Dr. Albert Rossi considers how to respond to the unforeseen events in life through Metropolitan Philaret of Moscow's morning prayer. O Lord, grant that I may meet the coming day in peace. Help me in all things to rely upon Thy Holy Will. In every hour of the day, reveal Thy will to me. Bless my dealings with all who surround me. Teach me to treat all that comes to me throughout the day with peace of soul, and with the firm conviction that Thy will governs all. In all my deeds and words, guide my thoughts and feelings. In unforeseen events, let me not forget that all are sent by Thee. Teach me to act firmly and wisely, without embittering and embarrassing others. Give me the strength to bear the fatigue of the coming day with all that it shall bring. Direct my will. Teach me to pray. Pray Thou Thyself in me. Amen.

Dr. Albert Rossi reflects on the opportunity to be in continual prayer with God throughout each moment of our days.

Bobby Maddex interviews Caleb Shoemaker and Elissa Bjeletich, the authors of the Ancient Faith Publishing book Blueprints for the Little Church, about how to effectively lead your family through the Advent fast and the Feast of the Nativity.

Bobby Maddex interviews Daniel Silver, Co-Chair of Doxacon, the Orthodox conference on science fiction and fantasy.

Bobby Maddex interviews Fr. Stephen Loposky about the 17th Annual Youth Worker and Camping Conference.

Bobby Maddex speaks with Dn. Joseph Matusiak, Ellie Bernasol, Ilmari Hayrynen, and Gabi Moussa about their experience at the 2023 Suprasl World Fellowship of Orthodox Youth event, held in Poland. To donate to this project please visit http://suprasl.org http://www.suprasl.org http://www.facebook.org/suprasl2022 http://www.instagram.com/suprasl_wfoy OR reach out to Dn. Joseph Matusiak @ jmatusiak@suprasl2022.org

Strictly speaking, the Orthodox Church does not celebrate doctrines, it celebrates events. On this homily given on the Sunday before Theophany, Fr Pat considers the event of Jesus's Incarnation.

The Parable of the Talents is often used as a reminder to be the best you can be. It's really about the structure of history and the Lordship of Jesus.

Guest: Perry C. Robinson, the editor of the popular Orthodox theology blog Energetic Procession, will share his perspectives on the perennial heterodox idea that God will eventually save/liberate everyone and why there is such a vibrant theological conversation on this subject going on within segments of Evangelicalism (Rob Bell).

Extending ATP Masters 1,000 events over two weeks has been a "backwards move", says two-time Grand Slam finalist Stefanos Tsitsipas.

Jannik Sinner is looking to cap a superb year with a first ATP Finals triumph. Here's everything you need to know about the event - and pick your tip for the title.

LME has acquired the Loch Ness Marathon and Festival of Running and Etape Loch Ness.

The Manx government scheme offering funding of up to 80% of costs aims to boost footfall for firms.

The initiative is named after Clare Wood who was murdered by her ex-boyfriend in 2009 in England.

Community groups say they are being asked to put plans in place for terrorism, bombs and drones.

Up to £991,000 will be spent on the major scheme to help reduce flooding.

A letter to Newport Town Council claims the animals face distress, fear and mental fatigue.

Wycombe Wanderers boss Matt Bloomfield says he has not heard anything from Coventry City about their vacant manager's job.

Events take place across the region to mark the armistice at the end of World War One.

A draft document with "ambitions" for Swindon town centre is being put to the council.

CWR presenter Vic Minett joins the London to Brighton Veteran Car Run 2024

BBC CWR's Clive Eakin chats to the 27-year-old ahead of this weekend versus Caldy!

Do you fancy Vernon's live 90's dance tour that's coming to the HMV Empire?

Championship leaders Coventry upset last year’s Premiership Rugby Cup runners-up Leicester Tigers as they storm to a 33-19 Pool B triumph at Welford Road.

Coventry owner Doug King confirms that ex-Chelsea and England great Frank Lampard is among the contenders for the Sky Blues job.

Volkswagen Group (VW) and Tesla rival Rivian have launched a joint venture, with the German car giant increasing its investment in the partnership. The two companies say the deal is […]

The post VW and Rivian launch joint venture, Elon Musk to lead ‘DOGE’ appeared first on ShinyShiny.

All 10 Formula 1 teams will participate in a new "season launch event" in February next year to reveal their liveries together.

