bluuebunny/crossref_metadata_2025 huggingface dataset:
Static and Dynamic Biomaterial Engineering for Cell Modulation
Authors: Park Hyung-Joon , Hong Hyunsik , Thangam Ramar , Song Min-Gyo , Kim Ju-Eun , Jo Eun-Hae , Jang Yun-Jeong , Choi Won-Hyoung , Lee Min-Young , Kang Heemin , Lee Kyu-Back
Published: April 2022

In the biological microenvironment, cells are surrounded by an extracellular matrix (ECM), with which they dynamically interact during various biological processes. Specifically, the physical and chemical properties of the ECM work cooperatively to influence the behavior and fate of cells directly and indirectly, which invokes various physiological responses in the body. Hence, efficient strategies to modulate cellular responses for a specific purpose have become important for various scientific fields such as biology, pharmacy, and medicine. Among many approaches, the utilization of biomaterials has been studied the most because they can be meticulously engineered to mimic cellular modulatory behavior. For such careful engineering, studies on physical modulation (e.g., ECM topography, stiffness, and wettability) and chemical manipulation (e.g., composition and soluble and surface biosignals) have been actively conducted. At present, the scope of research is being shifted from static (considering only the initial environment and the effects of each element) to biomimetic dynamic (including the concepts of time and gradient) modulation in both physical and chemical manipulations. This review provides an overall perspective on how the static and dynamic biomaterials are actively engineered to modulate targeted cellular responses while highlighting the importance and advance from static modulation to biomimetic dynamic modulation for biomedical applications.
Oxide Derived Copper for Electrochemical Reduction of CO2 to C2+ Products
Authors: Zahid Anum , Shah Afzal , Shah Iltaf
Published: April 2022

The electrochemical reduction of carbon dioxide (CO2) on copper electrode derived from cupric oxide (CuO), named oxide derived copper (ODCu), was studied thoroughly in the potential range of −1.0 V to −1.5 V versus RHE. The CuO nanoparticles were prepared by the hydrothermal method. The ODCu electrode was used for carbon dioxide reduction and the results revealed that this electrode is highly selective for C2+ products with enhanced current density at significantly less overpotential. This catalyst shifts the selectivity towards C2+ products with the highest Faradaic efficiency up to 58% at −0.95 V. In addition, C2 product formation at the lowest onset potential of −0.1 V is achieved with the proposed catalyst. X-ray diffraction and scanning electron microscopy revealed the reduction of CuO to Cu (111) nanoparticles during the CO2 RR. The intrinsic property of the synthesized catalyst and its surface reduction are suggested to induce sites or edges for facilitating the dimerization and coupling of intermediates to ethanol and ethylene.
Antimicrobial PMMA Bone Cement Containing Long Releasing Multi-Walled Carbon Nanotubes
Authors: Al Thaher Yazan , Khalil Raida , Abdelghany Sharif , Salem Mutaz S.
Published: April 2022

Prosthetic joint infections (PJIs) ensued from total joint replacement (TJR) pose a severe threat to patients that involve poor health outcomes, severe pain, death (in severe cases), and negative influence patients’ quality of life. Antibiotic-loaded bone cement (ALBC) is frequently used for the prevention and treatment of PJI. This work aims to study gentamicin release from carbon nanotubes (CNTs) incorporated in polymethyl methacrylate (PMMA) bone cement to prolong release over several weeks to provide prophylaxis from PJIs after surgery. Different CNT concentrations were tested with the presence of gentamicin as a powder or preloaded onto carboxyl functionalized CNTs. The different types of bone cement were tested for drug release, mechanical properties, water uptake, antimicrobial properties, and cytocompatibility with human osteoblast cells (MTT, LDH, alizarin red, and morphology). Results showed prolonged release of gentamicin from CNT-loaded bone cements over several weeks compared to gentamicin-containing bone cement. Additionally, the presence of CNT enhanced the percentage of gentamicin released without adversely affecting the nanocomposite mechanical and antimicrobial properties needed for performance. Cytotoxicity testing showed non-inferior performance of the CNT-containing bone cement to the equivalent powder containing cement. Therefore, the developed nanocomposites may serve as a novel PMMA bone cement to prevent PJIs.
Multilayer Coatings for Tribology: A Mini Review
Authors: Liu Yanfei , Yu Shengtao , Shi Qiuyu , Ge Xiangyu , Wang Wenzhong
Published: April 2022

