bluuebunny/crossref_metadata_2025 huggingface dataset:
Selenoxides as Excellent Chalcogen Bond Donors: Effect of Metal Coordination
Authors: Burguera Sergi , Gomila Rosa M. , Bauzá Antonio , Frontera Antonio
Published: December 2022

The chalcogen bond has been recently defined by the IUPAC as the attractive noncovalent interaction between any element of group 16 acting as an electrophile and any atom (or group of atoms) acting as a nucleophile. Commonly used chalcogen bond donor molecules are divalent selenium and tellurium derivatives that exhibit two σ-holes. In fact, the presence of two σ-hole confers to the chalcogen bonding additional possibilities with respect to the halogen bond, the most abundant σ-hole interaction. In this manuscript, we demonstrate that selenoxides are good candidates to be used as σ-hole donor molecules. Such molecules have not been analyzed before as chalcogen bond donors, as far as our knowledge extends. The σ-hole opposite to the Se=O bond is adequate for establishing strong and directional ChBs, as demonstrated herein using the Cambridge structural database (CSD) and density functional theory (DFT) calculations. Moreover, the effect of the metal coordination of the selenoxide to transition metals on the strength of the ChB interaction has been analyzed theoretically. The existence of the ChBs has been further supported by the quantum theory of atoms in molecules (QTAIM) and the noncovalent interaction plot (NCIPlot).
Docetaxel Loaded in Copaiba Oil-Nanostructured Lipid Carriers as a Promising DDS for Breast Cancer Treatment
Authors: Carvalho Fabiola Vieira de , Ribeiro Ligia Nunes de Morais , Moura Ludmilla David de , Rodrigues da Silva Gustavo Henrique , Mitsutake Hery , Mendonça Talita Cesarim , Geronimo Gabriela , Breitkreitz Marcia Cristina , de Paula Eneida
Published: December 2022

Breast cancer is the neoplasia of highest incidence in women worldwide. Docetaxel (DTX), a taxoid used to treat breast cancer, is a BCS-class-IV compound (low oral bioavailability, solubility and intestinal permeability). Nanotechnological strategies can improve chemotherapy effectiveness by promoting sustained release and reducing systemic toxicity. Nanostructured lipid carriers (NLC) encapsulate hydrophobic drugs in their blend-of-lipids matrix, and imperfections prevent drug expulsion during storage. This work describes the preparation, by design of experiments (23 factorial design) of a novel NLC formulation containing copaiba oil (CO) as a functional excipient. The optimized formulation (NLCDTX) showed approximately 100% DTX encapsulation efficiency and was characterized by different techniques (DLS, NTA, TEM/FE-SEM, DSC and XRD) and was stable for 12 months of storage, at 25 °C. Incorporation into the NLC prolonged drug release for 54 h, compared to commercial DTX (10 h). In vitro cytotoxicity tests revealed the antiproliferative effect of CO and NLCDTX, by reducing the cell viability of breast cancer (4T1/MCF-7) and healthy (NIH-3T3) cells more than commercial DTX. NLCDTX thus emerges as a promising drug delivery system of remarkable anticancer effect, (strengthened by CO) and sustained release that, in clinics, may decrease systemic toxicity at lower DTX doses.
Desorption of Ammonia Adsorbed on Prussian Blue Analogs by Washing with Saturated Ammonium Hydrogen Carbonate Solution
Authors: Usuda Hatsuho , Mishima Yoshie , Kawamoto Tohru , Minami Kimitaka
Published: December 2022

Prussian blue analogs (PBAs) have been reported as promising ammonia (NH3) adsorbents with a high capacity compared to activated carbon, zeolite, and ion exchange resins. The adsorbed NH3 was desorbed by heating and washing with water or acid. Recently, we demonstrated that desorption was also possible by washing with a saturated ammonium hydrogen carbonate solution (sat. NH4HCO3aq) and recovered NH3 as an NH4HCO3 solid by introducing CO2 into the washing liquid after desorption. However, this has only been proven for copper ferrocyanide and the relationship between the adsorption/desorption behavior and metal ions in PBAs has not been identified. In this study, we investigated the adsorption/desorption behavior of PBAs that are complexes of first row transition metals with hexacyanometalate anions. Six types of PBAs were tested in this study and copper ferricyanide exhibited the highest desorption/adsorption ratio. X-ray diffraction results revealed high structural stability for cobalt hexacyanocobaltate (CoHCC) and nickel ferricyanide (NiHCF). The Fourier transform infrared spectroscopy results showed that the NH3 adsorbed on the vacancy sites tended to desorb compared to the NH3 adsorbed on the interstitial sites as ammonium ions. Interestingly, the desorption/adsorption ratio exhibited the Irving-Williams order.
Antiproliferative Evaluation of Novel 4-Imidazolidinone Derivatives as Anticancer Agent Which Triggers ROS-Dependent Apoptosis in Colorectal Cancer Cell
Authors: Huang Jiuhong , Wang Juanli , Song Guiting , Hu Chunsheng , Xu Zhigang , Chen Zhongzhu , Xu Chuan , Yang Donglin
Published: December 2022

