bluuebunny/crossref_metadata_2025 huggingface dataset:
Biogenic Synthesis of Zero Valent Fe/Magnetite Fe3O4 Nanoparticles Using Caralluma acutangula and Application for Methylene Blue Dye Degradation under UV Light Irradiation
Authors: Alamier Waleed M. , El-Moselhy Medhat Mohamed , Bakry Ayyob M. , Hasan Nazim , Alamri Abdullah Ali
Published: October 2022

Biogenic synthesis of nanoparticles using plant extract is a promising trend in research to reduce chemical consumption and avoid wastewater treatment complications. In this work, the zero-valent Fe/Fe3O4 nanoparticles (Fe0/Fe3O4 NPs) were synthesized using Caralluma acutangula (CA) plant, widespread in the Jazan region in Saudi Arabia. The synthesis process involves hydrothermal treatment of plant extract and iron (III) mixture at 80 °C to facilitate the reduction reaction of iron (III) cations. The Fe0/Fe3O4 NPs were characterized by XRD, FTIR, SEM, EDX, TEM, XPS, TGA, UV, and SBET. The obtained data support the formation of Fe0/Fe3O4 NPs crystal structure with an average particle size of 9.6 nm and surface area of 89 m2.g−1. The biosynthesized Fe0/Fe3O4 NPs were then applied for the photodegradation of Methylene blue (MB) dye as one of the most common organic dyes in wastewater due to several industrial human activities. Different parameters for MB degradation were performed, such as kinetics and thermodynamics studies. The data obtained reflect the nonspontaneous endothermic process with 87.8 KJ. mol−1 activation energy (Ea).
Green Synthesis of Zinc Oxide Nanocrystals Utilizing Origanum majorana Leaf Extract and Their Synergistic Patterns with Colistin against Multidrug-Resistant Bacterial Strains
Authors: Yassin Mohamed Taha , Al-Askar Abdulaziz Abdulrahman , Maniah Khalid , Al-Otibi Fatimah O.
Published: October 2022

There is a crucial necessity for the formulation of efficient antimicrobial agents owing to the increasing prevalence of hospital-acquired bacterial infections triggered by multidrug-resistant microbes that result in significant deaths and illnesses around the world. Hence, the current investigation examined the antibacterial proficiency of zinc oxide nanoparticles formulated utilizing the green route against bacterial strains that were resistant to multiple drugs. In addition, the synergistic antibacterial action of ZnO nanoparticles (ZnO NPs) combined with colistin was investigated against the tested microbial strains to determine the efficiency of the bioinspired ZnO nanoparticles in boosting the antibacterial proficiency of colistin antibiotic. Incidentally, the bioinspired ZnO nanoparticles were synthesized using water extract of Origanum majorana leaves and these nanomaterials were physicochemically characterized using different analytical techniques. The bioactivity of the synthesized nanomaterials against multidrug-resistant bacterial strains was appraised using the agar diffusion method. The biogenic ZnO NPs at a concentration of 100 μg/disk revealed a compelling antimicrobial efficacy against the tested strains, expressing the maximum antimicrobial action against Escherichia coli strain with clear zone diameter of 38.16 ± 0.18 mm. The remarkable antibacterial proficiency might be accredited to the tiny particle size of the bioformulated ZnO NPs of 12.467 ± 1.36 nm. The net charge of ZnO nanomaterials was −14.8 mV while XRD analysis confirmed their hexagonal wurtzite structure. Furthermore, the bioformulated ZnO NPs showed a promising synergistic potency with colistin demonstrating respective synergism proportions of 91.05, 79.07, 75.04, 75.25, 56.28 and 10.60% against E. coli, Klebsiella pneumoniae, Acinetobacter baumannii, Salmonella typhimurium, Enterobacter cloacae, and Pseudomonas aeruginosa, respectively. In conclusion, the water extract of O. majorana leaves mediated green formulation of zinc oxide nanoparticles with unique physicochemical characteristics and effective antibacterial proficiency against the examined drug-resistant bacterial strains. These nanomaterials could be used in the synthesis of effective antibacterial coatings to control hospital acquired infections caused by multidrug-resistant bacterial pathogens.
Growth and Evaluation of Improved CsI:Tl and NaI:Tl Scintillators
Authors: Hawrami Rastgo , Ariesanti Elsa , Farsoni Abi , Szydel Diane , Sabet Hamid
Published: October 2022

