Information For Management Research Articles

The potential role of leukocytes cell population data (CPD) for diagnosing sepsis in adult patients admitted to the intensive care unit.

The aim of the study was to evaluate the predictive value of cell population data (CPD) parameters in comparison with procalcitonin (PCT) and C-reactive protein (CRP) for an early diagnosis of sepsis in intensive care unit (ICU). The effect of renal function on CPD, PCT and CRP, in septic and non-septic patients was also investigated. This is a retrospective, observational and single-center study, performed with data collected from patients consecutively admitted to the ICU of the Edoardo Bassini Hospital in Milan. Patients were divided in septic and non-septic according to Sepsis-III criteria. The control group was formed by critically ill patients without sepsis. Patients with sepsis were further divided in patients with sepsis and patients with septic shock. A significant difference between septic and non-septic patients was found for neutrophils complexity (NE-SSC), neutrophils fluorescence intensity (NE-SFL), width of dispersion of neutrophils fluorescence (NE-WY), monocytes complexity (MO-X), monocytes fluorescence intensity (MO-Y), PCT and CRP parameters. PCT, neutrophils sixe (NE-FSC), NE-WY, width of dispersion of neutrophils size (NE-WZ) and MO-X discriminated sepsis and septic-shock patients. CPD parameters were not influenced by renal function. CPD, PCT and CRP had a heterogeneous diagnostic performance efficiency in the prediction of sepsis. Overall, NE-SSC, NE-SFL, width of dispersion of neutrophils complexity (NE-WX), MO-X, MO-Y, PCT and CRP displayed the best diagnostic performance for sepsis. This study suggested that some CPD parameters (i.e.,NE-SFL and MO-X) might provide useful information for diagnosis and management of sepsis.

Treatment of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in stormwater using polishing columns with biochar and granular activated carbon.

Polycyclic Aromatic Hydrocarbons (PAHs) and Polychlorinated Biphenyls (PCBs) are recalcitrant organic pollutants often detected in stormwater. Various stormwater control measures (SCMs) can remove PAHs and PCBs by filtration, adsorption, and biodegradation. However, dissolved PAHs and PCBs remain present in the treated outflow of SCMs. To provide further treatment, two types of biochar (biochar A and biochar B), a granular activated carbon (GAC), and a regenerated GAC (RAC) were tested in a column study with synthetic stormwater containing PAHs and PCBs in low concentrations. Results showed that PAH concentrations were reduced by 98.2-99.8% (range: <limit of quantification (LOQ) - 1.89μg/L), while PCB concentrations were reduced by 86.0-97.9% (range: <LOQ - 0.7μg/L). The extraction of PCBs from the media showed that >62% of the PCBs accumulated at the first third of the column due to adsorption onto the black carbon material thus providing information for management. The difference in treatment performances of different PAHs and PCBs may be caused by the molecular structure of the contaminants. Lifetime calculations indicated that biochar had lifetimes >36-51 years, while GAC and RAC showed lifetimes longer than 147 and 206 years, respectively, based on predicted precipitation for scaled up designs. The results showed that biochar and activated carbons can be efficient adsorbents for stormwater polishing treatment. With consideration of efficiency, sustainability and cost, RAC is recommended for polishing treatment of dissolved PAHs and PCBs.

Enhanced accuracy and stability in automated intra-pancreatic fat deposition monitoring of type 2 diabetes mellitus using Dixon MRI and deep learning.

Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI. In this retrospective study, 534 patients from two centers who underwent upper abdomen MRI and completed multi-echo and double-echo Dixon MRI were included. A pancreatic segmentation model was trained on double-echo Dixon water images using nnU-Net. Predicted masks were registered to the proton density fat fraction (PDFF) maps of the multi-echo Dixon sequence. Deep semantic segmentation feature-based radiomics (DSFR) and radiomics features were separately extracted on the PDFF maps and modeled using the support vector machine method with 5-fold cross-validation. The first deep learning radiomics (DLR) model was constructed to distinguish T2DM from non-diabetes and pre-diabetes by averaging the output scores of the DSFR and radiomics models. The second DLR model was then developed to distinguish pre-diabetes from non-diabetes. Two radiologist models were constructed based on the mean PDFF of three pancreatic regions of interest. The mean Dice similarity coefficient for pancreas segmentation was 0.958 in the total test cohort. The AUCs of the DLR and two radiologist models in distinguishing T2DM from non-diabetes and pre-diabetes were 0.868, 0.760, and 0.782 in the training cohort, and 0.741, 0.724, and 0.653 in the external test cohort, respectively. For distinguishing pre-diabetes from non-diabetes, the AUCs were 0.881, 0.688, and 0.688 in the training cohort, which included data combined from both centers. Testing was not conducted due to limited pre-diabetic patients. Intraclass correlation coefficients between radiologists' pancreatic PDFF measurements were 0.800 and 0.699 at two centers, suggesting good and moderate reproducibility, respectively. The DLR model demonstrated superior performance over radiologists, providing a more efficient, accurate and stable method for monitoring IPFD and predicting the risk of T2DM and pre-diabetes. This enables IPFD assessment to potentially serve as an early biomarker for T2DM, providing richer clinical information for disease progression and management.

Deciphering abbreviations in Malaysian clinical notes using machine learning.

This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a Natural Language Processing system, that extracts clinical information for healthcare management. The model utilizes word embedding to ensure feasibility of use, not in real-time but for secondary analysis, within the constraints of low-resource settings. A Malaysian clinical embedding, based on Word2Vec model, was developed using 29,895 electronic discharge summaries. The embedding was compared against conventional rule-based and FastText embedding on two tasks: abbreviation detection and abbreviation disambiguation. Machine Learning classifiers were applied to assess performance. The Malaysian clinical word embedding contained 7 million word-tokens, 24,352 unique vocabularies, and 100 dimensions. For abbreviation detection, the Decision Tree classifier augmented with the Malaysian clinical embedding showed the best performance (F-score of 0.9519). For abbreviation disambiguation, the classifier with the Malaysian clinical embedding had the best performance for most of the abbreviations (F-score of 0.9903). Despite having a smaller vocabulary and dimension, our local clinical word embedding performed better than the larger non-clinical FastText embedding. Word embedding with simple machine learning algorithms can decipher abbreviations well. It also requires lower computational resources and is suitable for implementation in low-resource settings such as Malaysia. The integration of this model into MyHarmony will improve recognition of clinical terms, thus improving the information generated for monitoring Malaysian healthcare services and policymaking.

Mechanism and therapeutic potential of hippo signaling pathway in type 2 diabetes and its complications.

Loss of pancreatic islet cell mass and function is one of the most important factors in the development of type 2 diabetes mellitus, and hyperglycemia-induced lesions in other organs are also associated with apoptosis or hyperproliferation of the corresponding tissue cells. The Hippo signaling pathway is a key signal in the regulation of cell growth, proliferation and apoptosis, which has been shown to play an important role in the regulation of diabetes mellitus and its complications. Excessive activation of the Hippo signaling pathway under high glucose conditions triggered apoptosis and decreased insulin secretion in pancreatic islet cells, while dysregulation of the Hippo signaling pathway in the cells of other organ tissues led to proliferation or apoptosis and promoted tissue fibrosis, which aggravated the progression of diabetes mellitus and its complications. This article reviews the mechanisms of Hippo signaling, its individual and reciprocal regulation in diabetic pancreatic pathology, and its emerging role in the pathophysiology of diabetic complications. Potential therapeutics for diabetes mellitus that have been shown to target the Hippo signaling pathway are also summarized to provide information for the clinical management of type 2 diabetes mellitus.

The first geospatial dataset of irrigated fields (2020–2024) in Vojvodina (Serbia)

Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive. To address this, we created a georeferenced regional dataset consisting of location, crop type, and occurrence of the irrigation equipment which are essential information for mapping irrigated fields. Four main irrigated crops were considered: maize, soybean, sugar beet, and wheat. The dataset, consisting of a total of 1256 parcels, is created for Vojvodina, the main agricultural area in Serbia, spanning the period of five years (2020–2024). This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.

