Search Results (165)

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Autism Spectrum Disorder (ASD) seriously affects social, communication, and behavioral functions, and early accurate diagnosis is crucial to improve the prognosis of patients. Traditional diagnosis methods rely on professional doctors to make subjective diagnosis through scales, the feature extraction of existing machine learning methods is inefficient, and existing deep learning methods have limitations in capturing time-varying features and the joint expression of time–frequency features. To this end, this study proposes a time–frequency synergy network (TFSNet) to improve the accuracy of ASD EEG signal classification. The proposed Dynamic Residual Block (TDRB) was used to enhance time-domain feature extraction; Short-Time Fourier Transform (STFT), convolutional attention mechanism, and transformation technology were combined to capture frequency-domain information; and an adaptive cross-domain attention mechanism (ACDA) was designed to realize efficient fusion of time–frequency features. The experimental results show that the average accuracy of TFSNet on the University of Sheffield (containing 28 ASD patients and 28 healthy controls) and KAU dataset (containing 12 ASD patients and five healthy controls) reaches 98.68%and 97.14%, respectively, yielding significantly better results than the existing machine learning and deep learning methods. In addition, the analysis of model decisions through interpretability analysis techniques enhances its transparency and reliability. Full article

Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article

The addition of oxygen-releasing biomaterials into 3D-printed scaffolds presents a novel approach to enhancing bone scaffolds, yet no in vitro studies have demonstrated the effect of oxygen-generating filaments on scaffold biological and mechanical properties. This study introduces a polylactic acid (PLA)/calcium peroxide (CPO) composite filament, designed for oxygen release, which is a key factor for early-stage bone regeneration. The PLA/CPO composite filament was fabricated via wet-mixing, solvent evaporation, and hot-melt extrusion, followed by fused deposition modeling (FDM) with optimized parameters to achieve high structural fidelity (25% porosity, 0.60mm pore size). In vitro characterization, including mechanical, morphological, and biological assessments, demonstrated that, post-cell culturing, mechanical strength improved, which indicates improved scaffold resilience. The scaffold exhibited gradual oxygen release over a 3-day period, and gene expression analysis confirmed notable upregulation of osteogenic markers RUNX2, SPP1, and SP7 in vitamin D-supplemented conditions. The mechanical strength improved from approximately 2.8 MPa in the control group to 5.0 MPa in scaffolds cultured with osteogenic media. This study provides the first in vitro evidence that oxygen-releasing 3D-printed filaments can improve both mechanical properties and biological response in scaffolds, demonstrating the functional integration of sustained oxygen delivery, enhanced mechanical properties, and increased osteogenic activity in a single 3D-printed scaffold. Full article

This research explores the potential of green encapsules uploaded with eucalypt essential oil (EEOs) in enhancing their functionality and application in pest control, focusing on suitable ecotype selection from King Abdulaziz University (KAU) campus, Hada Al-Sham (HAS) village, and Briman district as well as optimizing extraction processes. Eucalypt hybrids’ leaves were collected from three different sites, and the EEOs were extracted using microwave-assisted steam distillation (MASD) and electric steam distillation (ESD) techniques. The physical and chemical properties of the EEO were determined. The identification of volatile chemical ingredients in the resulting EEOs was conducted using GC/MS after saponification and methylation procedures, and the ingredients were compared to those obtained from Eucalyptus globulus Labill, the ideal species containing the 1,8-cineol, the principal compound in its essential oil. The 1,8-cineole was found to be the major chemical constituent of the EEOs all over the two extraction methods, regardless of the ecotypes examined, and was interfered with other minor components such as 3-carene, α-pinene, α-myrcene, D-limonene, and α-terpinene. Eucalypt ecotypes grown at Hada Al-Sham village had the highest cineole content (59.29%) among the other sites studied. Compared to the ESD technique, MASD showed much promise because it is simple, facile, more ecofriendly and cost-effective, it kept oils true to their original form, and it allows to warm larger machines and spaces. The polymeric encapsules of either guar gum crosslinked by borax or sodium alginate crosslinked by calcium chloride were fabricated. Moreover, a bioassay screening of the encapsules uploaded with 1,8-cineole was evaluated against termite infection. The encapsules were found to be versatile tools with a wide range of applications; in particular, the alginate encapsules displayed superior characteristics. Furthermore, regardless of the encapsule type and the exposure duration, the mortality (%) of the insects was exceeded significantly for the high cineol concentrations compared to the lower ones for both alginate-based encapsules (ABEs) and guar gum-based encapsules (GGBEs). The higher the cineol concentrations, the higher the mortality percent of the termites. This finding can be attributed to the rapid toxic effect of the cineol compound at higher concentrations. Full article

