tna, the global leader in packaging and processing solutions, has launched a new main line spray system which delivers optimum coverage and flavour adhesion. 

What is your Austin home worth, right now? When considering a sale, it’s generally worth “market value”, defined loosely as the highest price a buyer would pay, and that a seller will accept in an open, competitive market. This assumes neither buyer nor seller are under any undue stress or duress external to the transaction itself. Many homeowners want to know their home value even when not considering a sale. Such as when protesting assessed property value at Travis County. Or maybe you are just curious, or you want to update your Net Worth spreadsheet with a current value. If you don’t want the “market” to determine your home value, because you don’t want to sell, there now exists a plethora of “Automated Valuation Model” (AVM) tools that will tell you the supposed value of your home online based on mathematical algorithms and data. The most well know is perhaps ... Read more

We naturally pick up microorganisms as we move about the world. Now, researchers have developed an AI tool that accurately links you to a particular location using a sample of the bugs you’ve collected on your travels – like a bacterial satellite navigation system.

Continue Reading

Category: AI in Health, Medical Innovations, Body & Mind

Tags: Microbiome, Bacteria, Location, Satellite Navigation, Lund University

Over the past three weeks, I’ve been assessing various aspects of the Canadian Urban Transit Research and Innovation Consortium’s (CUTRIC) positions, research, and publications on transit bus decarbonization. I’ve published ten articles directly about CUTRIC’s material and reports, or closely related and with serious implications for their claims that both ... [continued]

The post Canadian Transit Think Tank CUTRIC Chooses Inaccuracy, Irrelevancy, & Attack appeared first on CleanTechnica.

Fr. Lawrence Farley critically examines the hermeneutic of suspicion that marks modern liberal biblical scholarship.

Based on a survey of 2548 American adults conducted by Pew Research Center in 2021, this study finds that trust in the accuracy of news circulated on X (former Twitter) is positively correlated with following news sites on X, underscoring the crucial role of trust in news accuracy in shaping news-seeking behaviour. Trust in news accuracy also positively relates to political participation via X. Those who trust in news accuracy are more likely to perceive X as an effective tool for raising public awareness about political and social issues, as well as a positive force for democracy. However, exposure to misinformation weakens the connection between trust in news accuracy and users' perception about X as an effective tool for raising public awareness about political or social issues and as a positive driver for democracy.

Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of information systems implementations. Measurements theory and error analysis, specifically propagation of uncertainties methods, were used to derive analytical expressions for ROI errors. Monte Carlo simulation methodology was used to design and deliver a quantitative experiment to model costs and returns estimating errors and calculate ROI accuracies. Spreadsheet simulation (Microsoft Excel spreadsheets enhanced with Visual Basic for Applications) was used to implement Monte Carlo simulations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: Parasocial relationships are today established on social media between influencers and their followers. While marketing effects are well-researched, little is known about the meaning of such relationships and the psychological mechanisms behind them. This study, therefore, explores the questions: “How do followers on Instagram interpret explicit fiction cues from influencers?” and “What does this reveal about the meaning of parasocial attachment?” Background: With a billion-dollar advertising industry and leading in influencing opinion, Instagram is a significant societal and economic player. One factor for the effective influence of consumers is the relationship between influencer and follower. Research shows that disclosing advertisements surprisingly does not harm credibility, and sometimes even leads to greater trustworthiness and, in turn, willingness to purchase. While such reverse dynamics are measurable, the mechanisms behind them remain largely unexplored. Methodology: The study follows an explorative approach with in-depth interviews, which are analyzed with Mayring’s content analysis under a reconstructive paradigm. The findings are discussed through the lens of critical psychology. Contribution: Firstly, this study contributes to the understanding of the communicative dynamics of influencer-follower communication alongside the reality-fiction-gap model, and, secondly, it contributes empirical insights through the analysis of 22 explorative interviews. Findings: The findings show (a) how followers rationalize fiction cues and justify compulsive decision-making, (b) how followers are vulnerable to influences, and (c) how parasocial attachment formation overshadows rational logic and agency. The findings are discussed with regard to mechanisms, vulnerabilities, rationalizations and cognitive bias, and the social self, as well as the ethics of influencer marketing and politics. Recommendation for Researchers: The contribution is relevant to relationship research, group dynamics and societal organizing, well-being, identity, and health perspectives, within psychology, sociology, media studies, and pedagogy to management. Future Research: Future research might seek to understand more about (a) quantifiable vulnerabilities, such as attachment styles, dispositions, and demographics, (b) usage patterns and possible factors of prevention, (c) cognitive and emotional mechanisms involved with larger samples, (d) the impact on relationships and well-being, and (e) possible conditions for the potential of parasocial attachment.

