bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidative and Microbial Stability of a Traditional Appetizer: Aubergine Salad
Authors: Bozinou Eleni , Chatzimitakos Theodoros , Alexandraki Maria , Salakidou Chrysanthi , Dourtoglou Vassilis G. , Lalas Stavros I. , Elhakem Abeer , Sami Rokayya , Ashour Amal Adnan , Shafie Alaa , Athanasiadis Vassilis
Published: June 2022

An eggplant-based salad, called aubergine salad (AS), is a traditional appetizer and as such, is quite popular in the Mediterranean area. It is widely produced either on a home scale or on an industrial scale and widely consumed. However, there are cases where preservatives (such as sodium benzoate and potassium sorbate) are added in order to extend the shelf life of the product. In the present study, the stability of this delicatessen against oxidation and microbial spoilage was evaluated, with or without preservatives. The physicochemical properties of the salad were evaluated, along with the tocopherol content, resistance to oxidation, and microbial count. According to the results, it is evident that the induction period of AS is 16% (in the case that preservatives were used) and 26% (in the case without preservatives) increased, compared to a control sample (plain soybean oil). This can be attributed to the increased content in tocopherols, and more specifically to α-tocopherol. Furthermore, the addition of preservatives resulted in increased storage days and a reduction of microorganisms. However, in both cases, the AS-prepared salad exhibited a self-stabilization ability after 13 days, negating the need for preservatives.
Study on Characteristics and Control Strategy of Diesel Particulate Filters Based on Engine Bench
Authors: Sun Hao , Liu Yingshuai , Li Ning , Tan Jianwei
Published: June 2022

The ignition temperature of a diesel oxidation catalyst (DOC) and the internal temperature-field distribution of the diesel particulate filter (DPF) during active regeneration are investigated during an engine bench test in this study. Based on the dropped to idle (DTI) test, a test method is developed to determine the safe regeneration temperature of the DPF. The results show that when the inlet temperature of the DOC is more than 240 °C, the DOC begins ignition and reaches the target temperature of 600 °C set for active regeneration of DPF; when the inlet exhaust temperature of the DOC is between 240 and 280 °C, a higher injection rate is required to reduce the secondary pollution of HC and thus make the DPF reach the set target temperature as soon as possible. The active regeneration process of the DPF is divided into three stages. During ignition, the temperature of the DPF inlet and outlet increases rapidly and successively. The internal and outlet temperatures of DPF during regeneration are approximately 50 °C higher than the inlet temperature. At the end of regeneration, the DPF inlet to outlet temperature drops rapidly. A feed-forward design and feedback algorithm are used to verify the change in the target regeneration temperature. The overshoot of the DPF control strategy was less than 3%, and the steady-state temperature control error was less than 20 °C. The results of this study provide a basis for the safe control of DPFs’ active regeneration temperatures.
Pasteurellosis Vaccine Commercialization: Physiochemical Factors for Optimum Production
Authors: Oslan Siti Nur Hazwani , Tan Joo Shun , Yusoff Abdul Hafidz , Sulaiman Ahmad Ziad , Awang Mohd Azrie , Lazim Azwan Mat , Lim Si Jie , Oslan Siti Nurbaya , Saad Mohd Zamri , Ariff Arbakariya B.
Published: June 2022

Pasteurella spp. are Gram-negative facultative bacteria that cause severe economic and animal losses. Pasteurella-based vaccines are the most promising solution for controlling Pasteurella spp. outbreaks. Remarkably, insufficient biomass cultivation (low cell viability and productivity) and lack of knowledge about the cultivation process have impacted the bulk production of animal vaccines. Bioprocess optimization in the shake flask and bioreactor is required to improve process efficiency while lowering production costs. However, its state of the art is limited in providing insights on its biomass upscaling, preventing a cost-effective vaccine with mass-produced bacteria from being developed. In general, in the optimum cultivation of Pasteurella spp., production factors such as pH (6.0–8.2), agitation speed (90–500 rpm), and temperature (35–40 °C) are used to improve production yield. Hence, this review discusses the production strategy of Pasteurella and Mannheimia species that can potentially be used in the vaccines for controlling pasteurellosis. The physicochemical factors related to operational parameter process conditions from a bioprocess engineering perspective that maximize yields with minimized production cost are also covered, with the expectation of facilitating the commercialization process.
A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions
Authors: Wang Jiayi , Jiang Junjie , Zyryanov Grigory V. , Liu Yuanli
Published: June 2022