Improving the recognition of online learning engagement is a critical issue in educational information technology, due to the complexities of student behavior and varying assessment standards. Additionally, the scarcity of publicly available datasets for engagement recognition exacerbates this challenge. The majority of existing methods for detecting student engagement necessitate significant amounts of annotated data to capture variations in behaviors and interaction patterns. To address these limitations, we investigate few-shot learning (FSL) techniques to reduce the dependency on extensive training data. Transformer-based models have shown comprehensive results for video-based facial recognition tasks, thus paving new ground for understanding complicated patterns. In this research, we propose an innovative FSL model that employs a prototypical network with the vision transformer (ViT) model pre-trained on a face recognition dataset (e.g., MS1MV2) for spatial feature extraction, followed by an LSTM layer for temporal feature extraction. This approach effectively addresses the challenges of limited labeled data in engagement recognition. Our proposed approach achieves state-of-the-art performance on the EngageNet dataset, demonstrating its efficacy and potential in advancing engagement recognition research. Full article

The main objective of this paper is to investigate the performance of the Network Real-time Kinematic (NRTK) technique in hydrographic surveying and check whether it meets the International Hydrography Organization (IHO) minimum bathymetry standards for the safety of navigation hydrographic surveys. To this end, the KAU-Hydrography 2 vessel was used to conduct a hydrographic survey session at Sharm Obhur. NRTK corrections were streamed in real time from the KSA-CORS NTRIP server and GNSS data were collected at the same time at the base station using a Trimble SPS855 GNSS receiver. Multibeam records were collected using a Teledyne RESON SeaBat T50-P multibeam echosounder in addition to Valeport’s sound velocity profiler records and Applanix POSMV data. Applanix POSPac MMS 8.3 software was used to process the GNSS data of the base station along with the POSMV data to obtain the Smoothed Best Estimate of Trajectory (SBET) file, which is used as a reference solution. The NRTK solution is then compared with the reference solution. It is shown that the Total Horizontal Uncertainty (THU) and the Total Vertical Uncertainty (TVU) of the NRTK solution are 6.38 cm and 3.10 cm, respectively. Statistical analysis of the differences between the seabed surface generated using the NRTK solution and the seabed surface generated using the Post-Processed Kinematic (PPK) technique showed an average of −0.19 cm and a standard deviation of 2.4 cm. From these results, we can conclude that the KSA-CORS NRTK solution successfully meets IHO minimum bathymetry standards for the safety of navigation hydrographic surveys at a 95% confidence level for all orders of hydrographic surveys. Full article

This study analyzes the patent landscape of Saudi Arabia from the announcement of Saudi Vision 2030 in late April 2016 to September 2024, utilizing the Patsnap database to evaluate patent grants across various organizations. The findings reveal a gradual increase in patent registrations, with Saudi Aramco leading in patent grants, followed by King Faisal University (KFU), King Fahd University of Petroleum and Minerals (KFUPM), King Abdullah University of Science and Technology (KAUST), and King Abdulaziz University (KAU). SABIC, a prominent industry player in Saudi Arabia, has registered most of its patents using its European Head Office address and holds extensive collaborations with international partners, generating numerous patents. The analysis identifies the top patent offices where KSA organizations seek protection, including the United States Patent and Trademark Office (USPTO), Saudi Authority for Intellectual Property (SAIP), European Patent Office (EPO), the China National Intellectual Property Administration (CNIPA), and the German Patent and Trade Mark Office. However, the limited number of registrations at the SAIP highlights a need for improvement. The primary application domains encompass borehole/well accessories, measurement devices, organic chemistry, computing, and chemical/physical processes. The landscape reveals that Saudi Aramco and KFUPM focus predominantly on upstream and downstream technologies, while KAU, KFU, and KAUST concentrate on life sciences. Key findings indicate a significant increase in patent activity since the vision announcement, suggesting a growing focus on innovation within Saudi Arabia. However, the concentration of patents among a few major players (Saudi Aramco and SABIC) and the underrepresentation of patents filed with the Saudi Authority of Intellectual Property (SAIP) highlight areas for improvement. This study emphasizes the necessity to support small and medium enterprises (SMEs) and healthcare research institutions to foster broader participation in innovation and protect novel technologies. This research contributes valuable insights into the current state of patenting activities in Saudi Arabia and outlines opportunities for enhancing the country’s innovation ecosystem. Full article

