Search Results (2,363)

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition. Full article

This research has successfully addressed the technical challenge of generating nanobubbles in diesel fuel, which inherently lacks hydrophilic structures and charged ions, enabling the effective production of high-concentration nanobubble diesel fuel. This breakthrough lays a solid foundation for subsequent research into the combustion performance and combustion mechanism of high-concentration nanobubble fuels. Furthermore, it holds promising potential to advance high-concentration nanobubble fuel as a viable new type of energy source. A specialized device was designed to generate nanobubble-embedded diesel, and particle tracking analysis with n-hexadecane dilution was employed to quantify nanobubble concentration. The results demonstrate that the nanobubble concentration in diesel increases with both circulation time and pressure, reaching up to 5 × 10⁸ ± 3.1 × 10⁷ bubbles/mL under a pressure of 2.5 MPa. Stability tests indicate an initial rapid decay (50% reduction within one week), followed by a slower decline, which stabilizes at 4.5 × 10⁷ ± 3.13 × 10⁶ bubbles/mL after two months. Notably, nanobubble concentration has a minimal impact on the density and viscosity of diesel but slightly decreases its surface tension. This study presents a feasible method for preparing high-concentration nanobubble diesel, which lays a foundation for investigating the combustion mode and mechanism of nanobubble diesel fuel. With the goal of enhancing combustion efficiency and reducing pollutant emissions, this work further paves the way for the application of high-concentration nanobubble diesel as a new energy source in fields including automotive, marine, and aerospace industries. Full article

In the ever-evolving world full of technologies, blockchain proves itself to be the most secure way of dealing with tampering of data. This paper proposes an innovative model for tracking employees within facilities using RFID, IoT devices and blockchain technology implemented on the Hyperledger Fabric platform. The blockchain system supports a secure and tamper-proof recording of employee movement because it keeps the data in a decentralized system. Smart contracts automate activities like control access, generate alerts and create audit trails without the need for centralized management. This implementation shows a high level of security and efficiency, making it a good approach to improve monitoring and compliance within organizations. Full article

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines. Full article

Magnetic induction imaging technology, as a non-invasive detection method based on the principle of electromagnetic induction, has a wide range of applications in the field of materials science and engineering with the advantages of no radiation and fast imaging. However, it has not been improved to address the problems of high contact measurement interference and low spatial resolution of traditional strain detection methods in bulk materials engineering. For this reason, this study proposes a magnetic induction detection technique incorporating metal particle assistance and designs a hardware detection system based on an eight-coil sensor to improve the sensitivity and accuracy of strain detection. Through finite element simulation and an image reconstruction algorithm, the conductivity distribution reconstruction was realized. Taking asphalt concrete as the research object, particle-reinforced composite specimens with added metal particles were prepared. On this basis, a hardware detection system with eight-coil sensors was designed and constructed, and the functionality and stability of the system were verified. Using finite element analysis technology, two-dimensional and three-dimensional simulation models were established to focus on analyzing the effects of different coil turns and excitation parameters on the induced voltage signal. The method proposed in this study provides a new technical approach for non-contact strain detection in road engineering and can also be applied to other composite materials. Full article

The integration of technology in the classroom should be based on low-cost devices and affordable solutions, allowing educators to fully explore their potential benefits. Product design education is undergoing a profound transformation in response to these changes. The aim of this study is to demonstrate the integration of low-cost eye-tracking (ET) technology within a product design process. This research presents a practical case involving a group of design students who incorporated an ET device, as well as an alternative tracking method (AT) that simulates eye movement, to develop a product following a custom design methodology. The impact of both the methodology and the low-cost technology was evaluated through surveys administered to forty-seven students. The evaluation focused primarily on “utility, novelty, and relevance” as key aspects. The results showed consistently high approval ratings for both technologies. However, ET received significantly higher and more favorable evaluations. A detailed analysis of the evaluated elements indicated a strong preference for ET in terms of utility, novelty, and relevance. Furthermore, a correlational analysis revealed that students associated the integration of low-cost technology with usefulness and a positive experience. The findings of this case study highlight that low-cost devices and innovative methodologies are effective tools for enhancing teaching and learning experiences for students, educators, and researchers. Full article

