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Utilizing rare earth doped ceria in solid oxide cells (SOCs) engineering is indeed a strategy aimed at enhancing the electrochemical devices' durability and activity. Particularly, Gd-doped ceria (GDC) is actively used for barrier layer and catalytic additives in solid oxide fuel cells (SOFCs). In this study, experiments are conducted with La-doped CeO2 (LDC), in which the Ce sites are predominantly occupied by La, to prevent the formation of the Ce-Zr solid solution. This LDC is comparably used as a functional interlayer between the electrolyte and cathode if sintered at lower temperatures to avoid La2Zr2O7 impurity. In addition, the high substitution of La3+ into the ceria lattice improves the oxygen non-stoichiometry of LDC, leading to accelerated electrochemical high performance by the additional role of LDC for oxygen supplier capacitance at high current operation. Thus, it is confirmed that the improved SOFC high performance is achieved at the maximum power density (MPD) of ≈2.15 W cm-2 at 800 °C when the optimized LDC buffer layer is hired at the anode-supported typed-Samsung's SOFC by lowering the sintering temperature to prevent LDC's impurity reaction.

Apps that support telemedicine on the battlefield typically run on classified devices and transmit information over classified networks, whereas the medical data the apps create and transmit are unclassified. Current systems treat these data as classified, so a cross-domain solution is required to transfer the data back to an unclassified domain, which adds delays and costs to the process of transmitting critical data needed to treat injured warfighters. To address this gap, ATC-NY developed DroidChamber, which is a software-based Android system that enables multilevel security and which runs on smartphones and tablets. DroidChamber enables warfighters to execute apps in multiple security domains without risking information leakage. DroidChamber v6 is a collection of mobile device management and security containerization technologies targeting Android 6, and it applies these technologies by leveraging Linux kernel operating-system-level virtualization technologies. DroidChamber v10 takes advantage of Android Work Profiles and Samsung's Knox technology in Android 10, which provides the ability to provision separate "work" and "personal" profiles that securely isolate applications; apps within each profile can further be isolated through the built-in Android controls and the Knox Application Policy. DroidChamber is a software-based Android system, so it requires no added hardware to be attached or integrated with an end-user device. DroidChamber's innovation is isolation of specific Android app resources (e.g., networking) enforced with fine-grained security policies. With DroidChamber, a medic can connect their device to different security domains, giving a wider range of access to medical information in a tactical environment. DroidChamber improves telemedicine applications by enabling the warfighter to share information without requiring a cross-domain guard that may erroneously block some data. Using DroidChamber, a warfighter can use a single mobile device to manage/transmit data at different security levels, thereby reducing the cost and complexity of a mission.

Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as "possibly benign" and "possibly malignant." Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen's &#x3ba; = 0.73 (0.57-0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen's &#x3ba; = 0.79 (0.65-0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen's &#x3ba; = 0.73 (0.57-0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen's &#x3ba; = 0.80 (0.64-0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as "possibly malignant" by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.

We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincar&#xe9; plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung's Gear S3 and Galaxy Watch 3. The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors-30% and 66% lower-and mean heart rate and mean interbeat interval estimation errors-60% and 77% lower-when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

One of the barriers to the construction of consistent computer-based color vision tests has been the variety of monitors and computers. Consistency of color on a variety of screens has necessitated calibration of each setup individually. Color vision examination with a carefully controlled display has, as a consequence, been a laboratory rather than a clinical activity. Inevitably, smart phones have become a vehicle for color vision tests. They have the advantage that the processor and screen are associated and there are fewer models of smart phones than permutations of computers and monitors. Colorimetric consistency of display within a model may be a given. It may extend across models from the same manufacturer but is unlikely to extend between manufacturers especially where technologies vary. In this study, we measured the same set of colors in a JPEG file displayed on 11 samples of each of four models of smart phone (iPhone 4s, iPhone5, Samsung Galaxy S3, and Samsung Galaxy S4) using a Photo Research PR-730. The iPhones are white LED backlit LCD and the Samsung are OLEDs. The color gamut varies between models and comparison with sRGB space shows 61%, 85%, 117%, and 110%, respectively. The iPhones differ markedly from the Samsungs and from one another. This indicates that model-specific color lookup tables will be needed. Within each model, the primaries were quite consistent (despite the age of phone varying within each sample). The worst case in each model was the blue primary; the 95th percentile limits in the v' coordinate were &#xb1;0.008 for the iPhone 4 and &#xb1;0.004 for the other three models. The u'v' variation in white points was &#xb1;0.004 for the iPhone4 and &#xb1;0.002 for the others, although the spread of white points between models was u'v'&#xb1;0.007. The differences are essentially the same for primaries at low luminance. The variation of colors intermediate between the primaries (e.g., red-purple, orange) mirror the variation in the primaries. The variation in luminance (maximum brightness) was &#xb1;7%, 15%, 7%, and 15%, respectively. The iPhones have almost 2&#xd7; the luminance. To accommodate differences between makes and models, dedicated color lookup tables will be necessary, but the variations within a model appear to be small enough that consistent color vision tests can be designed successfully.

