This data article describes a large-scale Android application dataset built by enriching a previously released Google Play removal corpus with external malware intelligence from VirusTotal Premium v3 Enterprise and Quark-Engine 25.3.1. The source corpus contains 870,514 applications with 48 metadata features. Of these, 803,320 applications (92.3%) were matched to VirusTotal reports, 772,706 (88.7%) were successfully parsed from Quark-Engine scans, and 772,506 (88.7%) had paired analyses from both sources (with an additional 27 applications having VirusTotal data only, included in the summary dataset). The released paired dataset combines the original metadata with ten selected derived features-five from VirusTotal and four from Quark-Engine-yielding 58 analytical features per application, or 59 columns when the MD5 linkage key is included. The release contains processed extensive tables, a compact summary dataset, and five consensus-based validated subsets whose sizes range from 38,302 to 80,368 applications depending on the VirusTotal threshold.

Road crack images captured by smartphones suffer from low resolution, uneven illumination, and complex background interference. Mobile devices also have limited resources for real-time high-accuracy segmentation. A two-stage framework combines a high-accuracy dual-path teacher model with a knowledge-distilled lightweight student model. The teacher model integrates a CNN-KAN path for local texture extraction and a VMamba path for global context modeling at linear complexity. A dedicated KAN-based fusion module learns adaptive nonlinear mappings between the two feature streams. On public crack datasets, the teacher model achieves an mIoU of 0.8087 and an mDice of 0.9028. It is then transferred to a self-constructed smartphone dataset built from continuous 30 fps video, where it reaches an mIoU of 0.7084 with strong robustness to illumination and blur. A GAN-based super-resolution strategy further improves the mIoU by 4.01%. A ConvLSTM-based knowledge distillation framework compresses the teacher into a lightweight MobileViT student model. This reduces the parameter count from 57.80 M to 1.57 M and cuts the GPU inference time from 99.56 ms to 1.46 ms, while retaining an mIoU of 0.7078. The deployed student model runs at 15 to 20 frames per second on an Android smartphone. An ablation study confirms that the ConvLSTM-based temporal distillation contributes beyond standard distillation. This framework provides a practical solution for real-time road crack monitoring on smartphones.

This study proposes a practical framework for rapidly generating a fingerprinting-based radio map in GNSS-denied environments such as underground parking lots. The system employs vehicle-based simultaneous localization and mapping (SLAM) to estimate trajectories using data collected from onboard smartphones and an OBD2 interface. During normal driving, Bluetooth Low Energy (BLE) signals and inertial sensor measurements are recorded to construct spatial radio maps. To address device-dependent signal characteristics, Android and iOS smartphones are used simultaneously to generate platform-specific fingerprinting maps. To improve trajectory accuracy, a heading correction algorithm compensates for gyroscope drift during straight driving segments. Loop closures are then detected using both heading sequence similarity and BLE RSSI vector correlation within a graph-based SLAM framework. The optimized trajectory is integrated with BLE signal measurements to construct a spatial radio map. Experiments conducted in a large underground parking facility accommodating approximately 800 vehicles demonstrate that the entire radio map can be generated within approximately one hour of driving. The proposed approach significantly reduces the time and labor required for conventional site surveys while maintaining spatial consistency of the radio map. These results demonstrate the feasibility of scalable BLE-based indoor positioning in large GNSS-denied environments.

Post-cycle therapy (PCT) refers to the use of pharmacological agents-most commonly selective estrogen receptor modulators (SERMs), human chorionic gonadotropin (hCG), and aromatase inhibitors (AIs)-after cessation of androgen abuse to accelerate recovery of the hypothalamic-pituitary-gonadal (HPG) axis. Exogenous androgens suppress endogenous testosterone production through negative feedback, often resulting in a transient hypogonadal state that may persist for weeks to months, and occasionally longer, depending on cycle characteristics and individual factors. The rationale of PCT is to mitigate this suppression and reduce symptoms of androgen deficiency. However, evidence supporting its use is limited, with most data derived from observational studies or extrapolated from other forms of secondary hypogonadism. While some studies suggest that PCT may accelerate short-term hormonal and seminal recovery, spontaneous recovery occurs in the majority of individuals within months after cessation. Three perspectives can be distinguished. In real-world practice, PCT is widely used, often without medical supervision, driven by cultural norms and concerns about symptom persistence. From an academic standpoint, PCT is generally not recommended due to uncertain benefits and potential risks. A third perspective describes a pragmatic rationale for considering a short course of PCT in carefully selected individuals with significant symptom burden or risk of relapse into androgen abuse, while acknowledging that this approach is not supported by robust clinical evidence. This review aims to clarify the rationale, evidence gaps, and clinical implications of these perspectives to support informed clinical decision-making.

With the deepening of population aging in China, smart TVs have become the most frequently used home digital device for older adults, serving as their core channel for daily information access and entertainment. However, the mismatch between older adults' perceptual needs, declining sensory and cognitive abilities, and the complex interface design of smart TVs has severely exacerbated the digital divide among this group. Existing studies mostly focus on qualitative age-appropriate design strategies, and lack a targeted, quantitative usability evaluation system that integrates older adults' subjective perceptual needs and interface performance assessment. This research develops a multi-level usability evaluation index system for older adults' smart TV interfaces by integrating Kansei Engineering (KE) and Analytic Hierarchy Process (AHP)-fuzzy theory. The system includes 2 first-level, 5 s-level, and 25 third-level indicators. Taking Huawei (Harmony OS) and Skyworth (Android-based) smart TVs as cases, this research conducted a standardized usability test with 15 older adults aged 60-74 in a novice user scenario to eliminate the interference of prior learning experience. The evaluation system's robustness was preliminarily verified through model sensitivity analysis, with overall score fluctuations below 1.71% under ±10% perturbations of core indicator weights. The results show that within the user experience dimension, interactive usability (local weight: 0.4126) ranks highest in importance, followed by feedback usability (0.3275), which is identified as a high-priority optimization direction due to its low actual scores. Skyworth scored 83.11 in overall usability, while Huawei scored 81.95; both brands scored relatively low in user experience usability (around 79 points), with error prevention, recoverability, and generic function being major issues. The system developed in this study can effectively assess interface usability, providing data support and targeted design guidance for the age-friendly optimisation of smart TV brands. This study has two main limitations: the sample is concentrated in the Zhaoqing region of Guangdong Province, and only two mainstream smart TV systems are evaluated. Future research will broaden the sample size to ensure the universality of the conclusions.

Smartphones provide crucial but duplicate information in forensic investigations. The major deduplication tools designed for the cloud ignore constraints on phone storage, power, data fragmentation, and dynamic handling, leading to processing delays and limiting their efficient use. It also affects evidence integrity and increases the potential risk of data leakage. This paper proposes a data deduplication technique for resource-constrained Android devices with limited computing power and memory. The use of the Blake2b-Hash Algorithm in the proposed deduplication algorithm reduces overhead and enables dynamic memory allocation. Multiple strategies were proposed and tested using a wide range of Smartphones. The proposed algorithm is validated based on file analysis time, device storage and memory utilization, and storage overhead. The paper also assesses the hash function's cryptographic robustness. The experimental analysis demonstrates that the proposed method achieved a 45.90% processing time to identify 29.52% duplicate files. A 48.55% reduction in storage space was achieved, and battery consumption was reduced to 81.15% compared to MD5 and SHA224. It achieved up to 52.71% RAM optimization, along with only 0.0033% storage overhead to maintain the metadata of deleted files for forensic purposes. This proves the effectiveness and reliability of the proposed solutions in facilitating digital forensic analysis.

The hypothalamic-pituitary-gonadal (HPG) axis, particularly follicle-stimulating hormone (Fsh) and luteinizing hormone (Lh), is essential for the regulation of teleost reproduction. However, their expression dynamics and receptor distribution during gonadal development in silver pomfret remain poorly understood. This study aimed to characterize the expression profiles and cellular localization of Fshβ, Lhβ and their receptors (Fshr and Lhr) in the pituitary and gonads and to evaluate their potential roles in reproductive regulation. The different staining method and transmission electron microscopy (TEM) were used to analyse the structural changes of the pituitary gland during the reproductive season. Quantitative PCR (qPCR) and immunohistochemical (IHC) were used to detect the expression pattern of Fshβ and Lhβ. The results showed that the number of endocrine granules significantly increased before the reproductive season, and their electron density intensified. During the spawning season, the secretory granules gradually depleted, leading to the appearance of vacuolization. In the pituitary gland, the expression of fshβ in Stage IV was significantly higher in the female pituitary, but significantly higher in Stage V in the male. Fshβ and Lhβ signals were mainly located in the proximal pars distalis (PPD) and pars intermedia (PI) regions of the pituitary. In the gonad, Fshr signals were observed in the spermatogonia type A cells and Sertoli cells in testes, whereas Lhr signals were observed in spermatogonia and secondary spermatocytes in the testes. Fshr and Lhr signals were both observed in the follicle cells surrounding the oogonia and vitellogenic oocytes in the ovary of silver pomfret. The expression patterns of fshβ and fshr were essentially similar after different hormone treatments in the gonad and methyltestosterone (MT) could influence the expression of fshβ, lhβ and fshr in the testis. Taken together, our data suggest that Fshβ and Lhβ promote the development and maturation in the gonad, and Fshr and Lhr may be involved in ovary maturation and spawning.

Wearable electrocardiography (ECG) and smartphone technology create new opportunities for arrhythmia monitoring outside clinical environments. However, deployable mobile cardiac systems must address more than just classification accuracy. They must reliably acquire physiological signals under real-world conditions, protect the privacy of sensitive cardiac data, adapt to user- and sensor-specific variability, and minimize false alarms in daily use. We present Cardio-FL, a smartphone-based digital health prototype for privacy-preserving, adaptive arrhythmia monitoring.

Background/Objectives: Body mass index (BMI) is the most widely used measure for obesity classification, yet it does not directly measure body fat and may substantially underestimate obesity prevalence. Simultaneous evaluation of the diagnostic accuracy of bioelectrical impedance analysis (BIA), BMI, and waist-to-hip ratio (WHR) against dual-energy X-ray absorptiometry (DEXA) for obesity classification is lacking in Saudi Arabia. We aimed to quantify the diagnostic performance of these three surrogate measures against DEXA in Saudi adults. Methods: We conducted a cross-sectional diagnostic accuracy study reported according to the STARD guideline at Prince Sultan Military Medical City, Riyadh, Saudi Arabia. We enrolled a convenience sample of 399 adults aged 18-65 years recruited from primary health care and outpatient clinic waiting areas. Each participant underwent InBody 770 BIA, anthropometric measurements (BMI, WHR), and DEXA (Lunar iDXA, GE) on the same day. We evaluated BIA body fat percentage against DEXA body fat percentage, BMI ≥ 30 kg/m2 against DEXA body fat percentage and fat mass index (FMI)-defined obesity, and WHR against the DEXA android-to-gynoid ratio. We calculated sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating characteristic curve (AUC), overall and stratified by sex. Results: DEXA classified 86.0% of participants as having obesity, compared with 62.9% by BIA and 24.8% by BMI; against DEXA body fat percentage, BMI ≥ 30 kg/m2 had a sensitivity of 28.9% and specificity of 100%. When FMI replaced body fat percentage as the reference standard, BMI sensitivity increased to 68.5% (specificity 97.3%, AUC 0.95). BIA achieved a sensitivity of 72.3%, specificity of 94.4%, and AUC of 0.86. WHR had the lowest sensitivity for central obesity (54.0%, specificity 94.8%, AUC 0.80). BIA showed the strongest correlation with DEXA body fat percentage (Spearman ρ = 0.82), followed by BMI (ρ = 0.60). Conclusions: BMI alone missed two-thirds of participants with DEXA-defined obesity in this Saudi adult cohort. Although BIA was more sensitive, it still missed 27.7% of individuals with DEXA-defined obesity. These findings support integrating body composition assessment into clinical practice and suggest that national obesity prevalence estimates based on BMI alone substantially underestimate the true burden.