Scintillators play an important role in radiation detection and imaging. Thallium-doped cesium iodide (CsI:Tl) and sodium iodide (NaI:Tl) are two of the major scintillators that have been used for many applications for many decades. In this paper, we will present an improved scintillation performance of Bridgman method-grown CsI(Tl) and NaI(Tl) crystals developed by Xtallized Intelligence, Inc. (XI, Inc.), and we will compare the performance with commercially available CsI:Tl and NaI:Tl. In a preliminary testing using MicroFJ−60035−TSV silicon photomultipliers (ON Semiconductor), the newly developed and improved 12.5 × 12.5 × 6 mm3 CsI:Tl crystal has shown an energy resolution of 4.8% (FWHM) at 662 keV, compared to 7.2% obtained by a commercially available CsI:Tl with a size of 12.5 × 12.5 × 25 mm3. The energy resolution of 5.4% (FWHM) at 662 keV is obtained for the newly improved NaI:Tl crystal, compared to 7% obtained by a commercially available ∅1″ × 1″ NaI(Tl). The comparison of the photo peak channel locations shows that the improvements in both CsI:Tl and NaI:Tl, developed by XI, Inc., have produced much larger signal amplitudes (roughly double), when compared to the commercially available CsI(Tl) and NaI:Tl. Improved decay time constants are presented as well.
Synthesis, Molecular Docking, and Neuroprotective Effect of 2-Methylcinnamic Acid Amide in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)—An Induced Parkinson’s Disease Model
Authors: Chochkova Maya , Rusew Rusi , Kalfin Reni , Tancheva Lyubka , Lazarova Maria , Sbirkova-Dimitrova Hristina , Popatanasov Andrey , Tasheva Krasimira , Shivachev Boris , Petek Nejc , Štícha Martin
Published: October 2022

Parkinson’s disease (PD) has emerged as the second most common form of human neurodegenerative disorders. However, due to the severe side effects of the current antiparkinsonian drugs, the design of novel and safe compounds is a hot topic amongst the medicinal chemistry community. Herein, a convenient peptide method, TBTU (O-(benzotriazole-1-yl)-N,N,N′,N′-tetramethyluronium tetrafluoroborate), was used for the synthesis of the amide (E)-N-(2-methylcinnamoyl)-amantadine (CA(2-Me)-Am; 3)) derived from amantadine and 2-methylcinnamic acid. The obtained hybrid was studied for its antiparkinsonian activity in an experimental model of PD induced by MPTP. Mice (C57BL/6,male, 8 weeks old) were divided into four groups as follows: (1) the control, treated with normal saline (i.p.) for 12 consecutive days; (2) MPTP (30 mg/kg/day, i.p.), applied daily for 5 consecutive days; (3) MPTP + CA(2-Me)-Am, applied for 12 consecutive days, 5 days simultaneously with MPTP and 7 days after MPTP; (4) CA(2-Me)-Am +oleanoic acid (OA), applied daily for 12 consecutive days. Neurobehavioral parameters in all experimental groups of mice were evaluated by rotarod test and passive avoidance test. Our experimental data showed that CA(2-Me)-Am in parkinsonian mice significantly restored memory performance, while neuromuscular coordination approached the control level, indicating the ameliorating effects of the new compound. In conclusion, the newly synthesized hybrid might be a promising agent for treating motor disturbances and cognitive impairment in experimental PD.
Proton-Ion Conductivity in Hexagonal Wurtzite-Nanostructured ZnO Particles When Exposed to a Reducing Atmosphere
Authors: Li Jinpeng , Mushtaq Naveed , Arshad Naila , Shah M. , Irshad Muhammad , Yan Rong , Yan Senlin , Lu Yuzheng
Published: October 2022

Zinc oxide (ZnO), a direct wide band gap semiconductor (≥3.30 eV), has widespread potential for applications in energy devices and related industries. The initial physical demonstration of ZnO in ceramic fuel cells (CFCs) gave a new view of developing high ionic conductivity for multifunctional semiconductor technology. However, in the present work, we successfully synthesized highly textured nanoparticles of ZnO using a hydrothermal method followed by sintering in a reducing atmosphere. The resultant ZnO materials as electrolytes showed efficient ionic conductivity (5.28 × 10−2 S cm−1) and an excellent power density of 520 mW cm−2 ± 5% at 550 °C for low-temperature ceramic fuel cells (LT-CFCs). The achievement of enhanced ionic conductivity without any external ions or cation doping in the CFC was anticipated, since there was a rare possibility of vacancies in the bulk ZnO structure to conduct oxygen ions or protons. Therefore, we found that laterally the surfaces of the ZnO nanoparticles could be textured to become oxygen-deficient when sintered in an H2 atmosphere, which suggests a special mechanism for effective ionic transport. Furthermore, experimental analyses such as SEM, XPS, UV–visible, and EIS methods were performed to analyze the changes in the structural properties and mechanism of ionic transport in ZnO nanoparticles. The presented work provides insights into a novel approach for developing high ionic conductivity in electrolytes in low-cost semiconductor oxides such as ZnO for energy storage and conversion devices.
Variational Theory of Crystal Growth in Multicomponent Alloys
Authors: Dudorov Maxim V. , Drozin Alexander D. , Roshchin Vasiliy E.
Published: October 2022