1. Lego Horizon Adventures is a delightful, kid-friendly twist on Horizon Zero Dawn  Polygon
2. Lego Horizon Adventures Review  IGN
3. How LEGO Horizon Adventures was built with real LEGO bricks, out Nov 14  PlayStation
4. Lego Horizon Adventures: The Kotaku Review  Yahoo Entertainment
5. Lego Horizon Adventures Sylens voice actor revealed following Lance Reddick’s passing  Video Games Chronicle

1. B.C. ports lockout update: Union says it will challenge Ottawa’s intervention in work stoppages  Vancouver Sun
2. Union says it will challenge Ottawa's intervention in B.C. port work stoppages  CTV News
3. Labour minister forcing end of negotiations at Quebec ports marks 'dark day for workers' rights,' union says  CBC.ca
4. Canada orders ports to restart as labour disputes choke trade  Financial Post
5. Opinion: Stuck in the middle, and risking union support  The Globe and Mail

Des files d'attente, la nuit, devant les prￜfectures, pour tenter d'obtenir un rendez-vous afin de demander ou de renouveler un titre de sￜjour. C'ￜtait la rￜalitￜ au dￜbut des annￜes...

The operation of supply chain finance faces various risks, therefore, studying the operational risks of supply chain finance and corresponding preventive measures is of great significance. Firstly, classify the types of operational risks in supply chain finance. Secondly, based on the risk classification results, the decision tree method is used to evaluate the operational risks of supply chain finance. Finally, based on the risk assessment results, targeted risk prevention measures for supply chain finance operations are proposed, such as strengthening supplier management, optimising logistics and warehouse management, risk analysis and monitoring, and strengthening information security and data protection. The case analysis results show that the accuracy of the evaluation results of this method is higher, and the risk coefficient has been significantly reduced after applying this method, indicating that it can effectively reduce supply chain risk.

Aim/Purpose: This research aims to describe and demonstrate the results of an intervention through educational robotics to improve the computational thinking of student teachers. Background: Educational robotics has been increasing in school classrooms for the development of computational thinking and digital competence. However, there is a lack of research on how to prepare future teachers of Kindergarten and Elementary School in the didactic use of computational thinking, as part of their necessary digital teaching competence. Methodology: Following the Design-Based Research methodology, we designed an intervention with educational robots that includes unplugged, playing, making and remixing activities. Participating in this study were 114 Spanish university students of education. Contribution: This research helps to improve the initial training of student teachers, especially in the field of educational robotics. Findings: The student teachers consider themselves digital competent, especially in the dimensions related to social and multimedia aspects, and to a lesser extent in the technological dimension. The results obtained also confirm the effectiveness of the intervention through educational robotics in the development of computational thinking of these students, especially among male students. Recommendations for Practitioners: Teacher trainers could introduce robotics following these steps: (1) initiation and unplugged activities, (2) gamified activities of initiation to the programming and test of the robots, (3) initiation activities to Scratch, and (4) design and resolution of a challenge. Recommendation for Researchers: Researchers could examine how interventions with educational robots helps to improve the computational thinking of student teachers, and thoroughly analyze gender-differences. Impact on Society: Computational thinking and robotics are one of the emerging educational trends. Despite the rise of this issue, there are still few investigations that systematize and collect evidence in this regard. This study allows to visualize an educational intervention that favors the development of the computational thinking of student teachers. Future Research: Researchers could evaluate not only the computational thinking of student teachers, but also their didactics, their ability to teach or create didactic activities to develop computational thinking in their future students.

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.

In several manufacturing firms, inventory constitutes most of the current assets, and this underscores the importance of inventory management as a fundamental issue for the majority of the firms irrespective of their sizes. Therefore, the purpose of this research is to assess the factors that influence the effectiveness of inventory management of Malaysian SMEs in the manufacturing sector. The study employs PLS-SEM technique to test the hypotheses. The main findings show that documentation and records, inventory control system and qualified personnel have positive effects on effective inventory management of Malaysian SMEs in the manufacturing sector. The study also reveals that effective inventory management has a mediating effect on the relationship between documentation and records, inventory control system, qualified personnel and organisational performance. Therefore, the study recommends that Malaysian SMEs in the manufacturing sector should improve their approaches to embracing effective inventory management practices in order to enhance organisational performance.

