bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Key Risk Nodes and Invulnerability Analysis of Construction Supply Chain Networks
Authors: Wang Hongchun , Zhou Zixiang
Published: July 2024

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.
Investigation of an Ethanol Electroreforming Cell Based on a Pt1Ru1/C Catalyst at the Anode
Authors: Lo Vecchio Carmelo , Mosca Erminia , Trocino Stefano , Baglio Vincenzo
Published: July 2024

The production of H2 from renewable sources represents a crucial challenge for the planet’s future to achieve net zero emissions and store renewable energy. A possible alternative to water electrolysis (WE), which requires high potential (E > 1.48 V) to trigger the oxygen evolution reaction (OER), would be alcohol electrochemical reforming (ER), which implies the oxidation of short organic molecules such as methanol or ethanol. In ER, energy must be supplied to the system, but from a thermodynamic point of view, the energy request for the methanol or ethanol oxidation reaction is much lower than that of the OER. To study this process, an in-house 50 wt.% Pt1Ru1/C anodic catalyst was easily synthesized according to the Pt sulphite complex route and the impregnation of a carbon support (Ketjenblack, KB) and a Ru precursor. X-ray diffraction (XRD), X-ray fluorescence (XRF) spectroscopy, and Transmission Electron Microscopy (TEM) were used to characterize the structure, composition, and morphology of the catalyst. It appears that two distinct crystallographic phases of the Pt and Ru nanoparticles were encountered after the synthesis conducted by Ru impregnation. For the electrochemical measurements, ethanol electrooxidation (2 M CH3CH2OH) was studied first in a half cell with a rotating disc electrode (RDE) configuration under acid conditions and then in a direct ethanol electroreforming (or electrolysis) cell, equipped with a proton exchange membrane (PEM) as the electrolyte. The output current density was 0.93 A cm−2 at 1 V and 90 °C in 2 M ethanol. The remarkable current densities obtained in the alcohol electrolyzer at a low voltage are better than the actual state of the art for PEM ethanol ER.
Au Supported on Bovine-Bone-Derived Hydroxyapatite Catalyzes CO2 Photochemical Reduction toward Methanol
Authors: Gama-Lara Sergio Arturo , Vilchis-Néstor Alfredo Rafael , Amado-Piña Deysi , Natividad Reyna
Published: July 2024

In this work, gold-photo-catalyzed CO2 transformation was conducted and the effect of three variables with two levels was investigated: support (TiO2 and hydroxyapatite from bovine bone (BB)), Au content (5 and 10%) and activation wavelength (254 and 380–700 nm). Reactions were conducted in a stirred tank reactor by bubbling CO2 (9 × 10−3 dm3/min) in 0.1 dm3 of 0.5 M NaOH solution. The catalysts were synthesized using AuCl3, TiO2 and BB. Au nanoparticles were obtained by reduction with Hetheroteca inuloides, thus eliminating calcination and hydrogenation to reduce the gold species. By TEM, the particle size distribution was determined, and the synthesized nanoparticle sizes varied in the range of 9 to 19 nm, depending on the support and Au content. By UV–Vis spectroscopy, the energy band gaps of the prepared materials were 2.18 eV (10% Au/BB), 2.38 eV (5% Au/BB), 2.42 eV (BB), 3.39 eV (5% Au/TiO2), 3.41 eV (10% Au/TiO2) and 3.43 eV for pure TiO2. Methanol and formic and acetic acids were identified during the process. Selectivity toward methanol was found to be improved with the 10% Au/BB catalytic system.
Photocatalytic, Antimicrobial, and Cytotoxic Efficacy of Biogenic Silver Nanoparticles Fabricated by Bacillus amyloliquefaciens
Authors: Eid Ahmed M. , Hassan Saad El-Din , Hamza Mohammed F. , Selim Samy , Almuhayawi Mohammed S. , Alruhaili Mohammed H. , Tarabulsi Muyassar K. , Nagshabandi Mohammed K. , Fouda Amr
Published: July 2024

