bluuebunny/crossref_metadata_2025 huggingface dataset:
CEBP-β and PLK1 as Potential Mediators of the Breast Cancer/Obesity Crosstalk: In Vitro and In Silico Analyses
Authors: Accattatis Felice Maria , Caruso Amanda , Carleo Alfonso , Del Console Piercarlo , Gelsomino Luca , Bonofiglio Daniela , Giordano Cinzia , Barone Ines , Andò Sebastiano , Bianchi Laura , Catalano Stefania
Published: June 2023

Over the last two decades, obesity has reached pandemic proportions in several countries, and expanding evidence is showing its contribution to several types of malignancies, including breast cancer (BC). The conditioned medium (CM) from mature adipocytes contains a complex of secretes that may mimic the obesity condition in studies on BC cell lines conducted in vitro. Here, we report a transcriptomic analysis on MCF-7 BC cells exposed to adipocyte-derived CM and focus on the predictive functional relevance that CM-affected pathways/processes and related biomarkers (BMs) may have in BC response to obesity. CM was demonstrated to increase cell proliferation, motility and invasion as well as broadly alter the transcript profiles of MCF-7 cells by significantly modulating 364 genes. Bioinformatic functional analyses unraveled the presence of five highly relevant central hubs in the direct interaction networks (DIN), and Kaplan–Meier analysis sorted the CCAAT/enhancer binding protein beta (CEBP-β) and serine/threonine-protein kinase PLK1 (PLK1) as clinically significant biomarkers in BC. Indeed, CEBP-β and PLK1 negatively correlated with BC overall survival and were up-regulated by adipocyte-derived CM. In addition to their known involvement in cell proliferation and tumor progression, our work suggests them as a possible “deus ex machina” in BC response to fat tissue humoral products in obese women.
Beneficial Effects of Plant-Based Diets on Skin Health and Inflammatory Skin Diseases
Authors: Flores-Balderas Ximena , Peña-Peña Mario , Rada Karla M. , Alvarez-Alvarez Yamnia Q. , Guzmán-Martín Carlos A. , Sánchez-Gloria José L. , Huang Fengyang , Ruiz-Ojeda Dayanara , Morán-Ramos Sofía , Springall Rashidi , Sánchez-Muñoz Fausto
Published: June 2023

The human skin is a crucial organ that protects the organism from the outer environment. Skin integrity and health depend on both extrinsic and intrinsic factors. Intrinsic factors such as aging and genetic background contribute to weakened skin and disease susceptibility. Meanwhile, extrinsic factors including UV radiation, pollution, smoking, humidity, and poor diet also affect skin health and disease. On the other hand, healthy dietary patterns such as plant-based diets have gained popularity as a complementary therapy for skin health. A plant-based diet is defined as all diets based on plant foods, including an abundance of vegetables, fruits, beans, lentils, legumes, nuts, seeds, fungi, and whole grains, with limited or no animal products or processed foods. However, some authors also exclude or limit processed foods in the definition. Recent research has shown that these diets have beneficial effects on inflammatory skin diseases. This review explored the beneficial effects of plant-based diets on inflammatory skin diseases and plant-based functional foods on healthy skin. In conclusion, plant-based diets and plant-based functional foods may have beneficial effects on skin health through the gut microbiome.
Green Tea Induces the Browning of Adipose Tissue—Systematic Review
Authors: Macêdo Ana Paula Azevêdo , Gonçalves Mariane dos Santos , Barreto-Medeiros Jairza Maria , da Silva Neto Oscar Caetano , David Jorge Mauricio , Villarreal Cristiane Flora , Macambira Simone Garcia , Pereira Soares Milena Botelho , Couto Ricardo David
Published: June 2023