The provisions for a new variational theory of crystal growth in multicomponent metal melts were formulated. The developed theory is the generalization of the previously conducted studies of crystal growth under conditions of deviation from local equilibrium at the phase boundary. The description of the methods of non-equilibrium thermodynamics of interrelated physico-chemical processes occurring in the initial phase, on the interface of phases and inside the growing crystal, was compared with the variational description of the crystal growth as a macrobody. The developed approach made it possible to find the general expression for the crystal growth rate, considering the influence of thermal and diffusion processes, as well as taking into account the influence of nonstationary effects associated with deviation from the local equilibrium on the surface of the growing nucleus. The justification of the new method showed that when the condition of the local equilibrium on the surface of the growing crystal is satisfied, the resulting equations take the form of expressions that can be obtained by constructing the equation of a mass and internal energy balance for the system under consideration. As an example, the problem of crystal growth from a melt of eutectic composition was considered. The equation of the growth rate of the two-component nucleus of the stoichiometric composition was obtained, taking into account the influence of the local non-equilibrium effects on growth. The expressions obtained were compared with the known equations of the solute trapping theory.
Project “Biodiversity MARE Tricase”: A Species Inventory of the Coastal Area of Southeastern Salento (Ionian Sea, Italy)
Authors: Micaroni Valerio , Strano Francesca , Crocetta Fabio , Di Franco Davide , Piraino Stefano , Gravili Cinzia , Rindi Fabio , Bertolino Marco , Costa Gabriele , Langeneck Joachim , Bo Marzia , Betti Federico , Froglia Carlo , Giangrande Adriana , Tiralongo Francesco , Nicoletti Luisa , Medagli Pietro , Arzeni Stefano , Boero Ferdinando
Published: October 2022

Biodiversity is a broad concept that encompasses the diversity of nature, from the genetic to the habitat scale, and ensures the proper functioning of ecosystems. The Mediterranean Sea, one of the world’s most biodiverse marine basins, faces major threats, such as overexploitation of resources, pollution and climate change. Here we provide the first multi-taxa inventory of marine organisms and coastal terrestrial flora recorded in southeastern Salento (Ionian Sea, Italy), realized during the project “Biodiversity MARE Tricase”, which provided the first baseline of species living in the area. Sampling was carried out by SCUBA and free diving, fishing gears, and citizen science from 0 to 70 m. Overall, 697 taxa were found between March 2016 and October 2017, 94% of which were identified to the species level. Of these, 19 taxa represented new records for the Ionian Sea (36 additional new records had been reported in previous publications on specific groups, namely Porifera and Mollusca Heterobranchia), and two findings represented the easternmost records in the Mediterranean Sea (Helicosalpa virgula and Lampea pancerina). For eight other taxa, our findings represented the only locality in the Ionian Sea, besides the Straits of Messina. In addition to the species list, phenological events (e.g., blooms, presence of reproductive traits and behaviour) were also reported, with a focus on gelatinous plankton. Our results reveal that even for a relatively well-known area, current biodiversity knowledge may still be limited, and targeted investigations are needed to fill the gaps. Further research is needed to understand the distribution and temporal trends of Mediterranean biodiversity and to provide baseline data to identify ongoing and future changes.
Long-Term Ringing Data on Migrating Passerines Reveal Overall Avian Decline in Europe
Authors: Petras Tina , Vrezec Al
Published: October 2022

The loss of biodiversity is shaping today’s environment. Bird ringing is a citizen science research tool that can determine species population dynamics and trends over a large geographic area. We used a 17-year time series to assess population trends of 74 passerine species based on ringing data from autumn migration in Slovenia (south-central Europe). We defined seven guilds of species according to geographic location, ecological, migratory, breeding, and life-history traits. Almost all guilds showed declining trends, except for the group of species of northeastern European origin, which showed a stable trend. The greatest decline was in low-productivity wetland specialists. Forest birds, seed-eaters, and high-productivity species experienced the smallest declines. The general declines in avifauna across a range of life-history and behavioural traits, and across a range of spatial and ecological scales, suggest widespread environmental change in Europe. Our data indicates that recent trends are toward ecosystem homogeneity, with an impoverished avifauna, including a few species that are increasing in abundance. These are the species with higher productivity and flexible behaviour, such as short-distance migrants, that have the greatest chance of prevailing in the recently rapidly changing environment because of their ability to adapt to changes in a timely manner.
The Relationship between Visceral Adiposity and Nonalcoholic Fatty Liver Disease Diagnosed by Controlled Attenuation Parameter in People with HIV: A Pilot Study
Authors: Sebastiani Giada , Paisible Nathalie , Costiniuk Cecilia , Cox Joseph , Kablawi Dana , Klein Marina B. , Kronfli Nadine , Routy Jean-Pierre , Falutz Julian , Lebouché Bertrand , Guaraldi Giovanni
Published: October 2022

