bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Reflection of GaAs Nanowire Laser Using Short-Period, Symmetric Double Metal Grating Reflectors
Authors: Yu Qun , Wei Wei , Yan Xin , Zhang Xia
Published: April 2022

Owing to the high contrast of the refractive indices at the end facets of a nanowire, lasing emission can be achieved in an individual nanowire without external, reflected feedback. However, the reflection provided by the end facet is not high enough to lower the threshold gain, especially for nanowires with smaller diameters. This work proposes a novel structure of nanowire laser partially sandwiched in double Ag gratings. Compared to a nanowire with a single metal grating or without a grating, the parallel double metal gratings play the reflector role with higher reflectivity to enhance the round-trip feedback and reduce the threshold gain. The reflective properties are calculated using the finite elements method. Simulation results show that a high reflectivity of more than 90% can be achieved when the number of periods is more than 8. The reflectivity of double gratings is 2.4 times larger than that of the nanowire end facet for large nanowire diameters. When the nanowire has a small diameter of 150 nm, the reflectivity of double gratings is 17 times larger than that of the nanowire end facet. Compared to a single grating, the reflective performance of double gratings is much better. Owing to the highly reflective properties of the double gratings, nanowires partially sandwiched in the double gratings can realize lasing emission at a very low threshold gain, and the period of the grating can be very short to benefit on-chip interconnection systems.
Design and Analysis of a Hollow Metallic Microlattice Active Cooling System for Microsatellites
Authors: Chen Junming , Liu Longquan , Xu Wenjun , Huang Xiaobin , Sheng Haoqiang
Published: April 2022

Microsatellites have stringent demands for thermal dissipation systems with high efficiency but low weight, which is a difficult combination to obtain using current technologies. The design method of a new cooling system consisting of hollow metallic microlattice material filled with liquid is developed and proposed, and its heat dissipation performance is analyzed through experimental tests and numerical simulations. Through the analysis results of the influences of the microstructures of the hollow microlattice material, it is found that the effective coefficient (the number of channels taking part in convection) has the highest influence on the heat dissipation performance. Numerical simulation results illustrated that the heating surface temperature can be reduced to 301.7 K through special design, which can meet the heat dissipation requirement of most microsatellites. The new microlattice cooling system in this study improves heat dissipation performance while having very low structural weight, thus providing a feasible substitute for thermal control systems in microsatellites.
Mediterranean-Type Diets as a Protective Factor for Asthma and Atopy
Authors: Vassilopoulou Emilia , Guibas George V. , Papadopoulos Nikolaos G.
Published: April 2022

We are currently riding the second wave of the allergy epidemic, which is ongoing in affluent societies, but now also affecting developing countries. This increase in the prevalence of atopy/asthma in the Western world has coincided with a rapid improvement in living conditions and radical changes in lifestyle, suggesting that this upward trend in allergic manifestations may be associated with cultural and environmental factors. Diet is a prominent environmental exposure that has undergone major changes, with a substantial increase in the consumption of processed foods, all across the globe. On this basis, the potential effects of dietary habits on atopy and asthma have been researched rigorously, but even with a considerable body of evidence, clear associations are far from established. Many factors converge to obscure the potential relationship, including methodological, pathophysiological and cultural differences. To date, the most commonly researched, and highly promising, candidate for exerting a protective effect is the so-called Mediterranean diet (MedDi). This dietary pattern has been the subject of investigation since the mid twentieth century, and the evidence regarding its beneficial health effects is overwhelming, although data on a correlation between MedDi and the incidence and severity of asthma and atopy are inconclusive. As the prevalence of asthma appears to be lower in some Mediterranean populations, it can be speculated that the MedDi dietary pattern could indeed have a place in a preventive strategy for asthma/atopy. This is a review of the current evidence of the associations between the constituents of the MedDi and asthma/atopy, with emphasis on the pathophysiological links between MedDi and disease outcomes and the research pitfalls and methodological caveats which may hinder identification of causality. MedDi, as a dietary pattern, rather than short-term supplementation or excessive focus on single nutrient effects, may be a rational option for preventive intervention against atopy and asthma.
Maternal Iodine Intake and Neurodevelopment of Offspring: The Japan Environment and Children’s Study
Authors: Hisada Aya , Takatani Rieko , Yamamoto Midori , Nakaoka Hiroko , Sakurai Kenichi , Mori Chisato ,   the Japan Environment and Children’s Study (JECS) Group
Published: April 2022

