bluuebunny/crossref_metadata_2025 huggingface dataset:
Technology CAD (TCAD) Simulations of Mg2Si/Si Heterojunction Photodetector Based on the Thickness Effect
Authors: Yu Hong , Ji Shentong , Luo Xiangyan , Xie Quan
Published: August 2021

Research on infrared detectors has been widely reported in the literature. For infrared detectors, PbS, InGaAs, PbSe, InSb, and HgxCd1-xTe materials are the most widely used and have been explored for photodetection applications. However, these are toxic and harmful substances which are not conducive to the sustainable development of infrared detectors and are not eco-friendly. Mg2Si is a green, healthy, and sustainable semiconductor material that has the potential to replace these toxic and damaging photoelectric materials, making photoelectric detectors (PDs) green, healthy, and sustainable. In this work, we report on the results of our simulation studies on the PN junction Mg2Si/Si heterojunction PD. A model structure of Mg2Si/Si heterojunction PD has been built. The effects of Mg2Si and Si layer thickness on the optical and electrical performance of Mg2Si/Si heterojunction PD are discussed. For the purpose of this analysis, we consider electrical performance parameters such as I–V curve, external quantum efficiency (EQE), responsivity, noise equivalent power (NEP), detectivity, on-off ratio, response time, and recovery time. The simulation results show that the Mg2Si/Si heterojunction PD shows optimum performance when the thickness of Si and Mg2Si layers are 300 nm and 280 nm, respectively. For the optimized structure, the reverse breakdown voltage was found to be −23.61 V, the forward conduction voltage was 0.51 V, the dark current was 5.58 × 10−13 A, and the EQE was 88.98%. The responsivity was found to be 0.437 A/W, the NEP was 6.38 × 10−12 WHz1/2, and the detectivity was 1.567 × 1011 Jones. With the on-off ratio of 1566, the response time was found to be 0.76 ns and the recovery time was 5.75 ns. The EQE and responsivity peak wavelength of PD show a redshift as the thickness of Mg2Si increases. The Mg2Si heterojunction PD can effectively detect infrared light in the wavelength range of 400 to 1400 nm. The simulation results can be utilized to drive the development of green Mg2Si/Si heterojunction PD in the future.
A Cell’s Viscoelasticity Measurement Method Based on the Spheroidization Process of Non-Spherical Shaped Cell
Authors: Liu Yaowei , Zhang Yujie , Cui Maosheng , Zhao Xiangfei , Sun Mingzhu , Zhao Xin
Published: August 2021

The mechanical properties of biological cells, especially the elastic modulus and viscosity of cells, have been identified to reflect cell viability and cell states. The existing measuring techniques need additional equipment or operation condition. This paper presents a cell’s viscoelasticity measurement method based on the spheroidization process of non-spherical shaped cell. The viscoelasticity of porcine fetal fibroblast was measured. Firstly, we introduced the process of recording the spheroidization process of porcine fetal fibroblast. Secondly, we built the viscoelastic model for simulating a cell’s spheroidization process. Then, we simulated the spheroidization process of porcine fetal fibroblast and got the simulated spheroidization process. By identifying the parameters in the viscoelastic model, we got the elasticity (500 Pa) and viscosity (10 Pa·s) of porcine fetal fibroblast. The results showed that the magnitude of the elasticity and viscosity were in agreement with those measured by traditional method. To verify the accuracy of the proposed method, we imitated the spheroidization process with silicone oil, a kind of viscous and uniform liquid with determined viscosity. We did the silicone oil’s spheroidization experiment and simulated this process. The simulation results also fitted the experimental results well.
Precision Magnetometers for Aerospace Applications: A Review
Authors: Bennett James S. , Vyhnalek Brian E. , Greenall Hamish , Bridge Elizabeth M. , Gotardo Fernando , Forstner Stefan , Harris Glen I. , Miranda Félix A. , Bowen Warwick P.
Published: August 2021

Aerospace technologies are crucial for modern civilization; space-based infrastructure underpins weather forecasting, communications, terrestrial navigation and logistics, planetary observations, solar monitoring, and other indispensable capabilities. Extraplanetary exploration—including orbital surveys and (more recently) roving, flying, or submersible unmanned vehicles—is also a key scientific and technological frontier, believed by many to be paramount to the long-term survival and prosperity of humanity. All of these aerospace applications require reliable control of the craft and the ability to record high-precision measurements of physical quantities. Magnetometers deliver on both of these aspects and have been vital to the success of numerous missions. In this review paper, we provide an introduction to the relevant instruments and their applications. We consider past and present magnetometers, their proven aerospace applications, and emerging uses. We then look to the future, reviewing recent progress in magnetometer technology. We particularly focus on magnetometers that use optical readout, including atomic magnetometers, magnetometers based on quantum defects in diamond, and optomechanical magnetometers. These optical magnetometers offer a combination of field sensitivity, size, weight, and power consumption that allows them to reach performance regimes that are inaccessible with existing techniques. This promises to enable new applications in areas ranging from unmanned vehicles to navigation and exploration.
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
Authors: Mandal Moumita , Singh Pawan Kumar , Ijaz Muhammad Fazal , Shafi Jana , Sarkar Ram
Published: August 2021

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.
Classification of Ataxic Gait
Authors: Vyšata Oldřich , Ťupa Ondřej , Procházka Aleš , Doležal Rafael , Cejnar Pavel , Bhorkar Aprajita Milind , Dostál Ondřej , Vališ Martin
Published: August 2021

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.
ARMA-Based Segmentation of Human Limb Motion Sequences
Authors: Mei Feng , Hu Qian , Yang Changxuan , Liu Lingfeng
Published: August 2021

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.
A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases
Authors: Trakadas Panagiotis , Sarakis Lambros , Giannopoulos Anastasios , Spantideas Sotirios , Capsalis Nikolaos , Gkonis Panagiotis , Karkazis Panagiotis , Rigazzi Giovanni , Antonopoulos Angelos , Cambeiro Marta Amor , Gonzalez-Diaz Sergio , Conceição Luís
Published: August 2021

The provision of high data rate services to mobile users combined with improved quality of experience (i.e., zero latency multimedia content) drives technological evolution towards the design and implementation of fifth generation (5G) broadband wireless networks. To this end, a dynamic network design approach is adopted whereby network topology is configured according to service demands. In parallel, many private companies are interested in developing their own 5G networks, also referred to as non-public networks (NPNs), since this deployment is expected to leverage holistic production monitoring and support critical applications. In this context, this paper introduces a 5G NPN architectural approach, supporting among others various key enabling technologies, such as cell densification, disaggregated RAN with open interfaces, edge computing, and AI/ML-based network optimization. In the same framework, potential applications of our proposed approach in real world scenarios (e.g., support of mission critical services and computer vision analytics for emergencies) are described. Finally, scalability issues are also highlighted since a deployment framework of our architectural design in an additional real-world scenario related to Industry 4.0 (smart manufacturing) is also analyzed.
Nonprehensile Manipulation of Parts on a Horizontal Circularly Oscillating Platform with Dynamic Dry Friction Control
Authors: Kilikevičius Sigitas , Liutkauskienė Kristina , Fedaravičius Algimantas
Published: August 2021

This paper presents a novel method for nonprehensile manipulation of parts on a circularly oscillating platform when the effective coefficient of dry friction between the part and the platform is being dynamically controlled. Theoretical and experimental analyses have been performed to validate the proposed method and to determine the control parameters that define the characteristics of the part’s motion. A mathematical model of the manipulation process with dynamic dry friction control was developed and solved. The modeling showed that by changing the phase shift between the function for dynamic dry friction control and the function defining the circular motion of the platform, the part can be moved in any direction as the angle of displacement can be controlled in a full range from 0 to 2π. The nature of the trajectory and the mean displacement velocity of the part mainly depend on the width of the rectangular function for dynamic dry friction control. To verify the theoretical findings, an experimental setup was developed, and experiments of manipulation were carried out. The experimental results qualitatively confirmed the theoretical findings. The presented analysis enriches the classical theories of nonprehensile manipulation on oscillating platforms, and the presented findings are relevant for mechatronics, robotics, mechanics, electronics, medical, and other industries.
Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
Authors: Lee Byeong-ho , Park Kyoung-Min , Kim Yong-Hwa , Kim Seong-Cheol
Published: August 2021

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.
Stochastic Memristive Interface for Neural Signal Processing
Authors: Gerasimova Svetlana A. , Belov Alexey I. , Korolev Dmitry S. , Guseinov Davud V. , Lebedeva Albina V. , Koryazhkina Maria N. , Mikhaylov Alexey N. , Kazantsev Victor B. , Pisarchik Alexander N.
Published: August 2021