The biomass filtrate of the endophytic bacterial strain Bacillus amyloliquefaciens Fa.2 was utilized for the eco-friendly production of silver nanoparticles (Ag-NPs). The yellowish-brown color’s optical properties showed a maximum surface plasmon resonance at 415 nm. The morphological and elemental composition analysis reveals the formation of spherical shapes with sizes of 5–40 nm, and the Ag ion comprises the major component of the produced Ag-NPs. X-ray diffraction confirmed the crystalline structure, whereas dynamic light scattering reveals the high stability of synthesized Ag-NPs with a polydispersity index of 0.413 and a negative zeta potential value. The photocatalytic experiment showed the efficacy of Ag-NPs to degrade methylene blue with maximum percentages of 73.9 ± 0.5 and 87.4 ± 0.9% under sunshine and UV irradiation, respectively, compared with 39.8% under dark conditions after 210 min. Additionally, the reusability of Ag-NPs was still more active for the fifth run, with a percentage decrease of 11.6% compared with the first run. Interestingly, the biogenic Ag-NPs showed superior antimicrobial activity against different pathogenic Gram-negative bacteria (MIC = 6.25 µg mL−1), Gram-positive bacteria (MIC = 12.5 µg mL−1), and uni- and multicellular fungi (MIC = 12.5 µg mL−1). Moreover, the biosynthesized Ag-NPs could target cancer cells (Pc3 and Mcf7) at low concentrations compared with normal cell (Vero) lines. The IC50 of normal cells is 383.7 ± 4.1 µg mL−1 compared with IC50 Pc3 (2.5 ± 3.5 µg mL−1) and McF7 (156.1 ± 6.8 µg mL−1). Overall, the bacterially synthesized Ag-NPs showed multifunctional features to be used in environmental catalysis and biomedical applications.
Photocatalytic Activity of Metal- and Non-Metal-Anchored ZnO and TiO2 Nanocatalysts for Advanced Photocatalysis: Comparative Study
Authors: AlMohamadi Hamad , Awad Sameer A. , Sharma Ashwani Kumar , Fayzullaev Normurot , Távara-Aponte Arístides , Chiguala-Contreras Lincoln , Amari Abdelfattah , Rodriguez-Benites Carlos , Tahoon Mohamed A. , Esmaeili Hossein
Published: July 2024

This review article provides useful information on TiO2 and ZnO photocatalysts and their derivatives in removing organic contaminants such as dyes, hydrocarbons, pesticides, etc. Also, the reaction mechanisms of TiO2 and ZnO photocatalysts and their derivatives were investigated. In addition, the impact of adding metallic (e.g., Ag, Co, Pt, Pd, Cu, Au, and Ni) and non-metallic (e.g., C, N, O, and S) cocatalysts to their structure on the photodegradation efficiency of organic compounds was thoroughly studied. Moreover, the advantages and disadvantages of various synthesis procedures of ZnO and TiO2 nanocatalysts were discussed and compared. Furthermore, the impact of photocatalyst dosage, photocatalyst structure, contaminant concentration, pH, light intensity and wavelength, temperature, and reaction time on the photodegradation efficiency were studied. According to previous studies, adding metallic and non-metallic cocatalysts to the TiO2 and ZnO structure led to a remarkable enhancement in their stability and reusability. In addition, metallic and non-metallic cocatalysts attached to TiO2 and ZnO demonstrated remarkable photocatalytic efficiency in removing organic contaminants.
H3K27-Altered Diffuse Midline Glioma of the Brainstem: From Molecular Mechanisms to Targeted Interventions
Authors: Nonnenbroich Leo F. , Bouchal Samantha M. , Millesi Elena , Rechberger Julian S. , Khatua Soumen , Daniels David J.
Published: July 2024

Pediatric high-grade gliomas are a devastating subset of brain tumors, characterized by their aggressive pathophysiology and limited treatment options. Among them, H3 K27-altered diffuse midline gliomas (DMG) of the brainstem stand out due to their distinct molecular features and dismal prognosis. Recent advances in molecular profiling techniques have unveiled the critical role of H3 K27 alterations, particularly a lysine-to-methionine mutation on position 27 (K27M) of the histone H3 tail, in the pathogenesis of DMG. These mutations result in epigenetic dysregulation, which leads to altered chromatin structure and gene expression patterns in DMG tumor cells, ultimately contributing to the aggressive phenotype of DMG. The exploration of targeted therapeutic avenues for DMG has gained momentum in recent years. Therapies, including epigenetic modifiers, kinase inhibitors, and immunotherapies, are under active investigation; these approaches aim to disrupt aberrant signaling cascades and overcome the various mechanisms of therapeutic resistance in DMG. Challenges, including blood–brain barrier penetration and DMG tumor heterogeneity, require innovative approaches to improve drug delivery and personalized treatment strategies. This review aims to provide a comprehensive overview of the evolving understanding of DMG, focusing on the intricate molecular mechanisms driving tumorigenesis/tumor progression and the current landscape of emerging targeted interventions.
The Influence of Metabolic Risk Factors on the Inflammatory Response Triggered by Myocardial Infarction: Bridging Pathophysiology to Treatment
Authors: Ramos-Regalado Lisaidy , Alcover Sebastià , Badimon Lina , Vilahur Gemma
Published: July 2024