Reduction in thermal conductivity of monolayer MoS2 by large mechanical strains for efficient thermal management

Two-dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDC) have received extensive research interests and investigations in the past decade. In this research, we report the first experimental measurement of the in-plane thermal conductivity of MoS2 monolayer under a large mechanical strain using optothermal Raman technique. This measurement technique is direct without additional processing to the material, and MoS2’s absorption coefficient is discovered during the measurement process to further increase this technique’s precision. Tunable uniaxial tensile strains are applied on the MoS2 monolayer by stretching a flexible substrate it sits on. Experimental results demonstrate that, the thermal conductivity is substantially suppressed by tensile strains: under the tensile strain of 6.3%, the thermal conductivity of the MoS2 monolayer drops approximately by 62%. A serious of thermal transport properties at a group of mechanical strains are also reported, presenting a strain-dependent trend. It is the first and original study of 2D materials’ thermal transport properties under a large mechanical strain (> 1%), and provides important information that the thermal transport of MoS2 will significantly decrease at a large mechanical strain. This finding provides the key information for flexible and wearable electronics thermal management and designs.

Seed pretreatment with cloquintocet-mexyl protects wheat seedlings from fomesafen injury by promoting photosynthesis and decreasing oxidative stress.

Fomesafen is a selective herbicide widely used to control post-emergent broad-leaf weeds in soybean and peanut fields. Because of its persistent nature in soil, it can suppress subsequent crops, including wheat. There is limited information focusing on methods of protecting wheat from fomesafen injury by soil residue. Bioassay results showed slight variations in tolerance to fomesafen among 31 wheat cultivars. Soil-applied biochar (200 g m-2) could alleviate fomesafen injury at 2 mg L-1, and partially alleviate fomesafen injury at 4 mg L-1. Seed soaking in safeners cloquintocet-mexyl and mefenpyr-diethyl was more effective in protecting seedlings from fomesafen injury at 4 mg L-1 compared with 1,8-naphthalic anhydride, fenclorim or dichlormid. Indoor tests suggested that the combined application of biochar and cloquintocet-mexyl or mefenpyr-diethyl had a synergistic alleviating effect on wheat injury caused by fomesafen, which was further confirmed in a field trial. Wheat seeds treated with cloquintocet-mexyl increased expression of the fomesafen target protoporphyrinogen IX oxidase and light-harvesting chlorophyll a/b binding protein, leading to an increase in chlorophyll content and a decrease in oxidative stress of wheat exposed to fomesafen. Cloquintocet-mexyl treatment had no influence on the expression of P450 genes, toxic transport genes, most glutathione S-transferase (GST) genes, the enzyme activity of P450 or GSTs, and the metabolism rate of fomesafen. These results suggested that cloquintocet-mexyl acted by increasing photosynthesis and decreasing oxidative stress to alleviate injury to wheat exposed to fomesafen stress, showing no influence on fomesafen metabolism in wheat. This study provided valuable information for fomesafen injury management in wheat. © 2025 Society of Chemical Industry.

Core functions of primary care in Amathole district, South Africa: a descriptive study.

Strengthening primary care is a priority globally and for the South African health system. The current measurement tools in South Africa do not measure the core functions of primary care: access, comprehensiveness, coordination, continuity and person-centredness. A new regional version of the Primary Care Assessment Tool (PCAT) has just been validated and can measure these core functions. To field test the regional PCAT and measure the core functions of primary care performance. A descriptive cross-sectional survey in Amathole District, South Africa. Data were collected from 386 randomly selected patients from 40 clinics and six subdistricts. Data was collected using the REDCap mobile App and analysed in the Statistical Package for Social Sciences version 27. The median primary care score was 3.3 (IQR 3.2-3.5) where a score>3 was seen as acceptable performance and>3.5 as good. Person-centredness, coordination and utilisation were all scored as good (4.0 (IQR 4.0-4.0). Comprehensiveness (3.3 (IQR 2.9-3.6) and continuity (3.2 (IQR 3.1-3.6) were scored as acceptable. Access to care was scored as poor (1.7 (IQR 1.0-2.9). There were significant differences in primary care scores between subdistricts. Those with a worse health status or chronic condition gave lower scores. The most affluent and the poorest groups also gave lower scores. The district needs to focus on improving access to care as well as some aspects of comprehensiveness, continuity, and coordination. The newly validated regional PCAT provided the district with novel information for performance management and improvement.

Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism

This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields.

Prognostic Implications of the Bethesda System in Fine-Needle Aspiration for Follicular Thyroid Carcinoma.

Fine-needle aspiration is an effective tool for sampling thyroid nodules; its results are classified according to the Bethesda System for Reporting Thyroid Cytopathology (BSRTC), whose categories define malignancy risks. To compare the histologic outcomes and disease-free survival (DFS) with the preceding BSRTC categories, we hypothesized that the initial cytologic categories may reflect long-term outcomes in follicular thyroid carcinoma (FTC), similar to those observed in papillary thyroid carcinoma. This retrospective study enrolled 134 patients with FTC who underwent preoperative cytology between April 2011 and December 2020. Results were classified into 6 categories according to the BSRTC: nondiagnostic, benign, atypia of uncertain significance (AUS), follicular neoplasm (FN), suspicious for malignancy, or malignant. Overall, 8 of 134 patients (6.0%) were categorized as having a nondiagnostic FTC, 35 of 134 (26.1%) as benign, 51 of 134 (38.1%) as AUS, and 40 of 134 (29.9%) as FN. No lesions were classified as suspicious for malignancy or malignant. The nondiagnostic, AUS, and FN categories were associated with a progressively higher risk of vascular invasion, disease recurrence, and high-risk FTC, based on the 2022 World Health Organization classification (P for trend = .01, .01, and .01, respectively). Disease-free survival was lower in the FN group (log-rank P = .01). The initial BSRTC results may reflect not only the risk of malignancy but also the presence of vascular invasion and poor prognosis when the thyroid nodule is diagnosed as FTC. These results may provide prognostic information for therapeutic decision-making and clinical management of FTC.

Application of Big Data Analysis in Chinese Art Song Market Research

Data reliant on fisheries are essential for successful managing fisheries. A lack of fisheries data results in clarity regarding stock status, thereby jeopardizing the financial and food safety of the consumers reliant on that stock and heightening the risk of exploitation. Recent advancements in technology for gathering, managing, and evaluating fishery-related data offer a range of options to enhance and modernize fisheries’ information systems, significantly increasing data collecting and analytical capabilities. Notwithstanding the widespread availability of pertinent consumer electronics, incorporating sophisticated information systems into fisheries oversight is still atypical rather than the norm. The article delineates the present condition, obstacles, and prospective trajectories of advanced information systems in the fishing industry to elucidate the factors hindering their acceptance. Our examination of the utilization of fishery-dependent information technologies across several worldwide fisheries sectors reveals that technological progress is stalling due to a deficiency of trust and collaboration between fishers and management. The research advocates for a solution grounded in a cross-disciplinary approach to fisheries management that underscores the necessity of working together to solve problems among participants. In our suggested structure, data suggestions are essential for efficient fishing information systems, ensuring fishermen and managers acquire, access, and profit from data related to fishing while striving towards a shared objective. An innovative strategy for fisheries information systems will enhance data protection, precision, and settlement while minimizing costs and facilitating adaptive, adaptable, near real-time managing decisions to optimize fisheries performance.

Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms

Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage.

Large-Scale Urban 3D Geological Modeling Based on Multi-Method Coupling Under Multi-Source Heterogeneous Data Conditions

The development and utilization of urban underground space represents a crucial strategy for achieving sustainable urban development. Three-dimensional (3D) geological models provide a data foundation and technical support for research in urban planning and construction, as well as the prevention and control of environmental geological issues. However, current urban 3D geological modeling generally faces the challenge of multi-source heterogeneous modeling data. This often necessitates varying degrees of generalization in data processing, resulting in the majority of current urban 3D geological models being relatively coarse and insufficient to fulfill the demand for detailed geological information in contemporary urban development and management. Therefore, determining how to formulate or optimize the 3D geological modeling schemes to enhance the utilization of multi-source heterogeneous data is a key challenge in current urban 3D geological modeling. This study, taking the 3D geological structure modeling of Wuhan’s metropolitan development area (MDA) as an example, develops an automated scheme for standardizing modeling data based on multi-scale geological chronostratigraphy. By utilizing the standardized stratigraphy as a unified and independent geological framework for layered modeling, a high-precision 3D geological model of Wuhan’s MDA, characterized by large-scale and ultra-complex geological conditions, is constructed through a methodology that integrates the global discrete constrained points modeling approach with the global layered modeling approach, without generalizing the multi-source heterogeneous modeling data. This research not only holds significant practical implications for the prevention and control of comprehensive urban geological issues in Wuhan but also provides novel technical insights into the methodology of 3D urban geological modeling.

