bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Carbohydrate Diets and Mortality in Older Asian People: A 15-Year Follow-Up from a Prospective Cohort Study
Authors: Sun Ce , Zhang Wei-Sen , Jiang Chao-Qiang , Jin Ya-Li , Deng Xue-Qing , Woo Jean , Cheng Kar-Keung , Lam Tai-Hing , Thomas G. Neil , Xu Lin
Published: March 2022

The long-term effects of a low-carbohydrate diet (LCD) on mortality, accounting for the quality and source of the carbohydrate, are unclear. Hence, we examined the associations of LCDs with all-cause and cause-specific mortality in a prospective cohort study. A total of 20,206 participants (13.8% diabetes) aged 50+ years were included. Overall, vegetable-based and meat-based LCD scores were calculated based on the percentage of energy as total and subtypes of carbohydrates, fat, and protein. Cox regression analysis was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). During 294,848 person-years of follow-up, 4624 deaths occurred, including 3661 and 963 deaths in participants without and with diabetes, respectively. In all participants, overall LCD score was not associated with all-cause and cause-specific mortality, after multivariable adjustment. However, for the highest versus the lowest quartiles of vegetable-based LCD, the adjusted HRs (95%CIs) of all-cause and CVD mortality were 1.16 (1.05–1.27) and 1.39 (1.19–1.62), respectively. The corresponding values for highest versus lowest quartiles of meat-based LCD for all-cause and CVD mortality were 0.89 (0.81–0.97) and 0.81 (0.70–0.93), respectively. Similar associations were found in participants without diabetes. In patients with diabetes, the adjusted HR (95%CI) of CVD mortality for the highest versus the lowest quartiles of vegetable-based LCD was 1.54 (1.11–2.14). Although there were no significant associations with overall LCD score, we found that the vegetable-based LCD score was positively, whereas the meat-based LCD score was negatively, associated with all-cause and CVD mortality in older Asian people.
NUCB2/Nesfatin-1 Reduces Obesogenic Diet Induced Inflammation in Mice Subcutaneous White Adipose Tissue
Authors: Gharanei Seley , Ramanjaneya Manjunath , Patel Aaran Hitesh , Patel Vanlata , Shabir Kiran , Auld Callum , Karteris Emmanouil , Kyrou Ioannis , Randeva Harpal Singh
Published: March 2022

Background: Excess adipose tissue accumulation and obesity are characterised by chronic, low-grade, systemic inflammation. Nestfatin-1 is a neuropeptide derived from the precursor protein nucleobindin-2 (NUCB2), which was initially reported to exert anorexigenic effects. The present study aimed to investigate the effects of an obesogenic diet (OD; high-fat, high-sugar) in NUCB2 knockout (KO) mice and of nesfatin-1 treatment in LPS-stimulated 3T3-L1 preadipocytes. Methods: Subcutaneous white adipose tissue (Sc-WAT) samples from wild type (WT) and NUCB2 KO mice that were fed a normal diet (ND), or the OD for 12 weeks were used for RNA and protein extraction, as well as immunohistochemistry. 3T3-L1 cells were treated with 100 nM nesfatin-1 during differentiation and stimulated with 1 µg/mL LPS for measuring the expression and secretion of pro-inflammatory mediators by qPCR, western blotting, immunofluorescence, Bioplex, and ELISA. Results: Following the OD, the mRNA, protein and cellular expression of pro-inflammatory mediators (Tnfα, Il-6, Il-1β, Adgre1, Mcp1, TLR4, Hmbgb1 and NF-kB) significantly increased in the ScWAT of NUCB2 KO mice compared to ND controls. Adiponectin and Nrf2 expression significantly decreased in the ScWAT of OD-fed NUCB2 KO, without changes in the OD-fed WT mice. Furthermore, nesfatin-1 treatment in LPS-stimulated 3T3-L1 cells significantly reduced the expression and secretion of pro-inflammatory cytokines (Tnfα, Il-6, Il-1β, Mcp1) and hmgb1. Conclusion: An obesogenic diet can induce significant inflammation in the ScWAT of NUCB2 KO mice, involving the HMGB1, NRF2 and NF-kB pathways, while nesfatin-1 reduces the pro-inflammatory response in LPS-stimulated 3T3-L1 cells. These findings provide a novel insight into the metabolic regulation of inflammation in WAT.
Ultraprocessed Products as Food Fortification Alternatives: A Critical Appraisal from Latin America
Authors: Kroker-Lobos Maria F. , Mazariegos Mónica , Guamuch Mónica , Ramirez-Zea Manuel
Published: March 2022