Myocardial infarction (MI) sets off a complex inflammatory cascade that is crucial for effective cardiac healing and scar formation. Yet, if this response becomes excessive or uncontrolled, it can lead to cardiovascular complications. This review aims to provide a comprehensive overview of the tightly regulated local inflammatory response triggered in the early post-MI phase involving cardiomyocytes, (myo)fibroblasts, endothelial cells, and infiltrating immune cells. Next, we explore how the bone marrow and extramedullary hematopoiesis (such as in the spleen) contribute to sustaining immune cell supply at a cardiac level. Lastly, we discuss recent findings on how metabolic cardiovascular risk factors, including hypercholesterolemia, hypertriglyceridemia, diabetes, and hypertension, disrupt this immunological response and explore the potential modulatory effects of lifestyle habits and pharmacological interventions. Understanding how different metabolic risk factors influence the inflammatory response triggered by MI and unraveling the underlying molecular and cellular mechanisms may pave the way for developing personalized therapeutic approaches based on the patient’s metabolic profile. Similarly, delving deeper into the impact of lifestyle modifications on the inflammatory response post-MI is crucial. These insights may enable the adoption of more effective strategies to manage post-MI inflammation and improve cardiovascular health outcomes in a holistic manner.
Effects of Cannabis on Glutamatergic Neurotransmission: The Interplay between Cannabinoids and Glutamate
Authors: Chowdhury Kawsar U. , Holden Madison Elizabeth , Wiley Miles T. , Suppiramaniam Vishnu , Reed Miranda N.
Published: July 2024

There has been a significant increase in the consumption of cannabis for both recreational and medicinal purposes in recent years, and its use can have long-term consequences on cognitive functions, including memory. Here, we review the immediate and long-term effects of cannabis and its derivatives on glutamatergic neurotransmission, with a focus on both the presynaptic and postsynaptic alterations. Several factors can influence cannabinoid-mediated changes in glutamatergic neurotransmission, including dosage, sex, age, and frequency of use. Acute exposure to cannabis typically inhibits glutamate release, whereas chronic use tends to increase glutamate release. Conversely, the postsynaptic alterations are more complicated than the presynaptic effects, as cannabis can affect the glutamate receptor expression and the downstream signaling of glutamate. All these effects ultimately influence cognitive functions, particularly memory. This review will cover the current research on glutamate–cannabis interactions, as well as the future directions of research needed to understand cannabis-related health effects and neurological and psychological aspects of cannabis use.
Spatio-Temporal Regulation of Notch Activation in Asymmetrically Dividing Sensory Organ Precursor Cells in Drosophila melanogaster Epithelium
Authors: Pinot Mathieu , Le Borgne Roland
Published: July 2024

The Notch communication pathway, discovered in Drosophila over 100 years ago, regulates a wide range of intra-lineage decisions in metazoans. The division of the Drosophila mechanosensory organ precursor is the archetype of asymmetric cell division in which differential Notch activation takes place at cytokinesis. Here, we review the molecular mechanisms by which epithelial cell polarity, cell cycle and intracellular trafficking participate in controlling the directionality, subcellular localization and temporality of mechanosensitive Notch receptor activation in cytokinesis.
Microflow Injection System for Efficient Cu(II) Detection across a Broad Range
Authors: Ricart David , Dorado Antonio , Lao-Luque Conxita , Baeza Mireia
Published: July 2024

In this study, a modular, multi-step, photometric microflow injection analysis (micro-FIA) system for the automatic determination of Cu(II) in a bioreactor was developed. The system incorporates diverse 3D-printed modules, including a platform formed by a mixer module to mix Cu(II) with hydroxylamine, which reduces Cu(II) to Cu(I) linked to a diluter module via a Tesla valve, a chelation mixer module, a disperser module, and a detector module provided by an LED light source at λ = 455 nm and a light dependence resistor (LDR) as a light intensity detector. The system measures the color intensity resulting from the chelation between Cu(I) and neocuproine. The micro-FIA system demonstrated good capability for automatic and continuous Cu(II) determination, in a wide range of Cu concentrations, from 34 to 2000 mg L−1. The device exhibits a good repeatability (coefficient of variation below 2% across the measured concentration range), good reproducibility, and has an accuracy of around 100% between 600 and 1900 mg L−1. Real samples were analyzed using both the micro-FIA system and an atomic absorption spectroscopy method, revealing no statistically significant differences. Additionally, a Tesla valve located before the detector substituted a 3-way solenoid valve, eliminating the need for moving parts.
Novel Ultrasound-Guided Hydrodissection with 5% Dextrose for the Treatment of Occipital Neuralgia Targeting the Greater Occipital Nerve
Authors: Lam King Hei Stanley , Su Daniel Chiung-Jui , Wu Yung-Tsan , Janze Aeneas , Reeves Kenneth Dean
Published: July 2024