The Samsung Ombudsperson Commission was launched as an independent third-party institution following an agreement among Samsung Electronics, Supporters for Health and Right of People in Semiconductor Industry (Banolim in Korean, an independent NGO), and the Family Compensation Committee, in accordance with the industry accident prevention measure required by the settlement committee to address the issues related to employees who allegedly died from leukemia and other diseases as a result of working at Samsung's semiconductor production facilities. The Commission has carried out a comprehensive range of activities to review and evaluate the status of the company's occupational accidents management system, as well as occupational safety and health risk management within its facilities. Based on the results of this review, termed a comprehensive diagnosis, the Commission presented action plans for improvement to strengthen the company's existing safety and health management system and to effectively address uncertain risks in this area going forward. The Commission will monitor the execution of the suggested tasks and provide advice and guidance to ensure that Samsung's semiconductor and liquid crystal display production lines are safer.

Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health apps. The purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)-tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health apps and (2) to explore the factors associated with the use rate of the functions. To upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience of accessing Health4U in the 7-month period after November 2017, when the service was upgraded. Of the 17,624 users, 215 (1.22%) integrated daily steps data, 175 (0.99%) integrated weight data, 51 (0.29%) integrated blood sugar data, and 90 (0.51%) integrated blood pressure data. Overall, 61.95% (10,919/17,624) had one or more chronic diseases. For integration of daily steps data, 48.3% (104/215) of patients used the Apple Health app, 43.3% (93/215) used the S-Health app, and 8.4% (18/215) entered data manually. To retrieve medical documentation, 324 (1.84%) users downloaded PDF files and 31 (0.18%) users integrated their medical records into the Samsung S-Health app via the Consolidated-Clinical Document Architecture download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of &#x2265;60 years and &#x2265;80 years tended to use the download function less frequently than the others. This is the first study to examine the factors related to integration of lifelog data from Samsung S-Health and Apple Health apps into EMR-tethered PHRs and factors related to the retrieval of medical documents from PHRs. Our findings on the lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future.

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When asked about our weight, most of us can name a figure based on prior knowledge. And while stepping on a scale gives us the ability to know that exact number and track it routinely, it does not provide insights into our body?s composition. This, at the basic level, refers to proportions of fat and lean or fat-free mass (FFM) that comprise the human body. Conventionally, the body mass index (BMI), which is the ratio of body weight in kilograms to the square of its height in meters, and anthropometric parameters like waist circumference, waist-to-hip ratio, and skinfold thickness have been used to estimate the level of fatness. In fact, BMI is the de facto marker for stratifying individuals into underweight (<18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (>30 kg/m2) categories. Nonetheless, these metrics are limited in precisely characterizing individuals by percentages of body fat and muscle mass, particularly in epidemiological studies where these proportions vary across age, sex, and ethnic groups. Of note is also how, solely on the basis of BMI, a physically fit individual may be classified as overweight due to having a higher proportion of lean body mass, which outweighs fat. This highlights the importance of body composition in weight tracking and management.

The emergence of mHealth and the utilization of smartphones in physical activity interventions warrant a closer examination of validity evidence for such technology. This study examined the validity of the Samsung S Health application in measuring steps and energy expenditure. Twenty-nine participants (mean age 21.69&#x2009;&#xb1;&#x2009;1.63) participated in the study. Participants carried a Samsung smartphone in their non-dominant hand and right pocket while walking around a 200-meter track and running on a treadmill at 2.24&#x2009;m&#x2219;s-1. Steps and energy expenditure from the S Health app were compared with StepWatch 3 Step Activity Monitor steps and indirect calorimetry. No significant differences between S Health estimated steps and energy expenditure during walking and their respective criterion measures, regardless of placement. There was also no significant difference between S Health estimated steps and the criterion measure during treadmill running, regardless of placement. There was significant differences between S Health estimated energy expenditure and the criterion during treadmill running for both placements (both p&#x2009;<&#x2009;0.001). The S Health application measures steps and energy expenditure accurately during self-selected pace walking regardless of placement. Placement of the phone impacts the S Health application accuracy in measuring physical activity variables during treadmill running.

We investigated how intelligent virtual assistants (IVA), including Amazon's Alexa, Apple's Siri, Google Assistant, Microsoft's Cortana, and Samsung's Bixby, responded to addiction help-seeking queries. We recorded if IVAs provided a singular response and if so, did they link users to treatment or treatment referral services. Only 4 of the 70 help-seeking queries presented to the five IVAs returned singular responses, with the remainder prompting confusion (e.g., "did I say something wrong?"). When asked "help me quit drugs" Alexa responded with a definition for the word drugs. "Help me quit&#x2026;smoking" or "tobacco" on Google Assistant returned Dr. QuitNow (a cessation app), while on Siri "help me quit pot" promoted a marijuana retailer. IVAs should be revised to promote free, remote, federally sponsored addiction services, such as SAMSHA's 1-800-662-HELP helpline. This would benefit millions of IVA users now and more to come as IVAs displace existing information-seeking engines.

Apple AirDrop and Google/Samsung Quick Share are proximity file-transfer protocols used by over five billion devices, yet their application-layer security properties remain largely unstudied because both stacks are proprietary and undocumented. Both protocols are reachable from wireless proximity without any prior pairing and process complex serialized content (binary plists, CPIO archives, Protocol Buffers, UKEY2 handshakes) inside privileged daemons, making them attractive zero-click targets across multiple operating systems. We perform the first cross-platform reverse engineering and protocol-aware fuzzing study of both stacks. We reconstruct AirDrop's seven-layer state machine and DVZip adaptive compression from binary analysis, build AIRFUZZ, a protocol-aware fuzzer that mutates pre-compression representations, and complement it with targeted hand-written analyses of Samsung's Quick Share service and Google's Quick Share for Windows. We discover six vulnerabilities (V1-V6): three pre-authentication issues in macOS/iOS AirDrop (V1: Swift fatalError DoS in the HTTP path router; V2: unbounded XML plist recursion in Foundation; V3: NULL dereference in Network.framework's HTTP/1.1

Wearable devices are widely used for heart rate (HR) monitoring, yet their accuracy across diverse body compositions and skin tones remains uncertain. This study evaluated four wrist worn devices (Apple, Fitbit, Samsung, Garmin) in 58 Hispanic adults with Fitzpatrick skin types III to V during a cycling protocol alternating moderate (0.64 to 0.76 HRmax) and vigorous (0.77 to 0.95 HRmax) intensities. Criterion HR was obtained using a Polar H10 ECG, and accuracy was assessed using mean absolute error, mean absolute percentage error (MAPE), bias, and intraclass correlation coefficients. All devices showed significant deviation from criterion measures. Apple and Garmin demonstrated the lowest error, whereas Fitbit and Samsung exhibited greater inaccuracies. Higher BMI and darker skin tones were associated with increased MAPE. These biases disproportionately affect higher risk populations, underscoring the need for improved algorithms to ensure equitable health monitoring.

