bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Electrically Conductive Bio-Based Composites via Radical-Induced Cationic Frontal Photopolymerization
Authors: Moraru Dumitru , Cortés Alejandro , Martinez-Diaz David , Prolongo Silvia G. , Jiménez-Suárez Alberto , Sangermano Marco
Published: July 2024

Diglycidylether of vanillyl alcohol (DGEVA), in combination with mechanically recycled carbon fibers (RCFs), was used to make, via Radical-Induced Cationic Frontal Photopolymerization (RICFP), fully sustainable and bio-based conductive composites with good electrical conductivity and consequent Joule effect proprieties. Three different fiber lengths, using three different sieve sizes during the mechanical recycling process (0.2, 0.5, and 2.0 mm), were used in five different amounts (ranging from 1 to 25 phr). The samples were first characterized by dynamic mechanical thermal analysis (DMTA), followed byelectrical conductivity and Joule heating tests. More specifically, the mechanical properties of the composites increased when increasing fiber content. Furthermore, the composites obtained with the longest fibers showed the highest electrical conductivity, reaching a maximum of 11 S/m, due to their higher aspect ratio. In this context, the temperature reached by Joule effect was directly related to the electrical conductivity, and was able to reach an average and maximum temperatures of 80 °C and 120 °C, respectively, just by applying 6 V.
Folic-Acid-Conjugated Poly (Lactic-Co-Glycolic Acid) Nanoparticles Loaded with Gallic Acid Induce Glioblastoma Cell Death by Reactive-Oxygen-Species-Induced Stress
Authors: Ramalho Maria João , Alves Bruna , Andrade Stéphanie , Lima Jorge , Loureiro Joana Angélica , Pereira Maria Carmo
Published: July 2024

Glioblastoma (GBM) conventional treatment is not curative, and it is associated with severe toxicity. Thus, natural compounds with anti-cancer properties and lower systemic toxicity, such as gallic acid (GA), have been explored as alternatives. However, GA’s therapeutic effects are limited due to its rapid metabolism, low bioavailability, and low permeability across the blood–brain barrier (BBB). This work aimed to develop poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) modified with folic acid (FA), as its receptor is overexpressed in BBB and GBM cells, for GA delivery to enhance its therapeutic efficacy. The preparation of NPs was optimized by a central composite design (CCD). The obtained NPs showed physicochemical features suitable for drug internalization in BBB and tumor cells (sizes below 200 nm, monodispersity, and negative surface charge) and the ability to maintain a slow and sustained release for 40 days. In vitro studies using a human GBM cell line (U215) revealed the NPs’ ability to accumulate in the target cells, further promoting GA antiproliferative activity by inducing the production of intracellular reactive oxygen species (ROS). Furthermore, GA encapsulation in the developed nanosystems conferred higher protection to healthy cells.
Incorporation of Nanostructural Hydroxyapatite and Curcumin Extract from Curcuma longa L. Rhizome into Polylactide to Obtain Green Composite
Authors: Osial Magdalena , Wilczewski Sławomir , Godlewska Urszula , Skórczewska Katarzyna , Hilus Jakub , Szulc Joanna , Roszkiewicz Agata , Dąbrowska Agnieszka , Moazzami Goudarzi Zahra , Lewandowski Krzysztof , Wypych Tomasz P. , Nguyen Phuong Thu , Sumara Grzegorz , Giersig Michael
Published: July 2024

This study showed that a polylactide (PLA)-based composite filled with nanostructured hydroxyapatite (HAp) and a natural extract from the rhizome of Curcuma longa L. could provide an alternative to commonly used fossil-based plasticsfor food packaging. The incorporation of HAp into the PLA matrix had a positive effect on improving selected properties of the composites; the beneficial effect could be enhanced by introducing a green modifier in the form of an extract. Prior to the fabrication of the composite, the filler was characterized in terms of morphology and composition, and the composite was then fully characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), Raman and Fourier transform infrared spectroscopy (FT-IR), and the mechanical, thermal, thermomechanical, and optical properties were investigated. The proposed material exhibits antioxidant properties against DPPH radicals and antibacterial performance against Escherichia coli (E. coli). The results showed that the nanocomposite has the highest antioxidant and antibacterial properties for 10 wt% HAp with an average diameter of rod-shaped structures below 100 nm. In addition, the introduction of turmeric extract had a positive effect on the tensile strength of the nanocomposites containing 1 and 5% HAp. As the resulting material adsorbs light in a specific wavelength range, it can be used in the medical sector, food-packaging, or coatings.
Recent Advances in Synthesising and Applying Magnetic Ion-Imprinted Polymers to Detect, Pre-Concentrate, and Remove Heavy Metals in Various Matrices
Authors: Mabaso Nyeleti Bridget , Nomngongo Philiswa Nosizo , Nyaba Luthando
Published: July 2024

Magnetic ion-imprinted polymers (MIIPs) are an innovative material that combines the selectivity of ion imprinting with the ease of separation provided by magnetic properties. Recent advancements in MIIPs have shown that they have higher selectivity coefficients compared to non-imprinted materials. The synthesis of MIIPs involves creating specific recognition sites for target ions in magnetic nanomaterials. Various nanomaterials, such as graphene oxide, carbon nanotubes, and silica, have been incorporated into the IIPs to improve their analytical performance for different environmental applications, including metal extraction, monitoring, detection, and quantification. This review stresses the need to develop new monomers with a high affinity for the target analyte and to find supporting materials with groups that facilitate the effective removal of the target analyte. It also explores the influence of experimental parameters on metal determination.
Clinical Trials Gone Missing—A Potential Source for Publication Bias in Dentistry
Authors: Tomack Justin , Mascardo Kathleen , Chen Chia-Yu , Chen Tony , Li Xihao , Kim David M. , Machtei Eli E.
Published: July 2024

Background: The aim of this study was to examine publication bias associated with a failure to report research results of studies that were initially posted on the ClinicalTrials.gov registry and to examine factors associated with this phenomenon. Methods: A search was conducted in the ClinicalTrials.gov registry using six dental-related topics. Corresponding publications for trials completed between 2016 and 2019 were then searched using PUBMED, EMBASE and Google Scholar. For studies lacking matching publications, we emailed the primary investigator and received some additional data. For included studies, we recorded additional variables: industry funding, site setting (academic, private research facilities or private practice), design (single or multi-center), geographical location and commencement date vis a vis registration and publication dates. Results: A total of 744 entries were found, of which 7 duplicates were removed; an additional 67 entries just recently completed were removed. An additional 7 studies were in different fields and thus removed. Thus, 663 trials were included; of these, only 337 studies (50.8%) were published. The mean registration to publication interval was 29.01 ± 25.7 months, ranging from +142 to −34 months (post factum registration). Less than 1/3 of the studies were posted prior to commencement, of which much smaller proportions were published (37.3%). Studies that were posted after commencement (n = 462) had a much higher publication rate (56.7%), p < 0.001. Multi-center studies and those conducted in commercial facilities had much higher, though non-significant, publication rates (56.5% and 58.3%, respectively). Conclusions: With only half of the studies registered being published, a major source for publication bias is imminent.
Optimization of Collagen Scaffold with Cultured Autologous Chondrocytes for Osteochondritis Dissecans of the Knee: A Case Report
Authors: Valladares Nicolas , Jacobo-Jimenez Gibran J. , Lara-Palazuelos Nathaniel , Zavala-Cerna Maria G.
Published: July 2024

The treatment of osteochondritis dissecans of the knee has always been a challenge for orthopedic surgeons. We present a case report of a 38-year-old male with severe right knee pain after suffering from an indirect trauma and axial rotation of the knee, limiting knee functionality and impeding his ability to walk, with a diagnosis of osteochondritis dissecans in the trochlea of the knee, who underwent arthroscopic treatment with matrix-induced autologous chondrocyte implantation (MACI). After the surgery, a physical therapy protocol for MACI was implemented, and magnetic resonance images with cartilage mapping were used to evaluate the recovery of the lesion. A total recovery was observed and evaluated with the modified Cincinnati knee rating system (mCKRS). A discussion is provided with evidence and general recommendations for the use of MACI in the treatment of adult OCD of the knee as a possible alternative to conventional treatments. Our case shows a rapid improvement in pain and functionality 2 months after surgery that progressed to full recovery within 6 months.
Water Resources Monitoring in a Remote Region: Earth Observation-Based Study of Endorheic Lakes
Authors: Garnier Jeremie , Cicerelli Rejane E. , de Almeida Tati , Belo Julia C. R. , Curto Julia , Ramos Ana Paula M. , Valadão Larissa V. , Satge Frederic , Bonnet Marie-Paule
Published: July 2024

