Chromosome-level genome assembly of the sacoglossan sea slug Elysia timida (Risso, 1818).

BACKGROUND: Sequencing and annotating genomes of non-model organisms helps to understand genome architecture, the genetic processes underlying species traits, and how these genes have evolved in closely-related taxa, among many other biological processes. However, many metazoan groups, such as the extremely diverse molluscs, are still underrepresented in the number of sequenced and annotated genomes. Although sequencing techniques have recently improved in quality and quantity, molluscs are still neglected due to difficulties in applying standardized protocols for obtaining genomic data. RESULTS: In this study, we present the chromosome-level genome assembly and annotation of the sacoglossan sea slug species Elysia timida, known for its ability to store the chloroplasts of its food algae. In particular, by optimizing the long-read and chromosome conformation capture library preparations, the genome assembly was performed using PacBio HiFi and Arima HiC data. The scaffold and contig N50s, at 41.8 Mb and 1.92 Mb, respectively, are approximately 30-fold and fourfold higher compared to other published sacoglossan genome assemblies. Structural annotation resulted in 19,904 protein-coding genes, which are more contiguous and complete compared to publicly available annotations of Sacoglossa with respect to metazoan BUSCOs. We found no evidence for horizontal gene transfer (HGT), i.e. no photosynthetic genes encoded in the sacoglossan nucleus genome. However, we detected genes encoding polyketide synthases in E. timida, indicating that polypropionates are produced. HPLC-MS/MS analysis confirmed the presence of a large number of polypropionates, including known and yet uncharacterised compounds. CONCLUSIONS: We can show that our methodological approach helps to obtain a high-quality genome assembly even for a "difficult-to-sequence" organism, which may facilitate genome sequencing in molluscs. This will enable a better understanding of complex biological processes in molluscs, such as functional kleptoplasty in Sacoglossa, by significantly improving the quality of genome assemblies and annotations.

Respiratory pathogen dynamics in community fever cases: Jiangsu Province, China (2023-2024).

BACKGROUND: Respiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence. METHODS: From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. RESULTS: Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance. CONCLUSIONS: The spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months.

Prediction of acute onset of chronic cor pulmonale: comparative analysis of Holt-Winters exponential smoothing and ARIMA model.

BACKGROUND: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. METHODS: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. RESULTS: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. CONCLUSION: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings.

BACKGROUND: Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. METHODS: Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. RESULTS: The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. CONCLUSION: These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries.

BACKGROUND: Considering the rapidly spreading monkeypox outbreak, WHO has declared a global health emergency. Still in the category of being endemic, the monkeypox disease shares numerous clinical characters with smallpox. This study focuses on determining the most effective combination of autoregressive integrated moving average model to encapsulate time dependent flow behaviour of the virus with short run prediction. METHODS: This study includes the data of confirmed reported cases and cumulative cases from eight most burdened countries across the globe, over the span of May 18, 2022, to December 31, 2022. The data was assembled from the website of Our World in Data and it involves countries such as United States, Brazil, Spain, France, Colombia, Mexico, Peru, United Kingdom, Germany and Canada. The job of modelling and short-term forecasting is facilitated by the employment of autoregressive integrated moving average. The legitimacy of the estimated models is argued by offering numerous model performance indices such as, root mean square error, mean absolute error and mean absolute prediction error. RESULTS: The best fit models were deduced for each country by using the data of confirmed reported cases of monkeypox infections. Based on diverse set of performance evaluation criteria, the best fit models were then employed to provide forecasting of next twenty days. Our results indicate that the USA is expected to be the hardest-hit country, with an average of 58 cases per day with 95% confidence interval of (00-400). The second most burdened country remained Brazil with expected average cases of 23 (00-130). The outlook is not much better for Spain and France, with average forecasts of 52 (00-241) and 24 (00-121), respectively. CONCLUSION: This research provides profile of ten most severely hit countries by monkeypox transmission around the world and thus assists in epidemiological management. The prediction trends indicate that the confirmed cases in the USA may exceed than other contemporaries. Based on the findings of this study, it remains plausible to recommend that more robust health surveillance strategy is required to control the transmission flow of the virus especially in USA.

The impact of climate variation on the spread of Shiga toxin-producing E. coli.

