Search

An increasing number of consumers expect brands to engage in important societal issues. Brands like Procter & Gamble, Microsoft, and Gillette have launched ads promoting their stances on highly salient issues in public discourse. While corporate social responsibility (CSR) advertising research has explored issues such as skin cancer, heart disease, and abortion, this research stream has yet to investigate how an issue's salience in society influences consumer response. Because salient (versus nonsalient) issues make information more accessible and generate debate, the advertising context (i.e., public issue salience) may influence consumer response to CSR ads. Applying the salience theory of party competition to CSR advertising, this research shows how an issue's consensus level and individual issue importance differentially impact brand attitudes and purchase intentions when an issue is salient. Through a series of four experiments with real ads, the present investigation shows how and when public issue salience affects marketing outcomes. This research adopts an issue-centric view and contributes to CSR advertising scholarship by (a) opening a new theoretical corridor, (b) demonstrating empirically how the advertising context (public issue salience) influences marketing outcomes, (c) providing a more holistic, comparative advertising perspective on issue selection, and (d) generating new insight into individual issue importance. [ABSTRACT FROM AUTHOR]

Multiple research problems specific to advertising emerge from there, including whether and how the advertising industry should address the ethical and legal issues surrounding the possibility of AI-generated advertising content containing false or misleading information as well as strategies for designing and implementing AI-powered advertising in a socially responsible and ethical manner. In her editorial, the previous editor, Shelly Rodgers ([16]) proposed an AI classification schema to systematically understand and develop subdomains in the emerging AI advertising research field along the following three dimensions: AI type, AI function, and learning type. The lack of transparency and consumers' limited ability to understand AI technology and the mechanism and processes of AI-powered advertising would affect advertising outcomes. Since ChatGPT was released on November 30, 2022, followed by Microsoft's announcement of its artificial intelligence (AI)-powered new Bing search engine on February 7, 2023, and Google's Bard release on March 21, 2023, it seems AI generally has taken over conversations across all sectors of society. [Extracted from the article]

1.Challenges, strategies and new technologies in the field of biotransformation were presented and discussed at the 5th European Biotransformation Workshop, which was held on March 14, 2024 on the Novartis Campus in Basel, Switzerland.2. In this meeting report we summarise the presentations and discussions from this workshop.3. The topics covered are listed below:Advances in understanding drug induced liver injury (DILI) risks of carboxylic acids and targeted covalent inhibitorsBiotransformation of oligonucleotide-based therapeutics including automated software tools for metabolite identificationRecent advances in metabolite synthesisQualification and validation of a new compact Low Energy Accelerator Mass Spectrometry (LEA) system for metabolite profiling Advances in understanding drug induced liver injury (DILI) risks of carboxylic acids and targeted covalent inhibitorsBiotransformation of oligonucleotide-based therapeutics including automated software tools for metabolite identificationRecent advances in metabolite synthesisQualification and validation of a new compact Low Energy Accelerator Mass Spectrometry (LEA) system for metabolite profiling [ABSTRACT FROM AUTHOR]

Summary Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human‐like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS‐2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The use of cutting‐edge omics technology to edible fruits has transformed the disciplines of fruit biology, pre‐ and post‐harvest investigations, metabolite biosynthesis and the identification of novel therapeutic fruit bioactives for health by leveraging varied omics data. Combining modern analytical chromatography tools (LC, GC) with mass spectrometry has significantly improved our ability to examine complex fruit tissues or extracted components, advancing our understanding of the fruit metabolome. Studies aiming at understanding the full metabolome and future quality characteristics have concentrated on quantifying the number of metabolites in edible fruit species and cultivars from diverse geographical locations. These studies have also helped to develop new databases for precise and comprehensive qualitative analysis of metabolites, allowing for the analysis of metabolite biosynthesis pathways to identify differences in metabolites among developed hybrids, metabolite origins and potential derivatives. Bioactive metabolite information is currently being utilised to manage illnesses, provide nutrition and creation of novel food products. Furthermore, this research has helped us better understand fruit quality and how metabolites interact with biological systems. In conclusion, this review emphasises the importance of metabolomics approaches in studying fruit metabolomes in the context of current research perspectives. [ABSTRACT FROM AUTHOR]

