bluuebunny/crossref_metadata_2025 huggingface dataset:
EGCG Inhibits Tumor Growth in Melanoma by Targeting JAK-STAT Signaling and Its Downstream PD-L1/PD-L2-PD1 Axis in Tumors and Enhancing Cytotoxic T-Cell Responses
Authors: Ravindran Menon Dinoop , Li Yang , Yamauchi Takeshi , Osborne Douglas Grant , Vaddi Prasanna Kumar , Wempe Michael F , Zhai Zili , Fujita Mayumi
Published: October 2021

Over the last decade, therapies targeting immune checkpoints, such as programmed death-1 (PD-1), have revolutionized the field of cancer immunotherapy. However, low response rates and immune-related adverse events remain a major concern. Here, we report that epigallocatechin gallate (EGCG), the most abundant catechin in green tea, inhibits melanoma growth by modulating an immune response against tumors. In vitro experiments revealed that EGCG treatment inhibited interferon-gamma (IFN-γ)-induced PD-L1 and PD-L2 expression and JAK-STAT signaling. We confirmed that this effect was driven by inhibiting STAT1 gene expression and STAT1 phosphorylation, thereby downregulating the PD-L1/PD-L2 transcriptional regulator IRF1 in both human and mouse melanoma cells. Animal studies revealed that the in vivo tumor-inhibitory effect of EGCG was through CD8+ T cells and that the inhibitory effect of EGCG was comparable to anti-PD-1 therapy. However, their mechanisms of action were different. Dissimilar to anti-PD-1 treatment that blocks PD-1/PD-L1 interaction, EGCG inhibited JAK/STAT signaling and PD-L1 expression in tumor cells, leading to the re-activation of T cells. In summary, we demonstrate that EGCG enhances anti-tumor immune responses by inhibiting JAK-STAT signaling in melanoma. EGCG could be used as an alternative treatment strategy to target the PD-L1/PD-L2-PD-1 axis in cancers.
Perspectives on the Use of Algae in Agriculture and Animal Production
Authors: Fleurence Joël
Published: October 2021

Algae have been used in agriculture as fertilizers for a long time. Recently, they have also been applied to crops as biostimulants that target plant growth promotion and tolerance to biotic (herbivores, fungi, bacteria, viruses) or abiotic stresses. In addition, algae contain bioactive compounds that have been shown to maintain the health of domestic animals or aquaculture species. This opinion piece highlights different aspects of the present use of algae in agriculture and animal production and their future perspectives.
Arnica montana Cell Culture Establishment, and Assessment of Its Cytotoxic, Antibacterial, α-Amylase Inhibitor, and Antioxidant In Vitro Bioactivities
Authors: Nieto-Trujillo Aurelio , Cruz-Sosa Francisco , Luria-Pérez Rosendo , Gutiérrez-Rebolledo Gabriel Alfonso , Román-Guerrero Angélica , Burrola-Aguilar Cristina , Zepeda-Gómez Carmen , Estrada-Zúñiga María Elena
Published: October 2021

Arnica montana cell suspension culture could be a sustainable source of a vegetal material producer of secondary metabolites (SMs) possessing biological effects. Different plant growth regulator concentrations (0–5 mg/L) were tested in foliar explants to induce a callus that was used to establish a cell suspension culture. Growth kinetics was carried out for 30 days. A methanolic extract obtained from biomass harvested at 30 days of growth kinetics was fractionated, and three fractions were tested for bioactivities. We induced a callus with 1 mg/L of picloram and 0.5 mg/L of kinetin in foliar explants, which allowed for the establishment of a cell suspension culture, and the latter had the highest total SMs contents at day 30. Three fractions showed differences in total SMs contents, with the highest values per gram as follows: 270 mg gallic acid equivalent for total phenolic content, 200 mg quercetin equivalent for total flavonoid content, 83 mg verbascoside equivalent for total phenolic acid content, and 396 mg parthenolide equivalent for total sesquiterpene lactone content. The best bioactivities were 2–6 µg/mL for the 50% inhibition of 2,2-diphenyl-1-picrylhydrazyl radical, 30% cellular viability of lymphoma cells at 40 µg/mL, 17% inhibition against Escherichia coli and Staphylococcus aureus at 8 µg/disk, and α-amylase inhibition at 12% with 10 µg/mL. The total SMs contents were correlated with bioactivities.
Biochemical and Metabolic Plant Responses toward Polycyclic Aromatic Hydrocarbons and Heavy Metals Present in Atmospheric Pollution
Authors: Molina Lázaro , Segura Ana
Published: October 2021

Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) are toxic components of atmospheric particles. These pollutants induce a wide variety of responses in plants, leading to tolerance or toxicity. Their effects on plants depend on many different environmental conditions, not only the type and concentration of contaminant, temperature or soil pH, but also on the physiological or genetic status of the plant. The main detoxification process in plants is the accumulation of the contaminant in vacuoles or cell walls. PAHs are normally transformed by enzymatic plant machinery prior to conjugation and immobilization; heavy metals are frequently chelated by some molecules, with glutathione, phytochelatins and metallothioneins being the main players in heavy metal detoxification. Besides these detoxification mechanisms, the presence of contaminants leads to the production of the reactive oxygen species (ROS) and the dynamic of ROS production and detoxification renders different outcomes in different scenarios, from cellular death to the induction of stress resistances. ROS responses have been extensively studied; the complexity of the ROS response and the subsequent cascade of effects on phytohormones and metabolic changes, which depend on local concentrations in different organelles and on the lifetime of each ROS species, allow the plant to modulate its responses to different environmental clues. Basic knowledge of plant responses toward pollutants is key to improving phytoremediation technologies.
Hybrid Bio-Inspired Structure Based on Nacre and Woodpecker Beak for Enhanced Mechanical Performance
Authors: Ding Zhongqiu , Wang Ben , Xiao Hong , Duan Yugang
Published: October 2021

Materials with high strength and toughness have always been pursued by academic and industrial communities. This work presented a novel hybrid brick-and-mortar-like structure by introducing the wavy structure of the woodpecker beak for enhanced mechanical performance. The effects of tablet waviness and tablet wave number on the mechanical performance of the bio-inspired composites were analyzed. Compared with nacre-like composites with a flat tablet, the strength, stiffness and toughness of the novel hybrid nacre-like composite with tablet wave surface increased by up to 191.3%, 46.6% and 811.0%, respectively. The novel failure mode combining soft phase failure and tablet fracture revealed the key to the high toughness of composites. Finite element simulations were conducted to further explore the deformation and stress distribution of the hybrid brick-and-mortar-like structure. It showed that the hybrid brick-and-mortar-like structure can achieve a much better load transfer, which leads to greater tensile deformation in tablet before fracture, thus improving strength and energy absorption. These investigations have implications in the design of composites with high mechanical performance for aerospace, automobile and other manufacturing industries.
Superabsorbents and Their Application for Heavy Metal Ion Removal in the Presence of EDDS
Authors: Kołodyńska Dorota , Drozd Alicja , Ju Yongming
Published: October 2021

Three acrylic-based superabsorbents—TerraHydrogel®Aqua (THA), Zeba®Hydrogel (ZH) and Agro®Hydrogel (AH) were used to investigate the influence of chemical conditions on kinetic and adsorption behavior towards metal ions in the presence of a chelating agent of a new generation called ethylenediamine-N,N′-disuccinic acid (EDDS). The effects of relevant parameters—mainly including those of sorbent dose, pH of the solution and initial concentration of Cu(II), Zn(II), Mn(II) and Fe(III) complexes with EDDS as well as phase contact time and temperature—on the adsorption efficiency were studied in detail by the static method. The experimental data were also characterized by kinetic and adsorption parameters obtained based on the Langmuir and Freundlich models of sorption as well as the Lagergren, Ho and McKay and Weber–Morris models.
Thermal Devolatilization Kinetics of Dry Distiller’s Grains with Solubles (DDGS)
Authors: Branca Carmen , Di Blasi Colomba
Published: October 2021