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) is influenced by seasonality, but there is limited understanding of how specific climatic variables contribute to disease spread. This information aids in understanding disease transmission dynamics and could potentially inform public health modeling. METHODS: This retrospective cohort study analyzed public health data from Ontario, Canada, between 2012 and 2021, along with historical climate data from Environment Canada. We employed Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to assess how temperature and precipitation impact the incidence of STEC infections, measured per 10,000,000 population. RESULTS: The study included 1658 confirmed STEC cases. A significant correlation was found between STEC incidence and climatic variables. Each degree Celsius increase in maximum temperature was associated with a rise of 3 STEC cases per 10,000,000 population (Centers for Disease Control and Prevention (2024)). Additionally, each millimeter of increased precipitation correlated with an increase of 1.1 cases per 10,000,000 population. CONCLUSIONS: The findings demonstrate a significant impact of temperature and precipitation on STEC transmission, highlighting the importance of integrating meteorological data into public health surveillance. This integration may help inform public health responses and support healthcare systems in planning for future outbreaks. Further studies are needed to refine predictive models and develop effective early warning systems for clinical settings.

Spectrum occupancy prediction using LSTM models for cognitive radio applications.

In recent days, mobile traffic prediction has become a prominent solution for spectrum management-related operations for the next-generation cellular networks in Cognitive Radio (CR) applications. To achieve this, the binary dataset has been created from the captured data by monitoring the spectrum activities of nine different Long Term Evolution (LTE) frequency channels. We propose a Long Short Term Memory (LSTM) based Spectrum Occupancy Prediction (SOP) approach for modelling infrastructure-based cellular traffic systems. The different types of LSTM models, such as Convolutional, Convolutional Neural Network (CNN), Stacked, and Bidirectional have been generated via offline training and tested for the created binary datasets. Moreover, the prediction performance evaluation of the generated LSTM models has been calculated using Mean Absolute Error (MAE). The pro- posed LSTM-based SOP model has achieved 2.5% higher prediction accuracy than the Auto-Regressive Integrated Moving Average (ARIMA) statistical model, accurately aligning the traffic trend with the actual samples.

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses.

The present study aimed to predict the biofilm-formation ability of L. monocytogenes isolates obtained from cattle carcasses via the ARIMA model at different temperature parameters. The identification of L. monocytogenes obtained from carcass samples collected from slaughterhouses was determined by PCR. The biofilm-forming abilities of isolates were phenotypically determined by calculating the OD value and categorizing the ability via the microplate test. The presence of some virulence genes related to biofilm was revealed by QPCR to support the biofilm profile genotypically. Biofilm-formation of the isolates was evaluated at different temperature parameters (37 °C, 22 °C, 4 °C and - 20 °C). Estimated OD values were obtained with the ARIMA model by dividing them into eight different estimation groups. The prediction performance was determined by performance measurement metrics (ME, MAE, MSE, RMSE, MPE and MAPE). One week of incubation showed all isolates strongly formed biofilm at all controlled temperatures except - 20 °C. In terms of the metrics examined, the 3 days to 7 days forecast group has a reasonable prediction accuracy based on OD values occurring at 37 °C, 22 °C, and 4 °C. It was concluded that measurements at 22 °C had lower prediction accuracy compared to predictions from other temperatures. Overall, the best OD prediction accuracy belonged to the data obtained from biofilm formation at -20 °C. For all temperatures studied, especially after the 3 days to 7 days forecast group, there was a significant decrease in the error metrics and the forecast accuracy increased. When evaluating the best prediction group, the lowest RMSE at 37 °C (0.055), 22 °C (0.027) and 4 °C (0.024) belonged to the 15 days to 21 days group. For the OD predictions obtained at -20 °C, the 15 days to 21 days prediction group had also good performance (0.011) and the lowest RMSE belongs to the 7 days to 15 days group (0.007). In conclusion, this study will guide in using indicator parameters to evaluate biofilm forming ability to predict optimum temperature-time. The ARIMA models integrated with this study can be useful tools for industrial application and risk assessment studies using different parameters such as pH, NaCl concentration, and especially temperature applied during food processing and storage on the biofilm-formation ability of L. monocytogenes.

Using meta-learning to recommend an appropriate time-series forecasting model.

