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Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation of energy storage devices for any surplus energy. Business organisations always look for the use of price-prediction models with higher accuracy to help them maximise benefits. With the enhancement of Internet of Things (IoT) technology, data availability on energy demand, and hence the associated price prediction modelling has become more effective tools than before. However, price prediction has been a challenging task because of the uncertainty in the demand and supply and other external factors such as weather, and gas prices as these factors can influence the fluctuation of electricity prices. In this regard, the selection of an appropriate prediction model is crucial for business organisations. In this paper, an analytical study has been presented to predict short-term electricity prices in the UK market as a use case for a UK-based energy trading company. ARIMA, Prophet, XGBoost as well as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) algorithms have been analysed. In this study, UK Market Index Data (MID) from Elexon API data has been used that represent half-hourly electricity prices. In addition, gas prices, and initial demand out-turn data, as external factors, are introduced into the models for improving the accuracy and performance of these models. The comparative analysis shows that the ARIMA can handle only one external factor in its prediction model, while the Prophet and XGBoost can incorporate multiple external regressors in their models. Also, the models based on deep learning algorithms can deal with univariate and multivariate time series. The comparative analysis also revealed that the XGBoost model has better performance than the ARIMA and Prophet models, as has been found in earlier studies. With the extended analysis, it has been found that deep learning models outperform ARIMA, Prophet, and XGBoost models in terms of prediction accuracy. This extended comparative analysis gives the flexibility to choose the appropriate model selection for any organisation working in analogous business scenarios as of the business use case of this study. [ABSTRACT FROM AUTHOR]

Because scripture represents the direct word of God, it is the most sacred source for authority in the scriptural monotheisms. Scripture is conveyed to humanity through extraordinary individuals known as prophets. But if the purported prophet is a false prophet, then the conveyed message is false, resulting in the collapse of the structure upon which religious authority rests. This problem was recognized in the Hebrew Bible, and accusations of flawed prophecy and deceitful prophets figure prominently in relations between the scriptural monotheisms. Jews do not accept the authenticity of Jesus' role as messiah (and prophet) in the NT, and Jews and Christians do not accept the authenticity of Muhammad's role as prophet in the Qur'an. But the rejection is unidirectional. As a rule, established religions cannot accept new prophets, while new religions accept the prophets that lived before, though with certain qualifications. Each of the three classic scriptural monotheisms also declares an "end" to prophecy after the canonization of its scripture. Yet despite these deadlines, each acknowledges that God could nevertheless send another prophet, leaving open the wild card for new prophecy, new revelation, and even new religion; within each of the classic scriptural monotheisms, individuals arose after the canonization of scripture who were deemed by many to be prophets or something "like" prophets. This essay presents a preliminary phenomenology of prophethood, around which much of the religious polemics between the scriptural monotheisms are constructed. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is crucial for maintaining stable and affordable energy supplies, especially for vulnerable populations most affected by shortages and high costs. In the Netherlands, transmission system operator TenneT has raised concerns about potential electricity shortages by 2030. Rising energy prices and the impact of climate change on the energy demand further complicate today's energy market. Policymakers lack clear insights into demand patterns, which complicates the optimization of energy use and the protection of at-risk communities. Accurate and timely forecasts are essential for addressing these issues and supporting sustainable energy management. This research focuses on enhancing the accuracy and lead time of wintertime energy demand forecasts in the Netherlands using advanced machine learning. The ensemble model Prophet-LSTM is trained on hourly load consumption data combined with climate change-related and energy price predictors. The results demonstrate significant improvements over baseline models, achieving a Pearson correlation coefficient of r = 0.93 compared to r = 0.50 in prior studies, as well as accurate forecasts up to 180 days ahead, compared to 2 months. Incorporating climate change-related predictors is challenging due to multicollinearity, highlighting the importance of careful predictor selection. Including energy price predictors yielded modest yet hopeful results, suggesting their ability to optimize energy demand forecasting. [ABSTRACT FROM AUTHOR]

The dynamics of obedience and rebellion represent one of the most complex challenges throughout human history. It can be asserted that the core issue surrounding this phenomenon arises from the fact that individuals often struggle to discern whom or what they should adhere to. Indeed, the rebellion against entities that warrant obedience will lead to significant problems, obeying entities that should be rebelled against can also result in different problems. As a matter of fact, as stated in the Qur'ān, the first calamity that befell man (being expelled from heaven) occurred because of being deceived by the devil's whispers and not listening/obeying his advice. As stated in the relevant verses, as a first experience, humanity learned the adverse consequences of rebelling against the truth and obeying the falsehood. It can be said that this lesson given to the first ancestor of humanity is also a teaching to whom and how to obey, as well as when to rebel. This issue, which is crucial for humanity, is frequently addressed in the Qur'ān and it is aimed to enable human beings to cultivate their awareness of obedience/rebellion correctly. In this sense, while the importance and benefits of obedience are mentioned in some verses, the messages of not obeying, not bowing down and not surrendering are also provided. Because man, who is a magnificent being created by God, should not be a creature that blindly submits to everything. For this reason, in the Qur'ān, entities and individuals who should not be obeyed have been stated in general terms following the example set by the Prophet Muhammad. In this context, the prophet Muhammad has been commanded as never to obey infidels, hypocrites, transgressors and sinners. Likewise, especially in relations with the People of the Book, the commands not to follow their whims and not to be subject to their desires are frequently repeated. It can be said that these orders given to the Prophet Muhammad built a spirit of active struggle and resistance against evil in the characters of both him and the believers who believed in him. In this study, The doctrine of opposing and disobeying evil which primarly was entrusted to the Prophet Muhammad and all believ- ers by the Qur'ān was analyzed through the concepts of obedience and adherence. The study employed a document analysis method and sought to explore the nature of the consciousness of proper obedience and rebellion as exemplified in the life of the Prophet Muhammad. [ABSTRACT FROM AUTHOR]

Since the nineteenth century, Orientalist scholars have been preoccupied with recovering the original order of the Qur'an. In the past fifty years, the question appears to have reached an impasse with no sign of an emerging consensus. Divergences over the anchoring in space and time of the Qur'an have contributed to the diagnosis of "crisis" in (Western) Qur'anic studies. What do the project of determining Qur'anic chronology and its failure tell us about Qur'anic studies in Western academia? What do the current impasses reveal about the nature of the Qur'an as a text and as a theological object? This study analyses disagreements over the Qur'an's space and time by drawing on tools from literary theory and hermeneutics. It argues for a shift from a positivist to an interpretative paradigm. Moving away from the question of the actual historical order of the Qur'an, this article examines the making and the workings of diachronic readings of the Muslim scripture, with a particular focus on the works of two of the most prominent scholars in the field, Angelika Neuwirth and Nicolai Sinai. The aim of the exercise is double: first, to perform a critical reflection on the networks of meaning that we construct or inherit in our engagement with the Muslim scripture; and second, to apprehend better the nature of the Qur'an as an "open text" (Eco 1962) whose textual characteristics make it particularly prone to a number of interpretations. [ABSTRACT FROM AUTHOR]

The Harrist Church in Ivory Coast, West Africa, emerged from the ministry of Liberian William Wadé Harris who baptized between 10,000 and 200,000 people during his eighteen-month evangelistic tour, 1913–1915. This story is full of linguistic complexities and anomalies. Harris himself spoke only English and his own local Liberian Glebo language. He was therefore compelled to work through expatriate English-speaking merchants, knowledgeable of and conversant in local languages, as interpreters and translators in addressing the twelve ethnic groups who heard and accepted his message. Harris encouraged new converts to compose hymns in their own indigenous languages by transforming musical genres embedded in their local musical traditions. Additionally fascinating is that during this early colonial period, the twelve ethnic groups impacted by Harris's ministry lived in almost total isolation from each other and developed their own hymn traditions for thirty-five years (1914–1949), unaware of the existence of churches and worship patterns in neighboring ethnic districts. Only in 1949 did they suddenly become acquainted with the broader, multi-musical, multilingual reality of the Harrist movement. Since then, individual musicians and choirs from local congregations have gradually begun to sing a few of each other's songs, though the challenge of becoming a truly multicultural, multiethnic church remains a work in progress. Documentation of these developments include written colonial and early Protestant and Catholic missionary sources and a large number of eye-witness interviews. Primary research methods employed here come from four intersecting disciplines and theoretical frameworks: orality studies, with particular focus on oral sources in constructing historical narrative; religious phenomenology; mission history; and ethnodoxological research. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Culture & Haci Bektas Veli Research Quarterly is the property of Turkish Cultur & Haci Bektas Veli Research Quarterly and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the nineteenth century, Orientalist scholars have been preoccupied with recovering the original order of the Qur'an. In the past fifty years, the question appears to have reached an impasse with no sign of an emerging consensus. Divergences over the anchoring in space and time of the Qur'an have contributed to the diagnosis of "crisis" in (Western) Qur'anic studies. What do the project of determining Qur'anic chronology and its failure tell us about Qur'anic studies in Western academia? What do the current impasses reveal about the nature of the Qur'an as a text and as a theological object? This study analyses disagreements over the Qur'an's space and time by drawing on tools from literary theory and hermeneutics. It argues for a shift from a positivist to an interpretative paradigm. Moving away from the question of the actual historical order of the Qur'an, this article examines the making and the workings of diachronic readings of the Muslim scripture, with a particular focus on the works of two of the most prominent scholars in the field, Angelika Neuwirth and Nicolai Sinai. The aim of the exercise is double: first, to perform a critical reflection on the networks of meaning that we construct or inherit in our engagement with the Muslim scripture; and second, to apprehend better the nature of the Qur'an as an "open text" (Eco 1962) whose textual characteristics make it particularly prone to a number of interpretations. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water scarcity poses a significant challenge to social integration and economic development, necessitating efficient water management strategies. This study compares time series forecasting models, both classical, Holt–Winters and ARIMA, and modern, LSTM and Prophet, to determine the most accurate model for predicting water flow in public supply networks. Data from four rural Portuguese locations were used, with preprocessing ensuring quality and uniformity. Performance metrics were evaluated for both medium-term (10 days) and long-term (3 months) forecasts. Results indicate that classical models like Holt–Winters and ARIMA perform better for medium-term predictions, while modern models, particularly LSTM, excel in long-term forecasts by effectively capturing seasonal patterns. Future research should integrate additional variables and explore hybrid models to enhance forecasting accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to enhance the prediction of COVID-19 vaccination trends using a novel integrated forecasting model, facilitating better public health decision-making and resource allocation during the pandemic. As the COVID-19 pandemic continues to impact global health, accurately forecasting vaccination trends is critical for effective public health response and strategy development. Traditional forecasting models often fail to capture the complex dynamics of pandemic-driven vaccination rates. The analysis utilizes a comprehensive dataset comprising over 68,487 entries, detailing daily vaccination statistics across various demographics and geographic locations. This dataset provides a robust foundation for modeling and forecasting efforts. It utilizes advanced time series analysis techniques and machine learning algorithms to accurately predict future vaccination patterns based on the Hybrid Harvest model, which combines the strengths of ARIMA and Prophet models. Hybrid Harvest exhibits superior performance, with mean-square errors (MSEs) of 0.1323, and root-mean-square errors (RMSEs) of 0.0305. Based on these results, the model is significantly more accurate than traditional forecasting methods when predicting vaccination trends. It offers significant advances in forecasting COVID-19 vaccination trends through integration of ARIMA and Prophet models. The model serves as a powerful tool for policymakers to plan vaccination campaigns efficiently and effectively. [ABSTRACT FROM AUTHOR]

This study aims to analyze the life of the Prophet Muhammad through a Freudian analysis. Although Sigmund Freud’s study of Islam is considered inaccurate in portraying Islam as a simplified repetition of Judaism, his psychoanalytic approach is useful to study the historical development of the Prophet’s life and his early community. By closely reading Ibn Ishaq’s Sīrat Rasūl Allāh (The Biography of the Prophet) and related library sources, this study sheds a new light on the changes and transformations of Muhammad’s personality and responses to historical events. This approach differs from previous scholarship that relied more on chronological narration and transmission or spiritual-philosophical reflections. Through Freudian psychoanalysis that he employed in Moses and Monotheism, this study analyzes the development of Muhammad’s characters, approaches, and reactions to historical occurrences as recorded in Ibn Ishaq’s biography of the Prophet Muhammad. This study demonstrates that the driving force of the Prophet’s responses to historical realities is not the feeling of guilt as Freud suggests, but a traumatic experience that leads to a series of changing characters and reactions to different sociological, religious, and psychological contexts. This finding not only challenges the relevance of the Freudian approach in the study of Islam but also revisits traditional narratives of the Prophet Muhammad.

Production systems play a vital role in maximizing consumer satisfaction by efficiently transforming inputs such as labor, raw materials, and capital into products or services aligned with consumer demands. An order-based production takes place in poultry meat and meat products production facilities, which face various difficulties in meeting changing customer demands and managing the supply of raw materials. To optimize production and increase customer loyalty, these facilities use strategic scheduling, considering their daily production capacity and fluctuating customer orders. In this study, estimating which customer and product type the future order quantities will come from for the relevant facilities, increasing customer satisfaction by facilitating order processes and minimizing storage costs are discussed. With this study, the number of orders was estimated, and it was aimed to meet the orders in the most accurate way. In the estimations, the order data of a poultry meat and meat products production facility between 2013 and 2021 were used. Since the order figures will change every year in cases such as the customer working with the facility, growing, or shrinking, better results have been tried to be obtained with the arrangements made on the data set used and three different data sets have been obtained. Estimation processes were performed for these three data sets using LSTM and Prophet algorithms. While the RMSE value was 7.07 in the LSTM model in experimental studies, this value was obtained as 10.96 for Prophet. In the results obtained, it was observed that the arrangements made on the data set positively affected the accuracy of the estimations and the LSTM algorithm produced better results than the Prophet algorithm.

Evaluation of water quality and accurate prediction of water pollution indicators are key components in water resource management and water pollution control. The use of biological early warning systems (BEWS), in which living organisms are used as biosensors, allows for a comprehensive assessment of the aquatic environment state and a timely response in the event of an emergency. In this paper, we examine three machine learning algorithms (Theta, Croston and Prophet) to forecast bivalves’ activity data obtained from the BEWS developed by the authors. An algorithm for anomalies detection in bivalves’ activity data was developed. Our results showed that for one of the anomalies, Prophet was the best method, and for the other two, the anomaly detection time did not differ between the methods. A comparison of methods in terms of computational speed showed the advantage of the Croston method. This anomaly detection algorithm can be effectively incorporated into the software of biological early warning systems, facilitating rapid responses to changes in the aquatic environment.

Abstract Background Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Machine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now time for automated systems to take over modelling and monitoring tasks. However, there is a problem with the massive amount of time series data collected to build accurate forecasting systems. There may be inconsistencies in forecasting muscle forces due to the enormous amount of data. As a result, anomaly detection techniques play a significant role in detecting anomalous data. Detecting anomalies can help reduce redundancy and free up large storage space for storing relevant time‐series data. This paper employs several anomaly detection techniques, including Isolation Forest (iforest), K‐Nearest Neighbour (KNN), Open Support Vector Machine (OSVM), Histogram, and Local Outlier Factor (LOF). These techniques have been used by Long Short‐Term Memory (LSTM), Auto‐Regressive Integrated Moving Average (ARIMA), and Prophet models. The dataset used in this study contained raw measurements of body movements (kinematics) and the forces generated during walking (kinetics) of 57 healthy people (29 Female, 28 Male) without walking abnormalities or recent leg injuries. To increase the data samples, we used TimeGAN that generates synthetic time series data with temporal dependencies, aiding in training robust predictive models for muscle force prediction. The results are then compared with different evaluation metrics for five different samples. It is found that anomaly detection techniques with LSTM, ARIMA, and Prophet models provided better performance in forecasting muscle forces. The iforest method achieved the best Pearson's Correlation Coefficient (r) of 0.95, which is a competitive score with existing systems that perform between 0.7 and 0.9. The methodology provides a foundation for precision medicine, enhancing prognostic capability over relying solely on population averages. [ABSTRACT FROM AUTHOR]

