bluuebunny/crossref_metadata_2025 huggingface dataset:
State-Based Differential Privacy Verification and Enforcement for Probabilistic Automata
Authors: Teng Yuanxiu , Li Zhiwu , Yin Li , Wu Naiqi
Published: April 2023

Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework.
An Overview of the Vision-Based Human Action Recognition Field
Authors: Camarena Fernando , Gonzalez-Mendoza Miguel , Chang Leonardo , Cuevas-Ascencio Ricardo
Published: April 2023

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human–computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey concisely explores the vision-based human action recognition field and defines core concepts, including definitions and explanations of the common challenges and most used datasets. Additionally, we provide in an easy-to-understand manner the literature approaches and their evolution over time, emphasizing intuitive notions. Finally, we explore current research directions and potential future paths. The core goal of this work is to provide future works with a shared understanding of fundamental ideas and clear intuitions about current works and find new research opportunities.
Evaluation of Physics-Informed Neural Network Solution Accuracy and Efficiency for Modeling Aortic Transvalvular Blood Flow
Authors: Du Toit Jacques Francois , Laubscher Ryno
Published: April 2023

Physics-Informed Neural Networks (PINNs) are a new class of machine learning algorithms that are capable of accurately solving complex partial differential equations (PDEs) without training data. By introducing a new methodology for fluid simulation, PINNs provide the opportunity to address challenges that were previously intractable, such as PDE problems that are ill-posed. PINNs can also solve parameterized problems in a parallel manner, which results in favorable scaling of the associated computational cost. The full potential of the application of PINNs to solving fluid dynamics problems is still unknown, as the method is still in early development: many issues remain to be addressed, such as the numerical stiffness of the training dynamics, the shortage of methods for simulating turbulent flows and the uncertainty surrounding what model hyperparameters perform best. In this paper, we investigated the accuracy and efficiency of PINNs for modeling aortic transvalvular blood flow in the laminar and turbulent regimes, using various techniques from the literature to improve the simulation accuracy of PINNs. Almost no work has been published, to date, on solving turbulent flows using PINNs without training data, as this regime has proved difficult. This paper aims to address this gap in the literature, by providing an illustrative example of such an application. The simulation results are discussed, and compared to results from the Finite Volume Method (FVM). It is shown that PINNs can closely match the FVM solution for laminar flow, with normalized maximum velocity and normalized maximum pressure errors as low as 5.74% and 9.29%, respectively. The simulation of turbulent flow is shown to be a greater challenge, with normalized maximum velocity and normalized maximum pressure errors only as low as 41.8% and 113%, respectively.
Bio-Active Products from Mangrove Ecosystems
Authors: Lin Wenhan , Li Guoqiang , Xu Jing
Published: April 2023

Mangrove communities represent the coastal habitats located in intertidal zones or brackish waters of tropical and subtropical coastal areas in over 118 countries [...]
Creep of High-Strength Steel Coated with Plasma Sprayed Self-Fluxing Alloy
Authors: Moraes Denison A. , Almeida Gisele F. C. , Couto Antonio A. , Massi Marcos , Caliari Felipe R. , Lima Carlos R. C.
Published: April 2023

This article compares the creep testing behavior of AISI 4340 high-strength steel in the as-received and coated conditions. The coating material used was a NiCrBSi self-fluxing alloy. The microstructural characterization was carried out using optical and scanning electron microscopy. The creep tests were conducted at a temperature of 550 °C and with loads of 200, 250, and 300 MPa. The microstructure analysis of the deposited layer revealed some inclusions, very low porosity, and good adhesion to the substrate. The results of the creep tests indicated a decrease in the time to rupture under loads of 250 and 300 MPa for the coated steel. At a load of 200 MPa, the coated steel presented longer times to rupture and higher yield strength, demonstrating an improvement over the uncoated steel under these test condition. The fracture surface inspection showed a failure by a ductile fracture in both samples, with and without coating.
Effect of Rolling Reduction on Microstructures and Mechanical Properties of Sc-Containing Al-3.2Cu-1.5Li Alloy
Authors: Wang Jian , Kan Yun , Xu Wenting , Lu Yalin , Zhou Dongshuai , Li Shenao
Published: April 2023

The effects of various rolling reductions (64.7%, 72.1%, 81.4% and 88.2%) on the microstructures and mechanical behaviors of Sc-containing Al-3.2Cu-1.5Li-based alloys were experimentally investigated through XRD, SEM (equipped with EBSD), TEM and the tensile test. The results showed that the grains of hot-rolled and T6-treated alloys are mainly dominated by substructure features, and the addition of the trace Sc element can obviously impede the static and dynamic recrystallization due to the formation of fine and stable nano-Al3(Sc, Zr) particles. Additionally, the combined effects of fine grains, dispersion, substructure and precipitation strengthening make the alloys with a rolling reduction of 81.4% possess higher tensile properties. With an increase in the rolling reduction, the tensile strength of the alloys increases first and then decreases, while the elongation gradually increases and then reaches a plateau, which was ascribed to the variation in the grain size and substructure features.
Signatures of HIV and Major Depressive Disorder in the Plasma Microbiome
Authors: Taylor Bryn C. , Sheikh Andalibi Mohammadsobhan , Wandro Stephen , Weldon Kelly C. , Sepich-Poore Gregory D. , Carpenter Carolina S. , Fraraccio Serena , Franklin Donald , Iudicello Jennifer E. , Letendre Scott , Gianella Sara , Grant Igor , Ellis Ronald J. , Heaton Robert K. , Knight Rob , Swafford Austin D.
Published: April 2023

Inter-individual differences in the gut microbiome are linked to alterations in inflammation and blood–brain barrier permeability, which may increase the risk of depression in people with HIV (PWH). The microbiome profile of blood, which is considered by many to be typically sterile, remains largely unexplored. We aimed to characterize the blood plasma microbiome composition and assess its association with major depressive disorder (MDD) in PWH and people without HIV (PWoH). In this cross-sectional, observational cohort, we used shallow-shotgun metagenomic sequencing to characterize the plasma microbiome of 151 participants (84 PWH and 67 PWoH), all of whom underwent a comprehensive neuropsychiatric assessment. The microbial composition did not differ between PWH and PWoH or between participants with MDD and those without it. Using the songbird model, we computed the log ratio of the highest and lowest 30% of the ranked classes associated with HIV and MDD. We found that HIV infection and lifetime MDD were enriched in a set of differentially abundant inflammatory classes, such as Flavobacteria and Nitrospira. Our results suggest that the circulating plasma microbiome may increase the risk of MDD related to dysbiosis-induced inflammation in PWH. If confirmed, these findings may indicate new biological mechanisms that could be targeted to improve treatment of MDD in PWH.
Yield and Rhizosphere Soil Environment of Greenhouse Zucchini in Response to Different Planting and Breeding Waste Composts
Authors: Tie Jianzhong , Qiao Yali , Jin Ning , Gao Xueqin , Liu Yayu , Lyu Jian , Zhang Guobin , Hu Linli , Yu Jihua
Published: April 2023

Composting, planting, and breeding waste for return to the field is the most crucial soil improvement method under the resource utilization of agricultural waste. However, how the vegetable yield and rhizosphere soil environment respond to different composts is still unknown. Therefore, eight formulations were designed for compost fermentation using agricultural waste [sheep manure (SM), tail vegetable (TV), cow manure (CM), mushroom residue (MR), and corn straw (CS)] without fertilizer (CK1) and local commercial organic fertilizer (CK2) as controls to study the yield and rhizosphere soil environment of greenhouse zucchini in response to different planting and breeding waste compost. Applying planting and breeding waste compost significantly increased the soil’s organic matter and nutrient content. It inhibited soil acidification, which T4 (SM:TV:CS = 6:3:1) and T7 (SM:TV:MR:CS = 6:2:1:1) treatments affected significantly. Compared to CK2 treatment, T4 and T7 treatments showed a greater increase, with a significant increase of 14.69% and 11.01%, respectively. Therefore, T4, T7, and two control treatments were selected for high-throughput sequencing based on yield performance. Compared with the CK1 treatment, although multiple applications of chemical fertilizers led to a decrease in bacterial and fungal richness, planting and breeding waste compost maintained bacterial diversity and enhanced fungal diversity. Compared to CK2, the relative abundance increased in T7-treated Proteobacteria (Sphingomonas, Pseudomonas, and Lysobacter) and T4-treated Bacteroidetes (Flavobacterium) among bacteria. An increase in T4-treated Ascomycota (Zopfiella and Fusarium) and Basidiomycota among fungi and a decrease in T7-treated Mortierellomycota have been observed. Functional predictions of the bacterial Tax4Fun and fungal FUNGuild revealed that applying planting and breeding waste compost from the T4 treatment significantly increased the abundance of soil bacterial Metabolism of Cities, Genetic Information Processing, and Cellular Processes decreased the abundance of Pathotroph and Saprotroph-Symbiotroph fungi and increased the abundance of Saprotroph fungi. Overall, planting and breeding waste compost increased zucchini yield by improving soil fertility and microbial community structure. Among them, T4 treatment has the most significant effect, so T4 treatment can be selected as the optimized formulation of local commercial organic fertilizer. These findings have valuable implications for sustainable agricultural development.
Fe/S Redox-Coupled Mercury Transformation Mediated by Acidithiobacillus ferrooxidans ATCC 23270 under Aerobic and/or Anaerobic Conditions
Authors: Liu Yue , Gu Chenyun , Liu Hongchang , Zhou Yuhang , Nie Zhenyuan , Wang Yirong , Chen Lu , Xia Jinlan
Published: April 2023

Bioleaching processes or microbially mediated iron/sulfur redox processes in acid mine drainage (AMD) result in mineral dissolution and transformation, the release of mercury and other heavy metal ions, and changes in the occurrence forms and concentration of mercury. However, pertinent studies on these processes are scarce. Therefore, in this work, the Fe/S redox-coupled mercury transformation mediated by Acidithiobacillus ferrooxidans ATCC 23270 under aerobic and/or anaerobic conditions was studied by combining analyses of solution behavior (pH, redox potential, and Fe/S/Hg ion concentrations), the surface morphology and elemental composition of the solid substrate residue, the Fe/S/Hg speciation transformation, and bacterial transcriptomics. It was found that: (1) the presence of Hg2+ significantly inhibited the apparent iron/sulfur redox process; (2) the addition of Hg2+ caused a significant change in the composition of bacterial surface compounds and elements such as C, N, S, and Fe; (3) Hg mainly occurred in the form of Hg0, HgS, and HgSO4 in the solid substrate residues; and (4) the expression of mercury-resistant genes was higher in earlier stages of growth than in the later stages of growth. The results indicate that the addition of Hg2+ significantly affected the iron/sulfur redox process mediated by A. ferrooxidans ATCC 23270 under aerobic, anaerobic, and coupled aerobic–anaerobic conditions, which further promoted Hg transformation. This work is of great significance for the treatment and remediation of mercury pollution in heavy metal-polluted areas.
Effect of Food Matrix and Treatment Time on the Effectiveness of Grape Seed Extract as an Antilisterial Treatment in Fresh Produce
Authors: Ghorbani Tajani Anahita , Bisha Bledar
Published: April 2023

Listeriosis outbreaks were associated with contaminated fruits and vegetables, including cantaloupe, apples, and celery. Grape seed extract (GSE) is a natural antimicrobial with potential for reducing Listeria monocytogenes contamination in food. This study assessed the effectiveness of GSE to reduce L. monocytogenes on fresh produce and the impact of food matrices on its antilisterial activity. GSE showed MIC values of 30–35 μg/mL against four Listeria strains used in this study. A total of 100 g portions of cantaloupe, apples, and celery were inoculated with L. monocytogenes and treated with 100–1000 μg/mL of GSE for 5 or 15 min. Results were analyzed using Rstudio and a Tukey’s test. Treated produce had significantly lower L. monocytogenes counts than the control samples (p-value < 0.05). The inhibition was significantly higher on apples and lowest on cantaloupe. Moreover, a 15 min treatment was found to be more effective than a 5 min treatment in reducing L. monocytogenes on all produce types. The reduction in L. monocytogenes levels varied between 0.61 and 2.5 log10 CFU reduction, depending on the treatment concentration, duration, and produce matrix. These findings suggest that GSE is an effective antilisterial treatment for fresh produce, with varying levels of effectiveness depending on the food matrix and treatment time.
Self-Assembly of Silver Nanowire Films for Surface-Enhanced Raman Scattering Applications
Authors: Pang Yanzhao , Jin Mingliang
Published: April 2023

The development of SERS detection technology is challenged by the difficulty in obtaining SERS active substrates that are easily prepared, highly sensitive, and reliable. Many high-quality hotspot structures exist in aligned Ag nanowires (NWs) arrays. This study used a simple self-assembly method with a liquid surface to prepare a highly aligned AgNW array film to form a sensitive and reliable SERS substrate. To estimate the signal reproducibility of the AgNW substrate, the RSD of SERS intensity of 1.0 × 10−10 M Rhodamine 6G (R6G) in an aqueous solution at 1364 cm−1 was calculated to be as low as 4.7%. The detection ability of the AgNW substrate was close to the single molecule level, and even the R6G signal of 1.0 × 10−16 M R6G could be detected with a resonance enhancement factor (EF) as high as 6.12 × 1011 under 532 nm laser excitation. The EF without the resonance effect was 2.35 × 106 using 633 nm laser excitation. FDTD simulations have confirmed that the uniform distribution of hot spots inside the aligned AgNW substrate amplifies the SERS signal.
Predictive Role of Neutrophil-Percentage-to-Albumin Ratio (NPAR) in Nonalcoholic Fatty Liver Disease and Advanced Liver Fibrosis in Nondiabetic US Adults: Evidence from NHANES 2017–2018
Authors: Liu Chi-Feng , Chien Li-Wei
Published: April 2023

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent globally and includes chronic liver diseases ranging from simple steatosis to nonalcoholic steatohepatitis (NASH). The neutrophil-to-albumin ratio (NPAR) is a cost-effective, readily available biomarker of inflammation used to assess cancer and cardiovascular disease prognosis, and it may be of predictive value in NAFLD. This study was to evaluate the associations between the NPAR, the neutrophil-to-lymphocyte ratio (NLR), and the presence of NAFLD or advanced liver fibrosis, and to assess the predictive value of the NPAR in NAFLD in a nationally representative database. This population-based, cross-sectional, retrospective study analyzed the secondary data of adults with NAFLD or advanced liver fibrosis extracted from the National Health and Nutrition Examination Survey (NHANES) database 2017–2018. NHANES participants with complete information of vibration-controlled transient elastography (VCTE) and controlled attenuation parameter (CAP) were enrolled. A logistic regression analysis was used to determine the associations between the variables in the participants with and without NAFLD or advanced liver fibrosis. The mean values of the lymphocyte counts, neutrophil counts, NPAR, aspartate aminotransaminase (AST), alanine aminotransaminase (ALT), total cholesterol, triglycerides, and HbA1c were significantly higher in the participants with NAFLD than in those without NAFLD or advanced liver fibrosis. The mean blood albumin levels of the subjects without NAFLD or advancing fibrosis were considerably greater than those of the individuals with these conditions. The mean values of the NLR, NPAR, AST, ALT, triglycerides, lymphocyte count, neutrophil count, and HbA1c were significantly higher in patients with advanced fibrosis than in those without advanced fibrosis. A multivariate analysis showed that per unit increases in both the NLR and NPAR were significantly associated with an increased risk of developing NAFLD, while neither the NLR nor NPAR was significantly associated with higher odds of advanced fibrosis. In conclusion, the novel biomarker NPAR demonstrates a good association with NAFLD, along with participants’ clinical characteristics, in a nationwide population. The NPAR may serve as a biomarker for NAFLD and help clinicians refine the diagnosis and treatment of chronic liver disease.
The Prevalence of Undiagnosed Salmonella enterica Serovar Typhi in Healthy School-Aged Children in Osun State, Nigeria
Authors: Uwanibe Jessica N. , Kayode Tolulope A. , Oluniyi Paul E. , Akano Kazeem , Olawoye Idowu B. , Ugwu Chinedu A. , Happi Christian T. , Folarin Onikepe A.
Published: April 2023

Typhoid fever remains a significant public health concern due to cases of mis-/overdiagnosis. Asymptomatic carriers play a role in the transmission and persistence of typhoid fever, especially among children, where limited data exist in Nigeria and other endemic countries. We aim to elucidate the burden of typhoid fever among healthy school-aged children using the best surveillance tool(s). In a semi-urban/urban state (Osun), 120 healthy school-aged children under 15 years were enrolled. Whole blood and fecal samples were obtained from consenting children. ELISA targeting the antigen lipopolysaccharide (LPS) and anti-LPS antibodies of Salmonella Typhi, culture, polymerase chain reaction (PCR), and next-generation sequencing (NGS) were used to analyze the samples. At least one of the immunological markers was detected in 65.8% of children, with 40.8%, 37.5%, and 39% of children testing positive for IgM, IgG, and antigen, respectively. Culture, PCR, and NGS assays did not detect the presence of Salmonella Typhi in the isolates. This study demonstrates a high seroprevalence of Salmonella Typhi in these healthy children but no carriage, indicating the inability to sustain transmission. We also demonstrate that using a single technique is insufficient for typhoid fever surveillance in healthy children living in endemic areas.
Research on Germanium Photodetector with Multi-Mode Waveguide Input
Authors: Wu Longsheng , Lv Dongsheng , Zhao Nengyang , Wang Ruxue , Wu Aimin
Published: April 2023

In this work, a vertical N-I-P germanium (Ge) photodetector (PD) with a multi-mode waveguide input is presented. The fabricated devices exhibit a low dark current of 10 nA at bias of −1 V, and a high responsivity of exceeding 0.75 A/W over the wavelength range from 1270 to 1350 nm. High-frequency characteristics measurements show that the photodetector has a 3 dB opto-electrical (OE) bandwidth of 23 GHz under −3 V bias, which can be further improved by optimization of the photodetector configuration. A 50 Gb/s clear eye diagram with a non-return-to-zero (NRZ) modulation format is demonstrated. By using a single-mode excitation source, which is used to simulate light coming from the wavelength division multiplexing (WDM) devices, and sweeping its position, it is shown that the multi-mode input photodetector can be utilized in a WDM receiver to achieve both high responsivity and a flat-top passband.
Heterogeneity Assessment of Kenaf Breeding Field through Spatial Dependence Analysis on Crop Growth Status Map Derived by Unmanned Aerial Vehicle
Authors: Jang Gyujin , Kim Dong-Wook , Park Won-Pyo , Kim Hak-Jin , Chung Yong-Suk
Published: April 2023

The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the open-field is not always accepted, and requires a spatial dependence analysis to determine whether site-specific environmental factors exist. In this study, spatial dependence within the kenaf breeding field was assessed in a geo-tagged height map derived from an unmanned aerial vehicle (UAV). Local indicators of spatial autocorrelation (LISA) were applied to the height map using Geoda software, and the LISA map was generated in order to recognize the existence of kenaf height status clusters. The spatial dependence of the breeding field used in this study appeared in a specific region. The cluster pattern was similar to the terrain elevation pattern of this field and highly correlated with drainage capacity. The cluster pattern could be utilized to design random blocks based on regions that have similar spatial dependence. We confirmed the potential of spatial dependence analysis on a crop growth status map, derived by UAV, for breeding strategy design with a tight budget.
Organic Amendments Improved the Productivity and Bio-Fortification of Fine Rice by Improving Physiological Responses and Nutrient Homeostasis under Salinity Stress
Authors: Khan Imran , Mahmood Sikandar , Chattha Muhammad Umer , Bilal Chattha Muhammad , Ahmad Shahbaz , Awan Masood Iqbal , Alqahtani Fatmah M. , Hashem Mohamed , Alhaithloul Haifa Abdulaziz Sakit , Qari Sameer H. , Mahmood Faisal , Hassan Muhammad Umair
Published: April 2023

Salinity stress (SS) is major abiotic stress that is seriously limiting crop production across the globe. The application of organic amendments (OA) mitigate the effects of salinity and improves soil health and crop production on a sustainable basis. However, limited studies are conducted to determine the impact of farmyard manure (FYM) and press mud (PM) on the performance of rice crop. Therefore, we performed this study to determine the impacts of FYM and PM on the growth, physiological and biochemical attributes, yield, and grain bio-fortification of rice crop under SS. The experiment was comprised of SS levels; control, 6 and 12 dS m−1 SS and OA; control, FYM: 5%, press mud 5% and combination of FYM (5%) + PM (5%). Soil salinity imposed deleterious impacts on the growth, yield, and grain quality of rice, however, OA appreciably offset the deleterious impacts of SS and improved the growth, yield, and grain bio-fortification of rice crop. The combined application of FYM + PM improved the growth and yield of rice through an increase in chlorophyll contents, leaf water contents, anti-oxidant activities (ascorbate peroxidise: APX; catalase: CAT, peroxidise: POD and ascorbic acid: AsA), K+ accumulation and decrease in Na+/K+ ratio, electrolyte leakage, malondialdehyde (MDA), hydrogen peroxide (H2O2), Na+ accumulation. Moreover, the combined application of FYM + PM significantly improved the grain protein (5.84% and 12.90%), grain iron (40.95% and 42.37%), and grain zinc contents (36.81% and 50.93%) at 6 and 12 dS m−1 SS. Therefore, this study suggested that the application of FYM and PM augmented the growth, yield, physiology, biochemistry, and grain bio-fortification of rice and proved to be a good practice for better rice production in salt-affected soils.
Powdered Beverage from Native Plants from Argentina (Zuccagnia punctata and Solanum betaceum) Obtained by Spray-Drying: A Promising Source of Antioxidant Compounds
Authors: Correa Uriburu Florencia María , Zampini Iris Catiana , Maldonado Luis Maria , Gómez Mattson Milagros , Salvatori Daniela , Isla María Inés
Published: April 2023

In previous studies, the Argentinean native plants called Zuccagnia punctata (jarilla, pus pus, lata) and Solanum betaceum (chilto, tree tomato) were reported as new natural sources of antioxidant compounds, mainly chalcones, anthocyanins and rosmarinic acid derivates. The present study deals with the production of antioxidant beverages of Z. punctata (Zp) extract and chilto juice with honey as sweetener. A Zp extract and red chilto juice were obtained according to Food Code and characterized. The beverages were formulated by using maltodextrin (MD) with two dextrose equivalents (DE), 10 and 15, and then spray-dried at an inlet air temperature of 130 °C. The physicochemical, microscopical, phytochemical and functional characteristics of the powders were surveyed. The experiments carried out showed good physical properties for both formulations showing high water solubility with adequate features for handling, transport and storage. The chromatic parameters of both powdered beverages indicate orange–pink tones regardless of the wall material used. The total polyphenol and flavonoid content in the beverages were kept after spray-drying (92 and 100%, respectively). The anthocyanins were less stable under drying conditions (yield 58%). Both powdered beverages showed high scavenger capacity on ABTS•+, HO• and H2O2 (SC50 between 3.29 to 41.05 µg GAE/mL) and were able to inhibit xanthine oxidase (XOD) activity (CI50 between 91.35 and 114.43 µg GAE/mL). The beverages were neither toxic nor mutagenic in the concentration range with biological activity. The results obtained in the present work scientifically support the use of the powdered beverages of Argentinean native plants as antioxidant.
Edge/Fog Computing Technologies for IoT Infrastructure II
Authors: Kim Taehong , Yoo Seong-eun , Kim Youngsoo
Published: April 2023

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices [...]
Designing Ecological Auditory Feedback on Lower Limb Kinematics for Hemiparetic Gait Training
Authors: Kantan Prithvi Ravi , Dahl Sofia , Jørgensen Helle Rovsing , Khadye Chetali , Spaich Erika G.
Published: April 2023

Auditory feedback has earlier been explored as a tool to enhance patient awareness of gait kinematics during rehabilitation. In this study, we devised and tested a novel set of concurrent feedback paradigms on swing phase kinematics in hemiparetic gait training. We adopted a user-centered design approach, where kinematic data recorded from 15 hemiparetic patients was used to design three feedback algorithms (wading sounds, abstract, musical) based on filtered gyroscopic data from four inexpensive wireless inertial units. The algorithms were tested (hands-on) by a focus group of five physiotherapists. They recommended that the abstract and musical algorithms be discarded due to sound quality and informational ambiguity. After modifying the wading algorithm (as per their feedback), we conducted a feasibility test involving nine hemiparetic patients and seven physiotherapists, where variants of the algorithm were applied to a conventional overground training session. Most patients found the feedback meaningful, enjoyable to use, natural-sounding, and tolerable for the typical training duration. Three patients exhibited immediate improvements in gait quality when the feedback was applied. However, minor gait asymmetries were found to be difficult to perceive in the feedback, and there was variability in receptiveness and motor change among the patients. We believe that our findings can advance current research in inertial sensor-based auditory feedback for motor learning enhancement during neurorehabilitation.
UnVELO: Unsupervised Vision-Enhanced LiDAR Odometry with Online Correction
Authors: Li Bin , Ye Haifeng , Fu Sihan , Gong Xiaojin , Xiang Zhiyu
Published: April 2023

Due to the complementary characteristics of visual and LiDAR information, these two modalities have been fused to facilitate many vision tasks. However, current studies of learning-based odometries mainly focus on either the visual or LiDAR modality, leaving visual–LiDAR odometries (VLOs) under-explored. This work proposes a new method to implement an unsupervised VLO, which adopts a LiDAR-dominant scheme to fuse the two modalities. We, therefore, refer to it as unsupervised vision-enhanced LiDAR odometry (UnVELO). It converts 3D LiDAR points into a dense vertex map via spherical projection and generates a vertex color map by colorizing each vertex with visual information. Further, a point-to-plane distance-based geometric loss and a photometric-error-based visual loss are, respectively, placed on locally planar regions and cluttered regions. Last, but not least, we designed an online pose-correction module to refine the pose predicted by the trained UnVELO during test time. In contrast to the vision-dominant fusion scheme adopted in most previous VLOs, our LiDAR-dominant method adopts the dense representations for both modalities, which facilitates the visual–LiDAR fusion. Besides, our method uses the accurate LiDAR measurements instead of the predicted noisy dense depth maps, which significantly improves the robustness to illumination variations, as well as the efficiency of the online pose correction. The experiments on the KITTI and DSEC datasets showed that our method outperformed previous two-frame-based learning methods. It was also competitive with hybrid methods that integrate a global optimization on multiple or all frames.
Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition
Authors: Qian Lulu , Yu Tan , Yang Jianyu
Published: April 2023

Multi-scale feature fusion techniques and covariance pooling have been shown to have positive implications for completing computer vision tasks, including fine-grained image classification. However, existing algorithms that use multi-scale feature fusion techniques for fine-grained classification tend to consider only the first-order information of the features, failing to capture more discriminative features. Likewise, existing fine-grained classification algorithms using covariance pooling tend to focus only on the correlation between feature channels without considering how to better capture the global and local features of the image. Therefore, this paper proposes a multi-scale covariance pooling network (MSCPN) that can capture and better fuse features at different scales to generate more representative features. Experimental results on the CUB200 and MIT indoor67 datasets achieve state-of-the-art performance (CUB200: 94.31% and MIT indoor67: 92.11%).
A Novel Active Cooling System for Internal Combustion Engine Using Shape Memory Alloy Based Thermostat
Authors: Turabimana Pacifique , Sohn Jung Woo , Choi Seung-Bok
Published: April 2023