Inadequate maternal iodine intake affects thyroid function and may impair fetal brain development. This study investigated the association between maternal iodine intake during pregnancy and neurodevelopmental delay in offspring at 1 and 3 years of age using a nationwide birth cohort: the Japan Environment and Children’s Study. We assessed dietary iodine intake during pregnancy using a food frequency questionnaire and child neurodevelopment using the Japanese translation of the Ages and Stages Questionnaire, Third Edition. The risk of delay (score below the cut-off value) for fine motor domain at 1 year of age was increased in the lowest quintile iodine intake group compared with the fourth quintile iodine intake group. The risk of delay for problem-solving at 1 year of age was increased in the lowest and second quintile iodine intake group and decreased in the highest quintile iodine intake group. The risk of delay for communication, fine motor, problem-solving, and personal–social domains at 3 years of age was increased in the lowest and second quintile iodine intake group compared with the fourth quintile iodine intake group, while the risk of delay for fine motor and problem-solving domains was decreased in the highest quintile iodine intake group. Low iodine intake levels in pregnancy may affect child neurodevelopment.
The Effects of Omega-3 Supplementation on Depression in Adults with Cardiometabolic Disease: A Systematic Review of Randomised Control Trials
Authors: Arsenyadis Franciskos , Ahmad Ehtasham , Redman Emma , Yates Thomas , Davies Melanie , Khunti Kamlesh
Published: April 2022

Background: Omega-3 polyunsaturated fatty acids′ concurrent benefits for cardiometabolic and mental health are equivocal. Despite lack of evidence, up to a third of adults consume Omega-3 supplements. No review has yet been published to report effect on depression in this cardiometabolic population. Methods: We conducted a systematic review of double-blinded, controlled randomised trials to investigate the safety and effect of Omega-3 supplementation on depression scores in people with cardiometabolic diseases. Primary outcome was change in depression scores versus placebo. Secondary outcomes were side-effects, concurrent medication and adherence. Results: Seven trials reporting on 2575 (672 female) adults aged 39–73 were included. Omega-3 dosages ranged from 1–3 g with an intervention duration of 10–48 weeks. Six out of seven trials found no statistically or clinically significant change to depression scores compared to placebo. One trial favoured intervention (Relative Risk Reduction: 47.93%, 95% CI: 24.89–63.98%, p < 0.001). Sub-analyses showed clinically meaningful reductions in depression scores for those on antidepressants (Intervention: 20.9 (SD: 7.1), Placebo: 24.9 (SD: 8.5) p < 0.05) or with severe depression (−1.74; 95% CI −3.04 to −0.05, p < 0.05) in two separate trials. Side effects were comparable between treatment arms. Conclusions: Omega-3 supplementation is safe to use but not superior to placebo for depression in adults with concurrent cardiometabolic disease.
Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model
Authors: Chen Hao-Wei , Chen Yu-Chen , Lee Jung-Ting , Yang Frances M. , Kao Chung-Yao , Chou Yii-Her , Chu Ting-Yin , Juan Yung-Shun , Wu Wen-Jeng
Published: April 2022

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.
Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women’s Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda
Authors: Bulungu Andrea L. S. , Palla Luigi , Priebe Jan , Forsythe Lora , Katic Pamela , Varley Gwen , Galinda Bernice D. , Sarah Nakimuli , Nambooze Joweria , Wellard Kate , Ferguson Elaine L.
Published: April 2022

Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women’s time use in rural Eastern Ugandan. Women’s (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland–Altman limits of agreement (LOA) method of analysis and Cronbach’s coefficient alpha (time allocation) or Cohen’s κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (−1.1 to 3.7) and AWC-IAR (−3.2 to 3.2). Cronbach’s alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women’s time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women’s time allocations and nutrition outcomes.
A Mini Review on Isatin, an Anticancer Scaffold with Potential Activities against Neglected Tropical Diseases (NTDs)
Authors: Chowdhary Shefali , Shalini  , Arora Amandeep , Kumar Vipan
Published: April 2022