Willingness of medical students to work on the COVID-19 frontline during the pandemic in China: A nationwide population-based cross-sectional study.

The World Health Organization declared that coronavirus disease 2019 (COVID-19) constitutes an international public health emergency, which has strained health resources. In this study, we aimed to understand medical students' willingness to join the workforce fighting against the COVID-19 pandemic and identify factors associated with their decisions. We conducted a nationwide cross-sectional study using the Wen-Juan-Xing platform and a pre-designed questionnaire from 23 March to 19 April 2021. We conducted logistic regression analyses to identify the determinants associated with the willingness. Among the 5022 medical students, the majority (n = 4289, 85.40%) expressed willingness to work on the COVID-19 frontline. Logistic regression indicated that medical students' willingness to work on the COVID-19 frontline in China was associated with gender, region, reason for choosing medicine, having medical workers in the family, students whose family members, relatives or friends experienced COVID-19, and professional attitude. Females (odds ratio (OR) = 1.305; 95% confidence interval (CI) = 1.100-1.549; P = 0.0023), medical students from urban areas (OR = 1.295; 95% CI = 1.089-1.539; P = 0.0034), medical students whose choice of a medical career was their desire (OR = 1.579; 95% CI = 1.290-1.933; P < 0.0001), medical students whose parents or relatives are medical workers (OR = 1.266; 95% CI = 1.066-1.505; P = 0.0073), medical students whose family members, relatives, or friends have never been infected with COVID-19 (OR = 4.567; 95% CI = 3.002-6.947; P < 0.0001), and medical students with undisturbed of professional attitudes (OR = 4.280; 95% CI = 3.241-5.654; P < 0.0001) showed increased willingness to work on the COVID-19 frontline compared with their counterparts. Medical students demonstrated a strong willingness to contribute to COVID-19 work during the pandemic in China. The findings may provide valuable information for emergency management so that policymakers can maintain sufficient health resources and provide quality health care in similar health emergencies in the future.

Impact of Cash Holding on Firm Performance: Empirical Evidence from Pakistan

The study investigates the impact of cash holdings on firm performance, in the presences of firm size, debt ratio, and asset turnover. Using data from 91 firms over a six-year period (2017-2022), the study demonstrates the importance of cash reserves for boosting firm value and operating efficiency. The finding show a positive correlation between liquidity and business performance, emphasizing the strategic importance of cash management in corporate finance. This study, which examines key firm characteristics as well as cash holdings, gives insights into the intricate elements driving corporate success and valuable information for managers and policymakers trying to improve financial strategies for long-term growth.

Probabilistic Tsunami Hazard Assessment from Manila Trench to Shantou City in China

Manila Trench is an ocean trench that lies to the west of Luzon Island in the Philippines, which has the potential to generate a significant tsunami that could affect the coastal areas around it. This paper analyzes the tsunami hazard at the southern coastline of China, and more specifically at the city of Shantou. A Monte Carlo-type probabilistic tsunami hazard assessment (PTHA) with 5000 simulation runs was conducted using the tsunami propagation model COMCOT. A wide range of earthquake magnitudes were stochastically simulated in these zones, ranging from 7.0 to 9.0 in magnitude, and the peak nearshore tsunami amplitude (PNTA), for return periods of 100, 1,000 and 10,000 years, was determined by propagation simulations to be 0.15 m, 0.65 m and 1.75 m, respectively. Inundation simulations for the case study area, which considered various scenarios of sea level rise between the present day and the year 2100, were also conducted. The results indicate the severe influence that sea level rise could have on tsunami risks, which can provide valuable information for disaster risk managers and planners on how to develop adaptation pathways not only for this city but also for other points in the southeast coastline of China.

Mechanical Behavior and Failure Prediction of Cylindrical Lithium-Ion Batteries Under Mechanical Abuse Using Data-Driven Machine Learning

Abstract Mechanical failure prediction of lithium-ion batteries (LIBs) can provide important maintenance information and decision-making reference in battery safety management. However, the complexity of the internal structure of batteries poses challenges to the generalizability and prediction accuracy of traditional mechanical models. In view of these challenges, emerging data-driven methods provide new ideas for the failure prediction of LIBs. This study is based on an experimental data-driven application of machine learning (ML) models to rapidly predict the mechanical behavior and failure of cylindrical cells under different loading conditions. Mechanical abuse experiments including local indentation, flat compression, and three-point bending experiments were conducted on cylindrical LIB samples, and mechanical failure datasets for cylindrical cells were generated, including displacements, voltages, temperatures, and mechanical forces. Six ML models were used to predict the mechanical behavior of cylindrical batteries, four metrics were used to evaluate the prediction performance, the coefficients of determination of eXtreme Gradient Boosting (XGBoost) regression and random forest were 0.999, and the root-mean-square errors (RMSE) were lower than 0.015. It is shown that the integrated tree models tested in this study are suitable for the failure prediction of LIBs under the conditions of mechanical abuse. Also, the random forest prediction model outperforms other ML prediction models with the smallest RMSE values of 0.005, 0.0149, and 0.007 for local indentation, flat compression, and three-point bending, respectively. This work highlights the capability of ML algorithms for LIB safety prediction.

Firm‐Specific Economic Policy Uncertainty and Stock Price Crash Risk

ABSTRACTEconomic policy uncertainty (EPU) significantly impacts capital markets and financial activities. Prior studies present inconsistent results regarding the effect of EPU on stock price crash risk, possibly due to the usage of a macro‐level EPU index that assumes homogeneity in its effects across diverse firms. Using data from Chinese A‐share listed firms from 2008 to 2019, we adopt a text‐based method to measure firm‐specific economic policy uncertainty (FEPU) and examine its impact on future stock price crashes. We find that FEPU mitigates crash risk through two channels: monitoring enhancement and conservatism promotion, which deter the accumulation of negative information. The mitigating effect of FEPU is more pronounced for firms with greater managerial stock option incentives, overconfident CEOs, and higher reliance on government subsidies. Overall, our study highlights the crucial role of FEPU in reducing the expected costs of managerial information manipulation and managing stock return tail risks.

Exploring Beliefs, Concerns, Prenatal Care Advice, and Sources of Information About Gestational Weight Gain Among Immigrant Central American Pregnant Women in the United States.

Gestational weight gain (GWG) is critical for maternal and neonatal health, but excessive GWG can lead to complications such as gestational diabetes, hypertension, and increased obesity risk later in life. Minoritized and immigrant women often face higher risks of excessive GWG. This cross-sectional study assessed Central American women's beliefs and concerns about GWG, the receipt of advice from healthcare providers, and sources of information for healthy weight management during pregnancy. A cross-sectional survey was conducted with 93 pregnant women from El Salvador (31.2%), Guatemala (46.2%), and Honduras (22.6%). Most participants were married (91.4%), and 91.2% had household incomes below $40,000. Self-reported pre-pregnancy weight status varied significantly (p = 0.03), with more Guatemalans self-reporting as overweight (34.9%) compared to Salvadorans (10.3%) and Hondurans (19.1%). Beliefs about GWG varied significantly; 72.1% of Guatemalan women accepted "eating for two", while only 31.0% of Salvadorans did (p = 0.002). More Honduran women (90.5%) received weight gain recommendations from healthcare providers than Salvadorans (62.1%) and Guatemalans (60.5%) (p = 0.04). The Internet and family were common information sources on weight management, highlighting the need for culturally tailored health education. This study underscores critical differences in beliefs and access to prenatal care among pregnant Central American immigrant women, emphasizing the importance of culturally competent health education to support healthy pregnancy outcomes.