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Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.

Organic pollutants in water bodies pose a serious environmental problem, and photocatalytic technology is an efficient and environmentally friendly water treatment method. Titanium dioxide (TiO2) is a widely used photocatalyst, but it suffers from some drawbacks such as a narrow light response range, fast charge recombination, and low photocatalytic activity. To improve the photocatalytic performance of TiO2, this article reviews the preparation methods, performance evaluation, and applications of modified TiO2 photocatalysts. Firstly, the article introduces the effects of doping modification, semiconductor composite modification, and other modification methods on the structure and properties of TiO2 photocatalysts, as well as the common characterization techniques and activity test methods of photocatalysts. Secondly, the article discusses the effects and mechanisms of modified TiO2 photocatalysts on degrading dye, pesticide, and other organic pollutants in water bodies, as well as the influencing factors. Finally, the article summarizes the main achievements and advantages of modified TiO2 photocatalysts in degrading organic pollutants in water bodies, points out the existing problems and challenges, and prospects for the development direction and future of this field.

To improve the efficiency of the dim point target detection based on dynamic programming (DP), this paper proposes a multi-frame target detection method based on a DP ring network (DPRN). In the proposed method, first, the target trajectory is approximated using the piecewise linear function. The velocity space partition DP (VSP-DP) is used to accumulate the merit functions of a target on each piecewise linear trajectory segment to avoid the merit function diffusion in different velocity spaces. In addition, the velocity space matching DP (VSM-DP) is employed to realize the state transition of a target between adjacent piecewise linear trajectory segments. Then, the VSP-DP and VSM-DP are used to construct a DP network (DPN). Second, to suppress the merit function diffusion further, the sequential and reverse DPNs are connected in a head-to-tail manner to form a DPRN, and the merit function of the DPRN is obtained by averaging the merit functions of the sequential and reverse DPNs. Finally, the target trajectory is obtained by tracking the extreme points of the merit functions of the DPRN. The simulation and analysis results show that the proposed DPRN combines the advantages of high detection probability of the high-order DP and high execution efficiency of the first-order DP. The proposed DPRN is suitable for radars and infrared searching and tracking systems.

In this study, a potassium ferrate (K2FeO4)-modified biochar (Fe–BC) was prepared and characterized. Afterwards, Fe–BC was applied to activated periodate (PI) to degrade tetracycline (TC), an antibiotic widely used in animal farming. The degradation effects of different systems on TC were compared and the influencing factors were investigated. In addition, several reactive oxygen species (ROS) generated by the Fe–BC/PI system were identified, and TC degradation pathways were analyzed. Moreover, the reuse performance of Fe–BC was evaluated. The results exhibited that the Fe–BC/PI system could remove almost 100% of TC under optimal conditions of [BC] = 1.09 g/L, initial [PI] = 3.29 g/L, and initial [TC] = 20.3 mg/L. Cl−, HCO3−, NO3−, and humic acid inhibited TC degradation to varying degrees in the Fe–BC/PI system due to their quenching effects on ROS. TC was degraded into intermediates and even water and carbon dioxide by the synergistic effect of ROS generated and Fe on the BC surface. Fe–BC was reused four times, and the removal rate of TC was still maintained above 80%, indicating the stable nature of Fe–BC.

Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.

Selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) are reported to cause stress cardiomyopathy (SC). This study evaluated the association between SSRI/SNRI use and the occurrence of cardiomyopathy in the publicly available U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionate analysis and likelihood ratio tests were used to identify risk associated with SSRIs or SNRIs and the incidence of SC, using data from between from 2012 to 2022 acquired from the FAERS database. The study identified 132 individual case safety reports (ICSRs) of SC associated with SSRIs or SNRIs. Venlafaxine (48%) and fluoxetine (27%) were the most common antidepressants of the ICSRs. Approximately 80% of SC cases were reported in females, with individuals aged 45–65 years identified as a high-risk population. Both venlafaxine (ratio-scale information component [RSIC] 2.54, 95% CI 2.06–3.04) and fluoxetine (RSIC 3.20, 95% CI 2.31–4.47) were associated with SC, with likelihood ratio estimates of 3.55 (p = 0.02) for venlafaxine and 4.82 (p = 0.008) for fluoxetine. The median time to cardiomyopathy onset was 20 days, with hospitalization reported in 48.33% of patients. Venlafaxine and fluoxetine were associated with SC risk, particularly in middle-aged women. Caution should be exercised when using SSRIs or SNRIs combined with other serotonergic medications.

