bluuebunny/crossref_metadata_2025 huggingface dataset:
State-of-the-Art Review of Electrospun Gelatin-Based Nanofiber Dressings for Wound Healing Applications
Authors: Li Tao , Sun Mingchao , Wu Shaohua
Published: February 2022

Electrospun nanofiber materials have been considered as advanced dressing candidates in the perspective of wound healing and skin regeneration, originated from their high porosity and permeability to air and moisture, effective barrier performance of external pathogens, and fantastic extracellular matrix (ECM) fibril mimicking property. Gelatin is one of the most important natural biomaterials for the design and construction of electrospun nanofiber-based dressings, due to its excellent biocompatibility and biodegradability, and great exudate-absorbing capacity. Various crosslinking approaches including physical, chemical, and biological methods have been introduced to improve the mechanical stability of electrospun gelatin-based nanofiber mats. Some innovative electrospinning strategies, including blend electrospinning, emulsion electrospinning, and coaxial electrospinning, have been explored to improve the mechanical, physicochemical, and biological properties of gelatin-based nanofiber mats. Moreover, numerous bioactive components and therapeutic agents have been utilized to impart the electrospun gelatin-based nanofiber dressing materials with multiple functions, such as antimicrobial, anti-inflammation, antioxidation, hemostatic, and vascularization, as well as other healing-promoting capacities. Noticeably, electrospun gelatin-based nanofiber mats integrated with specific functions have been fabricated to treat some hard-healing wound types containing burn and diabetic wounds. This work provides a detailed review of electrospun gelatin-based nanofiber dressing materials without or with therapeutic agents for wound healing and skin regeneration applications.
Hybrid Enhancement of Surface-Enhanced Raman Scattering Using Few-Layer MoS2 Decorated with Au Nanoparticles on Si Nanosquare Holes
Authors: Ko Tsung-Shine , Chen Yen-Lun
Published: February 2022

By combining the excellent biocompatibility of molybdenum disulfide (MoS2), excellent surface-enhanced Raman scattering (SERS) activity of Au nanoparticles (Au NPs), and large surface area of Si nanosquare holes (NSHs), a structure in which MoS2 is decorated with Au NPs on Si NSHs, was proposed for SERS applications. The NSH structure fabricated by e-beam lithography possessed 500 nm of squares and a depth of approximately 90 nm. Consequently, a few-layer MoS2 thin films (2–4 layers) were grown by the sulfurization of the MoO3 thin film deposited on Si NSHs. SERS measurements indicated that MoS2 decorated with Au NPs/Si NSHs provided an extremely low limit of detection (ca. 10−11 M) for R6G, with a high enhancement factor (4.54 × 109) relative to normal Raman spectroscopy. Our results revealed that a large surface area of the NSH structure would probably absorb more R6G molecules and generate more excitons through charge transfer, further leading to the improvement of the chemical mechanism (CM) effect between MoS2 and R6G. Meanwhile, the electromagnetic mechanism (EM) produced by Au NPs effectively enhances SERS signals. The mechanism of the SERS enhancement in the structure is described and discussed in detail. By combining the hybrid effects of both CM and EM to obtain a highly efficient SERS performance, MoS2 decorated with Au NPs/Si NSHs is expected to become a new type of SERS substrate for biomedical detection.
Peculiarities of Zika Immunity and Vaccine Development: Lessons from Dengue and the Contribution from Controlled Human Infection Model
Authors: Santiago Helton C. , Pereira-Neto Tertuliano A. , Gonçalves-Pereira Marcela H. , Terzian Ana C. B. , Durbin Anna P.
Published: February 2022

The Zika virus (ZIKV) was first isolated from a rhesus macaque in the Zika forest of Uganda in 1947. Isolated cases were reported until 2007, when the first major outbreaks of Zika infection were reported from the Island of Yap in Micronesia and from French Polynesia in 2013. In 2015, ZIKV started to circulate in Latin America, and in 2016, ZIKV was considered by WHO to be a Public Health Emergency of International Concern due to cases of Congenital Zika Syndrome (CZS), a ZIKV-associated complication never observed before. After a peak of cases in 2016, the infection incidence dropped dramatically but still causes concern because of the associated microcephaly cases, especially in regions where the dengue virus (DENV) is endemic and co-circulates with ZIKV. A vaccine could be an important tool to mitigate CZS in endemic countries. However, the immunological relationship between ZIKV and other flaviviruses, especially DENV, and the low numbers of ZIKV infections are potential challenges for developing and testing a vaccine against ZIKV. Here, we discuss ZIKV vaccine development with the perspective of the immunological concerns implicated by DENV-ZIKV cross-reactivity and the use of a controlled human infection model (CHIM) as a tool to accelerate vaccine development.
Old and New Insights into Sporothrix schenckii Complex Biology and Identification
Authors: De Carolis Elena , Posteraro Brunella , Sanguinetti Maurizio
Published: February 2022

Sporothrix schenckii is a worldwide-distributed thermally dimorphic fungus, which usually causes a subacute to chronic infection through traumatic implantation or inoculation of its infectious propagules. The fungus encompasses a group of phylogenetically closely related species, thus named the S. schenckii complex, of which S. schenckiisensu stricto and S. brasiliensis are main causative species of sporotrichosis. Owing to a multifaceted molecular dynamic, the S. schenckii complex can switch between the mycelium and the yeast form. This characteristic along with a varying cell wall composition account for significant species-specific differences in the host range, virulence, and susceptibility to antifungal drugs. While culture remains the gold standard to diagnose sporotrichosis, polymerase chain reaction (PCR) or matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry-based methods have become an essential for accurate species identification in many clinical laboratories. If directly applied on tissue samples, molecular methods are helpful to improve both sensitivity of and time to the etiological diagnosis of sporotrichosis. This mini-review aims to put together the old and new knowledge on the S. schenckii complex biology and identification, with particular emphasis on the laboratory diagnosis-related aspects of disease.
Pathogenesis of the Pseudomonas aeruginosa Biofilm: A Review
Authors: Tuon Felipe Francisco , Dantas Leticia Ramos , Suss Paula Hansen , Tasca Ribeiro Victoria Stadler
Published: February 2022

Pseudomonas aeruginosa is associated with several human infections, mainly related to healthcare services. In the hospital, it is associated with resistance to several antibiotics, which poses a great challenge to therapy. However, one of the biggest challenges in treating P. aeruginosa infections is that related to biofilms. The complex structure of the P. aeruginosa biofilm contributes an additional factor to the pathogenicity of this microorganism, leading to therapeutic failure, in addition to escape from the immune system, and generating chronic infections that are difficult to eradicate. In this review, we address several molecular aspects of the pathogenicity of P. aeruginosa biofilms.
Detection of the ORF1 Gene Is an Indicator of the Possible Isolation of Severe Acute Respiratory Syndrome Coronavirus 2
Authors: Shirato Kazuya , Kakizaki Masatoshi , Tomita Yuriko , Kawase Miyuki , Takeda Makoto
Published: February 2022

In the ongoing coronavirus diseases 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), real-time RT-PCR based diagnostic assays have been used for the detection of infection, but the positive signal of real-time RT-PCR does not necessarily indicate the infectivity of the patient. Due to the unique replication system of the coronavirus, primer/probe sets targeted nucleocapsid (N) and spike (S) protein detect the abundantly synthesized subgenomic RNAs as well as the virus genome, possibly making the assay unsuitable for estimation of the infectivity of the specimen, although it has an advantage for the diagnostic tests. In this study, the primer/probe set targeting the open reading frame 1a (ORF1a) gene was developed to specifically detect viral genomic RNA. Then the relation between the ORF1a signal and infectivity of the clinical specimens was validated by virus isolation using VeroE6 cells, which constitutively express transmembrane protease, serine 2, (VeroE6/TMPRSS2). The analytical sensitivity of developed ORF1a set was similar to that of previously developed N and S sets. Nevertheless, in the assay of the clinical specimen, detection rate of the ORF1a gene was lower than that of the N and S genes. These data indicated that clinical specimens contain a significant amount of subgenomic RNAs. However, as expected, the isolation-succeeded specimen always showed an RT-PCR-positive signal for the ORF1a gene, suggesting ORF1a detection in combination with N and S sets could be a more rational indicator for the possible infectivity of the clinical specimens.
Comparison of the Photodynamic Effect of Two Chlorins, Photodithazine and Fotoenticine, in Gliosarcoma Cells
Authors: Fontana Letícia Corrêa , Pinto Juliana Guerra , Magalhães Jéssica Aparecida , Tada Dayane Batista , de Almeida Rainara Moreno Sanches , Pacheco-Soares Cristina , Ferreira-Strixino Juliana
Published: February 2022

The treatment and prognosis of cancers of the nervous system remain unfavorable to the patient, which makes it necessary to study alternative therapies as primary or adjuvant treatments to existing methods. Photodynamic Therapy (PDT) is a method that consists of combining a photosensitizer (PS), a light source at the appropriate wavelength, and molecular oxygen, forming reactive oxygen species (ROS), leading to death in the target cell. The objective of this work was to compare the effects of PDT with two chlorins, Photodithazine (PDZ) and Fotoenticine (FTC), in 9L/lacZ gliosarcoma cell lines. Both chlorins, together with an LED device at 660 nm with a fluence of 10 J/cm², were included in the study. It was observed that the response to therapy depends on the concentration and type of PS used. In addition, PDZ showed a higher quantum yield of singlet oxygen generation than FTC.
All-Dielectric Metasurface Based on Complementary Split-Ring Resonators for Refractive Index Sensing
Authors: Samadi Mohsen , Abshari Fatemeh , Algorri José F. , Roldán-Varona Pablo , Rodríguez-Cobo Luis , López-Higuera José M. , Sánchez-Pena José M. , Zografopoulos Dimitrios C. , Dell’Olio Francesco
Published: February 2022

Thanks to their lower losses and sharper resonances compared to their metallic counterparts, all-dielectric metasurfaces are attracting a quickly growing research interest. The application of such metasurfaces in the field of refractive index sensing is extremely attractive, especially due to the expected high performance and the simplicity of the sensing element excitation and readout. Herein, we report on an all-dielectric silicon metasurface based on complementary split-ring resonators (CSRRs) optimized for refractive index sensing. A quasi-bound state in the continuum (quasi-BIC) with an ultra-high quality factor can be excited in the near-infrared (NIR) window by violating the structure symmetry. By using the three-dimensional finite element method (3D-FEM), a refractive index sensor for biomedical applications with an ultra-high figure of merit (FoM > 100,000 RIU−1) has been designed, exploiting the quasi-BIC resonance. The proposed design strategy opens new avenues for developing flat biochemical sensors that are accurate and responsive in real time.
A Chaotic Second Order Oscillation JAYA Algorithm for Parameter Extraction of Photovoltaic Models
Authors: Jian Xianzhong , Cao Yougang
Published: February 2022

In order to identify the parameters of photovoltaic (PV) cells and modules more accurately, reliably and efficiently, a chaotic second order oscillation JAYA algorithm (CSOOJAYA) is proposed. Firstly, both logical chaotic map and the mutation strategy are brought in enhancing the population diversity and improving exploitation. Secondly, in order to better balance exploration and exploitation, the second order oscillation factor is added, which not only improves the diversity of the population, but also has strong exploration at the beginning of the iteration and strong exploitation at the end of the iteration. In order to balance the two abilities, the self-adaptive weight is introduced into the CSOOJAYA algorithm to regulate individuals the tendency of moving toward the optimal solution and escaping from the worst solution, so as to enhance the search efficiency and exploitation. In order to validate the behavior of CSOOJAYA, it is employed to the parameter identification problem of PV models. Finally, the experimental results show that CSOOJAYA delivers excellent behavior in the aspects of convergence, reliability and accuracy.
All-Optical Phase-Change Memory with Improved Performance by Plasmonic Effect
Authors: Sun Wei , Lu Yegang , Miao Libo , Zhang Yu
Published: February 2022

The combination of the integrated waveguide and phase-change materials (PCMs) provides a promising platform for reconfigurable and multifunctional photoelectric devices. Through plasmonic enhancement and the low loss propagation of the waveguide, the footprint and power consumption of the photoelectric device can be effectively improved. In this work, a metal double-ring structure embedded with phase change materials was proposed to utilize the plasmonic effect for enhancement of the light-matter interaction. In particular, the overall temperature difference in the PCM cell can be confined within 2 °C during the crystallization process, thus avoiding the interior heterogeneous crystallization. The insertion loss of the cell in amorphous and crystalline states at a wavelength of 1550 nm are 2.3 dB and 1.0 dB, respectively. A signal contrast ratio of 15.8% is achieved under the ultra-small footprint (50 × 90 nm2) at a wavelength of 1550 nm.
Onset of Optical Instabilities in the Nonlinear Optical Transmission of Heliconical Cholesteric Liquid Crystals
Authors: Gevorgyan Ashot H. , Simoni Francesco
Published: February 2022

We report the result of an accurate calculation of optical transmission of a light beam traveling along the helix direction of a heliconical cholesteric liquid crystal when the light intensity is affecting the director orientation by an effective optical torque. The study is based on the application of Ambartsumian’s layer addition modified method with the aim of taking into account the continuous modification of the wave field during propagation, which is quite strong when the light wavelength approaches the Bragg resonance. We show first, that the recently calculated red shift of the transmission gap is confirmed under the constant intensity approximation. Additionally, by taking into account the full modulation of the optical field occurring at wavelengths close to the Bragg resonance, a weak red shift is already observable at very low intensities, but quickly has the onset of instabilities in the optical transmission. We give the first account of this effect, which is dependent on the light intensity showing and that it corresponds to the onset of non-uniform distribution of the conical angle and pitch of the structure.
Welcome to Physchem: Status and Prospects
Authors: Sá Jacinto , Manzhos Sergei , Barone Vincenzo
Published: February 2022

We begin with passing on our very best wishes to the community for a healthy and prosperous 2022 [...]
Investigation of Calcium Forms in Lichens from Travertine Sites
Authors: Ručová Dajana , Đorđević Tamara , Baláž Matej , Weidinger Marieluise , Lang Ingeborg , Gajdoš Andrej , Goga Michal
Published: February 2022

Lichens are symbiotic organisms with an extraordinary capability to colonise areas of extreme climate and heavily contaminated sites, such as metal-rich habitats. Lichens have developed several mechanisms to overcome the toxicity of metals, including the ability to bind metal cations to extracellular sites of symbiotic partners and to subsequently form oxalates. Calcium is an essential alkaline earth element that is important in various cell processes. Calcium can serve as a metal ligand but can be toxic at elevated concentrations. This study investigated calcium-rich and calcium-poor sites and the lichen species that inhabit them (Cladonia sp.). The calcium content of these lichen species were analyzed, along with localized calcium oxalate formed in thalli collected from each site. The highest concentration of calcium was found in the lichen squamules, which can serve as a final deposit for detoxification. Interestingly, the highest content of calcium in Cladonia furcata was localized to the upper part of the thallus, which is the youngest. The produced calcium oxalates were species-specific. Whewellite (CaC2O4∙H2O) was formed in the case of C. furcata and weddellite (CaC2O4∙2H2O) was identified in C. foliacea.
Evaluation of the Impact of Different Management Methods on Tetranychus urticae (Acari: Tetranychidae) and Their Predators in Citrus Orchards
Authors: Assouguem Amine , Kara Mohammed , Mechchate Hamza , Al-Mekhlafi Fahd A. , Nasr Fahd , Farah Abdellah , Lazraq Abderahim
Published: February 2022

To evaluate the effectiveness of eco-friendly treatments based on detergents classified as non-hazardous and black soap on the pest Tetranychus urticae Koch 1836, and their predators (Euseius stipulatus Athias-Henriot, 1960, Typhlodromus sp., Phytoseiulus persimilis Athias-Henriot, 1957), different treatments were applied to citrus orchards planted with Valencia late (Orange) in the Mechraa Belksiri region of Morocco (T0 = control experiment; T1 = spirodiclofen 0.5 L/Ha; T2 = 125 L/Ha (5%) of black soap; T3 = detergent; 4 L/Ha of Oni product + 2 L/Ha of Tide product). The results obtained during the whole monitoring period indicated that the three treatments used, namely spirodiclofen, black soap, and detergents, ensured a reduction in the rate of population of the pest T. urticae compared to the untreated plot. In the untreated plot, the average was 45.01 A± 4.90 mobile forms, while the plot treated with spirodiclofen it was only 21.10 C ± 2.71, the black soap 31.49 B ± 3.35, and in the plot treated with detergents, the average was similar to that obtained by spirodiclofen (22.90 C ± 2.18). On the predators (E. stipulatus, P. persimilis, and Typhlodropmus sp.), the black soap and the treatment with detergents were less harmful compared to the chemical spirodiclofen.
Circumscription and Phylogenetic Position of Two Propagulose Species of Syntrichia (Pottiaceae, Bryophyta) Reveals Minor Realignments within the Tribe Syntricheae
Authors: Gallego M. Teresa , Cano María J. , Jiménez Juan A. , Guerra Juan
Published: February 2022

