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The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions. [ABSTRACT FROM AUTHOR]

This paper aims to explore key pillars of supply chain competitiveness (SCC) and understand how top supply chains remain competitive in the long term. The research design is divided into two phases. First, an extensive review of the scholarly SCC literature is conducted to identify the core pillars that help achieve SCC. Second, the literature published in practitioner outlets on the five selected companies of Gartner's Supply Chain Top 25 is scrutinized to understand how top supply chains apply the core pillars of SCC in practice. A total of 193 scientific and practitioner articles were analyzed to develop the key pillars of SCC. This study identified six key pillars of SCC in the literature: innovation, sustainability, collaboration, information technology, agility, and flexibility. It has been found that a combination of these pillars, if not all, will be required to remain competitive in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

The article focuses on the controversy surrounding the Department of Justice's antitrust lawsuit against Apple, which has received both praise and criticism. Topics include the criticism of the DOJ's narrow definition of the relevant market as either "smartphones" or "performance smartphones," the significance of market definition in antitrust cases, and the implications of the DOJ's strategic choice in framing the lawsuit.

We analyze whether industry competition influences analyst coverage decisions and whether analysts benefit from covering product market competitors. We find that analysts are more likely to cover a firm when this firm competes with more firms already covered by the analyst. We also find that the intensity of competition among these competitors is additionally important to the coverage decision. Moreover, we find that analysts who cover product market competitors are more likely to obtain analyst star status. These results are consistent with the importance to analysts of industry competition and product market knowledge accumulated through covering product market competitors. [ABSTRACT FROM AUTHOR]

While subjective methods like the Yanagihara system and the House-Brackmann system are standard in evaluating facial paralysis, they are limited by intra- and inter-observer variability. Meanwhile, quantitative objective methods such as electroneurography and electromyography are time-consuming. Our aim was to introduce a swift, objective, and quantitative method for evaluating facial movements. We developed an application software (app) that utilizes the facial recognition functionality of the iPhone (Apple Inc., Cupertino, USA) for facial movement evaluation. This app leverages the phone's front camera, infrared radiation, and infrared camera to provide detailed three-dimensional facial topology. It quantitatively compares left and right facial movements by region and displays the movement ratio of the affected side to the opposite side. Evaluations using the app were conducted on both normal and facial palsy subjects and were compared with conventional methods. Our app provided an intuitive user experience, completing evaluations in under a minute, and thus proving practical for regular use. Its evaluation scores correlated highly with the Yanagihara system, the House-Brackmann system, and electromyography. Furthermore, the app outperformed conventional methods in assessing detailed facial movements. Our novel iPhone app offers a valuable tool for the comprehensive and efficient evaluation of facial palsy. [ABSTRACT FROM AUTHOR]

The proposed model is a Deep Learning (DL) based method employing Long Short-Term Memory (LSTM) networks for forecasting stocks. The aim of this approach is forecasting stock prices of Apple Inc. using statistics on previous stock prices obtained from Tiingo. The proposed model consists of several stages of processing and modelling, including data cleaning, feature selection, feature scaling, model building, model evaluation, model improvement, and prediction. Cleaning, organising, and transforming raw data into a format appropriate for analysis are all parts of data pre-processing. Feature engineering involves the data extraction and selection of relevant features for accuracy improvement of the model. The scaling of features involves normalising the data to prevent bias in the model. The LSTM models are built and evaluated using multiple metrics such as Mean Squared Error (MAE) and Root Mean Squared Error (RMSE). The model is iteratively improved using a combination of hyperparameter tuning and feature engineering. Finally, the model is then used to forecast stock prices for the following 30 days, and the accuracy of the forecasts is determined. The proposed methodology is designed to outperform traditional LSTM models for predicting the future price of stock by incorporating novel techniques, for feature engineering and model refinement. The suggested design is a comprehensive approach for forecasting future stock prices using DL based techniques. The model is designed to be flexible and adaptable, allowing for customization for different datasets and prediction horizons. It represents a significant improvement over existing LSTM models for stock price prediction to be valuable in a variety of financial industry applications. This paper collects data from Tiingo API and uses stacked LSTM to train the model. The experimental results give only 0.0813 RMSE, which proves that the model is more accurate and precise. [ABSTRACT FROM AUTHOR]

