bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Stressing Conditions in a High Energy Ball Mill by Discrete Element Simulations
Authors: Burmeister Christine Friederike , Hofer Moritz , Molaiyan Palanivel , Michalowski Peter , Kwade Arno
Published: April 2022

The synthesis of sulfide solid electrolytes in ball mills by mechanochemical routes not only is efficient but also can enable the upscaling of material synthesis as required for the commercialization of solid-state battery materials. On a laboratory scale, the Emax high energy ball mill accounts for high stresses and power densities, as well as for temperature control, to prevent damage to the material and equipment even for long process times. To overcome the merely phenomenological treatment, we characterized the milling process in an Emax by DEM simulations, using the sulfide solid electrolyte LPS as a model material for the calibration of input parameters to the DEM, and compared it to a planetary ball mill for a selected parameter set. We derived mechanistic model equations for the stressing conditions depending on the operation parameters of rotational speed, media size and filling ratio. The stressing conditions are of importance as they determine the outcome of the mechanochemical milling process, thus forming the basis for evaluating and interpreting experiments and for establishing scaling rules for the process transfer to larger mills.
Was This Supposed to Be on the Test? Academic Leadership, Gender and the COVID-19 Pandemic in Denmark, Hungary, Romania, and United Kingdom
Authors: Constantinescu Sorana-Alexandra , Pozsar Maria-Henriete
Published: April 2022

Recent developments in workplace dynamics have made us even more aware of the importance of gender representation in all work-related decisions. Working from home during the pandemic, a decision that was generally the norm for European universities, forced us to rethink what are the main priorities when addressing the different needs of academic workers. The present paper tackles this overall issue from the perspective of gender representation, looking at the gender composition of the leadership structures of universities and their policy responses to employee needs. All the state-accredited universities in the following countries were included in the analysis: Romania, Denmark, Hungary and UK. These countries were chosen for the diversity in the state of their gender politics and in their overall quality of higher education. Primary results show not only that gender equality within academic leadership lags behind, but also that this lag may be associated with a poorer policy response to challenges typically faced by women during the COVID-19 pandemic.
Fast Neutron Scintillator Screens for Neutron Imaging Using a Layered Polymer-Phosphor Architecture
Authors: Chuirazzi William , Craft Aaron , Schillinger Burkhard , Mendoza Jesus , Cool Steven , Losko Adrian
Published: April 2022

Fast neutrons enable a nondestructive examination of dense, large, and highly attenuating samples due to their lower interaction probability compared to thermal neutrons. However, this also creates a challenge in fast neutron imaging, as the thicker sensors necessary to detect fast neutrons degrade an image’s spatial resolution due to scattering within the sensor and the indeterminate depth of interaction in the sensor. This work explores the advantages of a fast neutron imaging screen with a layered polymer-phosphor screen approach as opposed to a mixed polymer-phosphor screen typically used in fast neutron imaging. Proton recoil is the primary conversion mechanism for fast neutron imaging. Simulations showed that the recoil proton range of typical fast neutrons is approximately 200 µm, however, tests at Idaho National Laboratory revealed that the light output of these screens increased at much greater polymer thicknesses. The NECTAR fast neutron beamline at FRM II was used to test the imaging performance of layered fast neutron imaging screens. Distinguishing between the fast-neutron and γ-ray signals is a major challenge in fast neutron imaging because all fast neutron sources also produce γ-rays. A relative comparison between a control plate and the fast neutron screen was made to distinguish between a γ-ray and fast neutron signals. MCNP modeling quantified the γ-ray and fast neutron contributions to the images measured at NECTAR, which were approximately a 75% γ-ray image.
Potential of Alternative Organic Binders in Briquetting and Enhancing Residue Recycling in the Steel Industry
Authors: Mousa Elsayed , Ahmed Hesham , Söderström Daniel
Published: April 2022