BACKGROUND: There are various forecasting algorithms available for univariate time series, ranging from simple to sophisticated and computational. In practice, selecting the most appropriate algorithm can be difficult, because there are too many algorithms. Although expert knowledge is required to make an informed decision, sometimes it is not feasible due to the lack of such resources as time, money, and manpower. METHODS: In this study, we used coronavirus disease 2019 (COVID-19) data, including the absolute numbers of confirmed, death and recovered cases per day in 187 countries from February 20, 2020, to May 25, 2021. Two popular forecasting models, including Auto-Regressive Integrated Moving Average (ARIMA) and exponential smoothing state-space model with Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS) were used to forecast the data. Moreover, the data were evaluated by the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) criteria to label time series. The various characteristics of each time series based on the univariate time series structure were extracted as meta-features. After that, three machine-learning classification algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used as meta-learners to recommend an appropriate forecasting model. RESULTS: The finding of the study showed that the DT model had a better performance in the classification of time series. The accuracy of DT in the training and testing phases was 87.50% and 82.50%, respectively. The sensitivity of the DT algorithm in the training phase was 86.58% and its specificity was 88.46%. Moreover, the sensitivity and specificity of the DT algorithm in the testing phase were 73.33% and 88%, respectively. CONCLUSION: In general, the meta-learning approach was able to predict the appropriate forecasting model (ARIMA and TBATS) based on some time series features. Considering some characteristics of the desired COVID-19 time series, the ARIMA or TBATS forecasting model might be recommended to forecast the death, confirmed, and recovered trend cases of COVID-19 by the DT model.

Trends of older gastric cancer incidence, mortality, and survival in the highest gastric cancer risk area in China: 2010-2019 and prediction to 2024.

BACKGROUND: Gastric cancer is a major health problem worldwide, with a high incidence among older adults. Given the aging overall population, it was crucial to understand the current burden and prospective trend of older gastric cancer. This study aimed to analyze the temporal trends of the incidence, mortality, and survival of older gastric cancer in the highest gastric cancer risk area in China from 2010 to 2019, and to predict the future burden of older gastric cancer up to 2024. METHODS: The study was conducted in Gansu province, an area characterized by the highest gastric cancer incidence and mortality in China. The registration data of gastric cancer incidence and mortality from 2010 to 2019 were pooled from registries in the Gansu Cancer Registration System, while survival data were collected from the First Hospital of Lanzhou University, Lanzhou University Second Hospital, and Gansu Cancer Hospital. Chinese standard population in 2000 and the Segi's world standard population were applied to calculate the age-standardized rate. Joinpoint regression was used to analyze the average annual percentage change (AAPC) in cancer incidence and mortality. Autoregressive Integrated Moving Average (ARIMA) models were employed to generate forecasts for incidence and mortality from 2020 to 2024. RESULTS: Based on registry data from 2010 to 2019, the incidence and mortality rates of gastric cancer among older adults remained stable. The incidence rates declined from 439.65 per 100,000 in 2010 to 330.40 per 100,000 in 2019, with an AAPC of -2.59% (95% confidence interval[CI], -5.14 to 0.04, P = 0.06). Similarly, the mortality rate changed from 366.98 per 100,000 in 2010 to 262.03 per 100,000 in 2019, with an AAPC of -2.55% (95% CI, -8.77-4.08%, P = 0.44). In the hospital-based cohort, the decline in survival rates was reported among older patients with gastric cancer in the highest gastric cancer risk area in China, with the 3-year overall survival (OS) decreasing from 58.5% (95% CI, 53.5-63.2%) in 2010 to 34.4% (95%CI, 32.1-36.7%) in 2019, and the 3-year progression-free survival (PFS) decreasing from 51.3% (95%CI, 47.5-55.1%) in 2010 to 34.2% (95%CI, 32.0-36.3%) in 2019, respectively. Moreover, forecasts generated by ARIMA models revealed a significant decline in the incidence and mortality of older gastric cancer in China from 2020 to 2024. Specifically, the incidence rate of older gastric cancer was expected to decrease from 317.94 per 100,000 population in 2020 to 205.59 per 100,000 population in 2024, while the anticipated mortality rate was estimated to decrease from 222.52 per 100,000 population in 2020 to 186.22 per 100,000 population in 2024. CONCLUSION: From 2010 to 2019, the incidence and mortality of older gastric cancer remained stable in the highest gastric cancer risk area in China, while the survival rates showed a decline. Based on the ARIMA models, it was anticipated that there might be a continued decline in older gastric cancer incidence and mortality in the highest-risk area in China over the next five years.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index χ and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all χ ≤ 0.001 except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both χ = 0.3182 for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.

Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast.

BACKGROUND: This study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027. METHODS: This research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE. RESULTS: Based on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products. CONCLUSIONS: The growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient's blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.

Comparison of antipsychotic drug use in children and adolescents in the Netherlands before and during the COVID-19 pandemic.

This study aims to describe the patterns and trends in antipsychotic prescription among Dutch youth before and during the corona virus disease 2019 (COVID-19) pandemic (between 2017 and 2022). The study specifically aims to determine whether there has been an increase or decrease in antipsychotic prescription among this population, and whether there are any differences in prescription patterns among different age and sex groups. The study utilized the IADB database, which is a pharmacy prescription database containing dispensing data from approximately 120 community pharmacies in the Netherlands, to analyze the monthly prevalence and incidence rates of antipsychotic prescription among Dutch youth before and during the pandemic. The study also examined the prescribing patterns of the five most commonly used antipsychotics and conducted an autoregressive integrated moving average (ARIMA) analysis using data prior to the pandemic, to predict the expected prevalence rate during the pandemic. The prescription rate of antipsychotics for Dutch youth was slightly affected by the pandemic, with a monthly prevalence of 4.56 [4.50-4.62] per 1000 youths before COVID-19 pandemic and 4.64 [4.59-4.69] during the pandemic. A significant increase in prevalence was observed among adolescent girls aged 13-19 years. The monthly incidence rate remained stable overall, but rose for adolescent girls aged 13-19 years. Aripiprazole, and Quetiapine had higher monthly prevalence rates during the pandemic, while Risperidone and Pipamperon had lower rates. Similarly, the monthly incidence rates of Aripiprazole and Olanzapine went up, while Risperidone went down. Furthermore, the results from the ARIMA analysis revealed that despite the pandemic, the monthly prevalence rate of antipsychotic prescription was within expectation. The findings of this study suggest that there has been a moderate increase in antipsychotic prescription among Dutch youth during the COVID-19 pandemic, particularly in adolescent females aged 13-19 years. However, the study also suggests that factors beyond the pandemic may be contributing to the rise in antipsychotic prescription in Dutch youth.

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760-weight; 322,570-temperature; 155,373-video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.

Unmasking seasonal cycles in a high-input dairy herd in a hot environment: How climate shapes dynamics of milk yield, reproduction, and productive status.

This study aimed to predict the annual herd milk yield, lactation, and reproductive cycle stages in a high-input dairy herd in a zone with prolonged thermal stress. Also, the impact of climatic conditions on milk yield and productive and reproductive status was assessed. An autoregressive integrated moving average (ARIMA) model was used in data fitting to predict future monthly herd milk yield and reproductive status using data from 2014 to 2020. Based on the annual total milk output, the highest predicted percentage of milk yield based on the yearly milk production was in February (9.1%; 95% CI = 8.3-9.9) and the lowest in August (6.9%; 95% CI = 6.0-7.9). The predicted highest percentage of pregnant cows for 2021 was in May (61.8; 95% CI = 53.0-70.5) and the lowest for November (33.2%; 95% CI = 19.9-46.5). The monthly percentage of dry cows in this study showed a steady trend across years; the predicted highest percentage was in September (20.1%; CI = 16.4-23.7) and the lowest in March (7.5%; 4.0-11.0). The predicted days in milk (DIM) were lower in September (158; CI = 103-213) and highest in May (220; 95% CI = 181-259). Percentage of calvings was seasonal, with the predicted maximum percentage of calvings occurring in September (10.3%; CI = 8.0-12.5) and the minimum in April (3.2%; CI = 1.0-5.5). The highest predicted culling rate for the year ensuing the present data occurred in November (4.3%; 95% CI = 3.2-5.4) and the lowest in April (2.5%; 95% CI = 1.4-3.5). It was concluded that meteorological factors strongly influenced rhythms of monthly milk yield and reproductive status. Also, ARIMA models robustly estimated and forecasted productive and reproductive events in a dairy herd in a hot environment.

A dynamic hybrid model for efficient tuberculosis incidence rate prediction.

Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China.

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions.

Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.