Dynamic thermogravimetric analysis is applied to investigate the thermal devolatilization of dry distiller’s grain with solubles (DDGS), the major by-product of bioethanol plants. Compared with lignocellulosic biomass, the DDGS devolatilization occurs over a much wider temperature range and with slower rates. This reveals complex dynamics attributable to a peculiar chemical composition comprising, in addition to lignocellulose, proteins, starch and other minor components. The evolution of lumped volatile product classes is well described by a five-step reaction mechanism. The numerical solution of the ordinary differential equations together with a minimization of the objective function leads to activation energies invariant with the heating rate. The estimated values of 89, 120, 158, 102 and 113 kJ/mol are, on average, higher than those obtained under oxidative environments but still lower than those typically estimated for wood.
The Prediction of Possibilities of CO Poisoning and Explosion during Syngas Leakage in the UCG Process
Authors: Laciak Marek , Durdán Milan , Kačur Ján , Flegner Patrik , Benková Marta
Published: October 2021

Underground coal gasification (UCG) is an industrial process that converts coal into product gas (syngas). This technology makes it possible to obtain energy without mining coal and post-processing. The UCG process is considered a safe technology with various advantages over conventional mining techniques. However, a small amount of potentially dangerous syngas may escape from the UCG cavity, and it is necessary to pay attention to the safety of the process in this respect. This article analyses the impact of a syngas leak from UCG on a possible CO poisoning and explosion hazard in the vulnerable zones. Measured data from experiments and proposed mathematical models were used for the analysis of the UCG process and simulation studies. It is mainly a mathematical model mixing of gases, which evaluated the danger of explosion and CO poisoning in sensitive zones. This model predicts the composition of the syngas mixture with air because this mixture can be dangerous at a certain concentration. Simulation studies focused mainly on CO poisoning in vulnerable areas based on the measured data from laboratory experiments. Simulation studies have shown that the critical value of CO poisoning can achieve with a certain composition of syngas and its escape into vulnerable areas. The results of the studies here indicate a possible hazard. However, by monitoring and control of UCG process, this risk can be minimized. Based on the obtained results, the control of the supply of fresh air to the endangered area during the monitoring of the syngas composition was proposed to prevent the occurrence of possible poisoning.
Crop Insurance Policies in India: An Empirical Analysis of Pradhan Mantri Fasal Bima Yojana
Authors: Kaur Sandeep , Raj Hem , Singh Harpreet , Chattu Vijay Kumar
Published: October 2021

India is home to over one-third of all undernourished children worldwide, and it ranks 94th out of 107 nations in the Global Hunger Index 2020. Instability in production and market risks make agriculture a risky business and directly affect farmers’ income levels, thereby impacting food security. This review aimed to understand various features of different crop insurance policies in India and to analyze the Pradhan Mantri Fasal Bima Yojana’s (PMFBY) impacts on Indian farmers. A literature search was performed in all popular databases, including Scopus, Web of Science, ProQuest, AGRICOLA, AGRIS, and Google search engines, as well as annual Indian government reports. The keywords “Crop Insurance” OR “Pradhan Mantri Fasal Bima Yojana” OR “National Agriculture Schemes” AND “India” were searched to obtain relevant articles. By using cumulative data, we conducted a multiple regression analysis and a model was developed to estimate the effects of insurance characteristics on farmer coverage for the years 2017–2018 and 2018–2019. Agricultural insurance coverage under PMFBY remained low in terms of the number of farmers insured, the area insured, claims paid, and total farmers benefited. Compared to other schemes, the beneficiary and claim premium ratios were substantially lower under the PMFBY. The multiple regression analysis showed that farmers’ premiums have a significant effect on the number of farmers insured over time, although the subsidies do not have a significant influence in farmers’ insurance participation. Delays in claim settlement, the complexity of the system, and a lack of awareness among farmers are the major weaknesses of the PMFBY. Greater use of digital media could help spread awareness of these schemes among farmers.
Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps
Authors: Nitze Ingmar , Heidler Konrad , Barth Sophia , Grosse Guido
Published: October 2021

In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions.
Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery
Authors: Xie Congshuang , Chen Peng , Pan Delu , Zhong Chunyi , Zhang Zhenhua
Published: October 2021

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.
Evaluating the Farmland Use Intensity and Its Patterns in a Farming—Pastoral Ecotone of Northern China
Authors: Chen Xin , Zhang Guoliang , Jin Yuling , Mao Sicheng , Laakso Kati , Sanchez-Azofeifa Arturo , Jiang Li , Zhou Yi , Zhao Haile , Yu Le , Jiang Rui , Pan Zhihua , An Pingli
Published: October 2021

