bluuebunny/crossref_metadata_2025 huggingface dataset:
Beneficial Effects of Red Wine Polyphenols on Human Health: Comprehensive Review
Authors: Buljeta Ivana , Pichler Anita , Šimunović Josip , Kopjar Mirela
Published: January 2023

Polyphenols are secondary plant metabolites synthesized during the development of the grape berry as a response to stress conditions. They are important constituents in red wines that contribute to the sensory properties and antioxidant activity of wines. Due to the development of highly sophisticated analytical devices, it is now possible to characterize the structure of highly polymerized polyphenols and obtain a full polyphenol profile of red wines. Red wine polyphenols include the ones present in grapes as well as new polyphenol products formed during the winemaking process. Among them, the most important groups and their representatives are flavanols (catechin), stilbenes (trans-resveratrol), flavonols (quercetin) and hydroxybenzoic acids (gallic acid). It is known that polyphenols exhibit beneficial effects on human health, such as anti-inflammatory, anticarcinogenic and cardio-protective effects. Many studies have been conducted on the health effects of red wine polyphenols in cancer chemopreventive activities, neuroprotective effects and impact on cardiovascular diseases, gut microbiota in humans, etc. This review will provide major scientific findings on the impact of red wine polyphenols on human health as well as a review of polyphenols present in red wines and their main features.
Acknowledgment to the Reviewers of Coasts in 2022
Authors:   Coasts Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Dual-Type Flexible-Film Thermoelectric Generators Using All-Carbon Nanotube Films
Authors: Konagaya Ryota , Takashiri Masayuki
Published: January 2023

The long-term stability of n-type single-walled carbon nanotubes (SWCNTs) in air makes all-carbon thermoelectric generators (TEGs) viable. To increase the performance of TEGs, we developed a dual-type flexible-film thermoelectric generator (DFTEG). The vacuum filtering was used to form p- and n-type SWCNT films from ethanol-based dispersion and water-based solutions with cationic surfactant, respectively. DFTEGs were fabricated as follows: strip-shaped p- and n-type SWCNT films were attached on the top and back sides of a polyimide substrate, respectively, and were connected alternately in series by bending copper tapes on the edge of the polyimide substrate. The thermoelectric performance was measured after attaching the DFTEG outside a beaker full of water, where the water surface reached the center of the DFTEG. For a 10 mm long film and 15 p-n pairs, the DFTEG had an output voltage of 40 mV and a maximum power of 891 nW at a temperature difference of 25 K. The measured thermoelectric performance was significantly higher than that of the single-type TEG for almost the same SWCNT films. This result demonstrates that thermoelectric performance can be improved by using DFTEGs that are fabricated with optimum structural designs.
Novel Thermal Barrier Coatings with Phase Composite Structures for Extreme Environment Applications: Concept, Process, Evaluation and Performance
Authors: Ma Xinqing , Rivellini Kristina , Ruggiero Peter , Wildridge George
Published: January 2023

In this paper, a novel concept in the field of phase composite ceramics has been proposed and applied for creating the topcoats of durable thermal barrier coatings (TBCs), which is one of the most critical technologies for advanced high-efficiency gas turbine engines in extreme environments. The phase composite ceramic TBCs were designed to demonstrate superior and comprehensive performance-related merits, benefits, and advantages over conventional single-phase TBCs with a topcoat of 8YSZ or Gd2Zr2O7, including thermal phase stability, thermal shock durability, low thermal conductivity, and solid particle erosion resistance. In this paper, we review and summarize the development work conducted so far related to the phase composite ceramic concept, coatings processing, and experimental investigation into TBC behaviors at elevated temperatures (typically, ≥1250 °C) using different evaluation and characterization methods, including isothermal sintering, a burner rig test, a solid particle-impinging erosion test, and a CMAS corrosion test. Two-phase (t’+c) zirconia-based TBCs demonstrated improved thermal shock and erosion resistance in comparison to conventional single-phase (t’), 8YSZ TBC, and Gd2Zr2O7 TBC, when used separately. Additionally, a triple-phase (t’+c+YAG) TBC sample demonstrated superior CMAS resistance. The TBC’s damage modes and failure mechanisms for thermal phase stability, thermal cycling resistance, solid particle erosion behavior, and CMAS infiltration are also characterized and discussed in detail, in terms of microstructural characterization and performance evaluation.
Corrosion and Anticorrosion of Alloys/Metals: The Important Global Issue
Authors: Kania Henryk
Published: January 2023