This study seeks to identify the primary objectives of the narratives found in extensive passages of the Qur'an. These narratives primarily serve to guide and enlighten individuals. Moreover, they demonstrate that Islam and its principles are not new phenomena but rather a continuation of the teachings of previous prophets. The narratives aim to recount historical events, and to take a consideration for present and future generations. These narratives also provide a brief history of the ongoing struggle between belief and disbelief, and emphasize the consistent nature of human behavior in this struggle through different ages. By emphasizing the existence, unity, and omnipotence of Allah, these narratives provide evidence that Muhammad, who conveyed them through revelation without prior knowledge of past prophets and communities, is indeed the Messenger of Allah. This study aims to explain the main purposes of these Qur'anic narratives, which are dispersed throughout various works, and to explore why narratives are so prevalent in the Qur'an. The Qur'an functions as the primary source for this study, supplemented by narrative materials from classical tafsir and sirah sources, as well as modern literature. The main objectives of the Qur'anic narratives are presented in bullet points for clarity. The study reveals that, these narratives serve various other purposes in addition to its offering of advice and lessons. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones. [ABSTRACT FROM AUTHOR]

Evaluation of water quality and accurate prediction of water pollution indicators are key components in water resource management and water pollution control. The use of biological early warning systems (BEWS), in which living organisms are used as biosensors, allows for a comprehensive assessment of the aquatic environment state and a timely response in the event of an emergency. In this paper, we examine three machine learning algorithms (Theta, Croston and Prophet) to forecast bivalves' activity data obtained from the BEWS developed by the authors. An algorithm for anomalies detection in bivalves' activity data was developed. Our results showed that for one of the anomalies, Prophet was the best method, and for the other two, the anomaly detection time did not differ between the methods. A comparison of methods in terms of computational speed showed the advantage of the Croston method. This anomaly detection algorithm can be effectively incorporated into the software of biological early warning systems, facilitating rapid responses to changes in the aquatic environment. [ABSTRACT FROM AUTHOR]

Copyright of Firat University Faculty of Theology / Fırat Üniversitesi İlahiyat Fakültesi Dergisi is the property of Firat University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts. [ABSTRACT FROM AUTHOR]

Accurately identifying changes in carbon trading prices can provide reasonable reference indicators for a government's macrocontrol and can also help companies more effectively avoid risks brought by carbon emissions and increase the income of carbon assets. Based on the Prophet model, LSTM neural network model, and backpropagation (BP), this paper proposes a method to predict carbon trading prices using the ensemble learning model and uses the Hubei carbon trading market data to predict carbon trading prices. Results show that in terms of accuracy, the Prophet-LSTM-BP ensemble learning model achieves better predictive ability than existing models; its RMSE, MAE, and MAPE are 1.479, 0.951, and 2.135, respectively, which are markedly smaller than the Prophet model's 5.631, 4.471, and 9.661, and the LSTM model's 3.352, 3.105, and 6.880, respectively. Compared with the traditional time series ARIMA model, the MAPE of ARIMA reaches 12.933, which is nearly 1.5 times that of the Prophet model, nearly 2 times that of the LSTM model, and nearly 7 times that of the ensemble learning model. In terms of applicability, when the model is applied to the national carbon trading market, the difference in MAPE compared with the Hubei carbon trading market is only 0.6%, and the other parameters are not more than 16%. The model improves the relevant research on carbon trading price predicting, and concurrently, this method provides ideas for carbon trading price predicting in other carbon trading markets and promotes the sustainable development of the national carbon trading market. [ABSTRACT FROM AUTHOR]

Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector. [ABSTRACT FROM AUTHOR]

Although the binomial »Bible and communication« is very complex, in this paper, we will present some of, in our opinion, the most significant biblical horizons of communication. Word makes the first horizon. Like the human word, God's word is also information, invitation, revelation and fellowship. God also communicates with signs. The world created by the power of God's creative word is one of the privileged places of dialogue between man and God. A special communication mediator is also a prophet - a man of words. In this communication horizon, the word also played a leading role, as did various prophetic signs, among which the prophet's life has a special place. In Scripture, God, paradoxically, also speaks through silence. Sometimes, it is a consequence of man's sins; sometimes, it is God's powerful pedagogical tool; and in Jesus' death on the cross, according to Mk 15, 33-39, it is a revelation of love that transforms the cry of pain, not into the painful exhalation of the dying, but into the sigh of life of the woman giving birth. [ABSTRACT FROM AUTHOR]