In long-span bridges and high-rise buildings, closely spaced modes are commonly observed, which greatly increases the challenge of identifying modal parameters. Hilbert–Huang transform (HHT), a widely used method for modal parameter identification, first applies empirical mode decomposition (EMD) to decompose the acquired response and then uses the Hilbert transform (HT) to identify the modal parameters. However, the problem is that the deficiency of mode separation of EMD in HHT limits its application for structures with closely spaced modes. In this study, an improved HHT based on analytical mode decomposition (AMD) is proposed and is used to identify the modal parameters of structures with closely spaced modes. In the improved HHT, AMD is first employed to replace EMD for decomposing the measured response into several mono-component modes. Then, the random decrement technique is applied to the decomposed mono-component modes to obtain the free decay responses. Furthermore, the resulting free decay responses are analyzed by HT to estimate the modal parameters of structures with closely spaced modes. Examples of a simple three-degree-of-freedom system with closely spaced modes, a high-rise building under ambient excitation, and the Ting Kau bridge under typhoon excitations are adopted to validate the accuracy, effectiveness, and applicability of the proposed method. The results demonstrate that the proposed method can efficiently and accurately identify the natural frequencies and damping ratios of structures with closely spaced modes. Moreover, its identification results are more precise compared to those obtained using existing methods. Full article

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF₄-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments. Full article

We first briefly review the adventure of scale invariance in physics, from Galileo Galilei, Weyl, Einstein, and Feynman to the revival by Dirac (1973) and Canuto et al. (1977). In the way that the geometry of space–time can be described by the coefficients $g_{\mu \nu }$, a gauging condition given by a scale factor $\lambda \left(x^{\mu }\right)$ is needed to express the scaling. In general relativity (GR), $\lambda =1$. The “Large Number Hypothesis” was taken by Dirac and by Canuto et al. to fix $\lambda$. The condition that the macroscopic empty space is scale-invariant was further preferred (Maeder 2017a), the resulting gauge is also supported by an action principle. Cosmological equations and a modified Newton equation were then derived. In short, except in extremely low density regions, the scale-invariant effects are largely dominated by Newtonian effects. However, their cumulative effects may still play a significant role in cosmic evolution. The theory contains no “adjustment parameter”. In this work, we gather concrete observational evidence that scale-invariant effects are present and measurable in astronomical objects spanning a vast range of masses (0.5 ${\mathrm{M}}_{\odot }<$ M $<{10}^{14}$${\mathrm{M}}_{\odot }$) and an equally impressive range of spatial scales (0.01 pc < r < 1 Gpc). Scale invariance accounts for the observed excess in velocity in galaxy clusters with respect to the visible mass, the relatively flat/small slope of rotation curves in local galaxies, the observed steep rotation curves of high-redshift galaxies, and the excess of velocity in wide binary stars with separations above 3000 kau found in Gaia DR3. Last but not least, we investigate the effect of scale invariance on gravitational lensing. We show that scale invariance does not affect the geodesics of light rays as they pass in the vicinity of a massive galaxy. However, scale-invariant effects do change the inferred mass-to-light ratio of lens galaxies as compared to GR. As a result, the discrepancies seen in GR between the total lensing mass of galaxies and their stellar mass from photometry may be accounted for. This holds true both for lenses at high redshift like JWST-ER1 and at low redshift like in the SLACS sample. Of note is that none of the above observational tests require dark matter or any adjustable parameter to tweak the theory at any given mass or spatial scale. Full article

