AI Widens the Gap between the Rich and the Poor

Since entering the 21st century, high technology has developed at a rapid pace. The development of high technology has changed the methods of production and lifestyle of human beings. While enjoying the efficiency, comfort, and convenience brought by high technologies, people find that the gap between the rich and the poor has been widening. More and more attentions have been paid to the influences on the gap between the rich and the poor arising from the development of high technologies, especially from the development of artificial intelligence (AI) technology. This paper focuses on this social phenomenon and demonstrates that the development of AI will widen the gap between the rich and the poor. The paper will proceed with the discussion from three levels of human actives: individual, company, and country

Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach

Proposed clinical phases for the improvement of personalized treatment of checkpoint inhibitor–related pneumonitis

BackgroundCheckpoint inhibitor–related pneumonitis (CIP) is a lethal immune-related adverse event. However, the development process of CIP, which may provide insight into more effective management, has not been extensively examined.MethodsWe conducted a multicenter retrospective analysis of 56 patients who developed CIP. Clinical characteristics, radiological features, histologic features, and laboratory tests were analyzed. After a comprehensive analysis, we proposed acute, subacute, and chronic phases of CIP and summarized each phase’s characteristics.ResultsThere were 51 patients in the acute phase, 22 in the subacute phase, and 11 in the chronic phase. The median interval time from the beginning of CIP to the different phases was calculated (acute phase: ≤4.9 weeks; subacute phase: 4.9~13.1 weeks; and chronic phase: ≥13.1 weeks). The symptoms relieved from the acute phase to the chronic phase, and the CIP grade and Performance Status score decreased (P<0.05). The main change in radiologic features was the absorption of the lesions, and 3 (3/11) patients in the chronic phase had persistent traction bronchiectasis. For histologic features, most patients had acute fibrinous pneumonitis in the acute phase (5/8), and most had organizing pneumonia in the subacute phase (5/6). Other histologic changes advanced over time, with the lesions entering a state of fibrosis. Moreover, the levels of interleukin-6, interleukin-10 and high-sensitivity C-reactive protein (hsCRP) increased in the acute phase and decreased as CIP progressed (IL-6: 17.9 vs. 9.8 vs. 5.7, P=0.018; IL-10: 4.6 vs 3.0 vs. 2.0, P=0.041; hsCRP: 88.2 vs. 19.4 vs. 14.4, P=0.005).ConclusionsThe general development process of CIP can be divided into acute, subacute, and chronic phases, upon which a better management strategy might be based devised

AI Widens the Gap between the Rich and the Poor

Since entering the 21st century, high technology has developed at a rapid pace. The development of high technology has changed the methods of production and lifestyle of human beings. While enjoying the efficiency, comfort, and convenience brought by high technologies, people find that the gap between the rich and the poor has been widening. More and more attentions have been paid to the influences on the gap between the rich and the poor arising from the development of high technologies, especially from the development of artificial intelligence (AI) technology. This paper focuses on this social phenomenon and demonstrates that the development of AI will widen the gap between the rich and the poor. The paper will proceed with the discussion from three levels of human actives: individual, company, and country

Effect of Mass Proportion of Municipal Solid Waste Incinerator Bottom Ash Layer to Municipal Solid Waste Layer on the Cu and Zn Discharge from Landfill

Municipal solid waste incinerator (MSWI) bottom ash is often used as the protection layer for the geomembrane and intermediate layer in the landfill. In this study, three sets of simulated landfills with different mass proportion of MSWI bottom ash layer to municipal solid waste (MSW) layer were operated. Cu and Zn concentrations in the leachates and MSW were monitored to investigate the effect of MSWI bottom ash layer on the Cu and Zn discharge from the landfill. The results showed that the Zn discharge was dependent on the mass proportion of MSWI bottom ash layer. The pH of landfill was not notably increased when the mass proportion of MSWI bottom ash layer to MSW layer was 1 : 9, resulting in the enhancement of the Zn discharge. However, Zn discharge was mitigated when the mass proportion was 2 : 8, as the pH of landfill was notably promoted. The discharge of Cu was not dependent on the mass proportion, due to the great affinity of Cu to organic matter. Moreover, Cu and Zn contents of the sub-MSW layer increased due to the MSWI bottom ash layer. Therefore, the MSWI bottom ash layer can increase the potential environmental threat of the landfill

Serotype distribution and drug resistance analysis of 540 Salmonella spp. isolated from diarrhea cases from Zhongshan City

