bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Nanocoated Filaments for 3D Fused Deposition Modeling of Antibacterial and Antioxidant Materials
Authors: Abdullah Turdimuhammad , Qurban Rayyan O. , Abdel-Wahab Mohamed Sh. , Salah Numan A. , Melaibari Ammar AbdulGhani , Zamzami Mazin A. , Memić Adnan
Published: June 2022

Three-dimensional (3D) printing is one of the most futuristic manufacturing technologies, allowing on-demand manufacturing of products with highly complex geometries and tunable material properties. Among the different 3D-printing technologies, fused deposition modeling (FDM) is the most popular one due to its affordability, adaptability, and pertinency in many areas, including the biomedical field. Yet, only limited amounts of materials are commercially available for FDM, which hampers their application potential. Polybutylene succinate (PBS) is one of the biocompatible and biodegradable thermoplastics that could be subjected to FDM printing for healthcare applications. However, microbial contamination and the formation of biofilms is a critical issue during direct usage of thermoplastics, including PBS. Herein, we developed a composite filament containing polybutylene succinate (PBS) and lignin for FDM printing. Compared to pure PBS, the PBS/lignin composite with 2.5~3.5% lignin showed better printability and antioxidant and antimicrobial properties. We further coated silver/zinc oxide on the printed graft to enhance their antimicrobial performance and obtain the strain-specific antimicrobial activity. We expect that the developed approach can be used in biomedical applications such as patient-specific orthoses.
Mechanical, Dynamic-Mechanical, Thermal and Decomposition Behavior of 3D-Printed PLA Reinforced with CaCO3 Fillers from Natural Resources
Authors: Pavon Cristina , Aldas Miguel , Samper María Dolores , Motoc Dana Luca , Ferrandiz Santiago , López-Martínez Juan
Published: June 2022

This study evaluates the effect of CaCO3 fillers extracted from waste eggshells on 3D-printed PLA performance. Samples of neat PLA and PLA reinforced with CaCO3 fillers embedded with different wt.% were prepared using an FDM (fused deposition modeling) technology. The samples were examined using mechanical, dynamic mechanical, thermal, and thermal decomposition analyses. The results revealed increasing elastic moduli, tensile strength, and flexure as the filler content increased. The rheological results from the MFR tests showed that the filler content did not influence the PLA-based samples’ processability. Further, the thermal degradation of neat and various CaCO3-wt.%-reinforced PLA specimens revealed relatively small discrepancies in their exposure to the temperature increase, mainly concerning the eggshell organic components and volatile components, from their processability up to 300 °C. By contrast, the increased filler content positively shifted the peaks along the temperature scale at the maximum degradation rate. Additionally, the weight content of the natural reinforcement strongly influenced the surface wettability and appearance of the samples. Further, the SEM analysis featured both the presence of interlayer disturbances and the interfacial compatibility the PLA with the selected fillers.
Study on Different Shear Performance of Moso Bamboo in Four Test Methods
Authors: Huang Aiyue , Su Qin , Zong Yurong , Chen Xiaohan , Liu Huanrong
Published: June 2022

Bamboo is recognized as a potential and sustainable green material. The longitudinal-splitting and shear strengths of bamboo are weak but critical to its utilizations. To discuss the different shear performances of bamboo, the shear strength and behaviors of bamboo culm were investigated by four test methods: the tensile-shear, step-shear, cross-shear, and short-beam-shear methods. Then, the different shear performance and mechanisms were discussed. Results indicated that the shear strength was significantly different in the four test methods and was highest in the step-shear-test method but lowest in the tensile-shear-test method. Moreover, the typical load-displacement curves were different across the shear methods but were similar to the curves of the respective loading modes. The axially aligned fiber bundles played an important role in all the shear performances. In the tensile-shear method, specimens fractured at the interface of the bamboo-fiber bundles. However, compress-shear behaviors were a combination of compression and shear. Then, the cross-shear method, in compress-shear, was lower than that of the step-shear method because of oval-shaped bamboo culm sections of different thickness. In the short-beam shear method, the behaviors and shearing characteristics were like bending with the fiber bundle pulled out.
Construction of Tissue-Engineered Bladder Scaffolds with Composite Biomaterials
Authors: Li Wenjiao , Qi Na , Guo Tingting , Wang Chao , Huang Ziwei , Du Zhouyuan , Xu Dingwen , Zhao Yin , Tian Hong
Published: June 2022