Most metal materials commonly used in engineering corrode in atmospheric conditions [...]
Gd(OH)3 as Modifier of Iron Oxide Nanoparticles—Insights on the Synthesis, Characterization and Stability
Authors: Montiel Schneider María Gabriela , Rivero Paula Sofía , Muñoz Medina Guillermo Arturo , Sanchez Francisco H. , Lassalle Verónica Leticia
Published: January 2023

Magnetic resonance imaging is one of the most widely used diagnostic techniques, since it is non-invasive and provides high spatial resolution. Contrast agents (CAs) are usually required to improve the contrast capability. CAs can be classified as T1 (or positive) or T2 (or negative) contrast agents. Nowadays, gadolinium chelates (which generate T1 contrast) are the most used in clinical settings. However, the use of these chelates presents some drawbacks associated with their toxicity. Iron oxide magnetic nanoparticles (MNPs) have been extensively investigated as CA for MRI, especially for their capacity to generate negative contrast. The need for more efficient and safer contrast agents has focused investigations on the development of dual CAs, i.e., CAs that can generate both positive and negative contrast with a single administration. In this sense, nanotechnology appears as an attractive tool to achieve this goal. Nanoparticles can be modified not only to improve the contrast ability of the current CAs but also to enhance their biocompatibility, resolving toxicity issues. With the aim of contributing to the field of development of dual T1/T2 contrast agents for MRI, here, we present the obtained results of the synthesis of hybrid nanoparticles composed of magnetite/maghemite and gadolinium hydroxide. Exhaustive characterization work was conducted in order to understand how the hybrid nanoparticles were formed. The nanoparticles were extensively characterized through FTIR and UV–Vis spectroscopy, TEM and SEM microscopy, X-ray diffraction (XRD) analysis, dynamic light scattering, zeta potential, thermogravimetric analysis, energy-dispersive X-ray and vibrating-sample magnetometry. Stabilization studies were carried out to get an idea of the behavior of nanohybrids in physiological media. Special interest was given to the evaluation of Gd3+ leaching. It was found that carbohydrate coating as well as the adsorption of proteins on the surface may improve the stabilization of hybrid nanoparticles.
On the Influence of Initial Stresses on the Velocity of Elastic Waves in Composites
Authors: Kolpakov Alexander G. , Andrianov Igor V. , Rakin Sergey I.
Published: January 2023

The paper is devoted to the problem of propagation of elastic waves in composites with initial stresses. We suppose initial stresses are well within the elastic regime. We deal with the long-wave case and use the asymptotic homogenization technique based on the two-scale asymptotic approach. The main problem lies in solving the local (cell) problem, i.e., boundary value problem on a periodically repeating fragment of a composite. In general, the local problem cannot be solved explicitly. In our work, it is obtained for any initial stresses formulas, which is convenient for solving by standard codes. An analytical solution is obtained for small initial stresses. Asymptotic expansions used a small parameter characterizing the smallness of the initial stresses. In the zero approximation, composites without initial stresses are considered; the first approximation takes into account their influence on waves propagation. Two particular cases are considered in detail: laminated media and frame (honeycomb cell) composites. The analyzed frame composite can be used for the modeling of porous media. We select these two cases for the following reasons. First, the laminated and porous material are widely used in practice. Second, for these materials, the homogenized coefficients may be computed in the explicit form for an arbitrary value of the initial stresses. The dependence of the velocity of elastic waves on the initial stresses in laminated and homogeneous bodies differs. The initial tension increases the velocity of elastic waves in both cases, but the quantitative effect of the increase can vary greatly. For frame composites modeling porous bodies, the initial tension can increase or decrease the velocity of elastic waves (the initial tension decreases the velocity of elastic waves in the porous body with an inverted honeycomb periodicity cell). The decrease of the velocity of elastic waves is impossible in homogeneous media. The problem under consideration is related, in particular, to the core sample analysis in the geophysics. This question is discussed in the paper. We also analyzed some features of applications of asymptotic homogenization procedure for the dynamical problem of stressed composite materials, i.e., the nonadditivity of homogenization of sum of operators.
Acknowledgment to the Reviewers of Condensed Matter in 2022
Authors:   Condensed Matter Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Second-Neighbor Hopping Effects in the Two-Dimensional Attractive Hubbard Model
Authors: Fontenele Rodrigo Alves , Vasconcelos Nathan , Costa Natanael Carvalho , Paiva Thereza , dos Santos Raimundo Rocha
Published: January 2023