Objective To study the serotype distribution and drug resistance of Salmonella isolates from diarrhea cases in Zhongshan City from 2016 to 2017. Methods Serotype of 540 strains of Salmonella spp. was determined from diarrhea cases monitored and isolated in Zhongshan City from 2016 to 2017. Furthermore, broth microdilution method was used for drug sensitivity test. Results 540 Salmonella strains isolated in Zhongshan City from 2016 to 2017 covered 59 serotypes and there were 22 common serotypes. Among them, the dominant serotypes were monophasic variant of S. Typhimurium (41.9%, 226/540), S. Enteritidis (13.1%, 71/540), S. Typhimurium (9.3%, 50/540) and S. Stanley (9.3%, 50/540), accounting for 73.5% (397/540) of the total strains. Drug susceptibility test showed that the resistance rate of Salmonella to ampicillin, ampicillin/sulbactam and tetracycline was higher than 50.0%. However, resistance to cephalosporins varies considerably. The drug resistance rates of cefazolin, cefotaxime, ceftazidime and cefoxitin were 34.1% (184/540), 25.7% (139/540), 12.2% (66/540) and 1.5% (8/540), respectively. In addition, all Salmonella strains were sensitive to imipenem, while S. Stanley was sensitive to six antibiotics. The multiple drug resistance rate was 65.4% (353/540). The multidrug resistance rates of the 4 dominant serotypes of monophasic variant of S. Typhimurium, S. Enteritidis, S. Typhimurium and S. Stanley were 82.7% (187/226), 56.3% (40/71), 72.0% (36/50) and 16.0% (8/50) respectively. Conclusion Salmonella isolates from diarrhea cases in Zhongshan City show biodiversity. Salmonella has a high rate of multiple drug resistance, among which monophasic variant of S. Typhimurium is the most serious strain, which should be paid attention to

Phenol Adsorption Mechanism of Organically Modified Bentonite and Its Microstructural Changes

Bentonite was modified with cetyltrimethylammonium bromide (CTAB). The organically modified bentonite (OMB) was used to remove phenol from aqueous solution, the microstructural changes were characterized by X-ray diffraction (XRD) and scanning electronic microscopy (SEM), and phenol adsorption kinetic was obtained using batch adsorption test results. The results indicated that the rate of adsorption of phenol onto the OMB was positively correlated with the initial concentration, and the maximum adsorption capacity was found to be 10.1 mg/g at the initial concentration of 150 mg/L at 25 °C and pH 10. The investigations of adsorption kinetics models showed that the adsorption kinetic was better reflected by the pseudo-second-order kinetic model. Furthermore, the properties of the OMB samples with different adsorption times were obtained by SEM and XRD. The statistic analysis revealed that the pore diameter of the OMB samples decreased with the increasing adsorption time and gradually reached equilibrium

Investigation on the Emission and Diffusion of Hydrogen Sulfide during Landfill Operations: A Case Study in Shenzhen

This study investigated the emission and diffusion of hydrogen sulfide (H2S), as one of the odorous gases generated from landfills, in a municipal solid waste landfill of a south Chinese city. To this end, the flux of the H2S emissions in the working area of the landfill and its diffusion in the surrounding area were measured. The diffusion of the H2S was simulated at different wind speeds, wind directions, bare working areas of the landfill, heights of the landfill, and angles between the wind direction and the long side of the working area. The results indicated that the concentration of the H2S around the monitoring point ranged from 0 to 60 µg/m3, and the simulated data were consistent with the measured results. At a uniform wind direction, the pollution range of the H2S was narrow. Furthermore, with an increase in the height of the waste dump, the concentration of the H2S decreased in the working area but rose in the surrounding area. Notably, when the angle between the long side of the working area and the wind direction was 0°, the H2S largely spread along the extension cord of the long side of the working area. When the angle increased to 90°, the influence range of the H2S extended significantly. The working area in the landfill site should be regulated based on the monitored data to reduce the effect of this harmful gas on the living environment, and the health of the landfill staff and nearby residents

Phenol Adsorption Mechanism of Organically Modified Bentonite and Its Microstructural Changes

Bentonite was modified with cetyltrimethylammonium bromide (CTAB). The organically modified bentonite (OMB) was used to remove phenol from aqueous solution, the microstructural changes were characterized by X-ray diffraction (XRD) and scanning electronic microscopy (SEM), and phenol adsorption kinetic was obtained using batch adsorption test results. The results indicated that the rate of adsorption of phenol onto the OMB was positively correlated with the initial concentration, and the maximum adsorption capacity was found to be 10.1 mg/g at the initial concentration of 150 mg/L at 25 °C and pH 10. The investigations of adsorption kinetics models showed that the adsorption kinetic was better reflected by the pseudo-second-order kinetic model. Furthermore, the properties of the OMB samples with different adsorption times were obtained by SEM and XRD. The statistic analysis revealed that the pore diameter of the OMB samples decreased with the increasing adsorption time and gradually reached equilibrium