Background: Occipital neuralgia is a debilitating condition, and traditional treatments often provide limited or temporary relief. Recently, ultrasound-guided hydrodissection of the greater occipital nerve (GON) has emerged as a promising minimally invasive approach. Objectives: To describe two novel ultrasound-guided hydrodissections with 5% dextrose for GON and discuss their advantages, disadvantages, and considerations. Methods: Two cases are reported. Case 1 describes a lateral decubitus approach for hydrodissecting the GON between the semispinalis capitis (SSC) and obliquus capitis inferior (OCI) muscles. Case 2 details a cranial-to-caudal approach for hydrodissecting the GON within the SSC and upper trapezius (UT) muscles when the GON passes through these two muscles. Results: Both patients experienced significant and sustained pain relief with improvements in function. Conclusions: Ultrasound-guided GON hydrodissection using 5% dextrose is a promising treatment for occipital neuralgia. The lateral decubitus and cranial-caudal approaches provide additional options to address patient-specific anatomical considerations and preferences.
The Role of OCT in Follow-Up of Fungal Keratitis Caused by Beauveria bassiana in Contact Lens Wearer
Authors: Martínez-Gil Cristina , Roig-Revert María José , Fernández-López Ester , González-Pellicer Rosa María , Camarena-Miñana Juan José , Peris-Martínez Cristina
Published: July 2024

A 25-year-old Caucasic female was referred to our clinic after suffering from infectious keratitis in the right eye for a month. The patient was a contact lens user and had no history of ocular trauma. Furthermore, the patient did not report any relevant antecedent. The main complaint was intense photophobia and pain. Infectious keratitis remains one of the main complications of contact lens wear and can become a therapeutic challenge in some patients. Although the most frequent causal agent is bacterial, other causes such as herpes virus, Acanthamoeba or fungi should be considered when antimicrobial therapy does not work as expected clinically. Fungal keratitis normally appears on previously damaged corneas, but it can also develop in contact lens wearers. Beauveria bassiana is an unusual pathogen which has been diagnosed more frequently lately per the clinical reports in the last 30 years, so it can be included in the diagnostic scheme when a fungal keratitis is suspected. In clinical management, AS-OCT may be a functional tool to assess the evolution and monitor the response to microbial agents and surgery. Although more studies are needed, some characteristic features have been described and can help to diagnose a fungal keratitis against other infections. AS-OCT can also play an important role in monitoring the corneal scarring after the keratitis episode, and it may be useful to plan post-infection therapy for visual rehabilitation.
The Usefulness of 55° Wide-Field Spectral-Domain Optical Coherence Tomography in Monitoring the Features of Peripheral Subretinal Fluid Remnants after Rhegmatogenous Retinal Detachment Surgery
Authors: Carta Valentina , Lixi Filippo , Loiudice Pasquale , Frongia Francesca , Tatti Filippo , Delpiano Chiara , Cremonesi Pierluca , Peiretti Enrico
Published: July 2024

