bluuebunny/crossref_metadata_2025 huggingface dataset:
Outcome of Ivermectin in Cancer Treatment: An Experience in Loja-Ecuador
Authors: Jiménez-Gaona Yuliana , Vivanco-Galván Oscar , Morales-Larreategui Gonzalo , Cabrera-Bejarano Andrea , Lakshminarayanan Vasudevan
Published: February 2023

(1) Background: Cancer is one of the leading causes of death worldwide, and trends in cancer incidence and mortality are increasing over last years in Loja-Ecuador. Cancer treatment is expensive because of social and economic issues which force the patients to look for other alternatives. One such alternative treatment is ivermectin-based antiparasitic, which is commonly used in treating cattle. This paper analyzed ivermectin use as cancer treatment in the rural area of the Loja province and the medical opinion regarding the use of ivermectin in humans. (2) Methods: The study used a mixed methodology using different sampling techniques such as observation, surveys, and interviews. (3) Results: The main findings show that 19% of the participants diagnosed with cancer take medicines based on ivermectin as alternative therapy to the cancer control and treatment without leaving treatment such as chemotherapy, radiotherapy, or immunotherapy, while 81% use it to treat other diseases. (4) Conclusions: Finally, we identify that the interviewed not only use IVM as anticancer treatment, but it is also used as a treatment against other diseases. Although the participants’ opinions indicate that they feel improvements in their health after the third dose, the specialist considers that there is no authorization to prescribe these alternative treatments. In addition, they confirmed that currently, there is no scientific knowledge about the application of these treatments in humans and they do not recommend their application. Thus, the anticancer mechanism of ivermectin remains to be further investigated; therefore, we consider that it is important to continue with this research by proposing a new stage to evaluate and determine the pharmacological action of this type of drug through an in vitro study in different cultures of cancer cells.
The Low Variability of Tc24 in Trypanosoma cruzi TcI as an Advantage for Chagas Disease Prophylaxis and Diagnosis in Mexico
Authors: Becker Ingeborg , Miranda-Ortiz Haydee , Fernández-Figueroa Edith A. , Sánchez-Montes Sokani , Colunga-Salas Pablo , Grostieta Estefanía , Juárez-Gabriel Javier , Lozano-Sardaneta Yokomi N. , Arce-Fonseca Minerva , Rodríguez-Morales Olivia , Meneses-Ruíz Gabriela , Pastén-Sánchez Sergio , López Martínez Irma , González-Guzmán Saúl , Paredes-Cervantes Vladimir , Moreira Otacilio C. , Finamore-Araujo Paula , Canseco-Méndez Julio C. , Coquis-Navarrete Uriel , Rengifo-Correa Laura , González-Salazar Constantino , Alfaro-Cortés Myrna M. , Falcón-Lezama Jorge A. , Tapia-Conyer Roberto , Stephens Christopher R.
Published: February 2023

(1) Background: Chagas disease is the main neglected tropical disease in America. It is estimated that around 6 million people are currently infected with the parasite in Latin America, and 25 million live in endemic areas with active transmission. The disease causes an estimated economic loss of USD 24 billion dollars annually, with a loss of 75,200 working years per year of life; it is responsible for around ~12,000 deaths annually. Although Mexico is an endemic country that recorded 10,186 new cases of Chagas disease during the period of 1990–2017, few studies have evaluated the genetic diversity of genes that could be involved in the prophylaxis and/or diagnosis of the parasite. One of the possible candidates proposed as a vaccine target is the 24 kDa trypomastigote excretory–secretory protein, Tc24, whose protection is linked to the stimulation of T. cruzi-specific CD8+ immune responses. (2) Methods: The aim of the present study was to evaluate the fine-scale genetic diversity and structure of Tc24 in T. cruzi isolates from Mexico, and to compare them with other populations reported in the Americas with the aim to reconsider the potential role of Tc24 as a key candidate for the prophylaxis and improvement of the diagnosis of Chagas disease in Mexico. (3) Results: Of the 25 Mexican isolates analysed, 48% (12) were recovered from humans and 24% (6) recovered from Triatoma barberi and Triatoma dimidiata. Phylogenetic inferences revealed a polytomy in the T. cruzi clade with two defined subgroups, one formed by all sequences of the DTU I and the other formed by DTU II–VI; both subgroups had high branch support. Genetic population analysis detected a single (monomorphic) haplotype of TcI throughout the entire distribution across both Mexico and South America. This information was supported by Nei’s pairwise distances, where the sequences of TcI showed no genetic differences. (4) Conclusions: Given that both previous studies and the findings of the present work confirmed that TcI is the only genotype detected from human isolates obtained from various states of Mexico, and that there is no significant genetic variability in any of them, it is possible to propose the development of in silico strategies for the production of antigens that optimise the diagnosis of Chagas disease, such as quantitative ELISA methods that use this region of Tc24.
Identification of Prescribing Patterns in Hemodialysis Outpatients Taking Multiple Medications
Authors: Nagano Hiroyuki , Tomori Koji , Koiwa Mano , Kobayashi Shotaro , Takahashi Masahiro , Makabe Hideki , Okada Hirokazu , Kushiyama Akifumi
Published: February 2023