Pollutants in exhaust gases and the high fuel consumption of internal combustion engines remain key issues in the automotive industry despite the emergence of electric vehicles. Engine overheating is a major cause of these problems. Traditionally, engine overheating was solved using electric pumps and cooling fans with electrically operated thermostats. This method can be applied using active cooling systems that are currently available on the market. However, the performance of this method is undermined by its delayed response time to activate the main valve of the thermostat and the dependence of the coolant flow direction control on the engine. This study proposes a novel active engine cooling system incorporating a shape memory alloy-based thermostat. After discussing the operating principles, the governing equations of motion were formulated and analyzed using COMSOL Multiphysics and MATLAB. The results show that the proposed method improved the response time required to change the coolant flow direction and led to a coolant temperature difference of 4.90 °C at 90 °C cooling conditions. This result indicates that the proposed system can be applied to existing internal combustion engines to enhance their performance in terms of reduced pollution and fuel consumption.
A Low-Power Analog Integrated Implementation of the Support Vector Machine Algorithm with On-Chip Learning Tested on a Bearing Fault Application
Authors: Alimisis Vassilis , Gennis Georgios , Gourdouparis Marios , Dimas Christos , Sotiriadis Paul P.
Published: April 2023

A novel analog integrated implementation of a hardware-friendly support vector machine algorithm that can be a part of a classification system is presented in this work. The utilized architecture is capable of on-chip learning, making the overall circuit completely autonomous at the cost of power and area efficiency. Nonetheless, using subthreshold region techniques and a low power supply voltage (at only 0.6 V), the overall power consumption is 72 μW. The classifier consists of two main components, the learning and the classification blocks, both of which are based on the mathematical equations of the hardware-friendly algorithm. Based on a real-world dataset, the proposed classifier achieves only 1.4% less average accuracy than a software-based implementation of the same model. Both design procedure and all post-layout simulations are conducted in the Cadence IC Suite, in a TSMC 90 nm CMOS process.
Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement
Authors: Al-Omairi Hayder R. , Fudickar Sebastian , Hein Andreas , Rieger Jochem W.
Published: April 2023

Functional near-infrared spectroscopy (fNIRS) is an optical non-invasive neuroimaging technique that allows participants to move relatively freely. However, head movements frequently cause optode movements relative to the head, leading to motion artifacts (MA) in the measured signal. Here, we propose an improved algorithmic approach for MA correction that combines wavelet and correlation-based signal improvement (WCBSI). We compare its MA correction accuracy to multiple established correction approaches (spline interpolation, spline-Savitzky–Golay filter, principal component analysis, targeted principal component analysis, robust locally weighted regression smoothing filter, wavelet filter, and correlation-based signal improvement) on real data. Therefore, we measured brain activity in 20 participants performing a hand-tapping task and simultaneously moving their head to produce MAs at different levels of severity. In order to obtain a “ground truth” brain activation, we added a condition in which only the tapping task was performed. We compared the MA correction performance among the algorithms on four predefined metrics (R, RMSE, MAPE, and ΔAUC) and ranked the performances. The suggested WCBSI algorithm was the only one exceeding average performance (p < 0.001), and it had the highest probability to be the best ranked algorithm (78.8% probability). Together, our results indicate that among all algorithms tested, our suggested WCBSI approach performed consistently favorably across all measures.
Detection and Length Measurement of Cracks Captured in Low Definitions Using Convolutional Neural Networks
Authors: Kim Jin-Young , Park Man-Woo , Huynh Nhut Truong , Shim Changsu , Park Jong-Woong
Published: April 2023

Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in low definitions. Therefore, this paper presented a framework of detecting the regions of blurred, indistinct concrete cracks. The framework divides an image into small square patches which are classified into crack or non-crack. Well-known CNN models were employed for the classification and compared with each other with experimental tests. This paper also elaborated on critical factors—the patch size and the way of labeling patches—which had considerable influences on the training performance. Furthermore, a series of post-processes for measuring crack lengths were introduced. The proposed framework was tested on the images of bridge decks containing blurred thin cracks and showed reliable performance comparable to practitioners.
An Improvement Strategy for Indoor Air Quality Monitoring Systems
Authors: De Capua Claudio , Fulco Gaetano , Lugarà Mariacarla , Ruffa Filippo
Published: April 2023

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.
Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices
Authors: Bajwa Rayan , Yapici Murat Kaya
Published: April 2023

RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remained less explored. In this work, we report a computationally efficient generic design optimization methodology for RF-MEMS passive devices based on multi-objective heuristic optimization techniques, which, to the best of our knowledge, stands out as the first approach offering applicability to different RF-MEMS passives, as opposed to being customized for a single, specific component. In order to comprehensively optimize the design, both electrical and mechanical aspects of RF-MEMS device design are modeled carefully, using coupled finite element analysis (FEA). The proposed approach first generates a dataset, efficiently spanning the entire design space, based on FEA models. By coupling this dataset with machine-learning-based regression tools, we then generate surrogate models describing the output behavior of an RF-MEMS device for a given set of input variables. Finally, the developed surrogate models are subjected to a genetic algorithm-based optimizer, in order to extract the optimized device parameters. The proposed approach is validated for two case studies including RF-MEMS inductors and electrostatic switches, in which the multiple design objectives are optimized simultaneously. Moreover, the degree of conflict among various design objectives of the selected devices is studied, and corresponding sets of optimal trade-offs (pareto fronts) are extracted successfully.
Comparing Perfectionism, Cognitive Mindset, Constructive Thinking, and Emotional Intelligence in Gifted Students by Grade and Gender
Authors: Tsai Min-Ying
Published: April 2023

