Bobby Maddex interviews Fr. Sergius Halvorsen, Assistant Professor of Homiletics and Rhetoric at St. Vladimir’s Seminary and the Director of the school’s new Doctor of Ministry program. Joining him are two DMin students—Fr. Theodore Paraskevopoulos and Mr. Greg Abdalah.

Bobby Maddex interviews Dr. Christoph Schneider, the Academic Director of the Institute for Orthodox Christian Studies, about the institute's new MA program that is available via distance learning.

Bobby Maddex interviews Fr. David Randolph, the Chairman of Teleios Ministries and the Antiochian Orthodox Archdiocese Department of Prison Ministry.

Learn about the work going on in prisons as we talk with Fr. Duane Pederson from the Orthodox Christian Prison Ministry. Listen at the end of the interview to learn about their search for a new Executive Director. Inquiries can be emailed directly to OCPM"

Bobby Maddex interviews Dr. John Burgess, a professor at Pittsburgh Theological Seminary, about the graduate school's Eastern Christian Doctor of Ministry Cohort, a new academic offering presented in partnership with Antiochian House of Studies.

⁠The story of the Orthodox Deaconess is largely unknown today. When did they exist, and what was their function? In recent decades, there has been a call for restoring the female diaconate, causing no small debate between Orthodox proponents and opponents.⁠ In the first special edition of Ancient Faith Today Live, Fr. Tom Soroka and John Maddex take a deep dive into the topic with a full-length audio documentary, which will feature scholarly experts from both sides of the issue and reflect upon the views shared and what we can conclude about the Church’s wisdom on this issue today.

Most of us know about the so-called Great Schism, which tragically divided the Christian Church between East and West in 1054. But there was an earlier division in the 5th century, following the Fourth Ecumenical Council in Chalcedon in 451, which clarified how Jesus is both God and Man. Charges of heresy were brought, anathemas were proclaimed, and communion was broken. Which Churches did not accept the decision of the Council and the subsequent three Councils that followed? Today they are known as the Oriental Orthodox Churches, including the Coptic, Armenian, Syrian, Malankara, Eritrean, and Ethiopian Orthodox Churches. What specifically separates us theologically? Are there reasons to hope that we are closer to these believers than we thought? What efforts have been made to better understand each other in recent decades? On this special edition of Ancient Faith Today Live, Fr. Tom Soroka and John Maddex examine the causes of our division and consider what any path to unity might involve. Panelists include: Bishop (Dr.) Daniel (Findikyan) Dr. Peter Bouteneff Christine Chaillot Dr. David Ford Dr. Emmanuel Gergis Dr. Chad Hatfield Dr. Michael Ibrahim Rev. Dr. Joseph Lucas Dr. Sam Noble Rev Dr. Timothy Thomas

Fr. Tom Soroka and John Maddex will dive into the topic of Universalism and speak with Orthodox panelists who fall into one of three categories: Confident Universalists, Hopeful Universalists, and those who say Universalism was condemned as a heresy. We read in Scripture that God is not willing that any should perish (2 Peter 3:9). But we also read that It is appointed unto man once to die, and after that comes the judgment (Hebrews 9:27). There are those who claim that, since Christ died for all, we can be assured that all will indeed be saved and not face eternal condemnation. This is called “universalism” or “apocatastasis.” Was this teaching condemned by the Church? Who among the Church Fathers embraced universal salvation?

Three Second-Temple Prophets who were among those who prepared the world for the coming of the Messiah have much to teach us about how to keep the Lord uppermost in our hearts and lives.

“Christians have forgotten that the ministry of listening has been committed to them by Him who is Himself the great listener and whose work they should share. We should listen with the ears of God that we may speak the Word of God.” - Dietrich Bonhoeffer, "Life Together" Jesus describes the Kingdom as a feast: a place where we're all seen and known. But life is full of disconnection and loneliness. Those moments aren't a taste of the Kingdom. While we'll never be able to solve every problem, we can all be better listeners. And we can learn to do that with the simple techniques of active listening. The ministry of listening is something we often overlook. But it's a simple way to make the Kingdom present for people and give them a taste of God's love. As always, we've prepared a FREE downloadable workbook to help you act on what you'll learn. https://mailchi.mp/goarch/bethebee164

Guest: Frederica Mathewes-Green, ex-feminist turned pro-life Christian, author of Gender and Real Choices, and a cultural commentator for NPR, National Review, Beliefnet.com, and Ancient Faith Radio.

You’ve no-doubt heard of the Law of the Vital Few. It’s the 80/20 rule, which states that roughly 80-percent of the results come about from just 20-percent of the energy. But, if you were to take your 80-percent results and apply the 80/20 rule to them a few more times, what you end up discovering is […]

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He says female rugby players at the centre of allegations over WRU contract talks can meet him.

Relatives affected by the Legacy funeral directors probe are taking their campaign to Parliament.

Transport Minister Fiona Hyslop says she is pushing ferry operator CalMac to consider "all options" to maintain services.

Alfred Cannan says reducing Manx Care's overspend for the current financial year will be difficult.

The Dons fan used a ministerial car to watch Aberdeen play at Hampden three times in six months.

The law could force health staff to be open with patients and their families when mistakes are made.

Sink your teeth into London's tastiest exhibition.

What better time for some spooky music than Halloween week? And so today I am pleased to say the new m-orchestra mini-album A Blessing has been released for your listening delight! It features seven tracks, including the two singles that...

Apres la nomination d'Elon Musk a la tete d'un ministere de l'Efficacite gouvernementale, le ministre de la Fonction publique francais a exprime sa << hate >> de << partager les meilleures pratiques >>.

Query algorithms are used to compute Boolean functions. The definition of the function is known, but input is hidden in a black box. The aim is to compute the function value using as few queries to the black box as possible. As in other computational models, different kinds of query algorithms are possible: deterministic, probabilistic, as well as nondeterministic. In this paper, we present a new alternative definition of nondeterministic query algorithms and study algorithm complexity in this model. We demonstrate the power of our model with an example of computing the Fano plane Boolean function. We show that for this function the difference between deterministic and nondeterministic query complexity is 7^N versus O(3^N).

Hasard du calendrier ou volontￜ de prￜserver le nouveau Premier ministre ? Selon Mediapart, une enquￜte judiciaire ouverte par le parquet de Perpignan, potentiellement gￜnante pour Jean Castex, a ￜtￜ...

Voilￜ un "petit oubli" bien embￜtant. Selon Mediapart, "le garde des Sceaux, Eric Dupont-Moretti, a oubliￜ de dￜclarer au fisc et ￜ la Haute Autoritￜ pour la transparence de la vie publique...

C'est malin. A peine nommￜ ministre de la Santￜ, Franￜois Braun croyait avoir trouvￜ une astuce pour dￜsengorger les services d'urgences des hￜpitaux : demander aux patients d'appeler le 15,...

The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

This research aims to establish a valid and accurate measurement scale and identify consumer-driven characteristics for minimalism. The study has employed a hybrid approach to produce items for minimalism. Expert interviews were conducted to identify the items for minimalism in the first phase followed by consumer survey to obtain their response in second phase. A five-point Likert scale was used to collect the data. Further, data was subjected to reliability and validity check. Structural equation modelling was used to test the model. The findings demonstrated that there are five dimensions by which consumers perceive minimalism: decluttering, mindful consumption, aesthetic choices, financial freedom, and sustainable lifestyle. The outcome also revealed a high correlation between simplicity and well-being. This study is the first to provide a reliable and valid instrument for minimalism. The results will have several theoretical and practical ramifications for society and policymakers. It will support policymakers in gauging and encouraging minimalistic practices, which enhance environmental performance and lower carbon footprint.

In order to improve the quality of higher education, student satisfaction, and employment rate, a data mining based high-quality management method for higher education is proposed. Firstly, construct a high-quality evaluation system for higher education based on the principles of education quality evaluation. Secondly, the association rule mining method is used to construct a university education quality management model and determine the weight of the impact indicators for high-quality management of university education. Finally, the fuzzy evaluation method is used to determine the high-quality evaluation function of higher education, and the results of high-quality evaluation of higher education are obtained. High-quality management strategies are developed based on the evaluation results to improve the quality of education. The experimental results show that the student satisfaction rate of this method can reach 99.3%, and the student employment rate can reach 99.9%.

In order to improve the mental health level of college students, a data mining based mental health education strategy for college students is proposed. Firstly, analyse the characteristics of data mining and its potential value in mental health education. Secondly, after denoising the mental health data of college students using wavelet transform, data mining methods are used to identify the psychological crisis status of college students. Finally, based on the psychological crisis status of college students, measures for mental health education are proposed from the following aspects: building a psychological counselling platform, launching psychological health promotion activities, establishing a psychological support network, strengthening academic guidance and stress management. The example analysis results show that after the application of the strategy in this article, the psychological health scores of college students have been effectively improved, with an average score of 93.5 points.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.