Objective The aim of this work was the development of an augmented reality system including the functionality of conventional surgical navigation systems. Methods An application software for the Augmented Reality System HoloLens 2 from Microsoft was developed. It detects the position of the patient as well as position of surgical instruments in real time and displays it within the two-dimensional (2D) magnetic resonance imaging or computed tomography (CT) images. The surgical pointer instrument, including a pattern that is recognized by the HoloLens 2 sensors, was created with three-dimensional (3D) printing. The technical concept was demonstrated at a cadaver skull to identify anatomical landmarks. Results With the help of the HoloLens 2 and its sensors, the real-time position of the surgical pointer instrument could be shown. The position of the 3D-printed pointer with colored pattern could be recognized within 2D-CT images when stationary and in motion at a cadaver skull. Feasibility could be demonstrated for the clinical application of transsphenoidal pituitary surgery. Conclusion The HoloLens 2 has a high potential for use as a surgical navigation system. With subsequent studies, a further accuracy evaluation will be performed receiving valid data for comparison with conventional surgical navigation systems. In addition to transsphenoidal pituitary surgery, it could be also applied for other surgical disciplines. [ABSTRACT FROM AUTHOR]

Palbociclib (Ibrance; Pfizer) was approved for the management of metastatic breast cancer characterized by hormone receptor‐positive/human epidermal growth factor receptor 2 negative status. The objective of this study was to create a fast, precise, environmentally friendly, and highly sensitive ultra‐high‐performance liquid chromatography‐tandem mass spectrometry approach for quantifying palbociclib (PAB) in human liver microsomes with the application for assessing metabolic stability. The validation features were performed in agreement with the bioanalytical method validation standards outlined by the US Food and Drug Administration. The StarDrop software (WhichP450 and DEREK modules) was used in screening the metabolic lability and structural alerts of PAB. The separation of PAB and encorafenib (as an internal standard) was achieved on a C8 column, employing an isocratic mobile phase. The inter‐day and intra‐day accuracy and precision ranged from ‐6.00% to 4.64% and from ‐2.33% to 3.13%, respectively. The constructed calibration curve displayed a linearity in the range of 1–3000 ng/mL. The sensitivity of the established approach was proven by the lower limit of quantification of 0.73 ng/mL. The Analytical GREEness calculator results revealed the high level of greenness of the developed method. The PAB's metabolic stability (t1/2 of 18.5 min and a moderate clearance (Clint) of 44.8 mL/min/kg) suggests a high extraction ratio medication that matched the WhichP450 software results. [ABSTRACT FROM AUTHOR]

• Use of artificial intelligence in fibroid management. In recent years, the integration of Artificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising potential. Surgical treatment of fibroid is myomectomy if uterine preservation and fertility are the primary aims. AI usage begins with the involvement of LLM (Large Language Model) from the point when a patient visits a gynecologist, from identifying signs and symptoms to reaching a diagnosis, providing treatment plans, and patient counseling. Use of AI (ChatGPT versus Google Bard) in the surgical management of fibroid. Identifying the patient's problems using LLMs like ChatGPT and Google Bard and giving a treatment option in 8 clinical scenarios of fibroid. Data entry was done using M.S. Excel and was statistically analyzed using Statistical Package for Social Sciences (SPSS Version 26) for M.S. Windows 2010. All results were presented in tabular form. Data were analyzed using nonparametric tests Chi-square tests or Fisher exact test. p values < 0.05 were considered statistically significant. The sensitivity of both techniques was calculated. We have used Cohen's Kappa to know the degree of agreement. We found that on the first attempt, ChatGPT gave general answers in 62.5 % of cases and specific answers in 37.5 % of cases. ChatGPT showed improved sensitivity on successive prompts 37.5 % to 62.5 % on the third prompt. Google Bard could not identify the clinical question in 50 % of cases and gave incorrect answers in 12.5 % of cases (p = 0.04). Google Bard showed the same sensitivity of 25 % on all prompts. AI helps to reduce the time to diagnose and plan a treatment strategy for fibroid and acts as a powerful tool in the hands of a gynecologist. However, the usage of AI by patients for self-treatment is to be avoided and should be used only for education and counseling about fibroids. [ABSTRACT FROM AUTHOR]

