Accurate Description Research Articles

Static and dynamic power splitting protocol enabled in DF energy harvesting Half-Duplex relaying network: Ergodic capacity analysis

In this research, we propose and examine the DF Energy Harvesting Half-Duplex Relaying Network. For this system model, the DF Energy Harvesting Half-Duplex Relaying Network is analyzed in two cases: Static and Dynamic Power Splitting Protocol. The Ergodic Capacity (EC) is introduced and derived in relation to all the primary system parameters in order to evaluate system performance. The Monte Carlo simulation results demonstrate that the mathematical and simulation are in agreement, thereby confirming the accuracy of the analytical description.

أثر التجارة الإلكترونية على سلوك المستهلكين في قطاع تجارة التجزئة في المملكة العربية السعودية

E-commerce in Saudi Arabia is experiencing rapid growth, driven by technological advancements and government initiatives aligned with Vision 2030. This research aims to understand the impact of e-commerce on consumer behavior in the retail sector by examining factors influencing e-commerce adoption, the challenges consumers face, and how digital transformations affect traditional businesses. The study gathered data from consumer surveys, government reports, and previous studies, using analytical models to understand the relationships between variables and their impact on consumer behavior. Survey results revealed that e-commerce has significantly changed purchasing habits among Saudis, with many preferring online shopping due to convenience and a wide range of options. The key drivers for e-commerce adoption include convenience, time savings, and product variety, while economic factors like competitive pricing play a significant role in attracting consumers. Trust and security in e-commerce platforms are critical, as consumers seek assurance regarding the protection of their financial information and product quality. Consumers face challenges such as product description accuracy, return difficulties, and concerns about data privacy. Digital transformation pressures traditional businesses to adopt digital strategies to remain competitive. Small businesses face difficulties adapting but show potential for innovation through partnerships with major e-commerce platforms. Data shows a remarkable increase in e-commerce revenue in Saudi Arabia from SAR 38.8 billion in 2020 to SAR 204 billion in 2024, with the number of stores rising from 28,676 to 57,000 during the same period. To sustain this growth, companies need to invest in long-term strategies and diversify revenue streams. The Saudi government plays a crucial role in supporting this sector by improving digital infrastructure, enhancing cybersecurity, and supporting startups. By adopting these recommendations, the online shopping experience can be improved, ensuring customer satisfaction and the continued growth of this vital sector.

VCC-DiffNet: Visual Conditional Control Diffusion Network for Remote Sensing Image Captioning

Pioneering remote sensing image captioning (RSIC) works use autoregressive decoding for fluent and coherent sentences but suffer from high latency and high computation costs. In contrast, non-autoregressive approaches improve inference speed by predicting multiple tokens simultaneously, though at the cost of performance due to a lack of sequential dependencies. Recently, diffusion model-based non-autoregressive decoding has shown promise in natural image captioning with iterative refinement, but its effectiveness is limited by the intrinsic characteristics of remote sensing images, which complicate robust input construction and affect the description accuracy. To overcome these challenges, we propose an innovative diffusion model for RSIC, named the Visual Conditional Control Diffusion Network (VCC-DiffNet). Specifically, we propose a Refined Multi-scale Feature Extraction (RMFE) module to extract the discernible visual context features of RSIs as input of the diffusion model-based non-autoregressive decoder to conditionally control a multi-step denoising process. Furthermore, we propose an Interactive Enhanced Decoder (IE-Decoder) utilizing dual image–description interactions to generate descriptions finely aligned with the image content. Experiments conducted on four representative RSIC datasets demonstrate that our non-autoregressive VCC-DiffNet performs comparably to, or even better than, popular autoregressive baselines in classic metrics, achieving around an 8.22× speedup in Sydney-Captions, an 11.61× speedup in UCM-Captions, a 15.20× speedup in RSICD, and an 8.13× speedup in NWPU-Captions.