Isatin, chemically an indole-1H-2,3-dione, is recognised as one of the most attractive therapeutic fragments in drug design and development. The template has turned out to be exceptionally useful for developing new anticancer scaffolds, as evidenced by the increasing number of isatin-based molecules which are either in clinical use or in trials. Apart from its promising antiproliferative properties, isatin has shown potential in treating Neglected Tropical Diseases (NTDs) not only as a parent core, but also by attenuating the activities of various pharmacophores. The objective of this mini-review is to keep readers up to date on the latest developments in the biological potential of isatin-based scaffolds, targeting cancer and NTDs such as tuberculosis, malaria, and microbial infections.
Design, Synthesis and Evaluation of Fused Bicyclo[2.2.2]octene as a Potential Core Scaffold for the Non-Covalent Inhibitors of SARS-CoV-2 3CLpro Main Protease
Authors: Herlah Barbara , Hoivik Andrej , Jamšek Luka , Valjavec Katja , Yamamoto Norio , Hoshino Tyuji , Kranjc Krištof , Perdih Andrej
Published: April 2022

The emergence of SARS-CoV-2, responsible for the global COVID-19 pandemic, requires the rapid development of novel antiviral drugs that would contribute to an effective treatment alongside vaccines. Drug repurposing and development of new molecules targeting numerous viral targets have already led to promising drug candidates. To this end, versatile molecular scaffolds with high functionalization capabilities play a key role. Starting with the clinically used conformationally flexible HIV-1 protease inhibitors that inhibit replication of SARS-CoV-2 and bind major protease 3CLpro, we designed and synthesized a series of rigid bicyclo[2.2.2]octenes fused to N-substituted succinimides to test whether this core scaffold could support the development of non-covalent 3CLpro inhibitors. Inhibition assays confirmed that some compounds can inhibit the SARS-CoV-2 main protease; the most promising compound 11a inhibited 3CLpro in micromolar range (IC50 = 102.2 μM). Molecular simulations of the target-ligand complex in conjunction with dynophore analyses and endpoint free energy calculations provide additional insight and first recommendations for future optimization. The fused bicyclo[2.2.2]octenes can be used as a new potential starting point in the development of non-covalent SARS-CoV-2 3CLpro protease inhibitors and the study also substantiates the potential of this versatile scaffold for the development of biologically active molecules.
Timosaponin A3 Inhibits Palmitate and Stearate through Suppression of SREBP-1 in Pancreatic Cancer
Authors: Kim Yumi , Jee Wona , An Eun-Jin , Ko Hyun Min , Jung Ji Hoon , Na Yun-Cheol , Jang Hyeung-Jin
Published: April 2022

Timosaponin A3 (TA3) was demonstrated as a potent anticancer chemical by several studies. Although the effects of inhibiting growth, metastasis, and angiogenesis in various cancer cells were demonstrated through multiple mechanisms, the pharmacological mechanism of TA3 shown in pancreatic cancer (PC) is insufficient compared to other cancers. In this study, we aimed to explore the key molecular mechanisms underlying the growth inhibitory effects of TA3 using PC cells and a xenograft model. First, from the microarray results, we found that TA3 regulated INSIG-1 and HMGCR in BxPC-3 cells. Furthermore, we showed that inhibition of sterol regulatory element-binding protein-1 (SREBP-1) by TA3 reduced the fatty acid synthases FASN and ACC, thereby controlling the growth of BxPC-3 cells. We also tried to find mechanisms involved with SREBP-1, such as Akt, Gsk3β, mTOR, and AMPK, but these were not related to SREBP-1 inhibition by TA3. In the BxPC-3 xenograft model, the TA3 group had more reduced tumor formation and lower toxicity than the gemcitabine group. Interestingly, the level of the fatty acid metabolites palmitate and stearate were significantly reduced in the tumor tissue in the TA3 group. Overall, our study demonstrated that SREBP-1 was a key transcription factor involved in pancreatic cancer growth and it remained a precursor form due to TA3, reducing the adipogenesis and growth in BxPC-3 cells. Our results improve our understanding of novel mechanisms of TA3 for the regulation of lipogenesis and provide a new approach to the prevention and treatment of PC.
3,6′- and 1,6′-Dithiopomalidomide Mitigate Ischemic Stroke in Rats and Blunt Inflammation
Authors: Tsai Yan-Rou , Kim Dong Seok , Hsueh Shih-Chang , Chen Kai-Yun , Wu John Chung-Che , Wang Jia-Yi , Tsou Yi-Syue , Hwang Inho , Kim Yukyung , Gil Dayeon , Jo Eui Jung , Han Baek-Soo , Tweedie David , Lecca Daniela , Scerba Michael T. , Selman Warren R. , Hoffer Barry J. , Greig Nigel H. , Chiang Yung-Hsiao
Published: April 2022