Smartwatches are widely used to estimate caloric expenditure for weight management, clinical decision making, and public health monitoring. These devices combine photoplethysmography, accelerometry, and proprietary algorithms. However, prior studies report substantial error, and the influence of moderators such as skin tone and body fat percentage (BF) remains underexamined. This study tested whether smartwatch brand, BF, and Fitzpatrick skin type (III to V) predict caloric expenditure error relative to indirect calorimetry. Fifty eight Hispanic adults completed a single laboratory visit including a ten minute recumbent cycling protocol with alternating two minute moderate and vigorous intensity intervals, bracketed by rest and recovery. Participants wore four consumer devices: Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955. Energy expenditure was measured using a COSMED K5 metabolic system. After device specific data quality filtering, valid participant device pairings ranged from 44 to 52 per brand. One sample tests showed significant mean bias for three devices: Apple, Garmin, and Samsung. Fitbit showed no significant overall bias, althou

This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest research and internal specifications, the simulator provides a high-fidelity model of both the hardware data paths and the software control layers of the LPDDR5X-PIM block. This integrated hardware-software simulation approach enables precise evaluation of system performance and energy efficiency while maximizing PIM resource utilization. We have refined existing simulation frameworks to align with actual hardware implementation, ensuring consistent behavioral accuracy. Further technical details regarding the specific architecture and circuit design of the LPDDR5X-PIM will be disclosed in future publications

We investigate diagonal artifacts present in images captured by several Samsung smartphones and their impact on PRNU-based camera source verification. We first show that certain Galaxy S series models share a common pattern causing fingerprint collisions, with a similar issue also found in some Galaxy A models. Next, we demonstrate that reliable PRNU verification remains feasible for devices supporting PRO mode with raw capture, since raw images bypass the processing pipeline that introduces artifacts. This option, however, is not available for the mid-range A series models or in forensic cases without access to raw images. Finally, we outline potential forensic applications of the diagonal artifacts, such as reducing misdetections in HDR images and localizing regions affected by synthetic bokeh in portrait-mode images.

The idea of computational storage device (CSD) has come a long way since at least 1990s [1], [2]. By embedding computing resources within storage devices, CSDs could potentially offload computational tasks from CPUs and enable near-data processing (NDP), reducing data movements and/or energy consumption significantly. While the initial hard-disk-based CSDs suffer from severe limitations in terms of on-drive resources, programmability, etc., the storage market has witnessed the commercialization of solid-state-drive (SSD) based CSDs (e.g., Samsung SmartSSD [3], ScaleFlux CSDs [4]) recently, which has enabled CSD-based optimizations for avariety of application scenarios (e.g., [5], [6], [7]).

The growing prevalence of data-intensive workloads, such as artificial intelligence (AI), machine learning (ML), high-performance computing (HPC), in-memory databases, and real-time analytics, has exposed limitations in conventional memory technologies like DRAM. While DRAM offers low latency and high throughput, it is constrained by high costs, scalability challenges, and volatility, making it less viable for capacity-bound and persistent applications in modern datacenters. Recently, Compute Express Link (CXL) has emerged as a promising alternative, enabling high-speed, cacheline-granular communication between CPUs and external devices. By leveraging CXL technology, NAND flash can now be used as memory expansion, offering three-fold benefits: byte-addressability, scalable capacity, and persistence at a low cost. Samsung's CXL Memory Module Hybrid (CMM-H) is the first product to deliver these benefits through a hardware-only solution, i.e., it does not incur any OS and IO overheads like conventional block devices. In particular, CMM-H integrates a DRAM cache with NAND flash in a single device to deliver near-DRAM latency. This paper presents the first publicly available study for c

This study presents an in-depth analysis of the security landscape in Bluetooth Low Energy (BLE) tracking systems, with a particular emphasis on Apple AirTags and Samsung SmartTags, including their cryptographic frameworks. Our investigation traverses a wide spectrum of attack vectors such as physical tampering, firmware exploitation, signal spoofing, eavesdropping, jamming, app security flaws, Bluetooth security weaknesses, location spoofing, threats to owner devices, and cloud-related vulnerabilities. Moreover, we delve into the security implications of the cryptographic methods utilized in these systems. Our findings reveal that while BLE trackers like AirTags and SmartTags offer substantial utility, they also pose significant security risks. Notably, Apple's approach, which prioritizes user privacy by removing intermediaries, inadvertently leads to device authentication challenges, evidenced by successful AirTag spoofing instances. Conversely, Samsung SmartTags, designed to thwart beacon spoofing, raise critical concerns about cloud security and user privacy. Our analysis also highlights the constraints faced by these devices due to their design focus on battery life conservati