In the western Andes, climate changes have led to drastic ecological changes during the Pleistocene and Holocene. Given the debate surrounding precipitation pattern changes and the lack of research on lakes in the Chilean Altiplano, this study aims to assess recent climate changes. The paper presents an innovative methodology based on Google Earth Engine (GEE), utilizing fluctuations in water levels in endorheic lakes as natural precipitation indicators. Three lakes (Chungará, Miscanti, and Miniques) in isolated drainage systems were studied, where changes in water levels directly reflect rainfall variations. Data from Landsat-OLI 8, Landsat-ETM+, Landsat-TM 5, and MODIS spanning 31 years were processed using the Google Earth Engine platform. The shapes of the water bodies were extracted using hue saturation value (HSV) composites. The surface areas of the lakes were compared with precipitation data from national meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) using linear regression analyses. Both lake area and rainfall volume showed a decrease over time, with varying trends depending on environmental conditions. However, the analysis consistently indicates a reduction in the area and volume of Chilean lakes corresponding to observed rainfall patterns over the past three decades.
Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data
Authors: Schröder Tanja , Schmidt Susanne I. , Kutzner Rebecca D. , Bernert Hendrik , Stelzer Kerstin , Friese Kurt , Rinke Karsten
Published: July 2024

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.
An Investigation into the Effects of Correlated Color Temperature and Illuminance of Urban Motor Vehicle Road Lighting on Driver Alertness
Authors: Chen Quan , Pan Zelei , Wu Jinchun , Xue Chengqi
Published: July 2024

Current international optical science research focuses on the non-visual effects of lighting on human cognition, mood, and biological rhythms to enhance overall well-being. Nocturnal roadway lighting, in particular, has a substantial impact on drivers’ physiological and psychological states, influencing behavior and safety. This study investigates the non-visual effects of correlated color temperature (CCT: 3000K vs. 4000K vs. 5000K) and illuminance levels (20 lx vs. 30 lx) of urban motor vehicle road lighting on driver alertness during various driving tasks. Conducted between 19:00 and 20:30, the experiments utilized a human-vehicle-light simulation platform. EEG (β waves), reaction time, and subjective evaluations using the Karolinska Sleepiness Scale (KSS) were measured. The results indicated that the interaction between CCT and illuminance, as well as between CCT and task type, significantly influenced driver alertness. However, no significant effect of CCT and illuminance on reaction time was observed. The findings suggest that higher illuminance (30 lx) combined with medium CCT (4000K) effectively reduces reaction time. This investigation enriches related research, provides valuable reference for future studies, and enhances understanding of the mechanisms of lighting’s influence on driver alertness. Moreover, the findings have significant implications for optimizing the design of urban road lighting.
Chipless RFID Sensor for Measuring Time-Varying Electric Fields Using a Contactless Air-Filled Substrate-Integrated Waveguide Resonator
Authors: Amirkabiri Amirmasoud , Idoko Dawn , Kordi Behzad , Bridges Greg E.
Published: July 2024

This paper presents a wireless chipless resonator-based sensor for measuring the absolute value of an external time-varying electric field. The sensor is developed using contactless air-filled substrate-integrated waveguide (CLAF-SIW) technology. The sensor employs a low-impedance electromagnetic band gap structure to confine the electric field within the sensor’s air cavity. The air cavity is loaded with varactor diodes whose reverse bias voltage is modified by the to-be-measured external electric field. Variation in the external electric field results in a variation of the sensor’s resonant frequency. The CLAF-SIW sensor offers a high unloaded quality factor, which is required for a long-distance ringback-based interrogation system. A prototype of the proposed sensor is fabricated and tested. It can measure a time-varying external electric field up to 6.9 kV/m, has a sensitivity of 1.86 (kHz)/(V/m), and can be interrogated from a distance of 80 cm. The feasible maximum bandwidth of the external electric field is 25 kHz. The proposed sensor offers a compact planar multilayer structure that can easily be incorporated with a planar antenna and its size can be reduced by selecting a higher operating frequency without an increase in dielectric loss.
Architectures for Industrial AIoT Applications
Authors: Villar Eneko , Martín Toral Imanol , Calvo Isidro , Barambones Oscar , Fernández-Bustamante Pablo
Published: July 2024

Industry 4.0 introduced new concepts, technologies, and paradigms, such as Cyber Physical Systems (CPSs), Industrial Internet of Things (IIoT) and, more recently, Artificial Intelligence of Things (AIoT). These paradigms ease the creation of complex systems by integrating heterogeneous devices. As a result, the structure of the production systems is changing completely. In this scenario, the adoption of reference architectures based on standards may guide designers and developers to create complex AIoT applications. This article surveys the main reference architectures available for industrial AIoT applications, analyzing their key characteristics, objectives, and benefits; it also presents some use cases that may help designers create new applications. The main goal of this review is to help engineers identify the alternative that best suits every application. The authors conclude that existing reference architectures are a necessary tool for standardizing AIoT applications, since they may guide developers in the process of developing new applications. However, the use of reference architectures in real AIoT industrial applications is still incipient, so more development effort is needed in order for it to be widely adopted.
Ultrasound Image Temperature Monitoring Based on a Temporal-Informed Neural Network
Authors: Han Yuxiang , Du Yongxing , He Limin , Meng Xianwei , Li Minchao , Cao Fujun
Published: July 2024

Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25–65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues.
Modified Gold Screen-Printed Electrodes for the Determination of Heavy Metals
Authors: Celesti Consuelo , Giofrè Salvatore Vincenzo , Espro Claudia , Legnani Laura , Neri Giovanni , Iannazzo Daniela
Published: July 2024

Screen-printed electrodes (SPEs) are reliable, portable, affordable, and versatile electrochemical platforms for the real-time analytical monitoring of emerging analytes in the environmental, clinical, and agricultural fields. The aim of this study was to evaluate the electrochemical behavior of gold screen-printed electrodes (SPGEs) modified with molecules containing amino (Tr-N) or α-aminophosphonate (Tr-P) groups for the selective and sensitive detection of the toxic metal ions Pb2+ and Hg2+ in aqueous samples. After optimizing the analytical parameters (conditioning potential and time, deposition potential and time, pH and concentration of the supporting electrolyte), anodic square wave stripping voltammetry (SWASV) was used to evaluate and compare the electrochemical performance of bare or modified electrodes for the detection of Hg2+ and Pb2+, either alone or in their mixtures in the concentration range between 1 nM and 10 nM. A significative improvement in the detection ability of Pb2+ ions was recorded for the amino-functionalized gold sensor SPGE-N, while the presence of a phosphonate moiety in SPGE-P led to greater sensitivity towards Hg2+ ions. The developed sensors allow the detection of Pb2+ and Hg2+ with a limit of detection (LOD) of 0.41 nM and 35 pM, respectively, below the legal limits for these heavy metal ions in drinking water or food, while the sensitivity was 5.84 µA nM−1cm−2 and 10 µA nM−1cm−2, respectively, for Pb2+ and Hg2+. The reported results are promising for the development of advanced devices for the in situ and cost-effective monitoring of heavy metals, even in trace amounts, in water resources.
Weighted Attribute-Based Proxy Re-Encryption Scheme with Distributed Multi-Authority Attributes
Authors: Yi Wenlong , Wang Chuang , Kuzmin Sergey , Gerasimov Igor , Cheng Xiangping
Published: July 2024

Existing attribute-based proxy re-encryption schemes suffer from issues like complex access policies, large ciphertext storage space consumption, and an excessive authority of the authorization center, leading to weak security and controllability of data sharing in cloud storage. This study proposes a Weighted Attribute Authority Multi-Authority Proxy Re-Encryption (WAMA-PRE) scheme that introduces attribute weights to elevate the expression of access policies from binary to multi-valued, simplifying policies and reducing ciphertext storage space. Simultaneously, the multiple attribute authorities and the authorization center construct a joint key, reducing reliance on a single authorization center. The proposed distributed attribute authority network enhances the anti-attack capability of cloud storage. Experimental results show that introducing attribute weights can reduce ciphertext storage space by 50%, proxy re-encryption saves 63% time compared to repeated encryption, and the joint key construction time is only 1% of the benchmark scheme. Security analysis proves that WAMA-PRE achieves CPA security under the decisional q-parallel BDHE assumption in the random oracle model. This study provides an effective solution for secure data sharing in cloud storage.
Design of Wideband Flextensional Hydrophone
Authors: Kim Gihyeon , Kim Donghyun , Roh Yongrae
Published: July 2024

