bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Rheological and Thermomechanical Response of Acrylonitrile Butadiene Styrene/Silicon Nitride Nanocomposites in Material Extrusion Additive Manufacturing
Authors: Petousis Markos , Michailidis Nikolaos , Papadakis Vassilis M. , Korlos Apostolos , Mountakis Nikolaos , Argyros Apostolos , Dimitriou Evgenia , Charou Chrysa , Moutsopoulou Amalia , Vidakis Nectarios
Published: May 2023

The current research aimed to examine the thermomechanical properties of new nanocomposites in additive manufacturing (AM). Material extrusion (MEX) 3D printing was utilized to evolve acrylonitrile butadiene styrene (ABS) nanocomposites with silicon nitride nano-inclusions. Regarding the mechanical and thermal response, the fabricated 3D-printed samples were subjected to a course of standard tests, in view to evaluate the influence of the Si3N4 nanofiller content in the polymer matrix. The morphology and fractography of the fabricated filaments and samples were examined using scanning electron microscopy and atomic force microscopy. Moreover, Raman and energy dispersive spectroscopy tests were accomplished to evaluate the composition of the matrix polymer and nanomaterials. Silicon nitride nanoparticles were proved to induce a significant mechanical reinforcement in comparison with the polymer matrix without any additives or fillers. The optimal mechanical response was depicted to the grade ABS/Si3N4 4 wt. %. An impressive increase in flexural strength (30.3%) and flexural toughness (47.2%) was found. The results validate that these novel ABS nanocomposites with improved mechanical properties can be promising materials.
Programming Mechanism of Adipose Tissue Expansion in the Rat Offspring of Obese Mothers Occurs in a Sex-Specific Manner
Authors: Ibáñez Carlos A. , Lira-León Gabriela , Reyes-Castro Luis A. , Rodríguez-González Guadalupe L. , Lomas-Soria Consuelo , Hernández-Rojas Alejandra , Bravo-Flores Eyerahí , Solis-Paredes Juan Mario , Estrada-Gutierrez Guadalupe , Zambrano Elena
Published: May 2023

We investigated whether excessive retroperitoneal adipose tissue (AT) expansion programmed by maternal obesity (MO) affects adipocyte size distribution and gene expression in relation to adipocyte proliferation and differentiation in male and female offspring (F1) from control (F1C) and obese (F1MO) mothers. Female Wistar rats (F0) ate a control or high-fat diet from weaning through pregnancy and lactation. F1 were weaned onto a control diet and euthanized at 110 postnatal days. Fat depots were weighed to estimate the total AT. Serum glucose, triglyceride, leptin, insulin, and the insulin resistance index (HOMA-IR) were determined. Adipocyte size and adipogenic gene expression were examined in retroperitoneal fat. Body weight, retroperitoneal AT and adipogenesis differed between male and female F1Cs. Retroperitoneal AT, glucose, triglyceride, insulin, HOMA-IR and leptin were higher in male and female F1MO vs. F1C. Small adipocytes were reduced in F1MO females and absent in F1MO males; large adipocytes were increased in F1MO males and females vs. F1C. Wnt, PI3K-Akt, and insulin signaling pathways in F1MO males and Egr2 in F1MO females were downregulated vs. F1C. MO induced metabolic dysfunction in F1 through different sex dimorphism mechanisms, including the decreased expression of pro-adipogenic genes and reduced insulin signaling in males and lipid mobilization-related genes in females.
Advances in Nutrition, Dietary Supplements and Ergogenic Aids for Athletic Performance: Trends and Future Prospects
Authors: Bonilla Diego A. , Boullosa Daniel , Del Coso Juan
Published: May 2023

Sports nutrition is a scientific discipline that explores the relationship between nutrients and physical exercise performance [...]
Nutrition for Prevention and Control of Chronic Degenerative Diseases and COVID-19
Authors: Di Renzo Laura , Gualtieri Paola , Frank Giulia , De Lorenzo Antonino
Published: May 2023

