Search Results (516)

Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g ${\mathrm{L}}^{-1}$). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions. Full article

Background: Radiation therapy with or without systemic treatment for lung cancer can cause adverse reactions, e.g., fatigue. Affected patients may experience a decrease in physical activity and, as a consequence, be unable to receive the complete assigned treatment. Physical exercise may be helpful. A benefit of exercise regarding completion of systemic treatment and quality of life was previously suggested. Irradiated patients may benefit as well. A smartphone-based app reminding patients to make a minimum number of steps might positively influence physical activity. The prototype of an app called “Step Reminder” was recently finalized. The randomized APPAREL trial (NCT07627022) evaluates whether this app can improve physical activity during irradiation for lung cancer. Methods: The primary endpoint of this phase 2 trial focuses on the within-patient change regarding the mean number of daily steps (week 5 minus week 1). Secondary objectives include satisfaction with the application and its impact on the perception of digital health technology. Patients are randomized 1:1 to be treated with a standard approach (conventionally fractionated irradiation with or without systemic treatment) supported by the “Step Reminder” app (Arm A) or standard treatment without the app (Arm B). The app provides reminders several times per day to walk a pre-defined number of steps. To make sure that the required 28 patients are included in the primary analysis population (full analysis set), 32 patients have to be randomized. Results: At this stage, results are not available. Conclusions: It is expected that the app will have a positive effect on the patients’ physical activity (number of steps). Full article

Background/Objectives: This study aimed to describe dietary intake among adults 55 years and older living in the Central Division of Fiji and identify food sources of energy, macronutrients, and micronutrients. Methods: A retrospective dietary dataset consisting of 171 adults aged 55 and over was collected using the 24 h multiple-pass recall (24 h MPR) and assessed using the Intake24 Fiji smartphone application. Analyses were restricted to 147 participants with complete dietary record data. Energy, macronutrient, and selected micronutrient intakes were assessed against recommended amounts, and nutrient sources were reported for 15 food groups. Results: Mean daily energy intakes (6.6 MJ/day) were lower than recommended, especially for Those > 70 years (5.3 MJ). The participants had acceptable contributions of energy from fat (28%) and carbohydrates (56%) but lower contributions for protein (13%) than the recommended range (15–25%). The mean protein intake for men and women was 0.68 g/kg/day. More than half of these older adults consumed less than the Estimated Average Requirement (EAR) of retinol, thiamin, folate, vitamin C, vitamin E, calcium, iron, manganese, selenium, and zinc, while vitamin B₆ requirements were met by approximately half of the participants. The primary sources of energy and nutrients were from bread and bakery products, mixed dishes, and sugar-sweetened beverages. Conclusions: The dietary intakes of older adults in Fiji’s Central Division met carbohydrate and fat intake guidelines but were low in protein as well as retinol, thiamin, folate, vitamin C, vitamin E, and minerals such as calcium, iron, manganese, selenium, and zinc; however, approximately half of the participants met the vitamin B6 requirements. These findings highlight the need to improve overall diet quality for older Fijians by increasing the accessibility of nutrient-dense, protein-rich foods and addressing low energy and micronutrient intakes. Full article

This paper presents the design, development, and large-scale deployment of a vehicular IoT-based methane sensing system for urban environmental monitoring. The proposed solution integrates a low-cost metal oxide semiconductor (MOS) methane sensor with a dual chamber gas sampling mechanism, embedded processing, and wireless communication via 4G/5G networks using a smartphone as a gateway. Methane concentration data are collected from sensors installed in moving vehicles, georeferenced in real time using GNSS, and transmitted to a cloud-based platform for storage and analysis. Field experiments were conducted in the metropolitan region of São Paulo, Brazil, using 16 instrumented vehicles over a 20-month period, covering approximately 192,274 km and generating more than 48 million measurements. The results reveal spatially consistent methane concentration patterns and identify urban areas with elevated levels exceeding global background concentrations. A comparative analysis with a commercial infrared-based mobile methane monitoring system showed consistent agreement in the identification of spatial methane concentration patterns and potential emission hotspots. These results demonstrate the effectiveness of the proposed system for scalable urban methane monitoring. Full article