The advent of digital economy, more particularly platform markets has increased the role of 'data' massively. Data is the central fuel around which business strategies are made coupled with associated algorithms to increase the conversion rates. It is true that since today's markets are data-driven in nature, dominant undertakings having access to data may end up prohibiting competitors to scale up their networks by exhibiting some explicit or implicit exclusionary conduct against them. Moreover, platform economies exhibit certain distinct characteristics such as network effects, feedback loops, economies of scope and scale and switching costs which in reality end up facilitating ostracization of small or new players. Various antitrust cases against big tech giants such as Google, Microsoft etc. in the recent past have brought forth the reality of such exclusionary conduct. Considering the fact that data is not only needed but is essential for today's digital businesses, the question arises if sharing of data with market players in certain situations must be ensured. From the lens of antitrust, the central issue is whether data should be considered as an 'essential facility' and under what circumstances. Essential facilities doctrine is inspired by the firms' duty to share which initially was applicable for infrastructural facilities such as phone lines and bridges, but considering that firms may refuse to share data impeding innovation in today's era, the author argues that the said doctrine, with some qualifications, must be revisited to grant open access to data. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fusion of varied data sources are used to develop more informed flood extent maps. • SVM outscored the MLE, RF, Iso cluster, and Otsu's algorithms in all strategies. • PCA-based technique outperforms the other three approaches. • First and Second approaches performed better in VV than VH polarisation. Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, by integrating various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE, and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE, and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (C v) metrics. The findings show that a PCA-based technique with SVM is marginally superior (based on the overall accuracy and Kappa coefficient with a value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the C v metric. The SVM algorithm with a C v of 1.25 demonstrated the best performance, followed by the RF algorithm with C v of 1.5, and the MLE algorithm with C v of 2.5. The worst performing algorithm among all is found to be ISO with C v of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area. [ABSTRACT FROM AUTHOR]

The article presents an interview with Katherine Jones, a high-tech marketing analyst. She discusses the implications of Generative Artificial Intelligence (GenAI) in resume development, examining how AI tools can streamline the process for job applicants. Topics discussed include the potential for AI to create uniform resumes, the ease with which applicants can tailor resumes for specific positions, and the challenges recruiters may face with an influx of similar AI-generated resumes.

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Untargeted analysis of gas chromatography–high-resolution mass spectrometry (GC-HRMS) data is a key and time-consuming challenge for identifying metabolite markers in food authentication applications. Few studies have been performed to evaluate the capability of untargeted data processing tools for feature extraction, metabolite annotation, and marker selection from untargeted GC-HRMS data since most of them are focused on liquid chromatography (LC) analysis. In this framework, this study provides a comprehensive evaluation of data analysis tools for GC-Orbitrap-HRMS plant metabolomics data, including the open-source MS-DIAL software and commercial Compound Discoverer™ software (designed for Orbitrap data processing), applied for the geographical discrimination and search for thyme markers (Spanish vs. Polish differentiation) as the case study. Both approaches showed that the feature detection process is highly affected by unknown metabolites (Levels 4–5 of identification confidence), background signals, and duplicate features that must be carefully assessed before further multivariate data analysis for reliable putative identification of markers. As a result, Compound Discoverer™ and MS-DIAL putatively annotated 52 and 115 compounds at Level 2, respectively. Further multivariate data analysis allowed the identification of differential compounds, showing that the putative identification of markers, especially in challenging untargeted analysis, heavily depends on the data processing parameters, including available databases used during compound annotation. Overall, this method comparison pointed out both approaches as good options for untargeted analysis of GC-Orbitrap-HRMS data, and it is presented as a useful guide for users to implement these data processing approaches in food authenticity applications depending on their availability. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793). [ABSTRACT FROM AUTHOR]

Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system. [ABSTRACT FROM AUTHOR]

