bluuebunny/crossref_metadata_2025 huggingface dataset:
Climatic Zone and Soil Properties Determine the Biodiversity of the Soil Bacterial Communities Associated to Native Plants from Desert Areas of North-Central Algeria
Authors: Bona Elisa , Massa Nadia , Toumatia Omrane , Novello Giorgia , Cesaro Patrizia , Todeschini Valeria , Boatti Lara , Mignone Flavio , Titouah Houda , Zitouni Abdelghani , Lingua Guido , Vuolo Francesco , Gamalero Elisa
Published: June 2021

Algeria is the largest country in Africa characterized by semi-arid and arid sites, located in the North, and hypersaline zones in the center and South of the country. Several autochthonous plants are well known as medicinal plants, having in common tolerance to aridity, drought and salinity. In their natural environment, they live with a great amount of microbial species that altogether are indicated as plant microbiota, while the plants are now viewed as a “holobiont”. In this work, the microbiota of the soil associated to the roots of fourteen economically relevant autochthonous plants from Algeria have been characterized by an innovative metagenomic approach with a dual purpose: (i) to deepen the knowledge of the arid and semi-arid environment and (ii) to characterize the composition of bacterial communities associated with indigenous plants with a strong economic/commercial interest, in order to make possible the improvement of their cultivation. The results presented in this work highlighted specific signatures which are mainly determined by climatic zone and soil properties more than by the plant species.
Investigation of Iron Ore Mineral Distribution Using Aero-Magnetic Exploration Techniques: Case Study at Pocheon, Korea
Authors: Kim Bona , Jeong Soocheol , Bang Eunseok , Shin Seungwook , Cho Seongjun
Published: June 2021

We present our aeromagnetic survey results from an investigation of the iron ore mineral distribution in Pocheon, Korea, in the west-central area of the Korean Peninsula. A manned aeromagnetic system using a helicopter for regional exploration and an unmanned aeromagnetic system using a multicopter for high-resolution exploration were used for the survey. The inversion results of the magnetic data confirmed the possibility of the existence of a new iron ore body. Drilling was carried out based on inversion results and drilling revealed amphibolite including iron ore, as indicated by a strong magnetic response. The position and depth of the iron ore were consistent with the interpretation results of the magnetic data.
Lead Isotope Analysis of Geological Native Copper: Implications for Archaeological Provenance Research in the North American Arctic and Subarctic
Authors: Cooper H. Kory , Simonetti Antonio
Published: June 2021

The Indigenous inhabitants of Arctic and Subarctic North America had been using native copper for several centuries prior to sustained interaction with Europeans beginning in the 18th century. The connection, if any, between the use of copper in these two adjacent regions is, at present, unclear. The ability to determine the source of native copper artifacts found in greater northwestern North America would inform on the movement of copper via trade and exchange between, and aid in understanding the innovation and diffusion of native copper metallurgy among, ancestral Dene and Inuit People. This paper provides the results of a Lead Isotope Analysis (LIA) pilot study examining Pb isotope ratios of native copper samples from multiple locations in the northern regions of North America. The results from this preliminary study indicate some overlap in Pb isotope ratios between Arctic and Subarctic sources of native copper, and these nonetheless record distinct isotope signatures relative to those associated with other North American native Cu deposits.
TrainAR: A Scalable Interaction Concept and Didactic Framework for Procedural Trainings Using Handheld Augmented Reality
Authors: Blattgerste Jonas , Luksch Kristina , Lewa Carmen , Pfeiffer Thies
Published: June 2021

The potential of Augmented Reality (AR) for educational and training purposes is well known. While large-scale deployments of head-mounted AR headsets remain challenging due to technical limitations and cost factors, advances in mobile devices and tracking solutions introduce handheld AR devices as a powerful, broadly available alternative, yet with some restrictions. One of the current limitations of AR training applications on handheld AR devices is that most offer rather static experiences, only providing descriptive knowledge with little interactivity. Holistic concepts for the coverage of procedural knowledge are largely missing. The contribution of this paper is twofold. We propose a scalabe interaction concept for handheld AR devices with an accompanied didactic framework for procedural training tasks called TrainAR. Then, we implement TrainAR for a training scenario in academics for the context of midwifery and explain the educational theories behind our framework and how to apply it for procedural training tasks. We evaluate and subsequently improve the concept based on three formative usability studies (n = 24), where explicitness, redundant feedback mechanisms and onboarding were identified as major success factors. Finally, we conclude by discussing derived implications for improvements and ongoing and future work.
Mechanical and Magnetic Properties of Double Layered Nanostructures of Tin and Zirconium Oxides Grown by Atomic Layer Deposition
Authors: Tamm Aile , Piirsoo Helle-Mai , Jõgiaas Taivo , Tarre Aivar , Link Joosep , Stern Raivo , Kukli Kaupo
Published: June 2021

Double layered stacks of ZrO2 and SnO2 films, aiming at the synthesis of thin magnetic and elastic material layers, were grown by atomic layer deposition to thicknesses in the range of 20–25 nm at 300 °C from ZrCl4, SnI4, H2O, and O3 as precursors. The as-deposited nanostructures consisted of a metastable tetragonal polymorph of ZrO2, and a stable tetragonal phase of SnO2, with complementary minor reflections from the orthorhombic polymorph of SnO2. The hardness and elastic modulus of the stacks depended on the order of the constituent oxide films, reaching 15 and 171 GPa, respectively, in the case of top SnO2 layers. Nonlinear saturative magnetization could be induced in the stacks with coercive fields up to 130 Oe.
Sensing Behavior of Metal-Free Porphyrin and Zinc Phthalocyanine Thin Film towards Xylene-Styrene and HCl Vapors in Planar Optical Waveguide
Authors: Kari Nuerguli , Zannotti Marco , Giovannetti Rita , Maimaiti Patigu , Nizamidin Patima , Abliz Shawket , Yimit Abliz
Published: June 2021

The sensing behavior of a thin film composed of metal-free 5, 10, 15, 20-tetrakis (p-hydroxy phenyl) porphyrin and zinc phthalocyanine complex towards m-xylene, styrene, and HCl vapors in a homemade planar optical waveguide (POWG), was studied at room temperature. The thin film was deposited on the surface of potassium ion-exchanged glass substrate, using vacuum spin-coating method, and a semiconductor laser light (532 nm) as the guiding light. Opto-chemical changes of the film exposing with hydrochloric gas, m-xylene, and styrene vapor, were analyzed firstly with UV-Vis spectroscopy. The fabricated POWG shows good correlation between gas exposure response and absorbance change within the gas concentration range 10–1500 ppm. The limit of detection calculated from the logarithmic calibration curve was proved to be 11.47, 21.08, and 14.07 ppm, for HCl gas, m-xylene, and styrene vapors, respectively. It is interesting to find that the film can be recovered to the initial state with trimethylamine vapors after m-xylene, styrene exposures as well as HCl exposure. The gas-film interaction mechanism was discussed considering protonation and π-π stacking with planar aromatic analyte molecules.
A Cylindrical Triode Ultrahigh Vacuum Ionization Gauge with a Carbon Nanotube Cathode
Authors: Zhang Jian , Wei Jianping , Li Detian , Zhang Huzhong , Wang Yongjun , Zhang Xiaobing
Published: June 2021

In this study, a cylindrical triode ultrahigh vacuum ionization gauge with a screen-printed carbon nanotube (CNT) electron source was developed, and its metrological performance in different gases was systematically investigated using an ultrahigh vacuum system. The resulting ionization gauge with a CNT cathode responded linearly to nitrogen, argon, and air pressures in the range from ~4.0 ± 1.0 × 10−7 to 6 × 10−4 Pa, which is the first reported CNT emitter-based ionization gauge whose lower limit of pressure measurement is lower than its hot cathode counterpart. In addition, the sensitivities of this novel gauge were ~0.05 Pa−1 for nitrogen, ~0.06 Pa−1 for argon, and ~0.04 Pa−1 for air, respectively. The trend of sensitivity with anode voltage, obtained by the experimental method, was roughly consistent with that gained through theoretical simulation. The advantages of the present sensor (including low power consumption for electron emissions, invisible to infrared light radiation and thermal radiation, high stability, etc.) mean that it has potential applications in space exploration.
Optimization of the Electrospun Niobium–Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology
Authors: Fatile Babajide Oluwagbenga , Pugh Martin , Medraj Mamoun
Published: June 2021