Chronic degenerative diseases (CDD) are non-infectious, slow-progressing, and long-lasting diseases such as diabetes, heart disease, obesity, chronic respiratory diseases, neurodegenerative diseases, and cancer [...]
Beyond BMI
Authors: Bray George A.
Published: May 2023

This review examined the origins of the concept of the BMI in the work of Quetelet in the 19th century and its subsequent adoption and use in tracking the course of the pandemic of obesity during the 20th century. In this respect, it has provided a valuable international epidemiological tool that should be retained. However, as noted in this review, the BMI is deficient in at least three ways. First, it does not measure body fat distribution, which is probably a more important guide to the risk of excess adiposity than the BMI itself. Second, it is not a very good measure of body fat, and thus its application to the diagnosis of obesity or excess adiposity in the individual patient is limited. Finally, the BMI does not provide any insights into the heterogeneity of obesity or its genetic, metabolic, physiological or psychological origins. Some of these mechanisms are traced in this review.
Attenuation of Oxidative Stress and Regulation of AKT Signaling by Vanillic Acid during Bovine Pre-Implantation Embryo Development
Authors: El-Sheikh Marwa , Mesalam Ayman , Joo Myeong-Don , Sidrat Tabinda , Mesalam Ahmed Atef , Kong Il-Keun
Published: May 2023

Vanillic acid (VA) has shown antioxidant and anti-inflammatory activities in different cell types, but its biological effects in the context of early embryo development have not yet been clarified. In the current study, the impact of VA supplementation during in vitro maturation (IVM) and/or post-fertilization (in vitro culture; IVC) on redox homeostasis, mitochondrial function, AKT signaling, developmental competence, and the quality of bovine pre-implantation embryos was investigated. The results showed that dual exposure to VA during IVM and late embryo culture (IVC3) significantly improved the blastocyst development rate, reduced oxidative stress, and promoted fatty acid oxidation as well as mitochondrial activity. Additionally, the total numbers of cells and trophectoderm cells per blastocyst were higher in the VA-treated group compared to control (p < 0.05). The RT-qPCR results showed down-regulation of the mRNA of the apoptosis-specific markers and up-regulation of AKT2 and the redox homeostasis-related gene TXN in the treated group. Additionally, the immunofluorescence analysis showed high levels of pAKT-Ser473 and the fatty acid metabolism marker CPT1A in embryos developed following VA treatment. In conclusion, the study reports, for the first time, the embryotrophic effects of VA, and the potential linkage to AKT signaling pathway that could be used as an efficacious protocol in assisted reproductive technologies (ART) to improve human fertility.
High-Order Sinc-Correlated Model Vortex Beams
Authors: Wang Jixian , Mei Zhangrong , Mao Yonghua , Shi Xiaohui , Zhou Guoquan
Published: May 2023

We propose a new partially coherent vortex source model in which the spatial correlation function is a sinc function on the difference from the q-th power of the coordinates of two points of the source field. The beam radiated by such source is termed the high-order sinc-correlated model vortex (SCMV) beam. We derived the propagating formula of the cross-spectral density (CSD) function for SCMV beams in atmospheric disturbances. On the basis of the derived analytical expression, the behavior of the spectral density of the SCMV beams propagating in free space and atmosphere turbulence was investigated under comparative analysis. The results show that the spectral densities of such beams exhibited interesting novel features, which were significantly different from those of the trivial vortex beams.
Engineered Shellac Beads-on-the-String Fibers Using Triaxial Electrospinning for Improved Colon-Targeted Drug Delivery
Authors: Yang Yaoyao , Chen Wei , Wang Menglong , Shen Jiachen , Tang Zheng , Qin Yongming , Yu Deng-Guang
Published: May 2023

