Adjustable Parameters Research Articles

Modified fractional power allocation for downlink cell-free massive MIMO systems

Cell-free massive multiple-input multiple-output (mMIMO) significantly improves the spectral efficiency (SE) performance compared to conventional centralized mMIMO through its distributed antenna architecture. Fractional power allocation (FPA) algorithm is widely used for scalable power control with good performance in downlink (DL) of cell-free mMIMO. In this paper, we propose modified FPA (MFPA) and generalized FPA (GFPA) strategies for centralized and distributed precoding in the DL of cell-free networks, respectively. For the former, we abandon the traditional normalization of precoding vectors and introduce three adjustment parameters, which can dynamically adjust the power allocation of the DL according to the actual channel conditions. Regarding the latter, the GFPA strategy finds effective channel factors suitable for various distributed precoding schemes and correlates them with the power allocation coefficients of each user equipmen t(UE), enabling power allocation to adapt to multiple precoding schemes. Analysis and simulation results demonstrate that, under the MFPA strategy, UEs with poorer channel conditions can achieve higher SE, but at the expense of other UEs with better channel conditions. Under the GFPA strategy, UEs with better channel conditions experience significant SE improvements without sacrificing UEs performance with poorer channel conditions.

Finite-time annular domain [formula omitted] filtering for mean-field stochastic systems with Wiener and Poisson noises

This paper investigates the finite-time annular domain H2/H∞ filtering for mean-field stochastic systems with external disturbance, Wiener and Poisson noises. Initially, the novel concept of finite-time annular domain (FTAD) H2/H∞ filtering is introduced, which ensures the system’s mean square finite-time annular domain boundedness (MSFTADB) and minimizes the H2 and H∞ filtering performance indices. Next, using the Itô-Levy formula and the reverse differential Gronwall inequality, a less conservative sufficient condition for the existence of finite-time annular domain H2/H∞ filter is derived. Furthermore, a new algorithm is devised to establish the relationship between the ranges of adjustable parameters and the minimum value of H2 performance index. Finally, a practical example is provided to verify the effectiveness of the proposed method.

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25% to 99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.

Pediatric endocardial temporary pacemaker implantation: Clinical characteristics and outcomes from a Chinese National Regional Health Center

AbstractEndocardial temporary pacemakers (TP) are widely used in the treatment of cardiovascular diseases in adults, but their application and clinical experience in the treatment of pediatric cardiovascular cases are relatively limited. This study aims to share the experience of using endocardial TP at a pediatric medical center in China. The study included patients who received their first endocardial TP implantation in the Department of Cardiology at Children's Hospital of Chongqing Medical University, China, from 2016 to 2022. A retrospective analysis of their clinical data was conducted. Our study involved 45 children who underwent endocardial TP implantation. Among these, 25 children (55.56%) converted to sinus rhythm after pacemaker parameter adjustment and the pacemaker was successfully removed; 13 children (28.89%) did not recover sinus rhythm and ultimately required a permanent pacemaker; in 4 cases, the family refused permanent pacemaker implantation; in 1 case, the treatment was abandoned by the family; and 2 children died during hospitalization. Notably, among the 20 children with fulminant myocarditis (FM), 16 children (80.00%) converted to sinus rhythm after pacemaker parameter adjustment and the pacemaker was successfully removed; 3 children (15.00%) ultimately received a permanent pacemaker; and 1 child died during hospitalization. Endocardial TP have shown remarkable efficacy in the treatment of critical heart disease, especially FM.

Quantitative Eliashberg theory of the superconductivity of thin films.

A quantitative theory of the superconductivity of materials confined at the nanoscale in parameter-free agreement with experimental data has been missing so far.
We present a generalization, in the Eliashberg framework, of a BCS theory of superconductivity in good metals which are confined along one of the three spatial directions, such as thin films. In this formulation of the Eliashberg equations the approximation of taking the normal density of states (DOS) as its value at the Fermi level has been removed. By numerically solving these new Eliashberg-type equations, we find the dependence of the superconducting critical temperature $T_{c}$ on the confinement size $L$, in quantitative agreement with experimental data of Pb and Al thin films with no adjustable parameters. This quantitative agreement provides an indirect confirmation that, upon increasing the confinement, a crossover from a spherical-like Fermi surface, which contains two growing hole pockets caused by the confinement, to a strongly deformed Fermi surface, occurs. This topology of the Fermi sea is implemented in the new Eliashberg-type equations to reproduce the experimentally observed maximum in the critical superconducting temperature vs film thickness of ultra-thin Pb films.