(1) Background: An important concomitant of stroke is neuroinflammation. Pomalidomide, a clinically available immunomodulatory imide drug (IMiD) used in cancer therapy, lowers TNF-α generation and thus has potent anti-inflammatory actions. Well-tolerated analogs may provide a stroke treatment and allow evaluation of the role of neuroinflammation in the ischemic brain. (2) Methods: Two novel pomalidomide derivatives, 3,6′-dithiopomalidomide (3,6′-DP) and 1,6′-dithiopomalidomide (1,6′-DP), were evaluated alongside pomalidomide in a rat middle cerebral artery occlusion (MCAo) stroke model, and their anti-inflammatory actions were characterized. (3) Results: Post-MCAo administration of all drugs lowered pro-inflammatory TNF-α and IL1-β levels, and reduced stroke-induced postural asymmetry and infarct size. Whereas 3,6′- and 1,6′-DP, like pomalidomide, potently bound to cereblon in cellular studies, 3,6′-DP did not lower Ikaros, Aiolos or SALL4 levels—critical intermediates mediating the anticancer/teratogenic actions of pomalidomide and IMiDs. 3,6′-DP and 1,6′-DP lacked activity in mammalian chromosome aberration, AMES and hERG channel assays –critical FDA regulatory tests. Finally, 3,6′- and 1,6′-DP mitigated inflammation across rat primary dopaminergic neuron and microglia mixed cultures challenged with α-synuclein and mouse LPS-challenged RAW 264.7 cells. (4) Conclusion: Neuroinflammation mediated via TNF-α plays a key role in stroke outcome, and 3,6′-DP and 1,6′-DP may prove valuable as stroke therapies and thus warrant further preclinical development.
Impact of Climate Change on Phenology of Two Heat-Resistant Wheat Varieties and Future Adaptations
Authors: Ishtiaq Muhammad , Maqbool Mehwish , Muzamil Mahnoor , Casini Ryan , Alataway Abed , Dewidar Ahmed Z. , El-Sabrout Ahmed M. , Elansary Hosam O.
Published: April 2022

Climate change (CC) is a global threat to the agricultural system. Changing climatic conditions are causing variations in temperature range, rainfall timing, humidity percentage, soil structure, and composition of gases in environment. All these factors have a great influence on the phenological events in plants’ life cycle. Alternation in phenological events, especially in crops, leads to either lower yield or crop failure. In light of respective statement, the present study is designed to evaluate the climatic impacts on two heat-resistant wheat varieties (Sialkot–2008 and Punjab–2018). During the study, impacts of CC on wheat phenology and annual yield were predicted considering six climatic factors: maximum temp, minimum temperature, precipitation, humidity, soil moisture content, and solar radiation using two quantitative approaches. First, a two-year field experimental plot was set up at five different sites of study—each plot a bisect of two sites. Phenological changes of both varieties were monitored with respect to climatic factors and changes were recorded in a scientific manner. Secondly, experimental results were compared with Global climate models (GMC) models with a baseline range of the past 40 years (1970–2010) and future fifty years (2019–2068) under Representative Concentration Pathway (RCP) 8.5 model analysis. Field experiment showed a (0.02) difference in maximum temperature, (0.04) in minimum temperature, (0.17) in humidity, and about (0.03) significant difference in soil moisture content during 2019–2021. Under these changing climatic parameters, a 0.21% difference was accounted in annual yield. Furthermore, the results were supported by GMC model analysis, which was analyzed by Decision Support System for Agrotechnology Transfer (DSSAT) model. Results depicted that non-heat-resistant wheat varieties could cause up to a 6~13% reduction in yield during future 50 years (2019–2068)) compared with the last 40 years (1970–2010). A larger decline in wheat grain number relative to grain weight is a key reducer of wheat yield, under future climate change circumstances. Using heat-tolerant wheat varieties will not only assist to overcome this plethora but also provide a potential increase of up to 7% to 10% in indigenous environment. On the other hand, it was concluded that cultivating these heat-resistant varieties that are also ripening late culminates into enhanced thermal time chucks during the grain-filling period; hence, wheat yield will increase by 8% to 12%. In changing climatic conditions and varieties, ‘Punjab–2018′ will be the better choice for peasants and farm-land owners to obtain a better yield of wheat to cope with the necessities of food on the domestic and national level.
A Highly Active Chondroitin Sulfate Lyase ABC for Enzymatic Depolymerization of Chondroitin Sulfate
Authors: Fan Xiao-Man , Huang Jia-Ying , Ling Xiao-Min , Wei Wei , Su Wen-Bin , Zhang Ye-Wang
Published: April 2022

