWP) from P2OA with the help of other instruments for evaluation. Estimates of Zi are based on the principle that the top of the convective boundary layer is associated with both a marked inversion and a decrease in turbulence. Those two criteria are respectively manifested by larger RWP reflectivity and smaller vertical-velocity Doppler spectral width. With this in mind, we introduce a new UHF-deduced dimensionless parameter which weighs the air refractive index structure coefficient with the inverse of vertical velocity standard deviation to the power of x. We then search for the most appropriate local maxima of this parameter for Zi estimates with defined criteria and constraints such as temporal continuity. Given that Zi should correspond to fair-weather cloud base height, we use ceilometer data to optimize our choice of the power x and find that x=3 provides the best comparisons. The estimates of Zi by CALOTRITON are evaluated using different Zi estimates deduced from radiosounding according to different definitions. The comparison shows excellent results with a regression coefficient of up to 0.96 and a root-mean-square error of 71 m, which is close to the vertical resolution of the UHF RWP of 75 m, when conditions are optimal. In more complex situations, that is when the atmospheric vertical structure is itself particularly ambiguous, secondary retrievals allow us to identify potential thermal internal boundary layers or residual layers and help to qualify the Zi estimations. Frequent estimate errors are observed nevertheless; for example, when Zi is below the UHF RWP first reliable gate or when the boundary layer begins its transition to a stable nocturnal boundary layer.

Modeling the atmospheric refractive index structure parameter using macrometeorological observations

The dynamic fluctuations in the atmospheric refractive index, commonly referred to as optical turbulence, cause phase distortions of the electromagnetic waves propagating through the atmosphere. The consequent scintillations have large implications for free-space optical communication, laser remote sensing, and directed energy applications. The refractive index structure parameter (C n 2), quantifying the strength of these fluctuations, is usually estimated using high-frequency micrometeorological measurements, employing sonic anemometer-thermometers or scintillometers. Despite providing highly accurate information, these systems are immensely complex and costly, especially for frequent field applications and remote locations. In this study, we have developed an empirical multinomial model for estimating C n 2 using three-year macrometeorological data and validated it against collocated and concurrent micrometeorological measurements, from a tropical semi-arid location. This simpler model would be handy for applications in remote locations having weather station measurements alone.

Conditional convolutional GAN-based adaptive demodulator for OAM-SK-FSO communication.

The perturbation of atmosphere turbulence is a significant challenge in orbital angular momentum shift keying-based free space optical communication (OAM-SK-FSO). In this study, we propose an adaptive optical demodulation system based on deep learning techniques. A conditional convolutional GAN (ccGAN) network is applied to recover the distorted intensity pattern and assign it to its specified class. Compared to existing methods based on convolutional neural networks (CNNs), our network demonstrates powerful capability in recovering the distorted light beam, resulting in a higher recognition accuracy rate under the same conditions. The average recognition accuracy rates are 0.9928, 0.9795 and 0.9490 when the atmospheric refractive index structure constant $C_n^2$ is set at 3 × 10-13, 4.45 × 10-13, 6 × 10-13m-2/3, respectively. The ccGAN network provides a promising potential tool for free space optical communication.

SWIX: A Memory-efficient Sliding Window Learned Index

Data stream processing systems enable querying over sliding windows of streams of data. Efficient index structures for the streaming window are a crucial building block to enable querying the sliding window for operations such as aggregation and joins. This paper proposes SWIX, a novel memory-efficient learned index for sliding windows. Unlike conventional learned indexes that rely on tree structures to achieve logarithmic query cost, SWIX has a flat structure that uses substantially less memory and enables efficient query execution while having a low cost for index maintenance when inserting (and retraining). SWIX dynamically adapts itself to the real-time distribution shifts of data streams. SWIX outperforms existing indexes in terms of query execution time and memory footprint for workloads characterized by very frequent updates. Our results show that SWIX has a significantly smaller memory footprint than conventional, streaming, and learned indexes, using only 22% to 42% of the size compared to state-of-the-art approaches, yet outperforming them by up 1.2× to 1.6× on average (and up to 52×) in terms of query time, making it a space- and time-efficient method for indexing data streams. For concurrent learned indexes, Parallel SWIX can achieve up to 3.45× throughput with only 34% of memory consumption.