Agapé is a light–touch relationship enhancement smartphone app. This study used data from a longitudinal study of couples using the Agapé app to explore change within an array of behavioral processes to uncover patterns of interconnected change, thereby providing some of the first quantitative insights into how various relationship processes might be linked as relationships change over time. A sample of 405 couples in long-term relationships (810 partners, 50% women, 75% white, together M = 4.5 yrs, 50% living together, 33% currently dissatisfied) completed assessments across their first month of using Agapé. Men and women significantly improved on 15 of the 16 relationship processes assessed. As the study lacked a randomized control condition, it remains unclear if those improvements were due to using the Agapé app or to factors like expectancy effects, regression to the mean, self-selection, demand characteristics, or general participation effects. Network analyses explored correlational linkages among the self-reported pre–post changes observed. Results highlighted increases in three processes (quality time spent together, perceived partner responsiveness, and gratitude toward partner) as the processes most proximally linked to increases in relationship quality. The network findings also uncovered a number of patterns of interconnected change to be explored in future studies (e.g., increases in couples talking about their relationships to increases in mindful awareness within those relationships to increases in gratitude and quality time to increases in relationship quality). Thus, the results offer some of the first comprehensive multivariate (albeit correlational) insights toward understanding how relationship processes might work in concert with one another within a broader multivariate pattern of self-reported relationship change. Full article

In this paper, we propose a lightweight authentication and identification protocol based on different phosphorescent Strontium aluminate color labels. The excitation sources are pulsed UV LEDs emitting at 365 nm and 385 nm, causing different RGB-dependent time responses of the label that are measured using a smartphone recording at 30 FPS. The rise and decay time responses as measured by the red (R), green (G) and blue (B) pixels were separately analyzed and were found to follow a power law with individual parameters depending on the excitation wavelength, pulse duration and duty cycle, which serve as security features and are suitable for authentication purposes in IoT applications. Our solution uses simple cryptographic functions such as HMAC and XOR. We performed a security analysis of our protocol to prove its resistance to known attack vectors. The proposed scheme has minimal computation and communication costs and can be deployed on resource-constrained Internet of Things devices. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article

False base station (FBS) attacks exploit the attach window before the network authenticates to the device. Existing User Equipment (UE)-side detectors typically need either labeled attack data, which is scarce and does not generalize to unseen attacks, or models too heavy for the resource budget of a smartphone or embedded endpoint. This study presents a lightweight UE-side detector built on a first-order Markov chain over a four-tuple state of packet type, direction, message identifier, and access-network type. A single counting pass fits the 119 KB chain, and thresholds are derived from normal traffic, so no attack labels are consulted. The capture path requires root and Qualcomm modem diagnostic access. Attacks surface as low-probability transitions, rare field values, and anomalous pacing, fused into a per session verdict with per-message attribution. On 192 commercial, testbed, and public LTE and 5G captures, the detector flags 51 of 53 attacks at an F1 of 88.70% in leakage-free leave-one-session-out evaluation and 96.23% once calibration covers the scored sessions. In five-fold cross-validation its F1 of 86.21% trails the strongest supervised baselines by margins that are not statistically significant, and it records the lowest latency (0.46 ms) and smallest working set (8.8 MB) among the eleven detectors benchmarked. Full article

We developed a smartphone-assisted digital image-based optical biosensor array using a planar glass slide with sensor spots in a 2 × 5 array format for point-of-care multiplex detection of biomarkers. The detection is based on the integration of the capture antibody (Ab^C)-functionalized sensor array with a detection antibody-conjugated gold nanoparticle bioconjugate (AuNP@Ab^D) in the presence of interleukin-8 (IL8) to form a sandwich-type AuNP@Ab^D–IL8–Ab^C nanocomplex on the sensing spot surface. Thus, the colorimetric detection method can be applied to the quantitative analysis of IL8, a clinically relevant pro-inflammatory and pro-angiogenic biomarker. The sensing strategy utilizes digital image-based analysis via ImageJ software (V 1.54 g; Java 1.8.0_345 [64 − bit], Windows 8) to quantify the colorimetric signals generated by the light absorbance of surface-bound gold nanoparticles in response to an IL8 droplet sample of merely 8 μL on the planar glass surface, achieving a low detection limit of 0.23 pg/mL (27 fM) and good reproducibility with a coefficient of variation of 0.95%. Validation using IL8-spiked serum at concentrations of 1 × 10⁻⁹ M and 1 × 10⁻¹⁰ M showed minimal matrix effects with a detection accuracy of 99.5% and 106.1%, respectively. Hence, this low-cost portable digital image-based plasmonic nanoparticle-linked immunosorbent assay serves as an alternative to traditional enzyme-linked immunosorbent assays. Full article

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain’s Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence $c_t^i=\sigma \left(w_4^{\top }{h}_3\right)$ in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent’s 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective $\mathrm{NSW}={\left({\prod }_{i=1}^N{Q}_i\right)}^{1/N}$, whose gradient $\partial \mathrm{NSW}/\partial Q_i\propto 1/Q_i$ automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies $Q_i/{\sum }_j{Q}_j\ge 1/N$. Counterfactual advantage estimation correctly attributes each agent’s marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain’s Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone. Full article

Graphitic carbon nitride (g-C₃N₄), an emerging metal-free semiconductor material, has attracted considerable attention in the field of photoelectrochemical (PEC) sensing due to its unique electronic structure, excellent chemical stability, and visible-light responsiveness. This article systematically reviews recent advances in research on g-C₃N₄-based PEC sensors applied to water environment monitoring. First, the fundamental physicochemical properties of g-C₃N₄ are introduced, along with its advantages and limitations in PEC sensing applications. Subsequently, four main performance enhancement strategies are outlined: heterojunction construction (including type II, Z-scheme, and S-scheme heterojunction), elemental doping and defect engineering, morphology control and nanostructure design, as well as various signal amplification approaches such as self-powered systems, dual-mode detection, and cyclic amplification. Furthermore, the current application status of these sensors in detecting typical water pollutants, including heavy metal ions (e.g., Pb²⁺, Cu²⁺, Cd²⁺, Hg²⁺), antibiotics (e.g., tobramycin, norfloxacin, kanamycin), pesticide residues (e.g., chlorpyrifos, atrazine, glyphosate), and pathogenic microorganisms (e.g., Salmonella, Candida albicans), is comprehensively reviewed, with particular emphasis on detection sensitivity, selectivity, and real-sample performance. Finally, the remaining challenges in terms of long-term stability, anti-interference capabilities in complex matrices, portability, and multifunctional integration are analyzed, and future development directions are proposed, including smartphone-based intelligent sensing, CRISPR/Cas12a-assisted signal amplification, and multi-target high-throughput detection. This review aims to provide a reference for the rational design and practical application of g-C₃N₄-based PEC sensors in the field of water environment monitoring. Full article

This paper proposes disturbance detection algorithms to mitigate the oscillations in smartphone camera module actuators induced by external shocks (e.g., drop events). Smartphone camera modules operate under volumetric constraints with inter-component trade-offs. Specifically, the limited space leads to insufficient performance because actuators are unstable under external disturbances. To optimize actuator function, we define the dynamic model of a voice coil motor (VCM) actuator, a controller model, and a shock disturbance model and perform worst-case operational analysis with MATLAB/Simulink (R2015a) simulations. Moreover, we propose two disturbance detection techniques: a phase-based detection algorithm that statistically analyzes the phase difference between the control input and the position feedback signal to detect disturbances and a frequency-based detection algorithm that uses discrete Fourier transform (DFT) to identify the characteristic spectral component of disturbances at 500 Hz. According to the simulation results, both methods reduce recovery time upon disturbance. Furthermore, the frequency-based algorithm achieves faster recovery performance than the phase-based detection algorithm. The phase-based detection method offers low computational complexity but increased processing latency, while the frequency-based detection method requires more memory capacity. The proposed techniques are anticipated to improve the recovery time of smartphone camera modules under disturbances, thereby enhancing system robustness and contributing to a more stable user imaging experience by mitigating image blur. Full article