In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high- and low-frequency components, the corresponding decomposed components are not precise enough for the following infrared-visible fusion operations. This paper proposes a non-subsampled contourlet transform (NSCT) based decomposition method for image fusion, by which source images are decomposed to obtain corresponding high- and low-frequency sub-bands. Unlike MST, the obtained high-frequency sub-bands have different decomposition layers, and each layer contains different information. In order to obtain a more informative fused high-frequency component, maximum absolute value and pulse coupled neural network (PCNN) fusion rules are applied to different sub-bands of high-frequency components. Activity measures, such as phase congruency (PC), local measure of sharpness change (LSCM), and local signal strength (LSS), are designed to enhance the detailed features of fused low-frequency components. The fused high- and low-frequency components are integrated to form a fused image. The experiment results show that the fused images obtained by the proposed method achieve good performance in clarity, contrast, and image information entropy.

Antibiotic contamination in water has received significant attention in recent years for the reason that the residuals of antibiotics can promote the progression of antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs). It is difficult to treat antibiotics using conventional biological treatment methods. In order to investigate an efficient new method of treating antibiotics in water, in this study, microwave (MW) was employed in revitalizing peroxymonosulfate (PMS) to treat typical antibiotic tetracycline (TC). The Box–Behnken design (BBD) was applied to organize the experimental schemes. The response surface methodology (RSM) optimization was run to derive the best experimental conditions and validated using actual data. Moreover, the main mechanisms of PMS activation via MW were resolved. The results demonstrated that the relationship between TC removal rate and influencing factors was consistent with a quadratic model, where the P-value was less than 0.05, and the model was considered significant. The optimal condition resulting from the model optimization were power = 800 W, [PMS] = 0.4 mM, and pH = 6.0. Under such conditions, the actual removal of TC was 99.3%, very close to the predicted value of 99%. The quenching experiment confirmed that SO4•− and •OH were jointly responsible for TC removal.

The microphonic noise induced by the vibration from cryocoolers has been found to cause energy resolution degradation in vibration-sensitive instruments. In this paper, theoretical and experimental research on the vibration generation mechanism of an aerospace-grade coaxial pulse-tube cryocooler (CPTC) is presented. Accordingly, suggestions for suppressing the vibration of the pulse-tube cryocooler are provided. A vibration model for the Oxford-type dual-opposed linear compressor is established, and the mechanism of vibration induced by the compressor is theoretically analyzed. A numerical simulation indicates that deviations in the compressor’s inductance coefficient, electromagnetic force coefficient, and flexure spring stiffness coefficient significantly affect the axial vibration of the compressor. The theoretical and experimental studies show that the high-order harmonic vibrations of the compressor are determined by both the resonance of the flexure springs and the high-order harmonics of the driving power supply. Through experiments and simulations, it is revealed that the dynamic gas pressure only induces vibration axially at the cold tip, while the radial vibration at the cold tip is determined by the heat head ‘s vibration and the structural response characteristics of the cold finger.

Background and objective Sepsis is a major cause of mortality, particularly in patients with myocardial injury. The objective of this study was to evaluate the impact of dexmedetomidine, propofol, and midazolam on mortality and various outcomes in this population. Methods A retrospective cohort study was performed using the eICU database, encompassing 2,171 septic patients with myocardial injury. Patients were categorized into single- and multiple-sedative groups. The primary endpoint was 100-day mortality, with secondary endpoints encompassing hospital stay, intensive care unit (ICU) stay, mechanical ventilation (MV), and dialysis. Statistical analysis was conducted using Cox regression, Kaplan-Meier curves, and propensity score matching. Results Among 2,171 patients, dexmedetomidine was associated with lower 100-day mortality in patients with APACHE IV scores < 78.9, particularly in specific subgroups. In patients with APACHE IV scores ≥ 78.9, dexmedetomidine provided no mortality advantage over propofol. Midazolam was linked to higher mortality across all score ranges, and its combination with propofol resulted in worse outcomes compared to dexmedetomidine-propofol. No significant differences were found in hospital stay, ICU stay, or MV rates between the groups. Conclusion Dexmedetomidine improves prognosis in septic patients with myocardial injury, particularly in those with lower severity of illness, highlighting its potential as a preferred sedative choice in this population.