The Parable of Lazarus and the Rich man is a dramatic story about the end result of a selfish life. Fr Tom reminds us that our most fundamental call as Christians is, not only to love God, but also to love our neighbor. (Twentieth Sunday after Pentecost)

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.

Britain's Cameron Norrie will contest his first ATP final since February 2023 after a straight-set victory over Corentin Moutet at the Moselle Open.

Former Wales lock Jake Ball announces he will retire from professional rugby at the end of the season.

The General Medical Council says there are interim conditions on the doctor's registration.

A first win in the Premier League for 22 years lifts Town out of the relegation zone.

Hartlepool United appoint caretaker boss Lennie Lawrence, 76, as manager until the end of the season.

The event is designed to show how the joy art can bring people.

Josh Cobb departs Worcestershire after one-season stay on white-ball contract, while Olly Cox also exits.

Panto is back - oh yes it is!

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

1. Ottawa businesses brace for possible Canada Post strike before the holiday season  CTV News Ottawa
2. Tougher work conditions for postal workers at the heart of labour dispute, union says  The Globe and Mail
3. Ottawa businesses worry about potential postal disruption  CBC.ca
4. Calgary workers 'ready to go' if Canada Post, CUPW unable to reach agreement by Friday: union president  Calgary Herald
5. ‘Feeling the pinch’: Potential postal strike may be first of many, Manitoba prof says  Global News Toronto

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

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.

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

[…] occurred to me – but Karen Blakeman has posted this advice – SEASONAL OPENING TIMES – NEVER TRUST GOOGLE’S ANSWERS (OR BING’S) (Dec 29) – information about open and closed times of shops might not be right – always […]

This is my usual Christmas/New Year reminder to never trust Google’s answers (or Bing’s) on opening times of shops over the holiday season, especially if you are thinking of visiting small, local, independent shops. I was contemplating going to our True Food Co-operative but suspected that it might still be shut. A search on my … Continue reading Seasonal opening times – never trust Google’s answers (or Bing’s) →

Let Virgo season inspire your nails with a blend of earthy tones and flawless precision.

Relive the wildest moments that prove the VMAs are never short on drama or surprises!

People walk around a park amid heavy smoggy conditions in Lahore on November 7, 2024. —AFP

PARIS: Record-breaking heat, extreme weather events, air pollution and the spread of infectious disease: climate change poses an already vast yet rising threat to the health of humans around...

The trailer has left people feeling confused, somewhat surprised and also overjoyed. For me, it's disappointment.

Back in December 2023, FDA announced intention in the Federal Register and in a press release to form a new FDA Advisory Committee to be called the Genetic Metabolic Diseases Advisory Committee (GeMDAC). As noted in a recent posting here, … Continue reading →

'Euphoria' season three big update revealed

After a long wait, HBO has confirmed that season three of Euphoria will air in January 2025.

Casey Bloys, the network's head, shared the update after rumours of delays dogged the series.

"We are shooting 'Euphoria,'" the head honcho...

Priyanka Chopra introduces daughter Malti to 'Citadel' world

Priyanka Chopra is keeping a smooth balance between work and life as shooting for the second season of Citadel begins.

The actress, 42, just announced the return via an Instagram post where the crew for...

We are very happy to announce officialy the kunstform BMX Shop Team for the season 2015/2016! Our focus with the choice of riders is to cover every BMX Freestyle disciplines like BMX Park, BMX Street and BMX Flatlen and to give them support and motivation. For BMX Flatland we could get Kevin Nikulski and John Krämer. Shawn Hammer from Berlin and Miguel Franzem from Sindelfingen represent BMX Park. Both of them have a very nice attitude! For BMX Street is riding David "Arthur" Biedermann, a local rider from Stuttgart and Robin Kachfi who won the kunstform "Can you Sponsor me" Video Competition. Jonas Bader and Miguel Smajlji feeling home everywhere! We have planned some projects, so stay tune!

We are very happy to announce officialy the kunstform BMX Shop Team for the season 2016/2017! Our focus with the choice of riders is to cover every BMX Freestyle disciplines like BMX Park, BMX Street and BMX Flatland and to give them support and motivation. For BMX Flatland we could get Kevin Nikulski, John Krämer and Markus Schwital. Shawn Hammer from Berlin and Miguel Franzem from Sindelfingen represent BMX Park. For BMX Street is riding David "Arthur" Biedermann, a local rider from Stuttgart and Robin Kachfi who won the kunstform "Can you Sponsor me" Video Competition. Jonas Bader,Miguel Smajlji and Sven Avemaria feeling home everywhere! We have planned some projects, so stay tune!

On august 19th 2017 will happend the famous Brickyardtrails Season Ender Jam 2.0. in Brackenheim next to Heilbronn (Germany). It doesn't matter if you ride BMX or MTB, if you love trails then this will be the place for you. There will be a lot of contest like Whip Off, Ring the bell, Best Trick or Highest Air.

When: 19.08.2017 (from 2pm)

Wo: Brickyardtrails, Im Sommerrain 8, 74336 Brackenheim, Germany

Wettbewerbe: Whip Off, Ring the bell, Best Trick, Highest Air

Webpage: https://www.brickyardmafia.de/ oder Facebook Event

A new item has been added to the database: 6550800 Barracuda Seas.

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