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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]

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]