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s. Full article

Opening titles are more than introductory devices supporting the film they have been produced for; they are artistic and cultural artefacts with a powerful visual identity. Among the most emblematic figures in this design field, the graphic and motion designer Saul Bass (1920–1996) pioneered an approach that redefined the identity, the design, and the experience of cinematic title sequences, opening a path of experimentation aimed at bridging visual communication, moving images, stylistic innovation, and aesthetic synaesthesia, through a combination of sound, movement, and image into a single expressive unit. This article investigates Bass’s contribution through a historical-critical and comparative lens, reconstructing the network of artistic and technological influences that shaped his design philosophy. It analyzes a selection of Bass’s title sequences, highlights his connection to European modernism, and identifies the seeds of postmodern culture in several aspects of Bass’s work such as the merging of principles coming from design and animation studies, the ambition for technological experimentation, and the openness towards a mass audience. By framing Bass’s creative legacy as a form of visual heritage, the article examines the ways in which his kinetic typography and moving compositions can be, therefore, recognized as resources for art historians, media scholars, designers, and visual communication theorists to track down the first and impactful aesthetic and narrative experiments conducted in the postmodern and contemporary motion graphic design field. Full article

Sixth-generation (6G) wireless technology has facilitated the rapid development of the Internet of Things (IoT), enabling various end devices to be deployed in applications such as wireless multimedia sensor networks. However, most end devices encounter difficulties when dealing a large amount of IoT video data due to their lack of computational resources for visual object tracking. Discriminative correlation filter (DCF)-based tracking approaches possess favorable properties for resource-constrained end devices, such as low computational costs and robustness to motion blur and illumination variations. Most current DCF trackers employ multiple features and the spatial–temporal scale space to estimate the target state, both of which may be suboptimal due to their fixed feature dimensions and dense scale intervals. In this paper, we present an adaptive mapped-feature and scale-interval method based on DCF to alleviate the problem of suboptimality. Specifically, we propose an adaptive mapped-feature response based on dimensionality reduction and histogram score maps to integrate multiple features and boost tracking effectiveness. Moreover, an adaptive temporal scale estimation method with sparse intervals is proposed to further improve tracking efficiency. Extensive experiments on the DTB70, UAV112, UAV123@10fps and UAVDT datasets demonstrate the superiority of our method, with a running speed of 41.3 FPS on a cheap CPU, compared to state-of-the-art trackers. Full article

Visual body-tracking techniques have seen widespread adoption in applications such as motion analysis, human–machine interaction, tele-robotics and extended reality (XR). These systems typically provide 2D landmark coordinates corresponding to key limb positions. However, to construct a meaningful 3D kinematic model for body joint reconstruction, a mapping must be established between these visual landmarks and the underlying joint parameters of individual body parts. This paper presents a method for constructing a 3D kinematic model of the human hand using calibrated 2D landmark-tracking data augmented with depth information. The proposed approach builds a hierarchical model in which the palm serves as the root coordinate frame, and finger landmarks are used to compute both forward and inverse kinematic solutions. Through step-by-step examples, we demonstrate how measured hand landmark coordinates are used to define the palm reference frame and solve for joint angles for each finger. These solutions are then used in a visualization framework to qualitatively assess the accuracy of the reconstructed hand motion. As a future work, the proposed model offers a foundation for model-based hand kinematic estimation and has utility in scenarios involving occlusion or missing data. In such cases, the hierarchical structure and kinematic solutions can be used as generative priors in an optimization framework to estimate unobserved landmark positions and joint configurations. The novelty of this work lies in its model-based approach using real sensor data, without relying on wearable devices or synthetic assumptions. Although current validation is qualitative, the framework provides a foundation for future robust estimation under occlusion or sensor noise. It may also serve as a generative prior for optimization-based methods and be quantitatively compared with joint measurements from wearable motion-capture systems. Full article

Metal halide perovskites have appeared as a promising semiconductor for high-efficiency and low-cost photovoltaic technologies. However, their performance and long-term stability are dramatically constrained by defects at the surface and grain boundaries of polycrystalline perovskite films formed during the processing. Herein, we propose a defect-targeted passivation strategy using 2-chlorocinnamic acid (2-Cl) to simultaneously enhance the efficiency and stability of perovskite solar cells (PSCs). The crystallization kinetics, film morphology, and optical and electronic properties of the used formamidinium–cesium lead halide (FA_0.85Cs_0.15Pb(I_0.95Br_0.05)₃, FACs) absorber were modulated and systematically investigated by various characterizations. Mechanistically, the carbonyl group in 2-Cl coordinates with undercoordinated Pb²⁺ ions, while the chlorine atom forms Pb–Cl bonds, effectively passivating the surface and interfacial defects. The optimized FACs perovskite film was incorporated into inverted (p-i-n) PSCs with a typical architecture of ITO/NiO_x/PTAA/Al₂O₃/FACs/PEAI/PCBM/BCP/Ag. The optimal device delivers a champion power conversion efficiency (PCE) of 22.58% with an open-circuit voltage of 1.14 V and a fill factor of 82.8%. Furthermore, the unencapsulated devices retain 90% of their initial efficiency after storage in ambient air for 30 days and 83% of their original PCE after stress under 1 sun illumination with maximum power point tracking at 50 °C in a N₂ environment, demonstrating the practical potential of dual-site molecular passivation for durable perovskite photovoltaics. Full article