The political instability and poor governance of Zimbabwe since the Mugabe era have led to gross socio-economic deprivation that caused many citizens to sink into abject poverty. This has created a sense of insecurity and despondency among the citizens. Amid the suffering, the mega-church prophets rose, who seemed to provide an alternative route to recover from the political and socio-economic crisis. This article gives an overview of the crises in Zimbabwe and the rise of prophets Emmanuel Makandiwa and Prophet Walter Magaya and their doctrine of seeding. The focus of the article is interrogating the purpose of the doctrine of seeding in the life of the prophets, their followers and their challenges. This is achieved by employing the four tasks of theology conceptual framework. These tasks prompt four important theological questions that can assist in addressing societal challenges. This is a qualitative desktop research article that generates data from secondary sources such as books, journal articles, newspaper articles. Firstly, this article enunciates some of the contextual challenges that arose because of the rise of Magaya and Makandiwa. Secondly, it discusses the desire for survival by using the doctrine of seeding. Thirdly, the article concludes by analysing the mega-prophets’ doctrine of seeding. Contribution: The scholarly significance of this article lies in the current discourse of religious movement and theological challenges within the discipline of practical theology. It raises concerns about theology that is self-serving instead of seeking the greater good by addressing the social injustices prevailing in the Zimbabwean context.

Prophecy, prophet(s) and prophesying were usual religious and spiritual phenomena in ancient Israel which do pose challenges for the contemporary church. Sending of divine messages or revelation to the covenant people through the chosen spokespersons were part of deity and human transactions. The violent and crazy act of the prophet in the course of relating the messages of the divine has posed certain apprehensions and was often a source of fear in the people. Such manner of display by prophets is often done as a way to authenticate and make their oracles look as though they are original. The methodology employed in this brief study is an exegetical word study of key concepts and words as used in biblical texts and its applicability in African Initiated Churches. The researcher additionally employed a comparative approach on Ancient Israel and African Initiated Churches. The study discovered that in biblical times, ecstatic prophecy at times involved violence, crazy displays and emotional outbursts. Similarly, among the African Initiated Churches such practices are still employed with all the privileges and dangers attached to them. In the process of receiving or/and delivering divine messages, a state of ecstasy might be expressed by the recipients of the messages.

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.

Abstract In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013–2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.

This paper discusses the practical implications of the theological concept of the three roles of Christ's office which is one of the distinctive identities of Reformed theology with the term munus triplex in the realm of Christian education. This theological concept explains the identity and work of Christ in salvation by carrying out the role of office as prophet, priest and king. The purpose of this study is to explain and apply the concept of Munus Triplex as one of the tools to manifest Christian theology concretely and specifically in the pedagogical context by presenting the identity and work of Christ as the centre of salvation, where Christian educators need to build it through certain habits with students continuously. The habit is built intentionally in presenting educators as the image of Christ through the implementation of the role of the office of prophet, priest and king. Thus, the educator is expected to see Christ through the habits built by the Christian educator so that the educator can grow to love God and resemble Christ more and more. BAHASA INDONESIA ABSTRACT: Tulisan ini membahas tentang implikasi praktis dari konsep teologis tiga peran jabatan Kristus yang menjadi salah satu identitas khas dari teologi Reformed dengan istilah munus triplex dalam ranah pendidikan Kristen. Konsep teologis ini menjelaskan mengenai identitas dan karya Kristus dalam keselamatan dengan mengemban peran jabatan sebagai nabi, imam dan raja. Tujuan dalam penelitian ini adalah menjelaskan dan mengaplikasikan konsep Munus Triplex sebagai salah satu tools untuk mengejawantahkan teologi Kristen secara konkrit dan partikular dalam konteks pedagogik dengan menampilkan identitas dan karya Kristus sebagai pusat keselamatan, dimana pendidik Kristen perlu membangunnya melalui kebiasaan tertentu dengan naradidik secara kontinyu. Kebiasaan tersebut dibangun secara intensional dalam menampilkan pendidik sebagai gambar Kristus melalui pelaksanaan peran jabatan nabi, imam dan raja. Dengan demikian naradidik diharapkan dapat melihat Kristus melalui kebiasaan yang dibangun pendidik Kristem sehingga naradidik dapat bertumbuh semakin mencintai Tuhan dan menyerupai Kristus.

Because scripture represents the direct word of God, it is the most sacred source for authority in the scriptural monotheisms. Scripture is conveyed to humanity through extraordinary individuals known as prophets. But if the purported prophet is a false prophet, then the conveyed message is false, resulting in the collapse of the structure upon which religious authority rests. This problem was recognized in the Hebrew Bible, and accusations of flawed prophecy and deceitful prophets figure prominently in relations between the scriptural monotheisms. Jews do not accept the authenticity of Jesus’ role as messiah (and prophet) in the NT, and Jews and Christians do not accept the authenticity of Muhammad’s role as prophet in the Qur’an. But the rejection is unidirectional. As a rule, established religions cannot accept new prophets, while new religions accept the prophets that lived before, though with certain qualifications. Each of the three classic scriptural monotheisms also declares an “end” to prophecy after the canonization of its scripture. Yet despite these deadlines, each acknowledges that God could nevertheless send another prophet, leaving open the wild card for new prophecy, new revelation, and even new religion; within each of the classic scriptural monotheisms, individuals arose after the canonization of scripture who were deemed by many to be prophets or something “like” prophets. This essay presents a preliminary phenomenology of prophethood, around which much of the religious polemics between the scriptural monotheisms are constructed.