Flextensional transducers have been widely used as low-frequency projectors, and these characteristics can be used to develop hydrophones with wider receiver bandwidth and higher sensitivity than conventional products in low-frequency ranges. In this work, we designed flextensional hydrophones of all classes, and compared their acoustic receiver performance to select the most suitable class for a low-frequency broadband hydrophone. For this purpose, basic models of the hydrophones were constructed for all classes and the effects of various structural parameters on the acoustic receiver characteristics of the hydrophones were analyzed. Based on the results, the structure of the flextensional hydrophone of each class was designed to have the maximum receiver bandwidth by an optimization technique while maintaining the receiver voltage sensitivity over a certain level. A comparison of the designed performance led to the selection of the class IV flextensional hydrophone as the most promising one with the widest receiver fractional bandwidth and highest sensitivity.
A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices
Authors: Sun Qiyan , Li Pengbo , He Chentao , Song Qiming , Chen Jierui , Kong Xiangzeng , Luo Zhicong
Published: July 2024

In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.
Performance Correction and Parameters Identification Considering Non-Uniform Electric Field in Cantilevered Piezoelectric Energy Harvesters
Authors: Wang Xianfeng , Liu Hui , Zheng Huadong , Liu Guoxiong , Xu Dan
Published: July 2024

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer’s to the piezoelectric layer’s thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials’ electromechanical coupling coefficient squared, k312. It is also found that the upper-limit relative corrections are only related to k312, i.e., the higher k312 is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field.
Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy
Authors: Jafari Abdolabbas , Seth Kritarth , Werner Armin , Shi Shengjing , Hofmann Rainer , Hoyos-Villegas Valerio
Published: July 2024

Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model’s specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Surface Acoustic Wave Sensors for Wireless Temperature Measurements above 1200 Degree Celsius
Authors: Zhang Hong , Mu Danyu , Zhang Zichao , Zhang Jikai , Sun Jiabao , Jin Hao
Published: July 2024

High-temperature wireless sensing is crucial for monitoring combustion chambers and turbine stators in aeroengines, where surface temperatures can reach up to 1200 °C. Surface Acoustic Wave (SAW) temperature sensors are an excellent choice for these measurements. However, at extreme temperatures, they face issues such as agglomeration and recrystallization of electrodes, leading to loss of conductivity and reduced quality factor, hindering effective wireless signal transmission. This study develops an LGS SAW sensor with a Pt-10%Rh/Zr/Pt-10%Rh/Zr/Pt-10%Rh/Zr multilayer composite electrode structure to address these challenges. We demonstrate that the sensor can achieve wireless temperature measurements from room temperature to 1200 °C with an accuracy of 1.59%. The composite electrodes excite a quasi-shear wave on the LGS substrate, maintaining a Q-factor of 3526 at room temperature, providing an initial assurance for the strength of the wireless interrogation echo signal. The sensor operates stably for 2.18 h at 1200 °C before adhesion loss between the composite electrode and the substrate causes a sudden increase in resonant frequency. This study highlights the durability of the proposed electrode materials and structure at extreme temperatures and suggests future research to improve adhesion and extend the sensor’s lifespan, thereby enhancing the reliability and effectiveness of high-temperature wireless sensing in aerospace applications.
An Experimental Study of the Acoustic Signal Characteristics of Locked-Segment Damage Evolution in a Landslide Model
Authors: Zhu Xing , Chen Hui , Wu Zhanglei , Yang Shumei , Li Xiaopeng , Li Tiantao
Published: July 2024

Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.
Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment
Authors: Burger Gregor , Guna Jože
Published: July 2024

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers’ safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app’s efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.
Forecasting of the Unemployment Rate in Turkey: Comparison of the Machine Learning Models
Authors: Güler Mehmet , Kabakçı Ayşıl , Koç Ömer , Eraslan Ersin , Derin K. Hakan , Güler Mustafa , Ünlü Ramazan , Türkan Yusuf Sait , Namlı Ersin
Published: July 2024

Unemployment is the most important problem that countries need to solve in their economic development plans. The uncontrolled growth and unpredictability of unemployment are some of the biggest obstacles to economic development. Considering the benefits of technology to human life, the use of artificial intelligence is extremely important for a stable economic policy. This study aims to use machine learning methods to forecast unemployment rates in Turkey on a monthly basis. For this purpose, two different models are created. In the first model, monthly unemployment data obtained from TURKSTAT for the period between 2005 and 2023 are trained with Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms. The second model, which includes additional economic parameters such as inflation, exchange rate, and labor force data, is modeled with the XGBoost algorithm in addition to ANN and SVM models. The forecasting performance of both models is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings of the study show how successful artificial intelligence methods are in forecasting economic developments and that these methods can be used in macroeconomic studies. They also highlight the effects of economic parameters such as exchange rates, inflation, and labor force on unemployment and reveal the potential of these methods to support economic decisions. As a result, this study shows that modeling and forecasting different parameter values during periods of economic uncertainty are possible with artificial intelligence technology.
Cost-Related Drivers and Barriers of Passivhaus: A Systematic Literature Review
Authors: Panchalingam Kajavathani , Rasheed Eziaku Onyeizu , Rotimi James Olabode Bamidele
Published: July 2024

Passivhaus (PH) has gained global recognition for its energy-efficient features despite a 5% to 10% higher construction cost than traditional houses, especially within European countries. However, its adoption and popularity have not met the same fate in other countries like New Zealand. The higher upfront cost has been critical to the slow adoption of the PH movement in New Zealand. This study aimed to demystify the mist around the cost of PHs with a focus on the effects of drivers and barriers on their life cycle costs (LCCs). As such, a systematic literature review was conducted to provide a comprehensive understanding of the cost implications associated with PH. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) review method, we examined 71 past studies on PHs from 2005 to 2023. We found that the drivers of PHs include reduced heating demand, increased thermal comfort, and indoor air quality (IAQ). Research showed that the rising market for PHs is fueled by climate change, environmental awareness, innovative materials and technologies, individual commitment, improved regulations, pilot studies, research efforts, and governmental funding and initiatives. However, PHs face significant challenges such as increased complexity, advanced technology, higher initial investments compared to conventional and low-energy houses, national requirements, overheating, difficulties in affording the technologies, and a lack of options in the market. Despite the wealth of research on the economic aspects of PH, there is a lack of in-depth studies exploring the LCC of PHs focusing on cost commitments and benefits. Such studies are essential for assessing and optimising the cost-effectiveness of PH, considering different climates and regions, and comparing them with other low energy standards. The findings of our review provide a crucial focus for PH stakeholders in assessing the long-term financial viability of PH projects, thereby improving decision-making and facilitating effective planning for sustainable and cost-effective housing.
Analysis of the Management of Protected Areas in Galapagos: 60 Years after Its Declaration as a National Park
Authors: Maestro María , Pérez-Cayeiro María Luisa , Reyes Harry , Chica-Ruiz Juan Adolfo
Published: July 2024

The Galapagos Islands are one of the most emblematic protected areas on the planet and also one of the most studied. Their main economic activity is tourism, which has grown rapidly in recent years. The increase in tourists is associated with the increase in population and the introduction of invasive species, which puts conservation at risk. This makes adequate management even more necessary and relevant on an international scale since the economy of Galapagos depends on the good state of conservation of its ecosystems and species. Numerous studies have shown that social factors, rather than physical–natural ones, determine the success or failure of a marine protected area (MPA), so they must be urgently incorporated into all phases of MPA management. In this study, we analyzed the management of the protected areas of Galapagos, including the National Park and the Marine Reserve, with the purpose of highlighting their weaknesses and strengths. The methodology used presents five prescribed scenarios in which priority is given to one or other factors, each of which has an impact on the biophysical and socio-economic systems that are influenced by the MPA. These scenarios were called proactive, interactive, centralized, learning, and formal management. Data were obtained through a comprehensive literature review and primary data collection methods, including in-depth interviews with key stakeholders and DPNG managers, as well as direct observations. Interviewees covered the main relevant sectors in the archipelago (fishing, tourism, and conservation). The results show that the archipelago’s protected areas follow a proactive management model with a continuous feedback loop. This feedback is a key element in any adaptive management process, which also allows practitioners to anticipate future problems. Both public participation and strategic planning are essential.
iKern: Advanced Intrusion Detection and Prevention at the Kernel Level Using eBPF
Authors: Hadi Hassan Jalil , Adnan Mubashir , Cao Yue , Hussain Faisal Bashir , Ahmad Naveed , Alshara Mohammed Ali , Javed Yasir
Published: July 2024