The present research aimed to investigate the effect of working parameters on the electrospinning of niobium–tungsten oxide nanofibers and optimize the process using central composite design (CCD) based on the response surface methodology (RSM). An experiment was designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Meanwhile, a second-order prediction model of nanofibers diameter was fitted and verified using analysis of variance (ANOVA). The results show that the diameter of the nanofibers was significantly influenced by all the variables except the flow rate. Some second-order and cross factor interactions such as VD, DP, PF, PN, and P2 also have significant effects on the diameter of the nanofibers. The results of the ANOVA yielded R2 and adjusted R2 values of 0.96 and 0.93 respectively, this affirmed that the predictive model fitted well with the experimental data. Furthermore, the process parameters were optimized using the CCD method and a maximum desirability function of 226 nm was achieved for the diameter of the nanofibers. This is very close to the 233 nm diameter obtained from a confirmatory experiment using the optimum conditions. Therefore, the model is representative of the process, and it could be used for future studies for the reduction of the diameter of electrospun nanofibers.
Cubic Nanoparticles for Magnetic Hyperthermia: Process Optimization and Potential Industrial Implementation
Authors: Sánchez Omar Sánchez , Castelo-Grande Teresa , Augusto Paulo A. , Compaña José M. , Barbosa Domingos
Published: June 2021

Cubic nanoparticles are referred to as the best shaped particles for magnetic hyperthermia applications. In this work, the best set of values for obtaining optimized shape and size of magnetic particles (namely: reagents quantities and proportions, type of solvents, temperature, etc.) is determined. A full industrial implementation study is also performed, including production system design and technical and economic viability.
Recent Advances in Lipid-Based Drug Delivery
Authors: Nishida Koyo
Published: June 2021

Here, I report recent advances in lipid-based drug delivery systems, with a focus on their production, controlled drug release, targeting, and co-delivery [...]
Particle-Scale Modeling to Understand Liquid Distribution in Twin-Screw Wet Granulation
Authors: Kumar Ashish , Radl Stefan , Gernaey Krist V. , De Beer Thomas , Nopens Ingmar
Published: June 2021

Experimental characterization of solid-liquid mixing for a high shear wet granulation process in a twin-screw granulator (TSG) is very challenging. This is due to the opacity of the multiphase system and high-speed processing. In this study, discrete element method (DEM) based simulations are performed for a short quasi-two-dimensional simulation domain, incorporating models for liquid bridge formation, rupture, and the effect of the bridges on inter-particular forces. Based on the knowledge gained from these simulations, the kneading section of a twin-screw wet granulation process was simulated. The time evolution of particle flow and liquid distribution between particles, leading to the formation of agglomerates, was analyzed. The study showed that agglomeration is a rather delayed process that takes place once the free liquid on the particle surface is well distributed.
Film-Forming Systems for Dermal Drug Delivery
Authors: Pünnel Larissa Carine , Lunter Dominique Jasmin
Published: June 2021

Film-forming formulations represent a novel form of sustained release dermatic products. They are applied to the skin as a liquid or semi-solid preparation. By evaporation of the volatile solvent on the skin, the polymer contained in the formulation forms a solid film. Various film-forming formulations were tested for their water and abrasion resistance and compared with conventional semi-solid formulations. Penetration and permeation studies of the formulations indicate a potential utility as transdermal therapeutic systems. They can be used as an alternative to patch systems to administer a variety of drugs in a topical way and may provide sustained release characteristics.
Review of Pharmacokinetics and Pharmacogenetics in Atypical Long-Acting Injectable Antipsychotics
Authors: Toja-Camba Francisco José , Gesto-Antelo Nerea , Maroñas Olalla , Echarri Arrieta Eduardo , Zarra-Ferro Irene , González-Barcia Miguel , Bandín-Vilar Enrique , Mangas Sanjuan Victor , Facal Fernando , Arrojo Romero Manuel , Carracedo Angel , Mondelo-García Cristina , Fernández-Ferreiro Anxo
Published: June 2021

Over the last two decades, pharmacogenetics and pharmacokinetics have been increasingly used in clinical practice in Psychiatry due to the high variability regarding response and side effects of antipsychotic drugs. Specifically, long-acting injectable (LAI) antipsychotics have different pharmacokinetic profile than oral formulations due to their sustained release characteristics. In addition, most of these drugs are metabolized by CYP2D6, whose interindividual genetic variability results in different metabolizer status and, consequently, into different plasma concentrations of the drugs. In this context, there is consistent evidence which supports the use of therapeutic drug monitoring (TDM) along with pharmacogenetic tests to improve safety and efficacy of antipsychotic pharmacotherapy. This comprehensive review aims to compile all the available pharmacokinetic and pharmacogenetic data regarding the three major LAI atypical antipsychotics: risperidone, paliperidone and aripiprazole. On the one hand, CYP2D6 metabolizer status influences the pharmacokinetics of LAI aripiprazole, but this relation remains a matter of debate for LAI risperidone and LAI paliperidone. On the other hand, developed population pharmacokinetic (popPK) models showed the influence of body weight or administration site on the pharmacokinetics of these LAI antipsychotics. The combination of pharmacogenetics and pharmacokinetics (including popPK models) leads to a personalized antipsychotic therapy. In this sense, the optimization of these treatments improves the benefit–risk balance and, consequently, patients’ quality of life.
Photocatalytic Degradation of Malachite Green Dye from Aqueous Solution Using Environmentally Compatible Ag/ZnO Polymeric Nanofibers
Authors: Elkady Marwa F. , Hassan Hassan Shokry
Published: June 2021

An efficient, environmentally compatible and highly porous, silver surface-modified photocatalytic zinc oxide/cellulose acetate/ polypyrrole ZnO/CA/Ppy hybrid nanofibers matrix was fabricated using an electrospinning technique. Electrospinning parameters such as solution flow rate, applied voltage and the distance between needles to collector were optimized. The optimum homogenous and uniform ZnO/CA/Ppy polymeric composite nanofiber was fabricated through the dispersion of 0.05% wt ZnO into the dissolved hybrid polymeric solution with an average nanofiber diameter ranged between 125 and 170 nm. The fabricated ZnO-polymeric nanofiber was further surface-immobilized with silver nanoparticles to enhance its photocatalytic activity through the reduction of the nanofiber bandgap. A comparative study between ZnO polymeric nanofiber before and after silver immobilization was investigated using X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) and thermal gravimetric analysis (TGA). The photocatalytic degradation efficiency of the two different prepared nanofibers before and after nanosilver immobilization for malachite green (MG) dye was compared against various experimental parameters. The optimum degradation efficiency of nanosilver surface-modified ZnO-polymeric nanofibers was recorded as 93.5% for malachite green dye after 1 h compared with 63% for ZnO-polymeric nanofibers.
Impact of Macrodiols on the Morphological Behavior of H12MDI/HDO-Based Polyurethane Elastomer
Authors: Naheed Shazia , Zuber Mohammad , Salman Mahwish , Rasool Nasir , Siddique Zumaira , Shaik Mohammed Rafi , Sharaf Mohammed A. F. , Abdelgawad Abdelatty , Sekou Doumbia , Awwad Emad Mahrous
Published: June 2021

In this study, we evaluated the morphological behavior of polyurethane elastomers (PUEs) by modifying the soft segment chain length. This was achieved by increasing the soft segment molecular weight (Mn = 400–4000 gmol−1). In this regard, polycaprolactone diol (PCL) was selected as the soft segment, and 4,4′-cyclohexamethylene diisocyanate (H12MDI) and 1,6-hexanediol (HDO) were chosen as the hard segments. The films were prepared by curing polymer on Teflon surfaces. Fourier transform infrared spectroscopy (FTIR) was utilized for functional group identification in the prepared elastomers. FTIR peaks indicated the disappearance of −NCO and −OH groups and the formation of urethane (NHCOO) groups. The morphological behavior of the synthesized polymer samples was also elucidated using scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. The AFM and SEM results indicated that the extent of microphase separation was enhanced by an increase in the molecular weight of PCL. The phase separation and degree of crystallinity of the soft and hard segments were described using X-ray diffraction (XRD). It was observed that the degree of crystallinity of the synthesized polymers increased with an increase in the soft segment’s chain length. To evaluate hydrophilicity/hydrophobicity, the contact angle was measured. A gradual increase in the contact angle with distilled water and diiodomethane (38.6°–54.9°) test liquids was observed. Moreover, the decrease in surface energy (46.95–24.45 mN/m) was also found to be inconsistent by increasing the molecular weight of polyols.
Investigation of Flexible Arrayed Lactate Biosensor Based on Copper Doped Zinc Oxide Films Modified by Iron–Platinum Nanoparticles
Authors: Nien Yu-Hsun , Kang Zhi-Xuan , Su Tzu-Yu , Ho Chih-Sung , Chou Jung-Chuan , Lai Chih-Hsien , Kuo Po-Yu , Lai Tsu-Yang , Dong Zhe-Xin , Chen Yung-Yu , Huang Yu-Hao
Published: June 2021

Potentiometric biosensors based on flexible arrayed silver paste electrode and copper-doped zinc oxide sensing film modified by iron-platinum nanoparticles (FePt NPs) are designed and manufactured to detect lactate in human. The sensing film is made of copper-doped zinc oxide (CZO) by a radio frequency (RF) sputtering system, and then modified by iron-platinum nanoparticles (FePt NPs). The surface morphology of copper-doped zinc oxide (CZO) is analyzed by scanning electron microscope (SEM). FePt NPs are analyzed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The average sensitivity, response time, and interference effect of the lactate biosensors are analyzed by voltage-time (V-T) measurement system. The electrochemical impedance is analyzed by electrochemical impedance spectroscopy (EIS). The average sensitivity and linearity over the concentration range 0.2–5 mM are 25.32 mV/mM and 0.977 mV/mM, respectively. The response time of the lactate biosensor is 16 s, with excellent selectivity.
Late Quaternary Stratigraphy and Geochronology of the Spring Creek Drainage along the Southern High Plains Eastern Escarpment, Northwest Texas
Authors: Johnson Eileen , Hurst Stance , Moretti John A.
Published: June 2021

The eastern escarpment breaks of the Southern High Plains of Texas are both a geomorphic and ecotonal transition zone from the high plains surface to the Rolling Plains below. The geoarchaeological record on the Southern High Plains surface is well documented, but few studies have investigated the sediments, soils, and geochronology of the eastern escarpment. The current investigation has targeted the discontinuous remnants of Late Quaternary deposits within Spring Creek, a tributary within the upper Brazos River basin. A total of 19 profiles, core, and isolated exposure locations placed along a transect from Macy Fork through upper Spring Creek and 40 radiocarbon ages provide a composite sequence and geochronology that also documents the Late Pleistocene to Late Holocene paleoenvironments of this drainage. The resulting record illustrates a series of major changes in sediments and local habitats over the past ~11,550 radiocarbon years (13,469–13,390 calendar years), characterized primarily by reductions in available water and increasing aridity that peaked during the middle Holocene. This sequence provides significant context to an expanding record of Late Pleistocene to middle Holocene biota and cultures. Subsequent downcutting of the drainage post-6000 14C yr B.P. (6988–6744 calendar years) removed large sections of the depositional sequence. Local topography within Spring Creek drainage greatly impacted the preservation of these deposits. The remaining record provides some different insights than those available from the Southern High Plains record.
Polarization but Not Pillarization Catholicism and Cultural Change in Post-Transformation Poland
Authors: Sadlon Wojciech
Published: June 2021

The modern Polish cultural system emerges through the interplay between social structure and agency, which represent different properties and powers or, more precisely, ‘independent properties, capable of exerting autonomous influences.’ I argue that Polish Catholicism entered the transformation process to some extent as an agential force which shaped progressive social change and delivered ‘energy’ for the transformation of Polish society. However, the process of transformation has changed the public position of the Catholic Church as it is no longer an actor within a dominating social movement that thrived in the quest for freedom and human rights. Catholicism that has played a role in shaping social conflicts, still represents to some degree ‘a public good’. That is why, despite strong political polarization in Poland, there is no context for ‘pillarization’ within Polish society. The situation of Catholicism in the modern Polish social configuration should be described using the category of ‘polarization’ rather than ‘pillarization’. My study of Polish Catholicism confirms that social polarization in Poland is not a bottom-up process but rather, is driven from the top down as a cultural process.
Correcting Acedia through Wonder and Gratitude
Authors: Dahm Brandon
Published: June 2021

In the capital vices tradition, acedia was fought through perseverance and manual labor. In this paper, I argue that we can also fight acedia through practicing wonder and gratitude. I show this through an account of moral formation developed out of the insight of the virtues and vices traditions that character traits affect how we see things. In the first section, I use Robert Roberts’s account of emotions to explain a mechanism by which virtues and vices affect vision and thus moral formation. Then, by looking at the capital vices tradition, I argue that restless boredom is a primary construal of the vice of acedia. Third, I explain wonder and gratitude through the work of G.K. Chesterton and Roberts, respectively. In light of their accounts, I explain how the construals of wonder and gratitude are contrary to the construal of acedia. Finally, I offer some practices that encourage gratitude and wonder.
Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection
Authors: Zhou Quan , Huang Wenqian , Liang Dong , Tian Xi
Published: June 2021

A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100–2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky–Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels.
Review: Development and Technical Design of Tangible User Interfaces in Wide-Field Areas of Application
Authors: Krestanova Alice , Cerny Martin , Augustynek Martin
Published: June 2021

A tangible user interface or TUI connects physical objects and digital interfaces. It is more interactive and interesting for users than a classic graphic user interface. This article presents a descriptive overview of TUI’s real-world applications sorted into ten main application areas—teaching of traditional subjects, medicine and psychology, programming, database development, music and arts, modeling of 3D objects, modeling in architecture, literature and storytelling, adjustable TUI solutions, and commercial TUI smart toys. The paper focuses on TUI’s technical solutions and a description of technical constructions that influences the applicability of TUIs in the real world. Based on the review, the technical concept was divided into two main approaches: the sensory technical concept and technology based on a computer vision algorithm. The sensory technical concept is processed to use wireless technology, sensors, and feedback possibilities in TUI applications. The image processing approach is processed to a marker and markerless approach for object recognition, the use of cameras, and the use of computer vision platforms for TUI applications.
Impact of Patellar Tendinopathy on Isokinetic Knee Strength and Jumps in Professional Basketball Players
Authors: Dauty Marc , Menu Pierre , Mesland Olivier , Louguet Bastien , Fouasson-Chailloux Alban
Published: June 2021

Patellar tendinopathy is characterized by tendon pain which may reduce the level of performance. This study’s main aim was to compare isokinetic knee strength and jump performances at the start of the sport season between players with patellar tendinopathy and those without. Secondary aims were to assess the relationship between knee strength and jump function. Sixty-two professional basketball players were enrolled (mean age: 25.0 ± 4.0). All players performed knee isokinetic measurements, single leg countermovement jumps, and one leg hop tests. Correlations between knee strength and jump performances were examined. Twenty-four players declared a patellar tendinopathy and were compared to the 38 players without tendinopathy. The isokinetic quadriceps strength was lower in cases of patellar tendinopathy, and a camel’s back curve was observed in 58% of the cases of patellar tendinopathy. However, jump performances were preserved. No link was found between quadriceps and hamstring limb symmetry indexes at 60 and 180°/s with jumps. This preseason screening enabled us to identify the absence of consequences of patellar tendinopathy in professional basketball players. Jump performances were not altered, possibly due to compensatory strategies.
A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks
Authors: Oyekan John , Hutabarat Windo , Turner Christopher , Tiwari Ashutosh , He Hongmei , Gompelman Raymon
Published: June 2021

Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.
Fiber Bragg Grating Sensors for Underwater Vibration Measurement: Potential Hydropower Applications
Authors: de la Torre Oscar , Floris Ignazio , Sales Salvador , Escaler Xavier
Published: June 2021

The present paper assesses the performance and characteristics of fiber Bragg grating sensors, with a special interest in their applications in hydraulic machinery and systems. The hydropower industry is turning to this technology with high expectations of obtaining high quality data to validate and calibrate numerical models that could be used as digital twins of key assets, further strengthening the sector’s relevant position within industry 4.0. Prior to any validation, fiber Bragg grating sensors’ ability to perform well underwater for long periods of time with minimal degradation, and their ease of scalability, drew the authors´ attention. A simplified modal analysis of a partially submerged beam is proposed here as a first step to validate the potential of this type of technology for hydropower applications. Fiber Bragg grating sensors are used to obtain the beam’s natural frequencies and to damp vibrations under different conditions. The results are compared with more established waterproof electric strain gauges and a laser vibrometer with good agreement. The presence of several sensors in a single fiber ensures high spatial resolution, fundamental to precisely determine vibration patterns, which is a main concern in this industry. In this work, the beam’s vibration patterns have been successfully captured under different excitations and conditions.
Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding
Authors: Xia Kaishu , Saidy Clint , Kirkpatrick Max , Anumbe Noble , Sheth Amit , Harik Ramy
Published: June 2021