Background: Fat alterations are frequent in people with HIV (PWH) and predict worse cardiometabolic outcomes. Visceral adipose tissue (VAT) is associated with ectopic fat accumulation in the liver. We aimed to investigate nonalcoholic fatty liver disease (NAFLD) diagnosed by controlled attenuation parameter (CAP) as a potential marker of visceral adiposity in PWH. Methods: We conducted a prospective pilot study of HIV mono-infected patients undergoing metabolic characterization and paired CAP measured by transient elastography with dual-energy X-ray absorptiometry (DEXA) scan. NAFLD was defined as CAP ≥ 285 dB/m, in absence of alcohol abuse. Excess visceral adiposity was defined as VAT > 1.32 Kg. Pairwise correlation, area under the curve (AUC) and logistic regression analysis were employed to study the association between VAT and CAP. Results: Thirty patients were included, of whom 50% had NAFLD. CAP was correlated with VAT (r = 0.650, p < 0.001) measured by DEXA scan. After adjusting for duration of HIV infection, body mass index and waist circumference, CAP remained the only independent predictor of excess VAT (adjusted odds ratio 1.05, 95% confidence interval [CI] 1.01–1.10). The AUC analysis determined CAP had excellent performance to diagnose excess VAT (AUC 0.92, 95% CI 0.81–1.00), higher than BMI and waist circumference. The optimized CAP cut-off to diagnose excess VAT was 266 dB/m, with a sensitivity of 88.3% and a specificity of 84.6%. Conclusions: NAFLD diagnosed by CAP is associated with VAT in PWH independently of anthropometric measures of obesity. CAP may be a potential diagnostic marker of visceral adiposity in the practice of HIV medicine.
A Deep Learning Algorithm for Radiographic Measurements of the Hip in Adults—A Reliability and Agreement Study
Authors: Jensen Janni , Graumann Ole , Overgaard Søren , Gerke Oke , Lundemann Michael , Haubro Martin Haagen , Varnum Claus , Bak Lene , Rasmussen Janne , Olsen Lone B. , Rasmussen Benjamin S. B.
Published: October 2022

Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.
Risk Factors for Post-Traumatic Stress Disorder after Childbirth: A Systematic Review
Authors: El Founti Khsim Ijlas , Martínez Rodríguez Mirella , Riquelme Gallego Blanca , Caparros-Gonzalez Rafael A. , Amezcua-Prieto Carmen
Published: October 2022

Background: Post-traumatic stress disorder (PTSD) after birth has generated a growing interest in recent years. Although some risk factors associated with PTSD have been studied, information is still scarce to date on risk factors associated with PTSD. This systematic review aims to identify risk factors associated with the diagnosis of PTSD after childbirth. Methods: We searched on PubMed, Web of Science and SCOPUS databases, from inception to May 2022. Quality assessment of the articles was performed using the Newcastle-Ottawa Quality Assessment (“NOQAS”) scale. This systematic review was performed according to the PRISMA guidelines. Inclusion criteria were women with age ≥18 years; articles in English or Spanish; articles focused on physical, social, psychological, medical-obstetric, and environmental risk factors. Results: A total of n = 17,675 women were included among the studies in this systematic review. The main risk factors associated with PTSD after birth were obstetric interventions and obstetric violence such as emergency caesarean section or a non-compliant birth plan, a previous mental illness, having suffered from of a traumatic event or depression and/or anxiety, and having poor social support throughout pregnancy and/or during birth. Conclusions: Obstetric interventions, obstetric violence, experiencing a traumatic event or depression and/or anxiety, and a previous mental illness are factors associated with the diagnosis of PTSD after birth. Protective factors are multiparity, adherence to the mother’s birth plan and skin-to-skin contact.
Can Peri-Implant Marginal Bone Loss Progression and a-MMP-8 Be Considered Indicators of the Subsequent Onset of Peri-Implantitis? A 5-Year Study
Authors: Guarnieri Renzo , Reda Rodolfo , Zanza Alessio , Miccoli Gabriele , Nardo Dario Di , Testarelli Luca
Published: October 2022