Exploring Santoon's Customers' Preferences That Affect Their Purchase Decision For Buying Fashion Products

There is a significant shift towards online shopping in the digital era, particularly within the fashion industry. Despite this trend, Santoon, a clothing brand in Indonesia, faces challenges with online sales being considerably lower than offline sales, even though many of its customers and target market prefer purchasing fashion products online. This study aims to identify the preferences of Santoon's customers that impact their decision-making process when buying fashion products. Adopting a qualitative approach, the research utilizes semi-structured interviews to gather in-depth insights. A non-probability purposive sampling technique focused on Santoon's past customers and target market. The study reveals that design, quality, and sizing are the primary factors influencing customers' preferences for fashion products. Additionally, detailed product descriptions and customer reviews are crucial in shaping online shopping preferences. Although customers often visit offline stores to see and sample products, the final purchase decision is frequently made online. The findings underscore the importance of enhancing online product descriptions and leveraging customer reviews to build trust. Integrating online and offline shopping experiences is essential to cater to hybrid customer behaviour. Recommendations for Santoon include improving the detail and accuracy of online product descriptions, utilizing customer reviews to foster trust, and merging online and offline shopping experiences. Furthermore, optimizing Instagram marketing by posting content related to the daily use of Santoon's products is advised, given the significant overlap between the brand's target market and Indonesian Instagram users.

Insights from utilizing data of different quality levels for simulating barley performance under Nordic conditions: The Agricultural Production Systems SIMulator model evaluation

Abstract Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality (HQ) experimental data. Despite being demanded frequently, such data are still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of HQ experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data, the prediction accuracy of Agricultural Production Systems SIMulator significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the HQ data. Process descriptions regarding, for example, early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.

Toward Accurate Quantum Chemical Methods for Molecules of Increasing Dimension: The New Family of Pisa Composite Schemes.

The new versions of the Pisa composite scheme introduced in the present paper are based on the careful selection of different quantum chemical models for energies, geometries, and vibrational frequencies, with the aim of maximizing the accuracy of the overall description while retaining a reasonable cost for all the steps. In particular, the computation of accurate electronic energies has been further improved introducing more reliable complete basis set extrapolations and estimation of core-valence correlation, together with improved basis sets for third-row atoms. Furthermore, the reduced-cost frozen natural orbital (FNO) model has been introduced and validated for large molecules. Accurate molecular structures can be obtained avoiding complete basis set extrapolation and evaluating core-valence correlation at the MP2 level. Unfortunately, analytical gradients are not available for the FNO version of the model. Therefore, for large molecules, an accurate reduced-cost alternative is offered by evaluation of valence contributions with a double-hybrid functional in conjunction with the same MP2 contribution for core-valence correlation or by means of a one-parameter approximation. The same double-hybrid functional and basis set are employed to evaluate zero-point energies and partition functions. After the validation of the new models for small systems, a panel of molecular bricks of life has been used to analyze their performances for problems of current fundamental or technological interest. The fully black-box implementation of the computational workflow paves the way toward the accurate yet not prohibitively expensive study of medium- to large-sized molecules also by experimentally oriented researchers.

Irregular ANCF model and experimental investigations of the irregular membrane structures with variable cross-section

The membrane spacecraft structures are widely used for deep space exploration and on-orbit operation, but the dynamic responses of them is more complex. To advance the state of membrane structures, in this paper, a new ANCF membrane element is established to model dynamic equations of the membrane structure precisely and efficiently. Firstly, the irregular ANCF membrane element is studied by considering anomalistic boundary and variable cross-section. The mapping relations between the sub-ANCF element and the parent-ANCF element are illuminated by the Jacobian matrix, which makes the strain of each irregular element accurately calculated with the same shape functions. It is helpful to improve the description accuracy of inherent attributes and reduce the element number of dynamic systems. Further, the nonlinear dynamic model for membrane spacecraft system is established by the virtual work principle. Finally, the numerical examples and experiments are analyzed to verify the correctness of the proposed method and discuss the influence of nonlinear geometry and distortion on the dynamic behaviors. The results from the time domain and frequency domain analyses demonstrate that the distinct irregular geometric boundary of the irregular spacecraft can be more accurately modeled using the presented method. However, the difference becomes more pronounced when the distortion is too large. The research outcomes of this paper can provide theoretical guidance for the irregular spacecraft system, which is of great significance and urgently needs to be studied.