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.
An Indole-Based Fluorescent Chemosensor for Detecting Zn2+ in Aqueous Media and Zebrafish
Authors: Choe Donghwan , So Haeri , Park Soyoung , Lee Hangyul , Chae Ju Byeong , Kim Jiwon , Kim Ki-Tae , Kim Cheal
Published: August 2021

An indole-based fluorescent chemosensor IH-Sal was synthesized to detect Zn2+. IH-Sal displayed a marked fluorescence increment with Zn2+. The detection limit (0.41 μM) of IH-Sal for Zn2+ was greatly below that suggested by the World Health Organization. IH-Sal can quantify Zn2+ in real water samples. More significantly, IH-Sal could determine and depict the presence of Zn2+ in zebrafish. The detecting mechanism of IH-Sal toward Zn2+ was illustrated by fluorescence and UV–visible spectroscopy, DFT calculations, 1H NMR titration and ESI mass.
Assessment of the Use of Multi-Channel Organic Electrodes to Record ENG on Small Nerves: Application to Phrenic Nerve Burst Detection
Authors: Avdeew Yvan , Bergé-Laval Victor , Le Rolle Virginie , Dieuset Gabriel , Moreau David , Kergoat Loïg , Martin Benoît , Bernard Christophe , Gestreau Christian , Hernández Alfredo
Published: August 2021

Effective closed-loop neuromodulation relies on the acquisition of appropriate physiological control variables and the delivery of an appropriate stimulation signal. In particular, electroneurogram (ENG) data acquired from a set of electrodes applied at the surface of the nerve may be used as a potential control variable in this field. Improved electrode technologies and data processing methods are clearly needed in this context. In this work, we evaluated a new electrode technology based on multichannel organic electrodes (OE) and applied a signal processing chain in order to detect respiratory-related bursts from the phrenic nerve. Phrenic ENG (pENG) were acquired from nine Long Evans rats in situ preparations. For each preparation, a 16-channel OE was applied around the phrenic nerve’s surface and a suction electrode was applied to the cut end of the same nerve. The former electrode provided input multivariate pENG signals while the latter electrode provided the gold standard for data analysis. Correlations between OE signals and that from the gold standard were estimated. Signal to noise ratio (SNR) and ROC curves were built to quantify phrenic bursts detection performance. Correlation score showed the ability of the OE to record high-quality pENG. Our methods allowed good phrenic bursts detection. However, we failed to demonstrate a spatial selectivity from the multiple pENG recorded with our OE matrix. Altogether, our results suggest that highly flexible and biocompatible multi-channel electrode may represent an interesting alternative to metallic cuff electrodes to perform nerve bursts detection and/or closed-loop neuromodulation.
Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications
Authors: Englert Tim , Gruber Florian , Stiedl Jan , Green Simon , Jacob Timo , Rebner Karsten , Grählert Wulf
Published: August 2021

To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models.
Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial
Authors: Wang Sizhong , Chan Peter P. K. , Lam Ben M. F. , Chan Zoe Y. S. , Zhang Janet H. W. , Wang Chao , Lam Wing Kai , Ho Kevin Ki Wai , Chan Rosa H. M. , Cheung Roy T. H.
Published: August 2021

The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VASP) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM1 and VASP were significantly reduced (both Ps < 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM2, KAAI and KFM did not change in both groups across time (Ps ≥ 0.120). A six-week sensor-based gait retraining, compared with walking exercise, was an effective intervention to lower medial knee loading, relieve knee pain and improve symptoms for patients with early medial knee OA.
Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement
Authors: Wojtanowski Jacek , Zygmunt Marek , Drozd Tadeusz , Jakubaszek Marcin , Życzkowski Marek , Muzal Michał
Published: August 2021

Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.
No Fine-Tuning, No Cry: Robust SVD for Compressing Deep Networks
Authors: Tukan Murad , Maalouf Alaa , Weksler Matan , Feldman Dan
Published: August 2021