AbstractMass spectrometry (MS) enables precise identification and quantification of molecules, particularly when combined with chromatography. The advent of atmospheric pressure ionization (API) techniques allowed the efficient coupling of liquid chromatography with MS (LC-MS), extending analyses to nonvolatile and thermolabile compounds. API techniques present limitations such as low informative capacity and reproducibility of mass spectra, increasing instrument complexity and costs. Other challenges include analyzing poorly polar molecules and matrix effects (ME), which negatively impact quantitative analyses, necessitating extensive sample purification or using expensive labeled standards. These limitations prompted the exploration of alternative solutions, leading to the development of the Liquid Electron Ionization (LEI) interface. The system has demonstrated excellent robustness and reproducibility. LEI has been employed to analyze various compounds, including pesticides, drugs of abuse, phenols, polycyclic aromatic hydrocarbons (PAHs), phthalates, and many others. Its versatility has been validated with single quadrupole, triple quadrupole, and QToF detectors, operating in electron ionization (EI) or chemical ionization (CI) modes and with both reverse phase liquid chromatography (RPLC) and normal phase liquid chromatography (NPLC). LEI has also been successfully integrated with the Microfluidic Open Interface (MOI), Membrane Introduction Mass Spectrometry (MIMS), and Microfluidic Water-Assisted Trap Focusing (M-WATF), broadening its application scope and consistently demonstrating promising results in terms of sensitivity and identification power. The most recent advancement is the development of Extractive-Liquid Sampling Electron Ionization-Mass Spectrometry (E-LEI-MS), a surface sampling and real-time analysis technique based on the LEI concept. This review article offers a comprehensive and up-to-date picture of the potential of LEI. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusions Artificial intelligence (AI) tools have the potential to revolutionize many facets of medicine and medical sciences research. Numerous AI tools have been developed and are in continuous states of iterative improvement in their functionality.This study aimed to assess the performance of three AI tools: The Literature, Microsoft's Copilot and Google's Gemini in performing literature reviews on a range of dermatology topics.Each tool was asked to write a literature review on five topics. The topics chosen have recently had peer‐reviewed systematic reviews published. The outputs of each took were graded on their evidence and analysis, conclusions and references on a 5‐point Likert scale by three dermatologists who are working in clinical practice, have completed the UK dermatology postgraduate training examination and are partaking in continued professional development.Across all five topics chosen, the literature reviews written by Gemini scored the highest. The mean score for Gemini for each review was 10.53, significantly higher than the mean scores achieved by The Literature (7.73) and Copilot (7.4) (p < 0.001).This paper shows that AI‐generated literature reviews can provide real‐time summaries of medical literature across a range of dermatology topics, but limitations to their comprehensiveness and accuracy are apparent. [ABSTRACT FROM AUTHOR]

Through its influence on the gut microbiota, the feeding of Saccharomyces cerevisiae fermentation products (SCFP) has been a successful strategy to enhance the health of dairy cows during periods of physiological stresses. Although production and metabolic outcomes from feeding SCFP are well-known, its combined impacts on the ruminal microbiota and metabolome during gut barrier challenges remain unclear. To address this gap in knowledge, multiparous Holstein cows (97.1 ± 7.6 DIM [SD]; n = 8/group) fed a control diet (CON) or CON plus 19 g/d SCFP for 9 wk were subjected to a feed restriction (FR) challenge for 5 d, during which they were fed 40% of their ad libitum intake from the 7 d before FR. The DNA extracted from ruminal fluid was subjected to PacBio full-length 16S rRNA gene sequencing, real-time PCR of 12 major ruminal bacteria, and metabolomics analysis of up to 189 metabolites via GC/MS. High-quality amplicon sequence analyses were performed with the TADA (Targeted Amplicon Diversity Analysis), MicrobiomeAnalyst, PICRUSt2, and STAMP software packages, and metabolomics data were analyzed via MetaboAnalyst 5.0. Ruminal fluid metabolites from the SCFP group exhibited a greater α-diversity Chao 1 (P = 0.03) and Shannon indices (P = 0.05), and the partial least squares discriminant analysis clearly discriminated metabolite profiles between dietary groups. The abundance of CPla_4_termite_group , Candidatus Saccharimonas , Oribacterium , and Pirellula genus in cows fed SCFP was greater. In the SCFP group, concentrations of ethanolamine, 2-amino-4,6-dihydroxypyrimidine, glyoxylic acid, serine, threonine, cytosine, stearic acid, and pyrrole-2-carboxylic acid were greater in ruminal fluid. Both Fretibacterium and Succinivibrio abundances were positively correlated with metabolites across various biological processes: gamma-aminobutyric acid, galactose, butane-2,3-diol, fructose, 5-amino pentanoic acid, β-aminoisobutyric acid, ornithine, malonic acid, 3-hydroxy-3-methylbutyric acid, hexanoic acid, heptanoic acid, cadaverine, glycolic acid, β-alanine, 2-hydroxybutyric acid, methyl alanine, and alanine. In the SCFP group, compared with CON, the mean proportion of 14 predicted pathways based on metabolomics data was greater, whereas 10 predicted pathways were lower. Integrating metabolites and upregulated predicted enzymes (NADP+-dependent glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, serine: glyoxylate aminotransferase, and d -glycerate 3-kinase) indicated that the pentose phosphate pathway and photorespiration pathway were most upregulated by SCFP. Overall, SCFP during FR led to alterations in ruminal microbiota composition and key metabolic pathways. Among those, we identified a shift from the tricarboxylic acid cycle to the glyoxylate cycle, and nitrogenous base production was enhanced. [ABSTRACT FROM AUTHOR]