Friction and wear usually lead to huge energy loss and failure of machine pairs, which usually causes great economic losses. Researchers have made great efforts to reduce energy dissipation and enhance durability through advanced lubrication technologies. Single-layer coatings have been applied in many sectors of engineering, but the performance of single-layer coatings still has many limitations. One solution to overcome these limitations is to use a multilayer coating that combines different components with varied physical and chemical properties. In addition, multilayer coating with alternating layers only containing two components can lead to improved performance compared to a coating with only two different layers. This paper systematically reviews the design concept and properties of different types of multilayer coatings, including transition-metal nitride coatings, diamond-like carbon-based coatings, and other multilayer coatings. The inherent functional mechanisms of the multilayer structures are also detailed and discussed.
A Colorimetric Ag+ Probe for Food Real-Time Visual Monitoring
Authors: Yu Jiahang , Qi Jun , Li Zhen , Tian Huixin , Xu Xinglian
Published: April 2022

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance.
Acute Effects of Kawakawa (Piper excelsum) Intake on Postprandial Glycemic and Insulinaemic Response in a Healthy Population
Authors: Ramzan Farha , Jayaprakash Ramya , Pook Chris , Foster Meika , Miles-Chan Jennifer L. , Mithen Richard
Published: April 2022

Background: Piper excelsum (kawakawa) is an endemic shrub of Aotearoa, New Zealand, of cultural and medicinal importance to Māori. Its fruits and leaves are often consumed. These tissues contain several compounds that have been shown to be biologically active and which may underpin its putative health-promoting effects. The current study investigates whether kawakawa tea can modulate postprandial glucose metabolism. Methods: We report a pilot three-arm randomized crossover study to assess the bioavailability of kawakawa tea (BOKA-T) in six male participants with each arm having an acute intervention of kawakawa tea (4 g/250 mL water; 1 g/250 mL water; water) and a follow-up two-arm randomized crossover study to assess the impact of acute kawakawa tea ingestion on postprandial glucose metabolism in healthy human volunteers (TOAST) (4 g/250 mL water; and water; n = 30 (15 male and 15 female)). Participants consumed 250 mL of kawakawa tea or water control within each study prior to consuming a high-glycemic breakfast. Pre- and postprandial plasma glucose and insulin concentrations were measured, and the Matsuda index was calculated to measure insulin sensitivity. Results: In the BOKA-T study, lower plasma glucose (p < 0.01) and insulin (p < 0.01) concentrations at 60 min were observed after consumption of a high-dose kawakawa tea in comparison to low-dose or water. In the TOAST study, only plasma insulin (p = 0.01) was lower at 60 min in the high-dose kawakawa group compared to the control group. Both studies showed a trend towards higher insulin sensitivity in the high-dose kawakawa group compared to water only. Conclusions: Consuming kawakawa tea may modulate postprandial glucose metabolism. Further investigations with a longer-term intervention study are warranted.
The Development of the Davis Food Glycopedia—A Glycan Encyclopedia of Food
Authors: Castillo Juan J. , Couture Garret , Bacalzo Nikita P. , Chen Ye , Chin Elizabeth L. , Blecksmith Sarah E. , Bouzid Yasmine Y. , Vainberg Yael , Masarweh Chad , Zhou Qingwen , Smilowitz Jennifer T. , German J. Bruce , Mills David A. , Lemay Danielle G. , Lebrilla Carlito B.
Published: April 2022

The molecular complexity of the carbohydrates consumed by humans has been deceptively oversimplified due to a lack of analytical methods that possess the throughput, sensitivity, and resolution required to provide quantitative structural information. However, such information is becoming an integral part of understanding how specific glycan structures impact health through their interaction with the gut microbiome and host physiology. This work presents a detailed catalogue of the glycans present in complementary foods commonly consumed by toddlers during weaning and foods commonly consumed by American adults. The monosaccharide compositions of over 800 foods from diverse food groups including Fruits, Vegetables, Grain Products, Beans, Peas, Other Legumes, Nuts, Seeds; Sugars, Sweets and Beverages; Animal Products, and more were obtained and used to construct the “Davis Food Glycopedia” (DFG), an open-access database that provides quantitative structural information on the carbohydrates in food. While many foods within the same group possessed similar compositions, hierarchical clustering analysis revealed similarities between different groups as well. Such a Glycopedia can be used to formulate diets rich in specific monosaccharide residues to provide a more targeted modulation of the gut microbiome, thereby opening the door for a new class of prophylactic or therapeutic diets.
Lacticaseibacillus rhamnosus FM9 and Limosilactobacillus fermentum Y57 Are as Effective as Statins at Improving Blood Lipid Profile in High Cholesterol, High-Fat Diet Model in Male Wistar Rats
Authors: Zafar Hamza , Ain Noor ul , Alshammari Abdulrahman , Alghamdi Saeed , Raja Hafsa , Ali Amjad , Siddique Abubakar , Tahir Syeda Duaa , Akbar Samina , Arif Maryum , Alharbi Metab , Rahman Abdur
Published: April 2022

Elevated serum cholesterol is a major risk factor for coronary heart diseases. Some Lactobacillus strains with cholesterol-lowering potential have been isolated from artisanal food products. The purpose of this study was to isolate probiotic Lactobacillus strains from traditional yoghurt (dahi) and yogurt milk (lassi) and investigate the impact of these strains on the blood lipid profile and anti-obesity effect in a high cholesterol high fat diet model in Wistar rats. Eight candidate probiotic strains were chosen based on in vitro probiotic features and cholesterol reduction ability. By 16S rDNA sequencing, these strains were identified as Limosilactibacillus fermentum FM6, L. fermentum FM16, L. fermentum FM12, Lacticaseibacillus rhamnosus FM9, L. fermentum Y55, L. fermentum Y57, L. rhamnosus Y59, and L. fermentum Y63. The safety of these strains was investigated by feeding 2 × 108 CFU/mL in saline water for 28 days in a Wistar rat model. No bacterial translocation or any other adverse effects were observed in animals after administration of strains in water, which indicates the safety of strains. The cholesterol-lowering profile of these probiotics was evaluated in male Wistar rats using a high-fat, high-cholesterol diet (HFCD) model. For 30 days, animals were fed probiotic strains in water with 2 × 108 CFU/mL/rat/day, in addition to a high fat, high cholesterol diet. The cholesterol-lowering effects of various probiotic strains were compared to those of statin. All strains showed improvement in total cholesterol, LDL, HDL, triglycerides, and weight gain. Serum cholesterol levels were reduced by 9% and 8% for L. rhamnosus FM9 and L. fermentum Y57, respectively, compared to 5% for the statin-treated group. HDL levels significantly improved by 46 and 44% for L. rhamnosus FM9 and L. fermentum Y57, respectively, compared to 46% for the statin-treated group. Compared to the statin-treated group, FM9 and Y57 significantly reduced LDL levels by almost twofold. These findings show that these strains can improve blood lipid profiles as effectively as statins in male Wistar rats. Furthermore, probiotic-fed groups helped weight control in animals on HFCD, indicating the possible anti-obesity potential of these strains. These strains can be used to develop food products and supplements to treat ischemic heart diseases and weight management. Clinical trials, however, are required to validate these findings.
Gut Microbial Signatures of Distinct Trimethylamine N-Oxide Response to Raspberry Consumption
Authors: Franck Maximilien , de Toro-Martín Juan , V. Varin Thibault , Garneau Véronique , Pilon Geneviève , Roy Denis , Couture Patrick , Couillard Charles , Marette André , Vohl Marie-Claude
Published: April 2022

The aim of this exploratory study was to evaluate the gut microbial signatures of distinct trimethylamine N-oxide (TMAO) responses following raspberry consumption. Investigations were carried out in 24 subjects at risk of developing metabolic syndrome who received 280 g/day of frozen raspberries for 8 weeks. Blood and stool samples were collected at weeks 0 and 8. Inter-individual variability in plasma TMAO levels was analyzed, 7 subjects were excluded due to noninformative signals and 17 subjects were kept for analysis and further stratified according to their TMAO response. Whole-metagenome shotgun sequencing analysis was used to determine the impact of raspberry consumption on gut microbial composition. Before the intervention, the relative abundance of Actinobacteriota was significantly higher in participants whose TMAO levels increased after the intervention (p = 0.03). The delta TMAO (absolute differences of baseline and week 8 levels) was positively associated with the abundance of gut bacteria such as Bilophila wadsworthia (p = 0.02; r2 = 0.37), from the genus Granulicatella (p = 0.03; r2 = 0.48) or the Erysipelotrichia class (p = 0.03; r2 = 0.45). Changes in the gut microbial ecology induced by raspberry consumption over an 8-week period presumably impacted quaternary amines-utilizing activity and thus plasma TMAO levels.
Augmentation of Antidiabetic Activity of Glibenclamide Microspheres Using S-Protected Okra Powered by QbD: Scintigraphy and In Vivo Studies
Authors: Rizg Waleed Y. , Naveen N. Raghavendra , Kurakula Mallesh , Safhi Awaji Y. , Murshid Samar S. , Mushtaq Rayan Y. , Abualsunun Walaa A. , Alharbi Majed , Bakhaidar Rana B. , Almehmady Alshaimaa M. , Salawi Ahmad , Al Fatease Adel , Hosny Khaled M.
Published: April 2022

Successful drug delivery by mucoadhesive systems depends on the polymer type, which usually gets adherent on hydration. The intended polymers must sustain the association with biomembranes and preserve or accommodate the drug for an extended time. The majority of hydrophilic polymers tend to make weak interactions like noncovalent bonds, which hampers the positioning of dosage forms at the required target sites, leading to inefficient therapeutic outcomes. It is possible to overcome this by functionalizing the natural polymers with thiol moiety. Further, considering that S-protected thiomers can benefit by improving thiol stability at a broad range of pH and enhancing the residence period at the required target, 2-mercapto-nicotinic acid (MA) was utilized in this present study to shield the free thiol groups on thiolated okra (TO). S-protected TO (STO) was synthesized and characterized for various parameters. Glibenclamide-loaded microspheres were formulated using STO (G-STO-M), and the process was optimized. The optimized formulation has shown complete and controlled release of the loaded drug at the end of the dissolution study. Cell viability assay indicated that the thiolated S-protected polymers gelated very well, and the formulated microspheres were safe. Further, G-STO-M showed considerable in vivo mucoadhesion strength. The glucose tolerance test confirmed the efficacy of STO formulation in minimizing the plasma glucose level. These results favor S-protection as an encouraging tool for improving the absorption of poorly aqueous soluble drugs like glibenclamide.
Digital Storytelling Review in a Pharmacy Self-Care Course
Authors: Mills Jenna M. , Guy Jason W. , Oestreich Julie H.
Published: April 2022

Digital storytelling is a type of active learning that allows instructors to simulate real-life situations through a series of connected videos. While this technique has been used in other healthcare education disciplines, its use in pharmacy has not been well documented. A digital storytelling model was incorporated in a required self-care pharmacy course to assess if the technique was helpful to improve the knowledge, confidence, and satisfaction of students. Due to a shift in online learning, the self-care course offered a remote exam review session containing a digital storytelling model, and this approach was compared to an in-person exam review that followed a lecture-based model held earlier in the course. Pre- and post-knowledge assessments were given to determine the impact of the digital storytelling review. There were 50 students involved in both sessions and there was a 70% response rate in the digital storytelling group and a 90% response rate in the lecture-based group. Students’ knowledge numerically improved, but not to a statistically significant level for most questions. Nonetheless, students reported more confidence (p < 0.05) in their ability to pass the upcoming exam following the digital storytelling review. Thematic analysis revealed that the digital storytelling session was engaging and interactive, though time-management and breakout rooms could be further optimized. Based on these results, exam review in a required self-care pharmacy course using a digital storytelling format may be a suitable method for students to apply course content and may particularly be of utility in online or hybrid courses.
Habit: A Rylean Conception
Authors: Tsai Cheng-Hung
Published: April 2022

Tennis champion Maria Sharapova has a habit of grunting when she plays on the court. Assume that she also has a habit of hitting the ball in a certain way in a certain situation. The habit of on-court grunting might be bad, but can the habit of hitting the ball in a certain way in a certain situation be classified as intelligent? The fundamental questions here are as follows: What is habit? What is the relation between habit and skill? Is there such a thing as intelligent habit? In this paper I expound the nature of habit by developing and defending a Rylean conception of habit, according to which an acquired disposition is a habit if and only if the manifestation of the disposition is repeated, automatic, and uniform. One implication of this conception is that there is no such thing as intelligent habit. A practical application in athletic expertise is that sport coaches can help athletes go beyond repeated, automatic, and uniform dispositions in sport.
Inventory Model with Stochastic Demand Using Single-Period Inventory Model and Gaussian Process
Authors: Mejia Jose , Avelar-Sosa Liliana , Mederos Boris , García-Alcaraz Jorge L.
Published: April 2022

Proper inventory management is vital to achieving sustainability within a supply chain and is also related to a company’s cash flow through the funds represented by the inventory. Therefore, it is necessary to balance excess inventory and insufficient inventory. However, this can be difficult to achieve in the presence of stochastic demand because decisions must be made in an uncertain environment and the inventory policy bears risks associated with each decision. This study reports an extension of the single-period model for the inventory problem under uncertain demand. We proposed incorporating a Gaussian stochastic process into the model using the associated posterior distribution of the Gaussian process as a distribution for the demand. This enables the modeling of data from historical inventory demand using the Gaussian process theory, which adapts well to small datasets and provides measurements of the risks associated with the predictions made. Thus, unlike other works that assume that demand follows an autoregressive or Brownian motion model, among others, our approach enables adaptability to different complex forms of demand trends over time. We offer several numerical examples that explore aspects of the proposed approach and compare our results with those achieved using other state-of-the-art methods.
Environmental Benefits of Air Emission Reduction in the Waste Tire Management Practice
Authors: Ćetković Jasmina , Lakić Slobodan , Žarković Miloš , Vujadinović Radoje , Knežević Miloš , Živković Angelina , Cvijović Jelena
Published: April 2022

Montenegro faces serious challenges in terms of waste tire management. The main goal of our paper is to consider the financial and economic justification of the implementation of the first phase of the project of collection, takeover and transport, sorting, and storage of waste tires from the three municipalities in Montenegro. The financial feasibility analysis pointed out the need to organize the second phase of the project and the production of commercially usable and energy efficient products. That phase would lead to the desired commercial effects and will probably ensure the financial sustainability of the project. The economic feasibility analysis of the project included an assessment of the socio-economic benefits from the emission reduction of the first group of pollutants (PM, SOX, NOX, VOC, CO) as a consequence of the waste tires’ destruction, predominantly by combusting them. Unit values of pollution costs by types of gases, adjusted for Montenegro, were defined in the interval from 192 EUR/t for CO to 24,294 EUR/t for PM. We proved that the direct socio-economic benefits of this project are savings in the cost of environmental pollution. The total present value of discounted costs in the observed time period was calculated at the level of EUR 1,620,080, while the total present value of the positive socio-economic effects was estimated at EUR 1,991,180. Dynamic justification indicators suggest that this investment has a satisfactory socio-economic justification, i.e., the economic rate of return is higher than the opportunity cost of capital (ERR = 15.82%), the economic net present value is greater than 0 (ENPV = 371,100 EUR), and the benefit–cost ratio is greater than 1 (B/C ratio = 1.23).
Synthesis and Characterization of Bi2WxMo1−xO6 Solid Solutions and Their Application in Photocatalytic Desulfurization under Visible Light
Authors: Belousov Artem S. , Suleimanov Evgeny V. , Parkhacheva Alina A. , Fukina Diana G. , Koryagin Andrey V. , Titaev Dmitry N. , Lazarev Mikhail A.
Published: April 2022

Photocatalytic oxidative desulfurization has attracted much attention in recent years due to the continuous tightening of the sulfur content requirements in motor fuels and the disadvantages of the industrial hydrodesulfurization process. This work is devoted to the investigation of the photocatalytic activity of Bi2WxMo1−xO6 solid solutions (x = 1, 0.75, 0.5, 0.25, 0) in the oxidative desulfurization of hydrocarbons under visible light irradiation using hydrogen peroxide as an oxidant. The synthesized photocatalysts were characterized in detail using XRD, SEM, EDS, low-temperature nitrogen adsorption–desorption, and DRS. It was shown that the use of solid solutions Bi2WxMo1−xO6 with x = 0.5–0.75 leads to the complete oxidation of organosulfur compounds to CO2 and H2O within 120 min. The high photocatalytic activity of solid solutions (x = 0.5–0.75) is attributed to their ability to absorb more visible light, the presence of the corner-shared [Mo/WO6] octahedral layers, which may promote the generation and separation of photogenerated charges, and the hierarchical 3D flower-like structure. The reaction mechanism of the desulfurization was also analyzed in this work.
Evaluating the Energy Efficiency and Environmental Impact of COVID-19 Vaccines Coolers through New Optimization Indexes: Comparison between Refrigeration Systems Using HFC or Natural Refrigerants
Authors: Santos Alexandre F. , Gaspar Pedro D. , de Souza Heraldo J. L.
Published: April 2022

COVID-19 vaccines are used worldwide to promote immunity and, in that sense, vaccination is a step forward toward ending the pandemic. Nevertheless, current vaccines must be ultra-cold or cold-stored. Vaccine coolers’ energy demand and greenhouse gas emissions lead to a significant environmental impact. This article predicts the environmental and energy impacts of some COVID-19 vaccines: Moderna, Janssen, CoronaVac, Pfizer, AstraZeneca–Oxford–Covishield, and Sputnik V, in terms of carbon dioxide emissions using a new approach for the TEWI (Total Equivalent Warming Impact) methodology, with several options of refrigerants from halogenated to natural fluids such as propane, which is natural gas with low GWP (global warming potential). Through the application of new optimization indexes, it is concluded that the evaporation temperature of the refrigerant gas has a great influence on the sizing of the coolers. For example, for the same number of vaccines, the thermal load of Pfizer is more than double that of AstraZeneca–Covishield, CoronaVac, or Janssen, while the direct environmental impact is seven times greater. Another relevant factor is the choice of refrigerant. For example, the greenhouse effect varies greatly for the same brand of vaccine. The Moderna vaccine’s global warming potential (GWP) is 776 times higher using R-449A gas than using R-290 (propane gas). In Brazil, the refrigerators used to store the Pfizer vaccine have a total TEWI almost two times higher than the total TEWI of refrigerators using propane to store the Janssen vaccine. At this time of the pandemic, these optimization indexes can be used to support important decisions regarding the future selection of vaccine brands considering the energy consumption and environmental impact required for their storage.
Research on Fault Diagnosis of PST Electro-Hydraulic Control System of Heavy Tractor Based on Support Vector Machine
Authors: Ni Huiting , Lu Liqun , Sun Meng , Bai Xin , Yin Yongfang
Published: April 2022

Due to the harsh working environment of the tractor, the transmission can often be faulty. In order to ensure the reliability of its operation, it must be monitored and the fault discovered. In this paper, the support vector machine (SVM) method is used. The eigenvector conversion of the original data uses the following eigenvectors: Three fault modes (leakage fault of shift clutch hydraulic cylinder, blockage fault of oil passage, and blockage fault of proportional valve spool) are identified in matrix and laboratory (MATLAB) with the help of the library for support vector machines (LibSVM) toolkit, and the classification accuracy of test samples is 90%. The normal mode of the PST electro-hydraulic system and the three kinds of fault modes mentioned above are discriminated against, and the correct rate of fault diagnosis reaches 95%, which meets the needs of practical engineering. Analysis of the fault recording data of the power shifting transmission shift solenoid valve shows that the difference between fault pressure data and normal data is small, and the value of traffic data is greater. This method can realize the fault mode online recognition based on controller area network (CAN) communication, and the research results provide a theoretical basis for the fault diagnosis of the PST electro-hydraulic control system.
The Impact of Berberine Pharmaceutical Wastewater on Aerobic Granules Formation: Change of Granules’ Size
Authors: Zeng Ping , Wang Yan , Liu Yongqiang , Li Juan , Liu Fenghua , Chang Ming , Zhang Yizhang
Published: April 2022

As important parameters in the characterization of aerobic granulation, the shape and average diameter were related to substrates. The previous studies disclosed that the morphology change in aerobic granules was the result of growth and the relatively strong hydrodynamic shear force. No further exploration of the size distribution of the aerobic granules has been conducted. To better understand the impact of toxic compounds on aerobic granules’ growth during their formation, the properties of aerobic granules were traced over 81 days in 3 sequencing batch reactors fed with acetate and berberine wastewater, especially the particle size and size distribution. The results showed that the aerobic granules were cultivated by the simulated acetate wastewater (R1), simulated berberine wastewater (R2), and effluent from an anaerobic baffled reactor (ABR) reactor which was fed with industrial berberine wastewater (R3). The reactors exhibited different COD removal efficiencies, and the MLSS and MLVSS values affected by the different substrates which were in an order of R1 > R2 > R3. However, the SVI and SOUR, which were affected by several factors, showed more complicated results. The aerobic granules had the lowest microbial activity (SOUR), while the aerobic granules in R3 had the lowest settling ability among the three kinds of granules. For the three reactors with different influent compositions, the aerobic granulation process displayed a three-stage process separately. Compared with the granules fed with berberine wastewater, the granules fed with acetate in a stable operation period showed more independence from other periods.The size distribution was affected by substrates. The aerobic granules with a range of 0.3–1.0 µm occupied 77.0%, 67.0%, and 35.7% of the volume for R1, R2, and R3, respectively. The biomass less than 0.3 µm occupied 59.1% volume in R3. The components of the substrate had a great influence on the growth of aerobic granules, not only on the diameter but also on the size distribution.
Assessing the Precision of Quantum Simulation of Many-Body Effects in Atomic Systems Using the Variational Quantum Eigensolver Algorithm
Authors: Sumeet  , Prasannaa V Srinivasa , Das Bhanu Pratap , Sahoo Bijaya Kumar
Published: April 2022

The emerging field of quantum simulation of many-body systems is widely recognized as a very important application of quantum computing. A crucial step towards its realization in the context of many-electron systems requires a rigorous quantum mechanical treatment of the different interactions. In this pilot study, we investigate the physical effects beyond the mean-field approximation, known as electron correlation, in the ground state energies of atomic systems using the classical-quantum hybrid variational quantum eigensolver algorithm. To this end, we consider three isoelectronic species, namely Be, Li−, and B+. This unique choice spans three classes—a neutral atom, an anion, and a cation. We have employed the unitary coupled-cluster ansätz to perform a rigorous analysis of two very important factors that could affect the precision of the simulations of electron correlation effects within a basis, namely mapping and backend simulator. We carry out our all-electron calculations with four such basis sets. The results obtained are compared with those calculated by using the full configuration interaction, traditional coupled-cluster and the unitary coupled-cluster methods, on a classical computer, to assess the precision of our results. A salient feature of the study involves a detailed analysis to find the number of shots (the number of times a variational quantum eigensolver algorithm is repeated to build statistics) required for calculations with IBM Qiskit’s QASM simulator backend, which mimics an ideal quantum computer. When more qubits become available, our study will serve as among the first steps taken towards computing other properties of interest to various applications such as new physics beyond the Standard Model of elementary particles and atomic clocks using the variational quantum eigensolver algorithm.
Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan
Authors: Rafaqat Warda , Iqbal Mansoor , Kanwal Rida , Song Weiguo
Published: April 2022