Rail corrugation and roughness represent typical irregularities on railway and tramway tracks, which cause increased dynamic forces, high-frequency vibrations, reduced riding comfort, shorter track lifespan, higher maintenance costs, and increased noise levels. Roughness and corrugation can be measured by evaluating the unevenness of the rail longitudinal running surface, which can be conducted using handheld devices or trolleys (directly on the track). Alternatively, vehicle or track-based indirect methods offer practical solutions for determining the condition of the rail running surface. This paper presents a methodology for rail corrugation and roughness evaluation, using bogie frame vibration data from an instrumented in-service tramway vehicle operating on Zagreb’s tramway network. Furthermore, it investigates the effects of various factors on the evaluation method, including wheel roughness, lateral positioning, signal processing methods, horizontal geometry, wheel–rail contact force, and tramway vehicle vibroacoustic characteristics. It was concluded that a simplified methodology that did not include transfer functions or wheel roughness measurements yielded relatively good results for evaluating rail corrugation and roughness across several wavelength bands. To improve the presented methodology, future research should assess the vehicle’s vibroacoustic characteristics with experimental hammer impact tests, measure the influence of wheel roughness on wheel–rail contact and bogie vibrations, and refine the measurement campaign by increasing test runs, limiting speed variation, and conducting controlled tests. Full article

Background: Velocity-based training (VBT) requires precise measurement devices to monitor neuromuscular performance. PowerTrack^TM is a novel optoelectronic device designed to assess movement velocity in resistance training. This study aimed to evaluate the validity and reliability of PowerTrack^TM during the Smith machine back squat. Methods: Twenty experienced-trained men performed three repetitions at three submaximal loads (20, 50, and 70 kg) across two sessions. Velocity metrics—mean velocity (MV), mean propulsive velocity (MPV), and maximum velocity (Vmax)—were simultaneously recorded by PowerTrack^TM and the criterion device (MuscleLab^TM). Validity was assessed via ordinary least products (OLP) regression, Lin’s concordance correlation coefficient (CCC), and Bland–Altman plots. Reliability was determined using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), coefficient of variation (CV), and minimum detectable change (MDC). Results: PowerTrack showed high agreement with MuscleLab^TM for MPV and Vmax (slope ≈ 1.00; CCC = 0.95–0.97), while MV presented a proportional bias (slope = 0.83). ICCs ranged from 0.78 to 0.91 across loads, and SEM remained <0.09 m/s for all metrics, indicating excellent relative reliability and acceptable absolute precision. Conclusion: Despite a slight underestimation of MV at light loads, PowerTrack^TM proved to be a valid and reliable device for velocity monitoring in VBT contexts. Full article

Background: Hearing impairment is a significant public health issue globally, yet national data for Saudi Arabia remain sparse. Methods: Using data from the 2017 Disability Survey, we analysed 12 hearing-related indicators across 13 administrative regions. Descriptive statistics, logistic regression, cluster analysis, and residual mapping were applied to identify socio-demographic disparities and service gaps. Findings: Among 20,408,362 Saudi nationals, about 1,445,723 (7.1%) reported at least one functional difficulty. Of these, 289,355 individuals (1.4%) had hearing impairment, either alone or with other difficulties—229,541 (1.1%) had hearing impairment combined with other disabilities, while 59,814 (0.3%) had only hearing impairment. Females and males were equally affected. Notably, educational attainment and marital status significantly influenced device uptake; less-educated and divorced individuals were particularly underserved. Regionally, southern provinces (Al-Baha, Jazan, and Najran) demonstrated the highest unmet need due to geographic barriers, limited audiological resources, and socioeconomic constraints, reflecting compounded risks from consanguinity and rural isolation. Cluster analyses identified provinces requiring urgent attention, recommending mobile audiology units, tele-audiology services, and means-tested vouchers to enhance coverage. Conclusions: Despite Saudi Arabia’s existing public audiology services and a National Newborn Hearing Screening programme achieving 96% coverage, substantial gaps remain in follow-up care and specialist distribution, underscoring the necessity for systematic workforce tracking and enhanced rural incentives. International evidence from India and Brazil underscores the feasibility and cost-effectiveness (approximately USD 5200/QALY) of these recommended interventions. Implementing targeted provincial strategies, integrating audiological screening into routine healthcare visits, and aligning resource allocation with the WHO and Vision 2030 benchmarks will significantly mitigate hearing impairment’s health, social, and economic impacts, enhancing the quality of life and societal inclusion for affected individuals. Full article