Research on spatial carving method of glutenite reservoir based on opacity voxel imaging

The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.

Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome.

Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response. To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches. Cross-sectional study. The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed. Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT's descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT's explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval. This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content's depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

From Finite Element Simulations to Equivalent Circuit Models of Extracellular Neuronal Recording Systems Based on Planar and Mushroom Electrodes.

define a new methodology to build multi-compartment lumped-elements equivalent circuit models for neuron/electrode systems. the equivalent circuit topology is derived by careful scrutiny of accurate and validated multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of the neuron membrane ion channels, and signal acquisition by the electronic readout. robust and accurate circuit models are systematically derived, suited to represent the dynamics of the sensed extracellular signals over a wide range of geometrical/physical parameters (neuron and electrode sizes, electrolytic cleft thicknesses, readout input impedance, non-uniform ion channel distributions). FEM simulations point out phenomena that escape an accurate description by equivalent circuits; notably: steric effects in the thin electrolytic cleft and the impact of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model. our multi-compartment equivalent circuits match accurately the FEM simulations. They unveil the existence of an optimum number of compartments for accurate circuit simulation. FEM simulations suggest that while steric effects are in most instances negligible, the extracellular ion transport affects the reversal potentials and consequently the recorded signal if the electrolytic cleft becomes thinner than approximately 100 nm. the proposed methodology and circuit models improve upon the existing area and point contact models. The coupling between the extracellular concentrations and reversal potential highlighted by FEM simulations emerges as a challenge for future developments in lumped-element modeling of the neuron/sensor interface.

Time–Frequency-Multisqueezing Transform

In the real world, most signals encountered are nonstationary. It is essential to extract a time-frequency (TF) characteristics in such signals for an accurate description. Two parameters are usually applied to quantify the TF characteristics of a nonstationary signal, i.e., instantaneous frequency (IF) and group delay (GD). A post-processing strategy was adopted by two recently developed techniques, the synchrosqueezing transform (SST) and the time-reassigned SST (TSST) to accurately capture the change rules of IF and GD respectively. However, due to the diversity of modes in complex nonstationary signals, no existing technique has been used to effectively estimate both IF and GD simultaneously. To solve this problem, a post-processing analysis technique termed as time-frequency-multisqueezing transform (TFMST) is proposed in this paper where a so-called chirp rate (CR) discrimination criterion is established by considering the Gaussian window in the short-time Fourier transform. The proposed method can accurately categorize nonstationary signals containing harmonic- and impulsive-like components to achieve a concurrent description and ensure the recovery of original signals. The proposed method is validated by numerical simulation and real signal analyses.

MATHEMATICAL MODEL OF THE FREQUENCY CONVERTER SYSTEM OPERATION IN A SHIP'S AUTONOMOUS POWER SYSTEM

The article shows the process of building a model of the frequency converter control system for a ship's autonomous power system. This task was solved to simplify the mathematical description of the frequency converter for the ship's autonomous power system. As a result, a mathematical set of probable combinations of operating states of power switches and the corresponding values of generalised voltage vectors of the autonomous inverter using pulse width modulation were obtained. The modelling carried out on the basis of this data confirmed the correspondence of the obtained values of the phase voltage of the autonomous inverter to real energy processes. It is shown that it is important to further study the technical and methodological factors of increasing energy efficiency and the accuracy of the mathematical description of real links of power systems, which will ensure the process of a more qualitative study of energy processes in them.

Region-Focused Network for Dense Captioning

Dense captioning is a very critical but under-explored task, which aims to densely detect localized regions-of-interest (RoIs) and describe them with natural language in a given image. Although recent studies tried to fuse multi-scale features from different visual instances to generate more accurate descriptions, their methods still suffer from the lack of exploration of relation semantic information in images, leading to less informative descriptions. Furthermore, indiscriminately fusing all visual instance features will introduce redundant information, resulting in poor matching between descriptions and corresponding regions. In this work, we propose a Region-Focused Network (RFN) to address these issues. Specifically, to fully comprehend the images, we first extract the object-level features, and encode the interaction and position relations between objects to enhance the object representations. Then, to decrease the interference from redundant information about the target region, we extract the most relevant information to the region. Finally, a region-based Transformer is employed to compose and align the previous mined information and generate the corresponding descriptions. Extensive experiments on Visual Genome V1.0 and V1.2 datasets show that our RFN model outperforms the state-of-the-art methods, thus verifying its effectiveness. Our code is available at https://github.com/VILAN-Lab/DesCap .

MineObserver 2.0: A Deep Learning & In-Game Framework for Assessing Natural Language Descriptions of Minecraft Imagery

MineObserver 2.0 is an AI framework that uses Computer Vision and Natural Language Processing for assessing the accuracy of learner-generated descriptions of Minecraft images that include some scientifically relevant content. The system automatically assesses the accuracy of participant observations, written in natural language, made during science learning activities that take place in Minecraft. We demonstrate our system working in real-time and describe a teacher dashboard to showcase observations, both of which advance our previous work. We present the results of a study showing that MineObserver 2.0 improves over its predecessor both in perceived accuracy of the system's generated descriptions as well as in usefulness of the system's feedback. In future work, we intend improve system generated descriptions to give more teacher control and shift the system to perform continuous learning to more rapidly respond to novel observations made by learners.

Self-Trapped Excitons in Metal-Halide Perovskites Investigated by Time-Dependent Density Functional Theory.

We present a theoretical study of the formation of self-trapped excitons (STEs) and the associated broadband emission in metal-halide perovskites Cs4SnBr6 and Cs2AgInCl6, using time-dependent density functional theory (TDDFT) with the dielectric-dependent hybrid (DDH) functional. Our approach allows for an accurate description of the excitonic effect and geometry relaxation in the electronic excited states and yields optical gap, STE emission energy, and emission spectra in reasonable agreement with experiments. We point out the significance of considering geometry relaxations in the electronic excited state by showing that the exciton-phonon coupling computed in the ground-state atomic geometry is insufficient to describe the physical properties of STEs. Overall, we find that TDDFT with the DDH hybrid functional is a suitable approach for the study of the formation of STEs in perovskite and provides insights for designing metal-halide perovskites with tailored emission properties.

The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective

This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics. Rooted in the mean-field assumption, CMs oversimplify by assuming that every individual has the potential to “infect” any other, neglecting the inherent complexity of underlying network structures. Since social interactions follow a networked pattern with specific links between individuals based on social behaviors, joining classic CMs and network science in epidemiology becomes essential for a more authentic portrayal of epidemic spreading. This review delves into noteworthy research studies that, from various perspectives, elucidate how the synergy between networks and CMs can enhance the accuracy of epidemic descriptions. In conclusion, we explore research prospects aimed at further elevating the integration of networks within the realm of epidemiology, recognizing its pivotal role in refining our understanding of disease dynamics.

Validation of the Survey of Sleep Quality in the Pediatric Intensive Care Unit (SSqPICU).

Observational data suggest pediatric intensive care unit-related sleep and circadian disruption (PICU-SCD) affects many critically ill children. Multicenter trials exploring PICU-SCD have been impractical because measuring sleep in this setting is challenging. This study validates a questionnaire for caregivers to describe children's sleep in the PICU. This prospective, multicenter, case-control study enrolled caregivers of children in 4 PICUs or in a hospital-based sleep laboratory (controls). Survey items were compiled from validated adult ICU and pediatric in- and outpatient sleep questionnaires. Control responses were compared to polysomnography to determine accuracy. A score was calculated by summing the level of disruption of sleep timing, duration, efficiency, quality, and daytime sleepiness and irritability. In responses from 152 PICU and 61 sleep laboratory caregivers, sleep survey items had acceptable internal reliability (α = 0.75) and reproducibility on retest surveys (interclass correlation coefficient > 0.600). Caregivers could not assess sleep of sedated children. Factor analysis identified 3 subscales of PICU-SCD. Control parents had good agreement with polysomnography sleep onset time (κ = 0.823) and sleep onset latency (κ = 0.707). There was a strong correlation between sleep scores derived by parental reporting to those by polysomnography (r = .844, P < .001). Scores had a linear association with caregiver-reported child sleep quality. There were no site-specific differences in sleep quality. Nearly all respondents found the survey easy to understand and of appropriate length. The Survey of Sleep Quality in the Pediatric Intensive Care Unit provides a reliable, accurate description of inpatient sleep disruption in nonsedated children, generalizable across PICUs. It offers practical means to quantify PICU-SCD daily in future investigations. Hassinger AB, Mody K, Gomez R, etal. Validation of the Survey of Sleep Quality in the Pediatric Intensive Care Unit (SSqPICU). J Clin Sleep Med. 2024;20(8):1251-1258.

Scattering spectra models for physics.

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.

Automatic Expansion of Metadata Standards for Historic Costume Collections

Objective: This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive metadata for fashion collections (otherwise known as costume or dress and textile collections) through expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion in the controlled terms of a metadata schema. This project seeks to expand upon existing domain-specific schema, Costume Core, by enhancing the schema with a comprehensive set of descriptors. This enhancement will allow for more accurate and detailed descriptions of costume artifacts. This article describes this process and the outcomes of AI approaches for providing this metadata expansion, who this process is for, ethical considerations, and lessons learned.Methods: We approached our problem with an investigation into using word embeddings to aid in supporting the suggesting of new metadata terms. Several word embedding models were applied with the more descriptive one chosen for final use in a human-in-the-loop selection process. This selection process provided domain experts to identify which terms chosen by the model are of relevant value. We then compare what was chosen by the domain experts and what the model produced to get an idea as to the value the model provides in the process of metadata expansion.Results: The metadata expansion process was a success. An AI supported process aided domain experts in choosing relevant terms to include in their metadata schema. Therefore, the results were a methodology for using identified AI models for the problem, an interactive system to aid the domain experts (software system), and how to evaluate the results.Conclusion: The application of AI technologies (word embeddings) provided a successful pipeline for supporting domain experts to expand the metadata schema with additional descriptors.&amp;nbsp; Enhancing the metadata schema with additional descriptors improves its usability for fashion collection managers and allows for a more precise description of the artifacts. As a result, many new terms that were expertly chosen were added to the metadata schema.

Fluoride Ion Binding and Translocation in the CLCF Fluoride/Proton Antiporter: Molecular Insights from Combined Quantum-Mechanical/Molecular-Mechanical Modeling.

CLCF fluoride/proton antiporters move fluoride ions out of bacterial cells, leading to fluoride resistance in these bacteria. However, many details about their operating mechanisms remain unclear. Here, we report a combined quantum-mechanical/molecular-mechanical (QM/MM) study of a CLCF homologue from Enterococci casseliflavus (Eca), in accord with the previously proposed windmill mechanism. Our multiscale modeling sheds light on two critical steps in the transport cycle: (i) the external gating residue E118 pushing a fluoride in the external binding site into the extracellular vestibule and (ii) an incoming fluoride reconquering the external binding site by forcing out E118. Both steps feature competitions for the external binding site between the negatively charged carboxylate of E118 and the fluoride. Remarkably, the displaced E118 by fluoride accepts a proton from the nearby R117, initiating the next transport cycle. We also demonstrate the importance of accurate quantum descriptions of fluoride solvation. Our results provide clues to the mysterious E318 residue near the central binding site, suggesting that the transport activities are unlikely to be disrupted by the glutamate interacting with a well-solvated fluoride at the central binding site. This differs significantly from the structurally similar CLC chloride/proton antiporters, where a fluoride trapped deep in the hydrophobic pore causes the transporter to be locked down. A free-energy barrier of 10-15 kcal/mol was estimated via umbrella sampling for a fluoride ion traveling through the pore to repopulate the external binding site.