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.
Research Questions with PICO: A Universal Mnemonic
Authors: Nishikawa-Pacher Andreas
Published: June 2022

A well-formulated research question should incorporate the components of a ‘problem’, an ‘intervention’, a ‘control’, and an ‘outcome’—at least according to the PICO mnemonic. The utility of this format, however, has been said to be limited to clinical studies that pose ‘which’ questions demanding correlational study designs. In contrast, its suitability for descriptive approaches outside of clinical investigations has been doubted. This paper disagrees with the alleged limitations of PICO. Instead, it argues that the scheme can be used universally for every scientific endeavour in any discipline with all study designs. This argument draws from four abstract components common to every research, namely, a research object, a theory/method, a (null) hypothesis, and the goal of knowledge generation. Various examples of how highly heterogenous studies from different disciplines can be grounded in the single scheme of PICO are offered. The finding implies that PICO is indeed a universal technique that can be used for teaching academic writing in any discipline, beyond clinical settings, regardless of a preferred study design.
Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables
Authors: Thao Nguyen Thi Thanh , Khoi Dao Nguyen , Denis Antoine , Viet Luong Van , Wellens Joost , Tychon Bernard
Published: June 2022

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.
Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data
Authors: Asam Sarah , Gessner Ursula , Almengor González Roger , Wenzl Martina , Kriese Jennifer , Kuenzer Claudia
Published: June 2022

Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.
Convolutional Neural Network and Optical Flow for the Assessment of Wave and Tide Parameters from Video Analysis (LEUCOTEA): An Innovative Tool for Coastal Monitoring
Authors: Scardino Giovanni , Scicchitano Giovanni , Chirivì Marco , Costa Pedro J. M. , Luparelli Antonio , Mastronuzzi Giuseppe
Published: June 2022

Coastal monitoring is a topic continuously developing, which has been applied using different approaches to assess the meteo-marine features, for example, to contribute to the development of improved management strategies. Among these different approaches, coastal video monitoring coupled with recent machine learning and computer vision techniques has spread widely to assess the meteo-marine features. Video monitoring allows to obtain large spatially and temporally datasets well-distributed along the coasts. The video records can compile a series of continuous frames where tide phases, wave parameters, and storm features are clearly observable. In this work, we present LEUCOTEA, an innovative system composed of a combined approach between Geophysical surveys, Convolutional Neural Network (CNN), and Optical Flow techniques to assess tide and storm parameters by a video record. Tide phases and storm surge were obtained through CNN classification techniques, while Optical Flow techniques were used to assess the wave flow and wave height impacting the coasts. Neural network predictions were compared with tide gauge records. Furthermore, water levels and wave heights were validated through spatial reference points obtained from pre-event topographic surveys in the proximity of surveillance cameras. This approach improved the calibration between network results and field data. Results were evaluated through a Root Mean Square Error analysis and analyses of the correlation coefficient between results and field data. LEUCOTEA system has been developed in the Mediterranean Sea through the use of video records acquired by surveillance cameras located in the proximity of south-eastern Sicily (Italy) and subsequently applied on the Atlantic coasts of Portugal to test the use of action cameras with the CNN and show the difference in terms of wave settings when compared with the Mediterranean coasts. The application of CNN and Optical Flow techniques could represent an improvement in the application of monitoring techniques in coastal environments, permitting to automatically collect a continuous record of data that are usually not densely distributed or available.
Open Logistics: Blockchain-Enabled Trusted Hyperconnected Logistics Platform
Authors: Barenji Ali V. , Montreuil Benoit
Published: June 2022

The digitalization and adoption of advanced technologies in supply chain and logistics not only change the business model but also transfer logistics infrastructure to a service-oriented architecture and introduce new avenues concerning supply chain 4.0 (SC4.0). Sharing logistic assets between various businesses leads to improving logistics work, enhancing work productivity, and reducing logistics expenses and environmental impact. However, due to the lack of a secure, trustworthy, and open sharing platform, the companies are not willing to rely on sharing economics. Aiming to improve trust-ability, openness, and interoperability in the SC4.0, this paper presents a blockchain-enabled hyperconnected logistics platform. Firstly, the Open Logistic platform (OL) is proposed, and the key characteristics of this platform are explained. Secondly, the concept of proof of delivery (PoD) based on smart contracts is defined and developed to explore its rule-based management and control among the dynamic assets sharing. Thirdly, the Blockchain asset sharing service is designed and discussed in the context of asset sharing. Fourthly to evaluate the feasibility of the proposed platform, a simulation environment is developed, and OL is implemented based on the case study.
A Novel 65 nm Active-Inductor-Based VCO with Improved Q-Factor for 24 GHz Automotive Radar Applications
Authors: Behera Prangyadarsini , Siddique Abrar , Delwar Tahesin Samira , Biswal Manas Ranjan , Choi Yeji , Ryu Jee-Youl
Published: June 2022