A common technique for compressing a neural network is to compute the k-rank ℓ2 approximation Ak of the matrix A∈Rn×d via SVD that corresponds to a fully connected layer (or embedding layer). Here, d is the number of input neurons in the layer, n is the number in the next one, and Ak is stored in O((n+d)k) memory instead of O(nd). Then, a fine-tuning step is used to improve this initial compression. However, end users may not have the required computation resources, time, or budget to run this fine-tuning stage. Furthermore, the original training set may not be available. In this paper, we provide an algorithm for compressing neural networks using a similar initial compression time (to common techniques) but without the fine-tuning step. The main idea is replacing the k-rank ℓ2 approximation with ℓp, for p∈[1,2], which is known to be less sensitive to outliers but much harder to compute. Our main technical result is a practical and provable approximation algorithm to compute it for any p≥1, based on modern techniques in computational geometry. Extensive experimental results on the GLUE benchmark for compressing the networks BERT, DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage.
Can GIS Foster Conscious and Critical Learning in Geography? An Application from Students to a Real Case Included in the National Strategy for Inner Areas: Monti Dauni, Apulia (Italy)
Authors: Labianca Marilena
Published: August 2021

Digital technologies, the use of which has progressively increased over the last few years, could represent key tools in learning and active citizenship in development processes. In this sense, Geographical Information Systems (GIS) have gradually become an integral part of various disciplines and sectors. Even if the relevance and diffusion of GIS in the educational and pedagogical fields are increasing, their potential is still under-explored in the development of empowerment for participatory planning. Regarding the combination of geographical education and information technologies applied to the territories, the contribution they offer to the Sustainable Development Goals (SDG) is a question which remains little-investigated, and which represents both an innovative field of experimentation and significant opportunities, especially for marginal, inner areas. However, recent reforms, in particular in Italy, are considerably reducing the role of geography in the curricula, which is paradoxical for such complex contexts and the understanding of major global issues. The risk is that of producing a gap between the functional use of the tools and the capacity for their critical application. This paper aims to reflect on the integration of Geography and GIS through teaching experimentation, as applied to a real case study in the Apulia region (Monti Dauni Area) under the national strategy for inner areas, in order to understand how to use GIS as an active tool in education for sustainability, the awareness of the value of local resources, and active citizenship.
Exploring and Validating Container Operational Risk Scale in Container Shipping: The Case of Ethiopian Shipping and Logistics Service Enterprise
Authors: Abdulahi Efrah Wozir , Fan Luo
Published: August 2021

The risk associated with container shipping has been a major concern in recent decades. This study presents three major risk frameworks to systematically and inclusively explore and validate container operational risk scales based on risk factors derived from the extant literature. The three risk frameworks identified are risks related to information flow, risks related to physical flow, and risks related to payment flow. Each risk factor is grouped into sub-factors (dimensions), three factors for information flow, two factors for physical flow, and two factors for payment flow. The study uses Ethiopia as a case study and employed both qualitative and quantitative research methods. An interview survey was conducted to explore additional risk factors and validate the identified risk factors in container shipping, and a questionnaire survey was then accompanied to collect the relevant data. A pairwise comparison chart (PCC) was employed to rank the risk dimensions. The results showed that the container operational risk model is satisfactory by employing exploratory and confirmatory factor analysis. Furthermore, the PCC result indicates that risk of loss or damage of goods/assets, payment delay, and decrease in or total loss of payment were ranked first, second, and third, respectively, and consequently the most significant dimensions of the risk factors. This study provides a reliable and valid scale for measuring container operational risk in container shipping companies. It also unlocks future works for using the identified risk factors as guidelines for researchers and experts to design and develop container operational risk dimensions.
The Role of Substitute Industries for Revitalizing Shrinking Cities
Authors: Schackmar Jakob , Fleschurz René , Pallagst Karina
Published: August 2021

In past decades, many cities and regions have underwent structural transformations—e.g., in old industrialized “rust belts” or in peripheral rural areas. Many of these shrinking cities have to face the challenges of long-term demographic and economic changes. While shrinkage is often related to post-industrial transformations in the USA, in other countries, such as Germany, for example, the causes are related to changing demographics with declining birth rates and the effects of the German reunification. Many cities have tried to combat shrinkage and have thus developed a variety of policies and strategies such as the establishing of substitute industries. To assess the sustainability of this approach, this paper investigates the cities of Cleveland, USA and Bochum, Germany in a comparative analysis following the most similar/most different research design. The paper shows that substitute industries might lead to new development paths for shrinking cities; however, whether these paths stimulate sustainable development is dependent on those who are benefiting from these developments and to what extent.
Exploitation of Agro-Industrial Residues for the Formulation of a New Active and Cost Effective Biofungicide to Control the Root Rot of Vegetable Crops
Authors: Hashem Mohamed , Mostafa Yasser S. , Alamri Saad , Abbas Ahmed M. , Eid Ebrahem M.
Published: August 2021

This study aimed to produce an economic and stable biofungicide based on a new effective antagonistic strain (Trichoderma harzianum JF419706) via the exploitation of agro-industrial lignocellulosic residues as carriers for fungal growth to control the root rot diseases of vegetable crops. Trichoderma harzianum JF419706 showed a good resistance to a chemical fungicide with two-fold of the recommended dose. It was able to propagate on corn stovers amended with 20% of date molasses, as a very cheap substrate, up to 2.90 × 1016 CFU/g after 30 days. Formulation of the bioagent on the substrate as a fine powder (FTB) increased the shelf-life up to 8 months with good viability (9.37 × 1011 CFU/g). The bioagent propagated itself in the rhizospheric soil about two-fold of the initial inoculum. Application of the FTB, as a seed treatment, suppressed the root rot disease severity percentage of cucumber, lettuce, and tomato plants to 70.0%, 61.5%, and 53.8%, respectively, from the control. The crop yield increased by 50%, 35%, and 30% in the same order of the three crops. FTB promoted the growth and physiological processes of the plants significantly compared with the control. Our study recommends the application of the FTB as a cost-effective biofungicide and biofertilizer in crop management, singly or as a part of integrated pest management, to ensure the sustainability of green farming and reduce the chemical input in cultural practices.
Illegal Waste Dumping under a Municipal Solid Waste Charging Scheme: Application of the Neutralization Theory
Authors: Chu Amanda M. Y.
Published: August 2021

Illegal waste dumping has become a threat to human health and the global environment. In Hong Kong, the government has proposed a quantity-based municipal solid waste charging scheme to reduce waste. However, individuals may still dispose of waste improperly, even if such a scheme has been implemented. In this study, the neutralization theory was adopted and an online survey with 273 respondents was conducted to examine the reasons for improper dumping intentions. A principal component analysis identified two types of neutralization: intrinsic neutralization (including denial of responsibility, denial of injury, and defense of necessity) and extrinsic neutralization (including condemnation of the condemners and appeal to higher loyalties). A regression analysis showed that intrinsic neutralization and gender were significant factors for illegal waste dumping intentions when attitude toward illegal waste dumping was controlled.
Multi-Perspective Analysis of Household Carbon Dioxide Emissions from Direct Energy Consumption by the Methods of Logarithmic Mean Divisia Index and σ Convergence in Central China
Authors: Rong Yueyue , Jia Junsong , Ju Min , Chen Chundi , Zhou Yangming , Zhong Yexi
Published: August 2021

Currently, household carbon dioxide (CO2) emissions (HCEs) as one of the leading sources of greenhouse gas (GHG) have drawn notable scholarly concern. Thus, here, taking six provinces in the period of 2000–2017 of Central China as a case, we analyzed the characteristics and the driving factors of HCEs from direct energy consumption and three perspectives: Central China as a whole, urban-rural differences, and inter-provincial comparison. The drivers of direct HCEs were analyzed by the Logarithmic Mean Divisia Index (LMDI). The σ convergence was adopted for analyzing the trend of inter-provincial differences on the HCEs. The key findings are as follows. First, all the direct HCEs from three perspectives had an obvious growth trend. The total direct HCEs grew from 9596.20 × 104 tonnes in 2000 to 30,318.35 × 104 tonnes in 2017, with an increase of 2.16 times. Electricity and coal use were the primary sources. The urban and rural increases of direct HCEs were up 2.57 times and 1.77 times, respectively. The urban-rural gap of direct HCEs narrowed first and then widened. The direct HCEs in the six provinces varied significantly, but the gap was narrowing. Second, as a whole the per capita consumption expenditure and energy demand were the main drivers to the increment of HCEs, with cumulative contribution rates of 118.19% and 59.90%. The energy price effect was mainly responsible for the mitigation of HCEs. Third, the similar drivers’ trend can also be seen from the perspective of inter-provincial comparison. However, from the perspective of urban and rural difference, the population urban-rural structure effect played a reverse influence on both urban and rural areas. Thus, raising the energy prices appropriately, upgrading the residents’ consumption to a sustainable pattern, controlling the growth of population size reasonably, and optimizing the household energy structure might effectively mitigate the growth of HCEs in Central China.
Exploring the Intensity of Relationships with Vocational Education Centres: A Typology of Spanish SMEs
Authors: Lavía Cristina , Otero Beatriz , Albizu Eneka , Olazaran Mikel
Published: August 2021

Even though the availability of skilled labour and technological know-how is critical to the sustainability of small and medium-sized enterprises (SMEs), the relationships between industry and the vocational education system have received little attention in the recent literature regarding social aspects of innovation. The objective of this paper is to analyse the intensity of relationships between industrial SMEs and vocational education and training (VET) centres from the firms’ perspective. The study is based on a survey carried out with a sample of 1388 Spanish industrial SMEs with vocational education graduates among their employees. Multivariate hierarchical segmentation techniques were used in order to identify the main explanatory variables. As a result, we obtained a typology (“tree”) of eight organizational profiles associated to different intensity levels (from higher to lower) of relationships between firms and schools. The results show that most industrial SMEs maintain relations with vocational education centres, reflecting the importance of the latter for the companies. The organisational type having the highest level of relations refers to SMEs with experience in external cooperation (cooperation with other actors in innovation projects) which have vocational education employees (graduates) in technical areas and which are bigger in size. Likewise, the results suggest that fruitful collaboration between SMEs and vocational education centres depends on the existence of an established culture of innovation among the smaller firms. This work sheds light on economic and social sustainability. Its results and discussion are linked to the objectives of United Nations sustainable development goals and the recent communication from the European Commission to the European Parliament entitled “European skills agenda for sustainable competitiveness, social fairness, and resilience”.
Sustainability of Management Decisions in a Digital Logistics Network
Authors: Barykin Sergey Evgenievich , Borisoglebskaya Larisa Nikolaevna , Provotorov Vyacheslav Vasilyevich , Kapustina Irina Vasilievna , Sergeev Sergey Mikhailovich , De La Poza Plaza Elena , Saychenko Lilya
Published: August 2021

Globalization has given a powerful impetus to the development of international commercial activity and logistics management systems taking full advantage of cross-border networking. The solution lies at the intersection of information technologies, technical means of machine-to-machine (M2M) interaction, mobile high-speed networks, geolocation, cloud services, and a number of international standards. The current trend towards creating digital logistics platforms has set a number of serious challenges for developers. The most important requirement is the condition of sustainability of the obtained solutions with respect to disturbances in the conditions of logistics activities caused not only by market uncertainty but also by a whole set of unfavorable factors accompanying the transportation process. Within the framework of the presented research, the problem of obtaining the conditions for the stability of solutions obtained on the basis of mathematical models is set. At the same time, the processes of transferring not only discrete but also continuous material flows through complex structured networks are taken into account. This study contains the results of the analysis of the stability of solutions of differential systems of various types that simulate the transfer processes in network media. Initial boundary value problems for evolutionary equations and differential-difference systems are relevant in logistics, both for the discrete transportation of a wide range of goods and for the quasi-continuous transportation of, for example, liquid hydrocarbons. The criterion for the work of a logistics operator is the integral functional. For the mathematical description of the transport process of continuous and discrete media, a wide class of integrable functions are used, which adequately describe the transport of media with a complex internal rheological structure.
On the Use of Cloud Analysis for Structural Glass Members under Seismic Events
Authors: Mattei Silvana , Fasan Marco , Bedon Chiara
Published: August 2021

Current standards for seismic-resistant buildings provide recommendations for various structural systems, but no specific provisions are given for structural glass. As such, the seismic design of joints and members could result in improper sizing and non-efficient solutions, or even non-efficient calculation procedures. An open issue is represented by the lack of reliable and generalized performance limit indicators (or “engineering demand parameters”, EDPs) for glass structures, which represent the basic input for seismic analyses or q-factor estimates. In this paper, special care is given to the q-factor assessment for glass frames under in-plane seismic loads. Major advantage is taken from efficient finite element (FE) numerical simulations to support the local/global analysis of mechanical behaviors. From extensive non-linear dynamic parametric calculations, numerical outcomes are discussed based on three different approaches that are deeply consolidated for ordinary structural systems. Among others, the cloud analysis is characterized by high computational efficiency, but requires the definition of specific EDPs, as well as the choice of reliable input seismic signals. In this regard, a comparative parametric study is carried out with the support of the incremental dynamic analysis (IDA) approach for the herein called “dynamic” (M1) and “mixed” (M2) procedures, towards the linear regression of cloud analysis data (M3). Potential and limits of selected calculation methods are hence discussed, with a focus on sample size, computational cost, estimated mechanical phenomena, and predicted q-factor estimates for a case study glass frame.
Preparation and Characterization of Cattail-Derived Biochar and Its Application for Cadmium Removal
Authors: Wang Xiaoshu , Yan Zheng , Song Lingchao , Wang Yangyang , Zhu Jia , Xu Nan , Wang Jinsheng , Chang Ming , Wang Lei
Published: August 2021