Ultraprocessed products (UPPs), associated with obesity and non-communicable diseases (NCDs), are becoming predominant on the global market and a target for market-driven fortification initiatives. The aim of this article is to describe the implications of adding micronutrients to UPPs with excessive amounts of critical nutrients associated with NCDs and provide recommendations for legislation and policies. UPPs with added micronutrients such as breakfast cereals, sugar-sweetened beverages, powder beverages, fruit juices, sauces, and bouillon cubes, among others, are commonly available and heavily promoted in Latin American countries. Misleading advertising of UPPs with added micronutrients and with excessive content of sugar, fat, and salt might increase the consumption of such products, giving them a “health halo effect” that leads consumers to overestimate their nutritional quality and healthfulness. Although international collections of standards such as the Codex Alimentarius provide some guidelines on this matter, countries need to implement national legislations, through a food systems approach, to regulate the marketing and labeling of UPPs. Lastly, there is still the need to foster research to close knowledge gaps and help countries to guide the process of food fortification strategies from a regulatory standpoint.
Fermented Food in Asthma and Respiratory Allergies—Chance or Failure?
Authors: Dębińska Anna , Sozańska Barbara
Published: March 2022

In the last few decades, a dramatic increase in the global prevalence of allergic diseases and asthma was observed. It was hypothesized that diet may be an important immunomodulatory factor influencing susceptibility to allergic diseases. Fermented food, a natural source of living microorganisms and bioactive compounds, has been demonstrated to possess health-promoting potentials and seems to be a promising strategy to reduce the risk of various immune-related diseases, such as allergic diseases and asthma. The exact mechanisms by which allergic diseases and asthma can be alleviated or prevented by fermented food are not well understood; however, its potential to exert an effect through modulating the immune response and influencing the gut microbiota has been recently studied. In this review, we provide the current knowledge on the role of diet, including fermented foods, in preventing or treating allergic diseases and asthma.
Prevalence and Financial Losses of Cystic Echinococcosis in Slaughtered Goats at Gumbo Slab in Juba County, South Sudan
Authors: Nigo Kundu L. S. , John Bata T. , Lobojo Doris L. , Lita Emmanuel P. , Osman Abdinasir Yusuf , Shuaib Yassir A.
Published: March 2022

A cross-sectional study was conducted at Gumbo slaughter slab in Juba, Central Equatoria State (CES) in South Sudan, between October and December 2015, to determine the prevalence of cystic echinococcosis (CE) and to estimate the direct economic losses associated with condemnation of infected organs in slaughtered goats. A thorough postmortem examination was conducted on carcasses of a total of 1126 goats selected by systematic random sampling for detection of CE in the lungs, liver, heart, kidneys, and the spleen. Moreover, CE-related direct financial losses were estimated by using a standardized formula. The overall prevalence of CE was found to be 281 (24.9%, 95% CI 22.5–27.6). Furthermore, old age (OR = 2.61, p value < 0.001), Mubende breed (OR = 2.97, p value < 0.001), and Ugandan geographical origin (OR = 2.97, p value < 0.001) were associated with higher prevalence of CE. The lungs were the most affected organ with a prevalence of 55.2% followed by liver (44.1%), and the spleen (0.07%). A direct CE-related monetary loss of 78,820 South Sudanese Pounds (SSP) per year was estimated. This loss is equivalent to USD 43,788. In conclusion, CE is considerably prevalent and is associated with significant direct financial losses among goats slaughtered at Gumbo slaughter slab in Juba, CES, South Sudan.
Effect of ITPA Polymorphism on Adverse Drug Reactions of 6-Mercaptopurine in Pediatric Patients with Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis
Authors: Lee Yeonhong , Jang Eun Jeong , Yoon Ha-Young , Yee Jeong , Gwak Hye-Sun
Published: March 2022

6-Mercaptopurine (6-MP) is a cornerstone of the maintenance regimen for pediatric acute lymphoblastic leukemia (ALL). Inosine triphosphate pyrophosphatase (ITPA) is considered a candidate pharmacogenetic marker that may affect metabolism and 6-MP-induced toxicities; however, the findings are inconsistent. Therefore, we attempted to evaluate the effect of ITPA 94C>A polymorphism on 6-MP-induced hematological toxicity and hepatotoxicity through a systematic review and meta-analysis. A literature search for qualifying studies was conducted using the PubMed, Web of Science, and Embase databases until October 2021. Overall, 10 eligible studies with 1072 pediatric ALL patients were included in this meta-analysis. The results indicated that ITPA 94C>A was significantly associated with 6-MP-induced neutropenia (OR 2.38, 95% CI: 1.56–3.62; p = 0.005) and hepatotoxicity (OR 1.98, 95% CI: 1.32–2.95; p = 0.0009); however, no significant association was found between the ITPA 94C>A variant and 6-MP-induced leukopenia (OR 1.75, 95% CI: 0.74–4.12; p = 0.20). This meta-analysis demonstrated that ITPA 94C>A polymorphism could affect 6-MP-induced toxicities. Our findings suggested that ITPA genotyping might help predict 6-MP-induced myelosuppression and hepatotoxicity.
Development and Validation of an Inventory for Stressful Situations in University Students Involving Coping Mechanisms: An Interesting Cultural Mix in Ghana
Authors: Quansah Frank , Ankomah Francis , Hagan John Elvis , Srem-Sai Medina , Frimpong James Boadu , Sambah Francis , Schack Thomas
Published: March 2022