Several foods and nutrients are being studied extensively because they have a positive effect on thermogenesis and the browning of white adipose tissue. Therefore, this study aims to evaluate, through a systematic review, the effect of green tea for inducing browning of adipose tissue. The systematic review was built following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyze. We searched the following electronic databases: PubMed (Medline), Science Direct, Scopus, and Web of Science. We included ten experimental articles that used green tea to treat induced obesity in rodents. Green tea reduced the weight of white and brown adipose tissue, positively regulated gene expression and microRNA that regulate the metabolism of adipose tissue, and morphological changes were identified as beige tissue. According to the results found, the factors involved in this induction to browning are PPARγ, PGC-1α, UCP1, CPT, and PRDM16. Therefore, green tea promotes the browning of adipose tissue in rodents. It is important to emphasize the need for studies in obese humans to identify whether the same metabolic response occurs.
The Potential Therapeutic Application of Simvastatin for Brain Complications and Mechanisms of Action
Authors: Vuu Yen My , Kadar Shahib Ashraf , Rastegar Mojgan
Published: June 2023

Statins are common drugs that are clinically used to reduce elevated plasma cholesterol levels. Based on their solubility, statins are considered to be either hydrophilic or lipophilic. Amongst them, simvastatin has the highest lipophilicity to facilitate its ability to cross the blood-brain barrier. Recent studies have suggested that simvastatin could be a promising therapeutic option for different brain complications and diseases ranging from brain tumors (i.e., medulloblastoma and glioblastoma) to neurological disorders (i.e., Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease). Specific mechanisms of disease amelioration, however, are still unclear. Independent studies suggest that simvastatin may reduce the risk of developing certain neurodegenerative disorders. Meanwhile, other studies point towards inducing cell death in brain tumor cell lines. In this review, we outline the potential therapeutic effects of simvastatin on brain complications and review the clinically relevant molecular mechanisms in different cases.
Chronological Analysis of First-in-Class Drugs Approved from 2011 to 2022: Their Technological Trend and Origin
Authors: Okuyama Ryo
Published: June 2023

The discovery and development of first-in-class (FIC) drugs are becoming increasingly important due to increasing reimbursement pressure and personalized medication. To investigate the technological trends and origin of FIC drugs, the FIC drugs approved in the U.S. from January 2011 to December 2022 were analyzed. The analysis shows that previous major target families, viz. enzymes, G-protein coupled receptors, transporters, and transcription factors, are no longer considered major in recent years. Instead, the shares of secreted proteins/peptides and mRNAs have continuously increased from 2011–2014 to 2019–2022, suggesting that the target family of FIC drugs has shifted to molecules previously considered challenging as drug targets. Small molecules were predominant in 2011–2014, followed by a large increase in antibody medicines in 2015–2018 and further diversification of antibody medicine modalities in 2019–2022. Nucleic acid medicine has also continuously increased its share, suggesting that diversifying modalities supports the creation of FIC drugs toward challenging target molecules. Over half of FIC drugs were created by small and medium enterprises (SMEs), especially young companies established in the 1990s and 2000s. All SMEs that produced more than one FIC drug approved in 2019–2022 have the strong technological capability in a specific modality. Investment in modality technologies and facilitating mechanisms to translate academic modality technologies to start-ups might be important for enhancing FIC drug development.
Sb-Based Low-Noise Avalanche Photodiodes
Authors: Campbell Joe C. , David John P. R. , Bank Seth R.
Published: June 2023

Accurate detection of weak optical signals is a key function for a wide range of applications. A key performance parameter is the receiver signal-to-noise ratio, which depends on the noise of the photodetector and the following electrical circuitry. The circuit noise is typically larger than the noise of photodetectors that do not have internal gain. As a result, a detector that provides signal gain can achieve higher sensitivity. This is accomplished by increasing the photodetector gain until the noise associated with the gain mechanism is comparable to that of the output electrical circuit. For avalanche photodiodes (APDs), the noise that arises from the gain mechanism, impact ionization, increases with gain and depends on the material from which the APD is fabricated. Si APDs have established the state-of-the-art for low-noise gain for the past five decades. Recently, APDs fabricated from two Sb-based III-V compound quaternary materials, AlxIn1-xAsySb1-y and AlxGa1-xAsySb1-y, have achieved noise characteristics comparable to those of Si APDs with the added benefit that they can operate in the short-wave infrared (SWIR) and extended SWIR spectral regions. This paper describes the materials and device characteristics of these APDs and their performance in different spectral regions.
Crocus heuffelianus—A New Species for the Bulgarian Flora from Series Verni (Iridaceae)
Authors: Raycheva Tsvetanka , Stoyanov Kiril , Naimov Samir , Apostolova-Kuzova Elena
Published: June 2023

In the Pirin Mountains, at an elevation of around 1000 m, three populations of a new species of Bulgarian flora from the genus Crocus, series Verni, were discovered. The species was compared to the morphologically related C. veluchensis, and presented with diagnostic morphological and anatomical features. Despite the high degree of morphological similarity, the molecular analysis, which included sequences from all related species (C. cvijicii, C. dalmaticus, C. jablanicensis, C. rujanensis, C. sieberi subsp. atticus, and C. veluchensis), distinguished the Pirin Mountains’ populations, and revealed the closest relationship to C. heuffelianus. Despite the C. heuffelianus/C. verni complex’s uncertain taxonomic status, our findings on the local population, based on morphometric, anatomical, molecular, and geographic analyses, indicate its belonging to the putative allotetraploid C. heuffelianus of south-eastern Europe and the Balkans, and an expansion of its range to the southeast. Given the taxonomic uncertainty and unclear phylogenetic relationships of the taxa in the Crocus vernus complex, we considered it appropriate to accept our taxon as Crocus heuffelianus. So far, only C. tommasinianus Herb. has been found in Bulgarian flora from the Crocus series Verni, but in terms of altitude and morphological features, the species from our collection is close to the Balkan endemic C. veluchensis, which belongs to the C. sieberi aggregate. Morphologically, it differs by the dark, heart-shaped spots on the tip of the tepals, and the presence of one bract. A detailed comparative anatomical analysis between the three species of crocuses from the series Verni in Bulgaria shows discrete differences: the width of the white stripe and lacunar area are good distinguishing features, as are the number of conducting vessels.
3D-Printed Polyamide 12/Styrene–Acrylic Copolymer–Boron Nitride (PA12/SA–BN) Composite with Macro and Micro Double Anisotropic Thermally Conductive Structures
Authors: Chen Minhang , Chen Xiaojie , Zhang Junle , Xue Bingfeng , Zhai Shangyu , She Haibo , Zhang Yuancheng , Cui Zhe , Fu Peng , Pang Xinchang , Liu Minying , Zhang Xiaomeng
Published: June 2023

Anisotropic thermally conductive composites are very critical for precise thermal management of electronic devices. In this work, in order to prepare a composite with significant anisotropic thermal conductivity, polyamide 12/styrene–acrylic copolymer–boron nitride (PA12/SA–BN) composites with macro and micro double anisotropic structures were fabricated successfully using 3D printing and micro-shear methods. The morphologies and thermally conductive properties of composites were systematically characterized via SEM, XRD, and the laser flash method. Experimental results indicate that the through-plane thermal conductivity of the composite is 4.2 W/(m·K) with only 21.4 wt% BN, which is five times higher than that of the composite with randomly oriented BN. Simulation results show that the macro-anisotropic structure of the composite (caused by the selective distribution of BN) as well as the micro-anisotropic structure (caused by the orientation structure of BN) both play critical roles in spreading heat along the specified direction. Therefore, as-obtained composites with double anisotropic structures possess great potential for the application inefficient and controllable thermal management in various fields.
Phase Diagram of Pickering Emulsions Stabilized by Cellulose Nanocrystals
Authors: Perrin Louise , Desobry-Banon Sylvie , Gillet Guillaume , Desobry Stephane
Published: June 2023

Cellulose is a promising renewable and biocompatible biopolymer for stabilizing Pickering emulsions (PEs). In the present study, PEs were produced by low-frequency ultrasounds with cellulose nanocrystals (CNCs) and caprylic/capric triglycerides. Phase diagrams allowed to understand mechanisms of formation and long-term stabilization of PEs. Emulsion type, continuous phase viscosity, and yield of oil incorporation were studied after PEs formation. Droplet size, oil release, and stability were measured weekly up to 56 days of storage. Results showed that oil mass fraction above 70% w/w led to unstable W/O PEs. Lower oil mass fraction formed O/W PEs of stability depending on CNC content and oil mass fraction. Droplet size stability increased with CNCs/oil ratio. A very low CNCs/oil ratio led to phase separation and oil release. High CNC content stabilized oil droplets surface, increased aqueous phase viscosity, and prevented creaming. Highly stable PEs were produced for CNC content above 3% (w/w) and oil mass fraction below 50% (w/w). Mechanisms for PEs formation and stabilization were proposed for various CNC contents and oil mass fractions.
Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning
Authors: Hsieh Ping-Cheng
Published: June 2023

Stretch blow molding serves as the primary technique employed in the production of polyethylene terephthalate (PET) bottles. Typically, a stretch blow molding machine consists of various components, including a preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Of particular significance is the temperature control within the heating system, which significantly influences the quality of PET bottles, especially when confronted with environmental temperature changes between morning and evening during certain seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process heavily relies on the personnel’s experience, causing a production challenge for bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent approach for adjusting temperature control parameters. The proposed approach aims to address issues such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. Experimental results demonstrate that the proposed approach achieves automatic adjustment of temperature control parameters during the heating process, effectively mitigating the influence of environmental temperature changes and ensuring stable control of preform surface temperature within ±2 ℃ of the target temperature.
Distribution Characteristics of Heavy Metals in Different Particle Size Fractions of Chinese Paddy Soil Aggregates
Authors: Gong Cang , Shao Yang , Luo Min , Xu Diandou , Ma Lingling
Published: June 2023

In recent years, the migration and transformation of heavy metals (HMs) in soil has become a hot issue. Soil particle size has an important effect on the environmental behavior of HMs in soil. The distribution of HMs in soil is strongly affected by the size of a soil aggregate. In this study, paddy samples in both cultivated and uncultivated soils were collected from Anhui Province, China. The soils were sieved into six particle size fractions (diameters of >4000, 4000–2000, 2000–1000, 1000–250, 250–53, and <53 μm) and the wet digestion method was used to determine the concentrations of Cd, Cr, Pb, and As in the soil aggregates. The results showed that the surface soils were characterized by the largest proportion of coarsest aggregates of >4000 μm particles. Moreover, the concentrations of Cd, Cr, Pb, and As were elevated in cultivated paddy soils compared to uncultivated soils. The accumulation of HMs in all paddy soils increased with decreasing particle size. Although the smallest particle size fractions accounted for only 5.65–17.28%, they provided the highest distribution factor (DF) of Cr (1.35), As (1.25), Cd (1.28), and Pb (1.38). The highest contributions of HMs came from the coarser fractions (>2000 μm); however, for Cr, Pb, Cd, and As, the mass loading of <53 μm particles was up to 15.90%, 14.41%, 15.21%, and 15.70%, respectively. The highest content of HMs was found in the finest aggregate, with a pattern of decreasing with increasing aggregate size. In addition, the leachability behaviors of different HMs in different particle size aggregates were different. The leachability behaviors of Cr and Pb in the coarse particle size fractions were the highest, while the leachability behaviors of As and Cd in the less than 250 μm particle size fractions were 10–100 times higher than those of Cr and Pb. A correlation analysis showed that the particle distributions of Fe, TOC, and Mn had significant influences on the distributions of Pb, Cd, As, and Cr. This study provides a theoretical basis for the prevention, treatment, and remediation of HMs pollution in soil.
Interfacial Electron Transfer and Synergistic Effects on NiCo(CA)@M Microbars That Boost the Alkaline Oxygen Evolution Reaction
Authors: Liu Jiajia , Wang Xiao , Min Yulin , Li Qiaoxia , Xu Qunjie
Published: June 2023

The development of oxygen evolution reaction electrocatalysts with a low cost, high activity, and strong stability is of great significance to the breakthrough of energy conversion technology. Herein, a composite material (NiCo(CA)@M) was obtained by growing nickel-cobalt nanoparticles on MIL-88A in situ by a simple two-step solvent thermal method. The results show that NiCo(CA)@M composite has rich active sites, and the formation of the composite induces charge redistribution between NiCo(CA) and MIL-88A, effectively reducing the reaction energy barrier, while growth in situ was conducive to the improvement of material stability. Impressively, the NiCo(CA)@M catalyst achieved a current density of 10 mA cm−2 in alkaline electrolyte required an overpotential of only 270 mV and the Tafel slope was 69 mV dec−1. At the same time, the NiCo(CA)@M catalyst had excellent stability at a current density of 10 mA cm−2, and after the 16 h i-t test, the catalyst still had 91.1% current density retention. The electrocatalytic activity did not decay significantly after 2000 CV cycles.
Adsorptive Removal of Dye (Methylene Blue) Organic Pollutant from Water by Pine Tree Leaf Biomass Adsorbent
Authors: Sen Tushar Kanti
Published: June 2023

In this laboratory batch adsorption study, the raw pine tree leaf biomass solid waste adsorbent material was used for the removal of methylene blue (MB) dye from water at different physicochemical process conditions. The characteristics of adsorbents were determined for particle size, surface area, the existence of functional group identification, and the morphology of the adsorbent surface. The adsorption was performed at different process conditions, which include solution pH, dye concentrations, adsorbent doses, and temperature, respectively. In this study, it was found that MB dye adsorption increased with increases in solution pH and adsorbate MB dye concentration but decreased with adsorbent doses and temperature at fixed process conditions. The Langmuir isotherm model was best fitted with the experimental equilibrium data, with a higher linear regression coefficient (R2) value of 99.9% among the two widely used Langmuir and Freundlich adsorption isotherm model equations. The maximum Langmuir monolayer adsorption capacity of raw pine leaf was found to be 36.88 mg/g, which was comparable with other reported adsorbent capacities towards methylene blue (MB) dye adsorption. The value of the separation factor, RL, from the Langmuir isotherm model equation gives an indication of favorable adsorption. Thermodynamic parameters such as standard Gibbs free energy change (ΔG0), standard enthalpy change (ΔH0), and standard entropy change (ΔS0) indicated that the methylene blue (MB) dye adsorption by pine tree leaf biomass was spontaneous and exothermic in nature and that the mechanism of adsorption was mainly physical adsorption. Finally, limitations and future studies are also discussed here. The outcome of this batch adsorption study may result in the valorization of locally available large pine tree leaf residue waste, which could be used in water purification.
Adapting an Existing Empirical Algorithm for Microwave Land Surface Temperature Retrieval in China for AMSR2 Data
Authors: Zhang Quan , Wang Ninglian , Wu Yuwei , Chen An’an
Published: June 2023

To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise regression using the brightness temperature in different AMSR2 channels. The results indicate that the annual RMSE of the AMSR2 LST, compared to the reference Moderate Resolution Imaging Spectroradiometer (MODIS) LST from 2012 to 2020, varies between 3.26 K and 3.61 K in the daytime and 2.76 K and 2.96 K in the nighttime, respectively. The RMSE of the AMSR2 LST compared to the field measurements at the sites of the Beidahe river basin and Naqu regions varies between 4.16 K and 5.26 K in the daytime and 2.4 K and 5.17 K in the nighttime. The accuracy is relatively low in the warmer months and daytime due to the stronger solar radiation, and is also relatively low in western China due to the dominate highly fluctuating topography and barren and arid landcover. Generally, the accuracy of the AMSR2 LST is comparable with that of the AMSR-E LST retrieved by the predecessor algorithm, which facilitates coherent long-term applications using AMSR series LST datasets.
Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals
Authors: Liu Xiaoyan , Chen Liang , Jiao Zhenhang , Lu Xiangchen
Published: June 2023

Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning.
The Potential of Visual ChatGPT for Remote Sensing
Authors: Osco Lucas Prado , Lemos Eduardo Lopes de , Gonçalves Wesley Nunes , Ramos Ana Paula Marques , Marcato Junior José
Published: June 2023

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. These are known as Visual LLMs and one notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. These models’ abilities to process images based on textual inputs can revolutionize diverse fields, and while their application in the remote sensing domain remains unexplored, it is important to acknowledge that novel implementations are to be expected. Thus, this is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.
Sea Clutter Amplitude Prediction via an Attention-Enhanced Seq2Seq Network
Authors: Qu Qizhe , Chen Hao , Lei Zhenshuo , Li Binbin , Du Qinglei , Wang Yongliang
Published: June 2023

Sea clutter is a kind of ubiquitous interference in sea-detecting radars, which will definitely influence target detection. An accurate sea clutter prediction method is supposed to be beneficial while existing prediction methods are based on the one-step-ahead prediction. In this paper, a sea clutter prediction network (SCPNet) is proposed to achieve the k-step-ahead prediction based on the characteristics of sea clutter. The SCPNet takes a sequence-to-sequence (Seq2Seq) structure as the backbone, and a simple self-attention module is employed to enhance the ability of adaptive feature selections. The SCPNet takes the normalized amplitudes of sea clutter as inputs and is capable of predicting an output sequence with a length of k; the phase space reconstruction theory is also used to find the optimized input length of the sea clutter sequence. Results with the sea-detecting radar data-sharing program (SDRDSP) database show the mean square error of the proposed method is 1.48 × 10−5 and 8.76 × 10−3 in the one-step-ahead prediction and the eight-step-ahead prediction, respectively. Compared with four existing methods, the proposed method achieves the best prediction performance.
Quantifying Multifrequency Ocean Altimeter Wind Speed Error Due to Sea Surface Temperature and Resulting Impacts on Satellite Sea Level Measurements
Authors: Tran Ngan , Vandemark Douglas , Bignalet-Cazalet François , Dibarboure Gérald
Published: June 2023

Surface wind speed measurements from a satellite radar altimeter are used to adjust altimeter sea level measurements via sea state bias range correction. We focus here on previously neglected ocean radar backscatter and subsequent wind speed variations due to sea surface temperature (SST) change that may impact these sea level estimates. The expected error depends on the radar operating frequency and may be significant at the Ka band (36 GHz) frequency chosen for the new Surface Water and Ocean Topography (SWOT) satellite launched in December 2022. SWOT is expected to revolutionize oceanography by providing wide-swath Ka band observations and enhanced spatial resolution compared to conventional Ku band (14 GHz) altimetry. The change to the Ka band suggests a reconsideration of SST impact on wind and sea level estimates, and we investigate this in advance of SWOT using existing long-term Ku and Ka band satellite altimeter datasets. This study finds errors up to 1.5 m/s in wind speed estimation and 1.0 cm in sea level for AltiKa altimeter data. Future SWOT data analyses may require consideration of this dependence prior to using its radar backscatter data in its sea level estimation.
Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention
Authors: Nazir Amril , Mitra Rohan , Sulieman Hana , Kamalov Firuz
Published: June 2023

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.
Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques
Authors: Viciano-Tudela Sandra , Parra Lorena , Navarro-Garcia Paula , Sendra Sandra , Lloret Jaime
Published: June 2023

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.
Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals
Authors: Kulyabin Mikhail , Zhdanov Aleksei , Dolganov Anton , Maier Andreas
Published: June 2023

The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.
Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons
Authors: Guerrero-Rodriguez Jose-Maria , Cifredo-Chacon Maria-Angeles , Cobos Sánchez Clemente , Perez-Peña Fernando
Published: June 2023

Pyroelectric infrared sensors (PIR) are widely used as infrared (IR) detectors due to their basic implementation, low cost, low power, and performance. Combined with a Fresnel lens, they can be used as a binary detector in applications of presence and motion control. Furthermore, due to their features, they can be used in autonomous intelligent devices or included in robotics applications or sensor networks. In this work, two neural processing architectures are presented: (1) an analog processing approach to achieve the behavior of a presynaptic neuron from a PIR sensor. An analog circuit similar to the leaky integrate and fire model is implemented to be able to generate spiking rates proportional to the IR stimuli received at a PIR sensor. (2) An embedded postsynaptic neuron where a spiking neural network matrix together with an algorithm based on digital processing techniques is introduced. This structure allows connecting a set of sensors to the post-synaptic circuit emulating an optic nerve. As a case study, the entire neural processing approach presented in this paper is applied to optical flow detection considering a four-PIR array as input. The results validate both the spiking approach for an analog sensor presented and the ability to retrieve the analog information sent as spike trains in a simulated optic nerve.
Signal Quality Analysis of Single-Arm Electrocardiography
Authors: Wang Jia-Jung , Liu Shing-Hong , Tsai Cheng-Hsien , Manousakas Ioannis , Zhu Xin , Lee Thung-Lip
Published: June 2023

The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals’ mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI.
A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction
Authors: Wang Huiyun , Guo Maozu , Tian Le
Published: June 2023

Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is one of the effective means to prevent equipment failures. In order to reduce the error of equipment operation trend prediction, this paper proposes a method for equipment operation trend prediction based on a combination of signal decomposition and an Informer prediction model. Aiming at the problem of high noise in vibration signals, which makes it difficult to obtain intrinsic characteristics when directly using raw data for prediction, the original signal is decomposed once using the variational mode decomposition (VMD) algorithm optimized by the improved sparrow search algorithm (ISSA) to obtain the intrinsic mode function (IMF) for different frequencies and calculate the fuzzy entropy. The improved adaptive white noise complete set empirical mode decomposition (ICEEMDAN) is used to decompose the components with the largest fuzzy entropy to obtain a series of intrinsic mode components, fully combining the advantages of the Informer model in processing long time series, and predict equipment operation trend data. Input all subsequences into the Informer model and reconstruct the results to obtain the predicted results. The experimental results indicate that the proposed method can effectively improve the accuracy of equipment operation trend prediction compared to other models.
An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring
Authors: Yang Guanglin , Du Xiaoyong , Huang Lingyong , Wu Xuerui , Sun Ling , Qi Chengli , Zhang Xiaoxin , Wang Jinsong , Song Shaohui
Published: June 2023

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm’s development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.
Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning
Authors: Paracha Muhammad Arsalan , Sadiq Muhammad , Liang Junwei , Durad Muhammad Hanif , Sheeraz Muhammad
Published: June 2023

The advancements and reliance on digital data necessitates dependence on information technology. The growing amount of digital data and their availability over the Internet have given rise to the problem of information security. With the increase in connectivity among devices and networks, maintaining the information security of an asset has now become essential for an organization. Intrusion detection systems (IDS) are widely used in networks for protection against different network attacks. Several machine-learning-based techniques have been used among researchers for the implementation of anomaly-based IDS (AIDS). In the past, the focus primarily remained on the improvement of the accuracy of the system. Efficiency with respect to time is an important aspect of an IDS, which most of the research has thus far somewhat overlooked. For this purpose, we propose a multi-layered filtration framework (MLFF) for feature reduction using a statistical approach. The proposed framework helps reduce the detection time without affecting the accuracy. We use the CIC-IDS2017 dataset for experiments. The proposed framework contains three filters and is connected in sequential order. The accuracy, precision, recall and F1 score are calculated against the selected machine learning models. In addition, the training time and the detection time are also calculated because these parameters are considered important in measuring the performance of a detection system. Generally, decision tree models, random forest methods, and artificial neural networks show better results in the detection of network attacks with minimum detection time.
Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep
Authors: Jiang Chengxiang , Qi Jingwei , Hu Tianci , Wang Xin , Bai Tao , Guo Leifeng , Yan Ruirui
Published: June 2023

Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.
Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia: A State-of-the-Art Review
Authors: Vuijk Jessica G. J. , Klein Brinke Jeroen , Sharma Nikita
Published: June 2023

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD.
Food Price Bubbles: Food Price Indices of Turkey, the FAO, the OECD, and the IMF
Authors: Akyol Özcan Kübra
Published: June 2023

Food price bubbles are a phenomenon in which the cost of some food items or food commodities climbs quickly and unreasonably before collapsing due to reasons such as speculation, supply and demand imbalances, meteorological occurrences, and governmental actions. Governments and academics keep an eye on food prices to spot and address these bubbles. The aim of this paper is to analyze the food price bubble phenomenon despite the global slowdown by examining its origins, consequences, and policy solutions. The study’s contribution lies in its investigation of the presence of a potential bubble formation in food prices, which have experienced a surge globally and particularly in Turkey in recent years. The research examines the behavior of bubble formation in food prices in Turkey, the FAO, the OECD, and the IMF between 1992 and 2022 using the ADF, SADF, and GSADF tests. These tests can help identify when asset prices deviate significantly from their fundamental values and may be at risk of a sharp correction. The TUR, FAO, OECD, and IMF variables all show distinct bubble formations, with the TUR variable having 130 periods from 1995 to 2006, the FAO having 8 price bubble occurrences, the OECD having 12 different bubble formations, and the IMF having 4 distinct bubble formations. The results indicate the existence of food price bubbles that demonstrate disparities from their inherent values grounded in market fundamentals.