Green entrepreneurial intention (GEI) in the agriculture sector signifies agricultural businesses' strong determination to embrace environmentally sustainable practices and innovative eco-friendly approaches. To understand farmers' GEI, the research applied theories of entrepreneurial events and institutional theory. A model was developed and empirically validated through structural equation modelling (SEM). A questionnaire survey was used to collect data from 211 farmers from the southern region of India. Findings revealed that perceived desirability, perceived feasibility, mimetic pressure, and entrepreneurial mindset positively influenced GEI. Entrepreneurial mindset played a mediating role in strengthening the farmers GEI. This study contributes to understanding GEI in agriculture and informs strategies for promoting sustainable farming practices.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Can questions sent to Open-Educational-Resource (OER) websites such as Ask-An-Expert serve as indicators for students’ interest in science? This issue was examined using an online questionnaire which included an equal number of questions about the topics “space” and “nutrition” randomly selected from three different sources: a 5th-grade science textbook, the “Ask-An-Expert” website, and questions collected from other students in the same age group. A sample of 113 5th-graders from two elementary schools were asked to rate their interest level in finding out the answer to these questions without knowledge of their source. Significant differences in students’ interest level were found between questions: textbook questions were ranked lowest for both subjects, and questions from the open-resource were ranked high. This finding suggests that questions sent to an open-resource could be used as an indicator of students’ interest in science. In addition, the high correlation of interests expressed by students from the two schools may point to a potential generalization of the findings. This study contributes by highlighting OER as a new and promising indicator of student interest, which may help bring “student voices” into mainstream science teaching to increase student interest in science.

Aim/Purpose: The purpose of this paper is to introduce, describe, and document the methods involved in the preparation of a mindset intervention built into a freshmen development course, and established after years of longitudinal research, that is designed to have a positive impact on the outlook, achievement, and persistence of first generation and under-prepared students. Background: A number of studies conducted in the past fifteen years have concluded that grit, the persistence and perseverance to achieve goals, and growth mindset, the belief that skills and intelligence can be developed, are positive predictors of achievement; however, little focus has been placed on the implications at institutions purposed to educate minorities, first generation college students, and learners from diminished socio-economic backgrounds. Methodology: A series of models were created, custom self-assessment scales designed, and a lesson plan prepared purposed to deliver a mindset intervention to edify students about and change perceptions of grit, locus of control/self-efficacy, growth mindset, and goal setting. The mindset intervention, as presented in this paper, was delivered as part of a pilot implementation to students enrolled in a freshmen professional development course at a Mid-Atlantic HBCU in the Fall of 2019. Contribution: This qualitative paper documents an ongoing initiative while providing a workable template for the design and delivery of a mindset intervention that is believed will be highly effective with first generation and socio-economically disadvantaged learners. It represents the third paper in a five paper series. Findings: Prior research conducted by the authors shed light on the need to explore non-cognitive factors that may affect student performance such as grit, mindset, engagement, self-efficacy, and goal setting. The authors postulate that a carefully crafted mindset intervention delivered to freshmen students from traditionally underserved populations attending a minority serving institution in the mid-atlantic region of the United States will yield positive outcomes in terms of student success. Recommendations for Practitioners: As part of a commitment to positive student outcomes, faculty and administrators in higher education must be constantly exploring factors that may, or may not, impact student success. Recommendation for Researchers: Research is needed that explores elements that may help to contribute to the success of under prepared college students, in particular those who are from low income, first generation, and minority groups Impact on Society: Since, mindset interventions have been shown to be particularly effective with underserved students, it stands to reason that they should be adopted widely, and be effective at delivering positive outcomes, at HBCUs Future Research: The authors have introduced the mindset intervention with freshmen business students enrolled in a required professional development course. Results of the self-assessments and reflection questions are being collected and coded. Additionally, students are being administered a survey designed to measure the perceived efficacy of the initiative.