Steel production generates various types of residues that cannot be directly recycled in the production process without pre-treatment and agglomeration. In the present study, recipes were designed to develop briquettes in a blast furnace (BF) with the partial replacement of cement with alternative commercial organic binders, including molasses–lime, bitumen, keracoal, carboxymethyl cellulose, and wood tar. The briquettes were produced using a technical-scale vibrating machine and the mechanical strength was evaluated using drop test and standard tumbler index results. The reduction behaviour was investigated by thermogravimetric analysis (TGA) coupled with QMS. A heat and mass balance model (MASMOD) was used to evaluate the potential of developed briquettes to reduce the energy consumption and CO2 emissions from the BF. Although cement was superior in developing mechanical strength, bitumen was the best among the other alternative organic binders and provided sufficient strength to the briquettes at 2.0% addition, which corresponded to 18.2% replacement of total cement. The briquettes containing bitumen possessed a higher reduction rate and lower activation energy compared to cement. The MASMOD calculation demonstrated that the developed briquettes have the potential to provide annual savings of 15,000–45,000 tons of lump coke, 4500–19,500 tons of CO2 emissions, and 5000–20,000 tons of limestone in Swedish BFs.
Beyond Theological Aesthetics: Aesthetic Theology
Authors: Castro Sixto J.
Published: April 2022

In this text, I explore what I have termed “aesthetic theology”. After noting the transference of religious content, function, etc., to art in Modernity, an act that has made art a locus theologicus once again, I analyse one of the main consequences of this phenomenon: art is progressively being considered through what was once purely theological categories, thus giving rise to aesthetic theology. The implication is that some of the solutions that have arisen from theological debate might be useful in the philosophy of art. I also suggest that aesthetic theology can provide theology with a generalized way of reasoning based on aesthetic judgments—judgements formed by postulated consensus instead of forced judgements formed on conceptual grounds. I defend that the formulation of religious judgement has always been of the former sort, such that aesthetic theology may prove itself a useful tool for theologians in developing their thinking about, depiction of, representing, or approaching God.
The Impact of Health Impairment on Optimal Annuitization for Retirees
Authors: Asmuni Nurin Haniah , Tan Ken Seng , Purcal Sachi
Published: April 2022

Post retirement, annuities provide a steady stream of income for retirees. However, the annuitization rate is relatively small in the insurance market in many countries around the world. Prior studies have shown that a substandard health status in retirement reduces annuitization due to adverse selection. Recent innovation introduces an enhanced annuity plan where individuals with impaired health are entitled to higher annuity payments. However, this market is less explored in countries other than the UK. This paper aims to study the optimal annuitization rate where both standard and substandard annuity rates are offered in the market. The life cycle model in this paper incorporates multiple health states based on the likelihood of events and quality of life measures. Our framework consists of two important parts. First, we estimate the transition probabilities of all health states in our Markov model using reliable national data. Second, we derive the optimal consumption and annuitization solution to maximize a retiree’s expected lifetime utility given the uncertainty of future health risk. In addition, we also consider the bequest motive in our optimization problem. Our results show that the optimal annuitization is driven by the choice of bequest and risk-aversion parameters, as well as the health status of the annuitant. Whilst the health-dependent utility parameter only affects our results for certain cases.
High-Precision Measurement of Height Differences from Shadows in Non-Stereo Imagery: New Methodology and Accuracy Assessment
Authors: Rada Giacaman Camilo Andrés
Published: April 2022

The shadow-height method has been extensively used to extract the heights of buildings from the shadows they cast in non-stereo (single view) aerial and satellite imagery. However, the use of this method in Earth sciences has been limited, partially due to the relatively low accuracy reported, the fuzziness of shadow edges, the complexities of the scanning sensors, and a lack of software tools. In this paper, we present an enhanced shadow-height methodology offering significant accuracy improvement. These improvements are mainly the result of using a physical approach to model the illumination gradient through the edge of shadows and by leveraging meteorological data to precisely estimate atmospheric refraction. We validated 91 shadow-derived height estimations from images obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at three sites with latitudes between 33 and 78°S: The Andes Mountains, Sentinel Range, and Abbot ice shelf. Reference measurements were obtained from Global Navigation Satellite System (GNSS) surveys and the Ice, Cloud, and land Elevation Satellite (ICESat). The observed errors fell below 6% for small height differences (∼20 m) and below 2% for larger height differences (≳300 m). Our validation data cover solar elevations ranging from 3.7 to 42.2°, and we observed smaller absolute errors at lower solar elevations. This novel information can be valuable for studying surface elevation changes in present and old imagery and extending glacier volume variation time-series.
3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data
Authors: Bornik Alexander , Neubauer Wolfgang
Published: April 2022

The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeological interpretation of the resulting large 2D and 3D datasets, and related data from aerial archaeology or airborne remote sensing, etc., is a time-consuming and complex process, which requires the integration of all data at hand, respective three-dimensional imagination, and a broad understanding of the archaeological problem; therefore, informative 3D visualizations supporting the exploration of complex 3D datasets and supporting the interpretative process are in great demand. This paper presents a novel integrated 3D GPR interpretation approach, centered around the flexible 3D visualization of heterogeneous data, which supports conjoint visualization of scenes composed of GPR volumes, 2D prospection imagery, and 3D interpretative models. We found that the flexible visual combination of the original 3D GPR datasets and images derived from the data applying post-processing techniques inspired by medical image analysis and seismic data processing contribute to the perceptibility of archaeologically relevant features and their respective context within a stratified volume. Moreover, such visualizations support the interpreting archaeologists in their development of a deeper understanding of the complex datasets as a starting point for and throughout the implemented interactive interpretative process.
Hyperspectral Image Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Authors: Fang Bei , Liu Yu , Zhang Haokui , He Juhou
Published: April 2022

Hyperspectral image (HSI) classification has been marked by exceptional progress in recent years. Much of this progess has come from advances in convolutional neural networks (CNNs). Different from the RGB images, HSI images are captured by various remote sensors with different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus the model is prone to overfitting when using deep CNNs. In this paper, we first propose a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, the 3D convolution layer of AINet is replaced with two asymmetric inception units, i.e., a space inception unit and spectrum inception unit, to convey and classify the features effectively. In addition, we exploited a data-fusion transfer learning strategy to improve model initialization and classification performance. Extensive experiments show that the proposed approach outperforms all of the state-of-the-art methods via several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center (KSC).
Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels
Authors: Fan Mengzhuo , Liao Kuo , Lu Dengsheng , Li Dengqiu
Published: April 2022

Examining the characteristics of vegetation change and associated spatial patterns under different protection levels can provide a scientific basis for national park protection and management. Based on the dense time-series Landsat enhanced vegetation index (EVI) data between 1986 and 2020, we utilized the Wild Binary Segmentation (WBS) approach to detect spatial and temporal characteristics of abrupt, gradual, and total changes in Wuyishan National Park. The differences in vegetation change in three protection-level areas (strictly protected [Prots], generally protected [Prot], and non-protected [NP]) were examined, and the contributions to their spatial patterns were evaluated through Geodetector. The results showed the following: (1) The highest percentage of area without abrupt change was in Prots (39.89%), and the lowest percentage was in NP (17.44%). The percentage of abrupt change frequency (larger than three times) increased from 4.40% to 9.10% and 12.49% with the decreases in protection. The significance test showed that the difference in changed frequencies was not significant among these regions, but the interannual variation of abrupt change in Prots was significantly different from other areas. (2) The vegetation coverage of the Wuyishan National Park generally improved. The total EVI change (TEVI) showed that the positive percentage of Prots and Prot was 90.43% and 91.71%, respectively, slightly higher than that of NP (88.44%). However, the mean greenness change of NP was higher than that of Prots and Prot. (3) The park’s EVI spatial pattern in 1986 was the strongest factor determining the EVI spatial pattern in 2020; the explanatory power reduced as the protection level decreased. The explanation power (q value) of abrupt vegetation change was lower and increased as the protection level decreased. The interaction detection showed that EVI1986 and TEVI had the strongest explanatory powers, but the explanatory ability gradually weakened from 0.713 to 0.672 to 0.581 in Prots, Prot, and NP, respectively. This study provided a systematic analysis of vegetation changes and their impacts on spatial patterns.
Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction
Authors: Yoshii Tatsuki , Matsumura Naoto , Lin Chinsu
Published: April 2022

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.
Review of Recent Bio-Inspired Design and Manufacturing of Whisker Tactile Sensors
Authors: Sayegh Mohamad-Ammar , Daraghma Hammam , Mekid Samir , Bashmal Salem
Published: April 2022

Whisker sensors are a class of tactile sensors that have recently attracted attention. Inspired by mammals’ whiskers known as mystacial vibrissae, they have displayed tremendous potential in a variety of applications e.g., robotics, underwater vehicles, minimally invasive surgeries, and leak detection. This paper provides a supplement to the recent tactile sensing techniques’ designs of whiskers that only sense at their base, as well as the materials employed, and manufacturing techniques. The article delves into the technical specifications of these sensors, such as the resolution, measurement range, sensitivity, durability, and recovery time, which determine their performance. The sensors’ sensitivity varies depending on the measured physical quantity; for example, the pressure sensors had an intermediate sensitivity of 58%/Pa and a response time of around 90 ms, whereas the force sensors that function based on piezoelectric effects exhibited good linearity in the measurements with a resolution of 3 µN and sensitivity of 0.1682 mV/µN. Some sensors were used to perform spatial mapping and the identification of the geometry and roughness of objects with a reported resolution of 25 nm. The durability and recovery time showed a wide range of values, with the maximum durability being 10,000 cycles and the shortest recovery time being 5 ms. Furthermore, the paper examines the fabrication of whiskers at the micro- and nanoscales, as well as their contributions to mechanical and thermal behavior. The commonly used manufacturing techniques of 3D printing, PDMS casting, and screen printing were used in addition to several micro and nanofabrication techniques such as photolithography, etching, and chemical vapor deposition. Lastly, the paper discusses the main potential applications of these sensors and potential research gaps in this field. In particular, the operation of whisker sensors under high temperatures or high pressure requires further investigation, as does the design of sensors to explore larger topologies.
Miniaturized Bandpass Filter Using a Combination of T–Shaped Folded SIR Short Loaded Stubs
Authors: Yoon Kicheol , Kim Kwang Gi , Lee Tae-Hyeon
Published: April 2022

The consumption of multimedia content is ubiquitous in modern society. This is made possible by wireless local area networks (W–LAN) or wire service systems. Bandpass filters (BPF) have become very popular as they solve certain data transmission limitations allowing users to obtain reliable access to their multimedia content. The BPFs with quarter–wavelength short stubs can achieve performance; however, these BPFs are bulky. In this article, we propose a compact BPF with a T–shaped stepped impedance resonator (SIR) transmission line and a folded SIR structure. The proposed BPF uses a T–shaped SIR connected to a J–inverter structure (transmission line); this T–shaped SIR structure is used to replace the λg/4 transmission line seen in conventional stub BPFs. In addition, a folded SIR is added to the short stubs seen in conventional stub BPFs. This approach allows us to significantly reduce the size of the BPF. The advantage of a BPF is its very small size, low insertion loss, and wide bandwidth. The overall size of the new BPF is 2.44 mm × 1.49 mm (0.068λg × 0.059λg). The proposed BPF can be mass produced using semiconductors due to its planar structure. This design has the potential to be widely used in various areas including military, medical, and industrial systems.
Deep Prior Approach for Room Impulse Response Reconstruction
Authors: Pezzoli Mirco , Perini Davide , Bernardini Alberto , Borra Federico , Antonacci Fabio , Sarti Augusto
Published: April 2022

