Complex Interior Research Articles

Optimizing Indoor Localization Precision: Advancements in RFID Strategies for Enhanced Performance

Abstract RFID technology is being used more and more for complex interior localization, as a result of the quick spread of mobile devices and the smooth incorporation of artificial intelligence (AI). But conventional approaches have severe difficulties with antenna placement and constrained signal feature sizes, which limits their performance. To improve indoor localization and get over these drawbacks, this study presents a novel method that combines the Unscented Kalman Filter (UKF) with Wavelet Neural Networks (WNN).The most significant development is the incorporation of phase data into input vectors along with Received Signal Strength Indication (RSSI), which significantly increases accuracy. Empirical results show notable improvements of 88.9%, 99.2%, 95.2%, and 37.5% in critical metrics like Mean Localization Error (MLE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Localization Error Offset (LEO) in particular. This work highlights the critical role of sophisticated techniques like WNN and UKF, highlighting the importance of phase as a critical component, to improve the performance of RFID-based indoor localization. The results indicate a promising trajectory for future advancements in this field as well.

In silico full-angle high-dynamic range scattering of microscopic objects exploiting holotomography.

Accurate optical characterization of microscopic objects is crucial in academic research, product development, and clinical diagnosis. We present a method for obtaining full and high-dynamic range, angle-resolved light scattering attributes of microparticles, enabling distinction of variations in both overall morphology and detailed internal structures. This method overcomes previous limitations in observable scattering angles and dynamic range of signals through computationally assisted three-dimensional holotomography. This advancement is significant for particles spanning tens of wavelengths, such as human erythrocytes, which have historically posed measurement challenges due to faint side-scattering signals indicative of their complex interiors. Our technique addresses three key challenges in optical side-scattering analysis: limited observational angular range, reliance on simplified computational models, and low signal-to-noise ratios in both experimental and computational evaluations. We incorporate three-dimensional tomographic complex refractive index data from Fourier-transform light scattering into a tailored finite-difference time-domain simulation space. This approach facilitates precise near-to-far-field transformations. The process yields complete full-angle scattering phase functions, crucial for particles like Plasmodium falciparum-parasitized erythrocytes, predominantly involved in forward scattering. The resultant scattering data exhibit an extreme dynamic range exceeding 100 dB at various incident angles of a He-Ne laser. These findings have the potential to develop point-of-care, cost-effective, and rapid malaria diagnostic tools, inspiring further clinical and research applications in microparticle scattering.

Assessing Relations Among Visual Variables in Hotel Lobbies Using Deep Learning

To understand the relationships between cognitive variables like visual complexity, coherence, and colour contrast in interior spaces, direct numerical analysis is crucial. Conventional approaches are limited due to the brain's struggle to process visual information without cognitive manipulation. However, advancements in artificial intelligence enable direct examinations. This study used a convolutional neural network to assess the intensity of colour contrasts in images of 5-star hotel lobby interiors with high levels of coherence and complexity. The results indicated that visually complex lobby interiors have less warm-cool and dark-light contrast but more pronounced complementary contrast than coherent lobby interiors. Additionally, a negative correlation was identified between complementary and warm-cool contrasts across all perspectives. These results underscore the potential influence of specific colour contrast types on the cognitive experience of interiority.

Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.

Indoor navigation map design based on spatial complexity

ABSTRACT In the face of the complex interior structure of a building, indoor navigation is an important tool for people to arrive at their destination in large public buildings. In this research, we first analyze the complexity of indoor space, which mainly includes spatial entities with rich semantics, but corridors lacking in semantics, poor visibility, and multi-dimensional floors. Then, we designed various indoor navigation maps based on spatial complexity and conducted a comparative analysis between the existing maps and the ones we designed. Participants were recruited to perform three navigation tasks in the study area on-site. The results demonstrate the advantages of using a hierarchical landmark representation for improved spatial knowledge acquisition and the effectiveness of scene-enhanced directional guidance in areas with poor visibility. It is worth noting that the introduction of three-dimensional maps was found to have a positive impact on users’ spatial perception and their ability to sketch routes accurately. In summary, this evidence confirms that maps based on spatial complexity not only enhance navigation efficiency but also have the potential to improve the overall indoor navigation experience.