Background: 'Candidatus Phytoplasma mali', the causal agent of apple proliferation disease, exerts influence on its host plant through various effector proteins, including SAP11CaPm which interacts with different TEOSINTE BRANCHED1/ CYCLOIDEA/ PROLIFERATING CELL FACTOR 1 and 2 (TCP) transcription factors. This study examines the transcriptional response of the plant upon early expression of SAP11CaPm. For that purpose, leaves of Nicotiana occidentalis H.-M. Wheeler were Agrobacterium-infiltrated to induce transient expression of SAP11CaPm and changes in the transcriptome were recorded until 5 days post infiltration. Results: The RNA-seq analysis revealed that presence of SAP11CaPm in leaves leads to downregulation of genes involved in defense response and related to photosynthetic processes, while expression of genes involved in energy production was enhanced. Conclusions: The results indicate that early SAP11CaPm expression might be important for the colonization of the host plant since phytoplasmas lack many metabolic genes and are thus dependent on metabolites from their host plant. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, Apple Inc. switched its product launch from onsite events to pre-recorded online broadcasts. Yet this form has been carried on even though the pandemic has faded. It is unclear if this shift was accompanied by a move in language complexity and how the communication effect was influenced. This study analysed Apple's product launch events from 2016 to 2022 using quantitative linguistics metrics such as lexical complexity, word token length, entropy, readability, and sentence length distribution. The analysis adopted the reach-effect-durability framework for interpreting the effects of strategic communication. The results showed that Apple's online product launch events featured richer and longer words, increased text difficulty, and a decrease in the proportion of short sentences. Such changes happened in a balanced manner that can be interpreted in view of the driving and restraining forces resulting from the mode shift of the launch event. This study demonstrates the feasibility of assessing efforts and strategies used in a communication campaign through quantitative text analysis. [ABSTRACT FROM AUTHOR]

This paper builds on Khan's work on Amazon by transferring her normative framework to Apple. It explores the company's anti-competitive business practices, main antitrust concerns, and the currently proposed reform measures from an innovative business ethics and law (i.e., ordoliberal) perspective. The paper argues that one of the key issues with Apple is the company's closed ecosystem combined with its role as an internet gatekeeper. The E.U.'s Digital Markets Act, which aims to open Apple's ecosystem – via data portability, interoperability, and multi-homing requirements – is a necessary step in the right direction. Yet, it is insufficient to prevent the lock-in effects of a 'walled product garden' and ensure complete device and platform neutrality. That is, additional steps must be taken to overcome Apple's dual role as a platform operator and service provider and inhibit the company from engaging in anti-competitive business conduct. [ABSTRACT FROM AUTHOR]

This research investigates the mediating role of perceived brand authenticity (PBA) between brand experience (BE) and brand love (BL) of global high-tech brands Apple and Samsung. A quantitative study was conducted in Japan, India, and Portugal. The research found evidence that PBA is a multidimensional, reflective-formative higher-order construct composed of two lower-order components namely PBA Core and PBA Peripheral. The findings also contribute to understand how consumers are impacted by different BE and PBA dimensions using the lens of consumer culture theory and how BL is formed as a social-cultural phenomena. Finally, the study demonstrates for the first time that relationship intensity and self-authenticity moderate the effect of BE on PBA. Although limited to three countries and high-tech brands, the findings are of relevance to global brands by raising awareness that culture plays a key role in how consumers perceive authentic brand experiences and how passionate feelings for global brands can be strengthened. [ABSTRACT FROM AUTHOR]

This paper proposes a new test for unit root processes with a partial quadratic trend on an unknown break date, denoted as the URQ process herein. Such a process is extremely similar to the explosive bubble process, and both can capture the sharp rise in prices. We develop the asymptotic distributions under the local-to-unity hypothesis, which covers the URQ null and explosive root alternatives. Simulations show that the test has good finite sample performances and can differentiate explosive bubble processes from URQ processes. An application to the Kweichow Moutai and Apple stocks, which exhibit striking price rises during their respective sample periods, shows that both prices follow URQ processes. We further provide a fundamental analysis. The significant increases in earnings, returns, dividends, and fundamental score after the partial quadratic trend occurs provide evidence that a fundamental improvement rather than a bubble mainly drives such drastic price rises. [ABSTRACT FROM AUTHOR]

Applications for the stock market that can be relied upon to provide the information regular and professional investors need to make better-informed purchases are a boon to both. A well-thought-out sales approach may help buyers mitigate risk, zero in on the companies most likely to provide the highest returns, and increase their chances of making a purchase. Due to numerous interrelationships between various company values, executing stock market research utilizing batch processing methods provides an exceptionally tough task. Technology advancements, such as the licensing of global processes, usher in a new age for stock market forecasting. The present market climate has increased the significance of data applications. An interesting and new contribution made by this work is the proposal of a deep learning-based resilience time series model for forecasting future market values. By analyzing financial time series, this study aimed to build a sophisticated strategy for forecasting stock market prices. The use of artificial intelligence (AI) to forecast future market behavior is one of the most intriguing developments in this generation of technological breakthroughs. Particularly promising results have been found when applying deep learning techniques to the task of predicting the actions of market players. In this paper, we propose a method for forecasting the final prices of publicly traded firms like Tesla, Inc. and Apple, Inc. by merging a convolutional neural network with long short-term memory (LSTM). These two methods were both developed with the help of deep learning techniques. The time frame covered by statistics used in these projections is two years. To evaluate the performance of our deep learning models, we compared their market predictions using several different metrics, including mean squared error (MSE), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and Pearson's R. When compared to the options, the CNN-LSTM deep learning algorithm did the best. When comparing convolution neural networks CNN-LSTM model to the normal LSTM and a simpler deep learning variant, the latter showed to be more effective at forecasting stock market values. This was the case when examining all three variants together. [ABSTRACT FROM AUTHOR]