Inherently, there exists a significant security hole in sensor networks. The majority of sensors are not high-end Internet of Things (IoT) devices with sufficient computing resources. Connected sensors (physical nodes in real networks) are allocated to logical nodes and managed remotely by a supervisor in a virtual network. Data acquired by sensors are then collected by a data center on which artificial intelligence operates. If an adversary spoofs a logical node (e.g., an account in a transport layer security (TLS) session) of a vulnerable sensor on the network, then it can manipulate data input to artificial intelligence. Artificial intelligence cannot verify the integrity of the data input for learning. It is difficult to stop data poisoning with no countermeasures against session spoofing. To avoid session spoofing, physical and logical nodes must be linked seamlessly. One might think this can be achieved by utilizing Hardware Root-of-Trust (HRoT) based on a Physically Unclonable Function (PUF). However, a PUF is based on an expensive System-on-a-Chip (SoC), which has been specifically designed for high-end devices, like expensive smartphones. Many sensors (low-end and middle-end IoT devices) can hardly be protected with existing PUFs. Since the number of IoT devices with a PUF is insufficient to cover the entirety of IoT devices, an attacker can find a vulnerable IoT device with no PUF to perform session spoofing. This is the problem of numbers. To resolve it, we propose Physical Cyber Authentication (PCA). A Blockchain account (a logical node in a TLS session) is anchored to an integrated circuit (IC) chip inside a sensor, allowing Blockchain to manage sensor networks, which provides necessary data to artificial intelligence, thus forming a Blockchain of sensors. Full article

Traditional pedestrian inertial navigation (PDR) algorithms usually assume that the carrying mode of a smartphone is fixed and remains horizontal, while ignoring the significant impact of dynamic changes in the carrying mode on heading estimation, which is the core element of PDR algorithms. In practical application scenarios, pedestrians often change their way of carrying smart terminals (e.g., calling) according to their needs, corresponding to the difference in the heading estimation method; especially when the mode is switched, it will cause a sudden change in heading, which will lead to a significant increase in the localization error if it cannot be corrected in time. Existing smart terminal carrying mode recognition methods that rely on traditional machine learning or set thresholds have poor robustness; lack of universality, especially weak diagnostic ability for mutation; and can not effectively reduce the heading error. Based on these practical problems, this paper innovatively proposes a PDR framework that tries to overcome these limitations. Based on this research purpose, firstly, this paper classifies four types of common carrying modes based on practical applications and designs a CNN-LSTM hybrid model, which can classify the four common carrying modes in near real-time, with a recognition accuracy as high as 99.68%. Secondly, based on the mode recognition results, a multi-layer heading correction strategy is introduced: (1) introducing a quaternion-based universal filter (VQF) algorithm to realize the accurate estimation of initial heading; (2) designing an algorithm to accurately detect the mode switching point and developing an adaptive offset correction algorithm to realize the dynamic compensation of heading in the process of mode switching to reduce the impact of sudden changes; and (3) considering the motion characteristics of pedestrians walking in a straight line segment where lateral displacement tends to be close to zero. This study designs a heading optimization method with lateral displacement constraints to further inhibit the drifting of the heading caused by the slight swaying of the smart terminal. In this study, two validation experiments are carried out in two different environment—an indoor corridor and a tree shelter—and the results show that based on the proposed multi-layer heading optimization strategy, the average heading error of the system is lower than 1.5°, the cumulative positioning error is lower than 1% of the walking distance, and the root mean square error of the checkpoints is lower than 2 m, which significantly reduces the positioning error and shows the effectiveness of the framework in complex environments. Full article

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies. Full article