Various congenital and acquired urinary system abnormalities can cause structural damage to patients’ bladders. This study aimed to construct and evaluate a novel surgical patch encapsulated with adipose-derived stem cells (ADSCs) for bladder tissue regeneration. The surgical patch consists of multiple biomaterials, including bladder acellular matrix (BAM), collagen type I from rat tail, microparticle emulsion cross-linking polylactic-co-glycolic acid (PLGA)-chitosan (CS) with PLGA-sodium alginate (SA), and growth factors. ADSCs were seeded on the surgical patch. Approximately 50% of the bladder was excised and replaced with a surgical patch. Histological, immunohistochemical and urodynamic analyses were performed at the 2nd, 4th, and 8th weeks after surgery, respectively. The PLGA-CS, PLGA-SA or surgical patch showed no cytotoxicity to ADSCs. PLGA-CS cross-linked with PLGA-SA at a ratio of 5:5 exhibited a loose microporous structure and was chosen as the candidate for ADSC seeding. We conducted bladder repair surgery in rats using the patch, successfully presenting urothelium layers, muscle bundles, and vessel regeneration and replacing 50% of the rat’s natural bladder in vivo. Experiments through qualitative and quantitative evaluation demonstrate the application potential of the composite biomaterials in promoting the repair and reconstruction of bladder tissue.
Functional Porous Ionic Polymers as Efficient Heterogeneous Catalysts for the Chemical Fixation of CO2 under Mild Conditions
Authors: Dai Zhifeng , Long Yang , Liu Jianliang , Bao Yuanfei , Zheng Liping , Ma Jiacong , Liu Jiayi , Zhang Fei , Xiong Yubing , Lu Ji-Qing
Published: June 2022

The development of efficient and metal-free heterogeneous catalysts for the chemical fixation of CO2 into value-added products is still a challenge. Herein, we reported two kinds of polar group (−COOH, −OH)-functionalized porous ionic polymers (PIPs) that were constructed from the corresponding phosphonium salt monomers (v-PBC and v-PBH) using a solvothermal radical polymerization method. The resulting PIPs (POP-PBC and POP-PBH) can be used as efficient bifunctional heterogeneous catalysts in the cycloaddition reaction of CO2 with epoxides under relatively low temperature, ambient pressure, and metal-free conditions without any additives. It was found that the catalytic activities of the POP-PBC and POP-PBH were comparable with the homogeneous catalysts of Me-PBC and PBH and were higher than that of the POP-PPh3-COOH that was synthesized through a post-modification method, indicating the importance of the high concentration catalytic active sites in the heterogeneous catalysts. Reaction under low CO2 concentration conditions showed that the activity of the POP-PBC (with a conversion of 53.8% and a selectivity of 99.0%) was higher than that of the POP-PBH (with a conversion of 32.3% and a selectivity of 99.0%), verifying the promoting effect of the polar group (−COOH group) in the porous framework. The POP-PBC can also be recycled at least five times without a significant loss of catalytic activity, indicating the high stability and robustness of the PIPs-based heterogeneous catalysts.
Influence of Stress Level and Fibre Volume Fraction on Fatigue Performance of Glass Fibre-Reinforced Polyester Composites
Authors: Zaghloul Mahmoud Yousry , Zaghloul Moustafa Mahmoud Yousry , Zaghloul Mai Mahmoud Yousry
Published: June 2022

Fibre-reinforced polymeric composite materials are becoming substantial and convenient materials in the repair and replacement of traditional metallic materials due to their high stiffness. The composites undergo different types of fatigue loads during their service life. The drive to enhance the design methodologies and predictive models of fibre-reinforced polymeric composite materials subjected to fatigue stresses is reliant on more precise and reliable techniques for assessing their fatigue life. The influences of fibre volume fraction and stress level on the fatigue performance of glass fibre-reinforced polyester (GFRP) composite materials have been studied in the tension–tension fatigue scenario. The fibre volume fractions for this investigation were set to: 20%, 35%, and 50%. The tensile testing of specimens was performed using a universal testing machine and the Young’s modulus was validated with four different prediction models. In order to identify the modes of failure as well as the fatigue life of composites, polyester-based GFRP specimens were evaluated at five stress levels which were 75%, 65%, 50%, 40%, and 25% of the maximum tensile stress until either a fracture occurred or five million fatigue cycles was reached. The experimental results showed that glass fibre-reinforced polyester samples had a pure tension failure at high applied stress levels, while at low stress levels the failure mode was governed by stress levels. Finally, the experimental results of GFRP composite samples with different volume fractions were utilized for model validation and comparison, which showed that the proposed framework yields acceptable correlations of predicted fatigue lives in tension–tension fatigue regimes with experimental ones.
Quantitative Structure-Property Relationship (QSPR) of Plant Phenolic Compounds in Rapeseed Oil and Comparison of Antioxidant Measurement Methods
Authors: Platzer Melanie , Kiese Sandra , Asam Tobias , Schneider Franziska , Tybussek Thorsten , Herfellner Thomas , Schweiggert-Weisz Ute , Eisner Peter
Published: June 2022

Natural antioxidants are known for their ability to scavenge free radicals and protect oils from oxidation. Our aim was to study the structural properties such as the number of hydroxyl groups and Bors criteria of phenolic substances leading to high antioxidant activity in oil in order to analyze common trends and differences in widespread in vitro antioxidant assays. Therefore, 20 different phenolic substances were incorporated into rapeseed oil and were measured using pressurized differential scanning calorimetry (P-DSC) and the Rancimat method. The Bors criteria had the highest influence on the antioxidant effect in rapeseed oil, which is why myricetin (MYR), fulfilling all Bors criteria, reached the highest result of the flavonoids. In the Rancimat test and P-DSC, MYR obtained an increase in oxidation induction time (OIT) of 231.1 ± 44.6% and 96.8 ± 1.8%, respectively. Due to differences in the measurement parameters, the results of the Rancimat test and P-DSC were only partially in agreement. Furthermore, we compared the results to in vitro assays (ABTS, DPPH, FC and ORAC) in order to evaluate their applicability as alternative rapid methods. These analysis showed the highest correlation of the oil methods with the results of the DPPH assay, which is, therefore, most suitable to predict the antioxidant behavior of oil.
Lightweight Yolov4 Target Detection Algorithm Fused with ECA Mechanism
Authors: Wang Chunguang , Zhou Yulin , Li Junjie
Published: June 2022

For the task of garbage classification, to overcome the main disadvantages of the Yolov4 target detection algorithm, such as the large network model and lower detection accuracy for small objects, a lightweight Yolov4 target detection network based on the EfficientNet-B0 fusion ECA mechanism is presented. The lightweight EfficientNet was used to replace the original backbone network, which reduces the parameters of the network model and improves the detection accuracy. Moreover, a deep detachable convolution block replaced the common convolution block in the original network, which further reduced the number of parameters in the model. In the feature pyramid model PANet, a lightweight ECA attention mechanism was introduced to realize the weight analysis of the importance of different channel feature maps through cross-channel interaction, allowing the network to extract more obvious features with which to distinguish categories. Finally, a Soft-NMS algorithm was introduced in the post-processing stage of the detection frame to reduce the missed target detection rate in dense areas, which can improve the detection accuracy of the network and detection efficiency. As shown in the results, the size of the model was only 48 MB, and the mAP was 91.09%. Compared with the original Yolov4 network, the mAP was increased by 5.77% based on the 80% reduction in the model size. The recognition of small targets was also improved, which proved the effectiveness and robustness of the improved algorithm.
“Velvet Steel” Ministers for God and America: Eleanor Lansing Dulles and the Nineteenth-Century Legacy of Christianity and Nationalism
Authors: Phillips Victoria
Published: June 2022

The political impact of Dr. Eleanor Lansing Dulles has not been assessed in her capacity as a power broker who brought her theological understandings to Cold War United States policy. The deep influence of both her brothers—Allen, Director of the Central Intelligence Agency, and John Foster, Secretary of State under Dwight D. Eisenhower—on global affairs and diplomacy has been the topic of myriad studies. Works draw extensively on family biography, noting that both “nature and nurture” brought religion to US foreign policy. Including Dr. Dulles in the analysis provides nuance and complexity to definitions of Christian nationalism and underscores the legacy of both missionaries and religious thought in US foreign relations during the early Cold War. Contextualizing religiosity through a study of gender and the Dulles family legacy of female missionaries into the Cold War narrative builds upon the existing literature of the Dulles family, religion, and Cold War diplomacy to challenge concepts such as Christian internationalism, Christian nationalism, and Left–Right binaries. Diplomacy is revealed as her form of Christian missionary work in the secular sphere. Eleanor Lansing Dulles became a missionary not for a religion, but for a nation.
Hydrological Evaluation of Satellite-Based Precipitation Products in Hunan Province
Authors: Yan Yan , Wang Guihua , Nanding Nergui , Chen Weitian
Published: June 2022

The quality of satellite-based precipitation products including TMPA 3B42, IMERG-early, IMERG-final, and CMORPH-CRT, is evaluated by comparing with gauge observations in Hunan province of China between 2017 and 2019. By using the outputs of the Dominant River Routing Integrated with VIC Environment (DRIVE) model, the hydrological applications of gauge- and satellite-based precipitation products are analyzed by comparing them with streamflow observations. Furthermore, we conduct a case study considering Typhoon Bailu. It is found that IMERG-final can produce better results compared to the other three satellite-based products against gauge-based precipitation. In terms of discharge simulations, the gauge-based precipitation provides the most accurate results, followed by IMERG-final. During Typhoon Bailu, the peak of the mean gauge-based precipitation in the rainfall center (24.5°N–26°N, 111°E–114°E) occurred on 25 August 2019, whereas the daily streamflow reached its peak one day later, suggesting the lagged impact of precipitation on streamflow. From the Taylor diagram, the gauge-based precipitation is the most accurate for estimating the streamflow during Typhoon Bailu, followed by IMERG-final, IMERG-early, TMPA 3B42, and CMORPH-CRT, respectively. Overall, gauge-based precipitation has the best performance in terms of hydrological application, whereas IMERG-final performs the best among four satellite-based precipitation products.
Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements
Authors: Zhu Yun , Mallick Mahendra , Liang Shuang , Yan Junkun
Published: June 2022

The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) (2)error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) (2)error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.
RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery
Authors: Chhabra Aakash , Rüdiger Christoph , Yebra Marta , Jagdhuber Thomas , Hilton James
Published: June 2022

The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover.
Evaluating Effects of Medium-Resolution Optical Data Availability on Phenology-Based Rice Mapping in China
Authors: Liu Ruoqi , Zhang Geli , Dong Jinwei , Zhou Yan , You Nanshan , He Yingli , Xiao Xiangming
Published: June 2022

The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.
Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework
Authors: Llamas Ricardo M. , Valera Leobardo , Olaya Paula , Taufer Michela , Vargas Rodrigo
Published: June 2022

Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor  and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.
Correction: Hoffmann et al. Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks. Sensors 2021, 21, 1086
Authors: Hoffmann Raoul , Brodowski Hanna , Steinhage Axel , Grzegorzek Marcin
Published: June 2022

There was an affiliation missing in the original article [...]
A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System
Authors: Alarcón-Aldana Andrea Catherine , Callejas-Cuervo Mauro , Bastos-Filho Teodiano , Bó Antônio Padilha Lanari
Published: June 2022

This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.
SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs
Authors: Yoon Daegun , Oh Sangyoon
Published: June 2022

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a 5.62× and 3.15× speedup over the state-of-the-art graph processing frameworks Gunrock and Enterprise, respectively.
A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration
Authors: Pupa Andrea , Van Dijk Wietse , Brekelmans Christiaan , Secchi Cristian
Published: June 2022

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.
MiWEndo: Evaluation of a Microwave Colonoscopy Algorithm for Early Colorectal Cancer Detection in Ex Vivo Human Colon Models
Authors: Guardiola Marta , Dghoughi Walid , Sont Roberto , Garrido Alejandra , Marcoval Sergi , Neira Luz María , Belda Ignasi , Fernández-Esparrach Glòria
Published: June 2022

This study assesses the efficacy of detecting colorectal cancer precursors or polyps in an ex vivo human colon model with a microwave colonoscopy algorithm. Nowadays, 22% of polyps go undetected with conventional colonoscopy, and the risk of cancer after a negative colonoscopy can be up to 7.9%. We developed a microwave colonoscopy device that consists of a cylindrical ring-shaped switchable microwave antenna array that can be attached to the tip of a conventional colonoscope as an accessory. The accessory is connected to an external unit that allows successive measurements of the colon and processes the measurements with a microwave imaging algorithm. An acoustic signal is generated when a polyp is detected. Fifteen ex vivo freshly excised human colons with cancer (n = 12) or polyps (n = 3) were examined with the microwave-assisted colonoscopy system simulating a real colonoscopy exploration. After the experiment, the dielectric properties of the specimens were measured with a coaxial probe and the samples underwent a pathology analysis. The results show that all the neoplasms were detected with a sensitivity of 100% and specificity of 87.4%.
Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
Authors: Zhuo Xinran , Pan Jianxiong , Wang Huwei , Li Xiangming , Ye Neng
Published: June 2022

High frequency wireless communication aims to provide ultra high-speed transmissions for various application scenarios. The waveform design for high frequency communication is challenging due to the requirements for high spectrum efficiency, as well as good hardware compatibility. With high flexibility and low peak-to-average power ratio (PAPR), discrete Fourier transformation spreading-based orthogonal frequency division multiplexing (DFT-s-OFDM) can be a promising candidate waveform. To further enhance the spectral efficiency, we integrate faster-than-Nyquist (FTN) signaling in DFT-s-OFDM, and find that the PAPR performance can also be improved. While FTN can introduce increased inter-symbol interference (ISI), in this paper, we deploy an isotropic orthogonal transform algorithm (IOTA) filter for FTN-enhanced DFT-s-OFDM, where the compact time-frequency structure of the IOTA filter can significantly reduce the ISI. Simulation results show that the proposed waveform is capable of achieving good performance in PAPR, bit error rate (BER) and throughput, simultaneously, with 3.5 dB gain in PAPR and 50% gain in throughput.
HoloLens 1 vs. HoloLens 2: Improvements in the New Model for Orthopedic Oncological Interventions
Authors: Pose-Díez-de-la-Lastra Alicia , Moreta-Martinez Rafael , García-Sevilla Mónica , García-Mato David , Calvo-Haro José Antonio , Mediavilla-Santos Lydia , Pérez-Mañanes Rubén , von Haxthausen Felix , Pascau Javier
Published: June 2022

This work analyzed the use of Microsoft HoloLens 2 in orthopedic oncological surgeries and compares it to its predecessor (Microsoft HoloLens 1). Specifically, we developed two equivalent applications, one for each device, and evaluated the augmented reality (AR) projection accuracy in an experimental scenario using phantoms based on two patients. We achieved automatic registration between virtual and real worlds using patient-specific surgical guides on each phantom. They contained a small adaptor for a 3D-printed AR marker, the characteristic patterns of which were easily recognized using both Microsoft HoloLens devices. The newest model improved the AR projection accuracy by almost 25%, and both of them yielded an RMSE below 3 mm. After ascertaining the enhancement of the second model in this aspect, we went a step further with Microsoft HoloLens 2 and tested it during the surgical intervention of one of the patients. During this experience, we collected the surgeons’ feedback in terms of comfortability, usability, and ergonomics. Our goal was to estimate whether the improved technical features of the newest model facilitate its implementation in actual surgical scenarios. All of the results point to Microsoft HoloLens 2 being better in all the aspects affecting surgical interventions and support its use in future experiences.
Electric-Field-Based Guidance for Percutaneous Catheter Vessel Crossing
Authors: Diallo Mamadou , Eder Clemens , Brasier Daniel , Popa Sorin , Dickinson Robert
Published: June 2022

Percutaneous procedures to divert blood flow from one blood vessel to another can be performed with intravascular catheters but demand a method to align a crossing needle from one vessel to another. Fluoroscopic imaging alone is not adequate, and it is preferable to have a sensor on one catheter that detects the correct alignment of an incoming needle. This can be implemented by generating dipole electric fields from the crossing catheter which are detected by a receiving catheter in the target vessel and, thus, can calculate and display the degree of alignment, permitting the operator to rotate the crossing catheter to guarantee alignment when deploying a crossing needle. Catheters were built using this concept and evaluated in vitro. The results show that accurate alignment is achieved, and a successful crossing can be made. The concept is being further developed for further clinical evaluation.
Subjective Evaluation of Basic Emotions from Audio–Visual Data
Authors: Kadiri Sudarsana Reddy , Alku Paavo
Published: June 2022

Understanding of the perception of emotions or affective states in humans is important to develop emotion-aware systems that work in realistic scenarios. In this paper, the perception of emotions in naturalistic human interaction (audio–visual data) is studied using perceptual evaluation. For this purpose, a naturalistic audio–visual emotion database collected from TV broadcasts such as soap-operas and movies, called the IIIT-H Audio–Visual Emotion (IIIT-H AVE) database, is used. The database consists of audio-alone, video-alone, and audio–visual data in English. Using data of all three modes, perceptual tests are conducted for four basic emotions (angry, happy, neutral, and sad) based on category labeling and for two dimensions, namely arousal (active or passive) and valence (positive or negative), based on dimensional labeling. The results indicated that the participants’ perception of emotions was remarkably different between the audio-alone, video-alone, and audio–video data. This finding emphasizes the importance of emotion-specific features compared to commonly used features in the development of emotion-aware systems.
A Novel Method of Impeller Blade Monitoring Using Shaft Vibration Signal Processing
Authors: Liska Jindrich , Vasicek Vojtech , Jakl Jan
Published: June 2022

The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically.
Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5
Authors: Guo Shuyi , Li Lulu , Guo Tianyou , Cao Yunyu , Li Yinlei
Published: June 2022

COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%—an improvement of 2.1% compared to the baseline model (YOLOv5).
Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving
Authors: Yang Fan , Li Xueyuan , Liu Qi , Li Zirui , Gao Xin
Published: June 2022

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.
Enhancing the MEP Coordination Process with BIM Technology and Management Strategies
Authors: Teo Ya Hui , Yap Jun Hong , An Hui , Yu Simon Ching Man , Zhang Limao , Chang Jie , Cheong Kang Hao
Published: June 2022

Building Information Modeling (BIM) has been increasingly used in coordinating the different mechanical, electrical, and plumbing (MEP) services in the construction industries. As the construction industries are slowly adapting to BIM, the use of 2D software may become obsolete in the future as MEP services are technically more complicated to coordinate, due to respective services’ codes of practice to follow and limit ceiling height. The 3D MEP designs are easy to visualize before installing the respective MEP services on the construction site to prevent delay in the construction process. The aid of current advanced technology has brought BIM to the next level to reduce manual work through automation. Combining both innovative technology and suitable management methods not only improves the workflow in design coordination, but also decreases conflict on the construction site and lowers labor costs. Therefore, this paper tries to explore possible advance technology in BIM and management strategies that could help MEP services to increase productivity, accuracy, and efficiency with a lower cost of finalizing the design of the building. This will assist the contractors to complete construction works before the targeted schedule and meet the client’s expectations.
Design and Optimization of a Pressure Sensor Based on Serpentine-Shaped Graphene Piezoresistors for Measuring Low Pressure
Authors: Ren Xincheng , Liu Xianyun , Su Xin , Jiang Xingfang
Published: June 2022

This thesis describes a novel microelectromechanical system (MEMS) piezoresistive pressure sensor based on serpentine-shaped graphene piezoresistors paired with trapezoidal prisms under the diaphragm for measuring low pressure. The finite element method (FEM) is utilized to analyze the mechanical stress and membrane deflection to enhance the degree of stress concentration in this unique sensor. The functional relationship between mechanical performance and dimension variables is established after using the curve fitting approach to handle the stress and deflection. Additionally, the Taguchi optimization method is employed to identify the best dimensions for the proposed structure. Then, the suggested design is compared to the other three designs in terms of operating performance. It is revealed that the recommended sensor can significantly improve sensitivity while maintaining extremely low nonlinearity. In this study, three different types of serpentine-shaped graphene piezoresistors are also designed, and their sensing capability is compared to silicon. The simulation results indicate that the pressure sensor with Type 2 graphene piezoresistors has a maximum sensitivity of 24.50 mV/psi and ultra-low nonlinearity of 0.06% FSS in the pressure range of 0–3 psi.
Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning
Authors: Peng Bo , Zhang Hanbo , Yang Ni , Xie Jiming
Published: June 2022

For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.
Multi-Decadal Mapping and Climate Modelling Indicates Eastward Rubber Plantation Expansion in India
Authors: Das Pulakesh , Panda Rajendra Mohan , Dash Padmanava , Jana Anustup , Jana Avijit , Ray Debabrata , Tripathi Poonam , Kolluru Venkatesh
Published: June 2022

Automated long-term mapping and climate niche modeling are important for developing adaptation and management strategies for rubber plantations (RP). Landsat imageries at the defoliation and refoliation stages were employed for RP mapping in the Indian state of Tripura. A decision tree classifier was applied to Landsat image-derived vegetation indices (Normalized Difference Vegetation Index and Difference Vegetation Index) for mapping RPs at two-three years intervals from 1990 to 2017. A comparison with actual plantation data indicated more than 91% mapping accuracy, with most RPs able to be identified within six years of plantation, while several patches were detected after six years of plantations. The RP patches identified in 1990 and before 2000 were used for training the Maxent species distribution model, wherein bioclimatic variables for 1960–1990 and 1970–2000 were used as predictor variables, respectively. The model-estimated suitability maps were validated using the successive plantation sites. Moreover, the RPs identified before 2017 and the Shared Socioeconomic Pathways (SSP) climate projections (SSP126 and SSP245) were used to predict the habitat suitability for 2041–2060. The past climatic changes (decrease in temperature and a minor reduction in precipitation) and identified RP patches indicated an eastward expansion in the Indian state of Tripura. The projected increase in temperature and a minor reduction in the driest quarter precipitation will contribute to more energy and sufficient water availability, which may facilitate the further eastward expansion of RPs. Systematic multi-temporal stand age mapping would help to identify less productive RP patches, and accurate monitoring could help to develop improved management practices. In addition, the existing RP patches, their expansion, and the projected habitat suitability maps could benefit resource managers in adapting climate change measures and better landscape management.
Chief Executive Officers and the Value of US Airlines: The Moderating Effect of Carrier Type
Authors: Moon Joonho , Lee Won Seok , Shim Jimin
Published: June 2022

The purpose of this research is to explore the moderating effect of carrier type on the effects of upper echelon attributes. The study subject is the position of chief executive officer (CEO). The dependent variable is market-to-book value; the independent variables are CEO age, tenure, and educational background. The moderator is LCC. For the data collection, this study used COMPUSTAT and EXECUCOMP using the standard industry classification code 4512; the data are collected from Annual 10K and Linkedin. There are 15 airlines examined, and the study period is from 1999–2019; in total, 210 observations were used for the data analysis. Multiple regression analysis was the main instrument used to test the research hypotheses. The results indicate that both older CEOs in LCCs performed better in the market.