Magnesium silicate chimneys at the Strytan hydrothermal field, Iceland, as analogues for prebiotic chemistry at alkaline submarine hydrothermal vents on the early Earth

The Strytan Hydrothermal Field (SHF) in basaltic terrain in Iceland is one of the extant alkaline submarine hydrothermal vent systems favoured as analogues for where life on Earth may have begun. To test this hypothesis we analyse the composition, structure, and mineralogy of samples from hydrothermal chimneys generated at the SHF. We find that the chimney precipitates are composed of Mg-silicates including clays of the saponite-stevensite group (high Mg and Si, low Fe and Al), Ca-carbonates and Ca-sulfates. The chimneys comprise permeable structures with pores sizes down to 1 µm or less. Their complex interiors as observed with SEM (Scanning Electron Microscopy) and X-ray CT (computed tomography scanning), exhibit high internal surface areas. EDX (energy-dispersive X-ray spectroscopy) analysis reveals an increase in the Mg/Si ratio toward the chimney exteriors. Chemical garden analogue experiments produce similar Mg–silicate chimneys with porous internal structures, indicating that injection-precipitation experiments can be high-fidelity analogues for natural hydrothermal chimneys at the SHF. We conclude that SHF chimneys could have facilitated prebiotic reactions comparable to those proposed for clays and silica gels at putative Hadean to Eoarchean alkaline vents. Analysis of the fluid dynamics shows that these chimneys are intermediate in growth rate compared to faster black smokers though slower than those at Lost City. The SHF is proposed as a prebiotic alkaline vent analogue for basaltic terrains on the early Earth.

The dynamic centres of infrared-dark clouds and the formation of cores

ABSTRACT High-mass stars have an enormous influence on the evolution of the interstellar medium in galaxies, so it is important that we understand how they form. We examine the central clumps within a sample of seven infrared-dark clouds (IRDCs) with a range of masses and morphologies. We use 1-pc-scale observations from the Northern Extended Millimeter Array (NOEMA) and the IRAM 30m telescope to trace dense cores with 2.8-mm continuum, and gas kinematics in C18O, HCO+, HNC, and N2H+ (J = 1–0). We supplement our continuum sample with six IRDCs observed at 2.9 mm with the Atacama Large Millimeter/submillimeter Array (ALMA), and examine the relationships between core- and clump-scale properties. We have developed a fully automated multiple-velocity component hyperfine line-fitting code called mwydyn which we employ to trace the dense gas kinematics in N2H+ (1–0), revealing highly complex and dynamic clump interiors. We find that parsec-scale clump mass is the most important factor driving the evolution; more massive clumps are able to concentrate more mass into their most massive cores – with a log-normally distributed efficiency of around 9 per cent – in addition to containing the most dynamic gas. Distributions of linewidths within the most massive cores are similar to the ambient gas, suggesting that they are not dynamically decoupled, but are similarly chaotic. A number of studies have previously suggested that clumps are globally collapsing; in such a scenario, the observed kinematics of clump centres would be the direct result of gravity-driven mass inflows that become ever more complex as the clumps evolve, which in turn leads to the chaotic mass growth of their core populations.

Toward random tensor networks and holographic codes in CFT

In holographic CFTs satisfying eigenstate thermalization, there is a regime where the operator product expansion can be approximated by a random tensor network. The geometry of the tensor network corresponds to a spatial slice in the holographic dual, with the tensors discretizing the radial direction. In spherically symmetric states in any dimension and more general states in 2d CFT, this leads to a holographic error-correcting code, defined in terms of OPE data, that can be systematically corrected beyond the random tensor approximation. The code is shown to be isometric for light operators outside the horizon, and non-isometric inside, as expected from general arguments about bulk reconstruction. The transition at the horizon occurs due to a subtle breakdown of the Virasoro identity block approximation in states with a complex interior.