A recent story, featured on CORDIS News, focuses on EU BON and its achievements. Based on a recent project's publication in ‘Nature – Scientific Reports’ and information from the project, the new item discusses important project outcomes, including the European Biodiversity Portal, and their contribution to drawing a more accurate picture of current biodiversity to aid efforts for sustainable governance of natural resources.

Here is a snippet from the news feature on CORDIS: 

A recent article published in the journal ‘Scientific Reports’ states, ‘Monitoring schemes provide an important source of information on biodiversity change, guiding further research, conservation assessment and planning.’ The article cites The European Biodiversity Portal, designed and implemented by the EU-funded EU BON project that offers researchers, policy-makers, and others interested in biodiversity, easy access to insights on trends and modelling techniques.

The project worked on the establishment and adoption of new data standards, the development of tools to enable collaborative research and the encouragement of citizen-scientists. As a result, EU BON, by building the European Biodiversity Observation Network, has created advanced techniques for data analysis along with new approaches for modelling and strategies for future biodiversity monitoring.

Read the full publication here.

As someone who started in construction almost 40 years ago, there are a few truths that never change. 

Pricing details will be made available to RAD’s dealer channel and clients upon request.

Mobile Computing/AIDC (Data capture) Technology Report

Retail Technology Review spoke with leading representatives from the analyst and vendor communities about many of the current key talking points and areas of innovation taking place within the world automatic identification and data capture (AIDC)/mobile computing solutions.

Despite increasing concerns surrounding food waste, more than 89 million tonnes of food are thrown away every year in Europe. The grocery retail sector contributes some 5% to the total amount, often due to expired fresh produce, equating to more than 4.45 million tonnes.

The crystal structure of calcium atorvastatin trihydrate was redetermined from previously published synchrotron powder diffraction data to give a much-improved agreement with two independent density-functional theory calculations.

With ever-improving quantum-mechanical computational methods, the accuracy requirements for experimental crystal structures increase. The crystal structure of calcium atorvastatin trihydrate, which has 56 degrees of freedom when determined with a real-space algorithm, was determined from powder diffraction data by Hodge et al. [Powder Diffr. (2020), 35, 136–143]. The crystal structure was a good fit to the experimental data, indicating that the electron density had been captured essentially correctly, but two independent quantum-mechanical calculations disagreed with the experimental structure and with each other. Using the same experimental data, the crystal structure was redetermined from scratch and it was shown that it can be reproduced within a root-mean-square Cartesian displacement of 0.1 Å by two independent quantum-mechanical calculations. The consequences for the calculated energies and solubilities are described.

We explain analysis of RIXS, HERFD and XR-HERFD data to discover new physical processes in manganese and manganese-containing materials, by applying our new technique XR-HERFD, developed from high resolution RIXS and HERFD.

Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

Here, the novel technique of extended-range high-energy-resolution fluorescence detection (XR-HERFD) has successfully observed the n = 2 satellite in manganese to a high accuracy. The significance of the satellite signature presented is many hundreds of standard errors and well beyond typical discovery levels of three to six standard errors. This satellite is a sensitive indicator for all manganese-containing materials in condensed matter. The uncertainty in the measurements has been defined, which clearly observes multiple peaks and structure indicative of complex physical quantum-mechanical processes. Theoretical calculations of energy eigenvalues, shake-off probability and Auger rates are also presented, which explain the origin of the satellite from physical n = 2 shake-off processes. The evolution in the intensity of this satellite is measured relative to the full Kα spectrum of manganese to investigate satellite structure, and therefore many-body processes, as a function of incident energy. Results demonstrate that the many-body reduction factor S02 should not be modelled with a constant value as is currently done. This work makes a significant contribution to the challenge of understanding many-body processes and interpreting HERFD or resonant inelastic X-ray scattering spectra in a quantitative manner.

As the U.N. Climate Change Conference (COP27) approaches, a new report recommends steps to improve the accuracy and usability of greenhouse gas emissions information for decision-makers, including creating a global information clearinghouse.

Job safety audits can mitigate the risk of accidents. But many rely on slow processes designed with compliance.

Netvue Birdfy is dedicated to improving the quality of living by applying game-changing AI recognition technology.

Burhani Engineers Completes Design, Supply, Installation and Commissioning of Advanced Automatic Tank Gauging Solution.

Rating the Spine: Evaluating Accuracy and Red Flags…

The integration of 3D scanning and additive manufacturing has opened new possibilities in metrology. Analyzing the technical intricacies of 3D laser scanning hardware and software reveals their pivotal role in shaping engineering's future.

