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Holographic Data Storage: From Theory to Practical Systems is a primer on the design and building of a holographic data storage system covering the physics, Servo, Data Channel, Recording Materials, and optics behind holographic storage, the requirements of a functioning system, and its integration into \"real-life\" systems.

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences. Making large datasets findable, accessible, interoperable and reusable could accelerate technology development. Now, Jacobsson et al. present an approach to build an open-access database and analysis tool for perovskite solar cells.

Social Complex Networks in communication networks are pivotal for comprehending the impact of human-like interactions on information flow and communication efficiency. These networks replicate social behavior patterns in the digital realm by modeling device interactions, considering friendship, influence, and information-sharing frequency. A key challenge in communication networks is their dynamic topologies, driven by dynamic user behaviors, fluctuating traffic patterns, and scalability needs. Analyzing these changes is essential for optimizing routing and enhancing the user experience. This paper introduces a network model tailored for Opportunistic Networks, characterized by intermittent device connections and disconnections, resulting in sporadic connectivity. The model analyzes node behavior, extracts vital properties, and ranks nodes by influence. Furthermore, it explores the evolution of node connections over time, gaining insights into changing roles and their impact on data exchange. Real-world datasets validate the model’s effectiveness. Applying it enables the development of refined routing protocols based on dynamic influence rankings. This approach fosters more efficient, adaptive communication systems that dynamically respond to evolving network conditions and user behaviors.

Declining medication adherence remains a critical health care issue, often assessed through unreliable self-reporting methods. Wearable devices (WDs) may offer an objective means to improve adherence monitoring by continuously recording physiological and activity data. This study aimed to develop and internally validate personalized predictive models, utilizing objective physiological and activity data from WDs, for identifying missed medication doses. A 30-day prospective observational study was conducted with 8 participants who wore Apple Watches and used a dedicated iOS app. The app collected demographics, medication details, psychological factors, mealtimes, and daily missed dose events. WDs recorded time-series data (ie, activity, heart rate, sleep) at 3-minute intervals. Data were aggregated into 1-hour segments, and lag (6 and 12 h) as well as rolling (24 h) features were generated. Light Gradient Boosting Machine models were constructed for each individual's dosing regimen if the missed dose rate exceeded 20%. Two modeling approaches were compared: a group cross-validation (CV) model that grouped data by day to avoid data leakage from rolling features, and a nonrolling feature model that excluded rolling features and used leave-one-out CV. F1-score, accuracy, recall, and precision were assessed between the 2 models. Of the 15 enrolled participants, 8 completed the study; 4 had a missed dose rate above 20%. In these 4 individuals, the group CV model achieved F1-scores of 0.435 to 0.902, with accuracy ranging from 0.711 to 0.911, recall from 0.278 to 0.822, and a precision of 1.000 for the most robust regimens. The nonrolling feature model yielded F1-scores of 0.667 to 0.910, with accuracy ranging from 0.800 to 0.906, recall from 0.500 to 0.835, and a precision of 1.000. Morning dosing regimens generally showed higher predictive performance than evening or afternoon. Time-series features, particularly those reflecting 6-, 12-, and 24-hour patterns, emerged as key predictors, indicating that physiological and lifestyle variations prior to dosing strongly influenced missed dose events. Personalized predictive models using WD-derived data demonstrated high precision for detecting missed medication doses, especially in morning and evening regimens. These findings underscore the feasibility of employing continuous, objective physiological and activity data from WDs to forecast nonadherence events. Although the sample size was limited, restricting the generalizability of the results, this study demonstrates the potential of WD-based personalized prediction of medication adherence. Future work should involve larger populations for external validation, strategies to improve recall, especially for clinically critical medications, and careful consideration of real-world implementation challenges.

Pallet pooling is regarded as a sustainable and cost-effective measure for the industry, but challenging to advocate due to weak data and pallet authentication.In order to establish trust between end-users and pallet pooling services, we propose an end-to-end, bidirectional authentication system for transmitted data and pallets based on blockchain and Internet-of-things (IoT) technologies. In addition, secure data authentication fosters the pallet authenticity in the whole supply chain network, which is achieved by considering the tag, location, and object-specific features. To evaluate the object-specific features, the scale invariant feature transform (SIFT) approach is adopted to match key-points and descriptors between two pallet images. According to the case study, it is found that the proposed system provides a low bandwidth blocking rate and a high probability of restoring complete data payloads. Consequently, positive influences on end-user satisfaction, quality of service, operational errors, and pallet traceability are achieved through the deployment of the proposed system.

Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks’ management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation.

In 1998, the National Research Council published People and Pixels: Linking Remote Sensing and Social Science. The volume focused on emerging research linking changes in human populations and land use/land cover to shed light on issues of sustainability, human livelihoods, and conservation, and led to practical innovations in agricultural planning, hazard impact analysis, and drought monitoring. Since then, new research opportunities have emerged thanks to the growing variety of remotely sensed data sources, an increasing array of georeferenced social science data, including data from mobile devices, and access to powerful computation cyberinfrastructure. In this article, we outline the key extensions of the People and Pixels foundation since 1998 and highlight several breakthroughs in research on human-environment interactions. We also identify pressing research problems—disaster, famine, drought, war, poverty, climate change—and explore how interdisciplinary approaches integrating people and pixels are being used to address them.

Men's health represents an often-overlooked aspect of public health. Men have higher mortality rates worldwide and are more negatively affected by chronic conditions such as obesity and heart disease, as well as addiction to alcohol and tobacco. Men also have health issues such as prostate cancer and male sexual dysfunction which only affect them. Because of the skewed burden of morbidity and mortality on men, it is imperative from a public health perspective to make a concerted effort to specifically improve men's health. The use of wearable devices in medical practice presents a novel avenue to invest in men's health in a safe, easily scalable, and economic fashion. Wearable devices are now ubiquitous in society, and their use in the healthcare setting is only increasing with time. There are commercially available devices such as smart watches which are available to lay people and healthcare professionals alike to improve overall health and wellness, and there are also purpose-built wearable devices which are used to track or treat a specific disease. In our review of the literature, we found that while research in the field of wearable devices is still in its early stages, there is ample evidence that wearable devices can greatly improve men's health in the long-term.

Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable. The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm. Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation. The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27-0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 ± 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38-0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15-0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95-0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15-0.55 bpm, 0.01-0.53 bpm and -0.37-0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22-7.03 bpm, 3.25-6.82 bpm and 3.18-7.04 bpm while the lower limits of 95% LoA were -6.36-(-2.35) bpm, -6.46-(-2.30) bpm and -7.42-(-2.41) bpm. Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities.