bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of MERRA-2 Aerosol Optical and Component Properties over China Using SONET and PARASOL/GRASP Data
Authors: Ou Yang , Li Zhengqiang , Chen Cheng , Zhang Ying , Li Kaitao , Shi Zheng , Dong Jiantao , Xu Hua , Peng Zongren , Xie Yisong , Luo Jie
Published: February 2022

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is widely used as an advanced model dataset for the understanding of global climate change. However, independent validation and comparison of MERRA-2 are both insufficient and always desired. Therefore, in this study, the quantitative evaluation of MERRA-2 aerosol products was conducted over China for Aerosol Optical Depth (AOD), Angstrom exponent (AE), absorbing AOD (AAOD) and chemical components (black carbon (BC) and dust (DU)) using Sun sky radiometer Observation NETwork (SONET) ground-based measurements and POLDER-3/PARASOL satellite products generated by the GRASP algorithm. The available MERRA-2 monthly dataset and PARASOL/GRASP monthly and seasonal products were intercompared over China. MERRA-2 AOD (550 nm) show general good agreement with SONET and PARASOL/GRASP. For example, the correlation coefficients are usually 0.6–0.85 with SONET and 0.75–0.85 with PARASOL/GRASP, the bias is usually −0.293 to +0.008 with SONET. For AE and AAOD, the agreement is still reasonable. MERRA-2 is found to overestimate fine mode AE and to display a general underestimation of aerosol absorption over China. In addition, MERRA-2 BC and DU mass concentrations show spatial and quantitative consistency with PARASOL/GRASP components climatological products. The relatively high columnar BC mass concentration is observed around 1.5–2 mg/m3 over the East China industrial region and high DU mass concentration is around 150 mg/m3 near Taklimakan desert. MERRA-2 shows slightly higher BC and lower DU concentration than PARASOL/GRASP over East China. The evaluations with in situ BC measurements near surface verify the overestimation (MAE = +0.44 µg/m2) of MERRA-2 and underestimation (MAE = −0.38 µg/m2) of PARASOL/GRASP. The analysis demonstrates multi-source datasets, such as ground-based, space-borne remote sensing, in situ measurements, model simulation as well as reanalysis data, complement each other and can be used to refine aerosol characterization.
Comparative Approach of Unmanned Aerial Vehicle Restrictions in Controlled Airspaces
Authors: McTegg Stephen John , Tarsha Kurdi Fayez , Simmons Shane , Gharineiat Zahra
Published: February 2022

Recent public discourse regarding unmanned aerial vehicle (UAV) usage and regulation is focused around public privacy and safety. Most authorities have employed key guidelines and licensing procedures for piloting UAVs, however there is marginal consensus amongst regulators and a limited view towards unified procedures. This paper aims to analyze the key challenges that affect the use of UAVs and to determine if the current rules address those challenges. For this purpose: privacy, safety, security, public nuisance and trespass are tested. A set of criteria are developed to perform a comparative analysis against the existing UAV regulations to determine how they are meeting the specified criteria. Within this framework, five countries are selected: Australia, Canada, European Union (EU), United Kingdom (UK) and the United States of America (USA), with usage data and length of time between regulatory reviews ensuring any analysis is realized on updated protocols. The regulations of each country are then compared against the developed criteria. The findings show there are shortfalls with the majority of regulations failing to meet some criteria and the results confirm that key issues fail to be addressed. Finally, recommendations are suggested for filling the gaps in the regulations.
Non-Contact Diagnostics of the Geometry of a Historic Wooden Building as an Element of Periodic Safety Assessment
Authors: Lipecki Tomasz
Published: February 2022