The inductor was primarily developed on a low-voltage CMOS tunable active inductor (CTAI) for radar applications. Technically, the factors to be considered for VCO design are power consumption, low silicon area, high frequency with reasonable phase noise, an immense quality (Q) factor, and a large frequency tuning range (FTR). We used CMOS tunable active inductor (TAI) topology relying on cascode methodology for 24 GHz frequency operation. The newly configured TAI adopts the additive capacitor (Cad) with the cascode approach, and in the subthreshold region, one of the transistors functions as the TAI. The study, simulations, and measurements were performed using 65nm CMOS technology. The assembled circuit yields a spectrum from 21.79 to 29.92 GHz output frequency that enables sustainable platforms for K-band and Ka-band operations. The proposed design of TAI demonstrates a maximum Q-factor of 6825, and desirable phase noise variations of −112.43 and −133.27 dBc/Hz at 1 and 10 MHz offset frequencies for the VCO, respectively. Further, it includes enhanced power consumption that varies from 12.61 to 23.12 mW and a noise figure (NF) of 3.28 dB for a 24 GHz radar application under a low supply voltage of 0.9 V.
A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions
Authors: Lee Chun-Yao , Zhuo Guang-Lin , Le Truong-An
Published: June 2022

This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of −4 dB.
Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications
Authors: Moore Megan O. , Buehrer R. Michael , Headley William Chris
Published: June 2022

Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization.
Trust Model Concept for IoT Blockchain Applications as Part of the Digital Transformation of Metrology
Authors: Miličević Kruno , Omrčen Luka , Kohler Mirko , Lukić Ivica
Published: June 2022

Trends for the digital transformation of metrology and regulation of metrology through IT have some keywords in common with the main properties of the blockchain, such as traceability, immutability, and machine-readable documents. The possible applicability of the blockchain as an innovative IT solution for metrology regulation is known in the scientific community. Still, blockchain implementation must consider the entire metrology pyramid—the technical aspects and the legal framework intrinsic to metrology. This is also valid for possible IoT blockchain applications. In resolving the issues, this paper applies a bottom-up approach, starting from IoT devices analyzed as oracles and building up to the sole definition of measurement units, thereby discussing technical aspects concerning relevant standardization documents. The resulting trust model concept encompasses the vertical and horizontal traceability of the measurement results (oracle data), where normative standards and legal requirements are crucial for building trust. Conclusively, for practical implementations, it will be necessary to analyze blockchain properties and applicability with a view to the standard requirements, as shown for WELMEC.
Planning and Optimization of Software-Defined and Virtualized IoT Gateway Deployment for Smart Campuses
Authors: Ferreira Divino , Oliveira João Lucas , Santos  Carlos , Filho Tércio , Ribeiro Maria , Freitas Leandro Alexandre , Moreira Waldir , Oliveira-Jr Antonio
Published: June 2022

The Internet of Things (IoT) is based on objects or “things” that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goiás as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.
Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose
Authors: Wei Lei , Chen Jun , Ding Yi , Wang Fei , Zhou Jianjiang
Published: June 2022

Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly proposes a state transition matrix update-based extended Kalman filter (STMU-EKF) passive tracking algorithm. In this algorithm, the multi-feature fusion-based trajectory clustering is proposed to estimate the target state, and the state transition matrix is updated according to the estimated value of the motion model and the observation value of multi-station passive sensors. On this basis, considering that only using passive sensors for target tracking cannot often meet the requirements of high target tracking accuracy, this paper introduces active radar for indirect radiation and proposes a multi-sensor collaborative management model based on trajectory clustering. The model performs the optimal allocation of active radar and passive sensors by judging the accumulated errors of the eigenvalue of the error covariance matrix and makes the decision to update the state transition matrix according to the magnitude of the fluctuation parameter of the error difference between the prediction value and the observation value. The simulation results verify that the proposed multi-sensor collaborative target tracking algorithm can effectively improve the high-maneuvering target tracking accuracy to satisfy the radar’s LPI performance.
Characteristics of an Eight-Quadrant Corner Reflector Involving a Reconfigurable Active Metasurface
Authors: Gan Lin , Sun Guang , Feng Dejun , Li Jianbing
Published: June 2022

The traditional corner reflector is a type of classical passive jamming equipment but with several shortcomings, such as fixed electromagnetic characteristics and a poor response to radar polarization. In this paper, an eight-quadrant corner reflector equipped with an electronically controlled miniaturized active frequency-selective surface (MAFSS) for X band is proposed to obtain better radar characteristics controllability and polarization adaptability. The scattering characteristics of the new eight-quadrant corner reflector for different switchable scattering states (penetration/reflection), frequency and polarization are simulated and analyzed. Results show that the RCS modulation depth, which is jointly affected by the electromagnetic wave frequency and incident directions, can be maintained above 10 dB in the majority of directions, and even larger than 30 dB at the resonant frequency. Moreover, the RCS adjustable bandwidth can be as wide as 1 GHz in different incident directions.
Recognition Method of Knob Gear in Substation Based on YOLOv4 and Darknet53-DUC-DSNT
Authors: Qin Ronglin , Hua Zexi , Sun Ziwei , He Rujiang
Published: June 2022

When unattended substations are popular, the knob is a vital monitoring object for unattended substations. However, in the actual scene of the substation, the recognition method of a knob gear has low accuracy. The main reasons are as follows. Firstly, the SNR of knob images is low due to the influence of lighting conditions, which are challenging to extract image features. Secondly, the image deviates from the front view affected by the shooting angle; that knob has a certain deformation, which causes the feature judgment to be disturbed. Finally, the feature distribution of each kind of knob is inconsistent, which interferes with image extraction features and leads to weak spatial generalization ability. For the above problems, we propose a three-stage knob gear recognition method based on YOLOv4 and Darknet53-DUC-DSNT models for the first time and apply key point detection of deep learning to knob gear recognition for the first time. Firstly, YOLOv4 is used as the knob area detector to find knobs from a picture of a cabinet panel. Then, Darknet53, which can extract features, is used as the backbone network for keypoint detection of knobs, combined with DUC structure to recover detailed information and DSNT structure to enhance feature extraction and improve spatial generalization ability. Finally, we obtained the knob gear by calculating the angle between the line of the rotating center point and the pointing point and horizontal direction. The experimental results show that this method effectively solves the above problems and improves the performance of knob gear detection.
The Validity of the Push Band 2.0 on the Reactive Strength Index Assessment in Drop Jump
Authors: Montoro-Bombú Raynier , de la Paz Arencibia Lázaro , Buzzichelli Carlo , Miranda-Oliveira Paulo , Fernandes Orlando , Santos Amândio , Rama Luis
Published: June 2022

This study aims to verify the validity of the Push Band 2.0 (PB2.0) device on the reactive strength index (RSI) measurement, using a force plate (FP) and an optical sensor device, OptoJump (OPT), as a reference. Twenty trained athletes performed 60 drop jump trials with a height box of 30 cm. A randomized repeated measures study was conducted during a single session using the PB2.0, the OPT, and the plate force manually synchronized to obtain RSI data for each jump. Validity was analyzed by contrasting three measures: the intra-class correlation coefficient (ICC), the Bland–Altman test, and R2 coefficient of determination. Bland–Altman analysis showed that RSI and FP for PB2.0 (media = −0.047; IC 93.34%) of all data were within the confidence interval, indicating a statistically reliable result. The RSI measured by the OPT and PB2.0 also provided similar values (media = −0.047). These data are identical to other validity measures (ICC and linear correlation) but differ in the R2 values. The explained variation of PB2.0 measures attained only 29.3% of the FP (R2 = 0.293) and 29.5% (R2 = 0.295) of the OPT assessment, showing a very low determination coefficient. The results of this study point to caution in the use of PB2.0 when measuring RSI in scientific research.
The Effect of Blade Alignment on Kinematics and Plantar Pressure during the Execution of Goaltender-Specific Movement Patterns: A Case Study
Authors: Dunne Colin , Holmes Michael , Lockwood Kelly
Published: June 2022