The aim of this retrospective study was to investigate the relationship between the amount of early bone remodeling, the marginal bone loss (MBL) progression, and the peri-implant sulcular fluid concentration of active metalloproteinase-8 (a-MMP-8) and the incidence of peri-implantitis (P) over 5 years of implant function. It has been documented that dental implants with a high degree of early marginal bone loss (MBL) are likely to achieve additional increased MBL during function. Moreover, it has been speculated that early increased MBL might be a predictive factor for the subsequent onset of peri-implant inflammatory diseases. Clinical and radiographic data at implant placement (T0) and restoration delivery (TR) at 6 months (T1), 2 years (T2), and 5 years (T5) post-loading were retrospectively collected. MBL levels/rates (MBLr) and peri-implant sulcular fluid levels/rates of a-MMP-8 were assessed at TR, T1, T2, and T5. Implants were divided into two groups: group 1 with peri-implantitis (P+) and group 2 without peri-implantitis (P−). A multi-level simple binary logistic regression, using generalized estimation equations (GEEs), was implemented to assess the association between each independent variable and P+. A receiver operating characteristics (ROC) curve was used to evaluate an optimal cutoff point for T1 MBL degree and a-MMP-8 level to discriminate between P+ and P− implants. A total of 80 patients who had received 80 implants between them (39 implants with a laser-microtextured collar surface (LMS) and 41 implants with a machined collar surface (MS)) were included. Periapical radiographs and a software package were used to measure MBL rates. Peri-implant sulcular implant fluid samples were analyzed by a chairside mouth-rinse test (ImplantSafe®) in combination with a digital reader (ORALyzer®). Twenty-four implants (six with an LMS and eighteen with an MS) were classified as P+. No statistically significant association was found between the amount of early bone remodeling, MBL progression, and MBLr and the incidence of peri-implantitis. Implants with a-MMP-8 levels >15.3 ng/mL at T1 presented a significantly higher probability of P+. The amount of early marginal bone remodeling cannot be considered as an indicator of the subsequent onset of P, whereas high a-MMP-8 levels 6 months after loading could have a distinct ability to predict P.
Graph-Based Image Segmentation for Road Extraction from Post-Disaster Aerial Footage
Authors: Sebasco Nicholas Paul , Sevil Hakki Erhan
Published: October 2022

This research effort proposes a novel method for identifying and extracting roads from aerial images taken after a disaster using graph-based image segmentation. The dataset that is used consists of images taken by an Unmanned Aerial Vehicle (UAV) at the University of West Florida (UWF) after hurricane Sally. Ground truth masks were created for these images, which divide the image pixels into three categories: road, non-road, and uncertain. A specific pre-processing step was implemented, which used Catmull–Rom cubic interpolation to resize the image. Moreover, the Gaussian filter used in Efficient Graph-Based Image Segmentation is replaced with a median filter, and the color space is converted from RGB to HSV. The Efficient Graph-Based Image Segmentation is further modified by (i) changing the Moore pixel neighborhood to the Von Neumann pixel neighborhood, (ii) introducing a new adaptive isoperimetric quotient threshold function, (iii) changing the distance function used to create the graph edges, and (iv) changing the sorting algorithm so that the algorithm can run more effectively. Finally, a simple function to automatically compute the k (scale) parameter is added. A new post-processing heuristic is proposed for road extraction, and the Intersection over Union evaluation metric is used to quantify the road extraction performance. The proposed method maintains high performance on all of the images in the dataset and achieves an Intersection over Union (IoU) score, which is significantly higher than the score of a similar road extraction technique using K-means clustering.
Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities
Authors: Svanström Fredrik , Alonso-Fernandez Fernando , Englund Cristofer
Published: October 2022

Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. However, our final deployment has not included the latter due to its limited detection range. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors.
Approximate Optimal Curve Path Tracking Control for Nonlinear Systems with Asymmetric Input Constraints
Authors: Wang Yajing , Wang Xiangke , Shen Lincheng
Published: October 2022

This paper proposes an approximate optimal curve-path-tracking control algorithm for partially unknown nonlinear systems subject to asymmetric control input constraints. Firstly, the problem is simplified by introducing a feedforward control law, and a dedicated design for optimal control with asymmetric input constraints is provided by redesigning the control cost function in a non-quadratic form. Then, the optimality and stability of the derived optimal control policy is demonstrated. To solve the underlying tracking Hamilton–Jacobi–Bellman (HJB) equation in consideration of partially unknown systems, an integral reinforcement learning (IRL) algorithm is utilized using the neural network (NN)-based value function approximation. Finally, the effectiveness and generalization of the proposed method is verified by experiments carried out on a high-fidelity hardware-in-the-loop (HIL) simulation system for fixed-wing unmanned aerial vehicles (UAVs) in comparison with three other typical path-tracking control algorithms.
Unmanned Aerial Vehicle Adaptation to Facilitate Healthcare Supply Chains in Low-Income Countries
Authors: Gunaratne Kalani , Thibbotuwawa Amila , Vasegaard Alex Elkjær , Nielsen Peter , Perera H. Niles
Published: October 2022

Low-income countries are persistently suffering from last-mile logistics issues in healthcare supply chains. Therefore, it is high time to explore technological applications to overcome such inadequacies. The faster speed, low maintenance cost, and absence of road dependency in unmanned aerial vehicles (UAV) have popularized them as an alternative to road delivery. Hence, it is suggested as a solution to overcome the persisting distribution inefficiencies in healthcare logistics of low-income countries. According to the case study analysis conducted on the Sri Lankan vaccine cold chain, incorporating UAVs increases truck-space utilization and reduces the time consumed, cost incurred, and carbon dioxide emission in a delivery round. Moreover, the most suitable way to cover the initial setup cost of an unmanned aerial system (UAS) is by receiving aid from international donors. The capital cost also can be covered by government investments or via service outsourcing only if the number of flights per year is increased. Moreover, a homogenous (i.e., only UAV) solution was revealed to be more beneficial than a heterogeneous (i.e., truck and UAV) solution. However, due to the lack of technology literacy and willingness to change in low-income countries, it is recommended to initially execute a heterogeneous solution and expand to a homogeneous plan in the future years. However, it was evident that for a mixed-fleet solution to be advantageous, drone characteristics play a vital role. Hence, a UAV with specifications ideal for the use case must be utilized to garner the maximum benefits. Nevertheless, it was apparent that with the right implementation plan, UAVs possess the potential to overcome the shortcomings in the healthcare logistics of low-income countries.
Threshold Concepts Theory in Higher Education—Introductory Statistics Courses as an Example
Authors: Beitelmal Wesam H. , Littlejohn Ray , Okonkwo Paul C. , Hassan Israr U. , Barhoumi El Manaa , Khozaei Fatemeh , Hassan Abbas M. , Alkaaf Khalid Ahmed
Published: October 2022

The purpose of this research was to identify “threshold” concepts in introductory statistics courses by surveying engineering students. Threshold concepts are those ideas that bind a subject together and are fundamental to the thinking and practice within a discipline. In this exploratory study, the aim was to answer the following questions: What are the learners’ assessments of the proposed threshold concepts? Based on the learners’ assessments, which of the four threshold characteristics best describes the selected threshold concepts? What are the final proposed threshold concepts that incorporate the learners’ perspectives? What is the perception held by learners about the theory’s impact on the learning and teaching processes? Using qualitative and quantitative exploratory analysis to answer these questions, all students in the College of Engineering and Applied Science who took one of the 29 sections of the course Introductory Statistics offered at the University of Colorado at Boulder were surveyed. The findings reveal there are differences in threshold concepts identified by instructors and learners. The learners added 11 concepts to the 18 proposed by the instructors as threshold concepts. Based on these inputs, a list of potential threshold concepts incorporating the two perspectives—that of instructor and that of learner—was created, and a framework of knowledge to support curriculum design was developed. An important suggestion for future research is to explore how to incorporate these threshold concepts in redesigning the syllabi of such courses, depending on the proposed framework.
Improvement of the SPICE Model of Diode Based on Measurement and Nonlinear Fitting Random Optimization Algorithm
Authors: Ren Dan , Xu Gang , Li Jing-Qin , Pan Ze-Yu , Zhao Xuan , Du Ping-An
Published: October 2022

This paper proposes a modeling method for improvement of the SPICE Model of a diode based on measurement and a nonlinear fitting random optimization algorithm. First, the mechanism of electromagnetic interference generated by diodes at high frequencies was analyzed: dynamic characteristic parameters such as reverse recovery of diodes and junction capacitance. Second, the method of obtaining the I-V characteristic curve and junction capacitance characteristics of the diode through testing was introduced in detail, and a nonlinear fitting random optimization algorithm was proposed to calculate the static and dynamic characteristic parameters of the diode, which can be applied to improvement of the SPICE model of the diode. A simulation and test circuit were built, and the simulation and test results were compared in the time and frequency domains. The model in this study can not only truly reflect the peak voltage owing to the electromagnetic interference characteristics, but also ensure that the harmonic components and component amplitudes are within three times the fundamental frequency, verifying the accuracy of the electromagnetic compatibility model.
A Deep Learning Method for the Detection and Compensation of Outlier Events in Stock Data
Authors: Naidoo Vashalen , Du Shengzhi
Published: October 2022

