If you work in an outbound call center, you know how important it is to hone your skills and keep outperforming the competition. At the same time, you also know not to waste time with useless gimmicks and low-level advice that was written by AI in two seconds.  What you […]

The post 9 Cold Calling Tips To Increase Your Sales This Week appeared first on .

Bryan Webber says dentists are trained on the signs of mouth cancer, but people must seek help early.

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

Council leader Liam Robinson says that "detailed work" is underway to mitigate the budget deficit.

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

The authority says there was a backlog of planning applications following the incident.

The development and rising popularity of AI is not only impacting the tech we use, but also the career paths the next generation are taking. A recent study by laptop and tech […]

The post AI university course uptake increased by 453% over 5 years, claims study appeared first on Tech Digest.

Back in November 2016 Music Ally wrote an article about how Laura Marling fans could pre-save her new album on Spotify. This was the first ever pre-save. This functionality wasn’t (and still isn’t) an official Spotify tool, it was put together by David Emery (who now works at Apple Music) who was VP of global...

Listen to this week's news roundup (January 30, 2012) I really should have named today's update "Planes, Trains and Automobiles", since those were all involved with my commute this unusual morning! This week's podcasts is surely enough recorded from SFO Airport, so I hope you enjoy the atmosphere and the ...

Let anesthesiology assistants work for themselves

Michigan Rising and Mackinac Center sued MEDC over FOIA delays

Running and longevity: Sub-four-minute milers increase their lifespan by up to 9 years  Genetic Literacy Project

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.

Aim/Purpose: To examine the early perceptions (acceptability) and usability of EASEL (Education through Application-Supported Experiential Learning), a mobile platform that delivers reflection prompts and content before, during, and after an experiential learning activity. Background: Experiential learning is an active learning approach in which students learn by doing and by reflecting on the experience. This approach to teaching is often used in disciplines such as humanities, business, and medicine. Reflection before, during, and after an experience allows the student to analyze what they learn and why it is important, which is vital in helping them to understand the relevance of the experience. A just-in-time tool (EASEL) was needed to facilitate this. Methodology: To inform the development of a mobile application that facilitates real-time guided reflection and to determine the relevant feature set, we conducted a needs analysis with both students and faculty members. Data collected during this stage of the evaluation helped guide the creation of a prototype. The user experience of the prototype and interface interactions were evaluated during the usability phase of the evaluation study. Contribution: Both the needs analysis and usability assessment provided justification for continued development of EASEL as well as insight that guides current development. Findings: The interaction design of EASEL is understandable and usable. Both students and teachers value an application that facilitates real-time guided reflection. Recommendations for Practitioners: The use of a system such as EASEL can leverage time and location-based services to support students in field experiences. This technology aligns with evidence that guided reflection provides opportunities for metacognition. Recommendation for Researchers: Iterative prototyping, testing, and refinement can lead to a deliberate and effective app development process. Impact on Society: The EASEL platform leverages inherent functionality of mobile devices, such as GPS and persistent network connectivity, to adapt reflection tasks based on lo-cation or time. Students using EASEL will engage in guided reflection, which leads to metacognition and can help instructors scaffold learning Future Research: We will continue to advance the application through iterative testing and development. When ready, the application will be vetted in larger studies across varied disciplines and contexts.

The present study attempts to find out the determinants of FinTech adoption for financial inclusion by a microfinance institution in India. The factors such as efficiency, consistency, convenience, reliability are taken as predictors of organisational attitude. Similarly, organisational attitude, ease of use, and perceived benefits are considered as antecedents of organisational adoption intention of FinTech in microfinance institutions of India. The purposive sampling technique was used to get a filled survey instrument by target samples. The results indicate that convenience and consistency in the use of FinTech applications build a favourable attitude to adopt it. Furthermore, perceived benefits are the most important antecedents of the adoption intention of FinTech in the microfinance institution in India. Additionally, the reliability of the application has a positive but insignificant impact on organisational attitude to adopt FinTech. The implications of the present study are discussed.

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.

