Localized Solutions Research Articles

A fingerprint dictionary processing approach in indoor localization system based on wi-fi

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

PHARM-LC: What role can Community Pharmacy play in supporting people with long Covid? Protocol for a mixed methods study

Background Long Covid is a debilitating multifaceted condition, that is more prevalent in those from deprived areas, females and those with another disorder or disability. Those that live with Long Covid currently must endure a lack of services, support and have also reported being ‘gaslit’ by health care professionals. Primary care services such as GP surgeries are under immense pressure and have limited resources to offer those that live with Long Covid. Community pharmacy could be a localised solution for those that need support and advice for Long Covid as they have already proven to be a reliable source during the COVID-19 pandemic. This study aims to address gaps in research to explore the possible role that community pharmacy teams can play in supporting those living with Long Covid. Protocol A two-phase multi-method study using semi-structured interviews and co-design will be utilised. For phase one: two population groups; those with lived experience of Long Covid and community pharmacy team members will be interviewed to understand their current experiences of community pharmacy supporting those living with Long covid, as well as lived experiences and self-management (people with Long Covid), and training needs, and current practice (Community pharmacy teams). For phase two: a co-design approach will be utilised with key stakeholders to help inform online training via multiple workshops. This training will be then piloted by end users, who will provide post training feedback via a survey. Discussion Understanding the role that community pharmacy can play in supporting those with Long Covid and to facilitate development of this new support pathway to develop online training which provides pharmacy teams with a structured and cohesive approach to care.

How Irregular Geometry and Flow Waveform Affect Pulsating Arterial Mass Transfer.

Alzheimer's disease is a progressive degenerative condition that has various levels of effect on one's memory. It is thought to be caused by a buildup of protein in small fluid-filled spaces in the brain called perivascular spaces (PVS). The PVS often takes on the form of an annular region around arteries and is used as a protein-clearing system for the brain. To analyze the modes of mass transfer in the PVS, a digitized scan of a mouse brain PVS segment was meshed and used for computational fluid dynamics (CFD) studies. Tandem analyses were then carried out and compared between the mouse PVS section and a cylinder with commensurate dimensionless parameters and hydraulic resistance. The geometry pair was used to first validate the CFD model and then assess mass transfer in various advection states: no-flow, constant flow, sinusoidal flow, sinusoidal flow with zero net solvent flux, and an anatomically correct asymmetrical periodic flow. Two mass transfer situations were considered, one being a protein build-up and the other being a protein blend-down using a multitude of metrics. Bulk arterial solute transport was found to be advection-controlled. The consideration of temporal evolution and trajectories of contiguous protein bolus volumes revealed that flow pulsation was beneficial at bolus break-up and that additional local wall curvature-based geometry irregularities also were. Using certain measures, local solute peak concentration blend-down appeared to be diffusion-dominated even for high Peclet numbers; however, bolus size evolution analyses showed definite advection support.

A star selection strategy based on MDS and fast convex hull algorithm in COSPAS-SARSAT system

Abstract The space segment of the current COSPAS-SARSAT system is mainly supported by the Galileo system, and the positioning technology used is generally three-star time-difference positioning. Most of the antennas equipped in the Mission Control Centre (MCC) are not omnidirectional. In the case of Source of Search and Rescue (SAR) in a heavily shaded environment, it is necessary to use the different orbit time-sharing single satellite geolocations using TDOA, and the time-sharing will generate more satellite position data. In order to achieve the higher positioning accuracy of the solution, it is necessary to select the appropriate time and optimal geometrical distribution from the satellite positions. In this paper, we propose a satellite selection strategy based on Multiple Dimensional scaling (MDS) data degradation and a fast convex hull algorithm. The three-dimensional satellite data are downscaled to two-dimensional while maintaining their relative positional relationships, and the two-dimensional convex hull algorithm is used for Geometric Dilution of Precision (GDOP) minimum search star selection. By simulating the satellite position data in different orbits and time-sharing to complete the localization solution, the results show that compared with the traditional satellite selection method, the satellite selection strategy proposed in this paper is able to achieve better localization accuracy in a shorter period of time.

On examining the predictive capabilities of two variants of the PINN in validating localized wave solutions in the generalized nonlinear Schrödinger equation

We introduce a novel neural network structure called strongly constrained theory-guided neural network (SCTgNN), to investigate the behaviour of the localized solutions of the generalized nonlinear Schrödinger (NLS) equation. This equation comprises four physically significant nonlinear evolution equations, namely, the NLS, Hirota, Lakshmanan–Porsezian–Daniel and fifth-order NLS equations. The generalized NLS equation demonstrates nonlinear effects up to quintic order, indicating rich and complex dynamics in various fields of physics. By combining concepts from the physics-informed neural network and theory-guided neural network (TgNN) models, the SCTgNN aims to enhance our understanding of complex phenomena, particularly within nonlinear systems that defy conventional patterns. To begin, we employ the TgNN method to predict the behaviour of localized waves, including solitons, rogue waves and breathers, within the generalized NLS equation. We then use the SCTgNN to predict the aforementioned localized solutions and calculate the mean square errors in both the SCTgNN and TgNN in predicting these three localized solutions. Our findings reveal that both models excel in understanding complex behaviour and provide predictions across a wide variety of situations.

Effect of dispersoid formation on age hardening and precipitation behavior of an Al-Si-Mg(-Cu) alloy with Mn and Cr addition

In the present study, Mn and Cr elements were added to the alloy to induce the formation of the α-AlFeMnCrSi dispersoids during solution treatment. The effect of the dispersoids on subsequent age-hardening response and precipitation behavior was investigated. The results indicate that there is a partially coherent structure between the dispersoid and the α-Al matrix, but the coherence is weak. The precipitation of dispersoids resulted in accelerated age-hardening response and precipitation kinetics, but weakened peak-aged hardening effect. With the aging time increasing from 0 to 16 h, a new precipitation sequence was proposed in Mn+Cr modified Al-Si-Mg(-Cu) alloy: Supersaturated solid solution (SSSS) → GP zones (under-aged) → β'' + β' (peak-aged) → Si + β'' + β' + Mg2Si + Q + α (over-aged). Among them, the extra precipitates were heterogeneous nucleated on the dispersoids, which depended on the orientation relationship between the dispersoid and the precipitation, the interfacial energy of the dispersoid/matrix/precipitation, and the local solute element concentration. Heterogeneous precipitation of coarse particles on dispersoids resulted in the formation of precipitation-free zones (PFZ), which was mainly responsible for the weakened peak-age hardening effect. Moreover, compared to the base alloy, finer and denser precipitates were obtained in the matrix of the modified alloy, due to the increased density of dislocations induced by the dispersoids and the higher Mg supersaturation.

The impact of social network and resource endowment of smallholders on sustainable apple production

PurposeSmallholders are essential in ensuring food security; however, smallholder-dominated food production often involves high resource-environmental costs. This study analyzed the factors that differentiate horticultural practices, willingness to adopt technology and social networks between optimized practices (OPT) and farmer practices (FP) to provide localized and systematic solutions for the sustainable apple production.Design/methodology/approachTo explore the approach of smallholder-dominated sustainable apple production, 257 apple producers in the Bohai Bay region, a major apple planting area in China, were investigated. Life cycle assessment (LCA), emergy analysis and social network analysis methods were used for evaluation.FindingsThe results showed that the net economic profit and emergy sustainability index (ESI) in OPT was 15.8 × 104 RMB·ha-1 and 1.2, respectively, which were 126.9 and 128.0% higher than FP. In contrast, greenhouse gas (GHG) emissions under OPT was 29.3% lower than those under FP. OPT has a higher percentage of adoption of scientific fertilizer application and water-saving irrigation technologies compared to FP. OPT has strong learning abilities, more social resources (such as technical training and sharing technical experience with others) and connections with stakeholders in the apple supply chain. Optimizing smallholders' social capital, willingness to adopt technology, behavioral willingness and technological awareness can promote sustainable apple production.Originality/valueConsidering the horticultural practices employed by smallholders in conjunction with their social networks, these factors contributed to the transition of smallholder-led apple production toward sustainability. The findings provided viable options and a theoretical basis for smallholder-dominated crop production to move toward sustainability, with significant implications for policymakers.

The localized excitation on the Weierstrass elliptic function periodic background for the (3+1)-dimensional generalized variable-coefficient Kadomtsev-Petviashvili equation

In this paper, the linear spectral problem associated with the (3+1)-dimensional generalized variable-coefficient Kadomtsev-Petviashvili (gvcKP) equation with the Weierstrass function as the external potential is investigated based on the Lamé function, from which some new localized nonlinear wave solutions on the Weierstrass elliptic ℘-function periodic background are obtained by the Darboux transformation. The degenerate solutions on the ℘-function periodic background for the gvcKP equation can be derived by taking the limits of the half-periods ω 1, ω 2 of ℘(x), whose evolution and corresponding dynamics are also discussed. The findings show that nonlinear waves on the ℘-function periodic background behave as different types of nonlinear waves in different spaces, including periodic waves, vortex solitons and interaction solutions, aiding in elucidating some physical phenomena in the related fields, such as the physical ocean and nonlinear optics.

BO-GRNN: Machine Learning for Underwater Acoustic Source Localization

This article proposes a Bayesian optimization-based generalized regression neural network (BO-GRNN) framework for underwater source localization in shallow water environment. Specially, by leveraging kernel regression analysis, BO-GRNN fits a conditional probability density function using training and test samples to infer the source location of unseen test samples. Furthermore, BO-GRNN models the objective function as a Gaussian process (GP) based on observed samples, utilizing an acquisition function to sample around the best objective function values and high variance regions. By evaluating these samples and updating the GP iteratively, the optimal hyperparameters are identified, ultimately improving the training efficiency and localization performance of the GRNN. Simulation experiments and error analysis validate this framework’s effectiveness. Further validation using SWellEx-96 experimental data demonstrates the superior localization performance of this framework, even with limited data snapshots. This framework estimates the range and depth of underwater sources within a 10 km range, achieving mean absolute errors of less than 0.15 km for range and 0.2 m for depth. Compared to GRNN, this framework demonstrates a 5 times improvement in training efficiency. These advantages make BO-GRNN an efficient, accurate, and robust solution for underwater source localization.

Collision dynamics between breather and lump-type localized waves in the (3+1)-dimensional shallow water wave equation

In this paper, the collision dynamics of breather and lump-type localized waves in the (3+1)-dimensional shallow water wave equation are investigated in detail. Firstly, the auto-Bäcklund transformation and the linear representation of the (3+1)-dimensional shallow water wave equation are derived in virtue of the truncated Painlevé expansion method, which provide convenience in solving the (3+1)-dimensional shallow water wave equation. Secondly, based on the linear representation and the principle of linear superposition, the rational solutions in the exponential and polynomial forms are constructed. Tuning the free parameters of the rational solutions, localized waves of various patterns are obtained such as breather, lump-type localized waves and their hybrid structure. The anomalous inelastic interaction phenomenons of breather and lump-type localized waves are exhibited. Thirdly, combining the large-time behaviors of solution with the velocity relationship of localized waves, the dynamical properties and the classification of localized wave solutions are discussed in detail. Finally, we discuss the bound state of breather and lump-type localized waves under the velocity resonance condition, three different types of lump-breather molecules are displayed. The obtained results further enrich the structures and dynamical behaviors of localized waves. It is expected that the interaction phenomena taking place in the (3+1)-dimensional shallow water wave equation will be helpful in predicting or controlling some related shallow water wave phenomena.

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Preferential fatigue cracking at basal twist grain boundary (BTGB) in bimodal Ti-5Al-4V alloy: Dislocation activities and crack initiation

In recent years, (0001) twist grain boundaries (BTGBs) located in primary α grain clusters were identified as fatigue crack nucleation sites in different Ti alloys. In the present study, crack initiation was investigated in a bimodal Ti-5Al-4V alloy subjected to low-cycle fatigue and dwell-fatigue loadings at room temperature. The low fraction of primary α grains was not associated with a lack of sensitivity to BTGB cracking. Transmission electron microscopy and electron back-scattered diffraction were used to characterize BTGBs in the initial microstructure. The fatigue mechanisms were then analyzed with a focus on dislocation activity. αp grains adjacent to cracked BTGBs contained a high dislocation density. It was primarily composed of planar slip bands of <a> dislocations. In addition, <c+a> dislocations were noticed in the vicinity of cracked BTGBs. They supposedly pertain to crack tip plasticity during growth, and no evidence of a role of an incoming slip event in crack nucleation was obtained. Also, basal slip bands extending across adjacent grains were found to emerge from BTGBs. This feature provides an easier path for crack extension when growth along the grain boundary becomes difficult owing to a deviation from the basal plane. Atom probe tomography analyses evidenced V and Fe segregation at a grain boundary with a significant deviation from the BTGB configuration. This suggests a possible contribution of local solute segregation to the high cracking resistance of general αp / αp grain boundaries. This work provides new insights into the mechanisms involved in cracking of BTGB in Ti alloys subjected to cyclic loadings.

Dispersion of a non-uniform solute slug in pulsatile viscoelastic fluid flow

Solute transport in pulsatile viscoelastic fluid flow is relevant in nutrient transport and drug delivery in blood flow. Previous studies have extensively analyzed the effect of the shear-thinning nature of the blood but neglected the elastic property. The present study aims to fill this lacuna by analyzing the role of blood viscoelasticity on solute transport. To accomplish this, we study solute transport for a non-uniformly distributed solute slug in the pulsatile flow of an Oldroyd-B fluid through a tube in the presence of wall absorption. We employ Gill's procedure and Aris' method of moments to compute the transport coefficients Km(t) (m≤4). We also numerically solve the species transport equation using a finite difference scheme to directly determine local solute concentration C(t,z,r). Consistent results for a non-viscoelastic fluid predict a negative convection coefficient K1 and a positive effective diffusivity K2 for realistic values of the parameters. However, the present analysis predicts positive K1 and negative K2 for small tubes due to flow reversal caused by the fluid elasticity. For high Λ1, the amplitude of oscillation for K1 and K2 exhibits scaling K1∼Λ11.5 and K2∼Λ12 indicating an enhancement in the dispersion due to fluid elasticity, where Λ1 is the dimensionless relaxation time. The analysis of the skewness and (excess) kurtosis coefficients reveals inconsistency in previous studies on Newtonian fluids. Thus, we present consistent results not only for a viscoelastic fluid but also for a Newtonian fluid subjected to a pulsatile pressure gradient. In addition, the solute dispersion is significantly influenced by the non-uniformity of a solute slug. As the radius of a slug increases, solute dispersion reduces in short and moderate times; however, at large times, it is independent of the radius of a slug.

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution.

Identification of seizure sources in the brain is of paramount importance, particularly for drug-resistant epilepsy patients who may require surgical operation. Interictal epileptiform discharges (IEDs), which may or may not be frequent, are known to originate from seizure networks. Delayed responses (DRs) to brain electrical stimulation have been recently discovered. If DRs and IEDs come from the same location and the DRs can be accurately localized, there will be a significant step in identification of the seizure sources. The solution to this important question has been investigated in this paper. For this, we have exploited the morphology of these spike-type events, as well as the variability in their temporal locations, to develop new constraints for an adaptive Bayesian beamformer that outperforms the conventional and recently proposed beamformers even for identifying correlated sources. This beamformer is applied to an array (a.k.a mat) of cortical EEG electrodes. The developed approach has been tested on 300 data segments from five epileptic patients included in this study, which clinically represent a large population of candidates for surgical treatment. As the significant outcome of applying this beamformer, it is very likely (if not certain) that for an epileptic subject, the IEDs and DRs originate from the same location in the brain. This paves the way for a quick identification of the source(s) of seizure in the brain.

Angles-Only Cooperative Local Catalog Maintenance of Close-Proximity Satellite Systems

In space mission sets like on-orbit servicing and manufacturing, agents in close proximity may operate too close to yield resolved localization solutions to operators. This requires that the system maintain a catalog of its local neighborhood; however, this imposes a large burden on each agent to update its catalog. This paper considers the case of multiple satellites each maintaining their catalog. Specifically, this paper considers the case of numerous noncooperative objects and a collection of agents operating in the same local environment. The goal of each agent is to maintain their catalog of all bodies (objects and agents) within this neighborhood through onboard angles-only measurements and cooperative communication with the other agents. A central supervisor selects the target body for each agent, a local controller tracks the selected target body for each agent, and a local estimator coalesces both an agent’s measurements and state estimates provided by neighboring agents within the communication graph. Numerical results are provided to demonstrate the supervisor’s ability to select an appropriate target subject to an uncertainty threshold, the controller’s ability to track the selected target, and the estimator’s ability to maintain an accurate and precise estimate of each of the bodies in the local environment.

MMHCA: Multi-feature representations based on multi-scale hierarchical contextual aggregation for UAV-view geo-localization

In global navigation satellite system denial environment, cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle (UAV) systems. The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms, such as UAV-view and satellite-view images. However, images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform, view, and timing. The existing methods predominantly extract features by segmenting feature maps, which overlook the holistic semantic distribution and structural information of objects, resulting in loss of image information. To address these challenges, dilated neighborhood attention Transformer is employed as the feature extraction backbone, and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation (MMHCA) is proposed. In the proposed MMHCA method, the multi-scale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels, establishing feature associations of contextual information with global and local information in the image. Subsequently, the multi-feature representations method is utilized to obtain rich discriminative feature information, bolstering the robustness of model in scenarios characterized by positional shifts, varying distances, and scale ambiguities. Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques, showing outstanding results in UAV localization and navigation.

Issues of LNG producing companies entering the eastern markets and ways to overcome them

This article is devoted to identifying the features of the еastern liquefied natural gas (hereinafter referred to as LNG) markets and new opportunities for entering them. The purpose of this article is to study the problems of LNG producing companies entering the еastern markets and ways to overcome them. The work analyses the current state of the еastern LNG markets and their development prospects, existing barriers, including those caused by the conflict in the Middle East, and possible consequences of the escalation of the conflict. The authors consider the issues of developing localised infrastructure and technological solutions that increase the efficiency of supply chains, meet specific market needs and provide a competitive advantage in the еastern markets. Logframe and comparative analysis, systems approach, and modelling have been used in the research process. As a result, the study has identified a number of challenges for LNG producers, primarily related to geopolitical complexities, regulatory environment, and market development dynamics, to innovations, ecological norms, and understanding regional tensions that may impact supply chains and trade agreements. The findings of the article can be used when domestic companies develop strategies to enter the еastern LNG markets with existing contracts or expand them.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

Cost-Effective Localization of Mobile Robots Using Ultrasound Beacons and Differential Time-of-Flight Measurement

This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system’s ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems.

Addressing localized thermal comfort needs of the human body through advanced personal thermal management garments design and evaluation

Personal thermal management clothing has garnered significant attention in recent years for offering innovative solutions to address extreme environments. By examining the “human-clothing-environment” system, we have analyzed the contribution and developmental trajectory of personal thermal management clothing, considering the diverse human needs and application environments. Local variations in human physiology necessitate localized thermal comfort solutions provided by personal thermal management clothing. Despite advancements, many Personal thermal management garments still encounter challenges such as bulky size, heavy weight, intricate donning process, subpar performance, limited temperature control, and low durability. Furthermore, aspects like comfort, ergonomics, and aesthetics, tailored to specific usage scenarios, have often been overlooked. Enhancing the standardized design and evaluation processes for personal thermal management clothing items is crucial to better cater to individual preferences and usage demands, offering recommendations for development to align with individual requirements, enhance effectiveness, and promote energy conservation.