As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation.
Compound Context-Aware Bayesian Inference Scheme for Smart IoT Environment
Authors: Ullah Ihsan , Kim Ju-Bong , Han Youn-Hee
Published: April 2022

The objective of smart cities is to improve the quality of life for citizens by using Information and Communication Technology (ICT). The smart IoT environment consists of multiple sensor devices that continuously produce a large amount of data. In the IoT system, accurate inference from multi-sensor data is imperative to make a correct decision. Sensor data are often imprecise, resulting in low-quality inference results and wrong decisions. Correspondingly, single-context data are insufficient for making an accurate decision. In this paper, a novel compound context-aware scheme is proposed based on Bayesian inference to achieve accurate fusion and inference from the sensory data. In the proposed scheme, multi-sensor data are fused based on the relation and contexts of sensor data whether they are dependent or not on each other. Extensive computer simulations show that the proposed technique significantly improves the inference accuracy when it is compared to the other two representative Bayesian inference techniques.
Materials, Preparation Strategies, and Wearable Sensor Applications of Conductive Fibers: A Review
Authors: Li Xiuhong , Chen Shuang , Peng Yujie , Zheng Zhong , Li Jing , Zhong Fei
Published: April 2022

The recent advances in wearable sensors and intelligent human–machine interfaces have sparked a great many interests in conductive fibers owing to their high conductivity, light weight, good flexibility, and durability. As one of the most impressive materials for wearable sensors, conductive fibers can be made from a variety of raw sources via diverse preparation strategies. Herein, to offer a comprehensive understanding of conductive fibers, we present an overview of the recent progress in the materials, the preparation strategies, and the wearable sensor applications related. Firstly, the three types of conductive fibers, including metal-based, carbon-based, and polymer-based, are summarized in terms of their principal material composition. Then, various preparation strategies of conductive fibers are established. Next, the primary wearable sensors made of conductive fibers are illustrated in detail. Finally, a robust outlook on conductive fibers and their wearable sensor applications are addressed.
Clinical–Functional Evaluation and Test–Retest Reliability of the G-WALK Sensor in Subjects with Bimalleolar Ankle Fractures 6 Months after Surgery
Authors: Fernández-Gorgojo Mario , Salas-Gómez Diana , Sánchez-Juan Pascual , Barbado David , Laguna-Bercero Esther , Pérez-Núñez María Isabel
Published: April 2022

Ankle fractures can cause significant functional impairment in the short and long term. In recent years, gait analysis using inertial sensors has gained special relevance as a reliable measurement system. This study aimed to evaluate the differences in spatiotemporal gait parameters and clinical–functional measurements in patients with bimalleolar ankle fracture and healthy subjects, to study the correlation between the different variables, and to analyze the test–retest reliability of a single inertial sensor in our study population. Twenty-two subjects with bimalleolar ankle fracture six months after surgery and eleven healthy subjects were included in the study. Spatiotemporal parameters were analyzed with the G-WALK sensor. Functional scales and clinical measures were collected beforehand. In the ankle fracture group, the main differences were obtained in bilateral parameters (effect size: 0.61 ≤ d ≤ 0.80). Between-group differences were found in cadence, speed, stride length, and stride time (effect size: 1.61 ≤ d ≤ 1.82). Correlation was moderate (0.436 < r < 0.554) between spatiotemporal parameters and clinical–functional measures, explaining up to 46% of gait performance. Test–retest reliability scores were high to excellent (0.84 ≤ ICC ≤ 0.98), with the worst results in the gait phases. Our study population presents evident clinical–functional impairments 6 months after surgery. The G-WALK can be considered a reliable tool for clinical use in this population.
Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
Authors: Miriya Thanthrige Udaya S. K. P. , Jung Peter , Sezgin Aydin
Published: April 2022

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure’s surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and ℓ1-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ℓ1-norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.
Deep Learning Approach to UAV Detection and Classification by Using Compressively Sensed RF Signal
Authors: Mo Yongguang , Huang Jianjun , Qian Gongbin
Published: April 2022