In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.
A Novel Elastomer-Based Inclinometer for Ultrasensitive Bridge Rotation Measurement
Authors: Zhou De , Wang Ningbo , Fu Chaofeng , Guo Chuanrui , Zhao Yangping
Published: April 2022

Bridge deformation consists of cross-section rotation and deflection, which are crucial parameters for bridge capacity evaluation and damage detection. The maximum value of deflection usually happens at mid-span while for rotation it happens at two-ends. Therefore, compared with deflection, rotation is more convenient for in-situ measurement since the bridge pier can be the reference point. In this study, a high-precision inclinometer for bridge rotation measurement was conceptualized, designed, and validated. The proposed inclinometer converted the small rotation of bridge section into the deformation of an elastomer. Strain gauges were then utilized to measure the elastomer deformation and thus the bridge rotation can be obtained. The dimensions and modulus of the elastomer were designed and chosen based on the theoretical analysis. Characteristics of the inclinometer were calibrated in lab and in-situ experiments at an in-service bridge were conducted to validate its feasibility and robustness. Test results showed that the proposed inclinometer had excellent performance in resolution and accuracy, which indicate its great potential for future bridge health monitoring.
On the Robustness of Quantum Algorithms for Blockchain Consensus
Authors: Ullah Muhammad Asad , Setiawan Jason William , ur Rehman Junaid , Shin Hyundong
Published: April 2022

Blockchain has revolutionized many fields, such as distributed sensor networks, finance, and cryptocurrency. Consensus between distributed network nodes is at the core of such blockchain technologies. The three primary performance measures for any consensus algorithm are scalability, security, and decentralization. This paper evaluates the usefulness and practicality of quantum consensus algorithms for blockchain-enhanced sensor, and computing networks and evaluates them against the aforementioned performance measures. In particular, we investigate their noise robustness against quantum decoherence in quantum processors and over fiber-optic channels. We observe that the quantum noise generally increases the error rate in the list distribution. However, the effect is variable on different quantum consensus schemes. For example, the entanglement-free scheme is more affected than entanglement-based schemes for the local noise cases, while in the case of noisy optical fiber links, the effect is prominent on all quantum consensus schemes. We infer that the current quantum protocols with noisy intermediate-scale quantum devices and noisy quantum communication can only be employed for modular units in intraenterprise-level blockchain, such as Zilliqa, for sensor, and computing networks.
Identifying CO2 Seeps in a Long-Dormant Volcanic Area Using Uncrewed Aerial Vehicle-Based Infrared Thermometry: A Qualitative Study
Authors: Tămaș Dan Mircea , Kis Boglárka Mercédesz , Tămaș Alexandra , Szalay Roland
Published: April 2022

Ciomadul is a long-dormant volcanic area in the Eastern Carpathians of Romania. The study site, the Stinky Cave, and the surrounding areas are well-known for CO2, and H2S seeps. The gases from these seeps come with high flux and are of magmatic origin, associated with the volcanic activity of Ciomadul. In this study, an Uncrewed Aerial Vehicle coupled with a thermal infrared sensor is used to identify new seeps. In order to achieve this, we carried out several field campaigns, coupling image acquisition with the creation of digital outcrop models and orthomosaics. The study was carried out at low ambient temperatures to identify strong thermal anomalies from the gasses. Using this qualitative study method, we identified several new seeps. The total emission of the greenhouse gas CO2 in the Ciomadul area and other similar sites is highly underestimated. The practical application of this method will serve as a guide for a future regional rollout of the thermal infrared mapping and identification of CO2 seeps in the area.
Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items
Authors: Dong Han , Lu Jiping , Han Yafeng
Published: April 2022