Cognitive and behavioural coping strategies are relevant approaches for individuals such as university students as they attempt to manage stressful situations such as the COVID-19 pandemic and other academic-related pursuits within their social milieu. Although several instruments have been developed to measure the coping situations of such individuals, few studies have developed students’ specific coping inventories, with none in the African context. Of the few that exist, a culturally dominant code such as religion has been ignored by many scholars in the development of coping measures. In this study, a cultural-mix coping inventory was developed and validated using university students in Ghana. Two distinct interrelated objectives were addressed. First, the structure of the coping inventory was identified through principal component analysis. Further confirmatory factor as well as reliability analyses were then performed to provide evidence of construct validity of the scale. The outcome of the study revealed a sixteen-item psychometrically sound coping inventory with a four-dimensional structure, namely, active coping, religious coping, behaviour disengagement, and emotional support. The implications of the results are further discussed in detail.
«We Are Alone»: Intergenerational Religious Transmission and the Effect of Migration in Italy
Authors: Bossi Luca , Marroccoli Giulia
Published: March 2022

Although research shows a general decrease in religiosity in Western societies, religious transmission still seems to be a poorly studied phenomenon, especially in Italy. We do not know much about the dynamics of socialisation that take place in the family and the main factors that determine the success or failure of religious transmission from one generation to the next. Yet, the family context represents the first agency of socialisation to religion, from an early age, through experiences with parents, grandparents and relatives. On the other hand, in the age of religious diversification, the migration factor is becoming increasingly crucial for national religious landscapes. Nevertheless, religious transmission in foreign families and the impact of migration on family religiosity are still little studied. This contribution presents some research perspectives that have emerged from a wide-ranging survey, still in progress, on intergenerational transmission of religion in Catholic, Muslim and Orthodox families in Italy.
Ketogenic Diet and Physical Exercise on Managing Tarlov Cysts: A Case Report
Authors: Cannataro Roberto , Di Lorenzo Cherubino , Iazzolino Mario , Caroleo Maria Cristina , Gallelli Luca , De Sarro Giovambattista , Cione Erika
Published: March 2022

Tarlov Cysts is a pathological condition, with low incidence, characterized by a painful component with a strong impact on quality of life. The therapeutic options are surgery or analgesics and/or anti-inflammatory medications; however, the condition is still without resolution. Herein, we are reporting a case of a woman who expressly followed a low-calorie ketogenic diet program for 3 months. In addition to the change in diet, an appreciable decrease of weight (−5 kg) and body circumferences were recorded; there was also a marked improvement (evident from the questionnaires administered) in the quality of life, of sleep, and in the perception of pain. It is interesting to note how, in conjunction with the Christmas period, upon leaving the ketogenic regime, there was a recurrence of symptoms, confirming the beneficial effect of the low-caloric ketogenic diet at least on the management of pain and, very likely, on inflammation.
Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data
Authors: Orellana Felipe , Hormazábal Joaquín , Montalva Gonzalo , Moreno Marcos
Published: March 2022

Coastal areas concentrate a large portion of the country’s population around urban areas, which in subduction zones commonly are affected by drastic tectonic processes, such as the damage earthquakes have registered in recent decades. The seismic cycle of large earthquakes primarily controls changes in the coastal surface level in these zones. Therefore, quantifying temporal and spatial variations in land level after recent earthquakes is essential to understand shoreline variations better, and to assess their impacts on coastal urban areas. Here, we measure the coastal subsidence in central Chile using a multi-temporal differential interferometric synthetic aperture radar (MT-InSAR). This geographic zone corresponds to the northern limit of the 2010 Maule earthquake (Mw 8.8) rupture, an area affected by an aftershock of magnitude Mw 6.8 in 2019. The study is based on the exploitation of big data from SAR images of Sentinel-1 for comparison with data from continuous GNSS stations. We analyzed a coastline of ~300 km by SAR interferometry that provided high-resolution ground motion rates from between 2018 and 2021. Our results showed a wide range of subsidence rates at different scales, of analyses on a regional scale, and identified the area of subsidence on an urban scale. We identified an anomalous zone of subsidence of ~50 km, with a displacement <−20 mm/year. We discuss these results in the context of the impact of recent earthquakes and analyze the consequences of coastal subsidence. Our results allow us to identify stability in urban areas and quantify the vertical movement of the coast along the entire seismic cycle, in addition to the vertical movement of coast lands. Our results have implications for the planning of coastal infrastructure along subduction coasts in Chile.
SAR Image Segmentation by Efficient Fuzzy C-Means Framework with Adaptive Generalized Likelihood Ratio Nonlocal Spatial Information Embedded
Authors: Zhu Jingxing , Wang Feng , You Hongjian
Published: March 2022