A question-and-answer period will follow the webinar. A recorded version will be available for later viewing.

Styli and probe extensions can provide certainty for most measurements. Find the right tools made from the right materials for your quality needs.

The first part of this article series delved into the fundamentals of overcurrent protection, exploring the intricacies of relay coordination, the impact of source impedance, and the application of distance protection. These foundational concepts laid the groundwork for understanding how... Read more

The post Mastering Distance Protection and Calculations Part 2: Never Mess Up Protection Accuracy appeared first on EEP - Electrical Engineering Portal.

The discussion of labels and labeling tends to revolve around materials. But another part of the equation, and perhaps just as important, is the labeling machinery. Learn how companies like Primera Technology, Videojet and Xeikon are making game-changing advances in this area.

Special Technology Report: Warehouse Management Systems (WMS) and Voice-directed Picking.

Contributors include VDC Research, Gartner, Panorama Consulting, Frost & Sullivan, Cap Gemini, Aberdeen Strategy & Research, Manhattan Associates, Dakota Integrated Solutions, Ehrhardt Partner Group, Indigo Software, Renovotec.

Mobile Computing/AIDC (Data capture) Technology Report

Manufacturing & Logistics IT spoke with leading representatives from the analyst and vendor communities about many of the current key talking points and areas of innovation taking place within the world automatic identification and data capture (AIDC)/mobile computing solutions.

Our memories are more reliable than experts predicted

Ivan Matic
Jan 1, 2008; 7:132-144
Research

Amelia C. Peterson
Nov 1, 2012; 11:1475-1488
Technological Innovation and Resources

Jesper V. Olsen
Dec 1, 2005; 4:2010-2021
Technology

This study evaluates the diagnostic utility of PET/MRI for primary, locoregional, and nodal head and neck squamous cell carcinoma (HNSCC) through systematic review and metaanalysis. Methods: A systematic search was conducted using PubMed and Scopus to identify studies on the diagnostic accuracy of PET/MRI for HNSCC. The search included specific terms and excluded nonhybrid PET/MRI studies, and those with a sample size of fewer than 10 patients were excluded. Results: In total, 15 studies encompassing 638 patients were found addressing the diagnostic test accuracy for PET/MRI within the chosen subject domain. Squamous cell carcinoma of the nasopharynx was the most observed HNSCC subtype (n = 198). The metaanalysis included 12 studies, with pooled sensitivity and specificity values of 93% and 95% per patient for primary disease evaluation, 93% and 96% for locoregional evaluation, and 89% and 98% per lesion for nodal disease detection, respectively. An examination of a subset of studies comparing PET/MRI against PET/CT or MRI alone for evaluating nodal and locoregional HNSCC found that PET/MRI may offer slightly higher accuracy than other modalities. However, this difference was not statistically significant. Conclusion: PET/MRI has excellent potential for identifying primary, locoregional, and nodal HNSCC.

SURREY, British Columbia, Nov. 12, 2024 — Today, the Quantum Algorithms Institute (QAI) announced a partnership with Canadian companies, AbaQus and InvestDEFY Technologies, to solve common challenges in training machine learning […]

The post Quantum Algorithms Institute Drives Predictive Model Accuracy with Quantum Collaboration appeared first on HPCwire.

In this post, we will learn about the most important parameters for the analog simulators that affect the accuracy of the simulation results. We will also understand how each parameter limits the accuracy of the measurements for the DC and transient analyses.(read more)

Background

Computer-aided detection (CAD) systems hold promise for improving tuberculosis (TB) detection on digital chest radiographs. However, data on their performance in exclusively paediatric populations are scarce.

ALS, the most common form of motor neuron disease, can take a long time to diagnose, but a blood test could help doctors spot the condition sooner

From assembling delicate electronics to constructing safety-critical aerospace components, the margin for error has shrunk to almost nothing. To meet these rigorous standards, the manufacturing industry increasingly relies on robotic precision. Modern robotics, equipped with advanced sensors, grippers, and AI, allow manufacturers to complete intricate tasks with extraordinary accuracy. Technological Innovations Driving Robotic Precision Today’s […]

The post Robotic Precision in Manufacturing: Achieving High Accuracy for Complex Tasks appeared first on Chart Attack.

AI search engine embraces 2024 US election with live tracker and chatbot info hub.

medlinkAmyotrophic lateral sclerosis/medlink (ALS) is a medlinkneurodegenerative disease/medlink that affects neurons in the brain and spinal cord.

medlinkWearable devices/medlink, including medlinksmartphones/medlink and smartwatches, lack essential sensors for accurate energy expenditure

Digital land records, mobile data apps to be used for data collection