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures. Full article

Rayon is an extremely valuable cellulosic fiber in the global textile industry. Since cuprammonium rayon is more eco-friendly than other types of rayon fabrics, it was synthesized by regenerating α–cellulose isolated from wastepaper using a novel gaseous-ammoniation injection (GAI) process. This was achieved by preparing tetra–ammine copper hydroxide (cuoxam solution) via reacting copper sulfate and sodium hydroxide to produce copper hydroxide that was, finally, ammoniated by injecting the gas directly to the reaction vessel instead of using ammonium hydroxide applied by prior art. After that, the air-dried cellulose was chemically generated by dissolving it in a freshly prepared cuoxam solution and, subsequently, was regenerated by extruding it within a hardening bath constituted mainly from citric acid, producing the cuprammonium rayon (c. rayon). The properties of the fibrous, structural (XRD and mechanical), physical, and chemical features were investigated. It was found that the rayon was produced in a high yield (90.3%) with accepted properties. The fibrous properties of the rayon staple length, linear density, and fiber diameter were found to be 44 mm, 235 Tex, and 19.4 µm, respectively. In addition, the mechanical properties determined, namely tensile strength, elongation at break, modulus of elasticity, and breaking tenacity, were found to be 218.3 MPa, 14.3 GPa, 16.1%, and 27.53 cN/Tex, respectively. Based on this finding, and upon injecting the ammonia gas through the α–cellulose saturated and immersed in the Cu (OH)₂ to complete producing the cuoxam solvent, we find that theuse of an injection rate of 120 mL/minute to obtain the highest fibers’ tensile strength for the final product of the c. rayon is preferable. Utilization of higher rates will consume more amounts of the ammonia gas without gaining noticeable enhancement in the c. rayon’s mechanical quality. Accordingly, the GAI invention rendered the c. rayon favorable for use in making sustainable semisynthetic floss for either insulation purposes or spun threads for woven and nonwoven textile clothing. Full article

Alpha-cellulose, a unique, natural, and essential polymer for the fiber industry, was isolated in an ecofriendly manner using eleven novel systems comprising recycling, defibrillation, and delignification of prosenchyma cells (vessels and fibers) of ten lignocellulosic resources. Seven hardwood species were selected, namely Conocorpus erectus, Leucaena leucocephala, Simmondsia chinensis, Azadirachta indica, Moringa perigrina, Calotropis procera, and Ceiba pentandra. Moreover, three recycled cellulosic wastes were chosen due to their high levels of accumulation annually in the fibrous wastes of Saudi Arabia, namely recycled writing papers (RWPs), recycled newspapers (RNPs), and recycled cardboard (RC). Each of the parent samples and the resultant alpha-cellulose was characterized physically, chemically, and anatomically. The properties examined differed significantly among the ten resources studied, and their mean values lies within the cited ranges. Among the seven tree species, L. leucocephala was the best cellulosic precursor due to its higher fiber yield (55.46%) and holocellulose content (70.82%) with the lowest content of Klasson lignin (18.86%). Moreover, RWP was the best α-cellulose precursor, exhibiting the highest holocellulose (87%) and the lowest lignin (2%) content. Despite the high content of ash and other additives accompanied with the three lignocellulosic wastes that were added upon fabrication to enhance their quality (10%, 11%, and 14.52% for RWP, RNP, and RC, respectively), they can be considered as an inexhaustible treasure source for cellulose production due to the ease and efficiency of discarding their ash minerals using the novel CaCO₃-elimination process along with the other innovative techniques. Besides its main role for adjusting the pH of the delignification process, citric acid serves as an effective and environmentally friendly additive enhancing lignin breakdown while preserving cellulose integrity. Comparing the thermal behavior of the ten cellulosic resources, C. procera and C. pentandra exhibited the highest moisture content and void volume as well as having the lowest specific gravity, crystallinity index, and holocellulose content and were found to yield the highest mass loss during their thermal degradation based on thermogravimetric and differential thermal analysis in an inert atmosphere. However, the other resources used were found to yield lower mass losses. The obtained results indicate that using the innovative procedures of recycling, defibrillation, and delignification did not alter or distort either the yield or structure of the isolated α-cellulose. This is a clear indicator of their high efficiency for isolating cellulose from lignocellulosic precursors. Full article

We have studied the variations in the temperature-dependent absorption edge of a bulk InSe-layered semiconductor using photoconductivity (PC) measurements. From both the X-ray diffraction (XRD) and Raman experimental results, the structural phase of the as-prepared InSe sample was confirmed to be γ-polytype. Upon heating from 15 K to 300 K, the absorption edge of PC spectra was found to shift significantly toward lower energy, and the absorption edge as a function of temperature was further analyzed by the Varshni’s relationship and Bose–Einstein empirical equation. The Urbach energy as a function of temperature was obtained by fitting the absorption tail below the absorption coefficient of the PC spectrum, and the effective phonon energy can be derived from the temperature-dependent steepness parameter associated with Urbach energy. Our study indicates that the broadening of the absorption edge in the as-synthesized bulk γ-InSe is caused by a combination of electron/exciton–phonon interactions and thermal/structural disorder. Full article