Colorectal cancer (CRC) is one of the most common causes of cancer-related death worldwide, and more therapies are needed to treat CRC. To discover novel CRC chemotherapeutic molecules, we used a series of previously synthesized novel imidazolidin-4-one derivatives to study their anticancer role in several cancer cell lines. Among these compounds, compound 9r exhibited the best anticancer activity in CRC cell lines HCT116 and SW620. We further investigated the anticancer molecular mechanism of compound 9r. We found that compound 9r induced mitochondrial pathway apoptosis in HCT116 and SW620 cells by inducing reactive oxygen species (ROS) production. Moreover, the elevated ROS generation activated the c-Jun N-terminal kinase (JNK) pathway, which further accelerated apoptosis. N-acetylcysteine (NAC), an antioxidant reagent, suppressed compound 9r-induced ROS production, JNK pathway activation, and apoptosis. Collectively, this research synthesized a series of imidazolidin-4-one derivatives, evaluated their anticancer activity, and explored the molecular mechanism of compound 9r-induced apoptosis in CRC cells. The present results suggest that compound 9r has a potential therapeutic role in CRC. Hence, it deserves further exploration as a lead compound for CRC treatment.
Exploring the Dynamical Nature of Intermolecular Hydrogen Bonds in Benzamide, Quinoline and Benzoic Acid Derivatives
Authors: Wojtkowiak Kamil , Jezierska Aneta
Published: December 2022

The hydrogen bonds properties of 2,6-difluorobenzamide, 5-hydroxyquinoline and 4-hydroxybenzoic acid were investigated by Car–Parrinello and path integral molecular dynamics (CPMD and PIMD), respectively. The computations were carried out in vacuo and in the crystalline phase. The studied complexes possess diverse networks of intermolecular hydrogen bonds (N-H…O, O-H…N and O-H…O). The time evolution of hydrogen bridges gave a deeper insight into bonds dynamics, showing that bridged protons are mostly localized on the donor side; however, the proton transfer phenomenon was registered as well. The vibrational features associated with O-H and N-H stretching were analyzed on the basis of the Fourier transform of the atomic velocity autocorrelation function. The spectroscopic effects of hydrogen bond formation were studied. The PIMD revealed quantum effects influencing the hydrogen bridges providing more accurate free energy sampling. It was found that the N…O or O…O interatomic distances decreased (reducing the length of the hydrogen bridge), while the O-H or N-H covalent bond was elongated, which led to the increase in the proton sharing. Furthermore, Quantum Theory of Atoms in Molecules (QTAIM) was used to give insight into electronic structure parameters. Finally, Symmetry-Adapted Perturbation Theory (SAPT) was employed to estimate the energy contributions to the interaction energy of the selected dimers.
Advances in Micro- and Nanomechanics
Authors: Eremeyev Victor A.
Published: December 2022

Recent advances in technologies of design, manufacturing and further studies of new materials and structures result in an essential extension of classic models of continuum and structural mechanics [...]
Exploiting Polyphenol-Mediated Redox Reorientation in Cancer Therapy
Authors: Li Lei , Jin Ping , Guan Yueyue , Luo Maochao , Wang Yu , He Bo , Li Bowen , He Kai , Cao Jiangjun , Huang Canhua , Li Jingquan , Shen Zhisen
Published: December 2022

Polyphenol, one of the major components that exert the therapeutic effect of Chinese herbal medicine (CHM), comprises several categories, including flavonoids, phenolic acids, lignans and stilbenes, and has long been studied in oncology due to its significant efficacy against cancers in vitro and in vivo. Recent evidence has linked this antitumor activity to the role of polyphenols in the modulation of redox homeostasis (e.g., pro/antioxidative effect) in cancer cells. Dysregulation of redox homeostasis could lead to the overproduction of reactive oxygen species (ROS), resulting in oxidative stress, which is essential for many aspects of tumors, such as tumorigenesis, progression, and drug resistance. Thus, investigating the ROS-mediated anticancer properties of polyphenols is beneficial for the discovery and development of novel pharmacologic agents. In this review, we summarized these extensively studied polyphenols and discussed the regulatory mechanisms related to the modulation of redox homeostasis that are involved in their antitumor property. In addition, we discussed novel technologies and strategies that could promote the development of CHM-derived polyphenols to improve their versatile anticancer properties, including the development of novel delivery systems, chemical modification, and combination with other agents.
Components with Anti-Diabetic Activity Isolated from the Leaves and Twigs of Glycosmis pentaphylla Collected in Vietnam
Authors: Nguyen Minh Tuyet Thi , Hsu I-Chi , Liu Hui-Kang , Lin Yu-Chi , Chen Shu-Rong , Chang Fang-Rong , Cheng Yuan-Bin
Published: December 2022

A phytochemical investigation of the leaves and twigs of Glycosmis pentaphylla (Rutaceae), collected in Vietnam, yielded three new compounds named glyfuran (1), glyphyllamide (2), and glyphyllazole (3), along with twenty-five known compounds (4–28). The structures of isolates were determined by IR, MS, NMR, and UV data analyses. In the anti-diabetic activity screening, (+)-isoaltholacton (4), glycoborinine (17), 2′,4′-dihydroxy-4,6′-dimethoxychalcone (24), and flavokawain A (25) simultaneously exhibited inhibition of dipeptidyl peptidase-4 (DPP4) and stimulation of the glucagon-like peptide-1 (GLP-1) secretion on the murine intestinal secretin tumor cell line (STC-1).
Artificial Base-Directed In Vivo Formulation of Aptamer–Drug Conjugates with Albumin for Long Circulation and Targeted Delivery
Authors: Sun Yang , Geng Xinyao , Ma Yue , Qin Yu , Hu Shangjiu , Xie Yuquan , Wang Ruowen
Published: December 2022

Aptamer–drug conjugates (ApDCs) are potential targeted pharmaceutics, but their clinical applications are hampered by fast clearance in blood. Herein we report the construction of ApDCs modified with artificial base F and the study of biological activities. Two types of F-base-modified ApDCs were prepared, Sgc8-paclitaxel by conjugation and Sgc8-gemcitabine, by automated solid-phase synthesis. In vitro experiments showed that F-base-modified ApDCs retain the specificity of the aptamer to target cells and the biological stability is improved. In vivo studies demonstrated that the circulatory time is increased by up to 55 h or longer, as the incorporated F base leads to a stable ApDC-albumin complex as the formulation for targeted delivery. Moreover, conjugated drug molecules were released efficiently and the drug (paclitaxel) concentration in the tumor site was improved. The results demonstrate that an F-base-directed ApDC-albumin complex is a potential platform for drug delivery and targeted cancer therapy.
The Lack of Philosophical Knowledge in Che Guevara’s Pedagogy: Fetishizing Love for Justice and Rage against Imperialism at the Expense of Logos
Authors: Al-Kassimi Khaled
Published: December 2022

Most research on Ernesto “Che” Guevara has been concerned with emphasizing his ideological Marxist commitments and anti-imperial material objectives. These scholarly concerns usually constellate recycled subjective themes highlighting the revolutionary leader hating injustice, and loving justice, in tandem with the objective of eliminating imperialism and advancing a Third World project. In 2012, Che’s Apuntes filósoficos (Eng. Philosophical Notes) were published and highlighted that his exposure to philosophy regrettably occurred late in his life, and surprisingly, the difficulty he had in reading Marx and Hegel. The objective, therefore, of this multidisciplinary research navigating law, theology, philosophy, and politics is threefold. First, it alludes to and critiques the familiar pedagogy of Guevara emphasizing the importance of developing a “theory in action”, “learning through action”, being a “humanist”, and “leading by example”. Secondly, it considers the consequences of Che reifying emotion (eros) over reason (logos) thereby providing a possible answer to his “failed revolutionary story” in the Congo and Bolivia with his pedagogy involving an unstable compound mixing the emotion of compassion with rage thus clouding his reason. Finally, the third section highlights that we should not relegate emotion away from the sphere of political discourse, but rather harmonize it with reason to avoid chaotic and unpredictable errors based on subjective truths. Emphasizing the former at the expense of the latter—as maintained by a realist approach to International Relations and positivist jurisprudence accenting International Law—risks undermining scholarship challenging the immoral consequences arising from a naturalized assumption separating reason and revelation thus decriminalizing colonial practices characterizing the North and South.
Impact of In Vitro Degradation on the Properties of Samples Produced by Additive Production from PLA/PHB-Based Material and Ceramics
Authors: Balogová Alena Findrik , Trebuňová Marianna , Bačenková Darina , Kohan Miroslav , Hudák Radovan , Tóth Teodor , Schnitzer Marek , Živčák Jozef
Published: December 2022

The present study deals with preparing a polymer-based material with incorporated ceramics and monitoring changes in properties after in vitro natural degradation. The developed material is a mixture of polymers of polylactic acid and polyhydroxybutyrate in a ratio of 85:15. Ceramic was incorporated into the prepared material, namely 10% hydroxyapatite and 10% tricalcium phosphate of the total volume. The material was processed into a filament form, and types of solid and porous samples were prepared using additive technology. These samples were immersed in three different solutions: physiological solution, phosphate-buffered saline, and Hanks’ solution. Under constant laboratory conditions, changes in solution pH, material absorption, weight loss, changes in mechanical properties, and surface morphology were monitored for 170 days. The average value of the absorption of the solid sample was 7.07%, and the absorption of the porous samples was recorded at 8.33%, which means a difference of 1.26%. The least change in pH from the reference value of 7.4 was noted with the phosphate-buffered saline solution. Computed tomography was used to determine the cross-section of the samples. The obtained data were used to calculate the mechanical properties of materials after degradation. The elasticity modulus for both the full and porous samples degraded in Hanks’ solution (524.53 ± 13.4 MPa) has the smallest deviation from the non-degraded reference sample (536.21 ± 22.69 MPa).
A Semi-Analytical Method for Designing a Runner System of a Multi-Cavity Mold for Injection Molding
Authors: Lin Chung-Chih , Wu Tian-Cheng , Chen Yu-Shiang , Yang Bo-Yu
Published: December 2022

Multi-cavity mold design is an efficient approach to achieving mass production and is frequently used in plastic injection applications. The runner system of a multi-cavity mold delivers molten plastic to each cavity evenly and makes the molded product from each individual cavity possess an equivalent quality. Not only the dimensions, but also the invisible quality, e.g., the internal stress of the product is of great concern in regard to molding quality. Using commercial software to find an optimal solution for the runner system may be time-consuming in respect to iterations if the engineers lack empirical rules. The H-type runner system is often used due to an inherently balanced filling in multi-cavities. However, the shear heat inducing an imbalanced flow behavior requires the H-type runner system to be improved as the number of the cavities is increased. This work develops a methodology based on the rheological concept to determine the runner system of a multi-cavity mold semi-analytically. As the relation of the viscosity with respect to shear rate is known, the runner system can be constructed step-by-step via this method. The use of the proposed method helps to focus attention on the connection between the physical situation and its related mathematical model. The influences of the melt temperature and resin type can be easily investigated. Three design examples, a 16-cavity mold with a fishbone runner system, an 8-cavity mold with an arbitrary runner layout, and the influences of melt temperature and resin type on the runner design are demonstrated and validated by the commercial software. The proposed method shows its great benefit when a new runner design project is launched in the initial design stage and then cooperates with the commercial software for further modifications.
A New Strategy for Achieving Shape Memory Effects in 4D Printed Two-Layer Composite Structures
Authors: Rahmatabadi Davood , Aberoumand Mohammad , Soltanmohammadi Kianoosh , Soleyman Elyas , Ghasemi Ismaeil , Baniassadi Majid , Abrinia Karen , Zolfagharian Ali , Bodaghi Mahdi , Baghani Mostafa
Published: December 2022

In this study, a new strategy and design for achieving a shape memory effect (SME) and 4D printed two-layer composite structures is unveiled, thanks to fused deposition modeling (FDM) biomaterial printing of commercial filaments, which do not have an SME. We used ABS and PCL as two well-known thermoplastics, and TPU as elastomer filaments that were printed in a two-layer structure. The thermoplastic layer plays the role of constraint for the elastomeric layer. A rubber-to-glass transition of the thermoplastic layer acts as a switching phenomenon that provides the capability of stabilizing the temporary shape, as well as storing the deformation stress for the subsequent recovery of the permanent shape by phase changing the thermoplastic layer in the opposite direction. The results show that ABS–TPU had fixity and recovery ratios above 90%. The PCL–TPU composite structure also demonstrated complete recovery, but its fixity was 77.42%. The difference in the SME of the two composite structures is related to the transition for each thermoplastic and programming temperature. Additionally, in the early cycles, the shape-memory performance decreased, and in the fourth and fifth cycles, it almost stabilized. The scanning electron microscopy (SEM) photographs illustrated superior interfacial bonding and part integrity in the case of multi-material 3D printing.
Wave Dispersion Behaviors of Multi-Scale CNT/Glass Fiber/Polymer Nanocomposite Laminated Plates
Authors: Ebrahimi Farzad , Enferadi Alireza , Dabbagh Ali
Published: December 2022

In this paper, wave propagation in multi-scale hybrid glass fiber (GF)/carbon nanotube (CNT)/polymer nanocomposite plates is studied for the first time by means of refined higher-order plate theory. The hybrid nanocomposite consists of CNTs and glass fibers (GF) as reinforcing components distributed within a polymeric matrix. A hierarchical micromechanical approach is used to predict the effective mechanical properties of the hybrid nanocomposite, including the three-dimensional (3D) Mori-Tanaka method and the rule of mixture. Moreover, a refined-type higher-order shear deformation theory (HSDT) is implemented to take into account the influence of the shear deformation on the motion equations of the system. Then, the governing equations are achieved on the basis of the energy-based Hamilton’s principle. Finally, the derived equations will be solved analytically for the purpose of extracting the natural frequency of the continuous system. A set of numerical examples are provided to cover the effects of various parameters on the wave dispersion characteristics of the plate. It can be declared that the hybrid nanocomposite system can achieve higher wave frequencies compared with other types of composite structures. Additionally, it is found that the selection of the lay-ups and length-to-diameter ratio plays a significant role in the determination of the sandwich plate’s acoustic response.
Chiral Polymers from Norbornenes Based on Renewable Chemical Feedstocks
Authors: Nazarov Ivan V. , Zarezin Danil P. , Solomatov Ivan A. , Danshina Anastasya A. , Nelyubina Yulia V. , Ilyasov Igor R. , Bermeshev Maxim V.
Published: December 2022

Optically active polymers are of great interest as materials for dense enantioselective membranes, as well as chiral stationary phases for gas and liquid chromatography. Combining the versatility of norbornene chemistry and the advantages of chiral natural terpenes in one molecule will open up a facile route toward the synthesis of diverse optically active polymers. Herein, we prepared a set of new chiral monomers from cis-5-norbornene-2,3-dicarboxylic anhydride and chiral alcohols of various natures. Alcohols based on cyclic terpenes ((-)-menthol, (-)-borneol and pinanol), as well as commercially available alcohols (S-(-)-2-methylbutanol-1, S-(+)-3-octanol), were used. All the synthesized monomers were successfully involved in ring-opening metathesis polymerization, affording polymers in high yields (up to 96%) and with molecular weights in the range of 1.9 × 105–5.8 × 105 (Mw). The properties of the metathesis polymers obtained were studied by TGA and DSC analysis, WAXD, and circular dichroism spectroscopy. The polymers exhibited high thermal stability and good film-forming properties. Glass transition temperatures for the prepared polymers varied from −30 °C to +139 °C and, therefore, the state of the polymers changed from rubbery to glassy. The prepared polymers represent a new attractive platform of chiral polymeric materials for enantioselective membrane separation and chiral stationary phases for chromatography.
An Improved Line-Up Competition Algorithm for Unrelated Parallel Machine Scheduling with Setup Times
Authors: Xu Yuting , Shi Bin
Published: December 2022

It is well known that with the development of economic globalization and increasing competition in the market, enterprises are facing a huge challenge in the unrelated parallel machine scheduling problem with setup time (UPMST). Determining the processing order of all jobs and assigning machines to production scheduling has become more complex and has research implications. Moreover, a reasonable production scheduling scheme can not only complete the production plan efficiently but also contribute to reducing carbon emissions. In this paper, a mathematical model with the goal of the shortest completion time is studied for the UPMST problem. An improved line-up competition algorithm (ILCA) is proposed to solve this model, and the search accuracy and rate of the algorithm are improved by the newly proposed heuristic workpiece allocation rules and variation strategies. From the perspective of evaluation purposes, the effectiveness and stability of the method are significantly superior to other methods, and it is competitive in solving the UPMST problem.
Flow-Shop Scheduling Problem Applied to the Planning of Repair and Maintenance of Electromedical Equipment in the Hospital Industry
Authors: Mellado-Silva Rafael , Cubillos Claudio , Cabrera-Paniagua Daniel , Urra Enrique
Published: December 2022

In the literature, several approaches have been proposed to integrate and optimize product supply and construction processes associated with demand management. However, in Industry 4.0, there needs to be more studies related to applying techniques that directly affect the programming and reprogramming process that integrates the industries at the operational level. This document proposes a flow-shop scheduling procedure to address the problem of planning the repair of medical equipment in public hospitals whose main objective is to eliminate downtime and minimize total production time. The research stems from the practical problem of responding to clinical users who make use of critical equipment, such as mechanical respirators, due to COVID-19, and the limited quantity of this equipment, which makes it necessary to have repair planning processes that seek to keep the equipment in operation for the most extended amount of time. The novelty of this study is that it was applied to a critical and real problem in the industry with a high economic and social impact, which has not been explored previously. The results show improvements in the overall planning and execution of electro-medical equipment repair. Several improvements to the applied methods were identified as future work, such as the need to consider work interruptions and psychosocial effects on workers due to the stricter planning of execution times.
Burnout Levels in Italian Nurses during the First and the Second Wave in the COVID-19 Outbreak: A Pilot Cohort-Data Comparison
Authors: Vitale Elsa
Published: December 2022

(1) Background: Nurses show higher psychological distress associated with physical difficulties during the COVID-19 outbreak. To compare burnout levels among nurses engaged in the front line of patients with COVID-19 during the first wave and the second wave of the pandemic. (2) Methods: Two cohort online surveys were conducted thanks to the Google Modules function: the first one was administered from March 2020 to April 2020 and the second survey, which included the same questionnaire, was administered from September 2020 to October 2020. (3) Results: There were significant differences in the emotional exhaustion sub dimension (p < 0.001): The first wave group recorded higher levels than the second wave one. While, as regards to the values of the dimension of the depersonalization, the second wave group reported significantly higher values than the first wave group (p = 0.006). No significant difference was recorded for the personal accomplishment sub dimension (p = 0.108). By considering the gender variable, significant difference was reported in personal accomplishment sub dimension, as during the second wave, females recorded lower levels in personal accomplishment while males reported significantly higher levels in the same sub dimension than the first wave (p = 0.012); while no statistically significant differences were reported in burnout sub dimensions during the first and the second waves according to years of work experience. (4) Conclusions: The COVID-19 pandemic represents an important provocation for nurses all around the world and, at the same time, constitutes a learning lesson to improve better approaches for the subsequent waves. Several interventions could be introduced to moderate the mental health influence of the COVID-19 pandemic on nurses.
Ship Recognition for SAR Scene Images under Imbalance Data
Authors: Zhan Ronghui , Cui Zongyong
Published: December 2022

Synthetic aperture radar (SAR) ship recognition can obtain location and class information from SAR scene images, which is important in military and civilian fields, and has turned into a very important research focus recently. Limited by data conditions, the current research mainly includes two aspects: ship detection in SAR scene images and ship classification in SAR slice images. These two parts are not yet integrated, but it is necessary to integrate detection and classification in practical applications, although it will cause an imbalance of training samples for different classes. To solve these problems, this paper proposes a ship recognition method on the basis of a deep network to detect and classify ship targets in SAR scene images under imbalance data. First, RetinaNet is used as the backbone network of the method in this paper for the integration of ship detection and classification in SAR scene images. Then, taking into account the issue that there are high similarities among various SAR ship classes, the squeeze-and-excitation (SE) module is introduced for amplifying the difference features as well as reducing the similarity features. Finally, considering the problem of class imbalance in ship target recognition in SAR scene images, a loss function, the central focal loss (CEFL), based on depth feature aggregation is constructed to reduce the differences within classes. Based on the dataset from OpenSARShip and Sentinel-1, the results of the experiment suggest that the the proposed method is feasible and the accuracy of the proposed method is improved by 3.9 percentage points compared with the traditional RetinaNet.
Impact of Errors in Environmental Correction on Gravity Field Recovery Using Interferometric Radar Altimeter Observations
Authors: Wan Xiaoyun , Wang Fei , Guo Hengyang , Liu Bo
Published: December 2022

As a new type of altimeter, interferometric radar altimeter (InRA) has significant potential in marine gravity field recovery due to its high spatial resolution. However, errors in environmental correction on gravity field recovery using InRA observations are unclear. In this study, four kinds of these errors, including wet and dry troposphere, ionosphere, and sea state bias (SSB) correction errors, are simulated. The impact of these errors on gravity field recovery are analyzed and discussed. The results show that, among the four types of errors in environmental correction, the wet troposphere and SSB have a more significant impact on the accuracy of sea surface height computing, and the wet troposphere has the most significant impact on the accuracy of gravity field recovery. The maximum error of gravity anomaly caused by the wet troposphere residual errors is nearly 2 mGal, and the relative error of the recovered gravity anomaly is around 6.42%. We can also find that SSB has a little more significant impact than dry troposphere and ionosphere, where dry troposphere and ionosphere have an almost identical impact, on DV and GA inversion accuracy.
Adaptive Interaction Control of Compliant Robots Using Impedance Learning
Authors: Sun Tairen , Yang Jiantao
Published: December 2022

This paper presents an impedance learning-based adaptive control strategy for series elastic actuator (SEA)-driven compliant robots without the measurement of the robot–environment interaction force. The adaptive controller is designed based on the command filter-based adaptive backstepping approach, where a command filter is used to decrease computational complexity and avoid the requirement of high derivatives of the robot position. In the controller, environmental impedance profiles and robotic parameter uncertainties are estimated using adaptive learning laws. Through a Lyapunov-based theoretical analysis, the tracking error and estimation errors are proven to be semiglobally uniformly ultimately bounded. The control effectiveness is illustrated through simulations on a compliant robot arm.
Different Configurations of Radio-Frequency Atomic Magnetometers—A Comparative Study
Authors: Bevington Patrick , Chalupczak Witold
Published: December 2022

We comprehensively explore different optical configurations of a radio-frequency atomic magnetometer in the context of sensor miniaturisation. Similarities and differences in operation principles of the magnetometer arrangements are discussed. Through analysis of the radio-frequency and noise spectra, we demonstrate that all configurations provide the same level of atomic polarisation and signal-to-noise ratio, but the optimum performance is achieved for significantly different laser powers and frequencies. We conclude with possible strategies for system miniaturisation.
Structure and Base Analysis of Receptive Field Neural Networks in a Character Recognition Task
Authors: Goga Jozef , Vargic Radoslav , Pavlovicova Jarmila , Kajan Slavomir , Oravec Milos
Published: December 2022

This paper explores extensions and restrictions of shallow convolutional neural networks with fixed kernels trained with a limited number of training samples. We extend the work recently done in research on Receptive Field Neural Networks (RFNN) and show their behaviour using different bases and step-by-step changes within the network architecture. To ensure the reproducibility of the results, we simplified the baseline RFNN architecture to a single-layer CNN network and introduced a deterministic methodology for RFNN training and evaluation. This methodology enabled us to evaluate the significance of changes using the (recently widely used in neural networks) Bayesian comparison. The results indicate that a change in the base may have less of an effect on the results than re-training using another seed. We show that the simplified network with tested bases has similar performance to the chosen baseline RFNN architecture. The data also show the positive impact of energy normalization of used filters, which improves the classification accuracy, even when using randomly initialized filters.
An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications
Authors: Shah Syed Mohsin Ali , Usman Syed Muhammad , Khalid Shehzad , Rehman Ikram Ur , Anwar Aamir , Hussain Saddam , Ullah Syed Sajid , Elmannai Hela , Algarni Abeer D. , Manzoor Waleed
Published: December 2022

Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.
Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion
Authors: Al-Zyoud Izaldein , Laamarti Fedwa , Ma Xiaocong , Tobón Diana , El Saddik Abdulmotaleb
Published: December 2022

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.
Simulation Method for Blockchain Systems with a Public Chain
Authors: Liu Yang , Zhang Yuxi , Lin Zhiyuan , Wang Zhaoguo , Wang Xuan
Published: December 2022

The potential security problems of blockchain technology are constantly restricting the development process of related industrial applications. The cost of deploying a blockchain system in a real environment to conduct research on security issues is relatively high, and the related security analysis and verification are also destructive and irreproducible. Therefore, based on the idea of layered design, this paper proposes a blockchain system simulation platform. The blockchain system is divided into four layers in the simulation platform: the consensus layer, network layer, contract layer, and storage layer. In the consensus layer, the problem of computing resource waste is solved. In the network layer, a peer-to-peer network topology simulation is implemented. In the storage layer, the problem of redundant storage is solved. In the contract layer, the contract replay speed is accelerated. Finally, a prototype of an efficient blockchain simulation system is implemented based on the above methods.
Mathematical Modeling and Validation of Retransmission-Based Mutant MQTT for Improving Quality of Service in Developing Smart Cities
Authors: Ali Jawad , Zafar Mohammad Haseeb , Hewage Chaminda , Hassan Raheel , Asif Rameez
Published: December 2022

Unreliable networks often use excess bandwidth for data integration in smart cities. For this purpose, Messaging Queuing Telemetry Transport (MQTT) with a certain quality of service (QoS) is employed. Data integrity and data security are frequently compromised for reducing bandwidth usage while designing integrated applications. Thus, for a reliable and secure integrated Internet of Everything (IoE) service, a range of network parameters are conditioned to achieve the required quality of a deliverable service. In this work, a QoS-0-based MQTT is developed in such a manner that the transparent MQTT protocol uses Transmission Control Protocol (TCP)-based connectivity with various rules for the retransmission of contents if the requests are not entertained for a fixed duration. The work explores the ways to improve the overall content delivery probability. The parameters are examined over a transparent gateway-based TCP network after developing a mathematical model for the proposed retransmission-based mutant QoS-0. The probability model is then verified by an actual physical network where the repeated content delivery is explored at VM-based MQTT, local network-based broker and a remote server. The results show that the repeated transmission of contents from the sender improves the content delivery probability over the unreliable MQTT-based Internet of Things (IoT) for developing smart cities’ applications.
Immunosensors—The Future of Pathogen Real-Time Detection
Authors: Janik-Karpinska Edyta , Ceremuga Michal , Niemcewicz Marcin , Podogrocki Marcin , Stela Maksymilian , Cichon Natalia , Bijak Michal
Published: December 2022

Pathogens and their toxins can cause various diseases of different severity. Some of them may be fatal, and therefore early diagnosis and suitable treatment is essential. There are numerous available methods used for their rapid screening. Conventional laboratory-based techniques such as culturing, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) are dominant. However, culturing still remains the “gold standard” for their identification. These methods have many advantages, including high sensitivity and selectivity, but also numerous limitations, such as long experiment-time, costly instrumentation, and the need for well-qualified personnel to operate the equipment. All these existing limitations are the reasons for the continuous search for a new solutions in the field of bacteria identification. For years, research has been focusing on the use of immunosensors in various types of toxin- and pathogen-detection. Compared to the conventional methods, immunosensors do not require well-trained personnel. What is more, immunosensors are quick, highly selective and sensitive, and possess the potential to significantly improve the pathogen and toxin diagnostic-processes. There is a very important potential use for them in various transport systems, where the risk of contamination by bioagents is very high. In this paper, the advances in the field of immunosensor usage in pathogenic microorganism- and toxin-detection, are described.
A Review of Machine Learning for Near-Infrared Spectroscopy
Authors: Zhang Wenwen , Kasun Liyanaarachchi Chamara , Wang Qi Jie , Zheng Yuanjin , Lin Zhiping
Published: December 2022

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
Effective Area Measurements of Magnetic Pick-Up Coil Sensors for RFX-mod2
Authors: Abate Domenico , Cavazzana Roberto
Published: December 2022

A meaningful characterization of the magnetic configuration of toroidal plasmas requires the identification and estimation of the sources of error on each magnetic measurement of the overall diagnostic system. Thus, the correct characterization of magnetic pick-up coil sensors and the assessment of their reliability becomes a necessary requirement before their permanent installation in the RFX-mod2 experiment. The experimental characterization methodology developed for the three-axes magnetic pick-up coil sensors of RFX-mod2 experiment is presented here. The sensitivity of each sensor is evaluated not only by performing accurate measurements of the effective areas in a time-varying magnetic field, but also by checking the alignment of the magnetic axes through measurements of the effective areas at different rotation angles. Moreover, the effect of thermal cycles on measuring the effective area and the angle of misalignment are evaluated and analyzed.
Fast Near-Field Frequency-Diverse Computational Imaging Based on End-to-End Deep-Learning Network
Authors: Wu Zhenhua , Zhao Fafa , Zhang Man , Huan Sha , Pan Xueli , Chen Wei , Yang Lixia
Published: December 2022

The ability to sculpt complex reference waves and probe diverse radiation field patterns have facilitated the rise of metasurface antennas, while there is still a compromise between the required wide operation band and the non-overlapping characteristic of radiation field patterns. Specifically, the current computational image formation process with a classic matched filter and other sparsity-driven algorithms would inevitably face the challenge of a relatively confined scene information sampling ratio and high computational complexity. In this paper, we marry the concepts of a deep convolutional neural network with computational imaging literature. Compared with the current matched filter and compressed sensing reconstruction technique, our proposal could handle a relatively high correlation of measurement modes and low scene sampling ratio. With the delicately trained reconstruction network, point-size objects and more complicated targets can both be quickly and accurately reconstructed. In addition, the unavoidable heavy computation burden and essential large operation frequency band can be effectively mitigated. The simulated experiments with measured radiation field data verify the effectiveness of the proposed method.
Performance Level and Strike Type during Ground and Pound Determine Impact Characteristics and Net Force Variability
Authors: Beranek Vaclav , Stastny Petr , Novacek Vit , Słomka Kajetan J. , Cleather Dan
Published: December 2022

The evaluation of strike impact is important for optimal training, conditioning and tactical use. Therefore, the aim of this study was to evaluate ground and pound strikes, in terms of net force variability, across genders and performance levels. Eighty-one participants, professional men (n = 8, 37 ± 6 years, 195 ± 7 cm, 113 ± 27 kg), advanced men (n = 47, 26 ± 8 years, 180 ± 7 cm, 76 ± 11 kg), and advanced women (n = 26, 21 ± 1 years, 167 ± 6 cm, 61 ± 7 kg) performed three strikes from a kneeling position into a force plate on the ground. The elbow strike resulted in the highest impulse and the palm strike in the highest peak force for all three categories. These results support the recommendation that has previously been made to teach the palm strike to beginners and advanced tactical and combat athletes. The direct punch and elbow strike net force were characterized by a double peak curve, where the first peak variability explained 70.2–84% of the net force. The second peak was pronounced in professional men during elbow strikes, which explained 16% of net force variability. The strike type determines the impact net force and its characteristics, where palm strike is typical by highest peak impact tolerance and elbow strike by double force peak with high net force impulse.