From a documentary historical perspective, the commercial expansion of telecommunications systems from first-world countries that have maintained a dominant presence in Mexico for 100 years is analyzed. The activated FM receiver in cell phones, which Apple has decided not to activate, serves as an example of the need to regulate the power of multinational corporations, which diminishes visibility in public communication and social media. Neither the public pressure from authorities, industry, and civil society in Mexico, Canada, nor the United States has succeeded in dissuading the iPhone from allowing such signals on its devices, thereby favoring the sales of headphones, music services, and mobile broadband data payments. Contributions from direct contact with media representatives and regulatory bodies are included. [ABSTRACT FROM AUTHOR]

In Epic Games, Inc. v. Apple Inc., the United States District Court for the Northern District of California held that Apple did not operate a monopoly in its App Store marketplace, offered through its famously known mobile device, the "iPhone." Both parties appealed aspects of the district court's decision to the Ninth Circuit Court of Appeals, which affirmed the decision in full, except for the award of attorney fees. While this App Store dilemma continues to unfold, several companies, U.S. states, and even the U.S. Congress have sided in favor of Epic, trying to provide support in its fight for a fairer App Store. This Note examines where the district court erred, and where it correctly applied the law. It also analyzes Epic's future chances, as there is a lot of work to be done before victory can be claimed. [ABSTRACT FROM AUTHOR]

Apple cultivar classification is challenging due to the inter-class similarity and high intra-class variations. Human experts do not rely on single-view features but rather study each viewpoint of the apple to identify a cultivar, paying close attention to various details. Following our previous work, we try to establish a similar multiview approach for machine-learning (ML)-based apple classification in this paper. In our previous work, we studied apple classification using one single view. While these results were promising, it also became clear that one view alone might not contain enough information in the case of many classes or cultivars. Therefore, exploring multiview classification for this task is the next logical step. Multiview classification is nothing new, and we use state-of-the-art approaches as a base. Our goal is to find the best approach for the specific apple classification task and study what is achievable with the given methods towards our future goal of applying this on a mobile device without the need for internet connectivity. In this study, we compare an ensemble model with two cases where we use single networks: one without view specialization trained on all available images without view assignment and one where we combine the separate views into a single image of one specific instance. The two latter options reflect dataset organization and preprocessing to allow the use of smaller models in terms of stored weights and number of operations than an ensemble model. We compare the different approaches based on our custom apple cultivar dataset. The results show that the state-of-the-art ensemble provides the best result. However, using images with combined views shows a decrease in accuracy by 3% while requiring only 60% of the memory for weights. Thus, simpler approaches with enhanced preprocessing can open a trade-off for classification tasks on mobile devices. [ABSTRACT FROM AUTHOR]

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The scope of this pilot study was to assess the feasibility of using the laser imaging detection and ranging (LiDAR) technology for contactless 3D body scanning of sports athletes and deriving anthropometric measurements of the lower limbs using available software. An Apple iPad Pro 3rd Generation with embedded LiDAR technology in combination with the iOS application Polycam were used. The effects of stance width, clothing, background, lighting, scan distance and measurement speed were initially assessed by scanning the lower limbs of one test person multiple times. Following these tests, the lower limbs of 12 male and 10 female participants were scanned. The resulting scans of the lower limbs were complete for half of the participants and categorized as good in quality, while the other scans were either distorted or presented missing data around the shank and/or the thigh. Bland–Altman plots between the LiDAR-based and manual anthropometric measures showed good agreement, with the coefficient of determination from correlation analysis being R2 = 0.901 for thigh length and R2 = 0.830 for shank length, respectively. The outcome of this pilot study is considered promising, and a further refinement of the proposed scanning protocol and advancement of available software for 3D reconstruction are recommended to exploit the full potential of the LiDAR technology for the contactless anthropometric assessment of athletes. [ABSTRACT FROM AUTHOR]

This article considers the European Commission's approach to tackling various Apple anti-competitive strategies. It considers why the European Commission has not applied excessive pricing abuse of dominance jurisprudence to challenge Apple's high commission fees charged to app developers, instead preferring to use a variety of other approaches to restore competition to certain parts of Apple's closed ecosystem. [ABSTRACT FROM AUTHOR]

This paper explores the understudied question of how the number of product features that a digital platform complementor adds to a product is associated with its performance. Specifically, by integrating consumer behaviour literature into platform research, this paper focuses on three key performance dimensions considered as important for digital platform complementors. Considering the distinctive characteristics of digital platforms, this paper conceptualizes how additional features affect consumer acquisition, consumer retention, and product monetization of a digital platform complementor. This research question is empirically examined in the context of a health and fitness mobile platform in the US Apple iOS App Store, and the panel data analyses are complemented with a randomized online experiment to mitigate endogeneity concerns and lend further credence to the findings. This paper enhances platform research by unravelling an underexplored yet essential topic—how many features a digital platform complementor should include to achieve superior performance and by distinguishing among various key performance measurements on digital platforms. [ABSTRACT FROM AUTHOR]

With the current artificial intelligence (AI) boom, new innovative and accessible applications requiring minimal computer science expertise have been developed for discipline specific and mainstream purposes. Apple Intelligence, a new AI model developed by Apple, aims to enhance user experiences with new functionalities across many of its product offerings. Although designed for the everyday user, many of these advances have potential applications in neurosurgery. These include functionalities for writing, image generation, and upgraded integrations to the voice command assistant Siri. Future integrations may also include other Apple products such as the vision pro for preoperative and intraoperative applications. Considering the popularity of Apple products, particularly the iPhone, it is important to appraise this new technology and how it can be leveraged to enhance patient care, improve neurosurgical education, and facilitate more efficiency for the neurosurgeon. [ABSTRACT FROM AUTHOR]

Designers rely on sketching to visualize and refine their initial ideas, and virtual reality (VR) tools now facilitate sketching in immersive 3D environments. However, little research has been conducted on the differences in the visual and spatial processes involved in 3D versus 2D sketching and their effects on cognition. This study investigated potential differences in spatial and visual functions related to the use of 3D versus 2D sketching media by analyzing functional magnetic resonance imaging (fMRI) data. We recruited 20 healthy, right-handed students from the Department of Horticulture and Landscape Architecture with at least three years of experience in freehand landscape drawing. Using an Oculus Quest VR headset controller and a 12.9-inch iPad Pro with an Apple Pencil, we tested participants individually with 3D and 2D sketching, respectively. When comparing 2D and 3D sketches, our fMRI results revealed significant differences in the activation of several brain regions, including the right middle temporal gyrus, both sides of the parietal lobe, and the left middle occipital gyrus. We also compared different sketching conditions, such as lines, geometrical objects (cube), and naturalistic objects (perspective view of a tree), and found significant differences in the spatial and visual recognition of brain areas that support visual recognition, composition, and spatial perception. This finding suggests that 3D sketching environments, such as VR, may activate more visual–spatial functions during sketching compared to 2D environments. The result highlights the potential of immersive sketching environments for design-related processes and spatial thinking. [ABSTRACT FROM AUTHOR]

We study how Apple and independent repair service providers used different physical, regulatory, and digital instruments to influence each other's abilities to control the repair aftermarket of the Apple iPhone between 2007 and 2020. We show how the emergence of digital instruments for enacting control, made possible through emerging functionality for tethering, encryption, and temporary binding implemented in the iPhone itself, was shaped by and shaped the actions of Apple and the independent repair service providers, and led to a dominant tension between encrypted authorization and inscription of control through Apple and collective legal action by independent repair service providers. Our analysis provides a more nuanced understanding of control enactment by highlighting the implications of using different mediums for exercising control, and we provide a new way to understand the dialectics involved in enacting control in digital product aftermarkets. Our study provides insights that can inform the regulation of digital product aftermarkets--in particular, the ongoing debate about rights-to-repair legislation. [ABSTRACT FROM AUTHOR]

When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple's iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint. [ABSTRACT FROM AUTHOR]

This article examines the lawsuit between Apple Inc. and Rivos Inc. regarding the recruitment of Apple employees by Rivos. Apple accused the departing employees of misappropriating trade secrets and breaching intellectual property agreements, while Rivos countersued, claiming that Apple's contracts violated California law. The case was settled after two years of litigation. The article explores the issues surrounding corporate raiding, trade secrets, and employee mobility, as well as the tension between protecting corporate secrets and allowing employee movement. It provides background, analyzes the claims and counterclaims, and discusses the lessons learned from the lawsuit. [Extracted from the article]

Jumps in the paths of efficient asset prices have important economic implications. Motivated by the issue of testing for jumps based on noisy high‐frequency data, we develop a novel spot volatility estimator, which is obtained by minimizing the sum of some Huber loss functions, and use it as an ingredient for jump detection. This type of estimators is uniformly consistent in estimating the spot volatilities of the efficient price at numerous time points. We further demonstrate the consistency of the proposed jump test based on the property of the novel spot volatility estimator. We show that in finite samples, the proposed volatility estimator and the test perform favorably compared to some competitors through Monte Carlo simulations. We also illustrate our methodology with the stock prices of Apple and Microsoft. [ABSTRACT FROM AUTHOR]

Background: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Methods: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple's vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. Results: We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Conclusions: Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions. [ABSTRACT FROM AUTHOR]

The prevalence of digital devices in modern society has raised concerns about the potential negative effects of blue-light emissions on eye health and biological rhythms. Research into blue light emissions from digital devices and their potential impact on eye health emphasizes the importance of understanding and quantifying the extent and scope of blue light emissions produced by commonly used screens (smartphones, tablets, and computers). The goal of this study was to implement a set of methodologies to analyze this emission. A comparative study specifically evaluated three popular Apple devices: the iPhone 12 mini®, iPad Pro 12.9®, and the MacBook Pro®. The devices' spectral power was measured using a spectroradiometer while displaying text and a game at different brightness levels. The laboratory measurements are compared to known solar irradiance, and all devices tested show blue wavelength peaks between 445 nm and 455 nm, with no expected immediate photobiological risk. We quantified the spectral emission from the three device categories and concluded that blue light levels should not significantly affect eye health. The measurements carried out indicated that the blue irradiance received by the human eye during one minute outdoors is greater than the blue light received by digital devices in approximately 24 h. This study also examines the effectiveness of blue-blocking lenses from well-known brands. The research highlights the importance of quantifying blue light emissions and understanding their potential impact on eye health, so appropriate measures can be developed to mitigate, if needed, adverse effects on ocular structures. A need to clarify the efficacy and usefulness of blue-blocking ophthalmic lenses still remains. [ABSTRACT FROM AUTHOR]

Mass media companies can be elaborated as organizations that manipulate technological components for their various departments such as movie studios, publishing houses, radio and television station management teams that impact a large range of audience via vast communication strategies. The companies have also been referred to as media conglomerates, media groups, or media houses further illustrating their grasp over the global markets and their revenue structures. The largest media companies such as Apple, Disney, and Comcast among others, offer products and services to users that are diverse individuals as well as large organizations leading to significant revenues as well as challenges that have been further explored in a comprehensive manner in the study. IoT utilizes wireless networks that are without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental conditions. If you're wanting to integrate WSN into your business, our highly driven and experienced engineers can give you an all-encompassing solution. [ABSTRACT FROM AUTHOR]

The activities of enterprise employees can become a risk factor in terms of cybercrime, which is often associated with their lack of preparedness to counter potential cyber threats. Many businesses need a well-adjusted system of approaches to protect their internal data from all sorts of cyber threats. The objectives of the study are to analyze the practices of using systems to combat cybercrime phenomena in the corporate sphere. This analysis includes a review of the use of preventive measures by the personnel of top high-tech companies such as Apple and Samsung. The research implies to study the regulatory component of corporate systems for preventing cybercrime among personnel in the context of existing practices and approaches to the legal regulation of this issue. The study examines the relationship of corporate instructions in the field of cybersecurity. The results of the study indicate that the regulatory basis of countering internal cybercrime and data breach issues, as well as practical measures for its implementation cannot be equated, since the latter may have the nature of informal measures and do not contain formal reporting. [ABSTRACT FROM AUTHOR]

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock-price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment's predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. The results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft's opening prices, title polarity influencing Tesla's opening prices, and a positive impact of title polarity on Apple's closing prices. However, Tesla's stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock-market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment's financial implications, suggesting further investigation into smaller firms and other market sectors. [ABSTRACT FROM AUTHOR]

Laser scanning technology has long been the preferred method for capturing interior scenes in various industries. With a growing market, smaller and more affordable scanners have emerged, offering end products with sufficient accuracy. While not on par with professional scanners, Apple has made laser scanning technology accessible to users with the introduction of the new iPhone Pro models, democratizing 3D scanning. Thus, this study aimed to assess the performance of the iPhone's lidar technology as a low-cost solution for building documentation. Four scanning applications were evaluated to determine the accuracy, precision, and user experience of the generated point clouds compared with a terrestrial laser scanner. The results reveal varying performances on the same device, highlighting the influence of software. Notably, there is room for improvement, particularly in tracking the device's position through software solutions. As it stands, the technology is well suited for applications such as indoor navigation and the generation of quick floor plans in the context of building documentation. [ABSTRACT FROM AUTHOR]

Near‐constant Internet access through desktop or mobile devices has turned self‐service support forums into the first port of call for users seeking to troubleshoot product or service issues. The firms providing these products and services also benefit from this trend since it reduces user support costs by diverting service requests away from costlier support channels, such as help desks. For the continued success of such a forum, however, the managing entity must ensure that users receive timely solutions to their inquiries quickly and regularly. We develop a mathematical model of a user forum's operations to obtain a "white box" view of a user forum and reveal the support system's dynamics. Then, using a large and comprehensive dataset of questions and answers from Apple's iPhone user forum, we empirically estimate the forum's performance to validate the predictions of the mathematical model. Our results demonstrate that the predictions closely match the forum's actual performance, with an error of less than 10%. We then propose and analyze an optimal threshold policy that boosts a thread to rekindle user interest and demonstrate the benefit of our intervention policy in managing the iPhone forum. [ABSTRACT FROM AUTHOR]