The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m3/hm2, 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns.
Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China
Authors: Mao Yu , Michel Opelele Omeno , Yu Ying , Fan Wenyi , Sui Ao , Liu Zhihui , Wu Guoming
Published: October 2021

Spaceborne polarimetric synthetic aperture radar interferometry (PolInSAR) has the potential to deal with large-scale forest height inversion. However, the inversion is influenced by strong temporal decorrelation interference resulting from a large temporal baseline. Additionally, the forest canopy induces phase errors, while the smaller vertical wavenumber (kz) enhances the sensitivity of the inversion to temporal decorrelation, which limits the efficiency in forest height inversion. This research is based on the random volume over ground (RVoG) model and follows the assumptions of the three-stage inversion method, to quantify the impact of repeat-pass spaceborne PolInSAR temporal decorrelation on the relative error of retrieval height, and develop a semi-empirical improved inversion model, using ground data to eliminate the interference of coherence and phase error caused by temporal decorrelation. Forest height inversion for temperate forest in northern China was conducted using repeat-pass spaceborne L-band ALOS2 PALSAR data, and was further verified using ground measurement data. The correction of temporal decorrelation using the improved model provided robust inversion for mixed conifer-broad forest height retrieval as it addressed the over-sensitivity to temporal decorrelation resulting from the inappropriate kz value. The method performed height inversion using interferometric data with temporal baselines ranging from 14 to 70 days and vertical wavenumbers ranging from 0.015 to 0.021 rad/m. The R2 and RMSE reached 0.8126 and 2.3125 m, respectively.
Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
Authors: Alkhodari Mohanad , Zakaria Amer , Qaddoumi Nasser
Published: October 2021

A major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness; thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality.
Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
Authors: Gil Óscar , Garrell Anaís , Sanfeliu Alberto
Published: October 2021

Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.
Force Plate with Simple Mechanical Springs and Separated Noncontact Sensor Elements
Authors: Kawasaki Yuta , Ogawa Ami , Takahashi Hidetoshi
Published: October 2021

This paper reports on a force plate (FP) using mechanical springs and noncontact distance sensors. The ground reaction force (GRF) is one of the factors for clarify biomechanics, and FPs are widely used to measure it. The sensor elements of conventional FPs are mainly strain gauges. Thus, the mechanical properties of FP depend on the sensor element performance. If the FP performance must change, we must redesign the FP, including changing the sensor elements. Here, we proposed an FP that uses a measuring principle based on simple springs and noncontact sensors. The shape and performance of the proposed FP are expected to change easily. As a prototype device, we designed and fabricated an FP installed with 12 springs and four sensors for human walking. A planar coil and magnet were used as the sensor elements, and the sensor output was proportional to the vertical and horizontal displacements. The FP resonance frequency was 123 Hz, which was larger than the required specification. The calibration experiments showed that vertical and horizontal forces and moments could be measured independently. The FP’s resolutions were 1.9 N and 1.4 N in the anterior–posterior and vertical directions, respectively. Furthermore, the fabricated FP measured GRF similarly to the commercial FP when a human walked on the plate. These results suggest that the proposed method will be helpful for FPs with custom-made requirements.
Quality Assessment of Deep-Frying Palm Oil by Impedimetric Sensing with a Simple and Economic Electrochemical Cell
Authors: Kung Yi , Hsieh Bo-Chuan
Published: October 2021

Quality control of deep-frying oil is a global public health concern. A simple and economic electrochemical chamber composed of two bare screen-printed carbon electrodes (working area: 78.54 × 102 cm2; distance: 0.0055 cm; cell constant: 0.70 × 10−2 cm−1) was constructed for precisely acquiring the impedimetric responses of a high-resistance palm oil sample (RSD < 7%, n = 3). Good correlations between the measured impedance data (charge transfer resistance and logarithmic output impedance (Log Z) obtained in the frequency region <0.1 Hz) and the regulatory quality indicators (total polar compounds and acid value) were achieved (R2 > 0.97), suggesting that the proposed impedimetric sensing method is useful for accurately assessing the deteriorated condition of repeated frying oil. Applications for rapid screening can also be realized because the measurement times of Log Z at any given perturbation frequency from 0.01–1 Hz were all less than 3 min.
Wearable Sensors for Measurement of Viewing Behavior, Light Exposure, and Sleep
Authors: Bhandari Khob R. , Mirhajianmoghadam Hanieh , Ostrin Lisa A.
Published: October 2021