The invitation to Islam has persisted since the time of the revelation of the Qur’an. Despite all the obstacles and oppression during the Meccan period, the invitation process continued without interruption. With the Hijra, a new era began in terms of invitation. The invitation to Islam quickly garnered a positive response among the polytheist Arabs of Madinah. However, this invitation did not receive the same level of response from the Jews, who were among the ancient inhabitants of Madinah. The Jews of Madinah did not adopt a monolithic approach to the invitation. Some, who shed their prejudices and overcame the pressures of their community, became convinced that Muhammad (saw) was the awaited prophet of the end times and embraced Islam. However, some of them chose to disguise themselves as Muslims, either to safeguard their worldly interests or to undermine the Islamic religion from within. However, the majority of Jews declined the invitation to Islam, providing various excuses and making claims related to the end-time prophet and revelations. The excuses and claims presented in this manner are not only disconnected from reality but also, in some cases, contradictory. This study will employ the document analysis methodology. [ABSTRACT FROM AUTHOR]

The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control. [ABSTRACT FROM AUTHOR]

In March 2021, the Ever Given, a colossal 4000-meter-long container ship with a capacity of 20,000 TEUs (Twenty-foot equivalent units), became lodged in the Suez Canal for six days, causing a significant disruption. This accident blocked approximately 400 ships, impacting not only the vessel itself and the canal but also global trade and the already strained global supply chain post-pandemic. Our research leverages causal inference techniques to rigorously assess and quantify the causal effects of the Ever Given accident on the World Container Index (WCI). We conducted experiments using time series data from eight major global shipping routes, achieving statistically significant results with a confidence level of 99.89%. This research conclusively demonstrates that the Ever Given incident at the Suez Canal had a substantial impact on shipping container prices, quantifying the effect as a remarkable 40% price increase post-exposure. By offering companies the ability to apply causal inference in understanding cause-and-effect dynamics within their supply chain networks, this study equips them with the knowledge needed to make well-informed decisions, especially in times of disruption, thus facilitating the optimization of their supply chain configuration and operations. [ABSTRACT FROM AUTHOR]

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches, enhancing forest fire warnings and emergency response capabilities, and accurately budgeting potential carbon emissions resulting from fires. However, due to the unavailability of spatial information technology, such databases are extremely difficult to build reliably and completely in the non-satellite era. This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province, southwestern China. First, the forest fire danger index (FFDI) was improved by supplementing slope and aspect information. We compared the performances of three time series models, namely, the autoregressive integrated moving average (ARIMA), Prophet and long short-term memory (LSTM) in predicting the modified forest fire danger index (MFFDI). The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970. Following this, the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals, which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database. The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI, with a fitting determination coefficient (R²) of 0.709, mean square error (MSE) of 0.047, and validation R² and MSE of 0.508 and 0.11, respectively. Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas, which is higher than the results determined from the original FFDI (2 out of 7). This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study. [ABSTRACT FROM AUTHOR]

The problem of minimizing redundant resource reservation while maintaining QoS at an agreed level is crucial for modern information systems. Modern information systems can include a large number of applications, each of which uses computing resources and has its own unique features, which require a high level of automation to increase the efficiency of computing resource management processes. Objective. The purpose of this paper is to ensure the quality of IT services at an agreed level in the face of significant dynamics of user requests by developing and using a method of proactive automatic application scaling in Kubernetes. Method. This paper proposes a proactive horizontal scaling method based on the Prophet time series prediction algorithm. Prometheus metrics storage is used as a data source for training and validating forecasting models. Based on the historical metrics, a model is trained to predict the future utilization of computation resources using Prophet. The obtained time series is validated and used to calculate the required number of application replicas, considering deployment delays. Results. The experiments have shown the effectiveness of the proposed proactive automated application scaling method in comparison with existing solutions based on the reactive approach in the selected scenarios. This method made it possible to reduce the reservation of computing resources by 47% without loss of service quality compared to the configuration without scaling. Conclusions. A method for automating the horizontal scaling of applications in Kubernetes is proposed. Although the experiments have shown the effectiveness of this solution, this method can be significantly improved. In particular, it is necessary to consider the possibility of integrating a reactive component for atypical load patterns. [ABSTRACT FROM AUTHOR]

Production systems play a vital role in maximizing consumer satisfaction by efficiently transforming inputs such as labour, raw materials, and capital into products or services aligned with consumer demands. An order-based production takes place in poultry meat and meat products production facilities, which face various difficulties in meeting changing customer demands and managing the supply of raw materials. To optimize production and increase customer loyalty, these facilities use strategic scheduling, considering their daily production capacity and fluctuating customer orders. In this study, estimating which customer and product type the future order quantities will come from for the relevant facilities, increasing customer satisfaction by facilitating order processes and minimizing storage costs are discussed. With this study, the number of orders was estimated, and it was aimed to meet the orders in the most accurate way. In the estimations, the order data of a poultry meat and meat products production facility between 2013 and 2021 were used. Since the order figures will change every year in cases such as the customer working with the facility, growing, or shrinking, better results have been tried to be obtained with the arrangements made on the data set used and three different data sets have been obtained. Estimation processes were performed for these three data sets using LSTM and Prophet algorithms. While the RMSE value was 7.07 in the LSTM model in experimental studies, this value was obtained as 10.96 for Prophet. In the results obtained, it was observed that the arrangements made on the data set positively affected the accuracy of the estimations and the LSTM algorithm produced better results than the Prophet algorithm. [ABSTRACT FROM AUTHOR]

Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector.