Enzymatic preparation of low-molecular-weight chondroitin sulfate (LMWCS) has received increasing attention. In this work, a chondroitin sulfate lyase ABC (Chon-ABC) was successfully cloned, expressed, and characterized. The Km and Vmax of the Chon-ABC were 0.54 mM and 541.3 U mg−1, respectively. The maximal activity was assayed as 500.4 U mg−1 at 37 °C in pH 8.0 phosphate buffer saline. The half-lives of the Chon-ABC were 133 d and 127 min at 4 °C and 37 °C, respectively. Enzymatic preparation of LMWCS was performed at room temperature for 30 min. The changes between the substrate and product were analyzed with mass spectrometry (MS), high-performance liquid chromatography (HPLC), gel permeation chromatography (GPC), and nuclear magnetic resonance (NMR). Overall, the Chon-ABC from Bacteroides thetaiotaomicron is competitive in large-scale enzymatic preparation of LMWCS for its high activity, stability, and substrate specificity.
Rapid Vehicle Detection in Aerial Images under the Complex Background of Dense Urban Areas
Authors: Zhu Shengjie , Liu Jinghong , Tian Yang , Zuo Yujia , Liu Chenglong
Published: April 2022

Vehicle detection on aerial remote sensing images under the complex background of urban areas has always received great attention in the field of remote sensing; however, the view of remote sensing images usually covers a large area, and the size of the vehicle is small and the background is complex. Therefore, compared with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem. In this paper, we propose a single-scale rapid convolutional neural network (SSRD-Net). In the proposed framework, we design a global relational (GR) block to enhance the fusion of local and global features; moreover, we adjust the image segmentation method to unify the vehicle size in the input image, thus simplifying the model structure and improving the detection speed. We further introduce an aerial remote sensing image dataset with rotating bounding boxes (RO-ARS), which has complex backgrounds such as snow, clouds, and fog scenes. We also design a data augmentation method to get more images with clouds and fog. Finally, we evaluate the performance of the proposed model on several datasets, and the experimental results show that the recall and precision are improved compared with existing methods.
Fast Superpixel-Based Non-Window CFAR Ship Detector for SAR Imagery
Authors: Zhang Liang , Zhang Zhijun , Lu Shengtao , Xiang Deliang , Su Yi
Published: April 2022

Ship detection in high-resolution synthetic aperture radar (SAR) images has attracted great attention. As a popular method, a constant false alarm rate (CFAR) detection algorithm is widely used. However, the detection performance of CFAR is easily affected by speckle noise. Moreover, the sliding window technique cannot effectively differentiate between clutter and target pixels and easily leads to a high computation load. In this paper, we propose a new superpixel-based non-window CFAR ship detection method for SAR images, which introduces superpixels to CFAR detection to resolve the aforementioned drawbacks. Firstly, our previously proposed fast density-based spatial clustering of applications with noise (DBSCAN) superpixel generation method is utilized to produce the superpixels for SAR images. With the assumption that SAR data obeys gamma distribution, the superpixel dissimilarity is defined. Then, superpixels can be accurately used to estimate the clutter parameters for the tested pixel, even in the multi-target situations, avoiding the drawbacks of the sliding window in the traditional CFAR. Moreover, a local superpixel contrast is proposed to optimize CFAR detection, which can eliminate numerous clutter false alarms, such as man-made urban areas and low bushes. Experimental results with real SAR images indicate that the proposed method can achieve ship detection with a higher speed and accuracy in comparison with other state-of-the-art methods.
Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle
Authors: Qin Longjun , Mao Peng , Xu Zhenbang , He Yang , Yan Chunhua , Hayat Muhammad , Qiu Guo-Yu
Published: April 2022