The existence of multiplicative noise in synthetic aperture radar (SAR) images makes SAR segmentation by fuzzy c-means (FCM) a challenging task. To cope with speckle noise, we first propose an unsupervised FCM with embedding log-transformed Bayesian non-local spatial information (LBNL_FCM). This non-local information is measured by a modified Bayesian similarity metric which is derived by applying the log-transformed SAR distribution to Bayesian theory. After, we construct the similarity metric of patches as the continued product of corresponding pixel similarity measured by generalized likelihood ratio (GLR) to avoid the undesirable characteristics of log-transformed Bayesian similarity metric. An alternative unsupervised FCM framework named GLR_FCM is then proposed. In both frameworks, an adaptive factor based on the local intensity entropy is employed to balance the original and non-local spatial information. Additionally, the membership degree smoothing and the majority voting idea are integrated as supplementary local information to optimize segmentation. Concerning experiments on simulated SAR images, both frameworks can achieve segmentation accuracy of over 97%. On real SAR images, both unsupervised FCM segmentation frameworks work well on SAR homogeneous segmentation in terms of region consistency and edge preservation.
Integrated Archaeological Modeling Based on Geomatics Techniques and Ground-Penetrating Radar
Authors: Al-Ruzouq Rami , Abu Dabous Saleh , Abueladas Abdelrahman , Hosny Fatma , Ibrahim Fakhariya
Published: March 2022

Archaeological sites have been subjected to preservation efforts due to their significant historical and cultural value, as well as their vulnerability. Several advanced digital imageries and modeling technologies have been proposed in the literature and adopted in practice to obtain accurate data required to manage and restore archaeological sites. This study proposes an integrated data collection and analysis methodology combining aerial and close-range photogrammetry, geographic information systems (GIS), and ground-penetrating radar (GPR) technologies to capture essential geospatial and geophysical information for preserving archaeological sites. The integrated methodology was applied and demonstrated with data and information collected from the important archaeological site of Qaser Amra, which is an ancient castle located in the desert in Jordan. The proposed methodology generated various levels of details, including a 2.5-dimensional geo-reference model, a GIS vector site layout, and a three-dimensional (3D) textured model. Subsurface detection of anomalies was performed across the site using the GPR technology. Most anomalies indicated shallow subsurface walls buried within depths ranging from half to one meter and at different extensions. Moreover, based on the GPR data, the horizontal and vertical extent of Qaser Amra’s walls were defined using 3D imaging. The integrated 3D archaeological model of Qaser Amra can be used for archaeological documentation, maintenance and monitoring, conservation, tourism, and urban planning.
Development of a New Tropical Cyclone Strip Segment Retrieval Model for C-Band Cross-Polarized SAR Data
Authors: Lv Letian , Zhang Yanmin , Wang Yunhua , Jiang Wenzheng , Sun Daozhong
Published: March 2022

Compared with co-polarized (HH/VV) normalized radar cross-section (NRCS) backscattered from the sea surface, there is no saturation phenomenon in cross-polarized (HV/VH) NRCS when wind speed is greater than about 20 m/s, so cross-polarized synthetic aperture radar (SAR) images can be used for high wind speed monitoring. In this work, a new geophysical model function (GMF) is proposed to describe the relation of the C-band cross-polarized NRCS with wind speed and radar incidence angle. Here, sixteen ScanSAR wide mode SAR images acquired by RADARSAT-2 (RS-2) under tropical cyclone (TC) conditions and the matching wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Stepped-Frequency Microwave Radiometer (SFMR) are collected and divided into datasets A and B. Dataset A is used for analyzing the effects of the wind field and radar incidence angle on the reference noise-removed cross-polarized NRCS, and for proposing the new GMF for each sub-swath of the SAR images, while dataset B is used to retrieve wind speed and evaluate the validity of the new GMF. The comparisons between the wind speeds retrieved by the new GMF and the collocated ECMWF and SFMR data demonstrate the excellent performance of the new GMF for wind speed retrieval. To analyze the universality of the new GMF, wind speed retrievals based on 32 Sentinel-1A/B (S-1A/B) extra-wide-swath (EW) mode images acquired under TC conditions are also compared with the collocated wind speeds measured by the Soil Moisture Active Passive (SMAP) radiometer, and the retrieved wind speeds have RMSE of 3.667 m/s and a bias of 2.767 m/s. The successful applications in high wind speed retrieval of different tropical cyclones again supports the availability of the new GMF.
The Impact of Dynamic Emissivity–Temperature Trends on Spaceborne Data: Applications to the 2001 Mount Etna Eruption
Authors: Rogic Nikola , Bilotta Giuseppe , Ganci Gaetana , Thompson James O. , Cappello Annalisa , Rymer Hazel , Ramsey Michael S. , Ferrucci Fabrizio
Published: March 2022