In the course of a worldwide revision of the genus Syntrichia, we identified problems in the circumscription of some species of the genus as well as among some allied genera grouped in the tribe Syntricheae. This is the case for the two propagulose Syntrichia amphidiacea and S. gemmascens, closely related to Streptopogon. We analyzed phylogenetic relationships between these species, based on nuclear (ITS) and two plastid (trnL-F and trnG) markers and morphological features. Species delimitation using molecular data was consistent with our preliminary morphological inference. Phylogenetic analyses were performed using maximum likelihood and Bayesian inference methods. Our results placed Syntrichia amphidiacea in the Streptopogon clade. Syntrichia gemmascens is also included in Streptopogon in spite of the discrepancy of the ITS and plastid relationships, which could be evidence of an exchange of genetic material between species in various lineages in the Pottioideae. Streptopogon is maintained as a separate genus on the basis of morphology characters, and we consider the differentiation of laminal papillae and the presence of a stem central strand as new characters in the genus. We accept Sagenotortula as distinct genus sister to Syntrichia. We consider the lack of costal dorsal epidermis and the differentiation of a crescent-shaped costal dorsal stereid band as distinctive generic characters in Syntrichia. Additionally, we include Syntrichia percarnosa as a new synonym for S. breviseta. Three names are lectotypified.
In Vivo Imaging and Quantification of Carbon Tracer Dynamics in Nodulated Root Systems of Pea Plants
Authors: Metzner Ralf , Chlubek Antonia , Bühler Jonas , Pflugfelder Daniel , Schurr Ulrich , Huber Gregor , Koller Robert , Jahnke Siegfried
Published: February 2022

Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack of understanding of how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localised carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11C tracer towards individual nodules in a three-dimensional (3D) root system of pea (Pisum sativum). Nitrate was used for triggering a reduction of biological nitrogen fixation (BNF), which was expected to rapidly affect carbon allocation dynamics in the root-nodule system. The nitrate treatment led to a decrease in 11C tracer allocation to nodules by 40% to 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11C tracers in a PET approach to quantify C-allocation to individual nodules following a BNF reduction. Our study demonstrates the strength of using 11C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development.
Assessment of the Fertilization Capacity of the Aquaculture Sediment for Wheat Grass as Sustainable Alternative Use
Authors: Burducea Marian , Lobiuc Andrei , Dirvariu Lenuta , Oprea Eugen , Olaru Stefan Mihaita , Teliban Gabriel-Ciprian , Stoleru Vasile , Poghirc Vlad Andrei , Cara Irina Gabriela , Filip Manuela , Rusu Mariana , Zheljazkov Valtcho D. , Barbacariu Cristian-Alin
Published: February 2022

Periodic removal of sediment from aquaculture ponds is practiced to maintain their productivity and animal welfare. The recovery of sediment as a plant fertilizer could alleviate the costs of sediment removal. The objective of this study was to test the effects of a dried sediment, extracted from an aquaculture pond used for common carp cultivation, on the growth and physiology of potted wheat grass and the quality of the juice obtained from wheat grass. The results showed that sediment application did not produce significant morphological changes, although the values for plant height (16.94–19.22 cm), leaf area (19.67–139.21 mm2), and biomass (3.39–4.26 g/plant) were higher in sediment-grown plants. However, at a physiological level, the effect was negative, decreasing photosynthesis (0.82–1.66 μmol CO2 m2s−1), fluorescence ΦPSII (0.737–0.782), and chlorophyll content (1.40–1.83 CCI). The juice yield was reduced in the sediment treatments (46–58 g/100 g), while the quality was improved by increasing the content of phenols (2.55–3.39 µg/mL gallic acid equivalent), flavonoids (1.41–1.85 µg/mL quercetin equivalent), and antioxidant activity (47.99–62.7% inhibition of; 2,2-diphenyl-1-picrylhydrazyl). The positive results obtained in this study can be attributed to the moderate nutrient content of the sediment and a negligible concentration of heavy metals.
Phylogenetic and Expression Studies of Small GTP-Binding Proteins in Solanum lycopersicum Super Strain B
Authors: Al-Zahrani Hassan S. , Moussa Tarek A. A. , Alsamadany Hameed , Hafez Rehab M. , Fuller Michael P.
Published: February 2022

This investigation involved a comparative analysis of the small GTPase superfamily in S. lycopersicum super strain B compared to their analogues in leguminous and other non-leguminous species. The small GTPases superfamily members were recognized by tBLASTn searches. The sequences of amino acid were aligned using Clustal Omega and the analysis of phylogeny was performed with the MEGA7 package. Protein alignments were applied for all studied species. Three-dimensional models of RABA2, ROP9, and ROP10 from Solanum lycopersicum “Super strain B” were performed. The levels of mRNA of the Rab, Arf, Rop, and Ran subfamilies were detected in aerial tissues vs. roots. Significant divergences were found in the number of members and groups comprising each subfamily of the small GTPases and Glycine max had the highest count. High expression of Rab and Arf proteins was shown in the roots of legumes whilst in non-legume plants, the highest values were recorded in aerial tissues. S. lycopersicum super strain B had the highest expression of Rab and Arf proteins in its aerial tissues, which may indicate that diazotroph strains have supreme activities in the aerial tissues of strain B and act as associated N-fixing bacteria. The phylogenies of the small GTPase superfamily of the studied plants did not reveal asymmetric evolution of the Ra, Arf, Rop, and Ran subfamilies. Multiple sequence alignments derived from each of the Rab, Arf, and Rop proteins of S. lycopersicum super strain B showed a low frequency of substitutions in their domains. GTPases superfamily members have definite functions during infection, delivery, and maintenance of N2-fixing diazotroph but show some alterations in their function among S. lycopersicum super strain B, and other species.
Heterogeneous Biodiesel Catalyst from Steel Slag Resulting from an Electric Arc Furnace
Authors: Roushdy Mai Hassan
Published: February 2022

Biodiesel is one of the most environmentally friendly and renewable fuels, as it is a non-polluting fuel and is made from living resources, such as vegetable oils. The steel industry generates a variety of solid wastes, including electric arc furnace slag (EAFS). The synthesis of biodiesel from waste sunflower cooking oil was examined in this study, utilizing EAFS as a catalyst, which mainly contains ferric and ferrous oxides, calcium oxide, and silica. To evaluate their impact on biodiesel production, four independent variables were chosen: temperature (50–70 °C), catalyst loading (1–5%), methanol-to-oil (M:O) molar ratio (5–20), and time (1–4 h). The response surface methodology (RSM) was used to examine the impact of independent variables on reaction response, which is the biodiesel yield. This process was carried out using a design expert program by central composite design (CCD). A model was constructed, and showed that the biodiesel yield was directly proportional to all independent reaction parameters. The predicted model’s adequacy was investigated using analysis of variance (ANOVA), which showed that it is an excellent representative of the results. The optimization of reaction conditions was investigated in order to maximize biodiesel yield at minimal reaction temperature and time, achieving a 94% biodiesel yield at a 20:1 M:O molar ratio, 5% catalyst loading, 55.5 °C reaction temperature, and 1 h reaction time.
Stir Casting Routes for Processing Metal Matrix Syntactic Foams: A Scoping Review
Authors: Sánchez de la Muela Alejandro Miguel , Duarte Joana , Santos Baptista João , García Cambronero Luis Enrique , Ruiz-Román José Manuel , Elorza Francisco Javier
Published: February 2022

Metal matrix syntactic foams (MMSFs) are advanced lightweight materials constituted by a metallic matrix and a dispersion of hollow/porous fillers. Physical and mechanical properties can be fitted regarding matrix and filler properties and processing parameters. Their properties make them potential materials for sectors where density is a limiting parameter, such as transport, marine, defense, aerospace, and engineering applications. MMSFs are mainly manufactured by powder metallurgy, infiltration, and stir casting techniques. This study focuses on the current stir casting approaches and on the advances and deficiencies, providing processing parameters and comparative analyses on porosity and mechanical properties. PRISMA approaches were followed to favor traceability and reproducibility of the study. Stir casting techniques are low-cost, industrially scalable approaches, but they exhibit critical limitations: buoyancy of fillers, corrosion of processing equipment, premature solidification of molten metal during mixing, cracking of fillers, heterogeneous distribution, and limited incorporation of fillers. Six different approaches were identified; four focus on limiting buoyancy, cracking, heterogeneous distribution of fillers, and excessive oxidation of sensitive matrix alloys to oxygen. These improvements favor reaching the maximum porosity of 54%, increasing the fillers’ size from a few microns to 4–5 mm, reducing residual porosity by ±4%, synthesizing bimodal MMSFs, and reaching maximum incorporation of 74 vol%.
A Kurdish al-Qaida? Making Sense of the Ansar al-Islam Movement in Iraqi Kurdistan in the Early 2000s
Authors: Lia Brynjar
Published: February 2022

Initially construed as the vital link between Saddam Husayn’s Iraq and al-Qaida in the runup to the Iraq war, the Ansar al-Islam (AI) group formed in Iraqi Kurdistan in December 2001 has been the subject of intense debate and huge media coverage. In academic research, however, its history, evolution and affiliation have received surprisingly little academic scrutiny. Commonly depicted as an al-Qaida affiliated group or a sub-group controlled by al-Qaida’s emerging organization in Iraq (AQI), the AI group should—this article argues—instead be understood as a strong independent-minded group with an ideology and operational pattern distinct from that of AQI. Although sharing many commonalities, the AI and AQI became de facto rivals, not allies. Contrary to accepted wisdom, the AI and its first successor group remained a distinct Salafi-jihadi insurgent group largely focused on fighting ‘the near enemy’, i.e., Kurdish and Iraqi authorities. It strongly resisted repeated calls for joining al-Qaida’s new umbrella organization in Iraq, the Islamic State of Iraq (ISI) in 2006, and it paid no homage to AQI’s or ISI’s leaders. Also on the international level, the groups were fundamentally different. As opposed to al-Qaida’s terrorist plotting abroad, the AI’s international network were hierarchical structures, geared towards raising logistical and financial support as well as recruitment. The article highlights the need for greater attention to the complexities and nuances in patterns of contacts and cooperation between militant Islamist extremists. Informed by the growing scholarship on the multifaceted nature of contemporary jihadism, its numerous manifestations in local settings, and its strong internal rifts, this paper seeks to redress the early reductionist portrayal of the AI movement.
Remote Sensing to Characterize River Floodplain Structure and Function
Authors: Hauer F. Richard , Lorang Mark S. , Gonser Tom
Published: February 2022

Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodplain in western Montana to quantify both riparian and aquatic habitats and processes. LIDAR data provided a detailed bare earth DEM and vegetation canopy DEM. We classified river hydraulics and aquatic habitats using a combination of the satellite multispectral, airborne hyperspectral, and LIDAR data coupled with spatially-explicit acoustic Doppler velocity profile data of water depth and velocity. Velocity, depth, and Froude classifications were aggregated into similar hydraulic zones of river habitat classes. Thermal imagery data were coupled with field measurements of temperature and radon gas tracer to identify patterns of water exchange between the alluvial aquifer and the surface. We found a high complexity of aquatic surface temperatures and radon tracer linked to groundwater discharge from the alluvial aquifer. Airborne hyperspectral data were used to identify “hot spots” of periphyton production, which coincided with the complex nature of groundwater–surface water exchange. Airborne hyperspectral data provided differentiation of vegetation patches by dominant species. When the hyperspectral data were coupled to LIDAR first return metrics, we were able to determine vegetation canopy height and relative vegetation patch age classes. The integration of these various remote sensing sources allowed us to characterize the distribution and abundance of floodplain aquatic and riparian species and model processes of change through space and time.
An Estimation of the Backscattering Strength of Artificial Bubbles Using an Acoustic Doppler Current Profiler
Authors: Bae Ho Seuk , Kim Won-Ki , Son Su-Uk , Kim Woo-Shik , Park Joung-Soo
Published: February 2022

Acoustic Doppler current profilers (ADCPs) were developed to acquire water current velocities, as well as depth-dependent echo intensities. As the backscattering strength of an underwater object can be estimated from the measured echo intensity, the ADCP can be used to estimate plankton populations and distributions. In this study, the backscattering strength of bubble clusters in a water tank was estimated using the commercial ADCP as a proof-of-concept. Specifically, the temporal variations in the backscattering strength and the duration of bubble existence were quantitatively evaluated. Additionally, the PDSL (population density spectrum level) and VF (void fraction) of the artificial bubbles were characterized based on the obtained distribution characteristics using a PDPA (phase Doppler particle analyzer).
Extraction of Premature Newborns’ Spontaneous Cries in the Real Context of Neonatal Intensive Care Units
Authors: Cabon Sandie , Met-Montot Bertille , Porée Fabienne , Rosec Olivier , Simon Antoine , Carrault Guy
Published: February 2022

Cry analysis is an important tool to evaluate the development of preterm infants. However, the context of Neonatal Intensive Care Units is challenging, since a wide variety of sounds can occur (e.g., alarms and adult voices). In this paper, a method to extract cries is proposed. It is based on an initial segmentation between silence and sound events, followed by feature extraction on the resulting audio segments and a cry and non-cry classification. A database of 198 cry events coming from 21 newborns and 439 non-cry events was created. Then, a set of features—including Mel-Frequency Cepstral Coefficients—issued from principal component analysis, was computed to describe each audio segment. For the first time in cry analysis, noise was handled using harmonic plus noise analysis. Several machine learning models have been compared. The K-Nearest Neighbours approach showed the best results with a precision of 92.9%. To test the approach in a monitoring application, 412 h of recordings were automatically processed. The cries automatically selected were replayed and a precision of 92.2% was obtained. The impact of errors on the fundamental frequency characterisation was also studied. Results show that despite a difficult context, automatic cry extraction for non-invasive monitoring of vocal development of preterm infants is achievable.
A Low-Cost Sensor Network for Real-Time Thermal Stress Monitoring and Communication in Occupational Contexts
Authors: Sulzer Markus , Christen Andreas , Matzarakis Andreas
Published: February 2022