The study compared the differences among gifted students of different grades and genders concerning perfectionism, cognitive mindset, constructive thinking, and emotional intelligence. The study included 908 gifted primary-school students from third to sixth grade. The study used the t-test and analysis of variance methods, and four scales. Furthermore, the following were the conclusions. Firstly, gifted pupils of different grades and genders scored considerably differently on some perfectionism subscales. Second, there were notable differences in the cognitive mindset of gifted pupils in different grades. Lastly, there was a substantial difference between gifted pupils in different grades in the distrust-of-others subscale in the constructive-thinking scale. Finally, there was a substantial difference in introversion, interpersonal relationships, and mood among gifted students of different genders.
A Study on Urban Ethnic Segmentation in Kabul City, Afghanistan
Authors: Sarwari Fakhrullah , Ono Hiroko
Published: April 2023

The Pashtun, Tajik, and Hazara ethnic groups, divided into three zones, make up most of the ethnic segments in Kabul, the capital of Afghanistan. However, each ethnic settlement is subdivided into smaller geographical communities. In this research, we want to validate the existence of these ethnic communities and how these communities are valuable to the residents of Kabul city. We also aimed to analyze what facilities the community shares and why it is crucial to consider these communities while redeveloping the informal settlements in Kabul city. We did a literature review on ethnic segmentation, chose a case study in district 13 of Kabul, interviewed the residents and head of the community, and distributed socio-economic questionnaires made by the Ministry of urban development and land Afghanistan in three streets to determine how the ethnicities are subdivided. Ethnicities are divided into smaller sub-ethnic communities based on their migrated rural districts. People live together because of a communal identity known as Qawm. Each community shares a mosque, and to redevelop their areas, they want to be relocated near their current community of ethnicity. Moreover, ethnicity is a significant factor in choosing where to live in Kabul. Hence, the government policymaker must consider the importance of these communities in redeveloping informal settlements.
Identification of Key Design Phase-Related Risks in DBB Projects in the UAE—Towards Developing a BIM Solution
Authors: Badran Deena , AlZubaidi Radhi , Venkatachalam Senthilkumar
Published: April 2023

Building Information Modelling (BIM) has become a widely used concept in high-rise buildings in the United Arab Emirates (UAE). However, the risks associated with the design phase of multi-story high-rise building projects have, to date, not been addressed by the research studies conducted in the UAE. The results show that “Time Spent in Approval Process” is the main cause of delay, “Complying with the New Regulations and Rules” is the main cause of cost overrun and “Poor Coordination between the Design Disciplines” is the main cause of the quality deficiency. The results also indicate that “Poor Coordination between the Design Disciplines’’ is the only risk that has a high-risk index related to time, cost, and quality. “Change Initiated by the Client” and “Interface by the Client during the Design Process” are ranked among the top five risks which cause delay and cost overrun in UAE high-rise buildings. The paper mapped the root causes of the identified most significant risks against the possible BIM-based solutions. The results show that BIM can effectively mitigate 75% of the root causes of these risks and, further, BIM is also effective in managing the consequences of the root causes for the remaining the remaining 25%.
Digital Transformation, Green Innovation, and Pollution Abatement: Evidence from China
Authors: Peng Shizhong , Peng Haoran , Pan Shirong , Wu Jun
Published: April 2023

The advent of novel and potent digital technologies has substantially transformed ways enterprises undertake their production. How digital transformation will reshape the production model of enterprises and have an impact on pollution emissions is a crucial problem in existing research. In this paper, we construct a theoretical framework to illuminate the theoretical mechanism of firms employing digital technology to bring out pollution abatement effect. Using a series of firm-level datasets from China, this paper introduces fixed-effect specification to empirically examine the environmental effects triggered by digital transformation. We find a negative effect of digital transformation on firms’ pollution emissions. The results are robust when potential endogeneity and mismeasurement problems are controlled for. Factors related to green innovation and investments in pollution abatement are found to play an important role in shaping the nexus between digital transformation and firm-level pollution emissions. This paper provides supporting firm-level evidence for the pollution abatement effect of digital transformation, which is essential for accelerating the construction of the digital economy, promoting the synergistic effect of digital transformation and green development, and achieving a balanced development of economic growth and environmental governance.