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods.
Predictors of Serotonin Syndrome in Acute Poisoning with 5-Hydroxytryptamine Modulators
Authors: Sharif Asmaa F. , Almulhim Mubarak Nasir M. , Almosabeh Hadi Mohamed A. , Alshammasy Mohammed Essam A. , Aljeshi Ali Mohammed A. , Mufti Taher Mohammed A. , AlNasser Shahd , Al-Mulhim Khalid A. , AlMubarak Yousef A.
Published: July 2024

5-Hydroxytryptamine (5-HT) modulators are commonly prescribed medications with potentially life-threatening outcomes, particularly serotonin syndrome (SS). Early prediction of SS is critical not only to avoid lethal drug combinations but also to initiate appropriate treatment. The present work aimed to recognize the significant predictors of SS through a retrospective cross-sectional study that was conducted among patients exposed to an overdose of 5-HT modulators and admitted to a poison control center where 112 patients were enrolled. Of them, 21 patients were diagnosed with SS, and 66.7% of patients with SS were exposed to long-term co-ingestion. There was a noticeable surge in SS between April and May, and 52.4% of patients who suffered from SS were admitted after suicidal exposure (p < 0.05). Patients with SS showed severe presentation indicated by high-grade poison severity scores (PSS) and low Glasgow coma scales (GCS). PSS was a significant predictor of SS with an area under the curve of 0.879. PCO2, pulse, GCS, HCO3, and erythrocytic count were other significant predictors of SS. Combinations of serotonergic agents increase the likelihood of developing SS. Clinicians should be vigilant when prescribing a combination of serotonergic therapy, particularly for patients on illicit sympathomimetic and over-the-counter medications like dextromethorphan.
Bibliometric Insights into Balancing Efficiency and Security in Urban Supply Chains
Authors: Jinor Emmanuel , Bridgelall Raj
Published: July 2024

This study examines the balance between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies, such as blockchain, AI, and IoT, to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.
Detection of Known and Novel Virus Sequences in the Black Solider Fly and Expression of Host Antiviral Pathways
Authors: Walt Hunter K. , Jordan Heather R. , Meyer Florencia , Hoffmann Federico G.
Published: July 2024

The mass rearing of animals in close quarters can be highly conducive to microbe transmission, including pathogens. This has been shown multiple times in the case of important industrial insects such as crickets, silkworms, and honeybees. One industrial insect of increasing importance is the black soldier fly (Diptera: Hermetia illucens), as it can convert organic waste into high-quality protein and fatty acids. Along with this, they take up far less space than traditional protein sources, as millions of black soldier flies can be reared in a relatively small facility. Because of this, there is a growing interest in the pathogens that could impact black soldier fly-rearing efforts. So far, only three black soldier fly-associated viruses have been identified. We used metatranscriptomic sequencing to survey black soldier fly guts, frass, and diet for viruses. We detected sequences from two novel viruses. One, which we name Hermetia illucens sigma-like virus 1, is phylogenetically related to viruses of the genus Sigmavirus, which have been highly studied in Drosophila. The other novel virus, which we name Hermetia illucens inse-like virus 1, is the second double-stranded RNA virus of the order Ghabrivirales described in the black soldier fly, and groups within a new family of insect viruses called the Inseviridae. We also detected two black soldier fly-associated viruses previously identified by our group: BSF nairo-like virus 1 and BSF uncharacterized bunyavirus-like 1. Consistent with our previous study, these two viruses are found primarily in frass samples and occur together more often than expected at random. When analyzing host transcription, we found significant differences in gene expression for eight candidate antiviral genes in the black soldier fly when comparing samples with and without viral sequences. Our results suggest that black soldier fly–virus interactions are ongoing, and they could be of interest to black soldier fly producers.