The article presents a method of non-invasive diagnostics of a historic wooden church, built in the 18th century. Over the hundreds of years of its use, changes in the geometry of the structure have been observed. This article presents the requirements of so-called architectural and geodetic survey and the method of using terrestrial laser scanning to create a three-dimensional solid model of an object. The diagnostic tests performed made it possible to perform analysis based on a so-called point cloud, which is a virtual representation of a real object. In order to determine the basic parameters of the building, the area and volume of all rooms were determined. It was found that the object exhibited deformations that cannot be explained solely as a result of imperfections during climb and normal wear and tear during operation. Therefore, the changes in shape were assessed in detail by means of an assessment of the verticality of the pillars supporting two levels of the church, the verticality of the walls, and the inconsistency of the floors, as well as the shape and horizontality of the roof edge. Additional InSAR and FEM tests of the object’s location on the ground allowed identification of the cause of the object deformation as the influence of inhomogeneous groundwater relations under the building. Without prophylactic measures, this deformation phenomenon can be expected to worsen. The tests described should therefore be considered as essential in subsequent diagnostic cycles and permit future extended numerical FEM analysis.
Hybrid Blockchain Platforms for the Internet of Things (IoT): A Systematic Literature Review
Authors: Alkhateeb Ahmed , Catal Cagatay , Kar Gorkem , Mishra Alok
Published: February 2022

In recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study.
Virtual Reality Systems as an Orientation Aid for People Who Are Blind to Acquire New Spatial Information
Authors: Lahav Orly
Published: February 2022

This research aims to examine the impact of virtual environments interface on the exploration process, construction of cognitive maps, and performance of orientation tasks in real spaces by users who are blind. The study compared interaction with identical spaces using different systems: BlindAid, Virtual Cane, and real space. These two virtual systems include user-interface action commands that convey unique abilities and activities to users who are blind and that operate only in these VR systems and not in real space (e.g., teleporting the user’s avatar or pointing at a virtual object to receive information). This research included 15 participants who are blind, divided into three groups: a control group and two experimental groups. Varied tasks (exploration and orientation) were used in two virtual environments and in real spaces, with both qualitative and quantitative methodologies. The results show that the participants were able to explore, construct a cognitive map, and perform orientation tasks. Participants in both virtual systems used these action commands during their exploration process: all participants used the teleport action command to move their avatar to the starting point and all Virtual Cane participants explored the environment mainly by using the look-around mode, which enabled them to collect spatial information in a way that influenced their ability to construct a cognitive map based on a map model.
Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
Authors: Rosário Francisco , Monteiro Francisco A.
Published: February 2022

Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.
Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform
Authors: Wu Hao , Ma Xue , Wen Chenglin
Published: February 2022

Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the safety and reliability of the motor control system. For this reason, on the basis of the powerful extraction ability of the fractional Fourier transform (FRFT) for micro fault features, especially the extraction ability to fit fractional frequency domain faults, this paper intends to establish a multilevel fine fault diagnosis method for fractional-order or integer-order faults. Firstly, this is accomplished by performing the fractional Fourier transform on the acquired data with faults and feature extraction in the multilevel fractional frequency domain and then optimizing the feature extraction model. Secondly, one further step search method is established to determine the projection direction with the largest fault feature. Thirdly, taking the extracted multilevel fault features as input, a multilevel fine fault diagnosis method based on the SVM model is established. Finally, three typical digital simulation examples and actual operating data collected by the ZHS-2 multifunctional motor test bench with a flexible rotor are employed to verify the effectiveness, robustness, and accuracy of this new method. The main contribution and innovation of this paper are that the fractional Fourier transform method based on time domain and frequency domains is introduced. This method can extract the small fault features in the maximum projection direction of the signal in the fractional domain, but detection with other time–frequency methods is difficult; the extracted multilevel fault features are used as input, and the corresponding fault diagnosis model is established, which can improve the accuracy of fault detection and ensure the safe and reliable operation of industrial equipment.
Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins
Authors: Munjal Rohan , Arif Sohaib , Wendler Frank , Kanoun Olfa
Published: February 2022

A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons.
A High Precision and Multifunctional Electro-Optical Conversion Efficiency Measurement System for Metamaterial-Based Thermal Emitters
Authors: Liu Heng , Zhao Meng , Gong Yongkang , Li Kang , Wang Cong , Wei Yuchen , Wang Jun , Liu Guozhen , Yao Jinlei , Li Ying , Li Zheyi , Gao Zhiqiang , Gao Ju
Published: February 2022