The purpose of this study was to compare two wearable sensors to each other and to a questionnaire in an adult population. For one week, participants aged 29.2 ± 5.5 years (n = 25) simultaneously wore a Clouclip, a spectacle-mounted device that records viewing distance and illuminance, and an Actiwatch, a wrist-worn device that measures illuminance and activity. Participants maintained a daily log of activities and completed an activity questionnaire. Objective measures of time outdoors, near (10–< 60 cm) and intermediate (60–100 cm) viewing, and sleep duration were assessed with respect to the daily log and questionnaire. Findings showed that time outdoors per day from the questionnaire (3.2 ± 0.3 h) was significantly greater than the Clouclip (0.9 ± 0.8 h) and Actiwatch (0.7 ± 0.1 h, p < 0.001 for both). Illuminance from the Actiwatch was systematically lower than the Clouclip. Daily near viewing duration was similar between the questionnaire (5.7 ± 0.6 h) and Clouclip (6.1 ± 0.4 h, p = 0.76), while duration of intermediate viewing was significantly different between methods (p < 0.001). In conclusion, self-reported time outdoors and viewing behaviors were different than objective measures. The Actiwatch and Clouclip are valuable tools for studying temporal patterns of behavioral factors such as near work, light exposure, and sleep.
Comparative Assessment of Different Gold Nanoflowers as Labels for Lateral Flow Immunosensors
Authors: Taranova Nadezhda A. , Byzova Nadezhda A. , Pridvorova Svetlana M. , Zherdev Anatoly V. , Dzantiev Boris B.
Published: October 2021

Many studies have found that gold nanoparticles with branched surfaces (nanoflowers) are markers for immunosensors that provide higher sensitivity gains than the commonly used spherical gold nanoparticles. Although the analytical characteristics of nanoparticle-using systems vary significantly depending on their size and shape, the question of choosing the best gold nanoflowers remains open. This work presents a comparative study of a panel of 33 preparations of gold nanoflowers formed by varying several parameters: the size of spherical nanoparticles-nuclei, the concentrations of nuclei, and tetrachloroauric acid during growth. The sizes of the resulting particles, their sorption capacity under antibody immobilization, mobility along membranes for lateral flow assays, and the effects of these parameters on the limits of detection of lateral flow immunoassay are characterized. The optimality of preparations obtained by growing a 0.2% v/v solution of nuclei with a diameter of 10 or 20 nm with tetrachloroauric acid at a concentration of 0.12 mM was shown. With their use, lateral flow immune tests were developed to determine markers of acute myocardial infarction—fatty acids binding protein and troponins I and T. The use of gold nanoflowers obtained under the proposed protocols led to significant gains in the limits of detection—3 to 10 times under visual detection and over 100 times under instrumental detection—compared to spherical gold nanoparticles. The significant increase under instrumental detection is due to the label’s low nonspecific binding.
The Minimum AC Signal Model of Bipolar Transistor in Amplification Region for Weak Signal Detection
Authors: Huang Lidong , Miao Qiuyan , Su Xiruo , Wu Bin , Song Kaichen
Published: October 2021

This paper presents a minimum signal model via the AC small-signal model and the uncertainty principle, which reveals the minimum AC signal that can be amplified by a bipolar transistor. The Ebers—Moll model (EM3) can describe the small signal amplification process, but it is difficult to define the minimum amplifiable signal of the bipolar transistor. In this study, the correspondence relationship between the non-equilibrium carrier and the electric injection is proved, and the relationship between the life of the non-equilibrium carrier and the measurable signal is proposed by the uncertainty principle. Next, the limit of perceived minimum voltage is also derived in this paper. Then, combining with EM3 model, the minimum AC signal model of bipolar transistor is presented to calculate the minimum voltage signal of bipolar transistor that can be amplified. Finally, a number of the simulation and experiment results show that when the minimum signal in the model is used as input, the carrier concentration of the bipolar transistor does not change and the base electrode cannot perceive the signal, which verifies the validity of the minimum AC signal model.
An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
Authors: Rehman Amjad , Haseeb Khalid , Saba Tanzila , Lloret Jaime , Sendra Sandra
Published: October 2021

