Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop Yield Response Function and Ex Post Economic Thresholds: The Impacts of Crop Growth Stage-specific Weather Conditions on Crop Yield

There have been two parallel approaches to modeling crop yields as a function of weather (and management): crop growth simulation modeling and, more recently, parsimonious statistical models of crop yields that require many fewer variables to estimate. Our motivating objective is to take into account the phenological detail present in process-based crop simulation models to estimate much less data intensive empirical models capable of informing crop management and adaptation to climate change. In the field of climate econometrics (Hsiang, 2016), the crop yield response function has become one of the most widely studied topics in applied econometric analysis of climate impacts and adaptation. Since Schlenker and Roberts (2009), there have been continuous efforts to improve econometric specification of crop yield response functions: the importance of temperature relative to precipitation (Lobell and Burke, 2008), specification bias (Cooper et al., 2017), and spatial aggregation bias (Yun et al., 2015). Despite these advances, however, two fundamental economic and agronomic components are largely ignored: the economic meaning of ex post data (Hsiang, 2016), and differential impacts of weather in specific plant growth stages. To fill this gap, this study tries to answer two research questions: 1) how do we interpret estimated weather impacts as an ex-post economic thresholds in crop yield response functions? and 2) do temperature and precipitation have different impacts on crop yields at different plant growth stages? To this end, this paper starts from the discussion in Hsiang (2016) on the economic meaning of ex post data, and then, implements econometric crop yield response function analysis with county-level corn yields in the 12 state US Corn Belt region from 19812015.
Testing the Influence of Static and Dynamic Stress Perturbations On the Occurrence of a Shallow, Slow Slip Event in Eastern Taiwan

We report the first evidence for the detection of a slow slip event in the Longitudinal Valley, in eastern Taiwan. The slow event, which lasted about 3.5 days, has been detected by borehole strainmeters. It occurred at shallow depths (about 2 to 4 km), either on the Longitudinal Valley Fault or on the Central Range Fault. Here we investigate whether the event occurrence was influenced by transient and periodic stress perturbations, in particular by the June 2013 Mw 6.2 Nantou earthquake, which occurred about 60 km away and 6 days prior to the event. Modeled changes in Coulomb stress in the direction parallel to the geologic slip vector on the fault planes show negative static stress changes (approximately −1.5 to −1 kPa), while maximum dynamic stress changes generated by the surface waves are ranging from 5.5 to 14.5 kPa. We also observe that the slow event initiated during a maximum of Earth and ocean tidal Coulomb stress changes (about 0.8 to 1.5 kPa). Dynamic and static stress perturbations represent a few percents to tens of percents of the stress buildup through the slow rupture cycle. However, the absence of recurrent events during the 12 years of strain monitoring (2006 to 2018) prevents to estimate the recurrence interval of the slow event, which limits our ability to further interpret the link between the rupture and the perturbations. Finally, there is no large and unique load transient at the time of the initiation, therefore this single event may have likely occurred spontaneously.
Changes in Endometriosis-Associated Symptoms Following Immunization against SARS-CoV-2: A Cross-Sectional Study

Background: There are many reports about variations in the menstrual cycle after infection with SARS-CoV-2 or vaccination against it. However, data on SARS-CoV-2 infection or vaccination-related changes in menstruation-associated endometriosis-typical symptoms such as dysmenorrhea, dyspareunia, dyschezia, dysuria, and bloating are rare or missing. Methods: This retrospective study was performed as an online survey among employees and students at the University Hospital Ulm, Germany. Changes regarding the presence of mentioned symptoms and after immunization (vaccination and/or infection) were evaluated with the McNemar Test. Additionally, the risk factors associated with these changes and associations between a subjectively perceived general change in menstruation and changes in the symptoms were evaluated. Results: A total of 1589 respondents were included in the final analysis. Less than 4% of respondents reported the occurrence of new symptoms that they had not experienced before immunization. Overall, there was a significant reduction in the presence of dysmenorrhea, back pain, dyschezia, bloating, and dyspareunia after immunization against coronavirus (p < 0.001). Only 2.3% of all participants reported to have been diagnosed with endometriosis. Factors associated with changes in endometriosis-typical symptoms following immunization were body mass index, age, endometriosis, and thyroid disease. Conclusions: Our results provide unique data about a reduction in the incidence of endometriosis-associated symptoms as dysmenorrhea, dyschezia, and dyspareunia after immunization against COVID-19.
Complex associations between cancer progression and immune gene expression reveals early influence of transmissible cancer on Tasmanian devils

The world’s largest extant carnivorous marsupial, the Tasmanian devil, is challenged by Devil Facial Tumor Disease (DFTD), a fatal, clonally transmitted cancer. In two decades, DFTD has spread across 95% of the species distributional range. A previous study has shown that factors such as season, geographic location, and infection with DFTD can impact the expression of immune genes in Tasmanian devils. To date, no study has investigated within-individual immune gene expression changes prior to and throughout the course of DFTD infection. To explore possible changes in immune response, we investigated four locations across Tasmania that differed in DFTD exposure history, ranging between 2 and >30 years. Our study demonstrated considerable complexity in the immune responses to DFTD. The same factors (sex, age, season, location and DFTD infection) affected immune gene expression both across and within devils, although seasonal and location specific variations were diminished in DFTD affected devils. We also found that expression of both adaptive and innate immune genes starts to alter early in DFTD infection and continues to change as DFTD progresses. A novel finding was that the lower expression of immune genes MHC-II, NKG2D and CD8 may predict susceptibility to earlier DFTD infection. A case study of a single devil with regressed tumor showed opposite/contrasting immune gene expression patterns compared to the general trends observed across devils with DFTD infection. Our study highlights the complexity of DFTD’s interactions with the host immune system and the need for long-term studies to fully understand how DFTD alters the evolutionary trajectory of devil immunity.
An Ensemble Dynamic Model and Bio-Inspired Feature Selection Method-based Decision Support System for Predicting Multiple Organ Dysfunction Syndrome in the ICU

—Multiple Organ Dysfunction Syndrome (MODS) is one of the most common and severe conditions affecting patients admitted to intensive care units (ICUs). It is characterized by the simultaneous failure or dysfunction of at least two organ systems. Although no specific remedy for MODS has been identified to date, early diagnosis and adequate organ support can significantly improve patient outcomes. Identifying patients at risk of developing MODS in the ICU is challenging. Currently, several methods are used for this purpose, including scoring systems like SOFA and MOD Score, as well as machine learning-based approaches. However, these methods often have limitations. Some require invasive features, making them complex to use in a smart healthcare system. Others suffer from a lack of performance due to various problems, which can potentially lead to unreliable predictions. Feature selection can improve ML models’ performance. Recently, bio-inspired feature selection techniques have shown promise in improving the performance of machine learning methods in many domains, but their effectiveness in MODS prediction has not yet been evaluated. Additionally, research on early MODS prediction, particularly utilizing time-series data and dynamic ensemble methods, remains limited. To fill this gap, the present research used state-of-the-art machine learning algorithms, namely dynamic ensemble techniques, to predict patients at risk of developing MODS in the ICU. Dynamic ensembles are new methods that select an ensemble of the best-performing models for every new test case. We compared the performance of these models with full features and with feature selection. Three nature-inspired meta-heuristic optimization models, namely the binary bat algorithm (BBA), grey wolf optimization (GWO), and genetic algorithm (GA), were evaluated to select the optimal feature subset. The models were built using non-invasive patient features and time-series data from the first 12 hours of ICU admission. The results showed that feature selection significantly improved the performance of dynamic ensemble models. Notably, the METADES model, employing grey wolf optimization for feature selection, demonstrated the best performance in terms of accuracy(96.5%), F1 score (96.4%), precision (97.2%), recall (95.7%), and area under the ROC curve (AUC) (98.4%). These findings highlight the potential and effectiveness of our approach for early MODS prediction in ICUs.
A Systemic Immune Inflammation Index and PD-L1 (SP142) Expression as a Potential Combined Biomarker of the Clinical Benefit of Chemo-Immunotherapy in Extensive-Stage Small-Cell Lung Cancer

Background: No studies have identified combined biomarkers that may be more reasonable for the assessment of current chemo-immunotherapy in patients with extensive stage small-cell lung cancer (ES-SCLC). Methods: This study was conducted to investigate a combined biomarker with prognostic or predictive value in ES-SCLC. We determined the best independent prognostic biomarker among the four complete blood-count-derived inflammatory biomarkers (CBC-IBs). Subsequently, we analyzed the prognostic or predictive value of combining this independent CBC-IB with PD-L1 (SP142) expression. We prospectively assessed the SP142 analyses in tumor samples at diagnosis. Results: All in all, 55 patients with ES-SCLC were classified into four groups according to the systemic immune inflammation index (SII) (low/high) and SP142 (positive/negative). The best survival was observed in the low-SII/ SP142-positive group, whereas the worst survival was observed in the high-SII/SP142-negative group (p = 0.002). The combined SII-SP142 biomarker was better for predicting both survival and disease progression in patients with ES-SCLC. Conclusions: The combined SII-SP142 biomarker can be readily and universally obtained at a low cost in clinical practice, without requiring advanced genomics technology or specialized expertise. Although further studies are needed to confirm that the combined SII-SP142 biomarker is widely applicable, it should help clinicians to identify the best patients for combined chemotherapy with atezolizumab in ES-SCLC.
Angle Diagnosis of Blast Furnace Chute Through Deep Learning of Temporal Images

Blast furnace (BF) ironmaking requires flexible and precise burden distribution. As a critical distributing unit, the angle diagnosis of the BF chute is essential for operation under severe conditions. However, the existing methods must be performed during BF overhauls, which cannot satisfy timely and convenient diagnosis requirements. Inspired by cutting-edge computer vision technologies, a novel angle diagnosis method for BF chute based on deep learning of temporal images is proposed, which uses grayscale consistency transformation and data augmentation for data preprocesses, and a Res-LSTM-based deep neural network model for angle diagnosis. Experiments were conducted using chute motion videos collected from the BF top imaging system, and experimental results showed that our method can extract spatial–temporal features and accurately identify chute angles. The BF chute angle diagnosis system developed based on this method has been successfully running in No.7 BF ( $1750 m^{3}$ in volume) of Tranvic Steel Company, Ltd. in China for half a year. The system can accurately diagnose the chute angle in real time, for a diagnosis rate of 100%.
Measurable Residual Disease Detection in Acute Myeloid Leukemia: Current Challenges and Future Directions

Acute myeloid leukemia (AML) is an aggressive malignant disease with a high relapse rate due to the persistence of chemoresistant cells. To some extent, these residual cells can be traced by sensitive flow cytometry and molecular methods resulting in the establishment of measurable residual disease (MRD). The detection of MRD after therapy represents a significant prognostic factor for predicting patients’ individual risk of relapse. However, due to the heterogeneity of the disease, a single sensitive method for MRD detection applicable to all AML patients is lacking. This review will highlight the advantages and limitations of the currently available detection methods—PCR, multiparameter flow cytometry, and next generation sequencing—and will discuss emerging clinical implications of MRD test results in tailoring treatment of AML patients.
The Weather Forecast Using Data Mining Research Based on Cloud Computing.

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.
River Mixing in the Amazon as a Driver of Concentration‐Discharge Relationships

Large hydrological systems aggregate compositionally different waters derived from a variety of pathways. In the case of continental‐scale rivers, such aggregation occurs noticeably at confluences between tributaries. Here we explore how such aggregation can affect solute concentration‐discharge (C‐Q) relationships and thus obscure the message carried by these relationships in terms of weathering properties of the Critical Zone. We build up a simple model for tributary mixing to predict the behavior of C‐Q relationships during aggregation. We test a set of predictions made in the context of the largest world's river, the Amazon. In particular, we predict that the C‐Q relationships of the rivers draining heterogeneous catchments should be the most “dilutional” and should display the widest hysteresis loops. To check these predictions, we compute 10 day‐periodicity time series of Q and major solute (Si, Ca2+, Mg2+, K+, Na+, Cl‐, SO42− ) C and fluxes (F) for 13 gauging stations located throughout the Amazon basin. In agreement with the model predictions, C‐Q relationships of most solutes shift from a fairly “chemostatic” behavior (nearly constant C) at the Andean mountain front and in pure lowland areas, to more “dilutional” patterns (negative C‐Q relationship) toward the system mouth. More prominent C‐Q hysteresis loops are also observed at the most downstream stations. Altogether, this study suggests that mixing of water and solutes between different flowpaths exerts a strong control on C‐Q relationships of large‐scale hydrological systems.
Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.
The impact of social capital on the creation of customer participation value in virtual brand communities

Purpose: This paper aims to investigate the effect of social capital on the contributions of knowledge and the behavior of citizens towards customer participation and value co-creation in virtual brand communities. Design/Methodology/Approach: This study uses structural equation modelling (SEM) to analyse 213 questionnaires in order to validate a model that is based on social capital theory. This method examines the assumed theoretical connections assessing both the direct and indirect impacts of social capital on customer engagement in value co-creation. Findings: The findings indicate that social interaction ties, principles of reciprocity and trust and a shared vision of social capital positively influence customers' knowledge contribution behaviors and citizenship behaviors for engaging in value co-creating in virtual brand communities. Conclusion: The study concludes that societal capital plays a significant role in customer participation in value-creating behaviors within the communities of virtual brands. Research Limitations/Implications: The main limitation of the study is the potential bias of self-reported data and it proposes to conduct a longitudinal study to dynamically understand customer engagement value co-creation and explore the interactions between dimensions of social capital. Practical Implications: The study points out that strengthening social capital in virtual brand communities can motivate customers to participate more in knowledge contribution and civic behavior, thereby enhancing value co-creation. Therefore, companies should aim to strengthen interaction, trust and shared vision within the community to drive this process. Contribution to the Literature: This study expands the existing research on value co-creation by revealing the mechanism of social capital influencing the participation of customers in value co-creation in virtual brand communities.
The Influence of Geographical Environment on Public Social Trust: What Role Do Tourism Activities Play?

Social trust is derived from the interaction of environmental and social factors, which has important significance for the sustainable development of society and social governance. In particular, in the post-pandemic era, tourist activity will receive special attention in terms of its role in the development of the public’s social trust. On the basis of the sample of big data, this research takes China as an example to study the influences of different geographical and environmental elements on individuals’ social trust as well as the common role played by the tourist activity. The research showed that the geographical environment and tourism activities have interacting effects on public social trust. This influencing mechanism is specifically manifested as the rice-growing ratio and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination; pathogen stress and tourist supply level can exert interacting effects on the social trust of the residents in an area from which tourists originate; and economic development and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination. By doing so, this research provides theoretical support and practical suggestions for the recovery of the public’s social trust from the perspective of tourism geography in the post-pandemic era.
Effect of Prebiotic, Probiotic Bacteria and Symbiotic Diets Containing Bacillus Coagulans and Gum Arabic on Lipid Profile in Hypercholesterolemic Rats

Purpose: Hypercholesterolemia is a major problem for many societies as well as for health professionals due to the close association between cardiovascular disease and lipid abnormalities. The current study was conducted to determine the effect of prebiotic, probiotic bacteria and symbiotic diets containing Bacillus Coagulans and Gum Arabic on lipid profile in hypercholesterolemic rats. Methodology: The spore suspension was prepared and Gum Arabic was purchased as the natural product. 35 male albino rats (170 ± 5 g) were fed a cholesterol-supplemented diet to induce hypercholesterolemia. The rats were divided into 5 groups (n = 7). The group 1: basal diet (negative control), and the other four groups were given hypercholesterolemia diet supplemented with 25% lard and 2% cholesterol for six weeks to induce hypercholesterolemia in rats. They were divided as follows: Group 2: (positive control), Group 3: fed a supplemented basal diet with 5% Gum Arabic (prebiotic), Group 4: fed a supplemented basal diet with coagulase spores, and group 5: fed a basal diet supplemented with 5% Gum Arabic and spores per day of B. coagulans (synbiotic). Results: The findings showed a significant increase in the levels of total cholesterol and triacylglycerol (mg/dL) (P<0.05) in (positive control) fed the basic diet (B.D.), compared to (negative control). Unique Contribution to Theory, Practice and Policy: The Anti-Inflammatory Theory and The theory of using prebiotics to lower cholesterol and reduce its absorption. This study indicates that the theory used in the study has been validated. The best results that showed significant differences with the negative control group were achieved in the hypercholesterolemia groups treated with Gum Arabic (GA5%), where the highest mean value was recorded (28.54 ± 3.71 mg/dL), which was close to the negative control (36.33 ± 5.00 mg/dL). As for the groups that took B. coagulans and GA 5% + B. coagulans, a decrease in LDL-c, (VLDL-c), AST and AST levels, and the best result recorded was in the group GA 5% + B.coagulans. Histological examinations also showed that the liver sections in the fifth group showed a normal structure, except for slight degeneration of the liver cells. The best result was in the group of GA 5%+ B. coagulans treatment group compared to the other groups.