Background: This study aimed to assess the effectiveness of 55° wide-field (WF) spectral-domain (SD) optical coherence tomography (OCT) for detecting peripheral subretinal fluid (SRF) after surgery for rhegmatogenous retinal detachment (RRD). Methods: In this retrospective observational study, the retinal periphery was examined to evaluate the possible presence of persistent SRF after surgery. OCT scans were acquired in infrared mode to use any peripheral vessel as a landmark for better repeatability in monitoring fluid remnants. Results: A total of 80 patients (10% with high myopia) were examined using 55° WF SD OCT after successful pars plana vitrectomy (83.8%) or scleral buckling (16.3%) for RRD. A total of 18 patients (22.5%), 16 of whom underwent pars plana vitrectomy and 2 who underwent scleral buckling, showed SRF at the OCT examination during the follow-up. Potential risk factors associated with SRF persistence were analyzed, revealing a significative association with young age (p = 0.009). After a follow-up period of 7.05 ± 2.44 months (ranging from 3 to 12 months), a complete resorption in all patients (100%) within 12 months was observed. Best-corrected visual acuity significantly improved in both groups over time. Conclusion: Using 55° WF SD-OCT successfully assessed the course of SRF reabsorption, offering a viable alternative for all those realities where technologies such as ultra-wide-field (UWF) OCT are not available.
Odontogenic Orbital Cellulitis at the Crossroads of Surgeries: Multidisciplinary Management and Review
Authors: Pirvulescu Ruxandra A. , Vasile Victor A. , Romanitan Mihaela O. , Zemba Mihail , Ciulei Oana C. , Geamanu Aida , Anton Nicoleta , Popa Cherecheanu Matei
Published: July 2024

This article examines two cases of odontogenic orbital cellulitis, highlighting the complexities and interdisciplinary approaches required for effective management. We present two cases and describe the clinical challenges and treatment strategies employed. We report the diagnosis, treatment, and follow-up of patients who developed orbital cellulitis as a complication of an odontogenic infection. Our objective is to report and discuss the clinical aspects and management of this pathology compared to those observed in the literature. This study underscores the necessity for collaboration among various specialties, including ophthalmology, otolaryngology, oral surgery, radiology, and infectious disease, to address the multifaceted challenges posed by this condition. Effective management of orbital abscesses of odontogenic origin requires a timely and multidisciplinary approach for successful outcomes. This article emphasizes the importance of early diagnosis and coordinated care to prevent serious complications, such as vision loss or intracranial infections.
The Impact of Laboratory Automation on the Time to Urine Microbiological Results: A Five-Year Retrospective Study
Authors: Kritikos Antonios , Prod’hom Guy , Jacot Damien , Croxatto Antony , Greub Gilbert
Published: July 2024

Total laboratory automation (TLA) is a valuable component of microbiology laboratories and a growing number of publications suggest the potential impact of automation in terms of analysis standardization, streaking quality, and the turnaround time (TAT). The aim of this project was to perform a detailed investigation of the impact of TLA on the workflow of commonly treated specimens such as urine. This is a retrospective observational study comparing two time periods (pre TLA versus post TLA) for urine specimen culture processing. A total of 35,864 urine specimens were plated during the pre-TLA period and 47,283 were plated during the post-TLA period. The median time from streaking to identification decreased from 22.3 h pre TLA to 21.4 h post TLA (p < 0.001), and the median time from streaking to final validation of the report decreased from 24.3 h pre TLA to 23 h post TLA (p < 0.001). Further analysis revealed that the observed differences in TAT were mainly driven by the contaminated and positive samples. Our findings demonstrate that TLA has the potential to decrease turnaround times of samples in a laboratory. Nevertheless, changes in laboratory workflow (such as extended opening hours for plate reading and antibiotic susceptibility testing or decreased incubation times) might further maximize the efficiency of TLA and optimize TATs.
Clinical Applications and Future Directions of Smartphone Fundus Imaging
Authors: Ahn Seong Joon , Kim Young Hwan
Published: July 2024

The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.
Basophil Activation Test Predicts Cetuximab Anaphylaxis Severity in Alpha-Gal IgE-Positive Patients
Authors: Kopač Peter , Koren Ana , Bidovec-Stojkovič Urška , Košnik Mitja , Dejanović Luka , Mesti Tanja , Strojan Primož , Korošec Peter , Ocvirk Janja
Published: July 2024

Upon first exposure to cetuximab, hypersensitivity reactions can occur. We aimed to assess the utility of the basophil activation test (BAT) to alpha-gal and cetuximab for predicting severe reactions. We prospectively recruited 38 patients and evaluated sIgE to alpha-gal in all patients before the first application of cetuximab. In all alpha-gal-sensitized patients, we evaluated skin tests to meat extracts, gelatine, and cetuximab and performed BAT with alpha-gal and cetuximab. In 24% (9/38) of patients, sIgE to alpha-gal was >0.10 kUA/L, and 8/9 reacted to the cetuximab. Basophil activation tests with alpha-gal were positive in all sensitized patients and were higher in those with severe reactions (18.3% in grade 4 [n = 4] vs. 1.8% in grade 2 [n = 3] or no reaction [n = 1] at 3.3 ng/mL of alpha-gal; p = 0.03). All patients with severe grade 4 reactions had a positive CD63 BAT response to cetuximab compared to patients with moderate or no reaction, who all had negative BAT (57.7% vs. 0.9% at 500 µg/mL, 63.2% vs. 4.1% at 100 µg/mL, 58.2% vs. 2.7% at 10 µg/mL, and 32.1% vs. 3.3% at 1 µg/mL of cetuximab, respectively; p ≤ 0.001). In summary, before initiating cetuximab treatment, sIgE to alpha-gal should be assessed in all patients. To predict the severity of the reaction and to assess the risk of cetuximab-induced anaphylaxis, we should perform BATs with alpha-gal or more discriminative BATs with cetuximab.
Learning by Doing in VR: A User-Centric Evaluation of Lathe Operation Training
Authors: Conesa Julian , Martínez Antonio , Mula Francisco , Contero Manuel
Published: July 2024

This study presents the development and evaluation of an immersive virtual reality (VR) application designed for lathe operation training. The VR application, built using Unity for Oculus Rift headsets, aims to simulate a realistic lathe machining experience, allowing users to interact with the machine’s various controls and levers. The experimental analysis involved 20 s-year Mechanical Engineering students who performed machining tasks in the virtual environment. The usability and user experience of the application were assessed using the System Usability Scale (SUS) and a 12-item questionnaire. The SUS results yielded a high mean score of 96.25 (SD = 6.41), indicating excellent usability. The user experience evaluation also showed positive feedback, with high ratings for the sense of presence, realism, and usefulness for training purposes. However, some users reported minor physical discomforts such as dizziness. The study concludes that immersive VR is a valuable tool for enhancing training in lathe operations, offering an engaging and realistic experience that encourages active learning. Future work should focus on reducing physical discomfort and further improving the application’s realism and interactivity.
Long-Range Wireless Power Transfer for Moving Wireless IoT Devices
Authors: Colmiais Ivo , Dinis Hugo , Mendes Paulo M.
Published: July 2024

Wireless technologies are revolutionizing communications, with recent deployments, such as 5G, playing a key role in the future of the Internet of Things (IoT). Such progress is leading to an increasingly higher number of wirelessly connected devices. These require increased battery use and maintenance, consequently straining current powering solutions. Since most wireless systems rely on radiofrequency (RF) waves for communications and feature low-power technologies, it is increasingly feasible to develop and implement wireless power transfer solutions supported by RF. In this paper, a simultaneous wireless information and power transfer (SWIPT) solution targeting small mobile devices is presented. This solution uses beamforming to mitigate the path loss associated with the RF power propagation. It relies on an RF backscattering tracking algorithm to power moving devices. The feasibility to power wearable devices is demonstrated by tracking a walking individual (approximately 5 km/h) at a distance of 0.5 m while transferring a minimum of 6 dBm to a wearable device using 2 GHz RF signals. Simulations were used to determine the viability of such a solution to deliver useful power levels to a 1.2 × 1.4 m2 working area without exceeding specific absorption rate (SAR) limits.
Leakage Power Attack-Resilient Design: PMOS-Reading 9T SRAM Cell
Authors: Yang Muyu , Balasubramanian Prakash , Chen Kangqi , Oruklu Erdal
Published: July 2024

Non-invasive side-channel attacks (SCAs) based on leakage power analysis (LPA) have received more attention recently, since leakage current has gradually become more dominant with further scaled technologies. For SRAM cells, LPA exploits the correlation between data in memory cells and their corresponding leakage power. This paper proposes a novel SRAM design in 7 nm node for countering LPA attacks, based on a single-ended PMOS-reading 9T (nine-transistor) cell design. The leakage current imbalance, delay, stability, and robustness of SRAM cells are examined for the proposed memory cell architecture with layout designs, and results are compared against other SRAM cell designs. Simulation results and failure of LPA attacks in case studies confirm the enhanced resilient behavior for the new SRAM cell design.
Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy
Authors: Hu Hao , Deng Shiqi , Yan Wang , He Yanyong , Wu Yudong
Published: July 2024

The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors’ idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors’ production and manufacturing processes in advance.
Fast Noise Level Estimation via the Similarity within and between Patches
Authors: Wu Jiaxin , Jia Meng , Wu Shiqian , Xie Shoulie
Published: July 2024

Patch level-based noise level estimation (NLE) is often inaccurate and inefficient because of the harsh criteria required to select a small number of homogeneous patches. In this paper, a fast image NLE method based on a global search for similar pixels is proposed to solve the above problem. Specifically, the mean square distance (MSD) is first expressed in the form of the standard deviation (std) and mean value of image patches. Afterward, the two values, std and mean, are calculated and stored in advance. Then, a 2D statistical histogram and summed area table are adopted to speed up the search for similar patches. Further, the most similar pixels are selected from similar patches to obtain an initial estimation. Finally, we correct the deviation of the initial estimation by re-injecting noise for secondary estimation. Experimental results show that the proposed method outperforms the state-of-the-art techniques in fast NLE and guided denoising.
Control of an Energy-Harvesting System Using Sinha’s Theory for the Purpose of Energy Production
Authors: Cauz Luiz Oreste , Chavarette Fábio Roberto
Published: July 2024

The goal of this study was to develop a linear feedback control method to increase the energy produced for a parametrically excited energy-harvesting system. The chosen control design uses state feedback based on the Lyapunov–Floquet transformation to direct the system dynamics toward a previously chosen trajectory. The methodology of this work was based on the studies of Sinha and Butcher. They developed stability and control analysis techniques based on Floquet theory and the approximation of the state transition matrix of systems that have periodic coefficients in time. It was observed that the stability and instability conditions of the system were obtained from the choice of physical parameters related to the parametric excitation force. When the control technique was applied to the system, it was found that the suggested control effectively directed the system’s trajectory in the direction of the predetermined trajectory. The application of control significantly increased the energy generation compared with the uncontrolled system, which was seen when comparing the energy generated by the uncontrolled system and the net energy generated by the controlled system.
Full-Swing Nanosecond Delay Hybrid Level Shifter for Time-Critical Applications
Authors: Basha Mohammed Mahaboob , Poreddy Lachi Reddy , Madhurima Virupakshi , Gundala Srinivasulu , Syed Javed
Published: July 2024

A crucial component of digital integrated circuits with numerous power domains is the level shifter (LS). The traditional topologies are currently the mirror LS and cross-coupled LS. For full-swing level conversions from extremely low voltage to the nominal voltage of the supply, ahybrid LS is proposed in this paper, which is a combination of the current mirror LS and cross-coupled LS with a swing-aware output inverter. The proposed LS circuit is designed to ensure full swing, static current-free, and limited current-contention level conversions by preserving the benefits of the current mirror and cross-coupled LS, and using them to eliminate each other’s shortcomings. The proposed hybrid LS is designed and implemented by using 45-nm technology in Cadence Virtuoso tool. Pass transistors and current limiters with multiple thresholds are included for the suggested LS. The proposed LS provides voltage conversion from 0.10 V to 1.20 V. At alevel-shifting voltage of 0.20 V, the proposed hybrid LS at 1 MHz input frequency demonstrates a delay of 8.38 ns, an average power consumption of 3.81 µW, and an energy per transition of 26.64 fJ. Additionally, the suggested LS has low delay, good supply voltage scaling, and delay scalability.
The Frequency Regulation Scheme of a Wind Turbine Generator Based on Variable Tracking Operation
Authors: Xue Shuilian , Yang Zhiguo , Yin Zhilong , Yu Feng , Wang Zhiguo
Published: July 2024

This paper proposes a variable-power-tracking-operation-based frequency regulation scheme for a wind turbine generator (WTG) that employs a frequency-excursion-based control parameter to suppress frequency excursion under various wind speeds and disturbances. To this end, an improved system frequency response model is built, and then the frequency regulation characteristics of the WTG are investigated. The frequency-excursion-based control parameter is determined. Simulations based on the Matlab/Simulink simulator verify the effectiveness of the proposed variable-power-tracking-operation-based frequency regulation strategy. The simulation results clearly indicate that the suggested scheme is beneficial for reducing the maximum frequency deviation. The proposed frequency regulation scheme could enhance the networking capacity of wind farms and improve the capacity of the power grid to absorb renewable energy.
BiFPN-KPointNet-CBAM: Application of 3D Point Cloud Technology Based on Deep Learning in Measuring Vegetation
Authors: Liu Qihuanghua , Jiang Jianmin , Hu Jingyi , Zhong Songyu , Zou Fang
Published: July 2024