In recent years, rotating machinery fault diagnosis methods based on convolutional neural network have achieved much success. However, in real industrial environments, interfering signals are unavoidable, which may reduce the accuracy of fault diagnosis seriously. Most of the current fault diagnosis methods are of single input type, which may lead to the information contained in the vibration signal not being fully utilized. In this study, theoretical analysis and comprehensive comparative experiments are completed to investigate the time domain input, frequency domain input, and two types of time–frequency domain input. Based on this, a new fault diagnosis model, named multi-stream convolutional neural network, is developed. The model takes the time domain, frequency domain, and time–frequency domain images as input, and it automatically fuses the information contained in different inputs. The proposed model is tested based on three public datasets. The experimental results suggested that the model achieved pretty high accuracy under noise and trend items without the help of signal separation algorithms. In addition, the positive implications of multiple inputs and information fusion are analyzed through the visualization of learned features.
Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform
Authors: Leone Alessandro , Rescio Gabriele , Manni Andrea , Siciliano Pietro , Caroppo Andrea
Published: April 2022

Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different “confidence” levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an “augmented” dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 “confidence” levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia “confidence” levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.
A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI
Authors: Zahoor Mirza Mumtaz , Qureshi Shahzad Ahmad , Bibi Sameena , Khan Saddam Hussain , Khan Asifullah , Ghafoor Usman , Bhutta Muhammad Raheel
Published: April 2022

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.
Safety Engagement in the Workplace: Text Mining Analysis
Authors: Seo Hyun Jeong , Hong Ah Jeong
Published: April 2022

In order to derive safety engagement factors in the workplace and analyze the characteristics of the factors, we collected literature data to be analyzed by a systematic literature review and text mining analysis. We used safety, industrial, occupational, corporate, commitment, engagement, interaction, and participation as key search terms for literature selection and used 143 literature datasets for analysis. We divided the factors of workplace safety engagement into the organizational level and the individual level. In studies after 2005, texts at the individual psychological level appeared in large numbers. Although individual factors have been studied as subfactors at the organizational level, we confirmed that the two types of factors must interact for safety engagement in the workplace. We classified safety engagement factors into cognitive, emotional, behavioral, and relational factors. In particular, relational factors were mainly composed of factors that negatively affected engagement. In the follow-up study, we identified the maturity level among safety engagement factors as divided into four dimensions needed to create a safe workplace environment and to suggest a direction for employees to engage themselves in safety.
Separation of Serum and Plasma Proteins for In-Depth Proteomic Analysis
Authors: Paul Joseph , Veenstra Timothy D.
Published: April 2022

There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.
Inclusive Research and the Use of Visual, Creative and Narrative Strategies in Spain
Authors: Rojas-Pernia Susana , Haya-Salmón Ignacio
Published: April 2022

In recent decades, there have been many works on inclusive research that provide a clear framework on its meaning and the implications it entails. They also highlight the importance of addressing outstanding challenges, among others, to inquire after research strategies that respond to the diversity of situations in which people with intellectual disabilities find themselves. This article presents a research project carried out in Spain over a period of eighteen months by a team of researchers with and without intellectual disabilities. Specifically, we explore how the construction of enabling relationships, both dialogic and horizontal, requires giving greater emphasis to visual and creative methodological strategies, such as photographs, image-theatre, body-mapping, murals or visual presentations. The findings reported by the researchers and co-researchers have encouraged us to review some methodological premises such as our role as researchers or the type of relationships we establish. They also demonstrate the value of using a variety of collaborative enquiry strategies that recognise the agency of all researchers.
Doing Research Inclusively: Understanding What It Means to Do Research with and Alongside People with Profound Intellectual Disabilities
Authors: de Haas Catherine , Grace Joanna , Hope Joanna , Nind Melanie
Published: April 2022

Positive developments in inclusion in line with ‘Nothing about us without us’ have rarely extended to people with profound intellectual disabilities. Advances in inclusive research are in danger of leaving this group (and their families and allies) on the outside, with researchers relying on proxies at best, or more often omitting this group as ‘too difficult’ to include in the research process at all. This paper argues that finding a way for people with profound intellectual disabilities to belong in inclusive research is important. Using examples, small stories and photographs, it explores and illustrates potential ways to research with and alongside those with profound intellectual disabilities that celebrate different kinds of agency and personhood and that support relational autonomy. The paper concludes that rather than starting from how inclusive research is currently conceived, the starting point should be a deep knowledge of people with profound intellectual and multiple disabilities. The way forward is likely to be an inclusive research culture that can accommodate ‘being with’ as core to its research approach. This will enable the voices of people with profound intellectual disabilities to inform the research in creating intersubjective knowledge together.
Sunlight Degradation of the Aminophosphonate Diethylenetriamine Penta-(Methylenephosphonic Acid)
Authors: Kuhn Ramona , Jensch Robert , Fischer Thomas , Keuler Klaus , Bryant Isaac Mbir , Martienssen Marion
Published: April 2022

Aminophosphonate diethylenetriamine penta(methylenephosphonic acid) (DTPMP) is a scale inhibitor commonly used in several industries. DTPMP is suspected to cause anthropogenic pollution through discharge into the aquatic environment. DTPMP is assumed to be degraded by sunlight radiation. We recently predicted a preliminary degradation pathway of DTPMP applying UV treatment. Currently, we have not yet evidenced that DTPMP shows the same degradation pattern with natural sunlight. One major reason leads to the fact that the light spectrum emitted by UV lamps does not completely represent the natural sunlight spectrum, and the emitted UVB and UVA irradiation flux is much higher than for solar light. For that reason, the degradation pattern and kinetics might be different between artificial UV treatment and natural sunlight treatment. Here, we investigated whether DTPMP is degradable under natural sunlight radiation, and whether the degradation mechanisms determined through UV treatment are transferable to sunlight. We investigated five different treatment conditions, i.e., DTPMP degradation in direct or diffuse sunlight, in diffuse sunlight with addition of Ca2+ or Mg2+, and in diffuse sunlight with local TW. Our experiment was carried out from March 2021 to October 2021. We performed LC/MS analyses and measured the release of o-PO43−. DTPMP was degraded with all five treatment conditions. The fastest DTPMP degradation occurred in direct and diffuse sunlight without addition of bivalent cations. The addition of Ca2+ and Mg2+ resulted in inhibited degradation. Similar effects occurred for sunlight treatment with local TW. We evidenced different degradation mechanisms for DTPMP depending on the presence of alkaline earth metals as we previously proposed for UV-treated DTPMP. However, both degradation mechanisms of DTPMP belong to the same degradation pathway determined with UV treatment. Therefore, we conclude that DTPMP undergoes a similar degradation pathway in sunlight as compared to UV light.
Effect of Two Different Training Interventions on Cycling Performance in Mountain Bike Cross-Country Olympic Athletes
Authors: Schneeweiss Patrick , Schellhorn Philipp , Haigis Daniel , Niess Andreas Michael , Martus Peter , Krauss Inga
Published: April 2022

To improve performance in endurance sports, it is important to include both high-intensity and low-intensity training, but there is neither a universally accepted practice nor clear scientific evidence that allows reliable statements about the predominance of a specific training method. This randomized controlled trial compared the effects of a polarized training model (POL) to a low-intensity training model (LIT) on physiological parameters and mountain bike cross-country Olympic (XCO) race performance in eighteen competitive XCO athletes (17.9 ± 3.6 years). The superiority of one of the two methods could not be shown in this study. The results did not show statistically significant differences between POL and LIT, as both interventions led to slight improvements. However, a small tendency toward better effects for POL was seen for cycling power output during the race (4.4% vs. –2.2%), at the 4 mmol/L (6.1% vs. 2.8%) and individual anaerobic lactate threshold (5.1% vs. 2.3%), and for maximal aerobic performance (4.4% vs. 2.6%), but not for maximal efforts lasting 10 to 300 s. Despite the lack of significant superiority in this and some other studies, many athletes and coaches prefer POL because it produces at least equivalent effects and requires less training time.
Proactive Personality and Career Adaptability of Chinese Female Pre-Service Teachers in Primary Schools: The Role of Calling
Authors: Wen Ya , Liu Fei , Pang Liman , Chen Huaruo
Published: April 2022