Colon-targeted drug delivery is gradually attracting attention because it can effectively treat colon diseases. Furthermore, electrospun fibers have great potential application value in the field of drug delivery because of their unique external shape and internal structure. In this study, a core layer of hydrophilic polyethylene oxide (PEO) and the anti-colon-cancer drug curcumin (CUR), a middle layer of ethanol, and a sheath layer of the natural pH-sensitive biomaterial shellac were used in a modified triaxial electrospinning process to prepare beads-on-the-string (BOTS) microfibers. A series of characterizations were carried out on the obtained fibers to verify the process–shape/structure–application relationship. The results of scanning electron microscopy and transmission electron microscopy indicated a BOTS shape and core–sheath structure. X-ray diffraction results indicated that the drug in the fibers was in an amorphous form. Infrared spectroscopy revealed the good compatibility of the components in the fibers. In vitro drug release revealed that the BOTS microfibers provide colon-targeted drug delivery and zero-order drug release. Compared to linear cylindrical microfibers, the obtained BOTS microfibers can prevent the leakage of drugs in simulated gastric fluid, and they provide zero-order release in simulated intestinal fluid because the beads in BOTS microfibers can act as drug reservoirs.
Renewable Poly(Lactic Acid)Lignocellulose Biocomposites for the Enhancement of the Water Retention Capacity of the Soil
Authors: Cruz Fabian Dalila Rubicela , Durpekova Silvie , Dusankova Miroslava , Cisar Jaroslav , Drohsler Petra , Elich Ondrej , Borkova Marketa , Cechmankova Jarmila , Sedlarik Vladimir
Published: May 2023

This manuscript details the preparation and characterization of a renewable biocomposite material intended as a soil conditioner based on low-molecular-weight poly(lactic acid) (PLA) and residual biomass (wheat straw and wood sawdust). The swelling properties and biodegradability of the PLA-lignocellulose composite under environmental conditions were evaluated as indicators of its potential for applications in soil. Its mechanical and structural properties were characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Results showed that the incorporation of lignocellulose waste material into PLA increased the swelling ratio of the biocomposite by up to 300%. The application of the biocomposite of 2 wt% in soil enhanced its capacity for water retention by 10%. In addition, the cross-linked structure of the material proved to be capable of swelling and deswelling repeatedly, indicating its good reusability. Incorporating lignocellulose waste in the PLA enhanced its stability in the soil environment. After 50 days of the experiment, almost 50% of the sample had degraded in the soil.
Combining Chitosan Nanoparticles and Garlic Essential Oil as Additive Fillers to Produce Pectin-Based Nanocomposite Edible Films
Authors: dos Santos Vanessa Solfa , Lorevice Marcos Vinicius , Baccarin Graziela Solferini , da Costa Fabíola Medeiros , da Silva Fernandes Renan , Aouada Fauze A. , de Moura Márcia Regina
Published: May 2023

Edible films were produced by combining a pectin (PEC) matrix with chitosan nanopar-ticle (CSNP), polysorbate 80 (T80), and garlic essential oil (GEO) as an antimicrobial agent. CSNPs were analyzed for their size and stability, and the films, throughout their contact angle, scanning electron microscopy (SEM), mechanical and thermal properties, water vapor transmission rate, and antimicrobial activity. Four filming-forming suspensions were investigated: PGEO (control); PGEO@T80; PGEO@CSNP; PGEO@T80@CSNP. The compositions are included in the methodology. The average particle size was 317 nm, with the zeta potential reaching +21.4 mV, which indicated colloidal stability. The contact angle of the films exhibited values of 65°, 43°, 78°, and 64°, respec-tively. These values showed films with variations in hydrophilicity. In antimicrobial tests, the films containing GEO showed inhibition only by contact for S. aureus. For E. coli, the inhibition occurred in films containing CSNP and by direct contact in the culture. The results indicate a promising al-ternative for designing stable antimicrobial nanoparticles for application in novel food packaging. Although, it still shows some deficiencies in the mechanical properties, as demonstrated in the elongation data.
PET Waste Recycling into BTX Fraction Using In Situ Obtained Nickel Phosphide
Authors: Golubeva Maria , Mukhtarova Mariyam , Sadovnikov Alexey , Maximov Anton
Published: May 2023