Spaceborne detection and measurements of high-temperature thermal anomalies enable monitoring and forecasts of lava flow propagation. The accuracy of such thermal estimates relies on the knowledge of input parameters, such as emissivity, which notably affects computation of temperature, radiant heat flux, and subsequent analyses (e.g., effusion rate and lava flow distance to run) that rely on the accuracy of observations. To address the deficit of field and laboratory-based emissivity data for inverse and forward modelling, we measured the emissivity of ‘a’a lava samples from the 2001 Mt. Etna eruption, over the wide range of temperatures (773 to 1373 K) and wavelengths (2.17 to 21.0 µm). The results show that emissivity is not only wavelength dependent, but it also increases non-linearly with cooling, revealing considerably lower values than those typically assumed for basalts. This new evidence showed the largest and smallest increase in average emissivity during cooling in the MIR and TIR regions (~30% and ~8% respectively), whereas the shorter wavelengths of the SWIR region showed a moderate increase (~15%). These results applied to spaceborne data confirm that the variable emissivity-derived radiant heat flux is greater than the constant emissivity assumption. For the differences between the radiant heat flux in the case of variable and constant emissivity, we found the median value is 0.06, whereas the 25th and the 75th percentiles are 0.014 and 0.161, respectively. This new evidence has significant impacts on the modelling of lava flow simulations, causing a dissimilarity between the two emissivity approaches of ~16% in the final area and ~7% in the maximum thickness. The multicomponent emissivity input provides means for ‘best practice’ scenario when accurate data required. The novel approach developed here can be used to test an improved version of existing multi-platform, multi-payload volcano monitoring systems.
Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia
Authors: Sharma Saroj Kumar , Aryal Jagannath , Rajabifard Abbas
Published: March 2022

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.
Using Artificial Neuro-Molecular System in Robotic Arm Motion Control—Taking Simulation of Rehabilitation as an Example
Authors: Chen Jong-Chen
Published: March 2022

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people’s daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.
A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization
Authors: Andreasson Henrik , Larsson Jonas , Lowry Stephanie
Published: March 2022

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.
Identification of the Principle of Taste Sensors to Detect Non-Charged Bitter Substances by 1H-NMR Measurement
Authors: Ishida Misaki , Ide Haruna , Arima Keishiro , Zhao Zeyu , Matsui Toshiro , Toko Kiyoshi
Published: March 2022

A taste sensor with lipid/polymer membranes is attracting attention as a method to evaluate taste objectively. However, due to the characteristic of detecting taste by changes in membrane potential, taste sensors cannot measure non-charged bitter substances. Many foods and medicines contain non-charged bitter substances, and it is necessary to quantify these tastes with sensors. Therefore, we have been developing taste sensors to detect bitter tastes caused by non-charged substances such as caffeine. In previous studies, a sensor for detecting bitterness caused by caffeine and theobromine, theophylline, was developed, using a membrane modified with hydroxybenzoic acid (HBA) as the sensing part. The sensor was designed to form intramolecular hydrogen bonds (H-bonds) between the hydroxy group and carboxy group of HBA and to successively cause the intermolecular H-bonds between HBA and caffeine molecules to be measured. However, whether this sensing principle is correct or not cannot be confirmed from the results of taste sensor measurements. Therefore, in this study, we explored the interaction between HBA and caffeine by 1H-nuclear magnetic resonance spectroscopy (NMR). By the 1H NMR detection, we confirmed that both the substances interact with each other. Furthermore, the nuclear Overhauser effect (NOE) of intermolecular spatial conformation in solution was measured, by which 2,6-dihydroxybenzoic acid (2,6-DHBA) preferably interacted with caffeine via the H-bonding and stacking configuration between aromatic rings. Identifying the binding form of 2,6-DHBA to caffeine was estimated to predict how the two substances interact.
Steganography in IoT: Information Hiding with APDS-9960 Proximity and Gestures Sensor
Authors: Koptyra Katarzyna , Ogiela Marek R.
Published: March 2022

