Distance Information Research Articles

A Probabilistic Approach in the Search Space of the Molecular Distance Geometry Problem.

The discovery of the three-dimensional shape of protein molecules using interatomic distance information from nuclear magnetic resonance (NMR) can be modeled as a discretizable molecular distance geometry problem (DMDGP). Due to its combinatorial characteristics, the problem is conventionally solved in the literature as a depth-first search in a binary tree. In this work, we introduce a new search strategy, which we call frequency-based search (FBS), that for the first time utilizes geometric information contained in the protein data bank (PDB). We encode the geometric configurations of 14,382 molecules derived from NMR experiments present in the PDB into binary strings. The obtained results show that the sample space of the binary strings extracted from the PDB does not follow a uniform distribution. Furthermore, we compare the runtime of the symmetry-based build-Up (SBBU) algorithm (the most efficient method in the literature to solve the DMDGP) combined with FBS and the depth-first search (DFS) in finding a solution, ascertaining that FBS performs better in about 70% of the cases.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Electrotactile proprioception training improves finger control accuracy and potential mechanism is proprioceptive recalibration

This study presents a novel training technique, visual + electrotactile proprioception training (visual + EP training), which provides additional proprioceptive information via tactile channel during motor training to enhance the training effectiveness. In this study, electrotactile proprioception delivers finger aperture distance information in real-time, by mapping frequency of electrical stimulation to finger aperture distance. To test the effect of visual + EP training, twenty-four healthy subjects participated in the experiment of matching finger aperture distance with distance displayed on screen. Subjects were divided to three groups: the first group received visual training and the other two groups received visual + EP training with or without a post-training test with electrotactile proprioception. Finger aperture control error was measured before and after the training (baseline, 15-min post, 24-h post). Experimental data suggest that both training methods decreased finger aperture control error at 15-min post-training. However, at 24-h post-training, the training effect was fully retained only for the subjects who received visual + EP training, while it washed out for the subjects with visual training. Distribution analyses based on Bayesian inference suggest that the most likely mechanism of this long-term retention is proprioceptive recalibration. Such applications of artificially administered sense have the potential to improve motor control accuracy in a variety of applications.

Environmental supervision distance, government-enterprise collusion and corporate environmental information disclosure

Environmental supervision distance, government-enterprise collusion and corporate environmental information disclosure

Encryption Techniques in Near Field Communication (NFC): Challenges, Applications, and Future Directions

With the growth of the need of information transmission and communication, Near Field Communication(NFC) technique is widely used in different areas for its convenience and simplicity,especially those areas that require both high efficiency and low cost of information transmission. However, the security of NFC itself is not ensured due to the risk of information leakage caused by technical features of short distance information transmission. Information encryption techniques are able to help NFC to be more reliable and safer from the danger of data leakage. There are several kinds of encryption techniques for NFC encryption.Due to the impact of multiple different techniques on the universality of NFC, placing different encryption techniques under the same standard can be an effective research method to improve collaboration efficiency of different encryption techniques. This paper mainly introduces common encryption techniques for NFC,including their features and applicable scenarios, and attempt to conduct technical analysis on existing encryption technologies for finding possible improving directions.

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.

Remote sensing and feminist critique: Reappropriations of sensing across distance

Remote sensing technologies and Geographic Information Systems (GIS) have sparked diverse conceptual and critical responses, with feminist approaches in particular undergoing notable transformations. This article traces and contextualizes these changes, emphasizing the re-evaluation of the concept of “distance” in feminist critiques of remote sensing (RS) and GIS from the 1990s onwards. It highlights a shift from outright rejection of GIS/RS technologies as tools of masculinist, positivistic science to a redefinition of remotely sensed information and imbricated understandings of distance and proximity. The article argues that researchers view issues of masculinism from markedly different perspectives and re-evaluate the role of technology-mediated seeing and imagining, resulting in a re-evaluation of physical distance in its relationship to infrastructure, access, and care. What has transformed is what it means to be “critical” toward as well as the possibility to be “critical” with remote sensing from a feminist point of view.

Information transfer from spatial to social distance in rats: implications for the role of the posterior parietal cortex in spatial-social integration.