The annual production of plastic waste is a serious ecological problem as it causes substantial pollution of the environment. Polyethylene terephthalate, a material usually found in disposable plastic bottles, is one of the most popular material used for packaging in the world. In this paper, it is proposed to recycle polyethylene terephthalate waste bottles into benzene-toluene-xylene fraction using a heterogeneous nickel phosphide catalyst formed in situ during the polyethylene terephthalate recycling process. The catalyst obtained was characterized using powder X-ray diffraction, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy techniques. The catalyst was shown to contain a Ni2P phase. Its activity was studied in a temperature range of 250–400 °C and a H2 pressure range of 5–9 MPa. The highest selectivity for benzene-toluene-xylene fraction was 93% at quantitative conversion.
Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds
Authors: Rozsa Zoltan , Sziranyi Tamas
Published: May 2023

This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements makes the temporal up-sampling of point clouds possible. The only requirement of the system is a camera with a higher frame rate than the LIDAR equipped to the same vehicle, which is usually provided. The pipeline first utilizes optical flow estimations from the available camera frames. Next, optical expansion is used to upgrade it to 3D scene flow. Following that, ground plane fitting is made on the previous LIDAR point cloud. Finally, the estimated scene flow is applied to the previously measured object points to generate the new point cloud. The framework’s efficiency is proved as state-of-the-art performance is achieved on the KITTI dataset.
Has the Dominant Climatic Driver for the Carbon Budget of Alpine Grassland Shifted from Temperature to Precipitation on the Qinghai–Tibet Plateau?
Authors: Hu Zhigang , Niu Ben , Tang Jiwang , Zhang Yu , Xiang Mingxue , Zhang Xianzhou
Published: May 2023

The alpine grassland on the Qinghai–Tibet Plateau (AGQTP) has undergone severe climate change. Although the carbon budget of AGQTP proved to have altered significantly, the spatiotemporal dynamics and the driving mechanism of the changes remain debated. This study estimated the gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the AGQTP, based on remote sensing models, and analyzed their spatiotemporal dynamics and their climatic driving mechanism. Our results showed that the GPP, ER, and NEP increased at rates of 0.002 Pg C yr−2, 0.013 Pg C yr−2, and 0.0007 Pg C yr−2, respectively, during 2000–2020, with substantial spatiotemporal variability. The changes in GPP were influenced by both temperature and precipitation, while NEP and ER were primarily affected by precipitation and temperature, respectively. However, the primary climatic driver of the carbon budget may have shifted from temperature to precipitation around 2010, and the impact of temperature on carbon sink was limited by local water conditions. Furthermore, we found that climate change, particularly precipitation variation, had notable legacy effects on the carbon budget of the AGQTP. Our findings highlight that the climatic impact on the carbon budget is dynamic and long-lasting, rather than static and short-lived, which should be considered in ecosystem carbon budget simulations and other related studies.
Investigating Foliar Macro- and Micronutrient Variation with Chlorophyll Fluorescence and Reflectance Measurements at the Leaf and Canopy Scales in Potato
Authors: Oivukkamäki Jaakko , Atherton Jon , Xu Shan , Riikonen Anu , Zhang Chao , Hakala Teemu , Honkavaara Eija , Porcar-Castell Albert
Published: May 2023

Vegetation indices (VIs) related to plant greenness have been studied extensively for the remote detection of foliar nitrogen content. Yet, the potential of chlorophyll fluorescence (ChlF) and photoprotection-based indices such as the photochemical reflectance index (PRI) or the chlorophyll/carotenoid index (CCI) for the detection of a wide range of nutrients remains elusive. We measured the dynamics of foliar macro- and micronutrient contents in potato plants as affected by fertilization and water stress, along with leaf and canopy level observations of spectral reflectance and ChlF (or solar-induced fluorescence). ChlF and photoprotection-related indices were more strongly related to a wide range of foliar nutrient contents compared to greenness-based indices. At the leaf level, relationships were largely mediated by foliar chlorophyll contents (Cab) and leaf morphology, which resulted in two contrasting groupings: a group dominated by macronutrients N, P, K, and Mg that decreased during canopy development and was positively correlated with Cab, and a group including Cu, Mn, Zn, and S that increased and was negatively related to Cab. At the canopy-level, spectral indices were additionally influenced by canopy structure, and so their capacity to detect foliar nutrient contents depends on the spatiotemporal covariation between foliar Cab, morphology, and canopy structure within the observations.
Improvement of Crystal Identification Accuracy for Depth-of-Interaction Detector System with Peak-to-Charge Discrimination Method
Authors: Miyata Kento , Ogawara Ryo , Ishikawa Masayori
Published: May 2023

In positron emission tomography (PET), parallax errors degrade spatial resolution. The depth of interaction (DOI) information provides the position in the depth of the scintillator interacting with the γ-rays, thus reducing parallax errors. A previous study developed a Peak-to-Charge discrimination (PQD), which can separate spontaneous alpha decay in LaBr3:Ce. Since decay constant of GSO:Ce depends on Ce concentration, the PQD is expected to discriminate GSO:Ce scintillators with different Ce concentration. In this study, the PQD-based DOI detector system was developed, which can be processed online and implemented in PET. A detector was composed of four layers of GSO:Ce crystals and a PS-PMT. The four crystals were obtained from both the top and bottom of ingots with a nominal Ce concentration of 0.5 mol% and 1.5 mol%. The PQD was implemented on the Xilinx Zynq-7000 SoC board with 8ch Flash ADC to gain real-time processing, flexibility, and expandability. The results showed that the mean Figure of Merits in 1D between four scintillators are 1.5, 0.99, 0.91 for layers between 1st–2nd, 2nd–3rd, and 3rd–4th respectively, and the mean Error Rate in 1D between four scintillators are 3.50%, 2.96%, 13.3%, and 1.88% for layers 1, 2, 3, and 4, respectively. In addition, the introduction of the 2D PQDs resulted in the mean Figure of Merits in 2D greater than 0.9 and the mean Error Rate in 2D less than 3% in all layers.
A Laser Plane Attitude Evaluation Method for Rail Profile Measurement Sensors
Authors: Wang Le , Wang Hao , Han Qiang , Fang Yue , Wang Shengchun , Wang Ning , Li Guoqing , Ren Shengwei
Published: May 2023

The non-coplanar lasers on both sides of the rail during full-section rail profile measurement based on line-structured light vision will cause the measured profile to be distorted, resulting in measurement errors. Currently, in the field of rail profile measurement, there are no effective methods for evaluating laser plane attitude, and it is impossible to determine the degree of laser coplanarity quantitatively and accurately. This study proposes an evaluation method based on fitting planes in response to this problem. Real-time fitting of laser planes with three planar targets of different heights provides information about the laser plane attitude on both sides of the rails. On this basis, laser coplanarity evaluation criteria were developed to determine whether the laser planes on both sides of the rails are coplanar. Using the method in this study, the laser plane attitude can be quantified and accurately assessed on both sides, effectively resolving the problem with traditional methods that can only assess the laser plane attitude qualitatively and roughly, thereby providing a solid foundation for calibration and error correction of the measurement system.
Current Sensorless Based on PI MPPT Algorithms
Authors: de Brito Moacyr A. G. , Martines Guilherme M. S. , Volpato Anderson S. , Godoy Ruben B. , Batista Edson A.
Published: May 2023

This paper presents novel current sensorless maximum-power point-tracking (MPPT) algorithms based on compensators/controllers and a single-input voltage sensor. The proposed MPPTs eliminate the expensive and noisy current sensor, which can significantly reduce the system cost and retain the advantages of the widely used MPPT algorithms, such as Incremental Conductance (IC) and Perturb and Observe (P&O) algorithms. Additionally, it is verified that the proposed algorithms, especially the proposed Current Sensorless V based on PI, can reach outstanding tracking factors (TFs) such as the IC and P&O based on PI algorithms. In this sense, the insertion of controllers inside the MPPT gives them adaptive characteristics, and the experimental TFs are in the remarkable range of more than 99%, with an average yield of 99.51% and a peak of 99.80%.
VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR
Authors: Peng Gang , Zhou Yicheng , Hu Lu , Xiao Li , Sun Zhigang , Wu Zhangang , Zhu Xukang
Published: May 2023

For the existing visual–inertial SLAM algorithm, when the robot is moving at a constant speed or purely rotating and encounters scenes with insufficient visual features, problems of low accuracy and poor robustness arise. Aiming to solve the problems of low accuracy and robustness of the visual inertial SLAM algorithm, a tightly coupled vision-IMU-2D lidar odometry (VILO) algorithm is proposed. Firstly, low-cost 2D lidar observations and visual–inertial observations are fused in a tightly coupled manner. Secondly, the low-cost 2D lidar odometry model is used to derive the Jacobian matrix of the lidar residual with respect to the state variable to be estimated, and the residual constraint equation of the vision-IMU-2D lidar is constructed. Thirdly, the nonlinear solution method is used to obtain the optimal robot pose, which solves the problem of how to fuse 2D lidar observations with visual–inertial information in a tightly coupled manner. The results show that the algorithm still has reliable pose-estimation accuracy and robustness in many special environments, and the position error and yaw angle error are greatly reduced. Our research improves the accuracy and robustness of the multi-sensor fusion SLAM algorithm.
Correlations among Firing Rates of Tactile, Thermal, Gustatory, Olfactory, and Auditory Sensations Mimicked by Artificial Hybrid Fluid (HF) Rubber Mechanoreceptors
Authors: Shimada Kunio
Published: May 2023

In order to advance the development of sensors fabricated with monofunctional sensation systems capable of a versatile response to tactile, thermal, gustatory, olfactory, and auditory sensations, mechanoreceptors fabricated as a single platform with an electric circuit require investigation. In addition, it is essential to resolve the complicated structure of the sensor. In order to realize the single platform, our proposed hybrid fluid (HF) rubber mechanoreceptors of free nerve endings, Merkel cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles mimicking the bio-inspired five senses are useful enough to facilitate the fabrication process for the resolution of the complicated structure. This study used electrochemical impedance spectroscopy (EIS) to elucidate the intrinsic structure of the single platform and the physical mechanisms of the firing rate such as slow adaption (SA) and fast adaption (FA), which were induced from the structure and involved the capacitance, inductance, reactance, etc. of the HF rubber mechanoreceptors. In addition, the relations among the firing rates of the various sensations were clarified. The adaption of the firing rate in the thermal sensation is the opposite of that in the tactile sensation. The firing rates in the gustation, olfaction, and auditory sensations at frequencies of less than 1 kHz have the same adaption as in the tactile sensation. The present findings are useful not only in the field of neurophysiology, to research the biochemical reactions of neurons and brain perceptions of stimuli, but also in the field of sensors, to advance salient developments in sensors mimicking bio-inspired sensations.
Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
Authors: Zhang Yaping , Qi Xiaozhi , Wang Tao , He Yuanhang
Published: May 2023

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.
Adaptive Parameters for LoRa-Based Networks Physical-Layer
Authors: Silva Edelberto Franco , Figueiredo Lucas M. , de Oliveira Leonardo A. , Chaves Luciano J. , de Oliveira André L. , Rosário Denis , Cerqueira Eduardo
Published: May 2023

Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.
Carbon Rovings as Strain Sensor in TRC Structures: Effect of Roving Cross-Sectional Shape and Coating Material on the Electrical Response under Bending Stress
Authors: Dittel Gözdem , Cicek Irem Ecem , Bredol Michael , Gries Thomas
Published: May 2023

This study investigated the ability of electrically conductive carbon rovings to detect cracks in textile-reinforced concrete (TRC) structures. The key innovation lies in the integration of carbon rovings into the reinforcing textile, which not only contributes to the mechanical properties of the concrete structure but also eliminates the need for an additional sensory system, such as strain gauges, to monitor the structural health. Carbon rovings are integrated into a grid-like textile reinforcement that differs in binding type and dispersion concentration of the styrene butadiene rubber (SBR) coating. Ninety final samples were subjected to a four-point bending test in which the electrical changes of the carbon rovings were measured simultaneously to capture the strain. The mechanical results show that the SBR50-coated TRC samples with circular and elliptical cross-sectional shape achieved, with 1.55 kN, the highest bending tensile strength, which is also captured with a value of 0.65 Ω by the electrical impedance monitoring. The elongation and fracture of the rovings have a significant effect on the impedance mainly due to electrical resistance change. A correlation was found between the impedance change, binding type and coating. This suggests that the elongation and fracture mechanisms are affected by the number of outer and inner filaments, as well as the coating.
NanoFN10: A High-Contrast Turn-On Fluorescence Nanoprobe for Multiphoton Singlet Oxygen Imaging
Authors: Zanocco Renzo P. , Bresolí-Obach Roger , Nájera Francisco , Pérez-Inestrosa Ezequiel , Zanocco Antonio L. , Lemp Else , Nonell Santi
Published: May 2023