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

ANN-Assisted Beampattern Optimization of Semi-Coprime Array for Beam-Steering Applications

In this paper, an artificial neural network (ANN) has been proposed to estimate the required values of the adjustable parameters of a Semi-Coprime array with staggered steering (SCASS), which was proposed recently. By adjusting the amount of staggering and the sidelobe attenuation (SLA) factor of Chebyshev weights, SCASS can promise a quite small half-power beamwidth (HPBW) and a high peak-to-side-lobe ratio (PLSR), even when the beam is steered away from broadside direction. However, HPBW and PSLR cannot be improved simultaneously. There is always a trade-off between the two performance metrics. Therefore, in this paper, a mechanism has been introduced to minimize HPBW for a desired PSLR. The proposed ANN takes the array of architectural parameters, the required steering angle, and the desired performance metric, i.e., PSLR, as input and suggests the values of the adjustable parameters, which can promise the minimum HPBW for the desired PSLR and steering angle. To train the ANN, we have developed a dataset in Matlab by calculating HPBW and PSLR from the beampattern generated for a large number of combinations of all the variable parameters. It has been shown in this work that the trained ANN can suggest the optimum values of the adjustable parameters that promise the minimum HPBW for the given steering angle, PSLR, and array architectural parameters. The trained ANN can suggest the required adjustable parameters for the desired performance with mean absolute error within just 0.83%.

Optimizing non-toxic (CH3NH3)3Bi2I9 perovskite solar cells by SCAPS-1D

Abstract Low-dimensional bismuth-based perovskite solar cells (PSCs) have demonstrated some benefits over lead-based PSCs for nontoxicity and remarkable stability. These two factors are now the primary concerns in the photovoltaic community. The power conversion efficiency (PCE) of PSCs using the lead Pb-free chemical methylammonium bismuth iodide (CH3NH3)3Bi2I9 is severely limited due to the poor quality of the photoactive material. Herein, the photovoltaic performance of the (CH3NH3)3Bi2I9 PSCs was investigated. The objective of this study was to investigate the intrinsic impacts of (CH3NH3)3Bi2I9 perovskite by using SCAPS-1D (Solar Cell Capacitance Simulator) to simulate the PSCs and the adjustment of relevant physical parameters to closely match experimental results. Moreover, the cells were optimized based on (CH3NH3)3Bi2I9 film thickness, total defect density of (CH3NH3)3Bi2I9, optical bandgap, and interfacial defects. By conducting a comprehensive analysis of the current-voltage (J-V) plots and quantum efficiency feature, the best values of perovskite thickness, bandgap, and defect density were determined to be 100 nm, 1.6 eV, and 1014 cm-3, respectively. Furthermore, defects in the interfaces between the electron-transporting layer (ETL)/(CH3NH3)3Bi2I9 and hole transport layer (HTL)/(CH3NH3)3Bi2I9 were considered, and their influence on performance was also investigated. Accordingly, the optimized cell has realized a record PCE of 9.043% and a high quantum efficiency exceeding 60%, which is comparable to those of some Pb-free perovskite analogues. The operational temperature calucations showed that all parameters remain relatively constant with increasing temperature. Therefore, the results imply that the simulated Pb-free PSCs can be stable in a thermal environment. The proposed structural layout and optimization approach can encourage more study and actual applications for Pb-free organometallic perovskite solar cells.

A mobile electrical stimulator for therapeutic modulation of the vestibular system — design, safety, and functionality

Low-intensity noisy galvanic vestibular stimulation (nGVS) is a promising non-invasive treatment for enhancing vestibular perceptual performance and postural control in patients with chronic vestibular hypofunction. However, this approach has so far been studied mainly under laboratory conditions. Evidence indicates that continuous application of nGVS in daily life is necessary for it to be effective. To address this need, we have developed a mobile nGVS stimulator and conducted a series of pilot studies to evaluate its safety, tolerability, functionality, and therapeutic effects. The device is a lightweight, compact, and portable AC stimulator featuring a user-friendly interface for the individualized adjustment of nGVS parameters. It includes an integrated motion sensor that automatically activates stimulation during body movement and deactivates it during inactivity, optimizing its practical use in real-world settings. The stimulator adheres to strict safety standards and, in initial long-term use, has exhibited only mild side effects (e.g., skin irritation and headaches), likely attributable to the current electrode placement, which requires further optimization. As expected, the device consistently elicits known vestibular sensorimotor reflex responses in healthy individuals. Importantly, further pilot studies in healthy participants demonstrate that the device can reliably replicate known facilitating effects on vestibular perception and postural control. Together, these findings suggest that this mobile stimulation device can facilitate the translation of nGVS into therapeutic everyday use.

A local convergent ecological inference algorithm for RxC tables

ABSTRACT Over the years, a number of methods have been proposed to forecast the unknown inner-cell values of a set of related RxC contingency tables when only their margins are known. This is a classical problem that emerges in many areas, from economics to quantitative history, being particularly ubiquitous when dealing with electoral data in sociology and political science. However, the two current major algorithms to solve this problem, based on Bayesian statistics and iterative linear programming depend on adjustable (hyper-)parameters and do not yield a unique solution: their estimates tend to fluctuate (when convergence is reached) around a stationary distribution. Within the linear programming framework, this paper proposes a new algorithm (lclphom) that always converges to a unique solution, having no adjustable parameters. This characteristic makes it easy to use and robust to claims of hacking. Furthermore, after assessing lclphom with real and simulated data, lclphom is found to yield estimates of (almost) similar accuracy to the current major solutions, being more preferable to the other lphom-family algorithms the more heterogeneous the row-fraction distributions of the tables are. Interested practitioners can easily use this new algorithm as it has been programmed in the R-package lphom.

Total, dietary, and supplemental calcium intake and risk of all-cause, cardiovascular, and cancer mortality among U.S. adults: a prospective cohort study from the National Health and Nutrition Examination Survey.

The study aimed to investigate the association of total, dietary, or supplemental calcium intake with all-cause, cardiovascular, and cancer mortality in American adults. This prospective cohort study used the National Health and Nutrition Examination Survey from 2005 to 2018. Participants were categorized into tertiles based on calcium intake. Risks of all-cause, cancer, or cardiovascular mortality and the dose-response relationship were estimated using weighted Cox proportional hazard regression and restricted cubic splines, with adjustments for demographic characteristics, comorbidities, laboratory parameters, and dietary data. In total, 6172 participants were included (median age: 61 years), and 869 had died (CVD:217, cancer:224) during a median follow-up of 81 months. After adjusting for confounders, higher total calcium(≥ 1660mg/d) [HR, 95%CI: 0.867 (0.865-0.869)], dietary calcium(≥ 1075mg/d) [HR, 95%CI: 0.711 (0.709-0.713)], and supplemental calcium (≥ 600mg/d) [HR, 95%CI: 0.786 (0.784-0.787)] intake groups were associated with lower all-cause mortality risk compared to the lowest intake group. Similar beneficial associations were found for cardiovascular, cancer mortality, and across subgroups of various ages, genders, races and body mass indexes. In the dose-response analysis, a 'J-shaped' nonlinear relationship was observed between calcium intake and the risk of all-cause, cardiovascular, and cancer mortality. Higher levels of total, dietary, or supplemental calcium were associated with lower all-cause, cardiovascular, or cancer mortality. However, a nonlinear association between calcium intake and mortality suggested that excessive calcium intake beyond a certain threshold may increase mortality risk.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Nondispersive ultraviolet monitoring of pulsed H2S gas delivery

This article describes time-resolved optical measurements of H2S partial pressure and mass flow in a pulsed gas delivery system approximating injection conditions encountered during atomic layer deposition. A high-speed nondispersive ultraviolet (NDUV) gas analyzer design is employed for in-line H2S detection in a gas delivery line with flowing carrier gas. An in-place analyzer calibration performed in a reference cell yields an H2S detection limit of ≈1.4 Pa (at 22 °C) at a sampling rate of 1 kHz. Flow measurements performed on the delivery line are used to evaluate the effects of adjustable delivery parameters on the time-dependent injection system output. Short pulse widths exhibit partial pressure transients attributed to flow development within the different volumes of the delivery system. After ≈1.0 s of injection, steady-state flow is established across flow elements. A partial pressure of H2S in the delivery line is found to vary linearly with upstream H2S pressure, consistent with choked flow. A stronger scaling of partial pressure is evident when the flow coefficient of the downstream metering valve is adjusted. Estimated steady-state H2S flow rates in the range of 0.05–0.21 mg/s are observed within a limited range of valve flow coefficients. However, further increases in the flow coefficient do not result in increased flow, likely due to conductance limitations in downstream flow system components. The utility of NDUV absorption measurements for high-pressure pulsed gas delivery systems is discussed.

NIMG-37. “SYNTHETIC” PERFUSION MRI WITH ADJUSTABLE ACQUISITION PARAMETERS IN BRAIN TUMORS USING DYNAMIC SPIN-AND-GRADIENT-ECHO ECHOPLANAR IMAGING

Abstract INTRODUCTION Dynamic susceptibility contrast (DSC) perfusion MRI can assess normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) in brain tumors, which can aid in differential diagnosis, molecular profiling, and identification of disease progression. However, CBV-optimized and PSR-optimized protocols differ in terms of acquisition parameters. Additionally, DSC dependency on acquisition parameters limits the universalizability of diagnostic cutoffs for nrCBV and PSR across institutions and scanners. This study aims to generate “synthetic” DSC datasets with adjustable synthetic acquisition parameters using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) to: 1) simultaneously generate nrCBV and PSR with optimal acquisition parameters, 2) compare DSC datasets with heterogeneous external cohorts. METHODS Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection. Multiple synthetic DSC datasets with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo gradient-echo (GE) data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. For each patient, synthetic protocols were set to match guideline-compliant CBV-optimized protocols, PSR-optimized protocols, and protocols used in external cohorts from the literature, for comparison. RESULTS Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection. Conversely, suboptimal PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman’s ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. CONCLUSIONS Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with a single injection, facilitating use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion parameters across heterogeneous multicenter datasets.

Thermophysical Properties of Silicon Oxide Nanoparticles in Water and Ethylene Glycol–Water Dispersions

Measurements of transmission as well as thermophysical properties have been carried out for different concentrations of SiO2 nanoparticles (0, 1, 2, 5, 10, and 20 wt.%) in pure water (W) and ethylene glycol–water mixture (EG/W) in a weight ratio of 25/75, from 298 to 323 K at 100 kPa. In particular, the density, specific heat capacity, and thermal diffusivity are measured by a density meter, differential scanning calorimetry, and the laser flash method. In the case of 20 wt.% SiO2, transmission in the visible range is reduced by 9.3%. Simultaneously, the density rises linearly to 12.3% (in W) and 11.3% (in EG/W). The specific heat capacity decreases to 15.9% (in W) and 17.3% (in EG/W), while the thermal diffusivity rises to 16.4% (in W) and 20.4% (in EG/W). While the density measurements are in very good agreement with the literature, the measured values of the specific heat capacity deviate more than 5%, especially for concentrations below 5 wt.% SiO2. Moreover, it is shown that the thermal conductivity increases less for fluids in small gaps compared to other authors, which could be due to the suppression of the Brownian motion. Based on the measurement results, temperature- and concentration-dependent correlations for the investigated thermophysical properties are developed using two adjustable parameters. In general, these correlations show deviations of less than 4% from the experimental results, which will help to fill the gaps in the variation of experimental results due to size, SiO2 nanofluid production, and different measurement devices, and thus optimize solar thermal applications with SiO2 nanofluid.

Adaptive variable gain linear active disturbance rejection control parameter optimization in magnetic levitation

The magnetic levitation ball system is characterized by strong nonlinearity, multiple disturbances, and intrinsic instability, which has long been a thorny problem in the field of control engineering. The magnetic levitation system can be continuously and stably levitated, even in a complex environment. In this thesis, the model of the magnetic levitation ball system is firstly constructed according to the kinetic theory and simplified according to the actual situation. After that, a linear active disturbance rejection control (LADRC) controller for the magnetic levitation ball system is designed. Considering that there are many parameters in the LADRC controller and it is difficult to obtain the optimal control effect by manual tuning, an adaptive variable gain LADRC algorithm is proposed to optimize the control strategy of LADRC parameters. An adaptive iterative algorithm is used to adaptively adjust the bandwidth of the linear extended state observer (LESO) in LADRC, and the convergence of the algorithm is demonstrated by using the Lyapunov function. To solve the problem that the proportional and differential gains of the LADRC controller can only be adjusted unidirectionally and simultaneously, this paper proposes an error variable gain algorithm. The aim is to realize the non-unidirectional and more detailed adjustment of the controller parameters, and it is rigorously analyzed and demonstrated through simulation and experiment. The results show that the proposed algorithm is better than proportional–integral–derivative (PID), sliding mode control (SMC), and LADRC in terms of dynamic performance, steady-state performance, and anti-interference.

Research on lane detection algorithms based on GTNs

Abstract. Lane detection technology plays an important role in the lane departure warning and adaptive cruise control functions of autonomous vehicle systems. Traditional lane line detection includes experimental steps such as preyreatment, color space conversion, feature extraction, and lane line tracking, but there are still some problems such as recognition accuracy in complex environments and parameter adjustment dependency. To overcome these limitations, this study uses a novel method that can directly predict the parameters of the lane shape model, thus avoiding complex post-processing steps. This research method uses the network structure based on Graph Transformer Networks (GTNS) to capture richer structural information and contextual relationships, thereby improving the accuracy of detection. In terms of feature extraction, Graph Transformer Networks is used to accurately capture the structural information of the lane through the attention mechanism. At the same time, the results in the later stage show that our method has better advantages in accuracy and speed. In short, this optimization scheme not only improves the detection speed but also ensures a certain degree of accuracy. The method will perform better in the future after further optimization such as multi-sensor fusion and real-time optimization.

BLE Bluetooth remote-controlled car based on ESP32

Abstract. With the rapid advancement of information technology, Bluetooth technology has been widely used in the field of short-range wireless communication, playing an important role in information exchange. As one of the main technologies for short-range wireless communication, Bluetooth technology has broad application prospects, especially in robotic applications such as remote-controlled cars. This paper provides a detailed introduction to the design and implementation process of a Bluetooth remote-controlled car system based on the ESP32. It covers the classic Bluetooth and Bluetooth Low Energy (BLE) functionalities of the ESP32, the hardware design and assembly of the car (including the DC motor drive module, Bluetooth module, and speed measurement module), and methods for establishing a connection between the ESP32 and Bluetooth module, data transmission, and remote control of the car's movement. The paper also evaluates the system's performance through experiments, discusses the application and parameter adjustment of the PID control algorithm in enhancing speed control, and finally summarizes the research findings and suggests directions for future work.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

Ion-Specific Surface Tension of Aqueous Electrolyte Solutions: Analytical Insights from a Restricted Primitive Model.

The ion-specific surface tension of aqueous electrolyte solutions is of fundamental importance in physical chemistry, solution chemistry, electrochemistry, and biochemistry, yet it remains a challenge to be qualitatively predicted. In this work, an analytical theory of ion-specific surface tension is developed. By modeling the solution to a restricted primitive model (RPM), the surface tension increment of the electrolyte solution reduces to the surface tension of the RPM, which is further determined analytically by using the integral equation theory and the morphological thermodynamics theory. According to our formula, the surface tension increment of the electrolyte solution consists of a dominant and positive hard sphere contribution, which depends on the salt concentration, and a secondary electrostatic contribution, which depends on the inverse Debye length and the salt concentration. Our theory is applied to 1:1, 2:2, 1:2, 2:1, 3:1, and 3:2 electrolyte solutions with typical salt concentrations up to several mol/L and compared with experimental data. Without introducing any adjustable parameters, our theory leads to a good prediction of the surface tension increment of more than 50 kinds of aqueous solutions. Such a good agreement demonstrates the great potential of our theory for a fundamental understanding of specific ion effects in a variety of electrolyte solutions.