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Günümüzde kullanıcıların işlemlerinin büyük bir kısmını dijital platformlar üzerinden gerçekleştirmesi, mobil cihaz kullanım oranlarını önemli ölçüde artırmıştır. Mobil cihazlar birçok kolaylık ve avantaj sağlarken, beraberinde çeşitli sorunları ortaya çıkarmıştır. Fiziksel ortamda işlenen çoğu suç, artık dijital ortamlara taşınmıştır. Mobil cihazlara erişimin kolaylaşması, bu araçların, suç işleme aracı olarak kullanılmasını da gündeme getirmiştir. Teknolojinin hızla gelişmesi, mobil cihazların sürekli değişen dinamik yapısı ve her geçen gün yeni özellikler kazanması kullanıcılar açısından düşünüldüğünde geniş imkanlar sağlamakta ancak suç soruşturmalarında görev alan adli bilişim uzmanları için ise çeşitli zorluklar oluşturmaktadır. Mobil cihazların suç delili olarak elde edilmesinden, bu delillerin mahkemeye sunulmasına kadar geçen adli bilişim inceleme süreçlerinde birçok zorluklarla karşılaşılmaktadır. Mobil cihazların çok katmanlı yapısı, işletim sistemlerinin cihazdan cihaza farklılık göstermesi, her cihazda farklı uygulamaların bulunması, cihaz ya da cihaz içi bileşenlerin parola ile korunuyor olması bu zorluklar arasında yer almaktadır. Sürekli değişen ve gelişen mobil cihaz teknolojisinin yakından takip edilmesi adli bilişim inceleme süreçlerinin sağlıklı bir şekilde yürütülebilmesi açısından büyük önem taşımaktadır. Belirtilen sorunlar, araştırmanın temel motivasyonunu ve hareket noktasını oluşturmuştur. Araştırmada, 2019-2024 yılları arasında yayınlanan 19 makale üzerinden mobil cihazlarda adli analiz alanındaki gelişmeler sistematik literatür taraması yöntemiyle incelenmiştir. Araştırma kapsamında mobil cihazlarda kullanılan işletim sistemleri, cihaz türleri, kullanılan adli bilişim araçları ve incelenen uygulama türleri detaylı şekilde ele alınmıştır. İncelenen araştırmalar, mobil adli bilişimdeki teknolojik çeşitlilik ve karmaşıklığın, uzmanların çok yönlü bilgi ve becerilere sahip olmasını gerektirdiğini ortaya koymaktadır. Son bölümde, araştırmalara yönelik sonuçlar özetlenmiş ve gelecekte gerçekleştirilebilecek çalışmalara yönelik öneriler sunulmuştur.

This book presents the fundamentals of wireless communications and services, explaining in detail what RF spectrum management is, why it is important, which are the authorities regulating the use of spectrum, and how is it managed and enforced at the international, regional and national levels. The book offers insights to the engineering, regulatory, economic, legal, management policy-making aspects involved. Real-world case studies are presented to depict the various approaches in different countries, and valuable lessons are drawn. The topics are addressed by engineers, advocates and economists employed by national and international spectrum regulators. The book is a tool that will allow the international regional and national regulators to better manage the RF spectrum, and will help operators and suppliers of wireless communications to better understand their regulators.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

This paper assesses whether ExxonMobil Corporation has in the past misled the general public about climate change. We present an empirical document-by-document textual content analysis and comparison of 187 climate change communications from ExxonMobil, including peer-reviewed and non-peer-reviewed publications, internal company documents, and paid, editorial-style advertisements ('advertorials') in The New York Times. We examine whether these communications sent consistent messages about the state of climate science and its implications-specifically, we compare their positions on climate change as real, human-caused, serious, and solvable. In all four cases, we find that as documents become more publicly accessible, they increasingly communicate doubt. This discrepancy is most pronounced between advertorials and all other documents. For example, accounting for expressions of reasonable doubt, 83% of peer-reviewed papers and 80% of internal documents acknowledge that climate change is real and human-caused, yet only 12% of advertorials do so, with 81% instead expressing doubt. We conclude that ExxonMobil contributed to advancing climate science-by way of its scientists' academic publications-but promoted doubt about it in advertorials. Given this discrepancy, we conclude that ExxonMobil misled the public. Our content analysis also examines ExxonMobil's discussion of the risks of stranded fossil fuel assets. We find the topic discussed and sometimes quantified in 24 documents of various types, but absent from advertorials. Finally, based on the available documents, we outline ExxonMobil's strategic approach to climate change research and communication, which helps to contextualize our findings.