Innovations in material properties of goaltender skates have improved the protective characteristics of the boot, leading to redesign of the blade holder to resemble players’ holders. The redesigned blade holder introduces the ability to customize blade alignment, which may grant a performance advantage. We investigated the effect of blade alignment on kinematics and plantar pressure during the execution of two different goaltender-specific movement patterns: (1) the butterfly drop to recovery and (2) the lateral butterfly slide to recovery. The main objective of this study was to investigate the effect of three blade alignment conditions. The secondary objective was to compare two neutral alignment conditions, which was facilitated by studying the effects of two different holders on kinematics and plantar pressure during two goaltender-specific techniques. A male goaltender with professional experience completed an A–B–A design, executing five trials of A, B, and A for both movements with each blade alignment condition (n = 30 per collection, n = 90 overall) on synthetic ice in a controlled lab environment. Blade alignment conditions were defined by the alignment of the blade holder on the boot and the type of blade holder. Kinematic and plantar pressure data were collected simultaneously using 3D motion capture and in-skate pressure insoles, respectively. Increased butterfly drop velocity (2.07 ± 0.09 m/s) and peak plantar pressure (77.19 ± 2.67 psi) were revealed when executing the butterfly drop with medial alignment. This work suggests medial blade alignment may enable the goaltender to drop into the butterfly position faster, potentially increasing the likelihood of making a save.
Digital Transformation and Rule of Law Based on Peak CO2 Emissions and Carbon Neutrality
Authors: Yang Jiwen , Zheng Chaoran , Liu Hua
Published: June 2022

The promotion and implementation of carbon neutrality against peaking carbon dioxide emissions urgently need the support of science and technology and the backing provided by a guarantee of rule of law. The proposition, logic, and progression of digital responses to peaking carbon dioxide levels in the search for carbon neutrality are clearly reflected in the current era, employing big data to address the problems of inadequate central–local coordination and interaction, the inadequate application of the rule of law, campaign-style “carbon reduction” promotion, and scientific and technological support in the promotion and implementation of peak carbon dioxide emissions and carbon neutrality. We need to pay attention to the coordination of digital technology, the rule of law response, and the protection of people’s rights. First, in the process of digital carbon dioxide peaking and carbon neutrality, it is necessary to improve the credibility of carbon dioxide peaking and carbon neutrality through the “whole-process trace” and storage mechanism techniques that are made possible by blockchain technology. Second, it is necessary to refine the management of peak carbon dioxide emissions and carbon neutrality through “decentralization” and consensus mechanisms. Third, it is necessary to improve the effectiveness of governance in the management of peak carbon dioxide emissions and carbon neutrality through “non-falsifiability” and collaboration mechanisms. Fourth and finally, the conclusions of this paper are offered. First, from the aspect of smart city construction, it is necessary to promote the coordinated construction of a low-carbon city and smart city and explore the legal ramifications of low-carbon development in urban governance. Second, in corporate governance, we need to build a low-carbon-development digital platform to promote the integration of digital technology and corporate compliance. Third, in terms of global governance, we need to promote the rule of law in cyberspace to address global climate change, the low-carbon development of digital technology, and the low-carbon construction of a cyber society. Fourth, we need to emphasize the rights and obligations of different parties in the implementation mechanism of the rule of law on digital carbon peaks and carbon neutrality.
Sociotechnological Sustainability in Pasture Management: Labor Input and Optimization Potential of Smart Tools to Measure Herbage Mass and Quality
Authors: Hart Leonie , Quendler Elisabeth , Umstaetter Christina
Published: June 2022

Investing labor time in herbage measurements is important for precision pasture management. In this study, the labor input of three smart herbage measurement tools—multispectral imagery linked to an unmanned aerial vehicle (UAV), a semi-automated rising plate meter (RPM), and near-infrared reflectance spectroscopy (NIRS) of cut herbage samples—and of direct observation was modeled based on the REFA work element method. Three to five users were observed during work execution to identify best-practice workflows. Time measurements were conducted using video footage. The resulting standard times of work elements were used to model labor input for herbage measurements in different farm sizes (i.e., milking platforms of 6–100 ha) and subdivisions of a farm’s milking platform (i.e., 4–45 paddocks). Labor time requirement differed between the smart farming tools (0.7–5.9 h) depending on the farm size and milking platform scenario. The labor time requirement increased for all tools with an increase in farm size and was lowest for the RPM. For the UAV tool, it did not increase noticeably when the division of the milking platform changed. Nevertheless, the potential to save time was identified for the UAV and the NIRS. Therefore, the automation of certain steps in the workflows would contribute to sociotechnological sustainable pasture management.
Effects of Photovoltaic Solar Farms on Microclimate and Vegetation Diversity
Authors: Vervloesem Jeffrey , Marcheggiani Ernesto , Choudhury MD Abdul Mueed , Muys Bart
Published: June 2022