The emergence of superconductivity (SC) in lattice models, such as the attractive Hubbard one, has renewed interest since the realization of cold-atom experiments. However, reducing the temperature in these experiments is a bottleneck; therefore, investigating how to increase the energy scale for SC is crucial to cold atoms. In view of this, we examine the effects of next-nearest-neighbor hoppings (t′) on the pairing properties of the attractive Hubbard model in a square lattice. To this end, we analyze the model through unbiased Quantum Monte Carlo simulations for fixed density n=0.87, and perform finite-size scaling analysis to the thermodynamic limit. As our main result, we notice that the existence of further hopping channels leads to an enhancement of the pairing correlations, which, in turn, increases the ground-state order parameter Δ. Finally, at finite temperatures, for t′/t≠0, this enhancement of pairing correlations leads to an increase in the critical temperature Tc. That is, the fine-tuning of second-neighbor hoppings increases the energy scales for SC, and may be a route by which cold-atom experiments can achieve such a phase and to help us further understand the nature of this phenomenon.
Acknowledgment to the Reviewers of Cosmetics in 2022
Authors:   Cosmetics Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Using Smart Devices for Monitoring Elderly Patients in Rural Areas of Calabria after COVID-19 Vaccination: Experiences within the SI4CARE Project
Authors: Gallo Alessandro , Fregola Salvatore , Menon Margherita , Talarico Filomena , Fragkiadaki Stella , Kontaxopolou Dionysia , Vukojevic Katarina , Matijaca Danira , Miljkovic Miodrag , Kožetinac Srdjan , Merola Alessio , Dimovski Vlado , Kejar Anamarija , Radevic Ivan , Stevovic Danica , Martinovic Vlatka , Matesan Katjia , Matkovic Roberta , Piccoli Guido , Esposito Francesco , Mayera Nicola , Pedace Elisabetta , Semrov Darja , Guzzi Pietro ,   the SI4CARE Partners Members
Published: January 2023

The SI4CARE project is a transnational project which aims to develop both strategy and action plans to improve health and social care in the Adriatic–Ionian region. Starting from a survey of the status quo, each partner has developed some pilots to support the development and monitoring of the policy actions. In particular, partner number three, the Municipality of Miglierina, designed and developed a pilot related to the use of wearable devices for monitoring elderly patients in rural areas. With the collaboration of the complex unity of primary care (UCCP) of the Reventino area, the pilot is based on the use of smart wearable devices to monitor some parameters of older adults after their vaccinations for flu and covid. This paper focused on the design and implementation of the system. It describes its application in the Municipality of Miglierina. Presentation of the results and a discussion of the strengths and weaknesses will be presented, in detail, in future work. Finally, the possibility of extending the experiment to other Adriatic–Ionian regions is addressed.
Synthesis, Characterization, DFT, and Thermogravimetric Analysis of Neutral Co(II)/Pyrazole Complex, Catalytic Activity toward Catecholase and Phenoxazinone Oxidation
Authors: Boutaybi Mohamed El , Bouroumane Nadia , Azzouzi Mohamed , Aaddouz Mohamed , Bacroume Said , El Miz Mohamed , Touzani Rachid , Bahari Zahra , Zarrouk Abdelkader , El-Marghany Adel , Jama Charafeddine , Abu-Rayyan Ahmed , Warad Ismail
Published: January 2023

The pyrazole-pyridin-2-amine, as a tridentate pyrazole ligand, and its neutral Co(II)/pyrazole complex were prepared using a direct method with a high yield. The desired pyrazole ligand and its complex were subjected to several physicochemical and thermal analyses; moreover, the DFT-like optimization of MEP, HOMO/LUMO, and TD-DFT correlated well with their experimental relatives. Additionally, the oxidation catalytic activities of the Co(II)/pyrazole complex, such as the catecholase of catechol to o-quinone and the phenoxazinone of 2-aminophenol to 2-aminophenoxazinone, were also evaluated under mild RT conditions and atmospheric oxygen.
A Reconfigurable Terahertz Metamaterial Absorber for Gas Sensing Applications
Authors: Shruti  , Pahadsingh Sasmita , Appasani Bhargav , Srinivasulu Avireni , Bizon Nicu , Thounthong Phatiphat
Published: January 2023

Reconfigurable metamaterials have immense applications in sensing. A refractive index reconfigurable terahertz metamaterial absorber was investigated in this research for gas sensing applications. The absorption spectrum reconfigures with the changes in the surrounding medium’s refractive index. The proposed absorber displays positive permittivity and negative permeability at the resonance frequency of 3.045 THz indicating magnetic resonance. The design consists of concentric U-shaped rings that were optimally designed to perform the parametric analysis using the finite element method (FEM). The absorption bands offered by the structure were found to be insensitive to variation in polarization angles up to 60°. The outcome of this design approach yields a 99.75% absorption rate with a Q-factor of 87. Additionally, the equivalent circuit model of this proposed absorber was analyzed to estimate the resonance frequency, which reveals good agreement with the simulated ones. Moreover, the structure was designed for a refractive index ranging between 1 and 1.03 to detect harmful gases such as methane, chloroform, etc., with a high sensitivity of 3.01 THz/RIU (Refractive Index Unit) and figure of merit (FoM) of 86. This research work is potentially suitable for biological sensing and chemical industry applications.
Development of Luminescent Nd-Doped LaNbO Compound Thin Film Growth by Magnetron Sputtering for the Improvement of Solar Cells
Authors: Salas-Colera Eduardo , Tardío Miguel , García-Tabarés Elisa , Perea Belén , Crespillo Miguel L. , Muñoz-Santiuste Juan Enrique , Galiana Beatriz
Published: January 2023

Nd3+-doped LaNbO phosphor thin films were prepared by radio-frequency magnetron sputtering on Si substrates. The effects of a 1% Nd-doping concentration, after annealing at 1200 °C for 12 h, on the light-emitting properties of the sputtered thin films were characterized via several experimental techniques and deeply discussed. Photoluminescence characterization showed strong emission peaks typical of Nd3+ centers at 880 nm and 1060 nm when a 325 nm wavelength laser source was applied. Similar responses were detected in Nd3+-doped La3NbO7 powder samples fabricated by the solid-state reaction method. The coexistence of two phases (LaNbO4 and La3NbO7) in the thin films with higher nominal thickness was clearly identified based on different structural analyses. The promising results open the possibility for developing phosphor substrates as a preliminary step for the improvement of solar cells based on photon recycling mechanisms.
Acknowledgment to the Reviewers of Current Oncology in 2022
Authors:   Current Oncology Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Five-Fraction Stereotactic Radiotherapy for Brain Metastases—A Retrospective Analysis
Authors: Layer Julian P. , Layer Katharina , Sarria Gustavo R. , Röhner Fred , Dejonckheere Cas S. , Friker Lea L. , Zeyen Thomas , Koch David , Scafa Davide , Leitzen Christina , Köksal Mümtaz , Schmeel Frederic Carsten , Schäfer Niklas , Landsberg Jennifer , Hölzel Michael , Herrlinger Ulrich , Schneider Matthias , Giordano Frank A. , Schmeel Leonard Christopher
Published: January 2023

Purpose: To determine the safety and outcome profile of five-fraction stereotactic radiotherapy (FSRT) for brain metastases (BM), either as a definitive or adjuvant treatment. Methods: We assessed clinical data of patients receiving five fractions of 7 Gy each (cumulative physical dose of 35 Gy) to BM or surgical cavities. The primary endpoints were toxicity and radiation necrosis (RN) rates. Secondary endpoints were 1-year cumulative local control rate (LCR) and estimated overall survival (OS). Results: A total of 36 eligible patients receiving FSRT to a total of 49 targets were identified and included. The median follow up was 9 (1.1–56.2) months. The median age was 64.5 (34–92) years, the median ECOG score was 1, and the median Diagnostic-Specific Graded Prognostic Assessment (DS-GPA) score was 2. Treatment was well tolerated and there were no grade 3 adverse events or higher. The overall RN rate was 14.3% and the median time to RN was 12.9 (1.8–23.8) months. RN occurrence was associated with immunotherapy, young age (≤45 years), and large PTV. The cumulative 1-year local control rate was 83.1% and the estimated median local progression free-survival was 18.8 months. The estimated median overall survival was 11 (1.1–56.2) months and significantly superior in those patients presenting with RN. Conclusions: FSRT with 5 × 7 Gy represents a feasible, safe, and efficient fast track approach of intensified FSRT with acceptable LC and comparable RN rates for both the adjuvant and definitive RT settings.
Identification of Key Volatile Organic Compounds Released by Gastric Tissues as Potential Non-Invasive Biomarkers for Gastric Cancer
Authors: Mochalski Paweł , Leja Marcis , Ślefarska-Wolak Daria , Mezmale Linda , Patsko Veronika , Ager Clemens , Królicka Agnieszka , Mayhew Chris A. , Shani Gidi , Haick Hossam
Published: January 2023

Background: Volatilomics is a powerful tool capable of providing novel biomarkers for medical diagnosis and therapy monitoring. The objective of this study is to identify potential volatile biomarkers of gastric cancer. Methods: The volatilomic signatures of gastric tissues obtained from two distinct populations were investigated using gas chromatography with mass spectrometric detection. Results: Amongst the volatiles emitted, nineteen showed differences in their headspace concentrations above the normal and cancer tissues in at least one population of patients. Headspace levels of seven compounds (hexanal, nonanal, cyclohexanone, 2-nonanone, pyrrole, pyridine, and phenol) were significantly higher above the cancer tissue, whereas eleven volatiles (ethyl acetate, acetoin, 2,3-butanedione, 3-methyl-1-butanol, 2-pentanone, γ-butyrolactone, DL-limonene, benzaldehyde, 2-methyl-1-propanol, benzonitrile, and 3-methyl-butanal) were higher above the non-cancerous tissue. One compound, isoprene, exhibited contradictory alterations in both cohorts. Five compounds, pyridine, ethyl acetate, acetoin, 2,3-butanedione, and 3-methyl-1-butanol, showed consistent cancer-related changes in both populations. Conclusions: Pyridine is found to be the most promising biomarker candidate for detecting gastric cancer. The difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes, or pathways. The results of this study confirm that the chemical fingerprint formed by volatiles in gastric tissue is altered by gastric cancer.
H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner
Authors: Yacob Yasmin Mohd , Alquran Hiam , Mustafa Wan Azani , Alsalatie Mohammed , Sakim Harsa Amylia Mat , Lola Muhamad Safiih
Published: January 2023

Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
Perceptions of and Reflections on Aesthetic Education Training from the Perspective of Taiwanese Preschool Educators
Authors: Lee Liza , Chao Pei-Ju
Published: January 2023

Many studies have demonstrated that music has many benefits for children’s physical and mental development. However, many preschool educators have scant preparation for or a lack of experience in aesthetic education. The purpose of this study is to explore preschool educators’ perceptions of and reflections on aesthetic education training. We conducted a survey associated with three aspects: “aesthetics appreciation”, “aesthetics implementation”, and “aesthetics evaluation”. The results of the study show the majority of participants agreed that (1) musical participation would strengthen participants’ attitudes toward aesthetic appreciation through proper aesthetic training programs and (2) musical engagement would benefit young children by enhancing their aesthetic experiences. The attitudes toward aesthetic implementation for young children were more favorable among the middle-aged and experienced participants; (3) musical activities are also a good way to improve young children’s aesthetic abilities, which helps to create an aesthetic atmosphere in educational practice. These results indicate that it is vital for preschool educators to receive the appropriate training in aesthetics and continuous assessment to provide high-quality early education to young children.
Saturation in Qualitative Educational Technology Research
Authors: Daher Wajeeh
Published: January 2023

Saturation is a key construct used by qualitative research theorists as a way to verify the rigor and validity of a study. This study intends to examine how saturation is used within qualitative educational technology studies that addressed saturation. We selected journals which (a) follow a peer review process; (b) are considered influential in their fields and of high quality, as reflected in being in Q1 (first quartile) or Q2 (second quartile) in Scopus; and (c) publish qualitative research. The number of journals selected for this study was 20, and we selected 3 articles from each. The methodology in the chosen articles was interview-based, and the articles were published in the journal in the 5 recent years 2018–2022 (i.e., a 5-year review period). The research results indicated that 20 out of 60 of the participating studies paid attention to the definition of saturation, 25 out of 60 participating studies paid attention to ‘why saturation was sought’, and 26 out of 60 participating studies paid attention to ‘when saturation was sought’. All the participating studies paid attention to ‘how was saturation assessed’. The most-mentioned expression to indicate the achievement of saturation was the emergence of new information, themes, categories, and codes, where most of the participating studies used expressions related to this aspect.
Interactivity Recognition Graph Neural Network (IR-GNN) Model for Improving Human–Object Interaction Detection
Authors: Zhang Jiali , Mohd Yunos Zuriahati , Haron Habibollah
Published: January 2023

Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction detection algorithms, resulting in inaccurate interaction detection. To recognize invalid human–object interaction pairs, this paper proposes a model structure, the interactivity recognition graph neural network (IR-GNN) model, which can directly infer the probability of human–object interactions from a graph model architecture. The model consists of three modules: The first one is the human posture feature module, which uses key points of the human body to construct relative spatial pose features and further facilitates the discrimination of human–object interactivity through human pose information. Second, a human–object interactivity graph module is proposed. The spatial relationship of human–object distance is used as the initialization weight of edges, and the graph is updated by combining the message passing of attention mechanism so that edges with interacting node pairs obtain higher weights. Thirdly, the classification module is proposed; by finally using a fully connected neural network, the interactivity of human–object pairs is binarily classified. These three modules work in collaboration to enable the effective inference of interactive possibilities. On the datasets HICO-DET and V-COCO, comparative and ablation experiments are carried out. It has been proved that our technology can improve the detection of human–object interactions.
Linear Active Disturbance Rejection Control for a Laser Powder Bed Fusion Additive Manufacturing Process
Authors: Hussain S. Zahid , Kausar Zareena , Koreshi Zafar Ullah , Shah Muhammad Faizan , Abdullah Ahmd , Farooq Muhammad Umer
Published: January 2023

Functional metal parts with complicated geometry and internal features for the aerospace and automotive industries can be created using the laser powder bed fusion additive manufacturing (AM) technique. However, the lack of uniform quality of the produced parts in terms of strength limits its enormous potential for general adoption in industries. Most of the defects in selective laser melting (SLM) parts are associated with a nonuniform melt pool size. The melt pool area may fluctuate in spite of constant SLM processing parameters, like laser power, laser speed, hatching distance, and layer thickness. This is due to heat accumulation in the current track from previously scanned tracks in the current layer. The feedback control strategy is a promising tool for maintaining the melt pool dimensions. In this study, a dynamic model of the melt pool cross-sectional area is considered. The model is based on the energy balance of lumped melt pool parameters. Energy coming from previously scanned tracks is considered a source of disturbance for the current melt pool cross-section area in the control algorithm. To track the reference melt pool area and manage the disturbances and uncertainties, a linear active disturbance rejection control (LADRC) strategy is considered. The LADRC control technique is more successful in terms of rapid reference tracking and disturbance rejection when compared to the conventional PID controller. The simulation study shows that an LADRC control strategy presents a 65% faster time response than the PID, a 97% reduction in the steady state error, and a 98% reduction in overshoot. The integral time absolute error (ITAE) performance index shows 95% improvement for reference tracking of the melt pool area in SLM. In terms of reference tracking and robustness, LADRC outperforms the PID controller and ensures that the melt pool size remains constant.
Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network
Authors: Akhand M. A. H. , Rahat-Uz-Zaman Md. , Hye Shadmaan , Kamal Md Abdus Samad
Published: January 2023

Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.
An Approach towards Designing Logic Locking Using Shape-Perpendicular Magnetic Anisotropy-Double Layer MTJ
Authors: Divyanshu Divyanshu , Kumar Rajat , Khan Danial , Amara Selma , Massoud Yehia
Published: January 2023

In recent years, discovering various vulnerabilities in the IC supply chain has raised security concerns in electronic systems. Recent research has proposed numerous attack and defense mechanisms involving various nanoelectronic devices. Spintronic devices are a viable choice among various nanoelectronic devices because of their non-volatility, ease of fabrication with a silicon substrate, randomization in space and time, etc. This work uses a shape-perpendicular magnetic anisotropy-double oxide layer magnetic tunnel junction (s-PMA DMTJ) to construct a potential logic-locking (LL) defensive mechanism. s-PMA DMTJs can be used for more realistic novel solutions of secure hardware design due to their improved thermal stability and area efficiency compared to traditional MTJs. The LL system’s critical design range and viability are investigated in this work and compared with other two-terminal MTJ designs using various circuit analysis techniques, such as Monte Carlo simulations, eye diagram analysis, transient measurement, and parametric simulations. Hamming Distance of 25%, and output corruption coverage of 100% are achieved in the investigated test circuit.
Rotor Fault Diagnosis Method Using CNN-Based Transfer Learning with 2D Sound Spectrogram Analysis
Authors: Jung Haiyoung , Choi Sugi , Lee Bohee
Published: January 2023

This study discusses a failure detection algorithm that uses frequency analysis and artificial intelligence to determine whether a rotor used in an industrial setting has failed. A rotor is a standard component widely used in industrial sites, and continuous friction and corrosion frequently result in motor and bearing failures. As workers inspecting failure directly are at risk of serious accidents, an automated environment that can operate unmanned and a system for accurate failure determination are required. This study proposes an algorithm to detect faults by introducing convolutional neural networks (CNNs) after converting the fault sound from the rotor into a spectrogram through STFT analysis and visually processing it. A binary classifier for distinguishing between normal and failure states was added to the output part of the neural network structure used, which was based on the transfer learning methodology. We mounted the proposed structure on a designed embedded system to conduct performance discrimination experiments and analyze various outcome indicators using real-world fault data from various situations. The analysis revealed that failure could be detected in response to various normal and fault sounds of the field system and that both training and validation accuracy were greater than 99%. We further intend to investigate artificial intelligence algorithms that train and learn by classifying fault types into early, middle, and late stages to identify more specific faults.
Network Situation Assessment Method Based on Improved BP Neural Network
Authors: Du Zhiqiang , Yao Haitao , Fu Yanfang , Cao Zijian , Liang Hongtao , Ren Jinkang
Published: January 2023

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.
Urban Metabolism Characterization from the National to the Regional Scale: A Case Study of Lisbon
Authors: Cunha Sónia , Ferrão Paulo
Published: January 2023

Cities are complex systems with a key role in economic development and in improving the environmental sustainability of nations. Therefore, a comprehensive understanding of urban systems is fundamental to designing relevant policies and initiatives. Urban metabolism characterization may provide a holistic framework for analyzing urban systems in the context of the broader national metabolism. This paper provides a methodology to characterize the metabolism of an urban area, such as the Lisbon metropolitan area, taking as a source of data the socioeconomic metabolism of the country. The national metabolism is established by computing physical input-output tables. The downscaling to the urban level is accomplished with a variety of sector-specific parameters, from which an urban mass balance is established for a set of economic activities. The results obtained for the case study of Portugal and the city of Lisbon suggest that the share of services and the type of manufacturing industries in the economic structure of Lisbon, together with the higher resource productivity in the most representative economic sectors, are key aspects of the metabolism that contribute to Lisbon having higher overall resource productivity. The representation of the metabolism flows revealed key differences between the supply chains of Lisbon and the national flows, as well as a strong dependence of the Lisbon economy on material flows from other regions.
Climate–Growth Relationships in Laurus azorica—A Dominant Tree in the Azorean Laurel Forest
Authors: Pavão Diogo C. , Jevšenak Jernej , Silva Lurdes Borges , Elias Rui Bento , Silva Luís
Published: January 2023

Forests on oceanic islands, such as the Azores archipelago, enable interesting dendroclimatic research, given their pronounced climatic gradients over short geographical distances, despite the less pronounced seasonality. The Lauraceae play an essential ecological role in Macaronesian natural forests. An example is Laurus azorica (Seub.) Franco, a relevant species given its high frequency and physiognomic dominance in Azorean laurel forests. This study aims to quantify climate–growth relationships in L. azorica using a dendroecological approach. We sampled four stands at São Miguel and two stands at Terceira islands, for a total of 206 trees. Following standard dendrochronological methods and rigorous sample selection procedures, we obtained relatively low rbar values and high temporal autocorrelation. Using a stepwise Random Forest analysis followed by Generalized Linear Models calculation, we found prominent effects of present and previous year temperature, but a low precipitation signal on growth rings, with some model variation between stands. Our results agreed with previous observations for broad-leaved species with diffuse porous wood, contributing to increase the baseline dendroecological knowledge about Azorean forests. Due to the high levels of within- and between-stand variation, and to refine the climatic signal analysis, complementary approaches should be explored in the future.
Potential of Phylloplane Fungi from Mangrove Plant (Rhizophora apiculata Blume) as Biological Control Agents against Fusarium oxysporum f. sp. cubense in Banana Plant (Musa acuminata L.)
Authors: Shara Melya , Basyuni Mohammad , Hasanuddin
Published: January 2023

Phylloplane fungi is a non-pathogenic fungi on the leaf surface that can be used to control plant diseases caused by pathogens. One of the most damaging banana plant diseases is fusarium wilt, caused by the pathogenic fungi Fusarium oxysporum f. sp. Cubense (Foc). Mangrove plant Rhizophora apiculate is widely distributed and is a high-diversity area where microorganisms that produce anti-microbial compounds flourish. This plant can be used as a biological agent. This study aims to determine the various phylloplane fungi available from mangrove plant R. apiculata leaves and their potential use against Fusarium oxysporum f. sp. cubense (Foc) in banana plants (Musa acuminata L.). All 20 phylloplane fungi were identified through DNA sequencing with identities of 83.88–100%; of those 20, 3 were found that have antagonistic potential against Fusarium oxysporum f. sp. cubense (Foc): Lasiodiplodia theobromae (67.43%), Trichoderma harzianum (66.65), and Nigrospora sphaerica (65.33%). In the in vivo tests, the best inhibition of disease incidence was shown by treatment with Lasiodiplodia theobromae (11.11%). The present study confirmed that phylloplane fungi isolated from R. apiculata can inhibit fusarium wilt disease in banana plant.
What Drives Land Use Change in the Southern U.S.? A Case Study of Alabama
Authors: Adjei Eugene , Li Wenying , Narine Lana , Zhang Yaoqi
Published: January 2023

Land use change reflects fundamental transformations in society. To better understand factors contributing to current land use changes in Alabama, we expand on existing land use studies by employing a generalized least-square method nested in a system of equations for the analysis. We correct for endogeneity issues in our paper by incorporating a control function technique. Using repeated land use data from 1990–2018, we focus on analyzing factors affecting land use changes among timberland, agricultural, urban, and conservation land use types. Our results reveal that land quality factors influence land allocation and land use decisions. We also indicate that population density is a driver for replacing timberland for urban development and agricultural purposes. We show that interest rates are important factors in timberland use decisions as timberland investments are sensitive to capital cost. We provide a basis for future simulations of nationwide land use changes under different economic and policy scenarios, as we offer new insights and contribute to the existing knowledge into public policies that are related to land use planning and management.
Cold-Pressed Oilseed Cakes as Alternative and Sustainable Feed Ingredients: A Review
Authors: Rakita Slađana , Kokić Bojana , Manoni Michele , Mazzoleni Sharon , Lin Peng , Luciano Alice , Ottoboni Matteo , Cheli Federica , Pinotti Luciano
Published: January 2023

Due to the increasing demand for alternative protein feed ingredients, the utilization of oilseed by-products in animal nutrition has been sought as a promising solution to ensure cheap and environmentally sustainable feedstuffs. This review aimed to summarize the nutritional value of six cold-pressed cakes (rapeseed, hempseed, linseed, sunflower seed, camelina seed, and pumpkin seed) and the effects of their inclusion in diet for ruminant, pig, and poultry on nutrient digestibility, growth and productive performance, and quality of the products. The presented results indicated that these unconventional feed ingredients are a good protein and lipid source and have a balanced amino acid and fatty acid profile. However, contradictory results of animal production performances can be found in the literature depending on the cake type and chemical composition, dietary inclusion level, animal category, and trial duration. Due to the substantial amount of essential fatty acid, these cakes can be efficiently used in the production of animal products rich in n-3 and n-6 polyunsaturated fatty acids. However, the utilization of cakes in pig and poultry nutrition is limited because of the presence of antinutritive factors that can deteriorate feed intake and nutrient utilization.
Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System
Authors: Jiang Mengwei , Li Yiting , Song Jin , Wang Zhenjie , Zhang Li , Song Lijun , Bai Bingyao , Tu Kang , Lan Weijie , Pan Leiqing
Published: January 2023

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.
2D Linear Canonical Transforms on Lp and Applications
Authors: Yang Yinuo , Wu Qingyan , Jhang Seong-Tae
Published: January 2023

As Fourier transformations of Lp functions are the mathematical basis of various applications, it is necessary to develop Lp theory for 2D-LCT before any further rigorous mathematical investigation of such transformations. In this paper, we study this Lp theory for 1≤p<∞. By defining an appropriate convolution, we obtain a result about the inverse of 2D-LCT on L1(R2). Together with the Plancherel identity and Hausdorff–Young inequality, we establish Lp(R2) multiplier theory and Littlewood–Paley theorems associated with the 2D-LCT. As applications, we demonstrate the recovery of the L1(R2) signal function by simulation. Moreover, we present a real-life application of such a theory of 2D-LCT by encrypting and decrypting real images.
Acknowledgment to the Reviewers of Gastroenterology Insights in 2022
Authors:   Gastroenterology Insights Editorial Office
Published: January 2023

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