bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Antimicrobial, Anticancer and Anti-Oxidant Activities of Novel 2,3-Dihydropyrido[2,3-d]pyrimidine-4-one and Pyrrolo[2,1-b][1,3]benzothiazole Derivatives via Microwave-Assisted Synthesis
Authors: Al-Mutairi Aamal A. , Hafez Hend N. , El-Gazzar Abdel-Rhaman B. A. , Mohamed Marwa Y. A.
Published: February 2022

In our attempt towards the synthesis and development of effective antimicrobial, anticancer and antioxidant agents, a novel series of 2,3-dihydropyrido[2,3-d]pyrimidin-4-one 7a–e and pyrrolo[2,1-b][1,3]benzothiazoles 9a–e were synthesized. The synthesis of 2-(1,3-benzo thiazol-2-yl)-3-(aryl)prop-2-enenitrile (5a–e) as the key intermediate was accomplished by a microwave efficient method. Via a new variety oriented synthetic microwave pathway, these highly functionalized building blocks allowed access to numerous fused heteroaromatic such as 7-amino-6-(1,3-benzo thiazol-2-yl)-5-(aryl)-2-thioxo-2,3dihydropyrido [2,3-d]pyrimidin-4(1H)-one 7a–e and 1-amino-2-(aryl)pyrrolo[2,1-b][1,3]benzothiazole-3-carbonitrile derivatives 9a–e in order to study their antimicrobial and anticancer activity. The present investigation offers effective and rapid new procedures for the synthesis of the newly polycondensed heterocyclic ring systems. All the newly synthesized compounds were evaluated for antimicrobial, anticancer and antioxidant activity. Compounds 7a,d, and 9a,d showed higher antimicrobial activity than cefotaxime and fluconazole while the remaining compounds exhibited good to moderate activity against bacteria and fungi. An anticancer evaluation of the newly synthesized compounds against the three tumor cell lines (lung cell NCI-H460, liver cancer HepG2 and colon cancer HCT-116) exhibited that compounds 7a, d, and 9a,d have higher cytotoxicity against the three human cell lines compared to doxorubicin as a reference drug. These compounds also exhibited higher antioxidant activity and a great ability to protect DNA from damage induced by bleomycin.
Lipophilicity and Pharmacokinetic Properties of New Anticancer Dipyridothiazine with 1,2,3-Triazole Substituents
Authors: Morak-Młodawska Beata , Jeleń Małgorzata
Published: February 2022

The lipophilicity parameters (logPcalcd, RM0 and logPTLC) of 10 new active anticancer dipirydothiazines with a 1,2,3-triazole ring were determined theoretically using computational methods and experimentally by reversed-phase TLC. Experimental lipophilicity was assessed using mobile phases (a mixture of TRIS buffer and acetone) using a linear correlation between the RM retention parameter and the volume of acetone. The RM0 parameter was correlated with the specific hydrophobic surface b, revealing two congenerative subgroups: 1,2,3-triazole-1,6-diazaphenothiazines and 1,2,3-triazole-1,8-diazaphenothiazines hybrids. The RM0 parameter was converted into the logPTLC lipophilicity parameter using a calibration curve. The investigated compounds appeared to be moderately lipophilic. Lipophilicity has been compared with molecular descriptors and ADME properties. The new derivatives followed Lipinski’s, Ghose’s and Veber’s rules.
Biophysical and Biological Tools to Better Characterize the Stability, Safety and Efficacy of a Cosmeceutical for Acne-Prone Skin
Authors: Sommatis Sabrina , Capillo Maria Chiara , Maccario Cristina , Liga Elsa , Grimaldi Giulia , Rauso Raffaele , Bencini Pier Luca , Guida Stefania , Zerbinati Nicola , Mocchi Roberto
Published: February 2022

(1) Background: Acne is a widespread skin disease, especially among adolescents. Following the COVID-19 pandemic and the use of masks, the problem has been affecting a greater number of people, and the attention of the skin care beauty routine cosmetics has been focused on the “Maskne”, caused by the sebum excretion rate (SER) that stimulates microbial proliferation. (2) Methods: the present study was focused on the rheological characterization and quality assurance of the preservative system of an anti-acne serum. The biological effectiveness (cytotoxicity—skin and eye irritation—antimicrobial, biofilm eradication and anti-inflammatory activity) was evaluated in a monolayer cell line of keratinocytes (HaCaT) and on 3D models (reconstructed human epidermis, RHE and human reconstructed corneal epithelium, HCE). The Cutibacterium acnes, as the most relevant acne-inducing bacterium, is chosen as a pro-inflammatory stimulus and to evaluate the antimicrobial activity of the serum. (3) Results and Conclusions: Rheology allows to simulate serum behavior at rest, extrusion and application, so the serum could be defined as having a solid-like behavior and being pseudoplastic. The preservative system is in compliance with the criteria of the reference standard. Biological effectiveness evaluation shows non-cytotoxic and irritant behavior with a good antimicrobial and anti-inflammatory activity of the formulation, supporting the effectiveness of the serum for acne-prone skin treatment.
Michael Addition of 3-Oxo-3-phenylpropanenitrile to Linear Conjugated Enynones: Approach to Polyfunctional δ-Diketones as Precursors for Heterocycle Synthesis
Authors: Igushkina Anastasiya V. , Golovanov Alexander A. , Vasilyev Aleksander V.
Published: February 2022

Reaction of linear conjugated enynones, 1,5-diarylpent-2-en-4-yn-1-ones [Ar1C≡CCH=CHC(=O)Ar2], with 3-oxo-3-phenylpropanenitrile (NCCH2COPh) in the presence of sodium methoxide MeONa as a base in MeOH at room temperature for 4–26 h affords polyfunctional δ-diketones as a product of regioselective Michael addition to the double carbon–carbon bond of starting enynones. The δ-diketones have been formed as mixtures of two diastereomers in a ratio of 2.5:1 in good general yields of 53–98%. A synthetic potential of the obtained δ-diketones has been demonstrated by heterocyclization with hydrazine into substututed 5,6-dihydro-4H-1,2-diazepine.
Development and Validation of an HPLC Method for Simultaneous Determination of Capsaicinoids and Camphor in Over-the-Counter Medication for Topical Use
Authors: Lalić Martin , Soldić Ana , Lalić Andrija , Lalić Zdenka , Sertić Miranda
Published: February 2022

A reverse-phase high-performance liquid chromatography method was developed to determine and quantify capsaicin (trans-8-methyl-N-vanillyl-6- nonenamid), dihydrocapsaicin (8-methyl-N-vanillylnonanamide), and camphor (trimethylbicyclo[2.2.1]heptan-2-one). It is applicable in analyses of over-the-counter (OTC) medications for topical use and raw materials such as chili pepper oleoresin. Chromatographic separation was carried out on a C18 column using an isocratic flow of the mobile phase containing acetonitrile and ultrapure water in a ratio of 2:3, with pH adjusted to 3.2 using glacial acetic acid, and a flow rate of 1.5 mL/min. The concentration of the eluting compounds was monitored by a diode-array detector at a wavelength of 281 nm. The method was evaluated for several validation parameters, including selectivity, accuracy (confidence intervals < 0.05%), repeatability, and intermediate precision. The limit of detection (LOD) was determined to be 0.070 µg/mL for capsaicin, 0.211 µg/mL for dihydrocapsaicin, and 0.060 µg/mL for camphor. The limit of quantification (LOQ) was determined to be 0.212 µg/mL for capsaicin, 0.640 µg/mL for dihydrocapsaicin, and 0.320 µg/mL for camphor. Linearity was set in the range of 2.5–200 µg/mL for capsaicin and dihydrocapsaicin and 25–2000 µg/mL for camphor. The suggested analytical method can be used for quality control of formulated pharmaceutical products containing capsaicinoids, camphor, and propolis.
Pseudostichococcus Stands Out from Its Siblings Due to High Salinity and Desiccation Tolerance
Authors: Van Anh Tu , Glaser Karin
Published: February 2022

Desiccation and high salinity are two abiotic stressors that are related in terms of their effect on water homeostasis within cells. The success of certain aeroterrestrial microalgae is influenced by their ability to cope with desiccation, and in some cases, high salinity. The microalgae of the Stichococcus clade are ubiquitous in terrestrial habitats and are known to withstand desiccation and salinity stress by accumulating secondary metabolites. Nevertheless, it remains unclear if those two related stressors have a synergistic effect. Hence, we studied the effect of salinity on desiccation on various representative taxa within the Stichococcus clade. The results showed that in contrast to other Stichococcus taxa, Pseudostichococcus was able to recover fully after desiccation, with and without salinity stress. This observation was connected to elevated proline production under salinity stress and higher proline:sorbitol ratio in Pseudostichococcus to the other strains tested. In the other taxa, increasing salinity reduced their ability to withstand desiccation. This might have severe effects on microalgae in (semi)arid regions, where salinization of soils is an increasing threat also for agriculture. The results encourage further research to be done on the possible applications of this genus in salinity bioremediation, as it seems to be comparable to other halotolerant green algae used for this purpose.
Overexpression of a Gene Encoding Trigonelline Synthase from Areca catechu L. Promotes Drought Resilience in Transgenic Arabidopsis
Authors: Li Yilin , Ding Mengying , Cui Chuang , An Qiyuan , Wu Jiao , Zhou Guangzhen , Wan Yinglang , Bao Wenlong
Published: February 2022

Areca catechu L. is a commercially important palm tree widely cultured in tropical and subtropical areas. Its growth and production are severely hindered by the increasing threat of drought. In the present study, we investigated the physiological responses of areca seedlings to drought stress. The results showed that prolonged drought-induced yellowing on the overall area of most leaves significantly altered the chlorophyll fluorescence parameters, including maximum chemical efficiency (Fv/Fm), photochemical efficiency of PSII (Y(II)), photochemical chlorophyll fluorescence quenching (qP) and non-photochemical chlorophyll fluorescence quenching (NPQ). On the 10th day of drought treatment, the contents of proline in the areca leaves and roots increased, respectively, by 12.2 times and 8.4 times compared to normal watering. The trigonelline levels in the leaves rose from 695.35 µg/g to 1125.21 µg/g under 10 days of water shortage, while no significant changes were detected in the content of trigonelline in the roots. We determined the gene encoding areca trigonelline synthase (AcTS) by conducting a bioinformatic search of the areca genome database. Sequence analysis revealed that AcTS is highly homologous to the trigonelline synthases in Coffea arabica (CaTS 1 and CaTS 2) and all possess a conserved S-adenosyl- L-methionine binding motif. The overexpression of AcTS in Arabidopsis thaliana demonstrated that AcTS is responsible for the generation of trigonelline in transgenic Arabidopsis, which in turn improves the drought resilience of transgenic Arabidopsis. This finding enriches our understanding of the molecular regulatory mechanism of the response of areca to water shortage and provides a foundation for improving the drought tolerance of areca seedlings.
Understanding the Modus Operandi of Class II KNOX Transcription Factors in Secondary Cell Wall Biosynthesis
Authors: Nookaraju Akula , Pandey Shashank K. , Ahlawat Yogesh K. , Joshi Chandrashekhar P.
Published: February 2022

Lignocellulosic biomass from the secondary cell walls of plants has a veritable potential to provide some of the most appropriate raw materials for producing second-generation biofuels. Therefore, we must first understand how plants synthesize these complex secondary cell walls that consist of cellulose, hemicellulose, and lignin in order to deconstruct them later on into simple sugars to produce bioethanol via fermentation. Knotted-like homeobox (KNOX) genes encode homeodomain-containing transcription factors (TFs) that modulate various important developmental processes in plants. While Class I KNOX TF genes are mainly expressed in the shoot apical meristems of both monocot and eudicot plants and are involved in meristem maintenance and/or formation, Class II KNOXTF genes exhibit diverse expression patterns and their precise functions have mostly remained unknown, until recently. The expression patterns of Class II KNOX TF genes in Arabidopsis, namely KNAT3, KNAT4, KNAT5, and KNAT7, suggest that TFs encoded by at least some of these genes, such as KNAT7 and KNAT3, may play a significant role in secondary cell wall formation. Specifically, the expression of the KNAT7 gene is regulated by upstream TFs, such as SND1 and MYB46, while KNAT7 interacts with other cell wall proteins, such as KNAT3, MYB75, OFPs, and BLHs, to regulate secondary cell wall formation. Moreover, KNAT7 directly regulates the expression of some xylan synthesis genes. In this review, we summarize the current mechanistic understanding of the roles of Class II KNOX TFs in secondary cell wall formation. Recent success with the genetic manipulation of Class II KNOX TFs suggests that this may be one of the biotechnological strategies to improve plant feedstocks for bioethanol production.
Impact of Silica Ions and Nano Silica on Growth and Productivity of Pea Plants under Salinity Stress
Authors: Ismail Lamiaa M. , Soliman Magda I. , Abd El-Aziz Mohammed H. , Abdel-Aziz Heba M. M.
Published: February 2022

The present study was conducted to evaluate the effects of silicon (Si) and nano-silicon (NSi) on growth, yield, ions content, and antioxidant defense systems, including transcript levels of enzyme-encoding genes in Pisum sativum plants grown under salinity stress. Both Si and NSi were applied at the 3 mM level and NaCl was applied at 4 concentrations (100, 150, 200 and 250 mM). Vegetative growth, including plant height, leaf area, fresh and dry weights, and yield attributes were determined. Gene expression of antioxidant enzymes was analyzed, and their activities were determined. The results showed that salinity had deleterious effects on plant growth and yield. Salt-stressed plant leaves exhibited a greater activity of superoxide dismutase (SOD), peroxidase (POD), but a lower activity of catalase (CAT) when compared to the control. Na+ ions accumulated in roots and shoots of salinized plants. The application of Si and NSi significantly enhanced vegetative growth and relative water content (RWC), and caused significant increases in plant height, fresh and dry weight, total yield, and antioxidant defense systems. Si and NSi enhanced K+ content in roots and shoots under salinity treatment and decreased Na+ content in the studied tissues. It was concluded that the application of NSi was beneficial in improving the salt tolerance of Pisum sativum plants more than Si alone.
Sugar, Invertase Enzyme Activities and Invertase Gene Expression in Different Developmental Stages of Strawberry Fruits
Authors: Topcu Hayat , Degirmenci Ipek , Sonmez Duygu Ayvaz , Paizila Aibibula , Karci Harun , Kafkas Salih , Kafkas Ebru , Ercisli Sezai , Alatawi Aishah
Published: February 2022

The cultivated strawberry (Fragaria × ananassa) is octoploid (2n = 8x = 56) and has been the focused fruit species of which an increasing number of molecular and genetic research has been conducted in recent years. The aim of this study is to identify the relationships between sucrose metabolism, invertase enzyme activity and gene expression in four different fruit development periods (red, pink, green and white) of two commercially important strawberry varieties ‘Rubygem’ and ‘Fortuna’. The metabolite profiles (glucose, fructose, sucrose and total sugar content) of two varieties were discovered to be extremely similar. The highest amount of total sugar was found in red fruits, while the lowest was obtained from green fruits. Invertase represents one of the key enzymes in sucrose metabolism. The lowest invertase activity was obtained from the green fruits in ‘Rubygem’ and ‘Fortuna’ during four developmental periods. In these varieties, the amount of sucrose was found to be close to glucose and fructose and the lowest amount was detected in green period, while invertase activity was relatively high during red and pink periods and invertase gene expression was determined at high levels in both primers (St-4 and St-6) in the green period. The results of the study indicated that sugar content and invertase activity were positively correlated while enzyme activity and gene expression were negatively correlated.
Broken Riceberry (BR) Powder Production Using a Double Drum Dryer and Its Utilization in the Development of Instant Beverages
Authors: Rittisak Sriwiang , Charoen Ratchanee , Savedboworn Wanticha
Published: February 2022

This study was designed to examine broken riceberry (BR) powder production through the use of a double drum dryer. There were two stages involved: (1) to study the optimized drum drying conditions for BR powder using response surface methodology (RSM), and (2) to utilize BR powder for the development of instant beverages. According to the results, the optimum drum drying condition was a drum temperature of 125 °C and drum speed of 1.0 rpm. Under these conditions, BR powder contained moisture = 5.81%, water activity = 0.494, total color difference 15.45, DPPH scavenging activity = 73.93%, water solubility index (WSI) = 52.33%, and water absorption index (WAI) = 10.52. For the utilization of BR powder for developing instant beverages, the product quality was light purple in color, water activity = 0.354, DPPH scavenging activity = 45%, WSI = 65.75%, and WAI = 12.25. The developed instant beverage contained 4.28% moisture, 13.89% protein, 12.42% fat, 4.23% fiber, and 2.23% ash. The microbial properties were <10 CFU/g for aerobic plate count and yeast and mold. The overall liking score was ‘like moderately’. This study indicated that the BR powder is of good quality and has potential in the beverage industry.
Effect of Biochar and Microbial Inoculation on P, Fe, and Zn Bioavailability in a Calcareous Soil
Authors: Vahedi Roghayeh , Rasouli-Sadaghiani Mir Hassan , Barin Mohsen , Vetukuri Ramesh Raju
Published: February 2022

To identify effective ways of increasing the yield of crops grown in nutrient-poor calcareous soils, the combined effects of biochar addition and inoculation with plant growth promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF) on wheat growth and soil properties were investigated under rhizobox conditions. Measured soil properties included pH, electrical conductivity (EC), organic matter content (OM), the availability of P, Fe, and Zn in the rhizosphere, and the uptake of these elements by plants. Combined biochar addition and microbial inoculation were shown to significantly increase the concentration of available forms of P, Fe, and Zn in the soil when compared to non-biochar treatments. The highest soil pH (7.82) was observed following biochar addition without microbial inoculation. The EC following biochar addition and PGPR inoculation was significantly higher than the other treatments, and the soil OM content was highest when combining AMF inoculation with biochar addition. The available P content after AMF inoculation combined with biochar addition was 27.81% higher than the control conditions, and AMF inoculation increased Fe and Zn bioavailability by factors of 2.38 and 1.29, respectively, when combined with biochar addition relative to AMF inoculation alone. The simultaneous biochar addition and PGPR inoculation significantly increased P uptake by the plants. The highest shoot Fe and Zn uptake rates were observed after a simultaneous application of biochar and PGPR inoculation. Under these conditions, shoot uptake was higher than seen when combining biochar addition with AMF inoculation by factors of 1.64 and 1.21, respectively. In general, it can be concluded that combining inoculation with growth-promoting bacteria and biochar addition can effectively improve nutrient availability to plant and soil conditions.
The Design and Magnetic Field Analysis of a Double Rotor Permanent Magnet Braking Device
Authors: Tang Jinhua , Zuo Yanyan
Published: February 2022

During long-term continuous braking, high-intensity braking, or frequent braking, the temperature of the brake disc or brake drum will increase significantly, resulting in a decrease in the friction coefficient, the aggravation of the wear degree, and the dangerous heat recession of partial or even total loss of braking efficiency. This paper focuses on an innovative double rotor permanent magnet braking device, which is located on the inner side of the wheel hub, to improve the heat decay resistance of the friction braking device. The structure and principle of the double rotor permanent magnet braking device were given, and its main structural parameters were designed, calculated, and optimized. The geometric model and finite element model of the double rotor permanent magnet braking device were established. The static and transient magnetic field analysis and the braking torque characteristic analysis of the double rotor permanent magnet braking device were carried out by using Maxwell electromagnetic analysis software. The results show that the magnetic flux density in the working area of the double rotor permanent magnet braking device increases with the increase in rotation speed, the braking torque changes with the change of rotation speed, and the maximum braking torque occurs in the low-speed area, which is consistent with the theoretical calculation results. This provides a theoretical basis for the follow-up prototype test of the double rotor permanent magnet braking device.
A Study of Text Vectorization Method Combining Topic Model and Transfer Learning
Authors: Yang Xi , Yang Kaiwen , Cui Tianxu , Chen Min , He Liyan
Published: February 2022

With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from data in order to solve the optimization problem in complex systems. There are many forms of data storage. Among them, text data is an important data type that directly reflects semantic information. Text vectorization is an important concept in natural language processing tasks. Because text data can not be directly used for model parameter training, it is necessary to vectorize the original text data and make it numerical, and then the feature extraction operation can be carried out. The traditional text digitization method is often realized by constructing a bag of words, but the vector generated by this method can not reflect the semantic relationship between words, and it also easily causes the problems of data sparsity and dimension explosion. Therefore, this paper proposes a text vectorization method combining a topic model and transfer learning. Firstly, the topic model is selected to model the text data and extract its keywords, to grasp the main information of the text data. Then, with the help of the bidirectional encoder representations from transformers (BERT) model, which belongs to the pretrained model, model transfer learning is carried out to generate vectors, which are applied to the calculation of similarity between texts. By setting up a comparative experiment, this method is compared with the traditional vectorization method. The experimental results show that the vector generated by the topic-modeling- and transfer-learning-based text vectorization (TTTV) proposed in this paper can obtain better results when calculating the similarity between texts with the same topic, which means that it can more accurately judge whether the contents of the given two texts belong to the same topic.
Τhe Contribution of Mobile Mental Health Units to Community Psychiatric Care in Greece
Authors: Samakouri Maria , Evagorou Olympia , Frangouli-Sakellaropoulou Athena
Published: February 2022

The implementation of Mobile Mental Health Units (MMHUs) was a decisive step in the psychiatric reform and the development of community psychiatric care in Greece [...]
Surface Channeling of Charged and Neutral Beams in Capillary Guides
Authors: Dabagov Sultan , Dik Alexey
Published: February 2022

In this review work, the passage of charged and neutral beams through dielectric capillary guides is described from a uniform point of view of beams channeling in capillaries. The motion of beams into the hollow channels formed by the inner walls of capillaries is mainly determined by multiple small-angle scattering (reflection) and can be described in the approximation of surface channeling. It is shown that the surface interaction potential in the case of micro- and nano-capillaries is actually conditioned by the curvature of the reflecting surface. After presenting the analysis of previously performed studies on X-rays propagation into capillaries, which is valid for thermal neutrons, too, the surface channeling formalism is also developed for charged particle beams, in particular, moving in curved cylindrical capillaries. Alternative theories explaining experimental results on the beams passage through capillaries are based on simple thermodynamic estimates, on various diffusion models, and on the results of direct numerical simulations as well. Our work is the first attempt to explain the effective guiding of a charged beam by a capillary from the general standpoint of quantum mechanics, which made it possible to analytically explore the interaction potential for surface channeling. It is established that, depending on the characteristics of a projectile and a dielectric forming the channel, the interaction potential can be either repulsive or attractive; the limiting values of the potential function for the corresponding cases are determined. It has been demonstrated that the surface channeling behaviour can help in explaining the efficient capillary guiding for radiations and beams.
Comparison of Effects of Chemical and Food Waste-Derived Fertilizers on the Growth and Nutrient Content of Lettuce (Lactuca sativa L.)
Authors: Kang Sang-Mo , Adhikari Arjun , Bhatta Dibya , Gam Ho-Jun , Gim Min-Ji , Son Joon-Ik , Shin Jin Y. , Lee In-Jung
Published: February 2022

The current high rate of food waste production, concomitant with the global increase in population and food demand, has adverse effects on environmental and socio-economic conditions. However, food waste has been shown to be an efficient and safe source of fertilizer in agriculture practice. Moreover, minimizing the application of chemical fertilizers is a goal of sustainable agriculture. Considering these facts, we aimed to compare the effect of chemical fertilizer (CF-3,8 g·pot−1) and different doses of mixed food waste-derived fertilizer (MF-10.6 g·pot−1), two-fold MF (MF × 2), four-fold MF (MF × 4), and six-fold MF (MF × 6) in a popular salad crop, Lactuca sativa (lettuce). Our results showed the growth rates of lettuce plants receiving CF, MF, and MF×2 applications were essentially the same; however, plant biomass significantly dropped with MF × 6 treatment. The CF, MF, and MF × 2 treatments enhanced the chlorophyll content, chlorophyll fluorescence, and photosynthetic rate of the plants and improved transpiration efficiency and stomatal conductance. With respect to mineral elements, the K+ content was significantly enhanced with MF × 2 and MF × 4 treatment, whereas MF × 6-treated plants showed lower concentrations of Ca, P, Mg, and K+ as well as higher Na+ concentration. Biochemical analysis showed the elevation of abscisic acid level with increasing dose of MF, except in the MF × 6 treatment. The level of super oxide dismutase (SOD) dropped with CF treatment, was unchanged with MF, and significantly increased in MF×2 and MF × 4 treated plants. Subsequently, higher flavonoid content was observed in MF×2 and MF×4 plants. The current results demonstrate the potential of food waste as a source of organic fertilizer and a significant substitute for chemical fertilizer in the conventional agricultural practice driven by high production cost and environmental pollution.
A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series
Authors: Basu Treena , Menzer Olaf , Ward Joshua , SenGupta Indranil
Published: February 2022

Stock trading has tremendous importance not just as a profession but also as an income source for individuals. Many investment account holders use the appreciation of their portfolio (as a combination of stocks or indexes) as income for their retirement years, mostly betting on stocks or indexes with low risk/low volatility. However, every stock-based investment portfolio has an inherent risk to lose money through negative progression and crash. This study presents a novel technique to predict such rare negative events in financial time series (e.g., a drop in the S&P 500 by a certain percent in a designated period of time). We use a time series of approximately seven years (2517 values) of the S&P 500 index stocks with publicly available features: the high, low and close price (HLC). We utilize a Siamese type neural network for pattern recognition in images followed by a bootstrapped image similarity distribution to predict rare events as they pertain to financial market analysis. Extending on literature about rare event classification and stochastic modeling in financial analytics, the proposed method uses a sliding window to store the input features as tabular data (HLC price), creates an image of the time series window, and then uses the feature vector of a pre-trained convolutional neural network (CNN) to leverage pre-event images and predict rare events. This research does not just indicate that our proposed method is capable of distinguishing event images from non-event images, but more importantly, the method is effective even when only limited and strongly imbalanced data is available.
Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple Constraints
Authors: Zhao Baigan , Huang Yingping , Ci Wenyan , Hu Xing
Published: February 2022

This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models. Unlike the existing unsupervised methods, our method extracts the features from the optical flow rather than from the raw RGB images, thereby enhancing unsupervised learning. In addition, we exploit the forward-backward consistency check of the optical flow to generate a mask of the invalid region in the image, and accordingly, eliminate the outlier regions such as occlusion regions and moving objects for the learning. Furthermore, in addition to using view synthesis as a supervised signal, we impose additional loss functions, including optical flow consistency loss and depth consistency loss, as additional supervision signals on the valid image region to further enhance the training of the models. Substantial experiments on multiple benchmark datasets demonstrate that our method outperforms other unsupervised methods.
A Wearable System Composed of FBG-Based Soft Sensors for Trunk Compensatory Movements Detection in Post-Stroke Hemiplegic Patients
Authors: Lo Presti Daniela , Zaltieri Martina , Bravi Marco , Morrone Michelangelo , Caponero Michele Arturo , Schena Emiliano , Sterzi Silvia , Massaroni Carlo
Published: February 2022

In this study, a novel wearable system for the identification of compensatory trunk movements (CTMs) in post-stroke hemiplegic patients is presented. The device is composed of seven soft sensing elements (SSEs) based on fiber Bragg grating (FBG) technology. Each SSE consists of a single FBG encapsulated into a flexible matrix to enhance the sensor’s robustness and improve its compliance with the human body. The FBG’s small size, light weight, multiplexing capability, and biocompatibility make the proposed wearable system suitable for multi-point measurements without any movement restriction. Firstly, its manufacturing process is presented, together with the SSEs’ mechanical characterization to strain. Results of the metrological characterization showed a linear response of each SSE in the operating range. Then, the feasibility assessment of the proposed system is described. In particular, the device’s capability of detecting CTMs was assessed on 10 healthy volunteers and eight hemiplegic patients while performing three tasks which are representative of typical everyday life actions. The wearable system showed good potential in detecting CTMs. This promising result may foster the use of the proposed device on post-stroke patients, aiming at assessing the proper course of the rehabilitation process both in clinical and domestic settings. Moreover, its use may aid in defining tailored strategies to improve post-stoke patients’ motor recovery and quality of life.
Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework
Authors: Seco Teresa , Lázaro María T. , Espelosín Jesús , Montano Luis , Villarroel José L.
Published: February 2022

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
Authors: Salimibeni Mohammad , Mohammadi Arash , Malekzadeh Parvin , Plataniotis Konstantinos N.
Published: February 2022

Development of distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted an increasing surge of interest lately. Generally speaking, conventional Model-Based (MB) or Model-Free (MF) RL algorithms are not directly applicable to the MARL problems due to utilization of a fixed reward model for learning the underlying value function. While Deep Neural Network (DNN)-based solutions perform well, they are still prone to overfitting, high sensitivity to parameter selection, and sample inefficiency. In this paper, an adaptive Kalman Filter (KF)-based framework is introduced as an efficient alternative to address the aforementioned problems by capitalizing on unique characteristics of KF such as uncertainty modeling and online second order learning. More specifically, the paper proposes the Multi-Agent Adaptive Kalman Temporal Difference (MAK-TD) framework and its Successor Representation-based variant, referred to as the MAK-SR. The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty. The proposed MAK-TD/SR frameworks are evaluated via several experiments, which are implemented through the OpenAI Gym MARL benchmarks. In these experiments, different number of agents in cooperative, competitive, and mixed (cooperative-competitive) scenarios are utilized. The experimental results illustrate superior performance of the proposed MAK-TD/SR frameworks compared to their state-of-the-art counterparts.
An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection
Authors: Kareem Saif S. , Mostafa Reham R. , Hashim Fatma A. , El-Bakry Hazem M.
Published: February 2022

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.
Accuracy Type Test for Rogowski Coils Subjected to Distorted Signals, Temperature, Humidity, and Position Variations
Authors: Mingotti Alessandro , Costa Federica , Peretto Lorenzo , Tinarelli Roberto
Published: February 2022

Low-Power Instrument Transformers (LPITs) are becoming the first choice for distributed measurement systems for medium voltage networks. However, there are still a lot of challenges related to their operation. Such challenges include their accuracy variation when several influence quantities are acting on them. Among the most significant influence quantities are temperature, electromagnetic field, humidity, etc. Another aspect that increases the importance of studying the LPITs’ accuracy behavior is that, once installed, they cannot be calibrated for several years; hence, one cannot compensate for in-field conditions. Hence, this work aims at introducing a simple type test for a specific LPIT, the Rogowski coil. First, an experimental setup to assess the effect of temperature, humidity, and positioning on the power quality accuracy performance of the Rogowski coil is described. Second, from the results and the experience of the authors it has been possible to design a specific type test. The test has the aim of finding the limits of the accuracy variations of a single Rogowski coil. Afterwards, such limits can be used to compensate for the in-field measurements, obtaining an overall higher accuracy. The results of this work may contribute to the always-evolving standardization work on LPITs.
Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology
Authors: Entezami Alireza , Mariani Stefano , Shariatmadar Hashem
Published: February 2022

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen–Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.
Direct Measurement of the Reduced Scattering Coefficient by a Calibrated Random Laser Sensor
Authors: Tommasi Federico , Auvity Baptiste , Fini Lorenzo , Martelli Fabrizio , Cavalieri Stefano
Published: February 2022

The research in optical sensors has been largely encouraged by the demand for low-cost and less or non-invasive new detection strategies. The invention of the random laser has opened a new frontier in optics, providing also the opportunity to explore new possibilities in the field of sensing, besides several different and peculiar phenomena. The main advantage in exploiting the physical principle of the random laser in optical sensors is due to the presence of the stimulated emission mechanism, which allows amplification and spectral modification of the signal. Here, we present a step forward in the exploitation of this optical phenomenon by a revisitation of a previous experimental setup, as well as the measurement method, in particular to mitigate the instability of the results due to shot-to-shot pump energy fluctuations. In particular, the main novelties of the setup are the use of optical fibers, a reference sensor, and a peristaltic pump. These improvements are devoted to: eliminating optical beam alignment issues; improving portability; mitigating the variation in pump energy and gain medium performances over time; realizing an easy and rapid change of the sensed medium. The results showed that such a setup can be considered a prototype for a portable device for directly measuring the scattering of liquid samples, without resorting to complicated numerical or analytic inversion procedures of the measured data, once the suitable calibration of the system is performed.
Healing Hands: The Tactile Internet in Future Tele-Healthcare
Authors: Senk Stefan , Ulbricht Marian , Tsokalo Ievgenii , Rischke Justus , Li Shu-Chen , Speidel Stefanie , Nguyen Giang T. , Seeling Patrick , Fitzek Frank H. P.
Published: February 2022

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings.
American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM
Authors: Abdullahi Sunusi Bala , Chamnongthai Kosin
Published: February 2022

Complex hand gesture interactions among dynamic sign words may lead to misclassification, which affects the recognition accuracy of the ubiquitous sign language recognition system. This paper proposes to augment the feature vector of dynamic sign words with knowledge of hand dynamics as a proxy and classify dynamic sign words using motion patterns based on the extracted feature vector. In this method, some double-hand dynamic sign words have ambiguous or similar features across a hand motion trajectory, which leads to classification errors. Thus, the similar/ambiguous hand motion trajectory is determined based on the approximation of a probability density function over a time frame. Then, the extracted features are enhanced by transformation using maximal information correlation. These enhanced features of 3D skeletal videos captured by a leap motion controller are fed as a state transition pattern to a classifier for sign word classification. To evaluate the performance of the proposed method, an experiment is performed with 10 participants on 40 double hands dynamic ASL words, which reveals 97.98% accuracy. The method is further developed on challenging ASL, SHREC, and LMDHG data sets and outperforms conventional methods by 1.47%, 1.56%, and 0.37%, respectively.
Intelligent Clinical Decision Support
Authors: Pinsky Michael , Dubrawski Artur , Clermont Gilles
Published: February 2022

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.