Recently, the frequent occurrence of the misuse and intrusion of UAVs has made it a research challenge to identify and detect them effectively, and relatively high bandwidth and pressure on data transmission and real-time processing exist when sampling UAV communication signals using the RF detection method. In this paper, firstly, for data sampling, we chose a compressed sensing technique to replace the traditional sampling theorem and used a multi-channel random demodulator to sample the signal; secondly, for the detection and identification of the presence, type, and flight pattern of UAVs, a multi-stage deep learning-based UAV identification and detection method was proposed by exploiting the difference in communication signals between UAVs and controllers under different circumstances. The data samples are first passed by detectors that detect the presence of UAVs, then classifiers are used to identify the type of UAVs, and finally flight patterns are judged by the corresponding classifiers, for which two neural network structures (DNN and CNN) are constructed by deep learning algorithms and evaluated and validated by a 10-fold cross-validation method, with the DNN network used for detectors and the CNN network for subsequent type and flying mode classification. The experimental results demonstrate, first, the effectiveness of using compressed sensing for sampling the communication signals of UAVs and controllers; and second, the detecting method with multi-stage DL detects higher efficiency and accuracy compared with existing detecting methods, detecting the presence, type, and flight model of UAVs with an accuracy of over 99%.
A Novel DEM Block Adjustment Method for Spaceborne InSAR Using Constraint Slices
Authors: Wang Rui , Chai Huiming , Guo Bin , Zhang Li , Lv Xiaolei
Published: April 2022

The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability of adjustment parameters and the GCP distribution pattern theoretically using matrix perturbation theory. Second, we put forward the Constraint Slices (CSs) concept and first introduce CSs into the adjustment optimization model as constraint conditions rather than actual values as GCPs. Finally, we propose a novel DEM block adjustment method for spaceborne InSAR using CSs based on an optimization model with nonlinear constraints. The simulated experiment shows the instability of the conventional method and validates the proposed method under different parallel baseline errors. Four groups of real experiments were carried out according to the size of the uncontrolled area using twelve Co-registered Single-look Slant–range Complex (CoSSC) datasets for Henan Province, China. The adjustment results verified by the ICESat-2 ATL08 data demonstrate that the performance of the proposed method is better than the conventional method in the uncontrolled area; the corresponding improvements in adjustment accuracies compared with the conventional method are 0.13 m, 1.02 m, 2.12 m, and 8.18 m, respectively. At the same time, the proposed method can enhance the height consistency in overlapping areas, which is vital for seamless DEM production.
On Mechanical and Electrical Coupling Determination at Piezoelectric Harvester by Customized Algorithm Modeling and Measurable Properties
Authors: Perez-Alfaro Irene , Gil-Hernandez Daniel , Murillo Nieves , Bernal Carlos
Published: April 2022

Piezoelectric harvesters use the actuation potential of the piezoelectric material to transform mechanical and vibrational energies into electrical power, scavenging energy from their environment. Few research has been focused on the development and understanding of the piezoelectric harvesters from the material themselves and the real piezoelectric and mechanical properties of the harvester. In the present work, the authors propose a behavior real model based on the experimentally measured electromechanical parameters of a homemade PZT bimorph harvester with the aim to predict its Vrms output. To adjust the harvester behavior, an iterative customized algorithm has been developed in order to adapt the electromechanical coupling coefficient, finding the relationship between the harvester actuator and generator behavior. It has been demonstrated that the harvester adapts its elongation and its piezoelectric coefficients combining the effect of the applied mechanical strain and the electrical behavior as a more realistic behavior due to the electromechanical nature of the material. The complex rms voltage output of the homemade bimorph harvester in the frequency domain has been successfully reproduced by the proposed model. The Behavior Real Model, BRM, developed could become a powerful tool for the design and manufacturing of a piezoelectric harvester based on its customized dimensions, configuration, and the piezoelectric properties of the smart materials.
External Knowledge Linkages and the Evolution of Comparative Advantage: An Examination of Territorial Knowledge Dynamics in China
Authors: Wang Tengfei , Halkier Henrik , James Laura , Gu Renxu , Chi Cheng
Published: April 2022

In the era of the knowledge economy with the superfluidity of information, labor, and goods, the ability to establish external knowledge linkages has become an indispensable asset for the development of regional industries. Based on the assumption that knowledge spillovers decay with distance, several existing studies have explored the role of neighboring regions in local industrial upgrading. Meanwhile, a small but growing literature has explored the evolution of regional comparative advantage from the perspective of multi-location territorial knowledge dynamics (TKDs), exploring multi-locational knowledge interactions (including proximity interactions and distance interactions) and their regional economic effects in the process of knowledge flows. Inspired by the literature on multi-location TKDs, this paper examines two hypotheses: (1) In addition to local capabilities, external knowledge linkages also have a positive effect on local industrial upgrading; (2) the stronger the knowledge linkages, the more similar the regional comparative advantage. Through an analysis of data on authorized patent citation and the two-digit manufacturing industry from Chinese cities in 2011 and 2016, we find that the knowledge flow networks among Chinese cities are characterized by strong external knowledge linkages to both adjacent and distant regions. Further analysis reveals that a particular Chinese city has a higher probability of developing comparative advantages if it maintains strong knowledge linkages with a city specialized in the same industry. In addition, the comparative advantages of regions with strong knowledge linkages are more similar than regions with weak knowledge linkages.