Digital media corporations such as Alphabet—the parent company of Google—and Apple invade people's privacy and personal data and make high profits through their operating systems such as Android and iOS. The research question of this article is the following: is it possible to prevent corporations from engaging in unfair and unlawful practices concerning personal data processing by making regulations regarding the laws of data protection and competition? We argue that the laws of data protection, such as the GDPR (General Data Protection Regulation) and anti-trust laws in the EU, structure digital economies in such a way that while the corporations are held liable for the abuse of dominance in the related market, such laws do not affect protecting individuals' privacy and personal data. A better solution would be to create an environment where individuals have a choice, and where they can negotiate the terms and conditions. This is possible by amending existing laws and designating new provisions that prevent invasive and compelling manners of the companies that users are exposed to in practice. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the robustness and sensitivity of various continuous-time rough Volterra stochastic volatility models in relation to the process of market calibration. Model robustness is examined from two perspectives: the sensitivity of option price estimates and the sensitivity of parameter estimates to changes in the option data structure. The following sensitivity analysis consists of statistical tests to determine whether a given studied model is sensitive to changes in the option data structure based on the distribution of parameter estimates. Empirical study is performed on a data set consisting of Apple Inc. equity options traded on four different days in April and May 2015. In particular, the results for RFSV, rBergomi and α RFSV models are provided and compared to the results for Heston, Bates, and AFSVJD models. [ABSTRACT FROM AUTHOR]

Apple Inc. is an American technological company that designs, manufactures, and sells various products such as laptops, phones, accessories, streaming services, licensing services, and so much more. The paper explores the corporate responsibility and unethical business practices that arise from the global sourcing of Apple's products. The company established many relationships with suppliers in East Asia to meet the rigorous demands of consumers, promising high-quality products at the expense of factory workers. However, global sourcing comes with unethical business practices. There have been various cases against Apple's suppliers. This research highlights unfair working conditions, overworking employees, and environmental pollution that Apple's suppliers are guilty of. [ABSTRACT FROM AUTHOR]

Apple vinegar is highly recommended for nutrition due to its health benefits and bioactive components. However, the apple cultivar greatly influences the quality of the vinegar. In this research, our focus was on examining the impact of four different apple cultivars on the physicochemical attributes, chemical composition, as well as biological properties—including antidepressant and anti-inflammatory activities—of vinegar. Interestingly, the physicochemical properties of vinegar and the contents of acetic acid and polyphenols depend on the apple cultivars. HPLC chromatographic analysis showed that citric acid (820.62–193.63 mg/100 g) and gallic acid (285.70–54.40 µg/g) were mostly abundant in the vinegar samples. The in vivo results showed that administration of Golden Delicious apple vinegar (10 mL/kg) to adult Wistar rats reduced carrageenan-induced inflammation by 37.50%. The same vinegar sample exhibited a significant antidepressant effect by reducing the rats' immobility time by 31.07% in the forced swimming test. Due to its high acidity, Golden Delicious vinegar was found to be more effective against bacteria, particularly Bacillus subtilis and Candida albicans, resulting in a MIC value of 31.81 mg/mL. Furthermore, the antioxidant activity of various vinegar samples was found to be powerful, displaying optimal values of IC50 = 65.20 mg/mL, 85.83%, and 26.45 AAE/g in the DPPH, β-carotene decolorization and TAC assays, respectively. In conclusion, the apple cultivars used in this study impact the chemical composition and biological activities of vinegar, which may help demonstrate the importance of raw material selection for the production of vinegar. [ABSTRACT FROM AUTHOR]

The objective of companies is to seek profitability to guarantee their survival. To do this, they want their brand to be recognized and differentiated. Apple is a paradigmatic case in this sense. It is currently the most valuable brand in the world. Through a theoretical framework, a case study and ad-hoc research, the perception of the value of the Apple brand among iPhone users is analyzed. The brand identity acts together with the main attributes of the product to shape in the minds of the users an experience that is perceived as unique and valuable. [ABSTRACT FROM AUTHOR]

The task of detecting a structural change in conditional quantiles of time series with time-varying volatilities is vital in the field of financial time series, especially risk management. Therefore, in this study, we aim to construct a change point test to detect a quantile change by hybridizing the cumulative sum of squares test with the support vector quantile regression. Compared to the test employing the standard quantile regression, this approach not only provides more robust property against a high degree of nonlinearity of time series but also exhibits better performance for the datasets contaminated with high-frequency noises, as demonstrated in our simulation study. It also has merits to provide more diverse interpretations and a deeper understanding of financial time series than the volatility change point test, as illustrated in a real data analysis of three financial indices, namely, S&P500, Nasdaq composite index, and the stock price of Apple Inc. [ABSTRACT FROM AUTHOR]