Career adaptability has attracted extensive attention from international scholars as one of the important factors predicting the future sustainability of individuals. Proactive personality and calling have a positive influence on individual career development and life satisfaction, with reference to promoting the sustainable development of individuals. This study focuses on the positive effects of career adaptability and calling on shaping female primary school pre-service teachers and provides suggestions for training excellent elementary school teachers and building quality primary education. This study investigated the relationship between proactive personality, calling, and career adaptability among 707 female pre-service elementary school teachers in Chinese universities. The results showed that proactive personality positively predicts calling and that proactive personality and calling positively predict career adaptability. Calling partially mediated the relationship between proactive personality and career adaptability. Therefore, it is possible to enhance calling by cultivating the proactive personality of female pre-service teachers in elementary schools, thus further promoting the career adaptability of this group. On this basis, effective strategies should be adopted in the future to improve the career adaptability of female pre-service teachers in elementary schools for the sustainable development of this group.
Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources
Authors: Saleh Amr , Omran Walid A. , Hasanien Hany M. , Tostado-Véliz Marcos , Alkuhayli Abdulaziz , Jurado Francisco
Published: April 2022

Nowadays, the penetration level of renewable energy sources (RESs) has increased dramatically in electrical networks, especially in microgrids. Due to the replacement of conventional synchronous generators by RESs, the inertia of the microgrid is significantly reduced. This has a negative impact on the dynamics and performance of the microgrid in the face of uncertainties, resulting in a weakening of microgrid stability, especially in an islanded operation. Hence, this paper focuses on enhancing the dynamic security of an islanded microgrid using a frequency control concept based on virtual inertia control. The control in the virtual inertia control loop was based on a proportional-integral (PI) controller optimally designed by the Manta Ray Foraging Optimization (MRFO) algorithm. The performance of the MRFO-based PI controller was investigated considering various operating conditions and compared with that of other evolutionary optimization algorithm-based PI controllers. To achieve realistic simulations conditions, actual wind data and solar power data were used, and random load fluctuations were implemented. The results show that the MRFO-based PI controller has a superior performance in frequency disturbance alleviation and reference frequency tracking compared with the other considered optimization techniques.
The Use of Virtual Reality in Tourism Destinations as a Tool to Develop Tourist Behavior Perspective
Authors: Oncioiu Ionica , Priescu Iustin
Published: April 2022

The role of new technologies in tourism is changing rapidly, leading to the development of customer relationships through the use of virtual reality in the marketing of tourist destinations. In addition to focusing on the influence of travel intentions that has prevailed in practice so far, the use of VR is expected to have an impact on the travel experience on the spot. This exploratory research study was conducted with 824 respondents to identify the role of virtual reality in choosing a tourist destination, as well as the expectations of potential customers that could rekindle the tourism industry for a post-pandemic world. The results pointed out that highly used virtual reality applications for destination marketing aim to create a certain image for a tourist destination and to communicate this to the outside world in a consistent and coordinated manner. The findings also reinforce the importance of developing future scenarios for virtual reality as a decisive factor for strategic planning in the tourism sector.
Global Dairy Sector: Trends, Prospects, and Challenges
Authors: Bhat Rajeev , Di Pasquale Jorgelina , Bánkuti Ferenc Istvan , Siqueira Tiago Teixeira da Silva , Shine Philip , Murphy Michael D.
Published: April 2022

Currently, there is a strong need to find practical solutions towards meeting the expected efficiency and overcoming recurring sustainability challenges in the global dairy sector [...]