This article proposes a practical guide including good laboratory practices followed by a critical review about the application of isotope dilution mass spectrometry (ID-MS) measurement to radionuclide analyses. Several national and international scientific institutions (French Alternative Energies and Atomic Energy Commission, French Radioprotection and Nuclear Safety Institute, European Commission, International Atomic Energy Agency, and Belgian Nuclear Research Centre) participated in this review by sharing their good laboratory practices. ID-MS is one of the primary ratio methods of measurement in the International System of Units. It can produce highly accurate results, as potential sources of bias can be controlled. Many hints are given to obtain the best performance. In addition, a script written with the open source software Octave is provided to calculate the optimal parameters of the {sample–spike} mixture easily. Radionuclide analysis by ID-MS applies to many different fields (environmental, biological, reference materials, irradiated samples, process control, nuclear safeguards and nuclear forensics). [ABSTRACT FROM AUTHOR]

This article contributes to theory building in public relations by responding to calls for more public relations research on the social, cultural and ethical implications of AI as it explores the racial dynamics of AI. We examine three case examples in which AI, race and public relations intersect including: Microsoft's chatbot Tay, a Facebook topic recommendation feature, and FN Meka, an AI-powered rap avatar. We draw upon the public relations race and DEI literature to introduce a framework we coin Inclusive, Responsible, Communication in Artificial Intelligence (IRCAI). The IRCAI framework is used to illuminate the three case examples. We conclude that it is important for the public relations field to navigate the intersection of AI and race, and we reiterate that our IRCAI framework can facilitate such efforts within and beyond the context of race. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the fast penetration of Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security and privacy issues of the VR/AR applications have been attracting considerable attention. Most VR/AR systems adopt head-mounted devices (i.e., smart headsets) to interact with users and the devices usually store the users' private data. Hence, authentication schemes are desired for the head-mounted devices. Traditional knowledge-based authentication schemes for general personal devices have been proved vulnerable to shoulder-surfing attacks, especially considering the headsets may block the sight of the users. Although the robustness of the knowledge-based authentication can be improved by designing complicated secret codes in virtual space, this approach induces a compromise of usability. Another choice is to leverage the users' biometrics; however, it either relies on highly advanced equipments which may not always be available in commercial headsets or introduce heavy cognitive load to users. In this paper, we propose a vibration-based authentication scheme, VibHead, for smart headsets. Since the propagation of vibration signals through human heads presents unique patterns for different individuals, VibHead employs a CNN-based model to classify registered legitimate users based the features extracted from the vibration signals. We also design a two-step authentication scheme where the above user classifiers are utilized to distinguish the legitimate user from illegitimate ones. We implement VibHead on a Microsoft HoloLens equipped with a linear motor and an IMU sensor which are commonly used in off-the-shelf personal smart devices. According to the results of our extensive experiments, with short vibration signals (≤1s), VibHead has an outstanding authentication accuracy; both FAR and FRR are around 5%. [ABSTRACT FROM AUTHOR]

Background: Everyone, everywhere, should have access to COVID-19 vaccines. Vaccines are a key strategy to stop escalation of the Coronavirus disease-(COVID-19) pandemic. Vaccine Myths and hesitancy can be due to lack of knowledge, false beliefs, or anti-vaccine misinformation. Unfortunately, there has been a lot of deception surrounding vaccines and their development. When deciding whether to get vaccine, it’s important to separate myths from facts. Objectives: To determine the myths regarding Covid-19 vaccine among adults. Materials and Methods: A community based cross sectional study was done for three months among adults of an urban area. Data regarding sociodemographic details and myths pertaining to Covid-19 vaccine were collected and entered in Microsoft excel and presented as frequencies and percentages. Results: 193 adults took part in study. Maximum were aged 32-38years, 80% were literates and majority of them were males. Residing in nuclear families was common. The most common myths were related to vaccine having an effect on woman’s fertility (47.67%), side effects due to vaccine (56.99%) and not requiring vaccine if already infected with Covid-19 (55.95%) and researchers rushed the development of covid-19 vaccine which makes it not trustworthy (43.52%). Majority of the study participants considered vaccines can be developed for common cold, HIV and other diseases. Conclusion: The circumstances in different parts of world varies and while vaccines may be very effective at reducing number of people getting infected, they cannot assure cent percent protection, since virus can mutate for new variants. Ending vaccination myths may also takes some time but at least can be lessened significantly if there is enough effort and collaboration from everyone. [ABSTRACT FROM AUTHOR]