A Zero-drift GARCH (ZD-GARCH) model is recently proposed to study conditional and unconditional heteroskedasticity together. Despite its attractive statistical properties, our research demonstrates that the stability test based on this model fails when structural changes are present. To overcome this issue, we allow the Markov regime-switching (MRS) feature within the ZD-GARCH framework and propose an MRS-ZD-GARCH model. A revised stability estimator is further derived. The effectiveness of our proposed approach to test the stability with and without structural changes is evidenced via simulation studies. Using the empirical data of the S&P 500, NASDAQ and Apple returns, we show that the new model can also outperform the ZD-GARCH model in practice and provide more informative results. Therefore, the MRS-ZD-GARCH model could be a widely useful tool to study the stability of financial data and help address risk management issues in other contexts. [ABSTRACT FROM AUTHOR]

Apple is a symbol of the fusion of technology, design and consumer interest. Their constant pursuit of innovation and focus on the user make them a market leader. Their success goes beyond financial metrics, having a significant cultural impact on technology and lifestyle. In a dynamic market and with changing societal expectations, their commitment to excellence and sustainability will be vital in shaping their future. This report discusses Apple's marketing strategy, focusing on the product portfolio, external and internal environment, and competition. Innovative products with premium design and high functionality characterize Apple's product portfolio. Apple's external environment is dynamic and competitive, but the company benefits from strong brands, loyal customers and an efficient distribution network. Apple's internal environment is robust, with a focus on research and development, design and marketing. The report summarizes that Apple is a successful company with a well-defined marketing strategy that enables it to cope with the dynamic competitive environment. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA. [ABSTRACT FROM AUTHOR]

Background: Foreign body ingestion (FBI) represents the most common cause of emergent gastrointestinal endoscopy in children. FBI's management can be quite challenging for physicians because of the variability of the clinical presentation, and the decision tree becomes even more intricate because of patient-specific variables that must be considered in the pediatric age range (e.g., age of patients and neuropsychiatric disorders) in addition to the mere characteristics of the foreign body. We present an application for smartphones designed for pediatricians and pediatric surgeons based on the latest guidelines from the official pediatric societies. The app aims to help physicians manage FBI quickly and properly in children. Materials and methods: The latest pediatric FBI management guidelines were reviewed and summarized. The flow chart we obtained guided the development of a smartphone application. A questionnaire was administered to all pediatric surgeon trainees at our institute to test the feasibility and helpfulness of the application. Results: An app for smartphones was obtained and shared for free on the Google Play Store and Apple Store. The app guides the physician step by step in the diagnostic process, analyzing all patient- and foreign body-specific characteristics. The app consultation ends with a suggestion of the most proper decision to make in terms of further radiological investigations and the indication and timing of endoscopy. A questionnaire administered to trainees proved the app to be useful and easy to use. Conclusion: We developed an app able to help pediatricians and pediatric surgeons manage FBI in children, providing standardized and updated recommendations in a smart and easily available way. [ABSTRACT FROM AUTHOR]

The global smartphone market is one of the most dynamically developing markets with high competition. The presence of high-tech products in the smartphone market forces companies to act more cautiously and strategically. The success of companies depends on their level of meeting the demands of smartphone users. To this end, this study aims to determine the factors that are affected by the smartphone preferences of individuals and the effect level of these factors on company strategies. For this purpose, in this study, a survey was conducted in the TRA2 Region located in the east of Turkey. The obtained data from the survey was evaluated using the CHAID algorithm. In the study, it was seen that smartphone users liked the Apple the most in terms of perceived service quality. In addition, it has been observed that the highest level of expectation in terms of expected service quality is concentrated on the Apple brand. On the other hand, the most preferred Xiaomi was found to have the lowest brand loyalty. [ABSTRACT FROM AUTHOR]

In the contemporary American technology landscape, companies such as Apple, Amazon, Google, Meta, and Microsoft exercise unparalleled influence by offering ostensibly "gratis" services. This Article interrogates the veracity of this "free" paradigm, contending that consumers engage in a de facto quid pro quo by tendering personal data. We critically examine the application of the consumer welfare standard in antitrust jurisprudence, a standard that has enabled such corporations to amass significant market power without triggering traditional antitrust scrutiny. Courts have abstained from applying extant antitrust principles to these firms, largely due to an overreliance on consumer price as the litmus test for competitive harm under the consumer welfare standard. We posit that this misapplication perpetuates market concentration, thereby inhibiting innovation and disproportionately impacting vulnerable consumer demographics and small enterprises. To rectify this interpretive misstep, we advocate for the reconceptualization of data as a currency. Such a doctrinal shift would ameliorate the prevailing incongruities in the application of the consumer welfare standard, furnishing regulatory agencies and state Attorney Generals with a more nuanced metric for antitrust and consumer protection enforcement. The Article is segmented into four sections: (1) a reconceptualization of "free services" in the digital age, (2) an empirical analysis of burgeoning market concentration and its concomitant impact on technological innovation, (3) a historical exegesis of the consumer welfare standard's integration into antitrust law, and (4) a critique of the standard's role in diminishing consumer welfare. We conclude by proffering a suite of methodological frameworks designed to recalibrate the assessment of consumer welfare and market competition in technology sectors. This reevaluation is pivotal for the invigoration of competition and innovation within the American tech industry. [ABSTRACT FROM AUTHOR]