An “off-on” fluorescent nanoprobe for near-infrared multiphoton imaging of singlet oxygen has been developed. The nanoprobe comprises a naphthoxazole fluorescent unit and a singlet-oxygen-sensitive furan derivative attached to the surface of mesoporous silica nanoparticles. In solution, the fluorescence of the nanoprobe increases upon reaction with singlet oxygen both under one- and multiphoton excitation, with fluorescence enhancements up to 180-fold. The nanoprobe can be readily internalized by macrophage cells and is capable of imaging intracellular singlet oxygen under multiphoton excitation.
Low-Cost, Real-Time Polymerase Chain Reaction System with Integrated RNA Extraction
Authors: Kadja Tchamie , Sun Yvonne , Chodavarapu Vamsy P.
Published: May 2023

Rapid, easy-to-use, and low-cost systems for biological sample testing are important for point-of-care diagnostics and various other health applications. The recent pandemic of Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed an urgent need to rapidly and accurately identify the genetic material of SARS-CoV-2, an enveloped ribonucleic acid (RNA) virus, in upper respiratory specimens from people. In general, sensitive testing methods require genetic material extraction from the specimen. Unfortunately, current commercially available extraction kits are expensive and involve time-consuming and laborious extraction procedures. To overcome the difficulties associated with common extraction methods, we propose a simple enzymatic assay for the nucleic acid extraction step using heat mediation to improve the polymerase chain reaction (PCR) reaction sensitivity. Our protocol was tested on Human Coronavirus 229E (HCoV-229E) as an example, which comes from the large coronaviridae family of viruses that affect birds, amphibians, and mammals, of which SARS-CoV-2 is a member. The proposed assay was performed using a low-cost, custom-made, real-time PCR system that incorporates thermal cycling and fluorescence detection. It had fully customizable reaction settings to allow versatile biological sample testing for various applications, including point-of-care medical diagnosis, food and water quality testing, and emergency health situations. Our results show that heat-mediated RNA extraction is a viable extraction method when compared to commercial extraction kits. Further, our study showed that extraction has a direct impact on purified laboratory samples of HCoV-229E, but no direct impact on infected human cells. This is clinically relevant, as it allows us to circumvent the extraction step on clinical samples when using PCR.
From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization
Authors: Stojchevska Marija , De Brouwer Mathias , Courteaux Martijn , Ongenae Femke , Van Hoecke Sofie
Published: May 2023

Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available.
Design of a Focusing Mechanism Actuated by Piezoelectric Ceramics for TMA Telescope
Authors: Lu Ying , Chen Changzheng , Zhu Junqing
Published: May 2023

With the development of space telescopes towards high-resolution and intelligent imaging, the scale and complexity of the focal plane components of large-aperture, off-axis, three-mirror anastigmatic (TMA) optical systems are increasing. Traditional focal plane focusing technology reduces the system reliability and increases the scale and complexity. This paper proposes a three-degrees-of-freedom focusing system based on a folding mirror reflector, with a piezoelectric ceramic actuator as the driver. An environment-resistant flexible support was designed for the piezoelectric ceramic actuator through an integrated optimization analysis. The fundamental frequency of the large-aspect-ratio rectangular folding mirror reflector focusing mechanism was around 121.5 Hz. After testing, it was found to meet the requirements of the space mechanics environment. This system shows promise for application to other optical systems as an open-shelf product in the future.
A Face Detection and Standardized Mask-Wearing Recognition Algorithm
Authors: Yu Jimin , Zhang Xin , Wu Tao , Pan Huilan , Zhang Wei
Published: May 2023