In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.
Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
Authors: Lara-Doña Alejandro , Torres-Sanchez Sonia , Priego-Torres Blanca , Berrocoso Esther , Sanchez-Morillo Daniel
Published: October 2021

Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.
An Empathy Evaluation System Using Spectrogram Image Features of Audio
Authors: Zhang Jing , Wen Xingyu , Cho Ayoung , Whang Mincheol
Published: October 2021

Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.
The Intra- and Inter-Rater Reliability of a Hip Rotation Range-of-Motion Measurement Using a Smartphone Application in Academy Football (Soccer) Players
Authors: Spork Paul , O’Brien James , Sepoetro Morris , Plachel Maximilian , Stöggl Thomas
Published: October 2021

The clinical assessment of hip rotation range-of-motion (ROM) is important for managing hip and groin injuries in footballers. Previously published reliability studies on hip ROM have employed protocols that are difficult to replicate under everyday clinical conditions. This single trial, intra- and inter-rater reliability study included 41 male academy football (soccer) players, aged 14–15 years, from one European football academy. Passive hip internal rotation (IR) and external rotation (ER) ROM were measured in supine with hip and knee flexed to 90°. The ROM was determined using a smartphone application, with the smartphone attached to the lower leg. The tests were performed on two separate occasions, one week apart, by two different physiotherapists and on both sides (left and right hips). Reliability was evaluated using Intra-Class Correlation Coefficients (ICCs) and Minimal Detectable Change (MDC). Hip IR and ER ROM displayed moderate to good intra-rater agreement (ICCs 0.54–0.75), with MDCs ranging from 10.9° to 16.4°. Inter-rater reliability displayed poor to moderate reliability (ICCs 0.33–0.75), with MDCs ranging from 11.7° to 16.5°. A hip rotation ROM test using a smartphone application and a protocol closely reflecting everyday clinical conditions displayed moderate to good intra-rater reliability and poor to moderate inter-rater reliability. Due to the high MDCs, the practical applicability of this test procedure is limited and further refinement is necessary.
Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas
Authors: Dibaj Samira , Hosseinzadeh Aryan , Mladenović Miloš N. , Kluger Robert
Published: October 2021

The emergence of micromobility services in the form of dockless shared e-scooters has resulted in a wide range of behavioral changes in urban environments. In order to effectively steer these changes towards sustainability targets, the characteristics of e-scooter trips and users’ behaviors should be understood further. However, there is a lack of systematic literature reviews in this domain. To address this gap, we provide a two-fold systematic literature review. The first aspect focuses on the categorization of temporal and spatial patterns of shared e-scooter usage. The second aspect focuses on a deeper understanding of e-scooter users’ behaviors, utilizing the principles of persona design. The analysis of temporal patterns highlights the commonality of midday, evening, and weekend peak usage across cities, while spatial patterns suggest e-scooters are used for traveling to recreational and educational land use, as well as city center areas. The synthesis of findings on users’ behaviors has resulted in six categories, with four user types based on usage frequency (one time, casual, power, and non-adopters), and two motivation-based personas (users who are not satisfied with current mobility options and users who have had positive travel experience from e-scooter usage). The overall findings provide important lessons for evaluating this emerging mobility service, which should be considered for steering its development in public-private stakeholder networks.
Learning from Alliance Membership: An Empirical Study of Learning from the Failure of Their Alliance Members, Liability and Environmentally Sustainable Airline
Authors: Kim Euisin , Rhee Mooweon
Published: October 2021