Quantum computers represent a transformative frontier in computational technology, promising exponential speedups beyond classical computing limits. IBM Quantum has led significant advancements in both hardware and software, providing access to quantum hardware via IBM Cloud ® since 2016 and achieving a milestone with the world’s first accessible quantum computer. This paper explores IBM’s journey in quantum computing, focusing on its contributions to both hardware and software, as well as the development of practical quantum computers. We trace the evolution of IBM Quantum’s processors, from the early canary processors to the milestone of surpassing the 1000-qubit barrier. In addition to these technological strides, we delve into the practical applications of quantum computing, particularly within nine key industries: airlines, banking, healthcare, electronics, life sciences, and more. We also explore IBM Quantum’s case studies and strategic partnerships with organizations such as Boeing, CERN, ExxonMobil, and Cleveland Clinic, which are helping to bridge the gap between theoretical research and real-world applications. Further, we examine the key challenges and solutions in scaling quantum systems and achieving fault tolerance, highlighting IBM’s efforts toward building practical, fault-tolerant quantum systems capable of addressing real-world problems.

The combination of corporate-community conflicts and oil transnational corporations' (TNCs) rhetoric about being socially responsible has meant that the issue of community development and poverty reduction have recently moved from the periphery to the heart of strategic business thinking within the Nigerian oil industry. As a result, oil TNCs have increasingly responded to this challenge by adopting partnership strategies as a means to contribute to poverty reductions in their host communities as well as secure their social licence to operate. This paper critically examines the strengths and weaknesses of the different community development partnership (CDPs) initiatives employed by Shell, Exxon Mobil and Total to contribute to poverty reduction within their host communities in the Niger Delta, Nigeria. Drawing on empirical data and critical analysis, the paper argues that while the CDP initiatives by SPDC, MPN and EPNL have the potential to contribute to community development, the failure to integrate negative injunction duties into existing partnerships means that the partnerships make no difference to how oil TNCs conduct their core business operation. Consequently, CDPs have had limited positive impact on poverty reduction in the Niger Delta. The paper concludes by examining the implications of the emerging issues for partnership and poverty reduction.

The increasing integration of digital technologies into daily life has spurred a growing body of research in the field of digital psychology. This research has shed light on the potential benefits and drawbacks of digital technologies for mental health and well-being. However, the intricate relationship between technology and psychology remains largely unexplored. This study aimed to investigate the impact of mindfulness-based mobile apps on university students' anxiety, loneliness, and well-being. Additionally, it sought to explore participants' perceptions of the addictiveness of these apps. The research utilized a multi-phase approach, encompassing a correlational research method, a pretest-posttest randomized controlled trial, and a qualitative case study. Participants were segmented into three subsets: correlations (n = 300), treatment (n = 60), and qualitative (n = 20). Data were gathered from various sources, including the social anxiety scale, well-being scale, social media use integration scale, and an interview checklist. Quantitative data was analyzed using Pearson correlation, multiple regression, and t-tests, while qualitative data underwent thematic analysis. The study uncovered a significant correlation between social media use and the variables under investigation. Moreover, the treatment involving mindfulness-based mobile apps led to a reduction in students' anxiety and an enhancement of their well-being. Notably, participants held various positive perceptions regarding the use of these apps. The findings of this research hold both theoretical and practical significance for the field of digital psychology. They provide insight into the potential of mindfulness-based mobile apps to positively impact university students' mental health and well-being. Additionally, the study underscores the need for further exploration of the intricate dynamics between technology and psychology in an increasingly digital world.

Low precision deep neural network model quantization can further reveal stronger abilities of models such as shorter inference time, lower energy consumption and memory usage, but meanwhile induce performance degradation and instability during training. Straight Through Estimator (STE) is widely used in Quantization-Aware-Training (QAT) to overcome these shortcomings, and achieves good results on (2-, 3-, 4-bit) quantization. Different STE function may achieve different performance under various quantization precision settings. In order to explore the applicable bit-width settings range of STE functions and stabilize the training process, we propose Root Quantization. Root Quantization combines two estimators, the linear estimator and the root estimator. While linear estimator is based on existing methods of training quantizer and weights under task loss function, root estimator is based on high degree root and acts as a correction module to fine-tune the weights, which not only approximates the gradient of quantization error, but also makes the gradient more accurate. Root estimator can also adapt and adjust each layer’s root degree to the most suitable value through the task loss gradient. Extensive experimental results on CIFAR-10 and ImageNet, with different network architectures under various bit-width range, show the effectiveness of our method.

Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data can inform, support or even become an integral part of statistical production processes. In this article, we focus on arguably the most important problem hindering the application of mobile phone data in official statistics: detecting home locations. We argue that current efforts to detect home locations suffer from a blind deployment of criteria to define a place of residence and from limited validation possibilities. We support our argument by analysing the performance of five home detection algorithms (HDAs) that have been applied to a large, French, Call Detailed Record (CDR) data set (~18 million users, five months). Our results show that criteria choice in HDAs influences the detection of home locations for up to about 40% of users, that HDAs perform poorly when compared with a validation data set (resulting in 358-gap), and that their performance is sensitive to the time period and the duration of observation. Based on our findings and experiences, we offer several recommendations for official statistics. If adopted, our recommendations would help ensure more reliable use of mobile phone data vis-à-vis official statistics.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named “BraNet” for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client–server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader’s agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts’ accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model. Graphical abstract