Data based constitutive modelling of rate independent inelastic effects in composite cables using Preisach hysteresis operators

AbstractThis contribution aims at introducing first steps to develop hysteresis operator type inelastic constitutive laws for Cosserat rods for the simulation of cables composed of complex interior components. Motivated by the basic elements of Cosserat rod theory, we develop a specific approach to constitutive modelling adapted for this application. Afterwards, we describe the hysteretical behaviour arising from cyclic bending experiments on cables by means of the Preisach operator. As shown in pure bending experiments, slender structures such as electric cables behave inelastically, and open hysteresis loops arise with noticeable difference between the first load cycle and the following ones. The Preisach operator plays an important role in describing the input-output relation in hysteresis behaviours, and it can be expressed as a superposition of relay operators. Hence, a mathematical formulation of the problem is introduced, and a first attempt is made to determine the hysteresis behaviour that describes the relation between curvature and bending moment. Therefore, a suitable kernel function is identified in a way that its integration over the Preisach plane results in the bending moment of the specimen, and a comparison between different kernel functions is performed.

Interior ballistic two-phase flow model and its calculation for a mixed charge structure

The mixed charge for guns generally consists of two or more kinds of propellants. Medium and large caliber guns usually adopt the typical combination of tubular and granular propellants, not only meeting interior ballistic indexes but also improving launch safety. However, numerical investigations, charge design, and optimizations of such charge structures rely heavily on classical interior ballistic theory. To gain insight into some crucial phenomena in this complex interior ballistic process, a two-phase flow dynamic model of a 125 mm smoothbore gun with mixed charge (granular propellants, tubular propellants, and a combustible cartridge case) is developed. Unlike the previous form of solid governing equations, the conservation equations, surface temperature and combustion law functions are simultaneously established. The space-time conservation element and solution element (CE/SE) method is used to solve the mathematical model, simplifying the calculations of partial derivatives in source terms. Results show that the model can describe the combustion and flow, and the predicted projectile velocity agrees well with the measured one. In addition, the effects of the mass ratio of mixed propellants on interior ballistic performance are discussed in detail. As the tubular propellant mass decreases, ignition consistency deteriorates, particle accumulation intensifies, and the maximum pressure exceeds the permitted one, all of which may affect launch safety. The work in this paper provides a powerful tool for observing and predicting the interior ballistic process of the mixed charge.

Pickering Emulsion Droplet-Derived Multicompartmentalized Microspheres for Innovative Applications.

Multicompartmentalized microspheres with multilevel and complex interior structures have great potential in practical applications due to their cell-like structures and microscale dimension. The Pickering emulsion droplet-confined synthesis route has been demonstrated to be a promising strategy for fabricating multicompartmentalized microspheres. Since Pickering emulsion-templated formation of hollow microspheres is an interface-directed process in which the growth of shells occurs at the oil/water interface and the confined space of Pickering emulsion droplet accommodates a variety of behaviors, such as surfactant-guided assembly growth, confined pyrolysis transformation, tritemplated growth, and bottom-up assembly, the independent and free regulation of the interface and internal structure of microspheres is allowed. In this Perspective, we highlight the recent progress in the synthesis of microparticles with tunable interior structures via the Pickering emulsion droplet-based approach. And we delve into the innovative applications of these multilevel-structured microparticles benefiting from their biomimetic multicompartments. Finally, some fundamental challenges and opportunities are identified for regulating the interior structure within microspheres and promoting practical applications by virtue of the Pickering emulsion droplet-confined synthesis pathway.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g., inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning ”focus, path, and difficulty.” The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL.

