bluuebunny/crossref_metadata_2025 huggingface dataset:
Tumor Necrosis Factor-α Induces a Preeclamptic-like Phenotype in Placental Villi via Sphingosine Kinase 1 Activation
Authors: Fakhr Yuliya , Koshti Saloni , Habibyan Yasaman Bahojb , Webster Kirsten , Hemmings Denise G.
Published: March 2022

Preeclampsia (PE) involves inadequate placental function. This can occur due to elevated pro-inflammatory tumor necrosis factor-α (TNF-α). In other tissues, TNF-α signals via sphingosine kinase 1 (SphK1). SphK1 hinders syncytial formation. Whether this occurs downstream of TNF-α signaling is unclear. We hypothesized that placental SphK1 levels are higher in PE and elevated TNF-α decreases syncytial function, increases syncytial shedding, and increases cytokine/factor release via SphK1 activity. Term placental biopsies were analyzed for SphK1 using immunofluorescence and qRT-PCR. Term placental explants were treated after 4 days of culture, at the start of syncytial regeneration, with TNF-α and/or SphK1 inhibitors, PF-543. Syncytialization was assessed by measuring fusion and chorionic gonadotropin release. Cell death and shedding were measured by lactate dehydrogenase release and placental alkaline phosphatase-positive shed particles. Forty-two cytokines were measured using multiplex assays. Placental SphK1 was increased in PE. Increased cell death, shedding, interferon-α2, IFN-γ-induced protein 10, fibroblast growth factor 2, and platelet-derived growth factor-AA release induced by TNF-α were reversed upon SphK1 inhibition. TNF-α increased the release of 26 cytokines independently of SphK1. TNF-α decreased IL-10 release and inhibiting SphK1 reversed this effect. Inhibiting SphK1 alone decreased TNF-α release. Hence, SphK1 partially mediates the TNF-α-induced PE placental phenotype, primarily through cell damage, shedding, and specific cytokine release.
A Second Gamma-Glutamylpolyamine Synthetase, GlnA2, Is Involved in Polyamine Catabolism in Streptomyces coelicolor
Authors: Krysenko Sergii , Okoniewski Nicole , Nentwich Merle , Matthews Arne , Bäuerle Moritz , Zinser Alina , Busche Tobias , Kulik Andreas , Gursch Stephanie , Kemeny Annika , Bera Agnieszka , Wohlleben Wolfgang
Published: March 2022

Streptomyces coelicolor is a soil bacterium living in a habitat with very changeable nutrient availability. This organism possesses a complex nitrogen metabolism and is able to utilize the polyamines putrescine, cadaverine, spermidine, and spermine and the monoamine ethanolamine. We demonstrated that GlnA2 (SCO2241) facilitates S. coelicolor to survive under high toxic polyamine concentrations. GlnA2 is a gamma-glutamylpolyamine synthetase, an enzyme catalyzing the first step in polyamine catabolism. The role of GlnA2 was confirmed in phenotypical studies with a glnA2 deletion mutant as well as in transcriptional and biochemical analyses. Among all GS-like enzymes in S. coelicolor, GlnA2 possesses the highest specificity towards short-chain polyamines (putrescine and cadaverine), while its functional homolog GlnA3 (SCO6962) prefers long-chain polyamines (spermidine and spermine) and GlnA4 (SCO1613) accepts only monoamines. The genome-wide RNAseq analysis in the presence of the polyamines putrescine, cadaverine, spermidine, or spermine revealed indication of the occurrence of different routes for polyamine catabolism in S. coelicolor involving GlnA2 and GlnA3. Furthermore, GlnA2 and GlnA3 are differently regulated. From our results, we can propose a complemented model of polyamine catabolism in S. coelicolor, which involves the gamma-glutamylation pathway as well as other alternative utilization pathways.
Application of the MAHDS Method for Multiple Alignment of Highly Diverged Amino Acid Sequences
Authors: Kostenko Dimitrii O. , Korotkov Eugene V.
Published: March 2022

The aim of this work was to compare the multiple alignment methods MAHDS, T-Coffee, MUSCLE, Clustal Omega, Kalign, MAFFT, and PRANK in their ability to align highly divergent amino acid sequences. To accomplish this, we created test amino acid sequences with an average number of substitutions per amino acid (x) from 0.6 to 5.6, a total of 81 sets. Comparison of the performance of sequence alignments constructed by MAHDS and previously developed algorithms using the CS and Z score criteria and the benchmark alignment database (BAliBASE) indicated that, although the quality of the alignments built with MAHDS was somewhat lower than that of the other algorithms, it was compensated by greater statistical significance. MAHDS could construct statistically significant alignments of artificial sequences with x ≤ 4.8, whereas the other algorithms (T-Coffee, MUSCLE, Clustal Omega, Kalign, MAFFT, and PRANK) could not perform that at x > 2.4. The application of MAHDS to align 21 families of highly diverged proteins (identity < 20%) from Pfam and HOMSTRAD databases showed that it could calculate statistically significant alignments in cases when the other methods failed. Thus, MAHDS could be used to construct statistically significant multiple alignments of highly divergent protein sequences, which accumulated multiple mutations during evolution.
Assessment of the Photosynthetic Apparatus Functions by Chlorophyll Fluorescence and P700 Absorbance in C3 and C4 Plants under Physiological Conditions and under Salt Stress
Authors: Stefanov Martin A. , Rashkov Georgi D. , Apostolova Emilia L.
Published: March 2022

Functions of the photosynthetic apparatus of C3 (Pisum sativum L.) and C4 (Zea mays L.) plants under physiological conditions and after treatment with different NaCl concentrations (0–200 mM) were investigated using chlorophyll a fluorescence (pulse-amplitude-modulated (PAM) and JIP test) and P700 photooxidation measurement. Data revealed lower density of the photosynthetic structures (RC/CSo), larger relative size of the plastoquinone (PQ) pool (N) and higher electron transport capacity and photosynthetic rate (parameter RFd) in C4 than in C3 plants. Furthermore, the differences were observed between the two studied species in the parameters characterizing the possibility of reduction in the photosystem (PSI) end acceptors (REo/RC, REo/CSo and δRo). Data revealed that NaCl treatment caused a decrease in the density of the photosynthetic structures and relative size of the PQ pool as well as decrease in the electron transport to the PSI end electron acceptors and the probability of their reduction as well as an increase in the thermal dissipation. The effects were stronger in pea than in maize. The enhanced energy losses after high salt treatment in maize were mainly from the increase in the regulated energy losses (ΦNPQ), while in pea from the increase in non-regulated energy losses (ΦNO). The reduction in the electron transport from QA to the PSI end electron acceptors influenced PSI activity. Analysis of the P700 photooxidation and its decay kinetics revealed an influence of two PSI populations in pea after treatment with 150 mM and 200 mM NaCl, while in maize the negligible changes were registered only at 200 mM NaCl. The experimental results clearly show less salt tolerance of pea than maize.
A Character String-Based Stemming for Morphologically Derivative Languages
Authors: Imin Gvzelnur , Ablimit Mijit , Yilahun Hankiz , Hamdulla Askar
Published: March 2022

Morphologically derivative languages form words by fusing stems and suffixes, stems are important to be extracted in order to make cross lingual alignment and knowledge transfer. As there are phonetic harmony and disharmony when linguistic particles are combined, both phonetic and morphological changes need to be analyzed. This paper proposes a multilingual stemming method that learns morpho-phonetic changes automatically based on character based embedding and sequential modeling. Firstly, the character feature embedding at the sentence level is used as input, and the BiLSTM model is used to obtain the forward and reverse context sequence, and the attention mechanism is added to this model for weight learning, and the global feature information is extracted to capture the stem and affix boundaries; finally CRF model is used to learn more information from sequence features to describe context information more effectively. In order to verify the effectiveness of the above model, the model in this paper is compared with the traditional model on two different data sets of three derivative languages: Uyghur, Kazakh and Kirghiz. The experimental results show that the model in this paper has the best stemming effect on multilingual sentence-level datasets, which leads to more effective stemming. In addition, the proposed model outperforms other traditional models, and fully consider the data characteristics, and has certain advantages with less human intervention.
Differential Expression of Major Royal Jelly Proteins in the Hypopharyngeal Glands of the Honeybee Apis mellifera upon Bacterial Ingestion
Authors: Kim Yun-Hui , Kim Bo-Yeon , Kim Jin-Myung , Choi Yong-Soo , Lee Man-Young , Lee Kwang-Sik , Jin Byung-Rae
Published: March 2022

Honeybee vitellogenin (Vg) transports pathogen fragments from the gut to the hypopharyngeal glands and is also used by nurse bees to synthesize royal jelly (RJ), which serves as a vehicle for transferring pathogen fragments to the queen and young larvae. The proteomic profile of RJ from bacterial-challenged and control colonies was compared using mass spectrometry; however, the expression changes of major royal jelly proteins (MRJPs) in hypopharyngeal glands of the honeybee Apis mellifera in response to bacterial ingestion is not well-characterized. In this study, we investigated the expression patterns of Vg in the fat body and MRJPs 1–7 in the hypopharyngeal glands of nurse bees after feeding them live or heat-killed Paenibacillus larvae. The expression levels of MRJPs and defensin-1 in the hypopharyngeal glands were upregulated along with Vg in the fat body of nurse bees fed with live or heat-killed P. larvae over 12 h or 24 h. We observed that the expression patterns of MRJPs and defensin-1 in the hypopharyngeal glands and Vg in the fat body of nurse bees upon bacterial ingestion were differentially expressed depending on the bacterial status and the time since bacterial ingestion. In addition, the AMP genes had increased expression in young larvae fed heat-killed P. larvae. Thus, our findings indicate that bacterial ingestion upregulates the transcriptional expression of MRJPs in the hypopharyngeal glands as well as Vg in the fat body of A. mellifera nurse bees.
Consensus for the Treatment of Tinea Pedis: A Systematic Review of Randomised Controlled Trials
Authors: Ward Harry , Parkes Nicholas , Smith Carolyn , Kluzek Stefan , Pearson Richard
Published: March 2022

Objective: To systematically review literature enabling the comparison of the efficacy of pharmaceutical treatments for tinea pedis in adults. Design: Systematic review of randomised controlled trials (RCTs) with mycological cure as the primary outcome. Secondary outcomes did include the clinical assessment of resolving infection or symptoms, duration of treatment, adverse events, adherence, and recurrence. Eligibility Criteria: Study participants suffering from only tinea pedis that were treated with a pharmaceutical treatment. The study must have been conducted using an RCT study design and recording age of the participant > 16 years of age. Results: A total of seven studies met the inclusion criteria, involving 1042 participants. The likelihood of resolution in study participants treated with terbinafine was RR 3.9 (95% CI: 2.0–7.8) times those with a placebo. Similarly, the allylamine butenafine was effective by RR 5.3 (95% CI: 1.4–19.6) compared to a placebo. Butenafine was similarly efficacious to terbinafine RR 1.3 (95% CI: 0.4–4.4). Terbinafine was marginally more efficacious than itraconazole, RR 1.3 (95% CI: 1.1–1.5). Summary/Conclusion: Topical terbinafine and butenafine treatments of tinea pedis were more efficacious than placebo. Tableted terbinafine and itraconazole administered orally were efficacious in the drug treatment of tinea pedis fungal infection. We are concerned about how few studies were available that reported the baseline characteristics for each treatment arm and that did not suffer greater than 20% loss to follow-up. We would like to see improved reporting of clinical trials in academic literature. Registration name: Treatment’s for athlete’s foot—systematic review with meta-analysis [CRD42020162078].
Identification of New CTX Analogues in Fish from the Madeira and Selvagens Archipelagos by Neuro-2a CBA and LC-HRMS
Authors: Tudó Àngels , Rambla-Alegre Maria , Flores Cintia , Sagristà Núria , Aguayo Paloma , Reverté Laia , Campàs Mònica , Gouveia Neide , Santos Carolina , Andree Karl B. , Marques Antonio , Caixach Josep , Diogène Jorge
Published: March 2022

Ciguatera Poisoning (CP) is caused by consumption of fish or invertebrates contaminated with ciguatoxins (CTXs). Presently CP is a public concern in some temperate regions, such as Macaronesia (North-Eastern Atlantic Ocean). Toxicity analysis was performed to characterize the fish species that can accumulate CTXs and improve understanding of the ciguatera risk in this area. For that, seventeen fish specimens comprising nine species were captured from coastal waters inMadeira and Selvagens Archipelagos. Toxicity was analysed by screening CTX-like toxicity with the neuroblastoma cell-based assay (neuro-2a CBA). Afterwards, the four most toxic samples were analysed with liquid chromatography-high resolution mass spectrometry (LC-HRMS). Thirteen fish specimens presented CTX-like toxicity in their liver, but only four of these in their muscle. The liver of one specimen of Muraena augusti presented the highest CTX-like toxicity (0.270 ± 0.121 µg of CTX1B equiv·kg−1). Moreover, CTX analogues were detected with LC-HRMS, for M. augusti and Gymnothorax unicolor. The presence of three CTX analogues was identified: C-CTX1, which had been previously described in the area; dihydro-CTX2, which is reported in the area for the first time; a putative new CTX m/z 1127.6023 ([M+NH4]+) named as putative C-CTX-1109, and gambieric acid A.
Development of the Gastrointestinal Tract in Newborns as a Challenge for an Appropriate Nutrition: A Narrative Review
Authors: Indrio Flavia , Neu Josef , Pettoello-Mantovani Massimo , Marchese Flavia , Martini Silvia , Salatto Alessia , Aceti Arianna
Published: March 2022

The second and third trimesters of pregnancy are crucial for the anatomical and functional development of the gastrointestinal (GI) tract. If premature birth occurs, the immaturity of the digestive and absorptive processes and of GI motility represent a critical challenge to meet adequate nutritional needs, leading to poor extrauterine growth and to other critical complications. Knowledge of the main developmental stages of the processes involved in the digestion and absorption of proteins, carbohydrates, and lipids, as well as of the maturational phases underlying the development of GI motility, may aid clinicians to optimize the nutritional management of preterm infants. The immaturity of these GI systems and functions may negatively influence the patterns of gut colonization, predisposing to an abnormal microbiome. This, in turn, further contributes to alter the functional, immune, and neural development of the GI tract and, especially in preterm infants, has been associated with an increased risk of severe GI complications, such as necrotizing enterocolitis. Deeper understanding of the physiological colonization patterns in term and preterm infants may support the promotion of these patterns and the avoidance of microbial perturbations associated with the development of several diseases throughout life. This review aims to provide a global overview on the maturational features of the main GI functions and on their implications following preterm birth. We will particularly focus on the developmental differences in intestinal digestion and absorption functionality, motility, gut–brain axis interaction, and microbiomes.
Carotenoids, β-Apocarotenoids, and Retinoids: The Long and the Short of It
Authors: Harrison Earl H.
Published: March 2022

Naturally occurring retinoids (retinol, retinal, retinoic acid, retinyl esters) are a subclass of β-apocarotenoids, defined by the length of the polyene side chain. Provitamin A carotenoids are metabolically converted to retinal (β-apo-15-carotenal) by the enzyme β-carotene-15,15′-dioxygenase (BCO1) that catalyzes the oxidative cleavage of the central C=C double bond. A second enzyme β-carotene-9′-10′-dioxygenase cleaves the 9′,10′ bond to yield β-apo-10′-carotenal and β-ionone. Chemical oxidation of the other double bonds leads to the generation of other β-apocarotenals. Like retinal, some of these β-apocarotenals are metabolically oxidized to the corresponding β-apocarotenoic acids or reduced to the β-apocarotenols, which in turn are esterified to β-apocarotenyl esters. Other metabolic fates such as 5,6-epoxidation also occur as for retinoids. Whether the same enzymes are involved remains to be understood. β-Apocarotenoids occur naturally in plant-derived foods and, therefore, are present in the diet of animals and humans. However, the levels of apocarotenoids are relatively low, compared with those of the parent carotenoids. Moreover, human studies show that there is little intestinal absorption of intact β-apocarotenoids. It is possible that they are generated in vivo under conditions of oxidative stress. The β-apocarotenoids are structural analogs of the naturally occurring retinoids. As such, they may modulate retinoid metabolism and signaling. In deed, those closest in size to the C-20 retinoids—namely, β-apo-14′-carotenoids (C-22) and β-apo-13-carotenone (C-18) bind with high affinity to purified retinoid receptors and function as retinoic acid antagonists in transactivation assays and in retinoic acid induction of target genes. The possible pathophysiologic relevance in human health remains to be determined.
Progress Report: Antimicrobial Drug Discovery in the Resistance Era
Authors: Shinu Pottathil , Mouslem Abdulaziz K. Al , Nair Anroop B. , Venugopala Katharigatta N. , Attimarad Mahesh , Singh Varsha A. , Nagaraja Sreeharsha , Alotaibi Ghallab , Deb Pran Kishore
Published: March 2022

Antibiotic resistance continues to be a most serious threat to public health. This situation demands that the scientific community increase their efforts for the discovery of alternative strategies to circumvent the problems associated with conventional small molecule therapeutics. The Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report (published in June 2021) discloses the rapidly increasing number of bacterial infections that are mainly caused by antimicrobial-resistant bacteria. These concerns have initiated various government agencies and other organizations to educate the public regarding the appropriate use of antibiotics. This review discusses a brief highlight on the timeline of antimicrobial drug discovery with a special emphasis on the historical development of antimicrobial resistance. In addition, new antimicrobial targets and approaches, recent developments in drug screening, design, and delivery were covered. This review also discusses the emergence and roles of various antibiotic adjuvants and combination therapies while shedding light on current challenges and future perspectives. Overall, the emergence of resistant microbial strains has challenged drug discovery but their efforts to develop alternative technologies such as nanomaterials seem to be promising for the future.
Preparation of Thermochromic Microcapsules of Bisphenol A and Crystal Violet Lactone and Their Effect on Coating Properties
Authors: Zhao Wenting , Yan Xiaoxing
Published: March 2022

Thermochromic microcapsules were prepared with a thermochromic compound as core material and urea formaldehyde as wall material. The molar ratio of urea and formaldehyde, the mass ratio of core material to wall material, the concentration of emulsifier, and rotating speed were selected to make a four-level and three-factor L9(34) orthogonal test. It was found that the molar ratio of urea and formaldehyde had the greatest influence on the coating rate of microcapsules. The effects of molar ratio of urea and formaldehyde on the discoloration temperature and coating rate of microcapsules were studied. When the molar ratio of urea to formaldehyde was 1:1.6, the core material: wall material ratio was 4:7, the concentration of emulsifier was 5.0%, and the rotating speed was 1600 rpm, the performance of the microcapsules was the best. When the microcapsule content was 20.0%, the color difference of the paint film was the largest, the gloss and hardness of the paint film decreased with increasing microcapsule content, and the impact resistance of the paint film first increased and then decreased with increasing microcapsule content. The adhesion of the paint film remained unchanged, and was grade 1. When the microcapsule content was 20.0%, the performance of the paint film was at its best. This provides a basis for the application of thermochromic coatings.
AHSWFM: Automated and Hierarchical Surface Water Fraction Mapping for Small Water Bodies Using Sentinel-2 Images
Authors: Wang Yalan , Li Xiaodong , Zhou Pu , Jiang Lai , Du Yun
Published: March 2022

Accurately mapping surface water fractions is essential to understanding the distribution and area of small water bodies (SWBs), which are numerous and widespread. Traditional spectral unmixings based on the linear mixture model require high-quality prior endmember information, and are not appropriate in situations such as dealing with multiple scattering effects. To overcome difficulties with unknown mixing mechanisms and parameters, a novel automated and hierarchical surface water fraction mapping (AHSWFM) for mapping SWBs from Sentinel-2 images was proposed. AHSWFM is automated, requires no endmember prior knowledge and uses self-trained regression using scalable algorithms and random forest to construct relationships between the multispectral data and water fractions. AHSWFM uses a hierarchical structure that divides pixels into pure water, pure land and mixed water-land pixels, and predicts their water fractions separately to avoid overestimating water fractions for pure land pixels and underestimating water fractions for pure water pixels. Results show that using the hierarchical strategy can increase the accuracy in estimating SWB areas. AHSWFM predicted SWB areas with a root mean square error of approximately 0.045 ha in a region using more than 1200 SWB samples that were mostly smaller than 0.75 ha.
Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers
Authors: Stonevicius Edvinas , Uselis Giedrius , Grendaite Dalia
Published: March 2022

Climate change leads to more variable meteorological conditions. In many Northern Hemisphere temperate regions, cold seasons have become more variable and unpredictable, necessitating frequent river ice observations over long sections of rivers. Satellite SAR (Synthetic Aperture Radar)-based river ice detection models have been successfully applied and tested, but different hydrological, morphological and climatological conditions can affect their skill. In this study, we developed and tested Sentinel-1 SAR-based ice detection models in 525 km sections of the Nemunas and Neris Rivers. We analyzed three binary classification models based on VV, VH backscatter and logistic regression. The model sensitivity and specificity were used to determine the optimal threshold between ice and water classes. We used in situ observations and Sentinel-2 Sen2Cor ice mask to validate models in different ice conditions. In most cases, SAR-based ice detection models outperformed Sen2Cor classification because Sen2Cor misclassified pixels as ice in areas with translucent clouds, undetected by the scene classification algorithm, and misclassified pixels as water in cloud or river valley shadow. SAR models were less accurate in river sections where river flow and ice formation conditions were affected by large valley-dammed reservoirs. Sen2Cor and SAR models accurately detected border and consolidated ice but were less accurate in moving ice conditions. The skill of models depended on how dense the moving ice was. With a lowered classification threshold and increased model sensitivity, SAR models detected sparse frazil ice. In most cases, the VV polarization-based model was more accurate than the VH polarization-based model. The results of logistic and VV models were highly correlated, and the use of VV was more constructive due to its simpler algorithm.
Transforming 2D Radar Remote Sensor Information from a UAV into a 3D World-View
Authors: Weber Christoph , Eggert Marius , Rodrigo-Comino Jesús , Udelhoven Thomas
Published: March 2022

Since unmanned aerial vehicles (UAVs) have been established in geoscience as a key and accessible tool, a wide range of applications are currently being developed. However, not only the design of UAVs themselves is vital to carry out an accurate investigation, but also the sensors and the data processing are key parts to be considered. Several publications including accurate sensors are taking part in pioneer research programs, but less is explained about how they were designed. Besides the commonly used sensors such as a camera, one of the most popular ones is radar. The advantages of a radar sensor to perform research in geosciences are the robustness, the ability to consider large distances and velocity measurements. Unfortunately, these sensors are often expensive and there is a lack of methodological papers that explain how to reduce these costs. To fill this gap, this article aims to show how: (i) we used a radar sensor from the automotive field; and (ii) it is possible to reconstruct a three-dimensional scenario with a UAV and a radar sensor. Our methodological approach proposes a total of eleven stages to process the radar data. To verify and validate the process, a real-world scenario reconstruction is presented with a system resolution reaching from two to three times the radar resolution. We conclude that this research will help the scientific community to include the use of radars in their research projects and programs, reducing costs and increasing accuracy.
Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain
Authors: Mao Gang , Zhang Zhongzheng , Jia Sixiang , Noman Khandaker , Li Yongbo
Published: March 2022

Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions.
Closed-Form Power Normalization Methods for a Satellite MIMO System
Authors: Segneri Andrea , Baldominos Alejandro , Goussetis George , Mengali Alberto , Fonseca Nelson J. G.
Published: March 2022

The paper proposes a new set of normalization techniques for precoding/beamforming matrices applicable to broadband multiuser multiple-input multiple-output (MIMO) satellite systems. The proposed techniques adapt known normalization methods to account for the signal attenuation experienced by users due to the degradation of antenna gain and free space losses towards the edge of the coverage. We use, as an example, an array-fed reflector (AFR) antenna onboard a satellite in geosynchronous orbit (GEO), which provides a favorable trade-off between high-directivity, reconfigurability, and the requirement for digital processing, but suffers from high scan losses away from broadside due to optical aberrations when considered for global coverage applications. Three different precoding/beamforming techniques are employed, namely zero forcing (ZF), minimum mean squared error (MMSE), and matched filtering (MF). Low-complexity power normalization techniques digitally applied after the beamformer are introduced that, in the absence of any atmospheric effects, lead to iso-flux-like characteristics whilst satisfying the power constraint per feed. In comparison with other methods reported in the literature, mainly based on iterative algorithms, the proposed techniques consist in closed-form expressions to provide uniform signal-to-noise ratio (SNR) and signal-to-noise plus interference ratio (SNIR) across the users without significant impact on the payload sum rate. Numerical results are presented to comparatively demonstrate the achieved performance in terms of total capacity and distribution of SNR and SNIR at various noise and interference scenarios.
A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors
Authors: Shukla Niraj Kumar , Srivastava Rajeev , Mirjalili Seyedali
Published: March 2022

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.
Energy-Aware QoS MAC Protocol Based on Prioritized-Data and Multi-Hop Routing for Wireless Sensor Networks
Authors: Sakib Aan Nazmus , Drieberg Micheal , Sarang Sohail , Aziz Azrina Abd , Hang Nguyen Thi Thu , Stojanović Goran M.
Published: March 2022

Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol’s performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%.
Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks
Authors: Russell Joseph , Bergmann Jeroen H. M. , Nagaraja Vikranth H.
Published: March 2022

Intent sensing—the ability to sense what a user wants to happen—has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.
Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living
Authors: Guerra Bruna Maria Vittoria , Schmid Micaela , Beltrami Giorgio , Ramat Stefano
Published: March 2022

Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.
Multi-Device Nutrition Control
Authors: Cunha Carlos A. S. , Duarte Rui P.
Published: March 2022

Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human–computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users.
Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors
Authors: Contreras-Hernandez Jose L. , Almanza-Ojeda Dora L. , Ledesma Sergio , Garcia-Perez Arturo , Castro-Sanchez Rogelio , Gomez-Martinez Miguel A. , Ibarra-Manzano Mario A.
Published: March 2022

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.
Detection of Sulfur Dioxide by Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS)
Authors: Thalman Ryan , Bhardwaj Nitish , Flowerday Callum E. , Hansen Jaron C.
Published: March 2022

Sulfur dioxide (SO2) is an important precursor for the formation of atmospheric sulfate aerosol and acid rain. We present an instrument using Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) for the measurement of SO2 with a minimum limit of detection of 0.75 ppbv (3-σ) using the spectral range 305.5–312 nm and an averaging time of 5 min. The instrument consists of high-reflectivity mirrors (0.9985 at 310 nm) and a deep UV light source (Light Emitting Diode). The effective absorption path length of the instrument is 610 m with a 0.966 m base length. Published reference absorption cross sections were used to fit and retrieve the SO2 concentrations and were compared to fluorescence standard measurements for SO2. The comparison was well correlated, R2 = 0.9998 with a correlation slope of 1.04. Interferences for fluorescence measurements were tested and the BBCEAS showed no interference, while ambient measurements responded similarly to standard measurement techniques.
Automatic Real-Time Pose Estimation of Machinery from Images
Authors: Bertels Marcel , Jutzi Boris , Ulrich Markus
Published: March 2022

The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than 16 mm with the translation components and accuracies higher than 3 mrad with the rotation components. As a result, 3D point accuracies higher than 16 mm were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors.
CANon: Lightweight and Practical Cyber-Attack Detection for Automotive Controller Area Networks
Authors: Baek Youngmi , Shin Seongjoo
Published: March 2022

Automotive cyber-physical systems are in transition from the closed-systems to open-networking systems. As a result, in-vehicle networks such as the controller area network (CAN) have become essential to connect to inter-vehicle networks through the various rich interfaces. Newly exposed security concerns derived from this requirement may cause in-vehicle networks to pose threats to automotive security and driver’s safety. In this paper, to ensure a high level of security of the in-vehicle network for automotive CPS, we propose a novel lightweight and practical cyber defense platform, referred to as CANon (CAN with origin authentication and non-repudiation), to be enabled to detect cyber-attacks in real-time. CANon is designed based on the hierarchical approach of centralized-session management and distributed-origin authentication. In the former, a gateway node manages each initialization vector and session of origin-centric groups consisting of two more sending and receiving nodes. In the latter, the receiving nodes belonging to the given origin-centric group individually perform the symmetric key-based detection against cyber-attacks by verifying each message received from the sending node, namely origin authentication, in real-time. To improve the control security, CANon employs a one-time local key selected from a sequential hash chain (SHC) for authentication of an origin node in a distributed mode and exploits the iterative hash operations with randomness. Since the SHC can constantly generate and consume hash values regardless of their memory capacities, it is very effective for resource-limited nodes for in-vehicle networks. In addition, through implicit key synchronization within a given group, CANon addresses the challenges of a key exposure problem and a complex key distribution mechanism when performing symmetric key-based authentication. To achieve lightweight cyber-attack detection without imposing an additive load on CAN, CANon uses a keyed-message authentication code (KMAC) activated within a given group. The detection performance of CANon is evaluated under an actual node of Freescale S12XF and virtual nodes operating on the well-known CANoe tool. It is seen that the detection rate of CANon against brute-force and replay attacks reaches 100% when the length of KMAC is over 16 bits. It demonstrates that CANon ensures high security and is sufficient to operate in real-time even on low-performance ECUs. Moreover, CANon based on several software modules operates without an additive hardware security module at an upper layer of the CAN protocol and can be directly ported to CAN-FD (CAN with Flexible Data rate) so that it achieves the practical cyber defense platform.
Solar Energy in Argentina
Authors: Bragagnolo Julio A. , Taretto Kurt , Navntoft Christian
Published: March 2022

There is a large gap between the vast solar resources and the magnitude of solar energy deployment in Argentina. In the case of photovoltaics, the country only reached the 1000 GWh electricity generated yearly landmark in 2020. Solar thermal technology is even less developed, in part due to the low natural gas prices resulting from political strategies that aim to soften the impact of an unstable economy on family budgets. This review describes this gap by summarizing the current state of Argentine solar energy. We summarize the fundamental legal and strategic tools which are available for solar energy deployment, survey the penetration of solar energy into the country’s energy landscape, identify national contributions to the local value chain, and review past and present research and development achievements. Both photovoltaic and solar thermal technologies show a historical fluctuation between local technology development and imported technology and know-how. Finally, a discussion on the main ingredients required to abridge Argentina’s solar gap indicates that stronger, consistent long-term strategies are required in Argentina in order to take advantage of the present window of opportunity, and to play a considerable role in the global energy transition.
Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions
Authors: Qin Min , Zhu Wei , Zhao Shiyue , Zhao Yu
Published: March 2022

Artificial intelligence has been widely applied to e-commerce and the online business service field. However, few studies have focused on studying the differences in the effects of types of customer service on customer purchase intentions. Based on service encounter theory and superposition theory, we designed two shopping experiments to capture customers’ thoughts and feelings, in order to explore the differences in the effects of three different types of online customer service (AI customer service, manual customer service, and human–machine collaboration customer service) on customer purchase intention, and analyses the superposition effect of human–machine collaboration customer service. The results show that the consumer’s perceived service quality positively influences the customer’s purchase intention, and plays a mediating role in the effect of different types of online customer service on customer purchase intention; the product type plays a moderating role in the relationship between online customer service and customer purchase intention, and human–machine collaboration customer service has a superposition effect. This study helped to deepen the understanding of AI developers and e-commerce platforms regarding the application of AI in online business service, and provides reference suggestions for the formulation of more perfect business service strategies.