The results of traditional vegetation-measuring methods are mostly two-dimensional data, which can only convey limited information. The greening situation of many cities or regions in the world cannot be fully assessed by these results. In this regard, this paper proposes the use of the air–ground integrated point cloud data acquisition mode for measuring vegetation. This mode combines a backpack-mounted laser scanning system, a vehicle-mounted laser scanning system, and UAV tilt photography technology to collect greening data in a comprehensive park and along a municipal road in Guangzhou, China. To classify the collected greening data, we propose the BiFPN-KPointNet-CBAM model, which was derived from PointNet. The model was introduced to analyze the distribution of green plants in study areas. The experimental findings indicate that our model achieved a notable enhancement in the overall accuracy by approximately 8% compared with other state-of-the-art models. Compared with the traditional greening survey method, this method obtained three-dimensional and more accurate greening data, and thus, provides higher quality greening data for urban managers.
Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms
Authors: Li Peiyu , Wang Hui , Tian Guo , Fan Zhihui
Published: July 2024

Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.
Deep Learning for Abnormal Human Behavior Detection in Surveillance Videos—A Survey
Authors: Wastupranata Leonard Matheus , Kong Seong G. , Wang Lipo
Published: July 2024

Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep learning techniques for detecting abnormal human behaviors in surveillance video streams. We categorize the existing techniques into three approaches: unsupervised, partially supervised, and fully supervised. Each approach is examined in terms of its underlying conceptual framework, strengths, and drawbacks. Additionally, we provide an extensive comparison of these approaches using popular datasets frequently used in the prior research, highlighting their performance across different scenarios. We summarize the advantages and disadvantages of each approach for abnormal human behavior detection. We also discuss open research issues identified through our survey, including enhancing robustness to environmental variations through diverse datasets, formulating strategies for contextual abnormal behavior detection. Finally, we outline potential directions for future development to pave the way for more effective abnormal behavior detection systems.
Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network
Authors: Yin Changkui , Mao Yingchi , He Zhenyuan , Chen Meng , He Xiaoming , Rong Yi
Published: July 2024

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.
Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks
Authors: MT Suryadi , Aminanto Achmad Eriza , Aminanto Muhamad Erza
Published: July 2024

In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing model to detect cyber-attacks on Wi-Fi networks. The model analyzes network data, enabling quick identification of attack indications in the command room. The research involves simulations and analyses of various feature selection methods and classification models using a public dataset of cyber-attacks on Wi-Fi networks. The study identifies mutual information with 20 features such as the optimal feature reduction method and the Neural Network as the best classification method, achieving a 94% F1-Score within 95 s. These results demonstrate the proposed IDS’s ability to swiftly detect attacks, aligning with previous research findings.
Effect of Different Weight on the Movable PZT Device on the Damage Detection Performance of Electromechanical Impedance Technique
Authors: Choi Hee-Wook , Na Wongi S.
Published: July 2024

This study presents a novel approach to conducting the electromechanical impedance (EMI) technique for delamination detection in composite structures without the need for permanently attaching PZT (Lead Zirconate Titanate) transducers to the surface. Instead, a device is created that can be simply placed on top of the composite structure, enabling one to perform the EMI technique for detecting damage. The primary objective is to investigate the effectiveness of this device in detecting delamination within composite materials. Additionally, this study explores the impact of placing additional weight on top of the transducer to investigate the performance of the device subjected to higher pressure. Experimental results and analysis will be presented to evaluate the feasibility and reliability of this approach for non-destructive testing and structural health monitoring of composite components. This research is significant as it lays the groundwork for developing automated damage detection systems using robotics in the near future. By demonstrating the proposed concept that can be easily integrated into robotic platforms, this study contributes to the advancement of automation in structural health monitoring. Implementing this technique in robotic systems has the potential to revolutionize maintenance practices by enabling continuous, real-time monitoring of composite structures, enhancing safety, and minimizing downtime due to structural defects. Moreover, the investigation into the impact of additional weight on the transducer’s performance is crucial for setting minimum weight limits in robotic systems, ensuring optimal functionality and accuracy during automated damage detection tasks.
Physics-Informed Neural Network-Based VSC Back-to-Back HVDC Impedance Model and Grid Stability Estimation
Authors: Chang Minhyeok , Jung Yoongun , Kang Seokjun , Jang Gilsoo
Published: July 2024

With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant impedance measurement data from power electronic devices. In this paper, we propose a method for constructing impedance models of equipment with fewer impedance measurement data in voltage-source converter (VSC) back-to-back high-voltage direct current (HVDC) systems using physics-informed neural networks. Furthermore, given the power system states, we present a neural network approach to estimate grid stability at different operating points. Validation via PSCAD/EMTDC simulations and a PyTorch neural network confirmed the adequacy of these models.