Biochars produced from aquatic plants have attracted increasing attention for the removal of heavy metals from the environment. Therefore, biochars derived from the roots (CBR), stems (CBS) and leaves (CBL) of cattail were investigated in this paper for their higher adsorption capacity, particularly for Cd(II). The adsorption characteristics and the leaching of alkali (soil) metals within biochars obtained from the different tissues of cattail were also discussed. The results showed that the specific surface area of cattail root biochar reached 15.758 m2 g−1. Langmuir, Freundlich and D-R isotherm equations were used to fit the experimental data, and the last equation revealed the best fitting result. The adsorption kinetics for Cd(II) removal were determined by using two different models. The experimental data for CBR and CBS were in good agreement with the pseudo second-order model, whereas the pseudo first-order model provided a better fit for CBL. The amount of leached K reached 73.214 mg g−1 in CBS (55.087 mg g−1 in CBL), which was almost an order of magnitude higher than those of Mg and Ca. The experimental data showed that the leached Mg and Ca metals in CBL had maximum levels of 6.543 and 10.339 mg g−1, respectively. The mechanism of Cd(II) sorption by the biochar is complex and probably involves a combination of mass transfer, ion exchange, and mineral precipitation through the macropores and micropores of the biochar in the sorption process.
Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms
Authors: Velimirović Lazar Z. , Janković Radmila , Velimirović Jelena D. , Janjić Aleksandar
Published: August 2021

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.
Fundamental Theory of Torsion Gravity
Authors: Fabbri Luca
Published: August 2021

In this work, we present the general differential geometry of a background in which the space–time has both torsion and curvature with internal symmetries being described by gauge fields, and that is equipped to couple spinorial matter fields having spin and energy as well as gauge currents: torsion will turn out to be equivalent to an axial-vector massive Proca field and, because the spinor can be decomposed in its two chiral projections, torsion can be thought as the mediator that keeps spinors in stable configurations; we will justify this claim by studying some limiting situations. We will then proceed with a second chapter, where the material presented in the first chapter will be applied to specific systems in order to solve problems that seems to affect theories without torsion: hence the problem of gravitational singularity formation and positivity of the energy are the most important, and they will also lead the way for a discussion about the Pauli exclusion principle and the concept of macroscopic approximation. In a third and final chapter, we are going to investigate, in the light of torsion dynamics, some of the open problems in the standard models of particles and cosmology which would not be easily solvable otherwise.
Asymptotically Safe Gravity-Fermion Systems on Curved Backgrounds
Authors: Daas Jesse , Oosters Wouter , Saueressig Frank , Wang Jian
Published: August 2021

We set up a consistent background field formalism for studying the renormalization group (RG) flow of gravity coupled to Nf Dirac fermions on maximally symmetric backgrounds. Based on Wetterich’s equation, we perform a detailed study of the resulting fixed point structure in a projection including the Einstein–Hilbert action, the fermion anomalous dimension, and a specific coupling of the fermion bilinears to the spacetime curvature. The latter constitutes a mass-type term that breaks chiral symmetry explicitly. Our analysis identified two infinite families of interacting RG fixed points, which are viable candidates to provide a high-energy completion through the asymptotic safety mechanism. The fixed points exist for all values of Nf outside of a small window situated at low values Nf and become weakly coupled in the large Nf-limit. Symmetry-wise, they correspond to “quasi-chiral” and “non-chiral” fixed points. The former come with enhanced predictive power, fixing one of the couplings via the asymptotic safety condition. Moreover, the interplay of the fixed points allows for cross-overs from the non-chiral to the chiral fixed point, giving a dynamical mechanism for restoring the symmetry approximately at intermediate scales. Our discussion of chiral symmetry breaking effects provides strong indications that the topology of spacetime plays a crucial role when analyzing whether quantum gravity admits light chiral fermions.