This article examines how the smartphone contributes to the co-production of security through an analysis of Covid-19 contact-tracing apps. Building on existing research in security studies that mobilizes the science and technology concept of co-production, the article proposes the notion of 'appropriation' as a concrete way of extending our understanding of the public/private co-production of security. Appropriation highlights how consumer technology may be repurposed for security and shows how private-sector actors that own consumer technology not only influence, but actively condition the co-production of security. Bringing new, typically commercial, concerns to bear on security practices, appropriation also has the effect of complicating conventional understandings of the relationship between liberty and security. Focusing on the NHS Covid-19 app and its contentious relationship with Google/Apple's framework for digital contact-tracing, the article demonstrates how the smartphone enables private-sector actors to gain influence in the security domain. Google and Apple used their control over smartphone technology to compel the British health authorities to adopt a less effective but more privacy-preserving approach than they originally intended, and thus enforced a seemingly liberal response to an exceptional political situation. [ABSTRACT FROM AUTHOR]

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Resale platforms such as Swappa and ThredUP, which provide a channel for product‐holders to sell used products, have become common. Interestingly, in the presence of resale platforms, some firms, such as Apple, set lower rebates for the trade‐in‐for‐upgrade (TU) mode instead of implementing the trade‐in‐for‐upgrade‐and‐cash (TUC) mode as Huawei does. In this paper, we build game‐theoretical models to explore how a firm should adjust its trade‐in strategy (e.g., choose pricing and mode selection between TU and TUC) in reaction to the emergence of third‐party resale platforms. We derive several insights. First, we find that using the TU mode helps to encourage consumer repurchases, whereas the TUC mode may have a greater promotion effect on consumers' first purchases. Second, we show that in the TUC mode, the amount of the trade‐in rebate is not affected by the presence of the resale platform. Differently, in the TU mode, whether the firm should provide a more generous trade‐in rebate depends on the unit product cost when the resale platform is present. Third, in response to the resale platform, the firm should choose the TU mode to take advantage of the platform's promotion effect if the unit product cost is high and choose the TUC mode to avoid the platform's cannibalization effect if the unit product cost is low. To verify the robustness of our findings, we consider the effects of reduced consumer uncertainty and the dynamic pricing mechanism in the extended models. Our main findings concerning trade‐in rebate and mode selection remain valid. [ABSTRACT FROM AUTHOR]

Knowledge management structures (KMs) can be defined as structures whose purpose is to encourage people to build relationships, networks and trust. Global companies like Apple, Inc. have promoted these structures to transform its organisation into a knowledge‐based organisation. This paper identifies the role of national culture on four KMs namely open‐mindedness (OM), counter‐knowledge (CK), environmental innovation (EF) and customer capital (CC). In doing so, this study uses data collected from 354 end users of Apple (161 from Italy and 193 from Brazil). This paper provides evidence that while Apple end users from Brazil are more positively associated with higher levels of OM and CC, Italy users are more positively associated with higher levels of CK and EF. This paper discusses the role of the six dimensions of national culture–power distance, individualism, masculinity, uncertainty avoidance, long‐term orientation and indulgence of a nation that plays a significant role in how they impact the KMs. Hence, this study serves as an important contribution to global companies that are trying to develop their KMs to sustain competitive advantages. [ABSTRACT FROM AUTHOR]

The rise or fall of the stock markets directly affects investors' interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and Avigilon Corporation (AVGO). The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022 (seventeen years) with 4280 samples. As already noted, multiple studies have been performed to resolve this problem using linear regression, support vectormachines, deep long short-termmemory (LSTM), and many other models. In this research, the Hidden Markov Model (HMM) outperformed other employed machine learning ensembles, tree-basedmodels, the ARIMA (Auto Regressive IntegratedMoving Average)model, and long short-term memory with a robust mean accuracy score of 99.98. Other statistical analyses and measurements for machine learning ensemble algorithms, the Long Short-TermModel, and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data. Thus, the proposed research found the best model to be HMM, and LSTM was the second-best model that performed well in all aspects. A developedmodel will be highly recommended and helpful for earlymeasurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments. [ABSTRACT FROM AUTHOR]