This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data come from an external source or are pre-existing. In data input mode, the data to embed come directly from the sensor that may detect gestures or RGB color. The secrets are embedded in time-lapse photographs, which are later converted to videos. Selected hardware and steganographic methods allowed for smooth operation in the IoT environment. The system may cooperate with a digital camera and other sensors.
A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators
Authors: Vo Anh Tuan , Truong Thanh Nguyen , Kang Hee-Jun
Published: March 2022

Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error transformation to guarantee a prescribed performance within a finite time. Following the definitions mentioned, a modified integral nonlinear sliding-mode hyperplane is constructed from the transformed errors. By using the designed nonlinear sliding-mode surface and the super-twisting reaching control law, an advanced approach to the prescribed performance control was formed for the trajectory tracking control of uncertain robotic manipulators. The proposed controller exhibits improved properties, including estimated convergence speed and a predefined upper and lower limit for maximum overshoot during transient responses. Furthermore, the maximum allowable size of the control errors at the steady-state can be predefined and these errors will inevitably converge to zero within a finite time, while the proposed controller can provide a smooth control torque without the loss of its robustness. It is shown that the proposed control system is globally stable and convergent over a finite time. A comprehensive analysis of the effectiveness of the proposed control algorithm was already conducted via the simulation of an industrial robot manipulator.
Fully Customized Photoacoustic System Using Doubly Q-Switched Nd:YAG Laser and Multiple Axes Stages for Laboratory Applications
Authors: Jung Unsang , Choi Jin Hyuck , Choo Han Tae , Kim Gyu Ug , Ryu Jaemyung , Choi Hojong
Published: March 2022

We developed a customized doubly Q-switched laser that can control the pulse width to easily find weak acoustic signals for photoacoustic (PA) systems. As the laser was constructed using an acousto-optic Q-switcher, in contrast to the existing commercial laser system, it is easier to control the pulse repetition rate and pulse width. The laser has the following control ranges: 10 Hz–10 kHz for the pulse repetition rate, 40–150 ns for the pulse width, and 50–500 μJ for the pulse energy. Additionally, a custom-made modularized sample stage was used to develop a fully customized PA system. The modularized sample stage has a nine-axis control unit design for the PA system, allowing the sample target and transducer to be freely adjusted. This makes the system suitable for capturing weak PA signals. Images were acquired and processed for widely used sample targets (hair and insulating tape) with the developed fully customized PA system. The customized doubly Q-switched laser-based PA imaging system presented in this paper can be modified for diverse conditions, including the wavelength, frequency, pulse width, and sample target; therefore, we expect that the proposed technique will be helpful in conducting fundamental and applied research for PA imaging system applications.
Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data
Authors: Park Sangyong , Kim Jaeseon , Heo Yong Seok
Published: March 2022

To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and cost compared with other tasks such as classification and object detection, because the ground-truth label of every pixel in an image is required. Hence, it is practically demanding to train DCNNs using a limited amount of training data for semantic segmentation. Generally, training DCNNs using a limited amount of data is problematic as it easily results in a decrease in the accuracy of the networks because of overfitting to the training data. Here, we propose a new regularization method called pixel-wise adaptive label smoothing (PALS) via self-knowledge distillation to stably train semantic segmentation networks in a practical situation, in which only a limited amount of training data is available. To mitigate the problem caused by limited training data, our method fully utilizes the internal statistics of pixels within an input image. Consequently, the proposed method generates a pixel-wise aggregated probability distribution using a similarity matrix that encodes the affinities between all pairs of pixels. To further increase the accuracy, we add one-hot encoded distributions with ground-truth labels to these aggregated distributions, and obtain our final soft labels. We demonstrate the effectiveness of our method for the Cityscapes dataset and the Pascal VOC2012 dataset using limited amounts of training data, such as 10%, 30%, 50%, and 100%. Based on various quantitative and qualitative comparisons, our method demonstrates more accurate results compared with previous methods. Specifically, for the Cityscapes test set, our method achieved mIoU improvements of 0.076%, 1.848%, 1.137%, and 1.063% for 10%, 30%, 50%, and 100% training data, respectively, compared with the method of the cross-entropy loss using one-hot encoding with ground truth labels.
Simultaneous Quantification of 17 Cannabinoids in Cannabis Inflorescence by Liquid Chromatography-Mass Spectrometry
Authors: Hewavitharana Amitha K. , Gloerfelt-Tarp Francine , Nolan Matthew , Barkla Bronwyn J. , Purdy Sarah , Kretzschmar Tobias
Published: March 2022

