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Abstract Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

In this work, the thermal stability of four types of 18,650 lithium-ion batteries with LiCoO2 (LCO), LiFePO4 (LFP), LiNi0.8Co0.1Mn0.1O2 (NCM811) and LiNi0.8Co0.15Al0.05O2 (NCA) materials as cathodes are experimentally investigated by the accelerating rate calorimeter (ARC) and the isothermal battery testing calorimeter (iso-BTC) under adiabatic and isothermal conditions, respectively. The thermal runaway danger level of these batteries can be ranked as LCO > NCA > NCM811 >> LFP by judging from the values of Tmax and HRmax, nominal. The higher the nickel and cobalt content, the higher the lithium-ion battery capacity, but the worse the thermal stability. The Qtotal of NCA is the largest in the complete standard charge and discharge process, due to that the capacity of NCA is significantly higher than that of the other three batteries, resulting in remarkable increase in Qirre proportioned to the square of the current. When the ambient temperature rises, the energy release decreases owing to the decrease in the internal resistance of the battery. These studies are expected to have important implications for the subsequent safe design of commercial lithium-ion batteries with different cathode materials.

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset.

Petrochemical excess sludge, as hazardous solid waste, poses a great threat to the environment and is difficult to dispose of in an economic and environmentally friendly way. A new alternative of using the petrochemical excess sludge to prepare ceramsite is proposed. The relationship between the sintering behavior of dried excess sludge, including the composition, temperature, fluxing agent, and pore-forming agent addition, and the properties of ceramsite is investigated. The properties of ceramsite are primarily affected by the sintering temperature and the addition of a fluxing agent. Ceramsite with a sintering-expanded surface is prepared. Also, its water absorption is quite low, indicating an improvement in densification due to sintering. Moreover, the leaching toxicity of the heavy metals in the dried excess sludge and prepared ceramsite is also investigated. It reveals the feasibility of ceramsite preparation by sintering petrochemical excess sludge.

A 63-year-old man with a history of right plantar malignant melanoma (T3bN0M0, IIb) developed multiple metastases in bilateral lungs 19 months after surgery. Subsequent 18F-FDG PET/CT revealed multiple pulmonary metastases with intense FDG uptake and detected a hypermetabolic lesion in the lateral wall of the left ventricle, which was considered to be a cardiac metastasis of malignant melanoma. This lesion was later confirmed in the dynamic myocardial perfusion MR. This case demonstrates the effectiveness of 18F-FDG PET/CT in detecting occult cardiac metastases of malignant melanoma.

In the field of bioinformatics, understanding protein secondary structure is very important for exploring diseases and finding new treatments. Considering that the physical experiment-based protein secondary structure prediction methods are time-consuming and expensive, some pattern recognition and machine learning methods are proposed. However, most of the methods achieve quite similar performance, which seems to reach a model capacity bottleneck. As both model design and learning process can affect the model learning capacity, we pay attention to the latter part. To this end, a framework called Multistage Combination Classifier Augmented Model (MCCM) is proposed to solve the protein secondary structure prediction task. Specifically, first, a feature extraction module is introduced to extract features with different levels of learning difficulties. Second, multistage combination classifiers are proposed to learn decision boundaries for easy and hard samples, respectively, with the latter penalizing the loss value of the hard samples and finally improving the prediction performance of hard samples. Third, based on the Dirichlet distribution and information entropy measurement, a sample difficulty discrimination module is designed to assign samples with different learning difficulty levels to the aforementioned classifiers. The experimental results on the publicly available benchmark CB513 dataset show that our method outperforms most state-of-the-art models.

This study focuses on the recycling of a spent fluid catalytic cracking (FCC) catalyst to produce catalyst-based non-sintered bricks (CN-bricks) for the recovery of its aluminosilicate components and the solidification of heavy metals. The effects of the content of cement (10–20%), the proportion of FCC (10–40%), and the type of an activator (NaOH/Na2SiO3/Na2SO4) on the performance of a CN-brick were investigated in terms of the mechanical strength and leaching behavior. The results show that an optimal binder system of 20% cement + Na2SO4 could promote the compressive strength up to 42.3 MPa; the proportion of an optimal spent FCC catalyst of 20% could achieve the lowest porosity and water absorption. The microscopic mechanism of a cementitious process was analyzed by x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), proving that C-S-H and ettringite (AFt) are the two main hydration products of a CN-brick. Na2SO4 is superior to NaOH or Na2SiO3 as an activator since Na2SO4 takes advantage of the aluminum-rich property of a spent FCC catalyst and specifically promote the formation of a needle-like AFt. Quantitative environmental risk assessment for the utilization of a CN-brick on roads was carried out based on the leaching test of a toxicity characteristic leaching procedure (TCLP), NEN 7371 maximum availability test, and the hazard Index (HI) identification, and a final HI 0.0045 (

Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter. Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively. Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread.