In this study, a multifunctional high-vacuum system was established to measure the electro-optical conversion efficiency of metamaterial-based thermal emitters with built-in heaters. The system is composed of an environmental control module, an electro-optical conversion measurement module, and a system control module. The system can provide air, argon, high vacuum, and other conventional testing environments, combined with humidity control. The test chamber and sample holder are carefully designed to minimize heat transfer through thermal conduction and convection. The optical power measurements are realized using the combination of a water-cooled KBr flange, an integrating sphere, and thermopile detectors. This structure is very stable and can detect light emission at the μW level. The system can synchronously detect the heating voltage, heating current, optical power, sample temperatures (both top and bottom), ambient pressure, humidity, and other environmental parameters. The comprehensive parameter detection capability enables the system to monitor subtle sample changes and perform failure mechanism analysis with the aid of offline material analysis using scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction. Furthermore, the system can be used for fatigue and high-low temperature impact tests.
Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir
Authors: Yang Beibei , Xiao Ting , Wang Luqi , Huang Wei
Published: February 2022

It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method—the gated recurrent unit (GRU)—with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods—long short-term memory neural networks (LSTM) and random forest (RF)—were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.
Long-Latency Event-Related Potentials (300–1000 ms) of the Visual Insight
Authors: Lytaev Sergey
Published: February 2022

The line of insight research methods that have high temporal and surface resolution is not large—these are EEGs, EPs, and fMRI, as well as their combinations and various options for assessing temporal events of random understanding. The objective of this research was to study the classification of insight for visual illusory images consisting of several objects simultaneously according to the analysis of early, middle, late, and ultra-late components (up to 1000 ms) of event-related potentials (ERPs). ERP research on 42 healthy subjects (men) aged 20–28 years was performed. The stimuli were a line of visual images with an incomplete set of signs, as well as images-illusions, which, with different perceptions, represent different images. The results showed the similarity of the tests to correct recognition of fragments of unrecognition and double images. At the intermediate stage of perception (100–200 ms), in both cases, the activity of the central and frontal cortex decreased, mainly in the left hemisphere. At the later stages of information processing (300–500 ms), the temporal-parietal and occipital brain parts on the right were activated, with the difference that when double objects were perceived, this process expanded to 700–800 ms with the activation of the central and occipital fields of the right hemisphere. Outcomes allowed discussing two possible options for actualizing the mechanisms of long-term memory that ensure the formation of insight—the simultaneous perception of images as part of an illusion. The first of them is associated with the inhibition of the frontal cortex at the stage of synthesis of information flows, with the subsequent activation of the occipital brain parts. The second variant is traditional and manifests itself in the activation of the frontal brain areas, with the subsequent excitation of all brain fields by the mechanisms of exhaustive search.
Generation and Characterization of Stable Redox-Reporter Mammalian Cell Lines of Biotechnological Relevance
Authors: Perelmuter Karen , Tiscornia Inés , Comini Marcelo A. , Bollati-Fogolín Mariela
Published: February 2022

Cellular functions such as DNA replication and protein translation are influenced by changes in the intracellular redox milieu. Exogenous (i.e., nutrients, deterioration of media components, xenobiotics) and endogenous factors (i.e., metabolism, growth) may alter the redox homeostasis of cells. Thus, monitoring redox changes in real time and in situ is deemed essential for optimizing the production of recombinant proteins. Recently, different redox-sensitive variants of green fluorescent proteins (e.g., rxYFP, roGFP2, and rxmRuby2) have been engineered and proved suitable to detect, in a non-invasive manner, perturbations in the pool of reduced and oxidized glutathione, the major low molecular mass thiol in mammals. In this study, we validate the use of cytosolic rxYFP on two cell lines widely used in biomanufacturing processes, namely, CHO-K1 cells expressing the human granulocyte macrophage colony-stimulating factor (hGM-CSF) and HEK-293. Flow cytometry was selected as the read-out technique for rxYFP signal given its high-throughput and statistical robustness. Growth kinetics and cellular metabolism (glucose consumption, lactate and ammonia production) of the redox reporter cells were comparable to those of the parental cell lines. The hGM-CSF production was not affected by the expression of the biosensor. The redox reporter cell lines showed a sensitive and reversible response to different redox stimuli (reducing and oxidant reagents). Under batch culture conditions, a significant and progressive oxidation of the biosensor occurred when CHO-K1-hGM-CSF cells entered the late-log phase. Medium replenishment restored, albeit partially, the intracellular redox homeostasis. Our study highlights the utility of genetically encoded redox biosensors to guide metabolic engineering or intervention strategies aimed at optimizing cell viability, growth, and productivity.
Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN
Authors: Singh Ritesh Kumar , Rahmani Mohammad Hasan , Weyn Maarten , Berkvens Rafael
Published: February 2022