In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods.
Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning
Authors: Kulmar Marika , Müürsepp Ivo , Alam Muhammad Mahtab
Published: May 2023

In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.
On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements
Authors: Munz Hannah , Ingwersen Joachim , Streck Thilo
Published: May 2023

Barometric process separation (BaPS) is an automated laboratory system for the simultaneous measurement of microbial respiration and gross nitrification rates in soil samples. To ensure optimal functioning, the sensor system, consisting of a pressure sensor, an O2 sensor, a CO2 concentration sensor, and two temperature probes, must be accurately calibrated. For the regular on-site quality control of the sensors, we developed easy, inexpensive, and flexible calibration procedures. The pressure sensor was calibrated by means of a differential manometer. The O2 and CO2 sensors were simultaneously calibrated through their exposure to a sequence of O2 and CO2 concentrations obtained by sequentially exchanging O2/N2 and CO2/N2 calibration gases. Linear regression models were best suited for describing the recorded calibration data. The accuracy of O2 and CO2 calibration was mainly affected by the accuracy of the utilized gas mixtures. Because the applied measuring method is based on the O2 conductivity of ZrO2, the O2 sensor is particularly susceptible to aging and to consequent signal shifts. Sensor signals were characterized by high temporal stability over the years. Deviations in the calibration parameters affected the measured gross nitrification rate by up to 12.5% and affected the respiration rate by up to 5%. Overall, the proposed calibration procedures are valuable tools for ensuring the quality of BaPS measurements and for promptly identifying sensor malfunctions.
Collaboration for Sustainable Innovation Ecosystem: The Role of Intermediaries
Authors: Sultana Nasrin , Turkina Ekaterina
Published: May 2023

Innovation ecosystems have increasingly been studied from various perspectives, including connecting innovation ecosystems to sustainable development. Extant studies have found that innovation is important for sustainable development, collaboration is important for innovation, and intermediaries create necessary links and opportunities for the development of relations and cooperation between different actors in an ecosystem. What has been missing, however, is an explicit analysis of the process of collaboration in innovation ecosystems to ensure sustainability and the role of intermediaries in the process. To fill this void, this paper studies six organizations that act as intermediaries, using a multiple-case design approach. It analyzes the process of collaboration in innovation ecosystems and elucidates the role of intermediaries in the development of sustainable ecosystems. The findings indicate that the process of collaboration between actors in innovation ecosystems is an iterative process facilitated by intermediaries. By connecting different actors, intermediaries support the diffusion of innovation that has important implications for building sustainable innovation ecosystems and achieving Sustainable Development Goals (SDGs).
The Fluctuation Characteristics and Periodic Patterns of Potato Prices in China
Authors: Lu Hongwei , Li Tingting , Lv Jianfei , Wang Aoxue , Luo Qiyou , Gao Mingjie , Li Guojing
Published: May 2023

The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers’ ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities.
A Study on the Effect of Authenticity on Heritage Tourists’ Mindful Tourism Experience: The Case of the Forbidden City
Authors: Eck Thomas , Zhang Yiwen , An Soyoung
Published: May 2023

While heritage tourism has been explored widely in the tourism literature, there remains a need to further understand the antecedent variables that influence tourist experiences in such a context. This study applied mindfulness theory, investigating the effect of authenticity and mindfulness on the tourist experience and how the tourist experience influenced satisfaction and loyalty. A structural model was used with eight proposed hypotheses based on data collected in the spring of 2021 from 363 Chinese visitors to the Forbidden City. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were employed, enabling variable relations to be tested. Results revealed that authenticity significantly influenced mindfulness, and mindfulness significantly influenced tourists’ experience. Furthermore, two dimensions of tourists’ experience influenced satisfaction and satisfaction influenced loyalty.
Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy
Authors: Guan Shihao , Shang Yuqian , Zhao Chao
Published: May 2023

To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.