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.
MEG Source Localization via Deep Learning
Authors: Pantazis Dimitrios , Adler Amir
Published: June 2021

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
A Saliency-Based Sparse Representation Method for Point Cloud Simplification
Authors: Leal Esmeide , Sanchez-Torres German , Branch-Bedoya John W. , Abad Francisco , Leal Nallig
Published: June 2021

High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.
A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process
Authors: Shi Xin , Huang Gaolu , Hao Xiaochen , Yang Yue , Li Ze
Published: June 2021

The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to the deficiency of the coupling relationship of the two indicators while forecasting separately. However, the time lags, coupling, and uncertainties of production variables lead to the difficulty of multi-indicator synchronous prediction. In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multi-channel structure was designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction. The experimental results implemented by the actual raw data of the cement plant demonstrate that the proposed MWMC-CNN structure has a better performance than without the combination structure of the moving window multi-channel with convolutional neural network.
Analysis of Autoencoders for Network Intrusion Detection
Authors: Song Youngrok , Hyun Sangwon , Cheong Yun-Gyung
Published: June 2021

As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e., zero-day attacks) and also alleviate the burden of the laborious labeling task. Although the autoencoders are effective in detecting unknown types of attacks, it takes tremendous time and effort to find the optimal model architecture and hyperparameter settings of the autoencoders that result in the best detection performance. This can be an obstacle that hinders practical applications of autoencoder-based NIDS. To address this challenge, we rigorously study autoencoders using the benchmark datasets, NSL-KDD, IoTID20, and N-BaIoT. We evaluate multiple combinations of different model structures and latent sizes, using a simple autoencoder model. The results indicate that the latent size of an autoencoder model can have a significant impact on the IDS performance.
Monitoring Local Earthquakes in Central Italy Using 4C Single Station Data
Authors: Simonelli Andreino , Desiderio Matteo , Govoni Aladino , De Luca Gaetano , Di Virgilio Angela
Published: June 2021

In this study, performed on a set of twenty-two earthquakes that occurred in central Italy between 2019 and 2020, we will explore the possibility to locate the hypocenter of local events by using a ring laser gyroscope observing the vertical ground rotation and a standard broadband seismometer. A picking algorithm exploiting the four components (4C) polarization properties of the wavefield is used to identify the first shear onset transversely polarized (SH). The wavefield direction is estimated by correlation between the vertical rotation rate and the transverse acceleration. The picked times for Pg and Sg onsets are compared to the ones obtained after manual revision on the GIGS station seismometer. The results are compared with the location provided by the national monitoring service of the INGV.
A System for Monitoring of Broadband FMR Phenomenon in Low-Carbon Steel Films Subjected to Deformations
Authors: Psuj Grzegorz , Lopato Przemyslaw , Maciusowicz Michal , Herbko Michal
Published: June 2021

Stresses and deformations are some of the main factors influencing the mechanical and magnetic properties of steels. Resonance methods, based on the utilization of high-frequency electromagnetic fields, are the ones that can provide information about the course of the magnetization process. Moreover, according to skin effect, these methods may show sensitivity to surface deformations of the examined materials as well. As a rule, however, they are used to study the properties of materials of very limited sizes. This paper presents an approach in which a system based on the ferromagnetic resonance method FMR was considered for monitoring changes of characteristics related to magnetization dynamics of steel elements subjected to deformations. First, a solution was proposed, and then a numerical analysis, as well as a construction of the system, were presented. During the study, the possibility of carrying out measurements in a wide range of electromagnetic field conditions, enabling local inspection on structures, was also analysed. The system operation was verified using a set of samples made of low carbon steel film, representing distinct states of deformation. The obtained results make it possible to clearly distinguish changes in magnetic conditions, pointing to changes in the resultant magnetic anisotropy caused by the straining process.
A Comprehensive Spectroscopic Analysis of the Ibuprofen Binding with Human Serum Albumin, Part II
Authors: Ploch-Jankowska Anna , Pentak Danuta , Nycz Jacek E.
Published: June 2021