The matrix metalloproteinase (MMP) family is widely involved in the destruction of the pulp and apical tissues in the inflammatory process. MMP9 is closely related to oral inflammation. Nevertheless, the specific function of MMP9 during oral inflammation, as well as its mechanism, is not well understood. Our previous studies found that in experimentally induced apical periodontitis, more severe inflammation occurred in MMP9 knockout mice compared with the wild type mice. Moreover, the pathology phenomenon of alveolar bone destruction was even more evident in MMP9 knockout mice compared with the wild type mice. We proposed that MMP9 has “anti-inflammatory” properties. We aimed to study the effects of MMP9 on inflammatory response as well as on bone formation and bone destruction. We found a specific relationship between MMP9 and inflammation. qRT-PCR and Western blot revealed that the production of IL-1β, TNF-α, RANK, RANKL, TLR2, and TLR4 was reduced by MMP9 in LPS-stimulated MC3T3-E1 cells. Meanwhile, the expressions of OPG and OCN were increased by MMP9 in LPS-stimulated cells. MMP9 plays a protective role in LPS-induced inflammation, thereby providing new clues to the prevention and treatment of apical periodontitis.

Heat pipes transfer heat via phase transformation of the working fluid, where the working fluid will keep the temperature constant and absorb or release a large amount of latent heat during phase transformation. With the development of heat pipe technology, the isothermal performance of heat pipes has been gradually emphasized in many application fields. Most studies focused on the average temperature characteristics of one heat pipe or several heat pipes with the same type, and lacked a comprehensive analysis on the isothermal performance of different heat pipes. In this paper, previous studies on the application fields of the isothermal performance of heat pipes, the isothermal level of heat pipes used in different fields, and the methods to improve the isothermal performance of heat pipes are summarized. The parameters of the wick have little effect on the temperature uniformity of the heat pipe, while the arrangement of the wick has more influence on the uniformity of the heat pipe. The most suitable charge rate is 15% to 30% of the total inner volume, and the best start-up performance and isothermal performance is at approximately 45°.

For high-precision thermocouple calibration, the uniformity of the temperature field provided by the metal temperature equalizing block is low, and the structure of the gas-controlled heat pipe is complex. In order to improve the thermocouple calibration equipment, heat pipe technology can be used to provide the stability and uniformity of the temperature field of the equipment. The concentric annular heat pipe (CAHP) is completely placed in the heating furnace to provide a uniform temperature field, and limited studies consider this heating mode for alkali metal CAHPs. Specifically, no information is available on the effect of heating temperatures on the temperature distribution of the internal pipe of potassium CAHP. In this study, a temperature comparison based on potassium CAHP for high-precision thermocouple calibration was manufactured. The temperature stability and temperature uniformity of CAHP were measured, and the effects of heating temperature and heating mode on the isothermal performance in the metering wells of CAHP were studied. The CAHP can provide a very stable and uniform temperature field. Under uniform heating at 400 °C, the maximum temperature difference within 16 cm was 0.174 °C. After adjusting the heating mode, the maximum temperature difference was within 16 cm 0.095 °C. The CAHP can effectively reduce the influence of heating temperature fluctuation on the temperature in the metering well; the maximum temperature change rate of the metering wells affected by the heating furnace temperature was 0.0942 °C/°C.

In the geyser boiling mode, the working fluid state is divided into a boiling process and a quiet process, and the sodium-potassium (Na-K) alloy heat pipe can discontinuously transfer heat at each boiling. The overheating of the liquid working fluid at the bottom causes short-term boiling and forms slug bubble, the strong condensing ability quickly conducts heat from the evaporator section. And geyser boiling can occur before the working fluid forms continuous flow, so it transfers more heat at lower temperatures than natural convection cooling. In this study, the heat transfer process of a Na-K alloy heat pipe with forced convection cooling under different heating power was experimental studied. The geyser boiling mode can make the Na-K alloy heat pipe work below 650 °C and reduce the start-up time. In the process of geyser boiling, the heat transfer quantity was increased by the boiling frequency and the amount of vapor produced in a single boiling. The boiling temperature had no obvious change with the increased of heating power, and the condenser section temperature increased with the heating power.