The article studies the metaphysical roots of prophethood from the perspective of perennial philosophy. The main problem discussed here is what is meant by “prophet as logos”. The discussion is around the principles of metaphysic which is typical of perennial philosophy. Employing library research included in qualitative methodology, the study delves into the key texts of perennial philosophers about prophethood. The primary data were taken and interpreted from the masterpieces of the renowned perennial philosophers: Frithjof Schuon, Seyyed Hossein Nasr, and William Stoddart. The secondary data were from relevant literature from books and articles. The study reveals that prophet is a reflection of the Absolute which is the seed of the universe (cosmological function), the saver (prophetic function), and the guide (initiative function) of humans. In Islam system, the “prophet as logos” is represented by Muhammad. Physically, he is just a man delegated by Allah, meanwhile spiritually, Muhammad is the “logos”.

يتناول هذا البحث الفكاهةَ في الأدب العربيّ ومظاهرِها في السُّنَّةِ النُّبويَّة في طائفة من أحاديث النبي عليه السلام الذي كان يحرص على إلقاء كلامه على المخاطبين بأسلوب حكيم، ويتعامل مع أصحابه برحمة وشفقة كأخ حميم. وفي هذ الإطار تتناول الدراسة الأدب الفكاهي ومظاهره في السنة النبوية. في البداية نُسلِّط الضَّوءَ على أهميَّة هذا الفنِّ في الأدبِ العربي، وأهمَّ أشكاله وأنواعه في هذا الأدب، كما تتبعنا معنى الفكاهة اللغوي والاصطلاحي وأهميتها، وأثرها في المرء وانعكاساتها على المجتمع الإنساني. ورصد البحث مظاهر الفكاهة في السنة النبوية، مثلما تجلّت في نماذج من أحاديث النبي عليه السلام التي تعبر عن روح الدعابة اللفظية والعملية، كما شرح الجانب الفكاهي ووجه الفكاهة في تلك الأحاديث. اهتمت الدراسة بالبحث، من خلال الأحاديث المختارة، عن بعض الفوائد البشرية والاجتماعية الخفية التي أشار إليها النبي عليه السلام بمثل هذه الفكاهات. وذلك بعد أن شرحت الدراسة الأحاديث ووضحتها. واعتمدت هذه الدراسة على الرصد والتحقيق والتحليل في النماذج المختارة من السنة النبوية الشفهية والعملية ذات الصلة.

Education and teaching in Islamic history have started since the Prophet received the first revelation in Mecca. When the Prophet migrated to Medina, he founded a mosque as the center of Muslim activity. Thus, this study aims to describe the Flashback of the Mosque in the Trajectory of History. The method used is a literature study with a historical-theological approach, which was then analyzed descriptively and concluded. The results of the study show that the Flashback of the Mosque in the Trajectory of History at the time of the Prophet was very simple but meaningful because it was used as a center for the spiritual development of faith, economy, education, politics, cultural arrangement and all forms of human social order. Then, during the time of the companions, the Mosque underwent physical development, starting from the expansion of buildings to the construction of new mosques in areas successfully conquered by the Muslims, even though the roles and functions were still the same as those of the Prophet Muhammad. Then, during the Umayyad and Abasiyyah periods, mosques experienced rapid progress in infrastructure and art, where towering towers and large mosque domes appeared. Still, their role and function decreased because they were only used as religious centers.

Flood is India's most prevalent natural calamity, devastatingly affecting human lives, infrastructure, and agriculture. Predicting floods can help to mitigate the potential damage and conduct timely evacuation drives. This research proposes a deep-learning regression model to forecast flood runoff. Various climatological, hydrological, land, and vegetation-related data have been collected from multiple sources for 18 years (2002–2019) to create a comprehensive dataset for the Godavari River at the Perur water station in India. The relevant attributes identified through feature selection are river water level, precipitation, temperature, surface pressure, evaporation, soil water content, daily runoff, and average river flow. The selected features were fed into various time series prediction models like AutoRegressive Integrated Moving Average (ARIMA), Prophet, Neural Prophet, and Long Short-Term Memory (LSTM). The LSTM model obtained the best results achieving a Root Mean Squared Error (RMSE) value of 0.05, Mean Absolute Error (MAE) value of 0.007, Willmott's Index (WI) of 0.83, Legates-McCabe's Index (LMI) of 0.58, and R2 of 0.67 for a 1-day prediction with a look-back window of 183 days. The model is also trained to predict the flood runoff value for a week ahead. The proposed model can serve as an essential component in flood warning systems. HIGHLIGHTS Created a comprehensive dataset of various climatological, hydrological, land, and vegetation-related data.; Feature selection is used to identify top features related to the flood runoff.; Proposed a multivariate multi-step LSTM model for predicting the flood runoff for a day and a week ahead.; Compared the performance of the proposed LSTM model with other state-of-the-art models like ARIMA, Prophet, NeuralProphet.;

Every year India faces many cyclones and erratic monsoon seasons are common in recent times. Cyclones destroy the infrastructure and lead to loss of life and damage property in coastal areas. The agriculture sector is also affected by random and unexpected rainfall. In recent years, India gets rainfall during the harvest season which leads to financial loss. Also, the number of drought events is on the rise in the Indian subcontinent as the rainwater is not managed properly. Farmers need to know whether the monsoon rainfall pattern has been shifted or not and need to shift their agricultural activity accordingly to handle the impacts of climate change. From the rainfall and accumulated cyclone energy (ACE) data analysis, it is found that monsoon seasons in India are not shifted, but, rainfall is intense during the initial months of each monsoon season. ACE values are predicted using techniques such as ARIMA, LSTM, Prophet, and stacked ensemble with multi-layer perceptron. Based on the experimental results, the proposed stacked ensemble model achieves 91% accuracy.

The necessity of a prophet's presence in the leadership of Medina and the intricate relationship between prophecy and politics have long captivated scholars in Islamic philosophy. Avicenna, recognizing this connection, linked the prophet to the active intellect as a singular entity independent of the self. He established this relationship in developing a theoretical framework for the leadership of the citizens of Medina. Consequently, Avicenna's exploration of the nexus between politics and prophecy, grounded in religious sources and Avicennan philosophy, stands as a noteworthy achievement in political philosophy. This study endeavors to answer a pivotal question: On what basis did Avicenna validate the connection between prophecy and politics, and which theoretical framework did he rely on to deem the prophet fit for leadership in Medina? Employing a descriptive-analytical case study, this paper delves into a specific facet of Avicenna's political views. Applying Wilhelm Dilthey’s methodological hermeneutics in an interpretive approach, it concludes that the prophet's connection to the active intellect, serving as a link between the perceptible and rational worlds, justifies his political and religious leadership in Medina. Avicenna posited that, with strong intellectual, imaginative, and creative powers—the sources of miraculous acts—the prophet becomes indispensable for guiding Medina toward salvation in both the worldly and the hereafter. The prophet's leadership reaches its pinnacle by aligning his imaginative power and intellective soul (acquired intellect) with the active intellect, along with the realization of the revelation phenomenon, ultimately leading the people to happiness. According to Avicenna, the prophet's intellectual power is inclined to receive the light of the active intellect, attaining a level akin to the holy intellect. This connection prompts people to recognize its absence within themselves, compelling them to adhere to the laws of Sharia. Avicenna thus demonstrated that for citizens to attain salvation, the prophet's leadership is imperative due to his profound connection to the active intellect.

The current developments in New Prophetic Churches (NPCs) raise the necessity for theological research. There are some practices within these formations, similar to African spiritualities. Spirituality is defined and some reflections on African spiritualities highlighted. This article identifies the NPCs and African spiritualities and how the two converge towards spiritual formation for Christians in these churches. Literature review and social media are used to research and conclude the research question, which is the resurgence of African spiritualities in faith formation of this faith community. Justification for this resurgence is based on both theological and etymological analyses of features, especially occultism and deliverance. Research finding is that many believers abandon Christian faith or opt to be opponents of the Gospel when promised prophecies are not fulfilled. Hence a conclusion is arrived at as an appeal for the return to Christian apologetics and vivacious socio-cultural contextual studies in the church. Robust dialogue with NPC leaders is long overdue, as at the end the local pastors are the ones engaged with damage control after the prophet has passed. Minimum standards should be suggested to minimize emotional damage in individuals and communities of faith, and discipleship into Christian faith needs to be heightened.

No abstract abvailable.

This article unravels the destructive practice of bribery, which is ingrained in South African culture. Through the scenarios in the public transport industry and literature review, the article reveals that indeed bribery is a reality to reckon with. It is observed in all tiers of society such as politics, businesses and religious circles. Bribery explained as a ‘brown envelope syndrome’ is rife in the South African socio-political landscape. The objective of this article is to enlighten the readers that bribery from socio-theological perspectives is ethically unacceptable. It promotes corrupt political accountability, economic inequality, social disrespect and religious hypocrisy. The article starts with personal scenarios and then delves into the definition of bribery as payment in a form of money, favours or gifts, to influence a decision, to receive favours in turn, facilitate fast delivery of goods, gain an unfair advantage, avoid legal prosecution or to supply substandard products or services. The second part of the artilce discusses how bribery clashes with Christian ethics as pointed by Jesus in Mark 7:21–22, as bribery lies buried in human hearts and manifests itself in different sins. There is a clear biblical condemnation of bribery as detestable, and that by all means possible, it should be proscribed. Bribery in political circles is known as ‘brown envelope’, which means the currency exchanges between the corrupt and the corrupted. Solutions suggested is that citizens in their national democracies should break out of the shell and become vocal against bribery. The church should emerge behind the trees and come up with prophetic discernment, wisdom and be vocal against bribery. Personally, those exposed to bribery situations must maintain wisdom and learn how to refuse offers without endangering themselves. This research unravels bribery and its devastating effects of promoting corrupt political accountability, economic inequality, social disrespect and religious hypocrisy. Contribution: This article calls for communities and churches to be vocal against bribery and encourages personal accountability when confronted with bribery offers.