We investigated the relationship between multidrug administration and the characteristics, pathophysiology, and drug class in outpatients with hemodialysis. A retrospective cross-sectional study was conducted at Saitama Medical University Hospital in October 2018. Multidrug administration was defined as receiving either more than six drugs or more than the median number of drugs. The drugs used were represented by their anatomical classification codes in the Anatomical Therapeutic Chemistry Classification System (ATC classification). A latent class analysis (LCA) was used to identify clusters at risk of receiving multiple medications. A stepwise logistic regression analysis was performed to select ATC classifications prone to being involved in multidrug administration. As of October 2018, 98 outpatients with hemodialysis were enrolled in the study. In the LCA, when diabetes was the main primary disease, oral hypoglycemic agents available to dialysis patients were limited, but the number of drugs administered was large. Old age, poor nourishment, a long history of dialysis, and chronic nephritis were associated with multidrug administration among nondiabetic patients. In the second level of the ATC classification, the drugs frequently used were coded A02 (drugs for acid-related disorders), A07 (antidiarrheal agents, intestinal anti-inflammatory/anti-infective agents), B01 (antithrombotic agents), and N05 (psycholeptics). The prescribing patterns for either diabetic patients or nondiabetic elderly patients were identified in outpatients with hemodialysis taking multiple medications, and drugs for acid-related disorders, antidiarrheal agents, intestinal anti-inflammatory/anti-infective agents, antithrombotic agents, and psycholeptics are frequently used in those patients.
Application of the Improved Grinding Technology to Freeform Surface Manufacturing
Authors: Peng Lirong , Li Xingchang , Li Lingzhong , Cheng Qiang , Luo Xiao , Zhou Xiaoqin , Zhang Xuejun
Published: February 2023

In order to meet the manufacturing requirements of modern space remote sensors for high-precision freeform optical parts, the grinding technology and its application were studied. The objective of this paper was to improve the application effect of traditional grinding technology in the processing of hard and brittle materials, and then apply it in specific fields. Therefore, the influence of key process factors such as cutting speed and removal depth on subsurface damage (SSD) was studied based on orthogonal experiments, and an improved grinding technology characterized by low SSD and high surface shape accuracy was formed. Then, the effect of this grinding technology was further verified by the high-precision manufacturing of freeform surfaces. A surface of a 130 mm diameter freeform surface was machined by improved grinding technology and combined polishing technology, the final root mean square of surface shape reached 12.1 nm. The improved grinding technology can reduce SSD from 20 μm to 10 μm, and improve the manufacturing efficiency of freeform surfaces above 30% when the cut speed is 20 m/s and the remove depth is 10 μm. The proposed technology can be applied to the extreme manufacturing of hard and brittle materials.
Combined Impact of Irrigation, Potassium Fertilizer, and Thinning Treatments on Yield, Skin Separation, and Physicochemical Properties of Date Palm Fruits
Authors: Ghazzawy Hesham S. , Alqahtani Nashi , Munir Muhammad , Alghanim Naser S. , Mohammed Maged
Published: February 2023

Orchard cultural practices, i.e., irrigation, fertilizer, and fruit thinning, are crucially encompassed to enhance fruit yield and quality. Appropriate irrigation and fertilizer inputs improve plant growth and fruit quality, but their overuse leads to the degradation of the ecosystem and water quality, and other biological concerns. Potassium fertilizer improves fruit sugar and flavor and accelerates fruit ripening. Bunch thinning also significantly reduces the crop burden and improves the physicochemical characteristics of the fruit. Therefore, the present study aims to appraise the combined impact of irrigation, sulfate of potash (SOP) fertilizer, and fruit bunch thinning practices on fruit yield and quality of date palm cv. Sukary under the agro-climatic condition of the Al-Qassim (Buraydah) region, Kingdom of Saudi Arabia. To achieve these objectives, four irrigation levels (80, 100, 120, and 140% of crop evapotranspiration (ETc), three SOP fertilizer doses (2.5, 5, and 7.5 kg palm−1), and three fruit bunch thinning levels (8, 10, and 12 bunches palm−1) were applied. The effects of these factors were determined on fruit bunch traits, physicochemical fruit characteristics, fruit texture profile, fruit color parameters, fruit skin separation disorder, fruit grading, and yield attributes. The findings of the present study showed that the lowest (80% ETc) and highest (140% ETc) irrigation water levels, lowest SOP fertilizer dose (2.5 kg palm−1), and retaining the highest number of fruit bunch per tree (12 bunches) had a negative effect on most yield and quality attributes of date palm cv. Sukary. However, maintaining the date palm water requirement at 100 and 120% ETc, applying SOP fertilizer doses at 5 and 7.5 kg palm−1, and retaining 8–10 fruit bunches per palm had significantly positive effects on the fruit yield and quality characteristics. Therefore, it is concluded that applying 100% ETc irrigation water combined with a 5 kg palm−1 SOP fertilizer dose and maintaining 8–10 fruit bunches per palm is more equitable than other treatment combinations.
The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress
Authors: Liyanage Dilrukshi Kombala , Torkamaneh Davoud , Belzile François , Balasubramanian Parthiba , Hill Brett , Thilakarathna Malinda S.
Published: February 2023

Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.
The Study on Sea Buckthorn (Genus Hippophae L.) Fruit Reveals Cell Division and Cell Expansion to Promote Morphogenesis
Authors: Zhao Jing , Zhang Zhihua , Zhou Hongdan , Bai Zengfu , Sun Kun
Published: February 2023

Due to its unique flavor and high antioxidant content, the sea buckthorn (genus Hippophae L.) fruit is increasingly favored by consumers. Developing from the perianth tube, the sea buckthorn fruit varies greatly among species in both size and shape. However, the cellular regulation mechanism of sea buckthorn fruit morphogenesis remains unclear. This study presents the growth and development patterns, morphological changes, and cytological observations of the fruits of three Hippophae species (H. rhamnoides ssp. sinensis, H. neurocarpa, and H. goniocarpa). The fruits were monitored every 10–30 days after anthesis (DAA) for six periods in their natural population on the eastern margin of the Qinghai-Tibet Plateau in China. The results showed that the fruits of H. rhamnoides ssp. sinensis and H. goniocarpa grew in a sigmoid mode, while H. neurocarpa grew in an exponential mode under the complex regulation of cell division and cell expansion. In addition, cellular observations showed that the mesocarp cells of H. rhamnoides ssp. sinensis and H. goniocarpa were larger in the area with prolonged cell expansion activity, while H. neurocarpa had a higher cell division rate. Elongation and proliferation of the mesocarp cells were found to be essential factors affecting the formation of fruit morphology. Finally, we established a preliminary cellular scenario for fruit morphogenesis in the three species of sea buckthorn. Fruit development could be divided into a cell division phase and a cell expansion phase with an overlap between 10 and 30 DAA. In particular, the two phases in H. neurocarpa showed an additional overlap between 40 and 80 DAA. The description of the sea buckthorn fruit’s transformation and its temporal order may provide a theoretical basis to explore the growth mechanism of fruits and regulate their size through certain cultivation techniques.
Contrasting Dynamics of Littoral and Riparian Reed Stands within a Wetland Complex of Lake Cerknica
Authors: Ojdanič Nik , Zelnik Igor , Holcar Matej , Gaberščik Alenka , Golob Aleksandra
Published: February 2023

This contribution discusses the use of field measurements and remotely sensed data in an exploration of the effects of environmental parameters on the riparian and littoral stands of the common reed (Phragmites australis) in an intermittent wetland in Slovenia. For this purpose, we created a normalized difference vegetation index (NDVI) time series extending from 2017 to 2021. Data were collected and fitted to a unimodal growth model, from which we determined three different stages relating to the reed’s growth. The field data consisted of the above-ground biomass harvested at the end of the vegetation season. Maximal NDVI values at the peak of the growing season exhibited no useful relationship with the above-ground biomass at the end of the season. Intense and long-lasting floods, especially during the period of intense culm growth, hindered the production of common reeds, while dry periods and temperatures were helpful before reed growth began. Summer droughts exhibited little effect. Water level fluctuations exerted a greater effect on reeds at the littoral site due to more pronounced extremes. In contrast, more constant and moderate conditions at the riparian site benefited the growth and productivity of the common reed. These results can prove useful for decision making regarding common reed management at the intermittent lake Cerknica.
Metabolite profiling and bioactivity of Cicerbita alpina (L.) Wallr. (Asteraceae, Cichorieae)
Authors: Zheleva-Dimitrova Dimitrina , Petrova Alexandra , Zengin Gokhan , Sinan Kouadio Ibrahime , Balabanova Vessela , Joubert Olivier , Zidorn Christian , Voynikov Yulian , Simeonova Rumyana , Gevrenova Reneta
Published: February 2023

Cicerbita alpina (L.) Wallr. is a perennial herbaceous plant in the tribe Cichorieae (Lactuceae), Asteraceae family, distributed in the mountainous regions in Europe. In this study, we focused on the metabolite profiling and the bioactivity of C. alpina leaves and flowering heads methanol-aqueous extracts. The antioxidant activity of extracts, as well as inhibitory potential towards selected enzymes, involving in several human diseases, including metabolic syndrome (α-glucosidase, α-amylase, and lipase), Alzheimer’s disease, (cholinesterases: AChE, BchE), hyperpigmentation (tyrosinase), and cytotoxicity were assessed. The workflow comprised ultra-high-performance liquid chromatography—high-resolution mass spectrometry (UHPLC-HRMS). UHPLC-HRMS analysis revealed more than 100 secondary metabolites, including acylquinic, acyltartaric acids, flavonoids, bitter sesquiterpene lactones (STLs), such as lactucin, dihydrolactucin, their derivatives, and coumarins. Leaves showed a stronger antioxidant activity compared to flowering heads, as well as lipase (4.75 ± 0.21 mg OE/g), AchE (1.98 ± 0.02 mg GALAE/g), BchE (0.74 ± 0.06 mg GALAE/g), and tyrosinase (49.87 ± 3.19 mg KAE/g) inhibitory potential. Flowering heads showed the highest activity against α-glucosidase (1.05 ± 0.17 mmol ACAE/g) and α-amylase (0.47 ± 0.03). The obtained results highlighted C. alpina as a rich source of acylquinic, acyltartaric acids, flavonoids, and STLs with significant bioactivity, and therefore the taxon could be considered as a potential candidate for the development of health-promoting applications.
Finite Amplitude Oscillatory Convection of Binary Mixture Kept in a Porous Medium
Authors: Rameshwar Y. , Srinivas G. , Laroze D.
Published: February 2023

In the present study, the double-diffusive oscillatory convection of binary mixture, 3He–4He, in porous medium heated from below and cooled from above was investigated with stress-free boundary conditions. The Darcy model was employed in the governing system of perturbed equations. An attempt was made, for the first time, to solve these equations by using the nonlinear analysis-based truncated Fourier series. The influence of the Rayleigh number (R), the separation ratio (ψ) due to the Soret effect, the Lewis number (Le), and the porosity number (χ) on the field variables were investigated using the finite amplitudes. From the linear stability analysis, expressions for the parameters, namely, R and wavenumbers, were obtained, corresponding to the bifurcations such as pitchfork bifurcation, Hopf bifurcation, Takens–Bogdnanov bifurcation and co-dimension two bifurcation. The results reveal that the local Nusselt number (NL) increases with R. The total energy is enhanced for all increasing values of R. The deformation in the basic cylindrical rolls and the flow rate are enhanced with R. The trajectory of heat flow was studied using the heatlines concept. The influence of R on the flow topology is depicted graphically. It is observed that the intensity of heat transfer and the local entropy generation are increased as R increases.
A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment
Authors: Failing Johanna Marie , Abellán-Nebot José V. , Benavent Nácher Sergio , Rosado Castellano Pedro , Romero Subirón Fernando
Published: February 2023

Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app.
Performance of Biomass and Exopolysaccharide Production from the Medicinal Mushroom Ganoderma lucidum in a New Fabricated Air-L-Shaped Bioreactor (ALSB)
Authors: Supramani Sugenendran , Rejab Nur Ardiyana , Ilham Zul , Ahmad Rahayu , Show Pau-Loke , Ibrahim Mohamad Faizal , Wan-Mohtar Wan Abd Al Qadr Imad
Published: February 2023

Conventional stirred-tank bioreactor (STR) designs are optimised for cultures of bacteria but not fungal cultures; therefore, a new Air-L-Shaped Bioreactor (ALSB) was fabricated. The ALSB was designed to eliminate the wall growth and clumping of fungal mycelium in STRs. Ganoderma lucidum was used as a fungal model and its biomass and exopolysaccharide (EPS) production were maximised by optimising the agitation rate, glucose concentration, initial pH, and aeration via response surface methodology (RSM). The ALSB system generated 7.8 g/L of biomass (biomass optimised conditions: 110 rpm, 24 g/L glucose, pH 5.6, and 3 v/v of aeration) and 4.4 g/L of EPS (EPS optimised conditions: 90 rpm, 30 g/L glucose, pH 4, and 2.5 v/v of aeration). In combination, for both optimised conditions, biomass (7.9 g/L) and EPS (4.6 g/L) were produced at 110 rpm, 30 g/L glucose, pH 4, and 3 v/v of aeration with minimal wall growth. The data prove that the ALSB is a blueprint for efficient economical fungal cultivation.
An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study
Authors: Kaya Şükrü Mustafa , İşler Buket , Abu-Mahfouz Adnan M. , Rasheed Jawad , AlShammari Abdulaziz
Published: February 2023

Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.
Accuracy and Precision of a Novel Photogate System to Measure Toe Clearance on Stairs
Authors: Skervin Timmion K. , Thomas Neil M. , Schofield Andrew J. , Hollands Mark A. , Maganaris Constantinos N. , O’Brien Thomas D. , Baltzopoulos Vasilios , Foster Richard J.
Published: February 2023

Background: Toe clearance on stairs is typically measured using optoelectronic systems, though these are often constrained to the laboratory, due to their complex setups. Here we measured stair toe clearance through a novel prototype photogate setup and compared this to optoelectronic measurements. Methods: Twelve participants (age 22 ± 3 years) completed 25 stair ascent trials, each on a seven-step staircase. Toe clearance over the fifth step edge was measured using Vicon and the photogates. Twenty-two photogates were created in rows through laser diodes and phototransistors. The height of the lowest photogate broken at step-edge crossing was used to determine photogate toe clearance. A limits of agreement analysis and Pearson’s correlation coefficient compared the accuracy, precision and relationship between systems. Results: We found a mean difference of −1.5 mm (accuracy) between the two measurement systems, with upper and lower limits (precision) of 10.7 mm and −13.8 mm, respectively. A strong positive correlation was also found (r = 70, n = 12, p = 0.009) between the systems. Discussion: The results suggest that photogates could be an option for measuring real-world stair toe clearances, where optoelectronic systems are not routinely used. Improvements to the design and measurement factors may help to improve the precision of the photogates.
The Development of a Novel Headspace O2 Concentration Measurement Sensor for Vials
Authors: Chen Xiao , Sun Hao , Huang Wei , Jin Jiayi , Su Mingxu , Yang Huinan
Published: February 2023

In the process of manufacture and transportation, vials are prone to breakage and cracks. Oxygen (O2) in the air entering vials can lead to the deterioration of medicine and a reduction in pesticide effects, threatening the life of patients. Therefore, accurate measurement of the headspace O2 concentration for vials is crucial to ensure pharmaceutical quality. In this invited paper, a novel headspace oxygen concentration measurement (HOCM) sensor for vials was developed based on tunable diode laser absorption spectroscopy (TDLAS). First, a long–optical–path multi–pass cell was designed by optimizing the original system. Moreover, vials with different O2 concentrations (0%, 5%, 10%, 15%, 20%, and 25%) were measured with this optimized system in order to study the relationship between the leakage coefficient and O2 concentration; the root mean square error of the fitting was 0.13. Moreover, the measurement accuracy indicates that the novel HOCM sensor achieved an average percentage error of 1.9%. Sealed vials with different leakage holes (4, 6, 8, and 10 mm) were prepared to investigate the variation in the headspace O2 concentration with time. The results show that the novel HOCM sensor is non-invasive and has a fast response and high accuracy, with prospects in applications for online quality supervision and management of production lines.
Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone
Authors: Alvarado Eduardo , Grágeda Nicolás , Luzanto Alejandro , Mahu Rodrigo , Wuth Jorge , Mendoza Laura , Yoma Néstor Becerra
Published: February 2023

In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects’ vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.
Self-Sensing Variable Stiffness Actuation of Shape Memory Coil by an Inferential Soft Sensor
Authors: Sul Bhagoji Bapurao , Kaliaperumal Dhanalakshmi , Choi Seung-Bok
Published: February 2023

Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control.
Characterizing Ambient Seismic Noise in an Urban Park Environment
Authors: Saadia Benjamin , Fotopoulos Georgia
Published: February 2023

In this study, a method for characterizing ambient seismic noise in an urban park using a pair of Tromino3G+ seismographs simultaneously recording high-gain velocity along two axes (north-south and east-west) is presented. The motivation for this study is to provide design parameters for seismic surveys conducted at a site prior to the installation of long-term permanent seismographs. Ambient seismic noise refers to the coherent component of the measured signal that comes from uncontrolled, or passive sources (natural and anthropogenic). Applications of interest include geotechnical studies, modeling the seismic response of infrastructure, surface monitoring, noise mitigation, and urban activity monitoring, which may exploit the use of well-distributed seismograph stations within an area of interest, recording on a days-to-years scale. An ideal well-distributed array of seismographs may not be feasible for all sites and therefore, it is important to identify means for characterizing the ambient seismic noise in urban environments and limitations imposed with a reduced spatial distribution of stations, herein two stations. The developed workflow involves a continuous wavelet transform, peak detection, and event characterization. Events are classified by amplitude, frequency, occurrence time, source azimuth relative to the seismograph, duration, and bandwidth. Depending on the applications, results can guide seismograph selection (sampling frequency and sensitivity) and seismograph placement within the area of interest.
ConMLP: MLP-Based Self-Supervised Contrastive Learning for Skeleton Data Analysis and Action Recognition
Authors: Dai Chuan , Wei Yajuan , Xu Zhijie , Chen Minsi , Liu Ying , Fan Jiulun
Published: February 2023

Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approaches are based on extracting skeleton sequences through convolutional operations. Most of these architectures are implemented by learning spatial and temporal features through multiple streams. These studies have enlightened the action recognition endeavor from various algorithmic angles. However, three common issues are observed: (1) The models are usually complicated; therefore, they have a correspondingly higher computational complexity. (2) For supervised learning models, the reliance on labels during training is always a drawback. (3) Implementing large models is not beneficial to real-time applications. To address the above issues, in this paper, we propose a multi-layer perceptron (MLP)-based self-supervised learning framework with a contrastive learning loss function (ConMLP). ConMLP does not require a massive computational setup; it can effectively reduce the consumption of computational resources. Compared with supervised learning frameworks, ConMLP is friendly to the huge amount of unlabeled training data. In addition, it has low requirements for system configuration and is more conducive to being embedded in real-world applications. Extensive experiments show that ConMLP achieves the top one inference result of 96.9% on the NTU RGB+D dataset. This accuracy is higher than the state-of-the-art self-supervised learning method. Meanwhile, ConMLP is also evaluated in a supervised learning manner, which has achieved comparable performance to the state of the art of recognition accuracy.
Improved BDS-2/3 Satellite Ultra-Fast Clock Bias Prediction Based with the SSA-ELM Model
Authors: Ya Shaoshuai , Zhao Xingwang , Liu Chao , Chen Jian , Liu Chunyang
Published: February 2023

Ultra-fast satellite clock bias (SCB) products play an important role in real-time precise point positioning. Considering the low accuracy of ultra-fast SCB, which is unable to meet the requirements of precise point position, in this paper, we propose a sparrow search algorithm to optimize the extreme learning machine (SSA-ELM) algorithm in order to improve the performance of SCB prediction in the Beidou satellite navigation system (BDS). By using the sparrow search algorithm’s strong global search and fast convergence ability, we further improve the prediction accuracy of SCB of the extreme learning machine. This study uses ultra-fast SCB data from the international GNSS monitoring assessment system (iGMAS) to perform experiments. First, the second difference method is used to evaluate the accuracy and stability of the used data, demonstrating that the accuracy between observed data (ISUO) and predicted data (ISUP) of the ultra-fast clock (ISU) products is optimal. Moreover, the accuracy and stability of the new rubidium (Rb-II) clock and hydrogen (PHM) clock onboard BDS-3 are superior to those of BDS-2, and the choice of different reference clocks affects the accuracy of SCB. Then, SSA-ELM, quadratic polynomial (QP), and a grey model (GM) are used for SCB prediction, and the results are compared with ISUP data. The results show that when predicting 3 and 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~60.42%, 5.46%, and 57.59% and 72.27%, 44.65%, and 62.96% as compared with the ISUP, QP, and GM models, respectively. When predicting 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~53.16% and 52.09% and by 40.66% and 46.38% compared to the QP and GM models, respectively. Finally, multiday data are used for 6 h SCB prediction. The results show that the SSA-ELM model improves the prediction model by more than 25% compared to the ISUP, QP, and GM models. In addition, the prediction accuracy of the BDS-3 satellite is better than that of the BDS-2 satellite.
Configurable Pseudo Noise Radar Imaging System Enabling Synchronous MIMO Channel Extension
Authors: Bräunlich Niklas , Wagner Christoph W. , Sachs Jürgen , Del Galdo Giovanni
Published: February 2023

In this article, we propose an evolved system design approach to ultra-wideband (UWB) radar based on pseudo-random noise (PRN) sequences, the key features of which are its user-adaptability to meet the demands provided by desired microwave imaging applications and its multichannel scalability. In light of providing a fully synchronized multichannel radar imaging system for short-range imaging as mine detection, non-destructive testing (NDT) or medical imaging, the advanced system architecture is presented with a special focus put on the implemented synchronization mechanism and clocking scheme. The core of the targeted adaptivity is provided by means of hardware, such as variable clock generators and dividers as well as programmable PRN generators. In addition to adaptive hardware, the customization of signal processing is feasible within an extensive open-source framework using the Red Pitaya® data acquisition platform. A system benchmark in terms of signal-to-noise ratio (SNR), jitter, and synchronization stability is conducted to determine the achievable performance of the prototype system put into practice. Furthermore, an outlook on the planned future development and performance improvement is provided.
Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review
Authors: Cai Yujian , Li Xingguang , Li Jinsong
Published: February 2023

In recent years, the rapid development of sensors and information technology has made it possible for machines to recognize and analyze human emotions. Emotion recognition is an important research direction in various fields. Human emotions have many manifestations. Therefore, emotion recognition can be realized by analyzing facial expressions, speech, behavior, or physiological signals. These signals are collected by different sensors. Correct recognition of human emotions can promote the development of affective computing. Most existing emotion recognition surveys only focus on a single sensor. Therefore, it is more important to compare different sensors or unimodality and multimodality. In this survey, we collect and review more than 200 papers on emotion recognition by literature research methods. We categorize these papers according to different innovations. These articles mainly focus on the methods and datasets used for emotion recognition with different sensors. This survey also provides application examples and developments in emotion recognition. Furthermore, this survey compares the advantages and disadvantages of different sensors for emotion recognition. The proposed survey can help researchers gain a better understanding of existing emotion recognition systems, thus facilitating the selection of suitable sensors, algorithms, and datasets.
Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization
Authors: Papale Leonardo , Catini Alexandro , Capuano Rosamaria , Allegra Valerio , Martinelli Eugenio , Palmacci Massimo , Tranfo Giovanna , Di Natale Corrado
Published: February 2023

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.
Integration of Task-Based Exoskeleton with an Assist-as-Needed Algorithm for Patient-Centered Elbow Rehabilitation
Authors: Delgado Pablo , Yihun Yimesker
Published: February 2023

This research presents an Assist-as-Needed (AAN) Algorithm for controlling a bio-inspired exoskeleton, specifically designed to aid in elbow-rehabilitation exercises. The algorithm is based on a Force Sensitive Resistor (FSR) Sensor and utilizes machine-learning algorithms that are personalized to each patient, allowing them to complete the exercise by themselves whenever possible. The system was tested on five participants, including four with Spinal Cord Injury and one with Duchenne Muscular Dystrophy, with an accuracy of 91.22%. In addition to monitoring the elbow range of motion, the system uses Electromyography signals from the biceps to provide patients with real-time feedback on their progress, which can serve as a motivator to complete the therapy sessions. The study has two main contributions: (1) providing patients with real-time, visual feedback on their progress by combining range of motion and FSR data to quantify disability levels, and (2) developing an assist-as-needed algorithm for rehabilitative support of robotic/exoskeleton devices.
Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU
Authors: Cui Hongjiang , Chen Guanxin , Guan Ying , Zhao Huimin
Published: February 2023

Due to aerodynamic resistance, aerodynamic noise, and other problems, the further development of traditional high-speed electric multiple units (EMUs) on the open line has been seriously restricted, and the construction of a vacuum pipeline high-speed train system has become a new solution. In this paper, the Improved Detached Eddy Simulation (IDDES) is used to analyze the turbulent characteristics of the near wake region of EMU in vacuum pipes, so as to establish the important relationship between the turbulent boundary layer, wake, and aerodynamic drag energy consumption. The results show that there is a strong vortex in the wake near the tail, which is concentrated at the lower end of the nose near the ground and falls off from the tail. In the process of downstream propagation, it shows symmetrical distribution and develops laterally on both sides. The vortex structure far from the tail car is increasing gradually, but the strength of the vortex is decreasing gradually from the speed characterization. This study can provide guidance for the aerodynamic shape optimization design of the rear of the vacuum EMU train in the future and provide certain reference significance for improving the comfort of passengers and saving the energy consumption caused by the speed increase and length of the train.
Development of an Interactive Touchless Technology Based on Static-Electricity-Induced Luminescence
Authors: Abe Keina , Eguchi Taiga , Oyama Tatsuya , Fujio Yuki , Kikunaga Kazuya
Published: February 2023

Touchless technology has garnered significant interest in recent years because of its effectiveness in combating infectious diseases such as the novel coronavirus (COVID-19). The goal of this study was to develop an inexpensive and high-precision touchless technology. A base substrate was coated with a luminescent material that emitted static-electricity-induced luminescence (SEL), and it was applied at high voltage. An inexpensive web camera was used to verify the relationship between the non-contact distance to a needle and the applied-voltage-triggered luminescence. The SEL was emitted at 20–200 mm from the luminescent device upon voltage application, and the web camera detected the SEL position with an accuracy of less than 1 mm. We used this developed touchless technology to demonstrate a highly accurate real-time detection of the position of a human finger based on SEL.
A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties
Authors: Barrio-Conde Mikel , Zanella Marco Antonio , Aguiar-Perez Javier Manuel , Ruiz-Gonzalez Ruben , Gomez-Gil Jaime
Published: February 2023

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.