Uniform Method Research Articles

Uniform electric-field optimal design method using machine learning

The demand for uniform electric fields (UEFs) in engineering is very high, particularly in high-voltage devices. The existing methods encounter limitations in terms of optimization region and universality. Herein, we propose a method for designing UEFs based on finite element calculations of electromagnetic fields and machine learning. First, the electric-field distribution of the plate-to-plate electrode structure determined using three electrode-shape parameters (ESPs) is calculated using finite element software and is drawn. Thereafter, a dataset of 2000 images is created with different electric-field strength distributions using various ESPs. Net, we employ image-processing techniques to extract nine statistical features from the gray-level information in the images. Models are trained through machine learning to predict ESPs based on the gray-level features (GLFs). Finally, the electric-field strength distribution image of the expected ideal uniform field is artificially selected. In addition, the ESPs from which the uniform electric-field is produced are predicted by the models. The proposed method provides an accurate solution for optimizing the design of a uniform electric-field and a new approach for solving inverse problems of electric-field. This involves drawing the required electric-field strength distribution image for high-voltage engineering and obtaining the required ESPs.

A Catalog of Pulsar X-Ray Filaments

Abstract We present the first Chandra X-ray Observatory (CXO) catalog of “pulsar X-ray filaments,” or “misaligned outflows.” These are linear, synchrotron-radiating features powered by ultrarelativistic electrons and positrons that escape from bow shock pulsars. The filaments are misaligned with the (large) pulsar velocity, distinguishing them from the pulsar wind nebula (PWN) trail, which is also often visible in CXO ACIS images. Spectral fits and morphological properties are extracted for five secure filaments and three candidates using a uniform method. We present a search of archival CXO data for linear diffuse features; the known examples are recovered and a few additional weak candidates are identified. We also report on a snapshot CXO ACIS survey of pulsars with properties similar to the filament producers, finding no new filaments but some diffuse emission, including one PWN trail. Finally, we provide an updated model for the pulsar properties required to create filaments in light of these new observations.

Uniform multiple laminates interpolation model and design method for double–double laminates based on multi-material topology optimization

Double–Double (DD) laminates, incorporating a repetition of sub-plies featuring two groups of balanced angles, offer broad design flexibility together with the ease of design and manufacturing. In this work, a novel optimization design method is proposed for DD composite laminates based on multi-material topology optimization. First, the uniform multiple laminates interpolation (UMLI) model is proposed to describe the certainty of the stacking direction in multi-layer composite structures, inspired by the interpolation model in multi-material topology optimization. Specifically, the stiffness matrices of all alternative angle combinations of laminates are interpolated to form virtual laminates. The UMLI model eliminates the need for adding interlayer constraints during the optimization process. Then, the optimization problem is defined to minimize the compliance of the composite structures and is solved using the gradient-based optimization algorithm. Finally, the proposed method is applied to the design of the composite stiffened panel, the composite Unmanned Aerial Vehicle (UAV) wing, and the rear fuselage. The results demonstrate that the UMLI model and proposed optimization method have considerable potential in the angle optimization design of multi-layer structures.

A Standardized Mouse Model for Wound Infection with Pseudomonas aeruginosa

Pseudomonas aeruginosa is a highly drug-resistant pathogen known to impair wound healing and provoke inflammatory responses, potentially leading to immune dysregulation. This study aimed to systematically investigate the immune response mechanisms mediated by cytokines following P. aeruginosa infection through the development of a standardized wound model. Kunming mice were selected as experimental subjects and given 8 mm diameter lesions on their backs and inoculated with standard strains PAO1 and PA14. The key parameters assessed included changes in body weight, wound redness and swelling, bacterial dynamics, protein content in wound tissues, immune responses, and pathological alterations. The results demonstrated that pathogen invasion significantly inhibited wound healing, with healing rates in the infected groups (87.5 ± 6.3% and 77.1 ± 3.6%) being notably lower than those in the uninfected control group. P. aeruginosa persisted in the wounds for up to 12 days, with bacterial loads decreasing from 8 log to 2 log. Additionally, there was a marked reduction in the protein content of the wound tissue and an increase in the expression levels of inflammatory factors such as IL-1β and TNF-α. The thickness of granulation tissue and the number of neovessels were significantly lower compared to the uninfected control group. This study establishes a standardized paradigm for creating a mouse model of P. aeruginosa infection in wounds, emphasizing the importance of appropriate mouse strains, uniform wound preparation methods, and moderate inoculation doses for reliable and accurate experimental results. These elements will facilitate the assessment of changes across six key indicators post-infection, providing a foundational data set and technical support for future mechanistic investigations of P. aeruginosa infection and the development of targeted antimicrobial strategies.

A fuzzy control based strategy for adjusting band blade feed rate in constant power sawing

Abstract The variable cross-sectional shape of the blank reduces the efficiency of the sawing machine. Additionally, the wear of the band saw blade causes a mismatch between the feed rate and the load, further exacerbating the wear. This study studied a feed rate control strategy considering sawtooth wear to balance the relationship between cutting efficiency and band life. Initially, speed parameters for the cut-in and cut-out areas, along with acceleration and deceleration control algorithms, were established to ensure the smooth transition of the saw blade during the machining process. Additionally, a constant power sawing feed rate model that considers the influence of wear was developed, enabling dynamic adjustments to the feed rate based on the saw blade's wear state. This study also proposes a fuzzy control-based target current correction method, which dynamically adjusts the target current according to the current wear state of the saw blade, ensuring that sawing requirements were met under varying levels of wear. The accuracy of the sawing model and control strategy was verified through experiments. The results demonstrated that this strategy improved efficiency by 44% compared to the traditional uniform feed rate method. This study introduces a novel feed rate control strategy for sawing and offers an effective solution to the problem of saw tooth wear.

Odd-even mass differences of well and rigidly deformed nuclei in the rare earth region: A test of a newly proposed fit of average pairing matrix elements

Abstract We discuss a test of a recently proposed approach to determine average pairing matrix elements within a given interval of single-particle states (sp) around the Fermi level $\lambda$ as obtained in the so-called uniform gap method (UGM). It takes stock of the crucial role played by the averaged sp level density $\tilde{\rho}(e)$. These matrix elements are deduced within the UGM approach, from microscopically calculated $\tilde{\rho}(e)$ and gaps obtained from analytical formulae of a semi-classical nature. Two effects generally ignored in similar fits have been taken care of. They are: (a) the correction for a systematic bias in choosing to fit pairing gaps corresponding to equilibrium deformation solutions as discussed by M"{o}ller and Nix [Nucl. Phys. A 476, 1 (1992)] and (b) the correction for a systematic spurious enhancement of $\tilde{\rho}(e)$ for protons in the vicinity of $\lambda$, because of the local Slater approximation used for the treatment of the Coulomb exchange terms in most calculations (see e.g. [Phys. Rev C 84, 014310 (2011)]). This approach has been deemed to be very efficient upon performing Hartree-Fock + BCS (with seniority force and self-consistent blocking when dealing with odd nuclei) calculations of a large sample of well and rigidly deformed even-even rare-earth nuclei. The reproduction of their experimental moments of inertia has been found to be at least of the same quality as what has been obtained in a direct fit of these data [Phys. Rev C 99, 064306 (2019)]. We extend here the test of our approach to the reproduction, in the same region, of three-point odd-even mass differences centered on odd-$N$ or odd-$Z$ nuclei. The agreement with the data is again roughly of the same quality as what has been obtained in a direct fit, as performed in [Phys. Rev C 99, 064306 (2019)].

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: (1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long-distance measurement during point cloud measurement in dam areas. (2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. (3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. The experiment on the prototype arch dam shows that the proposed normal estimation method improves the classification accuracy of PointNet + + from 96.26 to 98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy is improved by 0.82%, 1.22%, and 0.22%, and the joint average intersection is improved by 0.0293, 0.0325, and 0.0104. The prototype arch dam experiment shows that our proposed method has a segmentation accuracy of 98.27%. Compared with 2-jets, Hough CNN, and iterative PCA, the overall accuracy has been improved by 0.82%, 1.22%, and 0.22%. This study provides a high-precision segmentation scheme for applications such as deformation detection of dam components based on point clouds.

A factor analysis based automatic optimization method of vibration sensor points for ship acoustic reconstruction

To address the challenge associated with optimizing the number and positioning of accelerometer points in ship acoustic reconstruction, factor analysis is applied to the automatic optimization of accelerometer position in this work according to the source independence of ship’s underwater radiated noise. Based on the numerical simulation method that calculates the vibration noise response under specific operation conditions, the minimum number and the optimal position of the accelerometers can be obtained through the factor analysis and multi-parameter comprehensive evaluation method. The results of acoustic reconfiguration and testing are compared using one test cabin as a validation object to verify the effectiveness of the proposed method. The results show that the automatic optimization algorithm can improve the reconstruction accuracy concerning the uniform distribution method. The error is reduced from 15.46% (1.5 dB) to 8.35% (0.8 dB), and the correlation coefficient is improved from 0.86 to 0.89. The semi-automatic optimization method is slightly lower in terms of errors and correlation coefficients compared to the automatic optimization algorithm. After increasing the number of monitoring positions for automatic optimization, the acoustic reconstruction accuracy is further improved, while the average error is reduced to 7.37% (0.7 dB) and the correlation coefficient is improved from 0.89 to 0.92. The experimental results are proximate to the conclusions of the simulation calculation. Even under the circumstance of multi-factor noise interference, the correlation coefficient of multi-position automatic optimization can still reach 0.8, in comparison with the uniform distribution method, the error is reduced from 32.49% (3.4 dB) to 25.94% (2.6 dB). The proposed method effectively captures the dynamic characteristics of complex structures and their impact on underwater radiated noise, and the integration of factor analysis can prominently enhance the computational efficiency, thereby providing technical support for engineering applications to the ship acoustic reconstruction.

Density gradient structure foams prepared by novel two-step foaming strategy: Performance, simulation and optimization

Functional gradient foam materials play a crucial role in meeting the diverse performance and functionality requirements of modern engineering. Density gradients enable the distribution of mechanical, dielectric, and optical properties within materials, exhibiting a gradient representation. However, creating density gradients within foams poses a significant challenge, especially for semi-crystalline polymers. The paper proposed a novel two-step foaming method for preparing polypropylene (PP) foams with density gradient structure (DGS). Initially, a pre-foaming process was conducted to prepare PP pre-foams with uniform structure (US) at low temperatures, followed by a secondary foaming process on partially saturated PP pre-foams to fabricate PP DGS foams. By adjusting the duration of partial saturation time, PP foams with various DGS can be achieved. Compared with the commonly used one-step foaming method and uniform foaming method in the literature, the DGS foams prepared by the two-step foaming method exhibit not only a significant enhancement in mechanical property but also achieve the lowest thermal conductivity, while maintaining comparable and outstanding sound insulation performance. Finally, a comprehensive evaluation model using COMSOL Multiphysics was developed for the DGS foams, providing insights for optimizing foam performance through process enhancements.

Meat Inspection Decisions Regarding Pig Carcasses Affected by Osteomyelitis at the Slaughterhouse: From Etiopathogenesis to Total Condemnation Criteria

Osteomyelitis is a significant cause of total carcass condemnation in pigs at the slaughterhouse. The decision for total condemnation of a pig carcass for osteomyelitis is often based on traditional perceptions of the risk of pyaemia, leading to controversy among Official Veterinarians (OV) in the industry. This review aims to provide a more comprehensive understanding of the etiopathogenesis of osteomyelitis in pigs, the microorganisms involved, and the risk factors. It also highlights the urgent need for a more uniform method to evaluate osteomyelitis cases, which could significantly reduce economic losses in the industry. Lesions originating from tail-biting, tail docking, castration, teeth resection, and raw management are described as risk factors for osteomyelitis. Osteomyelitis is caused by the entry of pathogens into the animal’s bloodstream through an open wound. Trueperella monocytogenes, Staphylococcus aureus, and Streptococcus spp. are the most described pathogens. At slaughter, OVs condemn carcasses with osteomyelitis due to pyaemia. Signs of acute disease are essential to identify pyaemia cases. In chronic cases, total carcass condemnation can be avoided depending on the number of lesions and vertebrae affected. A clear overall image of the problem would help authorities in various countries adopt a more homogenous approach.

Relation between Urine Cytological Findings and Renal Function in Patients with Kidney Stones in Taif, Saudi Arabia

Background and Objectives: Urine serves as a vital diagnostic fluid, and urine cytology analysis plays a crucial role in identifying urinary system illnesses such as bladder cancer and kidney stones. The Paris System for Reporting Urinary Cytology establishes a uniform method for diagnosing urinary tract cancer. This study aimed to provide valuable insights that can inform diagnostic strategies related to kidney stones and ultimately improve patient outcomes via the early detection of the cellular changes associated with kidney stones and their relation to kidney function tests. Materials and Methods: A comparative study was conducted and comprised two groups: group 1, consisting of 50 patients diagnosed with kidney stones, and group 2, comprising 50 patients diagnosed with other kidney diseases. Renal function tests and urinalysis (via the PAP staining of urine cellular deposits to detect nuclear changes) were performed, and the results were analyzed. Results: There was a statistically significant increase in urinary red blood cells, white blood cells, and nuclear reactive atypical changes in urinary sediments of kidney stone patients compared to the patients without stones, while there was a decrease in the estimated glomerular filtration rate (eGFR). eGFR showed a 96.7% specificity in detecting cases with nuclear reactive atypia. Conclusions: eGFR emerges as a reliable diagnostic marker for the comprehensive assessment of kidney stones, particularly when associated with nuclear atypia. The significant correlation between the indicators of chronic kidney disease, such as decreased eGFR, and the presence of kidney stones emphasizes the urgent need for efficient diagnostic practices.

Assisting denitrification and strengthening combustion by using ammonia/coal binary fuel gasification-combustion: Effects of injecting position and air distribution

The direct co-firing of ammonia (NH3) with coal is an effective approach for reducing carbon emissions from coal-fired plants. However, the limitations of NH3 combustion, such as its high ignition energy requirement and high NOx emissions, restrict its large-scale co-combustion in coal-fired units. In this study, a self-sustaining gasification–combustion experimental system was employed, and its denitrification performance and combustion strengthening capability for NH3/coal binary fuel under different NH3 injection positions and air distribution methods were evaluated. The results of co-firing 20% NH3 in the gasifier revealed that its operating temperature can be flexibly controlled within the optimal reaction temperature window of SNCR by adjusting the primary air ratio (λ1). Increasing λ1 was beneficial for NH3 and coal conversion and denitrification in the gasifier; at λ1 = 0.53, the conversion rate of NH3 and N2 reached 86.37% and 83.59%, respectively, with no NOx being detected at the gasifier outlet. Furthermore, increasing λ1 promoted the development of gasified char pore structure, thereby improving reactivity. After entering the down-fired combustor (DFC), increasing λ1 promoted NH3 and coal burnout and effectively controlled NOx emissions, reaching a level similar with that of pure coal under λ1 = 0.53. Co-firing 20% NH3 in the DFC also demonstrated that the introduction of inner secondary air was conducive to char burnout, however, increased NH3 slip. The outer secondary air promoted NH3 combustion, however, exacerbated NOx emissions and inhibited gasified char combustion. The uniform air distribution method balanced the combustion of NH3 and gasified char, and effectively controlled NOx emissions. When the same air distribution method was used, co-firing NH3 in the gasifier was more favourable for NOx control, whereas co-firing NH3 in the DFC benefited coal burnout. This study provides innovative ideas and serves as a reference for developing enhanced combustion and low NOx emission technologies for large-scale NH3 co-firing in coal-fired units.

Study on preparation and properties of GA/h-BN modified road petroleum asphalt based on ball milling method

ABSTRACT Graphene (GA), a two-dimensional nanomaterial, is noted for its exceptional mechanical, thermal and electrical properties. The integration of GA with road materials can potentially develop high-performance composite road materials. However, its application in road engineering is limited by complex preparation processes and high costs. This paper presents a GA/h-BN composite material developed through mechanical stripping using flake graphite and hexagonal boron nitride (h-BN). A mathematical model was created employing uniform design methods and optimised through numerical analysis to identify the optimal process parameters. Microscopic techniques, including X-ray diffraction and atomic force microscopy, confirmed that the GA/h-BN product consists of approximately three layers, forming a ‘graphene-hexagonal boron nitride-graphene’ nanolayer structure. The composite was then used to modify matrix asphalt, with performance tests determining the optimal dosage. The results indicated that adding 0.3% GA/h-BN significantly improved the asphalt's high- and low-temperature performance, fatigue resistance and cracking resistance. Moreover, the evaluation of the physical properties of matrix asphalt modified solely with h-BN revealed that, despite h-BN comprising 70% of the mass fraction in the GA/h-BN modifier, it exerted a minimal impact on the performance of the asphalt. This study introduces a novel source of raw materials for the application of GA in road engineering.

Evaluation method for uniformity of steel slag concrete aggregate based on improved YOLOv8

The use of steel slag as a replacement for natural aggregates in concrete effectively alleviates the issue of natural resource scarcity. The uniformity of aggregates in steel slag concrete directly affects its performance and durability. Therefore, precise segmentation of aggregates in sedimentation images is crucial for evaluating their uniformity. This paper not only proposes an improved instance segmentation model for accurate segmentation of steel slag concrete aggregates but also introduces a new method for assessing the uniformity of aggregates when steel slag replaces natural aggregates. This method quantitatively analyses the area, replacement rate, and spatial distribution of the aggregates and conducts a comprehensive quantitative analysis using the comprehensive uniformity evaluation coefficient, K. This approach allows for the evaluation of the uniformity of aggregates in sedimentation images of steel slag concrete. Experimental results show that the proposed improved model achieved an mAP50 of 98.3 %. Furthermore, the aggregate uniformity assessment method based on the improved model can objectively and accurately evaluate the uniformity of steel slag concrete aggregates.

Effectiveness and acceptability of different lifestyle interventions for women with polycystic ovary syndrome: protocol for a systematic review and network meta-analysis.

Women with polycystic ovary syndrome (PCOS) experience various metabolic, endocrine, reproductive and psychosocial manifestations. Lifestyle modification is crucial for the management of PCOS to reduce long-term complications. Nonetheless, the efficacy and acceptability of lifestyle interventions differs, and there are no uniform methods of clinical application. Hence, a systematic review and network meta-analysis (NMA) are needed to explore the efficacy and acceptability of lifestyle interventions to inform clinical practice. Ten databases (Cochrane Gynaecology and Fertility Specialised Register, Cochrane Register of Studies Online, PubMed, EMBASE (Excerpta Medica Database), PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Chinese National Knowledge Infrastructure, WanFang, VIP, and Sinomed) and four clinical trial registry platforms will be searched to identify literature published in English or Chinese reporting results of randomised clinical trials conducted to evaluate the effects of lifestyle interventions for women with PCOS. The reference lists of the included studies will be manually searched. Primary outcomes will include biochemical and clinical hyperandrogenism, recruitment and retention rates. Secondary outcomes will encompass menstrual regularity, ovulation, anthropometry and quality of life. Literature selection and extraction of data will be performed independently by at least two researchers. An NMA random-effects model will be implemented for amalgamating evidence. All treatments will be ranked based on the value of p. OpenBUGS will be used for Bayesian modelling, with output verifications generated in Stata and R. The quality of evidence supporting network estimates of major outcomes will also be appraised using the Grading of Recommendations Assessment, Development, and Evaluation framework. Ethical approval is not required for this review as no data will be collected from human participants. Results will be presented in a peer-reviewed publication. CRD42024499819.

An Effective and Controllable Platform with Ag-Stepped Nanocone Arrays Serving as Stable SERS Substrate

Facile, controllable and efficient SERS substrate preparation has always been the focus project in the SERS field, especially the solid-state nanoarray substrate. Herein, a simple and convenient method for preparing a series of homogeneous Ag-stepped nanocone arrays (Ag-SNCAs) using polymer nanocompression printing is presented. By analyzing the 1162[Formula: see text]cm[Formula: see text] characteristic peak of crystal violet (CV), the relative standard deviation (RSD) of the Raman intensity was only 4.71%. The detection limit of malachite green (MG) reached 0.001[Formula: see text]ppm, and it also has good SERS detection results for drug molecules such as melamine, methadone, morphine, methamphetamine and thiabendazole. This work provides a large area uniform and controllable preparation method of sharp cone nanostructure array, which has important application in plasmonic photocatalysis and deep light field regulations.

Distinct cellular uptake patterns of two anticancer unsymmetrical bisacridines and their metabolic transformation in tumor cells

Unsymmetrical bisacridines (UAs) represent a novel class of anticancer agents. Their high cytotoxicity towards multiple human cancer cell lines and inhibition of human tumor xenograft growth in nude mice signal their potential for cancer treatment. Therefore, the mechanism of their strong biological activity is broadly investigated. Here, we explore the efflux and metabolism of UAs, as both strongly contribute to the development of drug resistance in cancer cells. We tested two highly cytotoxic UAs, C-2028 and C-2045, as well as their glucuronic acid and glutathione conjugates in human cancer cell lines (HepG2 and LS174T). As a point of reference for cell-based systems, we examined the rate of UA metabolic conversion in cell-free systems. A multiple reaction monitoring (MRM)-mass spectrometry (MS) method was developed in the present study for analysis of UAs and their metabolic conversion in complex biological matrices. Individual analytes were identified by several features: their retention time, mass-to-charge ratio and unique fragmentation pattern. The rate of UA uptake and metabolic transformation was monitored for 24 h in cell extracts and cell culture medium. Both UAs were rapidly internalized by cells. However, C-2028 was gradually accumulated, while C-2045 was eventually released from cells during treatment. UAs demonstrated limited metabolic conversion in cells. The glucuronic acid conjugate was excreted, whereas the glutathione conjugate was deposited in cancer cells. Our results obtained from cell-free and cell-based systems, using a uniform MRM-MS method, will provide valuable insight into the mechanism of UA biological activity in diverse biological models.

Multi-Objective Combinatorial Optimization Algorithm Based on Asynchronous Advantage Actor–Critic and Graph Transformer Networks

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity.

A physics-driven sensor placement optimization methodology for temperature field reconstruction

Perceiving the global field from sparse sensors has been a grand challenge in the monitoring, analysis, and design of physical systems. In this context, sensor placement optimization is a crucial issue. Most existing works require large and sufficient data to construct data-based criteria, which are intractable in data-free scenarios without numerical and experimental data. To this end, we propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction using a physics-based criterion to optimize sensor locations. In our methodological framework, we firstly derive the theoretical upper and lower bounds of the reconstruction error under noise scenarios by analyzing the optimal solution, proving that error bounds correlate with the condition number determined by sensor locations. Furthermore, the condition number, as the physics-based criterion, is used to optimize sensor locations by the genetic algorithm. Finally, the best sensors are validated by reconstruction models, including non-invasive end-to-end models, non-invasive reduced-order models, and physics-informed models. Experimental results, both on a numerical and an application case, demonstrate that the PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude. Moreover, the PSPO method can achieve comparable reconstruction accuracy to the existing data-driven placement optimization methods.

EHR Interoperability Challenges Leveraging HL7 FHIR for Seamless Data Exchange in Healthcare

Electronic health records, often known as EHRs, have revolutionised the healthcare industry by making patient information more easily accessible and more efficient on the whole. There is still a huge obstacle to overcome in order to achieve seamless data transmission across different electronic health record systems. variances in data formats, terminologies, and standards often give rise to interoperability concerns. These variances impede the smooth flow of information across various systems and have the potential to cause problems. The Fast Healthcare Interoperability Resources (FHIR) standard, which is based on Health Level Seven (HL7), has emerged as a potentially useful option to overcome these difficulties. Through the provision of a uniform and standardized method for the sharing of healthcare data, FHIR is intended to facilitate the interchange of data in a manner that is both more efficient and trustworthy. The issues that are involved with electronic health record (EHR) interoperability are investigated in this study, as is the manner in which HL7 FHIR may ease the interchange of data in healthcare settings in a smooth manner. It starts out by providing an overview of the basic problems that exist in electronic health record (EHR) interoperability. These problems include data fragmentation, inconsistent data formats, and the absence of standardized protocols. HL7 FHIR is then presented in the article, along with a description of its fundamental principles. These concepts include the emphasis placed on interoperability, the simplicity of implementation, and the support for contemporary web technologies.