Through this research, we examined whether airlines vicariously learn more from accidents of alliance members. We set organizational learning as our dependent variable and defined it as a reduction in the subsequent accident rate. Our research also examined the moderating effect of liability (U.S. air carriers) by hypothesizing that U.S. air carriers are more likely to learn from alliance memberships. In sum, the results of our analyses showed that an airline is more likely to learn from alliance members’ failure experiences. Furthermore, findings of the moderating effect of liability (U.S. air carriers) revealed that U.S. air carriers are more likely to learn from alliance memberships. In addition, findings on the moderating effect of environmentally sustainable airlines revealed that an environmentally sustainable airline is more likely to learn from alliance memberships. This research was examined using the accidents database from the U.S. National Transportation Safety Board (NTSB) of world commercial airlines from 2008 to 2018. We contributed to the previous line of research that explored factors influencing organizational learning and the benefits of forming alliances. The findings of this research could apply to other fields with alliances and accidents.
Family Business, Resilience, and Ethnic Tourism in Yunnan, China
Authors: Lin Qingqing , Wen Julie Jie
Published: October 2021

The expansion in commodification through tourism and the transformation of communities and heritages into destination attributes have become the centre of tourism research facing the COVID-19 crisis. Since small family businesses comprise the majority of the tourism industry, theories and practices on surviving the crisis are urgently required. This research attempts to clarify the level of business resilience during the COVID-19 lock down when business can only be conducted at home with very few visitors. It provides insights on family businesses in Yunnan, China, moving ahead with ethnic tourism against the lockdown and shutdown of tourism industry. Data were collected through in-depth interviews. The researchers invited previous research respondents who agreed to continue for online interviews. Eight interviewees were selected by purposive sampling. Although the COVID-19 crisis was unexpected, external shocks need to be taken into account for business planning. Tourism will not necessarily grow massively in the total number of visitors as the destinations have become saturated. The diversification of a tourism business facilitates the resilience of the firm by venturing into other areas of practice, including taking advantage of online platforms in selling local food and herbs, live broadcasting the local flora and fauna, teaching school children in making the traditional pickles, and other business extensions from tourism. Businesses are working together with other stakeholders in the effort of overcoming the COVID-19 crisis. Previous visitors from not only China but also overseas, who have kept in touch with the hosts in Yunnan, are becoming part of the supporting team for the business. However, there appears to be a lack of coordination in the community when businesses are isolated. There is a call for skills in using technology for online business, complementarity in the community, and policy support in the ethnic family businesses. Small family businesses in ethnic Yunnan are actively adapting and progressing despite the odds. They are resilient in times of crisis, with a strong presence of entrepreneurship, diversification of activities, re-organising resources, and digital literacy. The research sheds light on how community-based small family businesses surviving the crisis through resilience, entrepreneurship, and celebrating their ethnic cultures in tourism. Small-scale tourism closely linked with the community, family, and people may provide more promising prospects for tourism.
Hierarchical Fusion Process of Destination Image Formation: Targeting on Urban Tourism Destination
Authors: Zhang Xuhui , Zhang Chen , Li Yanan , Xu Ziyu , Huang Zhenfang
Published: October 2021

Image has been widely accepted as a combination of perceived elements that are commonly discrete and static ones. ‘Discrete’ means that the elements are treated as separate ones with each other, with no interactions among them. ‘Static’ means that the elements would not be changed into other forms in the process of destination image formation. This study, thinking outside the box, tries to explore destination image formation through perceived elements and take their interactions and corresponding changes into account. Machine learning, as the core of artificial intelligence, is applied for data analysis in this study. Urban tourism destinations are targeted because of their variety and abundance of perceived elements. Data are collected from both interview and questionnaire surveys of tourists. Through several phases of analysis, this study finally finds that perceived elements do interact with each other and change into new forms level by level in tourism destination image formation. Specifically, there are four levels from bottom to top in the whole process of destination image formation, i.e., the individual-landscape layer, compound-atmosphere layer, dual-factor layer, and overall-image layer. In the bottom stage, elements are commonly numerous, separate, and concrete. With the interactive effects of the elements, they integrate with each other and generate some new forms in higher levels, which would be more general and abstract. Based on the findings, the dynamic fusion process and pyramid hierarchy of destination image formation are disclosed. This study explores destination image formation from a new perspective, considering perceived elements within a dynamic, synthetic system, and therefore provides practical insights into destination image construction in a more comprehensive and targeted way.