The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attribute of time series data allows us to predict the values that will follow in the series. Typical prediction models are limited to following the patterns in the data set without being able to compensate for anomalous periods. This research will attempt to find a machine learning method to detect outliers and then compensate for these detections in the prediction made. This concept was previously unimplemented, and therefore, it will make use of theoretical work on market forecasting, outliers and their effects, and machine learning methods. The ideas implemented in the paper are based upon the efficient market hypothesis (EMH), in which the stock price reflects knowledge about the market. The EMH hypothesis cannot account for consumer sentiment towards a stock. This sentiment could produce anomalies in stock data that have a significant influence on the movement of the stock market. Therefore, the detection and compensation of outliers may improve the predictions made on stock movements. This paper proposes a deep learning method that consists of two sequential stages. The first stage is an outlier detection model based on a long short-term memory (LSTM) network auto-encoder that can determine if an outlier event has occurred and then create an associated value of this occurrence for the next stage. The second stage of the proposed method uses a higher-order neural network (HONN) model to make a prediction based on the output of the first stage and the stock time series data. Real stock data and standalone prediction models are used to validate this method. This method is superior at predicting stock time series data by compensating for outlier events. The improvement is quantifiable if the data set contains an adequate amount of anomalous periods. We may further apply the proposed method of compensating for outliers in combination with other financial time series prediction methods to offer further improvements and stability.
Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire
Authors: Neto Joaquim , Morais António Jorge , Gonçalves Ramiro , Coelho António Leça
Published: October 2022

The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.
Survey on Multi-Path Routing Protocols of Underwater Wireless Sensor Networks: Advancement and Applications
Authors: Shovon Iftekharul Islam , Shin Seokjoo
Published: October 2022

Underwater wireless sensor networks (UWSNs) are a prominent research topic in academia and industry, with many applications such as ocean, seismic, environmental, and seabed explorations. The main challenges in deploying UWSN are high ocean interference and noise, which results in longer propagation time, low bandwidth, and changes in network topology. To mitigate these problems, routing protocols have been identified as an efficient solution. Over the years, several protocols have been proposed in this direction and among them, the most popular are the ones that use multi-path propagation. However, there is a lack of compilation of studies that highlight the advancement of multi-path routing protocols of UWSN through the years. Hence, getting a heuristic idea of the existing protocols is crucial. In this study, we present a comprehensive survey of UWSNs multi-path routing protocols and categorize them into three main categories; energy-based routing protocols, geographic information-based routing protocols, and data-based routing protocols. Furthermore, we sub-classify them into several categories and identify their advantages and disadvantages. In addition, we identify the application of UWSN, open challenges and compare the protocols. The findings of our study will allow researchers to better understand different categories of UWSN multi-path routing protocols in terms of their scope, advantages, and limitations.
Multiscale Brain Network Models and Their Applications in Neuropsychiatric Diseases
Authors: Lu Meili , Guo Zhaohua , Gao Zicheng , Cao Yifan , Fu Jiajun
Published: October 2022

With the rapid development of advanced neuroimaging techniques, understanding the brain in terms of structural and functional connectomes has become one of the frontier topics in neuroscience. Different from traditional descriptive brain network models, which focused on single neuroimaging modal and temporal scales, multiscale brain network models consisting of mesoscopic neuronal activity and macroscopic functional dynamics can provide a mechanistic understanding for brain disorders. Here, we review the foundation of multiscale brain network models and their applications in neuropsychiatric diseases. We first describe some basic elements of a multiscale brain network model, including network connections, dynamics of regional neuronal populations, and model fittings to different metrics of fMRI. Secondly, we draw comparisons between multiscale brain network models and other large-scale brain models. Additionally, then we survey the related applications of multiscale brain network models in understanding underlying mechanisms of some brain disorders, such as Parkinson’s disease, Alzheimer’s disease, and Schizophrenia. Finally, we discuss the limitations of current multiscale brain network models and future potential directions for model development. We argue that multiscale brain network models are more comprehensive than traditional single modal brain networks and would be a powerful tool to explore neuronal mechanisms underlying different brain disorders measured by neuroimaging.
QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach
Authors: Mohapatra Soumya Snigdha , Kumar Rakesh Ranjan , Alenezi Mamdouh , Zamani Abu Taha , Parveen Nikhat
Published: October 2022

As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster.
Content and Other Resources Recommendations for Individuals with Intellectual Disability: A Review
Authors: Apostolidis Konstantinos , Mezaris Vasileios , Papadogiorgaki Maria , Bei Ekaterini S. , Livanos George , Zervakis Michalis E.
Published: October 2022

In this review paper, we look into how a recommendation system can be adapted to and support people with intellectual disability (ID). We start by reviewing and comparing the main classes of techniques for general-purpose content recommendation. Then, centering on individuals with ID, we collect information on their special needs that may be relevant to or affected by content recommendation tasks. We review the few existing recommendation systems specifically designed or adapted to the needs of this population and finally, based on the reviewed literature sources, we catalog the traits that a future content recommendation system should have in order to respond well to the identified special needs. We hope this listing of desirable traits and future directions in our concluding sections will stimulate research towards opening the doors to the digital world for individuals with ID.
Hardware Acceleration and Implementation of YOLOX-s for On-Orbit FPGA
Authors: Wang Ling , Zhou Hai , Bian Chunjiang , Jiang Kangning , Cheng Xiaolei
Published: October 2022

The rapid development of remote sensing technology has brought about a sharp increase in the amount of remote sensing image data. However, due to the satellite’s limited hardware resources, space, and power consumption constraints, it is difficult to process massive remote sensing images efficiently and robustly using the traditional remote sensing image processing methods. Additionally, the task of satellite-to-ground target detection has higher requirements for speed and accuracy under the conditions of more and more remote sensing data. To solve these problems, this paper proposes an extremely efficient and reliable acceleration architecture for forward inference of the YOLOX-s detection network an on-orbit FPGA. Considering the limited onboard resources, the design strategy of the parallel loop unrolling of the input channels and output channels is adopted to build the largest DSP computing array to ensure a reliable and full utilization of the limited computing resources, thus reducing the inference delay of the entire network. Meanwhile, a three-path cache queue and a small-scale cascaded pooling array are designed, which maximize the reuse of on-chip cache data, effectively reduce the bandwidth bottleneck of the external memory, and ensure an efficient computing of the entire computing array. The experimental results show that at the 200 MHz operating frequency of the VC709, the overall inference performance of the FPGA acceleration can reach 399.62 GOPS, the peak performance can reach 408.4 GOPS, and the overall computing efficiency of the DSP array can reach 97.56%. Compared with the previous work, our architecture design further improves the computing efficiency under limited hardware resources.
Super-Resolution of Remote Sensing Images for ×4 Resolution without Reference Images
Authors: Li Yunhe , Wang Yi , Li Bo , Wu Shaohua
Published: October 2022

Sentinel-2 satellites can provide free optical remote-sensing images with a spatial resolution of up to 10 M, but the spatial details provided are not enough for many applications, so it is worth considering improving the spatial resolution of Sentinel-2 satellites images through super-resolution (SR). Currently, the most effective SR models are mainly based on deep learning, especially the generative adversarial network (GAN). Models based on GAN need to be trained on LR–HR image pairs. In this paper, a two-step super-resolution generative adversarial network (TS-SRGAN) model is proposed. The first step is having the GAN train the degraded models. Without supervised HR images, only the 10 m resolution images provided by Sentinel-2 satellites are used to generate the degraded images, which are in the same domain as the real LR images, and then to construct the near-natural LR–HR image pairs. The second step is to design a super-resolution generative adversarial network with strengthened perceptual features, to enhance the perceptual effects of the generated images. Through experiments, the proposed method obtained an average NIQE as low as 2.54, and outperformed state-of-the-art models according to other two NR-IQA metrics, such as BRISQUE and PIQE. At the same time, the comparison of the intuitive visual effects of the generated images also proved the effectiveness of TS-SRGAN.
Hiding and Extracting Important Information in Encrypted Images by Using the Transformation of All Possible Permutations and VQ Codebook
Authors: Chi Heng-Xiao , Chang Chin-Chen , Wang Xu , Lin Chia-Chen
Published: October 2022

Due to its applications in cloud computing, research on reversible data hiding in encrypted images (RDHEI) is becoming more and more important. This paper proposes a reversible data hiding scheme for encrypted images that utilizes an all-permutation technique to embed data into encrypted images. The proposed scheme follows a block-wise data hiding process. Message extraction and image restoration are performed by the receiver using the trained vector quantization (VQ) codebook. This scheme can provide a high embedding rate and reduce the hardware burden on the receiver.
Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming
Authors: Duan Yanliang , Gong Yanping , Yang Xiaohui , Cao Weiping
Published: October 2022

Adaptive beamforming can efficiently contract interference and noise. Due to high sensitivity of the beamformer to model mismatch, the capability of interference reduction will critically degrade when the signal model mismatch occurs, particularly when the sampling sequence contains the desired signal. For the purpose of enhancing the robustness of beamformers to signal model mismatch, we propose a new robust adaptive beamforming (RAB) method. Firstly, the precise steering vector (SV) associating with the desired signal is estimated by employing the minimum norm of subspace projection (MNSP) approach. Secondly, the nominal interference SVs are estimated via the maximum entropy power spectrum. Subsequently, the corrected interference SVs and powers are obtained by oblique projection. Finally, the interference-plus-noise covariance matrix (INCM) is reconstructed, and the proposed RAB is obtained. Multiple simulations are carried out and demonstrate the robustness of the proposed RAB method.