In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease and poor nutrition of plants. Neural networks and other AI techniques need an initial set of measurement campaigns along with extensive datasets as a training set to baseline and evolve different applications. This paper presents LoRaWAN-based greenhouse monitoring datasets over a period of nine months. The dataset has both the network and sensing information from multiple sensor nodes for tomato crops in two different greenhouse environments. The goal is to provide the research community with a dataset to evaluate performance of LoRaWAN inside a greenhouse and develop more efficient PA monitoring techniques. In this paper, we carried out an exploratory data analysis to infer crop growth by analyzing just the LoRaWAN signals and without inclusion of any extra hardware. This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse. We developed this proof of concept of joint communication and sensing by using generated dataset from the “Proefcentrum Hoogstraten” greenhouse in Belgium. Results for the proposed method yield a root mean square error of 10% in detecting the average plant height inside a greenhouse. In future, we can use this concept of landscape sensing for different supplementary use-cases and to develop optimized methods.
Research on Position and Torque Loading System with Velocity-Sensitive and Adaptive Robust Control
Authors: Li Zijia , Chen Guangrong , Zhang Chenyang
Published: February 2022

In this paper, the research emphasis focuses on the tracking precision of position loading and torque loading systems with velocity-sensitive and adaptive robust control. Compared with conventional PID control, the improved PID control based on velocity-sensitive fully manifests its superiority to position loading. By contrast, we analyzed the possible influence for the control difference of conventional PID and velocity-sensitive with PID. Furthermore, for the purpose of accurate torque loading, a mathematical model was established through dynamics analysis and the adaptive robust controller, where the adaptive robust control algorithm is designed to generate the reference position trajectory for the servo system in the upper controller while closed-loop position tracking is performed in the underlying controller, was built based on a state space equation. In the end, an experimental platform was built to verify the feasibility and advantages of the position and torque loading system with the velocity-sensitive and adaptive robust control.
Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches
Authors: Kotenko Igor , Izrailov Konstantin , Buinevich Mikhail
Published: February 2022

Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.
Optimized Isolation of Safranal from Saffron by Solid-Phase Microextraction (SPME) and Rotatable Central Composite Design-Response Surface Methodology (RCCD-RSM)
Authors: Revelou Panagiota-Kyriaki , Mouzoula Spyridoula , Xagoraris Marinos , Evangelaras Haralambos , Papadopoulos George K. , Pappas Christos S. , Tarantilis Petros A.
Published: February 2022

Safranal is the main aroma component of saffron stigmas. It is also a great antioxidant with known pharmacological properties and is a potent indicator for the grading and authentication of saffron. In this study, the optimum extraction conditions of safranal from saffron stigmas were investigated using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) and response surface methodology (RSM). A rotatable-central composite design was applied, and a linear regression model has been used for the model building. The optimized factors were as follows: sample weight (15 mg), water volume (4 mL), exposure time in the headspace (20 min), and extraction temperature (45 °C). All factors were found significant; however, extraction temperature and exposure time were the most important for the isolation of safranal. The obtained model was successfully validated with a test set of saffron samples analyzed under the optimum extraction conditions. The optimized SPME extraction conditions of safranal found in this study contribute to the efforts towards the detection of saffron authentication and adulteration.
The Past, Present and Future Direction of Government-Supported Active Aging Initiatives in Japan: A Work in Progress
Authors: Someya Yoshiko , Hayashida Cullen T.
Published: February 2022

Active aging programs are seen as an important strategy for the long-term sustainability of Japan given population aging and fertility decline trends. This paper reviews Japan’s commitment to active aging initiatives since the 1960s with a focus on the development of senior clubs, welfare centers for the elderly and senior colleges. The changing patterns of their popularity are discussed in relation to the increased options available today and the changes taking place in the family structure with both a macro historical review and a case study to demonstrate how programs have been implemented with national and local funding support. A description of the U.S. experience is used to demonstrate the comparative level of commitment that Japan has made to support healthy aging. The recrafting of the active aging motif as shogai gen’eki, with its emphasis on continued employment, may suggest a redirection of the preferred role of Japan’s older adults in the future.
Occupational Risk Assessment in School Food Services: Instruments’ Construction and Internal Validation
Authors: Ferreira Jeane dos Santos , Araújo Maria da Purificação Nazaré , Barroso Rosemary da Rocha Fonseca , Botelho Raquel Braz Assunção , Zandonadi Renata Puppin , Raposo António , Han Heesup , Araya-Castillo Luis , Ariza-Montes Antonio , Akutsu Rita de Cássia Coelho de Almeida
Published: February 2022

Occupational risk assessment in school food services (SFS) should include factors related to occupational hygiene, physical–functional planning, worker health, and good meal production practices. This study aimed to develop an instrument to assess occupational risk in school food services and perform content validation and semantic evaluation. The research included three steps: (i) instrument’s development; (ii) content validation (Delphi); and (iii) semantic evaluation (focus group). After an extensive literature review, four instruments were developed. The Delphi was validated by the Content Validity Coefficient (CVC), with an agreement greater than 90%. The semantic analysis was performed through four meetings using a focus group. After the content and semantic evaluation, there were four final instruments: Identification of Socioeconomic and Demographic Conditions of Food Handlers and Geographical Mapping of SFS (30 items); Knowledge and Attitudes in Food Hygiene Assessment (33 items); Perception of Occupational Risks (16 items); and Mapping of Occupational Risks in SFS (97 items). Instrument’s content validation and semantic evaluation contributed to constructing and validating forms for analyzing occupational risks and adjusting respondents’ language and sociocultural components.
Building Envelope and the Outdoor Microclimate Variable of Vernacular Houses: Analysis on the Environmental Elements in Tropical Coastal and Mountain Areas of Indonesia
Authors: Hermawan Hermawan , Švajlenka Jozef
Published: February 2022

Temperature and humidity are essential factors in analyzing a building’s thermal performance. This research presents the differences in field measurements of vernacular houses in coastal and mountain areas in Indonesia. Field measurements were taken for five consecutive days in four vernacular houses. The variables were measured at the beginning and at the peak of the rainy season. Analysis included a combination of graphic and descriptive methods. The research results show that the location difference between coast and mountain results in a relatively high difference in temperature (43.6%). The outdoor temperature in the mountain area is lower than that of the coastal area. The outdoor humidity of the mountain area is 0.69% higher than that of the coastal area. In the tropical coastal area, the outdoor temperature of the exposed-brick house is 0.94% lower than that of the coastal wooden house. The outdoor air humidity of the brick house is 0.89% higher than that of the coastal wooden house. In the tropical mountain area, the outdoor temperature of the exposed-stone house is 2.47% lower than that of the wooden house. The outdoor air humidity of the stone house is 0.4% lower than that of the wooden house. The outdoor conditions affect the indoor conditions of the respective houses. These microclimatic differences are influenced by micro-environmental factors, such as the density of surrounding buildings, amount of vegetation, and shading. The research shows that height difference is the most dominant factor influencing outdoor microclimate. Regional microclimate becomes the basis for determining the most suitable envelope materials in different areas. The innovative contribution of the work is, among other benefits, the identification of factors that influence the wellbeing of the buildings’ users in the researched geographical area and the analysis of the interaction of the external and internal environment of buildings. From the above facts, it follows that the results of this work can contribute to the development of prediction models to determine the type of cover, material, shape, and load-bearing elements needed to create comfortable and energy-efficient buildings.
Analysis of Malfunctions in Selected Parking Systems in the Czech Republic
Authors: Mikšíková Simona , Ulčák David , Kuda František
Published: February 2022

The underlying cause of the problem with parking is the imbalance between demand and supply in key areas where demand for parking is high, but parking is limited due to excessive land prices. Newly-developed automated multi-storey parking facilities are able to automatically pick up and place cars on different storeys, with minimal requirements on space. In the paper we concentrate on Automated Parking Systems and their malfunction rate. Specifically, we analyzed two automated systems located in the Czech Republic, in the towns of Brno and Slaný. We employed time series and various testing hypotheses to compare the malfunction rate of the systems, and used information from practice. We conclude with an evaluation and a brief description of the optimization of systems and the use of innovative tools.
Spatial Spillover Effect and Threshold Effect of Digital Financial Inclusion on Farmers’ Income Growth—Based on Provincial Data of China
Authors: Li Yanling , Wang Mengxin , Liao Gaoke , Wang Junxia
Published: February 2022

The effect of digital financial inclusion on farmers’ income growth is worth theoretical discussion and empirical testing. Based on the sample data of 30 provinces in the Chinese mainland from 2011 to 2019, this paper adopted the spatial Durbin model and the panel threshold model to empirically study the impact of digital financial inclusion on the growth of farmers’ income; furthermore, the heterogeneity of the impact was studied in terms of the difference of provincial economic development level. The results showed that: first, digital financial inclusion can significantly promote farmers’ income growth; second, digital financial inclusion has a positive spatial spillover effect on farmers’ income growth; third, the impact of digital financial inclusion on farmers’ income growth has a double threshold effect: farmers’ income growth increases with the development of digital financial inclusion; and fourth, the impact of digital financial inclusion on farmers’ income growth is heterogeneous in terms of provincial economic development level.
A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora)
Authors: Lerone Laura , Mengoni Chiara , Di Febbraro Mirko , Krupa Hannah , Loy Anna
Published: February 2022

The Eurasian otter is endangered in Italy, only surviving in southern river basins. The spatial and social structure of a population living at the border of the current range was explored through a noninvasive genetic study along 174 km of the Sangro river. Sampling was conducted in 2011 and 2012, collecting spraints and anal jellies at 62 marking sites. Samples were successfully genotyped at 13 nuclear microsatellites and the ZFX/ZFY locus for molecular sexing, resulting in 14 distinct genotypes (4 females, 2 possible females, 8 males), from 35 marking sites. Mean captures/recaptures rate was 3.8 captures/individual, with males being recaptured more frequently than females. Spatial overlap among individuals was analyzed through a linear regression model fitted against sibship categories and sex pairing. Nine out of the fourteen genotyped individuals belonged to three full-sib clusters, while five individuals had no full-sibs in the population. Full-sibs overlapped more than half-sibs, while male–male pairs showed significantly higher spatial overlap than both male–female and female–female pairs. Estimated mean density was 0.152 otters/km and 2.4 individuals/10 × 10 km grid cell. Accordingly, the 3440 grid cells of otter occurrence in Italy could likely host about 8000 otters, suggesting the current population has become larger than the minimum viable population size.
How Fashionable Are We? Validating the Fashion Interest Scale through Multivariate Statistics
Authors: Weber Sabine , Weber Olaf
Published: February 2022

A person’s fashion interest describes how familiar a person is with fashion. There are major differences among consumers in terms of fashion interest that can be used as a segmentation criterion for markets. Understanding the drivers of clothing consumption can be used to develop strategies to address consumption habits, including overconsumption. Consequently, many studies have developed questionnaires and interview guidelines to define fashion interest or other fashion-related attitudes and behaviors. However, there is a gap in research about validating fashion scales. This study validates a fashion interest scale by comparing a random sample with a control group of fashion students, demonstrating differentiation between groups. We used principal component analysis (PCA) to explore the scale’s homogeneity and t-tests and analysis of variance (ANOVA) to validate the scale. The results suggest that the scale is homogeneous and has high validity. We conclude that the scale can be used as a tool to segment markets to gain faster and higher quality data and as a benchmark for other studies.
Environmental Performance of a Cost-Effective Energy Renovation at the Neighbourhood Scale—The Case for Social Housing in Braga, Portugal
Authors: Barbosa Ricardo , Almeida Manuela , Briones-Llorente Raúl , Mateus Ricardo
Published: February 2022

It is increasingly recognised that the energy renovation of the existing building stock will be determinant for achieving 2030 and 2050 decarbonisation targets in Europe. As operational energy is being dramatically reduced through regulatory efforts and funding from the European Union, the relevance of the environmental performance of these interventions becomes higher, namely regarding embodied energy and carbon emissions associated with the materials that compose the renovation solutions. Although some studies address these impacts in buildings, the range of studies focusing on the neighbourhood scale is limited. This article presents a methodological framework combining a life cycle cost assessment (LCC) and a life cycle assessment (LCA). The purpose is to assess the relevance of embodied energy and carbon emissions on the cost-effectiveness of building renovation solutions towards nZEB at the neighbourhood scale by comparing an operational energy approach and a whole life cycle approach in a case study of a social housing neighbourhood in Braga, Portugal. The results suggest an increase in indicators values demonstrating a negative impact on the achievable reduction of both energy and emissions when the whole life cycle approach is considered, which can constitute a critical point for policy formulation in the decarbonisation of the built environment.
Exploring the Relationship between Abusive Management, Self-Efficacy and Organizational Performance in the Context of Human–Machine Interaction Technology and Artificial Intelligence with the Effect of Ergonomics
Authors: Lin Shanyu , Döngül Esra Sipahi , Uygun Serdar Vural , Öztürk Mutlu Başaran , Huy Dinh Tran Ngoc , Tuan Pham Van
Published: February 2022

(1) Background: Our study aims to explore the impact of abusive management and self-efficacy on corporate performance in the context of artificial intelligence-based human–machine interaction technology in enterprise performance evaluation. (2) Methods: Surveys were distributed to 578 participants in selected international companies in Turkey, Taiwan, Japan, and China. To reduce uncertainty and errors, the surveys were rigorously evaluated and did not show a normal distribution, as it was determined that 85 participants did not consciously fill out the questionnaires, and the questionnaires from the remaining 493 participants were used. By using the evaluation model of employee satisfaction based on a back propagation (BP) neural network, we explored the manifestation and impact of abusive management and self-efficacy. Using the listed real estate businesses as an example, we proposed a deep learning BP neural network-based employee job satisfaction evaluation model and a human–machine technology-based employee performance evaluation system under situational perception, according to the design requirements of human–machine interaction. (3) Results: The results show that the human–machine interface can log in according to the correct verbal instructions of the employees. In terms of age and education level variables, employees’ perceptions of leaders’ abusive management and self-efficacy are significantly different from their job performances, respectively (p < 0.01). (4) Conclusions: artificial intelligence (AI)-based human–machine interaction technology, malicious management, and self-efficacy directly affect enterprise performance and employee satisfaction.
Study on Frost-Suppression Characteristics of Superhydrophobic Aluminum Surface Heat Exchanger Applied in Air Source Heat Pump
Authors: Gu Yaxiu , He Guixiang , Li Shuaipeng , Ding Weiqi , Li Hanlin , Duan Jiahui
Published: February 2022

In order to solve the frosting problem of air source heat pump (ASHP) outdoor heat exchange under low-temperature and low-humidity conditions, a superhydrophobic aluminum (Al) surface with a contact angle (CA) of 158.3° was prepared by chemical etching. The microscopic characteristics of droplet condensation and the freezing process of a superhydrophobic surface were revealed through visual experiments and theoretical analysis. On this basis, the frost-suppression effect of a superhydrophobic Al-based surface simulating the distribution of actual heat exchanger fins was preliminarily explored. The results demonstrated that, due to the large nucleation energy barrier and the coalescence-bounce behavior of droplets, the condensed droplets on the superhydrophobic surface appeared late and their quantity was low. The thermal conductivity of the droplets on a superhydrophobic surface was large, so their freezing rate was low. The frosting amount on the superhydrophobic Al-based surface was 69.79% of that of the bare Al-based surface. In turn, the time required for melting the frost layer on the superhydrophobic Al-based surface was 64% of that on the bare Al-based surface. The results of this study lay an experimental and theoretical foundation for the application of superhydrophobic technology on the scale of heat exchangers.
Innovative Industrial Use of Bamboo as Key “Green” Material
Authors: Borowski Piotr F. , Patuk Iaroslav , Bandala Erick R.
Published: February 2022

This study investigates the use of bamboo in different industrial sectors, including construction and energy, to highlight its mechanical properties, resources, and innovative use through information gathered from Ethiopia, Guinea, and Georgia (Caucasus) as study cases. Research shows that bamboo is a common plant and an easily accessible material possessing remarkable characteristics for different applications in different countries. The main goal of this study is to highlight the properties of bamboo that make it an interesting material with applications in several economic branches as a green material positively influencing the environment. The results of this study show a utilitarian use of bamboo in industries where production is based on bamboo or bamboo-related materials and wide possibilities for using bamboo in innovative and creative ways. Bamboo’s mechanical and physicochemical properties are discussed as well as its potential as a raw material for use in composites or for the production and processing of semi-finished products and parts of end devices, all with a view to its positive environmental impact.