Research on Sensorless Control of Built-in Permanent Magnet Synchronous Motor Based on Extended Kalman Filter Algorithm

The permanent magnet synchronous motor (PMSM) is a system of complex interior structure coupling and nonlinear, variable parameters. In order to track and observe the position and speed of PMSM in real time, the extended Kalman filter (EKF) algorithm is used in this paper to track the speed and position of the built-in PMSM. The influence of the Q and R covariance matrix on the observation effect is studied by using the control variable method of trial and error. The reliability of the EKF on the observation function of velocity position is verified by MATLAB simulation.

Characteristic Research on Electromagnetic Response of Two‐Dimensional Complex Structure Targets Based on Meshless Method

AbstractActual geological targets exhibit complex structures and fine grids can improve the modeling accuracy. However, the generation of fine grids is usually difficult. Moreover, the traditional apparent resistivity method can only obtain the approximate range of the target, and cannot accurately distinguish complex structures, including surface and interior complex structures. To address the above problems, this paper proposed to use the meshless method to model complex structure targets. Two‐dimensional (2D) numerical simulation of the electromagnetic (EM) response was performed in the frequency domain. The average relative error was 2.88% compared to the analytical solution. Furthermore, an analysis method of relative difference in the apparent resistivity (Da) was proposed, and the Da between irregular target and regular target was calculated. Two kinds of irregular targets were designed, one was a complex surface target with concave or convex semicircles embedded on the surface, the other was a complex interior target with high and low resistivity anomalies embedded inside. The calculation and analysis results show that the Da method can characterize the electrical difference caused by structure change compared with the traditional method. Our proposed method can facilitate the accurate detection of the surface and interior structure in the target and promote the development of EM methods in the field of complex geological resource detection.

Smartphone LiDAR Data: A Case Study for Numerisation of Indoor Buildings in Railway Stations.

The combination of LiDAR with other technologies for numerisation is increasingly applied in the field of building, design, and geoscience, as it often brings time and cost advantages in 3D data survey processes. In this paper, the reconstruction of 3D point cloud datasets is studied, through an experimental protocol evaluation of new LiDAR sensors on smartphones. To evaluate and analyse the 3D point cloud datasets, different experimental conditions are considered depending on the acquisition mode and the type of object or surface being scanned. The conditions allowing us to obtain the most accurate data are identified and used to propose which acquisition protocol to use. This protocol seems to be the most adapted when using these LiDAR sensors to digitise complex interior buildings such as railway stations. This paper aims to propose: (i) a methodology to suggest the adaptation of an experimental protocol based on factors (distance, luminosity, surface, time, and incidence) to assess the precision and accuracy of the smartphone LiDAR sensor in a controlled environment; (ii) a comparison, both qualitative and quantitative, of smartphone LiDAR data with other traditional 3D scanner alternatives (Faro X130, VLX, and Vz400i) while considering three representative building interior environments; and (iii) a discussion of the results obtained in a controlled and a field environment, making it possible to propose recommendations for the use of the LiDAR smartphone at the end of the numerisation of the interior space of a building.

Magnetic anomaly inversion through the novel barnacles mating optimization algorithm

Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local search algorithms depends on a well-designed initial model located close to the parameter set providing the global minimum. On the other hand, global optimization and metaheuristic algorithms that have the ability to scan almost the entire model space do not need an assertive initial model. Thus, these approaches are increasingly incorporated into parameter estimation studies and are also gaining more popularity in the geophysical community. In this study we present the Barnacles Mating Optimizer (BMO), a recently proposed global optimizer motivated by the special mating behavior of barnacles, to interpret magnetic anomalies. This is the first example in the literature of BMO application to a geophysical inverse problem. After performing modal analyses and parameter tuning processes, BMO has been tested on simulated magnetic anomalies generated from hypothetical models and subsequently applied to three real anomalies that are chromite deposit, uranium deposit and Mesozoic dike. A second moving average (SMA) scheme to eliminate regional anomalies from observed anomalies has been examined and certified. Post-inversion uncertainty assessment analyses have been also implemented to understand the reliability of the solutions achieved. Moreover, BMO’s solutions for convergence rate, stability, robustness and accuracy have been compared with the solutions of the commonly used standard Particle Swarm Optimization (sPSO) algorithm. The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods.

Electric Spark Deposition of Antibacterial Silver Coating on Microstructured Titanium Surfaces with a Novel Flexible Brush Electrode.

Infection caused by orthopedic titanium implants, which results in tissue damage, is a key factor in endosseous implant failure. Given the seriousness of implant infections and the limitations of antibiotic therapy, surface microstructures and antimicrobial silver coatings have emerged as prominent research areas and have displayed certain antimicrobial effects. Researchers are now working to combine the two to produce more effective antimicrobial surfaces. However, building robust and homogeneous coatings on complex microstructured surfaces is a tough task due to the limits of surface modification techniques. In this study, a novel flexible electrode brush (silver brush) instead of a traditional hard electrode was designed with electrical discharge machining, which has the ability to adapt to complex groove interiors. The results showed that the use of flexible electrode brush allowed silver to be deposited uniformly in titanium alloy microgrooves. On the surface of Ag-TC4, a uniformly covered deposit was visible, and it slowly released silver ions into a liquid environment. In vitro bacterial assays showed that a Ag-TC4 microstructured surface reduced bacterial adhesion and bacterial biofilm formation, and the antibacterial activity of Ag-TC4 against Staphylococcus aureus and Escherichia coli was 99.68% ± 0.002 and 99.50% ± 0.007, respectively. This research could lay the groundwork for the study of antimicrobial metal bound to microstructured surfaces and pave the way for future implant surface design.

WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research.

Conformational changes in mitochondrial complex I of the thermophilic eukaryote Chaetomium thermophilum.

Mitochondrial complex I is a redox-driven proton pump that generates proton-motive force across the inner mitochondrial membrane, powering oxidative phosphorylation and ATP synthesis in eukaryotes. We report the structure of complex I from the thermophilic fungus Chaetomium thermophilum, determined by cryoEM up to 2.4-Å resolution. We show that the complex undergoes a transition between two conformations, which we refer to as state 1 and state 2. The conformational switch is manifest in a twisting movement of the peripheral arm relative to the membrane arm, but most notably in substantial rearrangements of the Q-binding cavity and the E-channel, resulting in a continuous aqueous passage from the E-channel to subunit ND5 at the far end of the membrane arm. The conformational changes in the complex interior resemble those reported for mammalian complex I, suggesting a highly conserved, universal mechanism of coupling electron transport to proton pumping.

Dynamo Simulations of Jupiter's Magnetic Field: The Role of Stable Stratification and a Dilute Core

AbstractUnderstanding Jupiter's present‐day interior structure and dynamics is key to constraining planetary accretion models. In particular, the extent of stable stratification (i.e., non‐convective regions) in the planet strongly influences long‐term cooling processes, and may record primordial heavy element gradients from early in a planet's formation. Because the Galileo entry probe measured a subsolar helium abundance, Jupiter interior models often invoke an outer stably stratified region due to helium rain. Additionally, Juno gravity data suggest a deeper, potentially stratified dilute core extending halfway through the planet. However, fits to Jupiter's gravitational data are non‐unique, and outstanding uncertainty over the equations of state for hydrogen and helium remain. Here, we use high‐resolution numerical magnetohydrodynamic simulations of Jupiter's magnetic field to place constraints on the extent of stable stratification within the planet. We find that compared to traditional interior models, an upper stably stratified layer between 0.9 and 0.95 Jupiter radii (RJ) helps to explain both Jupiter's dipolar magnetic field and zonal winds. In contrast, an extended dilute core that is entirely stably stratified (no convective layers) yields significantly worse fits to both. However, our models with extended deep stratification still generate dipolar magnetic fields if an upper stratified region is also present. Overall, we find that a planet with a dilute core i.e., strongly stably stratified is increasingly challenging to reconcile with Jupiter's magnetic field and winds. Thus if a dilute core is present, alternative modalities such as a fully convective dilute core, a complex multilayered interior structure, or double diffusive convection may be required.