We examine when and why creative role identity causes entitlement and unethical behaviors and how this relationship can be reduced. We found that the relationships among the creative identity, entitlement, and dishonesty are contingent on the perception of creativity being rare. Four experiments showed that individuals with a creative identity reported higher psychological entitlement and engaged in more unethical behaviors. Additionally, when participants believed that their creativity was rare compared to common, they were more likely to lie for money. Moreover, manipulation of rarity of creative identity, but not practical identity, increased psychological entitlement and unethical acts. We tested for the mediating effect of psychological entitlement on dishonesty using both measurement of mediation and experimental causal chain approaches. We further provide evidence from organizations. Responses from a sample of supervisor-subordinate dyads demonstrated that employees reporting strong creative identities who perceived creativity as rare in their work-group rather than common were rated as engaging in more unethical behaviors by their supervisors. This paper extends prior theory on negative moral consequences of creativity by shedding new light on assumption regarding the prevalence of creativity and the role psychological entitlement plays.

Imagine a rifle that doesn't just shoot; it speaks to the soul of the hunter, the adventurer, the collector. Enter the Nosler Limited Edition M21 Carbon Rifle.

KUALA LUMPUR: The PriceCatcher app will continue to be improved with data-sharing on prices through the collaboration between the Domestic Trade and Cost of Living Ministry (KPDN) and two supermarket chains, Mydin and Redtick, said Minister, Datuk Armizan Mohd Ali.

He said that this commitment is an initiative that reflects transparency in transactions and business ethics to avoid price manipulation or profiteering at the expense of consumers.

“Previously, the price data displayed in the PriceCatcher app was entirely sourced from field price monitoring officers, which limited the coverage area and the number of premises uploaded to the app.

“...the signing of this MoU (Memorandum of Understanding) marks a pioneering effort to improve the app by enabling automated data sharing from the involved supermarkets to be displayed in the ‘Supermarket Price Sharing’ section,“ he told reporters after the MoU signing ceremony on price data sharing in Subang Jaya today.

Mydin Mohamed Holdings Bhd, managing director Datuk Dr Ameer Ali Mydin, and KPDN secretary-general Datuk Seri Mohd Sayuthi Bakar were also present.

Armizan said that this collaboration will serve as a benchmark for expanding the data-sharing initiative to other supermarkets and premises.

According to Armizan, the PriceCatcher app previously displayed price information for 480 consumer goods, with daily updates for 186 items, weekly updates for 220 items, and monthly updates for 74 items.

“Up until Nov 7, 459,998 users nationwide uploaded the app, however, the active usage rate is 10,00 per week.

“We are taking an additional approach to add more information in the app without adding more price monitoring officers by adopting a self-reporting system or data sharing from retail sector players,“ he said, adding that the app serves as a reference for users and fosters the habit of checking prices of items before buying.

CYBERJAYA: Pure play enterprise Internet of Things (IoT) solutions provider SMRT Holdings Bhd (SHB) posted a net profit of RM7.04 million for the first quarter (Q1) ended September 30, 2024, an increase of 0.82% from RM6.99 million posted in the same quarter last year.

The increase was due to a higher-margin revenue mix, realisations of economies of scale from the higher number of managed sites and reduced administrative expenses.

Revenue for Q1 decreased 10.4% to RM16.5 million compared to RM18.42 million posted in Q1 last year.

SHB group managing director Maha Palan said the company’s key markets in Malaysia and Indonesia continue to show growth trajectory.

“Our previous strategic entry into the Philippines’ financial services sector has laid the foundation for further growth, and we are now actively exploring new opportunities in the country,“ he said.

On the venture into new verticals, Palan said the group’s IoT deployments for the water utility sector are delivering positive results and will tangibly contribute to results in this financial year.

Meanwhile, SHB has appointed Au Wong Lian (Kit) as its new group CEO, effective November 8, 2024.

Au brings over 30 years of experience in the technology and telecommunications industries, during which he has held leadership positions in various leading companies, including TimeDotCom and Microsoft Malaysia.

“Given Au’s extensive experience, deep domain expertise, and proven track record in driving growth and profitability, I am confident he will help lead SHB to the next level.

“More importantly, there is a strong alignment in corporate culture and core values between Au and our team, ensuring a smooth integration that will support our shared vision of leading the provision of IoT services across the Asean region,“ Palan said.

KUALA LUMPUR: A group of planters and stakeholders in the East West One Group (EWOG) schemes urgently calls on Pacific Trustees Bhd (PTB) to release the funds necessary for the company’s approved rehabilitation and restructuring (R&R) exercise.

The majority of EWOG’s investors, represented by Thirunavukarasu Illamurugan, Yong Chin Koi, and Mahadevan Kathirgamathamby, are concerned that PTB’s continued withholding of these funds could further damage the company’s financial health, potentially leading to irreversible losses.

To recap, EWOG obtained planters’ approval of the company’s R&R exercise across all three schemes: East West One Planter’s Scheme (EWOP), East West Horizon Planter’s Scheme, and East-West Planter Scheme 1.

EWOG, in a statement, said the past few years have seen significant challenges that have severely impacted plantation operations, including the global Covid-19 pandemic, La Niña weather phenomena, industry-wide labour shortages, land disputes with landowners, and repeated injunctions that prevented timely convening of planters’ meetings from addressing these issues.

These cumulative challenges have compounded the company’s cash flow problems, resulting in an inability to meet payment obligations.

According to a statement by EWOG, despite the overwhelming support for the R&R plan from planters and stakeholders at the August 12 Planters’ Meeting, critical rehabilitation work on EWOG’s plantation assets remains stalled due to this delay.

For over a year, the plantation palms have relied solely on natural soil fertility, with no structured fertilisation or agronomic practices.

Prompt initiation of the R&R program is essential to restoring the plantation’s productivity.

This program leverages enhanced agronomic practices and inputs to increase fresh fruit bunch (FFB) production.

With crude palm oil (CPO) prices currently above RM4,000 per ton and projected to hold through 2025, the company has a unique window to capitalise on these favourable market conditions.

Proceeds from FFB sales could also partially offset ongoing rehabilitation costs, creating a sustainable pathway to recovery.

“Every day of delay further impacts our ability to restore the plantation and diminishes potential returns for all investors,” said Thirunavukarasu in the statement.

“These funds, specifically held in trust for the plantation’s rehabilitation, need to be released without further delay,“ he said in the statement.

According to a recent court filing by East West Horizon Plantation Bhd, the management continues to face challenges due to PTB’s reluctance to finalise necessary trust deeds despite ongoing efforts from EWOG’s management and legal team.

This impasse prevents the release of funds crucial for the R&R efforts, posing increased risks to the plantation assets and investor returns.

The investors’ representatives stressed that “a swift resolution is essential to launch the rehabilitation efforts and generate returns for all stakeholders.”

“It is time to move past the standstill and allow the EWOG group to implement the R&R plan for the benefit of all involved.”

I didn’t think Google News (http://news.google.co.uk/) could get any worse but I was wrong. The previous revamp was bad enough: no more advanced search, useless and irrelevant personalisation options, and don’t even think about trying to set up sensible alerts. Alerts were never that good at the best of times but were not improved one iota … Continue reading Somebody, please put Google News out of its misery →

Cemu - Wii U Emulator is a highly experimental freeware to emulate Wii U applications on your computer. It isn't an alpha or even a beta, but experimental. [License: Freeware | Requires: 11|10|8|7 | Size: 25-59 MB ]

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Visa investing in building digital payment infrastructure in the country to make process less costly, more manageable

Special training for officers and provision of armoured vehicles were key directives from the Punjab CM today

AI-heavy update, of more than 2GB download size, coming to the Galaxy S23 series, Galaxy Z Fold 5, and Galaxy Z Flip 5

The first shot to use Section 230 to force adversarial interoperability on platforms has hit a setback. Earlier this year, we wrote about an absolutely fascinating lawsuit that was an attempt to activate a mostly-ignored part of Section 230 in a really interesting way. Most people know about Section 230 for its immunity protections for […]

A representational image depicting a lady relaxing on a couch. — Freepik

A new study has disclosed that older adults, who are excessively sleepy during the day or have prominent sleep issues, are at an increased risk for a pre-dementia condition known as motoric cognitive risk...

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...

Less is more? Every so often is it worthwhile to look back at FDA to see what they had to say in a given year, and in addition, how they said it. One might not think that a large agency … Continue reading →

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 →

Nintendo has filed a devastating lawsuit against a gamer who not only live-streamed games before their commercial release, but used emulators and pirated ROMs to do so. Jesse Keighin, aka EveryGameGuru, faces claims of unauthorized public performance and reproduction, contributory infringement and inducement for sharing links to emulators and pirated ROMs, plus violations of the anti-circumvention and circumvention device trafficking components of the DMCA.

From: TF, for the latest news on copyright battles, piracy and more.