Humans and other social animals can represent and navigate complex networks of social relationships in ways that are suggestive of representation and navigation in space. There is some evidence that cortical regions initially required for processing space have been adapted to include processing of social information. One candidate region for supporting both spatial and social information processing is the posterior parietal cortex (PPC). We examined the hypothesis that rats can transfer or generalize distance information across spatial and social domains and that this phenomenon requires the PPC. In a novel apparatus, rats learned to discriminate two conspecifics positioned at different spatial distances (near vs. far) in a goal-driven paradigm. Following spatial learning, subjects were tested on probe trials in which spatial distance was replaced with social distance (cagemate vs. less familiar conspecific). The PPC was chemogenetically inactivated during a subset of probe sessions. We predicted that, in control probe trials, subjects would select conspecifics whose social distance matched the previously learned spatial distance. That is, if trained on the near distance, the rat would choose the highly familiar cagemate, and if trained on the far distance, the rat would choose the less familiar conspecific. Subjects learned to discriminate conspecifics based on spatial distance in our goal-driven paradigm. Moreover, choice for the appropriate social distance in the first probe session was significantly higher than chance. This result suggests that rats transferred learned spatial information to social contexts. Contrary to our predictions, PPC inactivation did not impair spatial to social information transfer. Possible reasons are discussed. To our knowledge, this is the first study to provide evidence that spatial and social distance are processed by shared cognitive mechanisms in the rat model.

MisORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

Transcriptomic Signature of Spatial Navigation in Brains ofDesert Ants.

Navigation is crucial for central-place foragers to locate food and return to the nest. Cataglyphis ants are renowned for their advanced navigation abilities, relying on landmark cues and path integration. This study aims to uncover the transcriptomic basis of exceptional spatial learning in the central nervous system of Cataglyphis niger. Ants navigated a maze with a food reward, and we examined expression changes linked to correct decisions in subsequent runs. Correct decisions correlated with expression changes in the optic lobes, but not the central brain, showing a downregulation of genes associated with sucrose response and Creb3l1. The latter gene is homologous to Drosophila crebA, which is essential for long-term memory formation. To understand how ants use distance information during path integration, we analyzed expression shifts associated with the last distance traveled. We uncovered a transcriptomic footprint in the central brain, but not in the optic lobes, with genes enriched for energy consumption and neurological functions, including neuronal projection development, synaptic target inhibition, and recognition processes. This suggests that transcriptional activity in the central brain is necessary for estimating distance traveled, which is crucial for path integration. Our study supports the distinct roles of different brain parts for navigation in Cataglyphis ants.

Acpro: fusion of embedded ambient light sensor and image sensor for visible light positioning

Abstract With the increasing demand for indoor positioning, visible light positioning (VLP) systems have become an optics technologies research hotspot due to their high accuracy and avoidance of electromagnetic interference. However, current photodiode (PD)-based and image sensor (IS)-based VLP systems still suffer from high costs at the receiver, and the problem of the trade-off between field of view (FoV) and positioning accuracy. Therefore, we propose a smartphone-based low-cost, and high-precision VLP system, Acpro. The system combines the smartphone’s embedded ambient light sensor and IS as the receiver for the positioning. First, for the low sampling rate of the ambient light sensor, we use the frequency aliasing characteristic to identify the LED’s beacon through the dynamic undersampling principle. Then, to increase the FoV and positioning accuracy, we use the standard distance information corrected by the IS to improve the noise distance calculated by the ambient light sensor. Further, the corrected distance information is used for positioning. Extensive results show that our system can achieve sub-meter positioning accuracy.

Hyperspectral endmember extraction using convexity based purity index

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

Word vector embedding and self-supplementing network for Generalized Few-shot Semantic Segmentation

Under the condition of sufficient base class samples and a few novel class samples, Generalized Few-shot Semantic Segmentation (GFSS) classifies each pixel in the query image as base class, novel class, or background. A standard GFSS approach involves two training stages: base class learning and novel class updating. However, inter-class interference and information loss which contribute to the poor performance of GFSS, have not been synthetical considered. To address the problem, we propose an Embedded-Self-Supplementing Network (ESSNet), i.e., semantic word embedding and query set self-supplementing information to enhance segmentation accuracy. Specifically, the semantic word embedding module employs distance information between word vectors to assist the model in learning the distance between class prototypes. In order to transform the semantic word vector prototypes from the semantic space to the visual embedding space, we designed a triplet loss function to supervise the word vector embedding module, where the word vector prototype serves as an anchor and positive-negative samples are collected among the general features of the support image. To compensate for the information loss caused by using prototypes to represent classes, we propose a self-supplementing module to mine the information contained in the query image. Specifically, this module first makes a preliminary prediction on the query image, then selects high-confidence area to form pseudo labels, and finally uses pseudo labels to extract query prototypes to supplement the missing information. Extensive experiments on PASCAL-5i and COCO-20i show that ESSNet has superior performance and outperforms state-of-the-art methods in all settings.

Bibliometric Mapping: Research Development on the Topic of Quality Management on Google Scholar Using Vosviewer

This research aims to map the development of quality management research indexed in Google Scholar and analyze topics related to this topic. By adopting a qualitative descriptive method, it was carried on through three stages: (1) indexed journal with the help of Google Scholar PoP (Publish or Perish) software; (2) journals were saved as an RIS (Research Information System) file; and (3) bibliometric analysis was performed using VOS viewer. This search focused on articles discussing quality management topics in the period 2010–2023, with a limit on the number of documents of 900. The research results showed that the amount of research on quality management had fluctuated. The most publications occurred in the specified year range, namely 83 articles. The topics most discussed were quality management systems, performance, top management, education, total quality management, relations, and improvement, while those that were still very few were distance education, skills, conflict management, competence, social development, information management, and career adaptability. Therefore, these topics were likely to be of concern to the latest research on quality management in the future because there was still very little research on them.

Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations

Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.

Linear polarization study of open clusters in the anticenter direction: Signature of the spiral arms

Aims. Our objective is to investigate the distribution of dust and associated large-scale structures of the Galaxy using optical linear polarization measurements of various open clusters located at different distances in the Galactic anticenter direction. Methods. We present R-band linear polarization observations of stars toward five open clusters: Kronberger 1, Berkeley 69, Berkeley 71, Berkeley 19, and King 8 in the anticenter direction. The polarization observations were carried out using the ARIES (Aryabhatta Research Institute of Observational Sciences) IMaging POLarimeter mounted on the 104 cm Sampurnanand telescope of ARIES, Nainital, making it the first study to target the polarization observations toward distant clusters (~6 kpc). We combined the observed polarization data with the distance information from the Gaia space telescope to infer the dust distribution along the line of sight. Results. The variation in the degree of polarization and extinction with distance reveals multiple dust layers in each cluster direction. In addition, common foreground-dust layers detected toward different cluster directions highlight global features such as spiral arms. Our results show that the dust clouds at 2 kpc toward Berkeley 69 and Berkeley 71 coincide with the Perseus arm, while the dust layer at ~4 kpc toward the distant clusters Berkeley 19 and King 8 indicates the presence of the Outer arm. The large-scale dust distribution obtained by combining our polarization results with previous polarization studies of nearby open clusters suggests that the anticenter direction is characterized by a low-extinction homogeneous dust distribution with a somewhat uniform orientation of the plane-of-sky component of the magnetic field along the line of sight. Conclusions. Our study demonstrates that polarization is useful as a tool for studying the large-scale dust distribution and structural features where kinematic distance methods are inadequate and cannot provide accurate distances to the dust clouds. The global dust distribution in the anticenter direction shows signatures of the intervening spiral arms.

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (<1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

How emissions and distance do influence the choice of eco-tourists?

ABSTRACT Given the growing environmental impact of tourism, understanding how information influences tourists’ sustainable choices is essential. This study focuses on the relatively under-researched area of how information on carbon emissions and travel distance affects tourists’ environmentally friendly choices at destinations across various time intervals. Hypothetical dichotomous questions were employed in three online surveys with 991 participants. T-tests were used to compare the effects of carbon emissions and distance information over eight, five, three, and one-hour intervals. The results indicate that both carbon emissions and distance information positively influence preferences for environmentally friendly behaviour. Additionally, more available time can lead to less environmentally friendly choices. These findings offer valuable insights for policymakers and tourism stakeholders aiming to foster sustainable practices in the industry.