The MoBiMet (Mobile Biometeorology System) is a low-cost device for thermal comfort monitoring, designed for long-term deployment in indoor or semi-outdoor occupational contexts. It measures air temperature, humidity, globe temperature, brightness temperature, light intensity, and wind, and is capable of calculating thermal indices (e.g., physiologically equivalent temperature (PET)) on site. It visualizes its data on an integrated display and sends them continuously to a server, where web-based visualizations are available in real-time. Data from many MoBiMets deployed in real occupational settings were used to demonstrate their suitability for large-scale and continued monitoring of thermal comfort in various contexts (industrial, commercial, offices, agricultural). This article describes the design and the performance of the MoBiMet. Alternative methods to determine mean radiant temperature (Tmrt) using a light intensity sensor and a contactless infrared thermopile were tested next to a custom-made black globe thermometer. Performance was assessed by comparing the MoBiMet to an independent mid-cost thermal comfort sensor. It was demonstrated that networked MoBiMets can detect differences of thermal comfort at different workplaces within the same building, and between workplaces in different companies in the same city. The MoBiMets can capture spatial and temporal differences of thermal comfort over the diurnal cycle, as demonstrated in offices with different stories and with different solar irradiances in a single high-rise building. The strongest sustained heat stress was recorded at industrial workplaces with heavy machinery.
Smart Contract Vulnerability Detection Model Based on Multi-Task Learning
Authors: Huang Jing , Zhou Kuo , Xiong Ao , Li Dongmeng
Published: February 2022

The key issue in the field of smart contract security is efficient and rapid vulnerability detection in smart contracts. Most of the existing detection methods can only detect the presence of vulnerabilities in the contract and can hardly identify their type. Furthermore, they have poor scalability. To resolve these issues, in this study, we developed a smart contract vulnerability detection model based on multi-task learning. By setting auxiliary tasks to learn more directional vulnerability features, the detection capability of the model was improved to realize the detection and recognition of vulnerabilities. The model is based on a hard-sharing design, which consists of two parts. First, the bottom sharing layer is mainly used to learn the semantic information of the input contract. The text representation is first transformed into a new vector by word and positional embedding, and then the neural network, based on an attention mechanism, is used to learn and extract the feature vector of the contract. Second, the task-specific layer is mainly employed to realize the functions of each task. A classical convolutional neural network was used to construct a classification model for each task that learns and extracts features from the shared layer for training to achieve their respective task objectives. The experimental results show that the model can better identify the types of vulnerabilities after adding the auxiliary vulnerability detection task. This model realizes the detection of vulnerabilities and recognizes three types of vulnerabilities. The multi-task model was observed to perform better and is less expensive than a single-task model in terms of time, computation, and storage.
A Fast Electrical Resistivity-Based Algorithm to Measure and Visualize Two-Phase Swirling Flows
Authors: Sattar Muhammad Awais , Garcia Matheus Martinez , Portela Luis M. , Babout Laurent
Published: February 2022

Electrical resistance tomography (ERT) has been used in the literature to monitor the gas–liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the relevant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10× less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000× faster than solving the inverse problem non-iteratively via the Gauss–Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the average estimation error below 6 mm in the worst-case scenario.
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
Authors: Makroum Mohammed Amine , Adda Mehdi , Bouzouane Abdenour , Ibrahim Hussein
Published: February 2022

(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.
Detection of E. coli Bacteria in Milk by an Acoustic Wave Aptasensor with an Anti-Fouling Coating
Authors: Spagnolo Sandro , De La Franier Brian , Davoudian Katharina , Hianik Tibor , Thompson Michael
Published: February 2022

Milk is a significant foodstuff around the world, being produced and consumed in large quantities. The safe consumption of milk requires that the liquid has an acceptably low level of microbial contamination and has not been subjected to spoiling. Bacterial safety limits in milk vary by country but are typically in the thousands per mL of sample. To rapidly determine if samples contain an unsafe level of bacteria, an aptamer-based sensor specific to Escherichia coli bacteria was developed. The sensor is based on an ultra-high frequency electromagnetic piezoelectric acoustic sensor device (EMPAS), with the aptamer being covalently bound to the sensor surface by the anti-fouling linker, MEG-Cl. The sensor is capable of the selective measurement of E. coli in PBS and in cow’s milk samples down to limits of detection of 35 and 8 CFU/mL, respectively, which is well below the safe limits for commercial milk products. This sensing system shows great promise for the milk industry for the purpose of rapid verification of product safety.
E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model
Authors: Aljasim Mustafa , Kashef Rasha
Published: February 2022

The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.