Objective:To examine the training and detraining effects of a group exercise activity program for the institutional residents with functional dependency.Method:Residents with functional dependency from two nursing homes (older than 60 years) were recruited.A total of 41 residents were randomly assigned to the intervention (n=18) or the control groups (n=23). The intervention group received group exercise three times per week for ten weeks, the control group maintained the original life style in the institute. Outcome measures included passive range of motion (PROM), muscle strength and upper extremity motor function. The measures were assessed at baseline (pre-test), after 11 weeks (post-test), and at 23 weeks follow-up.Result:At the end of the trial, the exercise subjects demonstrated significant improvement on ROM, muscle strength and upper extremity motor function over the control group (PP

The excess sludge in the petrochemical industry is large in quantity, complex in composition, and highly harmful, and its rational disposal is of great significance for environmental protection and sustainable development. In the present study, a classification and disposal strategy for the excess sludge in the petrochemical industry is proposed. The strategy first analyzes the dioxin, flammability, corrosivity, reactivity, and leaching properties of the sludge, from which the waste type of the sludge (general waste or hazardous waste) can be determined. Then, methods of disposal can be selected depending on the type of waste and the corresponding risk analysis, enabling rationalized disposal of the sludge. To verify the effectiveness and practicability of the proposed sludge classification and disposal strategy, research on petrochemical excess sludge samples (i.e., Ah, Bl, and Cq) originated from three different regions in China is carried out as a case study. The component analysis of the above three sludge samples revealed that they are all general wastes. In addition, the possibility of employing Cq sludge for landfill, soil modification, and greening mud, as well as the risk of landfill and incineration disposal in solid waste landfills are investigated. Furthermore, natural radioactive elements uranium and thorium in Cq sludge sample are studied. The results show that Cq sludge cannot be used for landfill, soil modification, and greening mud due to excessive arsenic content. The proposed strategy provides a basis for the selection of reasonable petrochemical excess sludge disposal methods.

Microvesicles (MVs) derived from mesenchymal stem cells (MSCs) have been shown to promote angiogenesis. This study was aimed to shed a light on the mechanisms by analyzing the angiogenesis-promoting compositions of MSC-MVs. Also we try to figure out the impact of hypoxia on angiogenesis.MVs were isolated from the culture supernatants of MSCs under hypoxia/normoxia and serum-deprivation condition. The morphological features of MVs were revealed by an electron microscope and the origin of the MVs was identified by a bead-bound assay. An antibody array was used to analyze the expression of angiogenic cytokines from MVs and the parent MSCs as well. The major candidate factors were screened and the results were validated by immune blotting.MSC-MVs were around 80 nm in diameter. They expressed CD29, CD44, and CD73, but not CD31 and CD45. Antibody array showed that both MSCs and MVs expressed many angiogenesis-promoting biomolecules, including interleukin-6 (IL-6), basic fibroblast growth factors (bFGF), and recptor of urokinase-type plasminogen activator (UPAR). MSC-MVs contained angiogenin, vascular endothelial growth factor (VEGF), monocyte chemotactic protein-1 (MCP-1) and the receptor-2 for vascular endothelial growth factor at higher levels than the parent MSCs. Under hypoxic condition most cytokines were expressed in greater quantity than normoxic in MSCs while in MVs there was no significant difference between hypoxic and normoxic conditions except UPAR, Angiogenin, VEGF, IGF, Tie-2/TEK, and IL-6 which were higher in MVs under hypoxic conditions than those in normoxic condition.Upon serum-deprivation condition, MSCs could secrete MVs that contain a variety of factors contributing to their angiogenesis-promoting function. And among them, Angiogenin, VEGF, MCP-1, VEGF R2 might be of greater importance than the other cytokines. Also UPAR, Angiogenin, VEGF, IGF, Tie-2/TEK, IL-6 might be responsible for hypoxia-augmented proangiogenic effects of MVs.