With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment and monitoring of urban vegetation damage after typhoons is important, as they contribute to post-disaster recovery and resilience efforts. Hence, this study examined the application of the easy-to-use and cost-effective Unmanned Aerial Vehicle (UAV) oblique photography technology and proposed an improved detection and diagnostic measure for the assessment of street-level damage to urban vegetation caused by the super typhoon Mangkhut in Shenzhen, China. The results showed that: (1) roadside trees and artificially landscaped forests were severely damaged; however, the naturally occurring urban forest was less affected by the typhoon. (2) The vegetation height of roadside trees decreased by 20–30 m in most areas, and that of artificially landscaped forests decreased by 5–15 m; however, vegetation height in natural forest areas did not change significantly. (3) The real damage to vegetation caused by the typhoon is better reflected by measuring the change in vegetation height. Our study validates the use of UAV remote sensing to accurately measure and assess the damage caused by typhoons to roadside trees and urban forests. These findings will help city planners to design more robust urban landscapes that have greater disaster coping capabilities.
Progress towards an HF Radar Wind Speed Measurement Method Using Machine Learning
Authors: Wyatt Lucy R.
Published: April 2022

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined.
Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations
Authors: Sravani Charakola , Kishore Pangaluru , Jiang Jonathan H. , Rao S. Vijaya Bhaskara
Published: April 2022

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model’s future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model’s performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets.
Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults
Authors: Wang Shuaijie , Miranda Fabio , Wang Yiru , Rasheed Rahiya , Bhatt Tanvi
Published: April 2022

Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (>64%) and NLOB (>90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.
Wearable and Portable GPS Solutions for Monitoring Mobility in Dementia: A Systematic Review
Authors: Cullen Anisha , Mazhar Md Khadimul Anam , Smith Matthew D. , Lithander Fiona E. , Ó Breasail Mícheál , Henderson Emily J.
Published: April 2022

Dementia is the most common neurodegenerative disorder globally. Disease progression is marked by declining cognitive function accompanied by changes in mobility. Increased sedentary behaviour and, conversely, wandering and becoming lost are common. Global positioning system (GPS) solutions are increasingly used by caregivers to locate missing people with dementia (PwD) but also offer a non-invasive means of monitoring mobility patterns in PwD. We performed a systematic search across five databases to identify papers published since 2000, where wearable or portable GPS was used to monitor mobility in patients with common dementias or mild cognitive impairment (MCI). Disease and GPS-specific vocabulary were searched singly, and then in combination, identifying 3004 papers. Following deduplication, we screened 1972 papers and retained 17 studies after a full-text review. Only 1/17 studies used a wrist-worn GPS solution, while all others were variously located on the patient. We characterised the studies using a conceptual framework, finding marked heterogeneity in the number and complexity of reported GPS-derived mobility outcomes. Duration was the most frequently reported category of mobility reported (15/17), followed by out of home (14/17), and stop and trajectory (both 10/17). Future research would benefit from greater standardisation and harmonisation of reporting which would enable GPS-derived measures of mobility to be incorporated more robustly into clinical trials.
MR-FPN: Multi-Level Residual Feature Pyramid Text Detection Network Based on Self-Attention Environment
Authors: Kang Jianjun , Ibrayim Mayire , Hamdulla Askar
Published: April 2022

With humanity entering the age of intelligence, text detection technology has been gradually applied in the industry. However, text detection in a complex background is still a challenging problem for researchers to overcome. Most of the current algorithms are not robust enough to locate text regions, and the problem of the misdetection of adjacent text instances still exists. In order to solve the above problems, this paper proposes a multi-level residual feature pyramid network (MR-FPN) based on a self-attention environment, which can accurately separate adjacent text instances. Specifically, the framework uses ResNet50 as the backbone network, which is improved on the feature pyramid network (FPN). A self-attention module (SAM) is introduced to capture pixel-level relations, increase context connection, and obtain efficient features. At the same time, the multi-scale enhancement module (MEM) improves the expression ability of text information, extracting strong semantic information and integrating the multi-scale features generated by the feature pyramid. In addition, information regarding the upper features will cause loss when the feature pyramid is passed down step by step, and multi-level residuals can effectively solve this problem. The proposed model can effectively improve the fusion ability of the feature pyramid, provide more refined features for text detection, and improve the robustness of text detection. This model was evaluated on CTW1500, Total-Text, ICDAR2015, and MSRA-TD500 datasets of different kinds and achieved varying degrees of improvement. It is worth mentioning that the F-measure of 83.31% obtained by this paper on the Total-Text dataset exceeds that of the baseline system by 5%.
Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data
Authors: Ta Quoc-Bao , Huynh Thanh-Canh , Pham Quang-Quang , Kim Jeong-Tae
Published: April 2022