Aim/Purpose This paper aims to illustrate, through an exploratory ideographic case study, how a Complex Thinking framework can inform the design of scientific events and the facilitation of scientific Inter and Transdisciplinary groups towards positive emergent outcomes, both at the level of the functioning of the group and the collective complexity of their thinking. Moreover, it aims to show how the choice of facilitation strategies can contribute to positive emergent outcomes in the context of a fully online event, with its inherent constraints. Finally, this study aims to conduct an exploratory qualitative evaluation of the participants’ experiences during School, with a focus on the processes and how they relate to the aims of the School and the goals of the facilitation. Background Science needs to embrace modes of knowing capable of generating more complex (differentiated, integrated, recursively organized, emergent), ecologically fit, and creative responses, to meet the complexity of the world’s challenges. New formats and strategies are required that attend to the facilitation of Inter and Transdisciplinary scientific events and meetings, towards creative and complex outcomes. A Complex Thinking framework provides suggestions for the facilitation of Inter and Transdisciplinary meetings and events through targeting key properties which may lead to the emergence of complex and creative outcomes. Methodology We adopt an ideographic case study approach to illustrate how a complex systems approach, in particular a Complex Thinking framework, grounded in an enactive view of cognition, guided the design choices and the facilitation strategies of an online Inter and Transdisciplinary Advanced Training School (Winter School). We aim to illustrate how the facilitation strategies were selected and used to promote deep and creative interactions within the constraints of an online environment. We adopt an exploratory qualitative approach to investigate the participants’ reports of their experiences of the School, in light of the principles and goals that guided its design and facilitation. Contribution This paper opens a new area of theoretical and applied research, under the scope of a Complex Thinking framework, focused on the facilitation of Inter and Transdisciplinarity at scientific events, meetings, and discussions towards complex and creative outcomes. Findings The results of the exploratory qualitative analysis of the participants’ experiences regarding the event suggest a critical role of its methodology in fostering rich, deep, and constructive interactions, in leading to the emergence of a collective group experience, to the integration of ideas, and in facilitating transformative personal experiences, under the effects of the emergent group processes. It suggests that the strategies employed were successful, anticipating and overcoming the particular constraints of an online event. Recommendations for Practitioners This case study suggests that a Complex Thinking framework can fruitfully guide the design of facilitation strategies and activities for scientific events and meetings, activating a number of key relational processes that contribute to or boost the emergence of positive group experiences and the production and integration of novel ideas. Recommendations for Researchers This study calls for action-oriented and applied research focused on the developmental evaluation of innovations, regarding the facilitation of scientific creativity and integration, within the scope of a Complex Thinking approach. Impact on Society This paper calls for new modes of organization and formats of scientific activities, suggesting that Inter and Transdisciplinary events and meetings may benefit from intentional management and facilitation of interactions between participants to produce transformative impacts. It demonstrates the importance of the organizational principles used to plan and run events that engage multiple and various societal agents, from academics to practitioners and social activists, towards enhancing their richness and relevance to complex real-world challenges. Future Research This study highlights the need for process-focused systematic case study research using complex systems-informed designs to explore how and which facilitation strategies may promote which (interaction of) properties of Complex Thinking and associated processes and how, and under which conditions, these lead to more complex and creative outcomes.

Aim/Purpose: The aim of the present paper is to present a Noise Decision (ND) scale. First, it reports the development and validation of the instrument aimed at examining organizational factors that have an influence on decision-making and the level of noise. Second, it validates this rating scale by testing its discriminant and convergent validity with other measures to assess decision-making qualities. Background: According to the literature, the concept of noise is the unwanted variability present in judgments. The notion of noise concerns the systematic influence to which individuals are exposed in their environment. The literature in the field has found that noise reduction improves the perception of work performance. Methodology: The first study involves the development of a scale (composed of 36 items) consisting of semi-structured interviews, item development, and principal component analysis. The second study involves validation and convergent validity of this scale. In the first study, there were 43 employees from three medium-sized Italian multinationals. For the second study, a sample of 867 subjects was analysed. Contribution: This paper introduces the first scale aimed at assessing noise within individuals and, in the organizational context, within employees and employers. Findings: Results show that the estimated internal reliability for each of the ND subscales and also the correlations between the subscales were relatively low, suggesting that ND correctly measures the analyzed components. Furthermore, the validation of the psychometric qualities of the ND allowed for the assertion that the influence of noise is present in the decision-making process within the context of work environments, validating the initial hypotheses. Recommendation for Researchers: This paper aims to improve theory and research on decision-making; for example, by providing a possible implementation for scales for evaluating decision-making skills. Furthermore, detecting and limiting noise with a systematic method could improve both the quality of decisions and the quality of thought processes. Future Research: Given the measurement of ND, the study can be a starting point for future research on this topic. Since there is no literature about this construct, it would be necessary to spend more time researching, so that the topic becomes clearer. System noise has been tested by some researchers with a “noise audit,” which means giving the same problem to different people and measuring the differences in their responses. Repeating this kind of audit in conjunction with the ND in a specific work environment could be helpful to detect but also measure the influence of noise.

An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.

Ph.D. Dissertation Defense Modeling and Extracting Information about Cybersecurity Events from Text Taneeya Satyapanich 9:30-11:30 Monday, 18 November, 2019, ITE346? People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with […]

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