With an increasing appreciation for the unique pharmacological properties associated with distinct, individual cannabinoids of Cannabis sativa, there is demand for accurate and reliable quantification for a growing number of them. Although recent methods are based on highly selective chromatography-mass spectrometry technology, most are limited to a few cannabinoids, while relying on unnecessarily sophisticated and expensive ultra-high performance liquid chromatography and tandem mass spectrometry. Here we report an optimised, simple extraction method followed by a reliable and simple high performance liquid chromatography method for separation. The detection is performed using a time-of-flight mass spectrometer that is available in most natural products research laboratories. Due to the simplicity of instrumentation, and the robustness resulting from a high resolution in the chromatography of isobaric cannabinoids, the method is well suited for routine phytocannabinoid analysis for a range of applications. The method was validated in terms of detection and quantification limits, repeatability, and recoveries for a total of 17 cannabinoids: detection limits were in the range 11–520 pg when using a 1 µL sample injection volume, and the recovery percentages ranged from 85% to 108%. The validated method was subsequently applied to determine cannabinoid composition in the inflorescences of several medicinal Cannabis sativa varieties.
Design, Analysis and Implementation of Bidirectional DC–DC Converters for HESS in DC Microgrid Applications
Authors: Punna Srinivas , Mailugundla Rupesh , Salkuti Surender Reddy
Published: March 2022

This research proposes an enhanced converter for a hybrid energy storage system (HESS) for a multi-input bidirectional DC–DC power converter (MIPC). When batteries are used for energy storage, their charge and discharge rates are low, putting the battery under current stress and shortening its life. Because of their increased power density, supercapacitors (SCs) can react quickly to abrupt fluctuations and solve this problem. SCs, on the other hand, cannot be utilized for storage since they cannot provide power for prolonged periods of time. Batteries and supercapacitors are employed together in HESSs because their opposing characteristics make them an ideal pair for energy storage. An MIPC is used to connect the HESS to the DC microgrid. The MIPC allows for decoupled battery and SC power regulation, as well as energy transfer across storage devices inside the system. A controller has been developed to regulate both HESS charging and discharging operations, making it a unified controller for DC microgrid applications. The proposed model predictive control (MPC) provided better DC grid voltage restoration to step change in PV generation and load demand over the traditional proportional integral (PI) control scheme. The MPC method minimizes current strains, extends battery life and enhances overall system performance in response to a step change in PV power and load demand as well as providing quicker DC grid voltage control. Simulation and experimental data for the proposed controller were created by varying PV generation and load demand, resulting in faster DC link voltage regulation.
Identification of Potential Valid Clients for a Sustainable Insurance Policy Using an Advanced Mixed Classification Model
Authors: Chen You-Shyang , Lin Chien-Ku , Lin Yu-Sheng , Chen Su-Fen , Tsao Huei-Hua
Published: March 2022

Due to the social awareness of risk control, we are witnessing the popularization of the insurance concept and the rapid development of financial insurance. The performance of the insurance industry is highly competitive; thus, in order to develop new and old business from existing clients, information on the renewal of client premiums, purchase of new policies, and new client referrals has become an important research topic in this field. However, based on a review of published literature, few scholars have engaged in relevant research on the above topics by data mining, which motivated the formation of this study, hoping to bridge this gap. We constructed 10 mixed classification prediction models (called Models A–J) using advanced data mining techniques. Moreover, 19 conditional attributes (coded as X1–X19) were selected from the collected insurance client database, plus three different decision attributes (coded as X20–X22): whether to pay the renewal insurance premium, whether to buy a new insurance policy, and whether to introduce new clients. In terms of technical methods, we used two data pretreatment techniques, attribute selection and data discretization, combined with different methods of disassembly in proportion and data cross-validation to conduct data analysis of the collected experimental data set. We also combined and calculated 23 important classification algorithms (or classifiers) in seven different classifications of data mining techniques (i.e., decision tree, Bayes, Function, Lazy, Meta, Mise, and Rule). In terms of the experimental results of insurance data, this study has the following important contributions and findings: (1) finding the best classifier; (2) finding the optimal mixed classification model; (3) determining the best disassembly in proportion; (4) comparing the performance of different disassembly in proportion and data cross-validation methods; (5) determining the important factors influencing the decision attribute “whether to purchase a new insurance policy”, including the time interval to the first purchase, the number of valid policies, the total number of purchased policies, the family salary structure, and gender; and (6) building a knowledge base of decision rules and criteria with the decision tree C4.5 technology, which shall be provided to relevant stakeholders such as insurance dealers and insurance salespeople as a reference for looking for valid clients in the future, and is conducive to the rapid expansion of insurance business. Finally, the important research findings and management implications of this study can serve as a basis for further study of sustainable insurance by academic researchers.
Screening of Plant Species Response and Performance for Green Belt Development: Implications for Semi-Urban Ecosystem Restoration
Authors: Anake Winifred U. , Bayode Faith O. , Jonathan Hassana O. , Omonhinmin Conrad A. , Odetunmibi Oluwole A. , Anake Timothy A.
Published: March 2022

Screened plant species with potential for green belt development can act as eco-sustainable tools for restoring the polluted ecosystem. Eight plant species from two study locations in Ado-Odo, Ota, Ogun State, Nigeria, were examined to identify their air pollution response and performance by deploying two air pollution indices, namely air pollution tolerance index (APTI) and anticipated performance index (API). APTI results identified all screened plants as sensitive species suitable as bio-indicators of air pollution, with Ficus auriculata (2.42) common to the non-industrial location being the most sensitive. API scores categorized Ficus auriculata (56.25%) as a moderate performer, while Syzygium malaccense (75%) and Mangifera indica (75%) were identified as very good performers, suitable for green belt development. The relationship between each biochemical parameter with APTI was investigated using regression analysis and two-way analysis of variance. The model result showed a significant relationship between each biochemical parameter with APTI, and relative water content had the highest influence on APTI (R2 = 0.99436). Both indices (APTI and API) are suitable for screening and recommending native plant species for cultivation in the polluted environment, thus promoting ecological restoration. Hence, Syzygium malaccense, Mangifera indica and Ficus auriculata, respectively, were recommended for green belts design. Further intensive screening to identify tolerant species and best to excellent performer’s trees suitable for restoring the ecosystem is advised.
Influence of Supplementary Cementitious Materials on Fresh Properties of 3D Printable Materials
Authors: Teixeira João , Schaefer Cecília Ogliari , Maia Lino , Rangel Bárbara , Neto Rui , Alves Jorge Lino
Published: March 2022

The development of printers and materials for 3D Printing Construction during the last two decades has allowed the construction of increasingly complex projects. Some of them have broken construction speed records due to the simplification of the construction process, particularly in non-standard geometries. However, for performance and security reasons the materials used had considerable amounts of Portland cement (PC), a constituent that increases the cost and environmental impact of 3D Printable Materials (3DPM). Supplementary Cement Materials (SCM), such as fly ash, silica fume and metakaolin, have been considered a good solution to partially replace PC. This work aims to study the inclusion of limestone filler, fly ash and metakaolin as SCM in 3DPM. Firstly, a brief literature review was made to understand how these SCM can improve the materials’ 3DP capacity, and which methods are used to evaluate them. Based on the literature review, a laboratory methodology is proposed to assess 3DP properties, where tests such as slump and flow table are suggested. The influence of each SCM is evaluated by performing all tests on mortars with different dosages of each SCM. Finally, a mechanical extruder is used to extrude the developed mortars, which allowed us to compare the results of slump and flow table tests with the quality of extruded samples.
Multi-Analytical Investigation of the Oil Painting “Il Venditore di Cerini” by Antonio Mancini and Definition of the Best Green Cleaning Treatment
Authors: Macchia Andrea , Biribicchi Chiara , Carnazza Paola , Montorsi Stefania , Sangiorgi Nausicaa , Demasi Giuseppe , Prestileo Fernanda , Cerafogli Eleonora , Colasanti Irene Angela , Aureli Helene , Zappelli Margherita , Ricca Michela , La Russa Mauro Francesco
Published: March 2022

This paper describes the multi-analytical approach implemented for the study of the oil painting Il Venditore di Cerini made by Antonio Mancini in 1878. The research was carried out to characterize both the original stratigraphy and the alleged non-original varnish on the surface. SEM/EDS analysis showed the presence of pigments already detected in other paintings by Antonio Mancini. Multispectral imaging, DinoLite microscope, and FT-IR ATR spectroscopy revealed significant data regarding the invention of the “graticola” method—a technique implemented by Mancini to respect the proportions of the figures—also proving the presence of an aged layer of non-original shellac on the surface. The yellow/brownish tone of the varnish was hiding the real shapes of the figure, requiring a selective removal of the aged coating. The proposed cleaning systems were chosen among the green chemical alternatives present in the market, aiming at promoting a sustainable development in the Cultural Heritage field. The selection was made according to the Fd parameter of the cleaning systems—which defines the energy from dispersion forces between molecules—in relation to what is defined in the literature as the suitable Fd value for the removal of the shellac. The best-performing green cleaning system proved to be the Polar Varnish Rescue GEL—a gelled acetals mixture developed by YOCOCU APS—for its effectiveness in selectively remove the aged shellac while preserving the integrity of the original stratigraphy.