The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector\'s carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry\'s carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province\'s total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi\'s power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

The traditional method for power load forecasting is susceptible to various factors, including holidays, seasonal variations, weather conditions, and more. These factors make it challenging to ensure the accuracy of forecasting results. Additionally, there is a limitation in extracting meaningful physical signs from power data, which ultimately reduces prediction accuracy. This paper aims to address these issues by introducing a novel approach called VCAG (Variable Mode Decomposition-Convolutional Neural Network-Attention Mechanism-Gated Recurrent Unit) for combined power load forecasting. In this approach, we integrate Variable Mode Decomposition (VMD) with Convolutional Neural Network (CNN). VMD is employed to decompose power load data, extracting valuable time-frequency features from each component. These features then serve as input for the CNN. Subsequently, an attention mechanism is applied to give importance to specific features generated by the CNN, enhancing the weight of crucial information. Finally, the weighted features are fed into a Gated Recurrent Unit (GRU) network for time series modeling, ultimately yielding accurate load forecasting results.To validate the effectiveness of our proposed model, we conducted experiments using two publicly available datasets. The results of these experiments demonstrate that our VCAG method achieves high accuracy and stability in power load forecasting, effectively overcoming the limitations associated with traditional forecasting techniques. As a result, this approach holds significant promise for broad applications in the field of power load forecasting.

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry.

Introduction Acute appendicitis is a common reason for acute abdominal pain. It has a high perforation rate of 20%. Diagnosis of acute appendicitis is usually through well-known clinical signs and symptoms. Radiologic imaging is by and large carried out in peculiar cases with indistinct signs and symptoms. Although various scoring methods are available for screening and diagnosis, those have inadequate validity to accurately predict the severity of acute appendicitis. From the differential counts, the neutrophil-to-lymphocyte ratio (NLR) is an economical and straightforward measure of subclinical inflammation. NLR may be a useful marker for predicting the onset and severity of appendicitis because of the insight it gives into immunological and inflammatory pathways. In this study, we aimed to determine the association between NLR and acute appendicitis among adult patients to differentiate between perforated and non-perforated appendicitis in a tertiary care hospital in Tamil Nadu, India. Methods This was a cross-sectional study conducted in the Department of General Surgery of a deemed university in Chennai, Tamil Nadu. The study was conducted from March 2022 to December 2022. Patients aged 18 years and above undergoing appendicectomy surgery were included in the study. Patients with hematology disorders, chronic kidney disease, chronic liver disease, chronic obstructive pulmonary disease, asthma, cancer, or auto-immune diseases, and any viral, bacterial, or parasitic infections were excluded. Pregnant women were also excluded from the study. After obtaining informed consent from the patients, blood samples were collected as and when they were diagnosed as acute appendicitis. Laboratory analysis for complete hemogram including white blood cell (WBC) count, neutrophil, and lymphocyte count was carried out using an automated hematology analyzer. Prevalence of perforated appendicitis was reported as a percentage. The receiver-operating characteristic (ROC) curve was developed for NLR in differentiating perforated and non-perforated appendicitis. Data were entered in Microsoft Excel 2023. These analyses were carried out in STATA 12.0 (StataCorp, College Station, Texas, USA). Results A total of 212 patients aged 18 years and above were included in the study. Among them 93 (43.9%) were male and 119 (56.1%) were female. Prevalence of perforated appendicitis observed intra-operatively was 29.7% and non-perforated appendicitis was 70.3%. The mean (SD) of NLR among patients with perforated appendicitis was 8.8 (5.1) and non-perforated appendicitis was 3.2 (2.4) with a statistically significant difference (p-value < 0.0001). ROC curve with a cut-off value of 3.78 NLR, had sensitivity of 65.9% and specificity of 93.1% in differentiating perforated and non-perforated appendicitis. The positive predictive value (PPV) and negative predictive values (NPV) were reported as 85.7% and 81.2%, respectively. Conclusion NLR has a reasonable validity in differentiating perforated and non-perforated appendicitis. NLR may be useful in low-resource settings where routine confirmatory radiological procedures like computed tomography scans are not available.

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models.

OBJECTIVE: This study aimed to provide the basic data needed to estimate future urologist supply and demand by applying various statistical models related to healthcare utilization.
METHODS: Data from multiple sources, including the Yearbook of Health and Welfare Statistics, Korean Hospital Association, Korean Medical Association, and the Korean Urological Association, were used for supply estimation. Demand estimation incorporated data on both clinical and non-clinical urologists, along with future population estimates. In-and-out moves and demographic methods were employed for supply estimation, while the Bureau of Health Professions model was utilized for demand estimation. Supply estimation assumptions included fixed resident quotas, age-specific death rates, migration rates, and retirement age considerations. Demand estimation assumptions included combining clinical and nonclinical urologist demands, adjusting population size for age-related healthcare usage variations. Urologist productivity was determined by adjusting productivity levels to 100%, 90%, and 80% of the base year based on actual clinical practice volumes.
RESULTS: Estimations of both demand and supply consistently indicate an oversupply of urologists until 2025, followed by an expected shortage by 2035 owing to increased deaths and retirements attributed to the aging urologist population. This shortage becomes more pronounced when employing more reliable models, such as logit or ARIMA (autoregressive integrated moving average), underscoring the growing need for urologists in the future.
CONCLUSIONS: All estimation models estimated an oversupply of urologists until 2025, transitioning to a deficit due to reduced supply thereafter. However, considering potential unaccounted factors, greater effort is needed for accurate predictions and corresponding measures.

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.

A landslide involves the downward movement of a mass of rock, debris, earth, or soil. Landslides happen when gravitational forces and other types of shear stresses on a slope surpass the shear strength of the materials. Additionally, landslides can be triggered by processes that weaken the shear strength of the slope\'s material. Shear strength primarily depends on two factors such as frictional strength, which is the resistance to movement between the interacting particles of the slope material, and cohesive strength, which is the bonding between those particles. A landslide is a terrible natural disaster that causes much damage to both human life and the economy. It often occurs in steep mountainous areas or hilly regions, ranging in scale from medium to large. It progresses slowly (20-50 mm/year), but when it occurs, it can move at a speed of 3 m/s. Therefore, early detection or prevention of this disaster is an essential and significant task. This paper developed a method to collect and analyze data, with the purpose of determining the possibility of landslide occurrences to reduce its potential losses.•The proposed method is convenient for users to grasp information of landslide phenomenon.•A machine learning model is applied to forecast landslide phenomenon.•Internet of things (IoT) system is utilized to manage and send a warning text to individual email address and mobile devices.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

BACKGROUND: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China.
METHODS: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk.
RESULTS: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China.
CONCLUSIONS: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.