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable. This study presents a deep learning method to identify corroded bolts in steel structures using a mask region-based convolutional neural network (Mask-RCNN) trained on synthesized data. The Resnet50 integrated with a feature pyramid network is used as the backbone for feature extraction in the Mask-RCNN-based corroded bolt detector. A four-step data synthesis procedure is proposed to autonomously generate the training datasets of corroded bolts with different severities. Afterwards, the proposed detector is trained by the synthesized datasets, and its robustness is demonstrated by detecting corroded bolts in a lab-scale steel structure under varying capturing distances and perspectives. The results show that the proposed method has detected corroded bolts well and identified their corrosion levels with the most desired overall accuracy rate = 96.3% for a 1.0 m capturing distance and 97.5% for a 15° perspective angle.
Enhancing the Radar Cross-Range Resolution in Ultra-Fast Radar Scans by Utilizing Frequency Coded Sub-Channels
Authors: Baer Christoph , Karsch Nicholas , Kaesbach Robin , Musch Thomas
Published: April 2022

This contribution handles a single-channel radar method that utilizes frequency-coded sub-channels for enabling cross-range resolution. Because of the sub-channel coding, the whole area of interest (AOI) is scanned within a single radar measurement. To further enhance the cross-range resolution, the sub-channels’ antenna beams are overlaid in this work, resulting in multiple coding signatures. Next to the operation theory, hardware components, such as coding filters and antennas, as well as signal processing methods, are presented and discussed in detail. A final measurement campaign that investigates several radar scenarios reveals high detection properties and proves the applicability of the proposed radar method.
Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas
Authors: Zafar Noureen , Haq Irfan Ul , Chughtai Jawad-ur-Rehman , Shafiq Omair
Published: April 2022

With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial–temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.
Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors
Authors: Zhang Yajun , Hao Dongmei , Yang Lin , Zhou Xiya , Ye-Lin Yiyao , Yang Yimin
Published: April 2022

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.
Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm
Authors: Zheng Huan , Xiao Feng , Sun Shijie , Qin Yali
Published: April 2022

The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the AdaBoost Algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.
Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction
Authors: Gomez-Perez Sandra L. , Zhang Yanyu , Byrne Cecily , Wakefield Connor , Geesey Thomas , Sclamberg Joy , Peterson Sarah
Published: April 2022

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin’s Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland–Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65–0.87). Bland–Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland–Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).
Accuracy Report on a Handheld 3D Ultrasound Scanner Prototype Based on a Standard Ultrasound Machine and a Spatial Pose Reading Sensor
Authors: Chifor Radu , Marita Tiberiu , Arsenescu Tudor , Santoma Andrei , Badea Alexandru Florin , Colosi Horatiu Alexandru , Badea Mindra-Eugenia , Chifor Ioana
Published: April 2022

The aim of this study was to develop and evaluate a 3D ultrasound scanning method. The main requirements were the freehand architecture of the scanner and high accuracy of the reconstructions. A quantitative evaluation of a freehand 3D ultrasound scanner prototype was performed, comparing the ultrasonographic reconstructions with the CAD (computer-aided design) model of the scanned object, to determine the accuracy of the result. For six consecutive scans, the 3D ultrasonographic reconstructions were scaled and aligned with the model. The mean distance between the 3D objects ranged between 0.019 and 0.05 mm and the standard deviation between 0.287 mm and 0.565 mm. Despite some inherent limitations of our study, the quantitative evaluation of the 3D ultrasonographic reconstructions showed comparable results to other studies performed on smaller areas of the scanned objects, demonstrating the future potential of the developed prototype.
Mobile Robots for In-Process Monitoring of Aircraft Systems Assemblies
Authors: Auledas-Noguera Marc , Liaqat Amer , Tiwari Ashutosh
Published: April 2022

Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications.