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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]

Copyright of INES Journal: The Journal of International Education Science / Uluslararası Eğitim Bilimleri Dergisi is the property of INES Journal: The Journal of International Education Science 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.)

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]

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.

Many interpreters have observed allusions to Elijah and Elisha in the signs of the Gospel of John. Older studies typically evaluated the parallels to determine either John's use of a signs source, the provenance of the Gospel, or the christology of early Christian communities. But several recent studies taking a more narrative-oriented approach have asked what kind of christology is generated when one reads the signs in light of the miracles of Elijah and Elisha. They have generally concluded that the resulting prophetic christology functions as a stepping-stone toward higher christologies. On the intertextual reading of this article, however, the prophetic christology relates to other christologies not as a stepping-stone but in terms of the theology of prophecy from Kings: the signs of Jesus validate his prophetic authority, and Jesus takes up not only the role of Elijah and Elisha but also the role of the prophets' God. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering 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.)

Objective: This article tries to show how and in what manner Prophet Muhammad was able to interact with the rulers of the period in his time and environment and advance his mission and present monotheism to the world and achieve success. In fact, the main goal of this article is to investigate interpersonal communication methods and in general their behavioral and interactive dimensions with the rulers of their time, which can be considered in the field of communication, especially religious communication. Methodology: The present study is descriptive-analytical and has been analyzed by referring to library and scientific sources and referring to the verses of the Quran and existing interpretations. Findings: Throughout history, the divine prophets have seriously fought against all kinds of idolatry, and this struggle has been shared by all the prophets. The second thing that the divine prophets have fought against the specific deviations of their people and population. For example, the people of the period of Prophet Noah (peace be upon him) were worldly, dependent on the world, and arrogant. Conclusion: The Prophet wants to establish justice in the world. The Prophet wants to create justice so that he can solve the problems that humans want to solve, and propose those problems, and since the revelation came to the Prophets, one of their problems has been the meaning of opposing those who were oppressors. They were cruel, tyrannical, bloodthirsty, and each of them opposed them in a different way, and it should not be thought that any prophet sat in his house and only prayed or gave orders. No, it's not like that, they said the commandments in order to implement them, Ibrahim said the issues, and then he removed the idols, which at that period was like someone daring to the Almighty God in our religion. [ABSTRACT FROM AUTHOR]

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]

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]

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.)

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]

Copyright of Journal of Islamic Civilization Studies / İslam Medeniyeti Araştırmaları Dergisi (IMAD) is the property of Islam Medeniyeti Arastirmalari Dergisi 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.)

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.)

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.

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.

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 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.

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]

Summary: Opportunistic network (OppNet) belongs to the category of Mobile Ad‐hoc Networks (MANETs), a kind of Delay Tolerant Network (DTN), where the wireless nodes are completely mobile and the data transmission routes are dynamic. The major challenge in developing a routing model for such a network is the unpredictable nature of the movement of the nodes. In this paper, a spatiotemporal prediction model based on human mobility pattern is proposed using Bayesian posterior probability (BPPR) where several clusters are identified within the network and the day and time duration of nodes visiting those clusters are recorded. The Bayesian posterior probability is then used to determine the probability of the neighbor node visiting the destination's cluster. If the calculated probability for that node is higher than a specified threshold, the packet will be forwarded. A comparison of the results obtained on simulation is made with benchmark models—Epidemic, Prophet, HBPR, EDR, NexT, and EBC, to name a few, where it is found that on average the proposed model outperforms the other models in terms of delivery probability, hop count and number of messages dropped by around 23.89%, 24.8%, 24.4%, 37%, 11%, and 42% respectively, with varying number of nodes, TTL, message generation interval, and buffer size. Similar improvements have been observed in terms of the other two metrics. In terms of overhead ratio, the proposed model outperforms Epidemic, Prophet, HBPR, NexT, and EBC. However, as the number of nodes and TTL are varied, BPPR performs better than NexT by around 9% and 12%, respectively, in terms of average latency. [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]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer 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.)

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]

al-Qurʾān al-Karīm was understood and practised by the Prophet and his Companions. Although this understanding and living constitutes the objective ground of tafsīr, after the Prophet, there were disagreements in the meaning and interpretation of many verses due to differences in knowledge, approach, qirāʾa and rivāyah. One of the verses that is the subject of dispute in this way is verse 110 of sūrat Yūsuf. From the information conveyed, it can be seen that the commentators in the context of the verse discussed whether the prophets despaired of what, from whom, and why, and who and whom did or did not make conjectures about what. In this study, in which it is tried to determine which of these views in the sources of tafsīr and qirāʾa is the most accurate, the subject is discussed in the context of classical and modern sources. Since the subject is associated with the attribute of prophets' el-ʿiṣme, how el-ʿiṣme is understood and how it should be understood is also included. From the information in the sources of tafsīr, it can be seen that commentators such as Muqātil, al-Ṭabarī and Ibn ʻĀshūr interpreted the verse as, "when the prophets despaired of the faith of their people and they denied them", Ḥaḍrat ʻĀʼishah said that, "the prophets despaired of the faith of their people and thought that the believers had denied them about the coming of divine help" and commentators such as Ibn ʻAbbās Zamakhsharī, Ibn Taymīyah, al-Biqāʻī and Sayyid Quṭb have understood it to mean that "the prophets, under the influence of their own souls, despaired of the coming of divine support or chastisement, and they had a misconception about the reality of the promise of Allah Almighty." Since the main determinant in the meaning preferences of the commentators was their conception of the prophet and their perception of el-ʿiṣme, it was determined that their preferences of qirāʾa and meaning were shaped accordingly. It is also seen that the understanding of el-ʿiṣme as sinlessness and innocence leads to a misunderstanding of the possibilities and limits of human beings in general, especially the prophets, who are shown to humanity as an uswat hasanah. As a result, it is understood that the most accurate view is to understand the verse as "despairing of divine help for a short time due to the influence of their own souls" by evaluating it within the context of the context of the sūrat Yūsuf, the integrity of the verse and the prophets such as Nūḥ, Yūnus, Yaʻqūb and Yūsuf during the testing processes, without taking the idea of el-ʿiṣme into the scope. [ABSTRACT FROM AUTHOR]

Copyright of Kırıkkale Law Journal (KLJ) is the property of Kirikkale Law Journal 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.)

Due to the influence of environment and other factors, there are often abnormal data in dam monitoring data and the detection of abnormal data plays an indispensable role in the normal operation of the dam. However, the accuracy of traditional anomaly detection algorithms for dam monitoring data often fails to meet the requirements. In this paper, an anomaly detection algorithm based on Prophet GMM was proposed. The better fitting performance of Prophet algorithm was used to fit the dam data and the residual sequence was obtained from the fitting data and the actual data. Then, the residual sequence was clustered by GMM algorithm to accurately identify the abnormal value. The test results show that the method proposed in this paper can accurately identify outliers for different types of dam monitoring data. Compared with the traditional detection algorithm, it has significantly improved the detection indicators of precision, recall and accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

Copyright of Journal of Ottoman Literature Studies / Divan Edebiyatı Araştırmaları Dergisi is the property of Journal of Ottoman Literature Studies 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.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Beytulhikme: An International Journal of Philosophy is the property of Beytulhikme: An International Journal of Philosophy 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.)