Human serum albumin (HSA) is the most abundant human plasma protein. HSA plays a crucial role in many binding endos- and exogenous substances, which affects their pharmacological effect. The innovative aspect of the study is not only the interaction of fatted (HSA) and defatted (dHSA) human serum albumin with ibuprofen (IBU), but the analysis of the influence of temperature on the structural modifications of albumin and the interaction between the drug and proteins from the temperature characteristic of near hypothermia (308 K) to the temperature reflecting inflammation in the body (312 K and 314 K). Ibuprofen is a non-steroidal anti-inflammatory drug. IBU is used to relieve acute pain, inflammation, and fever. To determine ibuprofen’s binding site in the tertiary structure of HSA and dHSA, fluorescence spectroscopy was used. On its basis, the fluorescent emissive spectra of albumin (5 × 10−6 mol/dm3) without and with the presence of ibuprofen (1 × 10−5–1 × 10−4 mol/dm3) was recorded. The IBU-HSA complex’s fluorescence was excited by radiation of wavelengths of λex 275 nm and λex 295 nm. Spectrophotometric spectroscopy allowed for recording the absorbance spectra (zero-order and second derivative absorption spectra) of HSA and dHSA under the influence of ibuprofen (1 × 10−4 mol/dm3). To characterize the changes of albumin structure the presence of IBU, circular dichroism was used. The data obtained show that the presence of fatty acids and human serum albumin temperature influences the strength and type of interaction between serum albumin and drug. Ibuprofen binds more strongly to defatted human serum albumin than to albumin in the presence of fatty acids. Additionally, stronger complexes are formed with increasing temperatures. The competitive binding of ibuprofen and fatty acids to albumin may influence the concentration of free drug fraction and thus its therapeutic effect.
Diversity as Immigration Governmentality: Insights from France
Authors: Doytcheva Milena
Published: June 2021

This article aims to examine the ambiguous connections between immigration, diversity politics, and white supremacy in twenty-first century France by considering them both theoretically and empirically. It offers to elucidate the ways in which the recent growth and expansion of the diversity framework in Europe and France have gone hand in hand with the unfolding of particularly repressive migration policies, hostility towards migrants, and outright institutional racism. Drawing on qualitative longitudinal data on corporate diversity policies, based on semi-structured interviews (n = 86), the article also relies on secondary data analysis from other policy domains (migration, education, urban development), favoring a globally comparative lens. First, I engage with some major trends of the recent reinvention of diversity at the EU level, underscoring the ambiguous effects of Europeanizing antiracism and nondiscrimination in a reverse sequence; second, I critically revisit the ways in which this European reinvention, combined with the legal universalization of equal opportunity, has given rise to the articulation of “white diversity” conceptions; then I explore their even more problematic nexus with governing migration. Finally, I call for a critical scrutiny of how universalized and thoroughly individualized notions of diversification may emerge as instrumental in upholding hegemonic whiteness, in the fields of race relations as well as international migration.
Assessing the Impact of Nonprofit Organizations on Multi-Actor Global Governance Initiatives: The Case of the UN Global Compact
Authors: Hengevoss Alice
Published: June 2021

This study empirically assesses the impact of nonprofit organizations (NPOs) on multi-actor global governance initiatives. Multi-actor global governance initiatives have emerged to strengthen joint action among different societal actors to tackle transnational social and environmental issues. While such initiatives have received a great deal of academic attention, previous research has primarily focused on businesses’ perspectives. In light of the important role of NPOs within such initiatives, critically addressing NPOs’ role by assessing their impact on the effectiveness of such initiatives is crucial. This article builds on the United Nations Global Compact (UNGC)—the largest multi-actor global governance initiative in the world—and offers a panel analysis on a unique dataset including 820 NPOs from 68 different countries. The findings suggest that NPOs have indeed strengthened the UNGC over time, yet their engagement explains only a small fraction of differences in UNGC activity across countries. This study contributes to the emerging research on nonprofits’ social responsibility by fostering the actorhood thesis, which places higher responsibility for the impact and requirements for accountability on NPOs. Furthermore, the study supports discussions about the increasing political role of NPOs by providing the first empirical evidence for their political leadership and impact in multi-actor global governance initiatives.