The need for energy and the increasing importance of climate change mitigation are leading to a conversion from conventional to renewable energy sources. Solar photovoltaic (PV) power has seen the most significant increase among all renewable energy sources. However, most of these installations are land-based, significantly changing global land use (LU). The real impacts, whether positive or negative, are poorly understood. This study was undertaken to have a better understanding of the impacts of solar parks on the microclimate and vegetation dynamics. First, different solar parks were visited to take measurements of the surface temperature (Tsurf), photosynthetic active radiation (PAR), air temperature (Tair), and humidity (RH) to quantify the microclimate and perform a vegetation relevé. The measurements were taken at different positions: underneath, in between, and outside solar panels. For vegetation, the data were first converted to diversity indices, which in turn contributed to a multi-indicator land use impact assessment that evaluated effects on vegetation, biodiversity, soil and water. Solar parks had clear effects on microclimate: if the panels were high enough from the ground, they could lower the Tsurf by providing shade and enough airflow. Additionally, the multidimensional functional diversity (FD) analysis of the vegetation indicated that there was less light at a higher humidity and lower temperature underneath the panels. Interestingly, the species underneath the panels also preferred a lower pH and a higher nitrogen level. Finally, the land use impact assessment found that the total land use impact for a wheat field was higher than that of the solar park, which suggests that the conversion of conventional intensive agriculture to a solar park would be beneficial.
Housing Sustainability: The Effects of Speculation and Property Taxes on House Prices within and beyond the Jurisdiction
Authors: Rauf Muhammad Adil , Weber Olaf
Published: June 2022

Housing plays an essential role in sustainable governance due to its socio-economic and environmental connection. However, the relationship between governance policies, market behavior, and socio-economic outcomes varies geographically and demographically. Therefore, segregated policies developed and implemented may fail to achieve their desired objectives because of the sensitivity of housing policies for their connection to human wellbeing. The effectiveness of housing policies in geographically connected regions is one of the areas that has received little attention in the Canadian context. The study follows a multi-step empirical method using a multiple linear regression model and a difference-in-difference approach to assessing the geographical variation of speculation and property taxes on housing markets. The study confirms that speculation taxes are not an effective tool in curbing house prices. Similarly, considering the role of property taxes in providing public services, delinking property taxes from a potential contributor to house prices would provide a better lens to develop local housing policies. Furthermore, the study also confirms that the housing market can be better assessed at a local scale, considering geographical influence in conjunction with investment trends.
Review of Thermochemical Technologies for Water and Energy Integration Systems: Energy Storage and Recovery
Authors: Castro Oliveira Miguel , Iten Muriel , Matos Henrique A.
Published: June 2022

Thermochemical technologies (TCT) enable the promotion of the sustainability and the operation of energy systems, as well as in industrial sites. The thermochemical operations can be applied for energy storage and energy recovery (alternative fuel production from water/wastewater, in particular green hydrogen). TCTs are proven to have a higher energy density and long-term storage compared to standard thermal storage technologies (sensible and latent). Nonetheless, these require further research on their development for the increasing of the technology readiness level (TRL). Since TCTs operate with the same input/outputs streams as other thermal storages (for instance, wastewater and waste heat streams), these may be conceptually analyzed in terms of the integration in Water and Energy Integration System (WEIS). This work is set to review the techno-economic and environmental aspects related to thermochemical energy storage (sorption and reaction-based) and wastewater-to-energy (particular focus on thermochemical water splitting technology), aiming also to assess their potential into WEIS. The exploited technologies are, in general, proved to be suitable to be installed within the conceptualization of WEIS. In the case of TCES technologies, these are proven to be significantly more potential analogues to standard TES technologies on the scope of the conceptualization of WEIS. In the case of energy recovery technologies, although a conceptualization of a pathway to produce usable heat with an input of wastewater, further study has to be performed to fully understand the use of additional fuel in combustion-based processes.
Review of the Impacts of Climate Change on Ports and Harbours and Their Adaptation in Spain
Authors: Portillo Juan Nerea , Negro Valdecantos Vicente , del Campo Jose María
Published: June 2022

Climate change is one of the issues of greatest concern to today’s society. The increase in temperatures has affected sea levels, polar masses and extreme events, among others. There are many scientific studies that analyze the impacts of climate change on coastal communities, but most of them focus on beach erosion and coastal recession. Scientific literature on the effects of climate change on ports and harbors and their adaptation is much less abundant. Ports are essential for the economy and society of their cities, so studying the impact of climate change on them is an urgent need. The Mediterranean and the Spanish Mediterranean coast is one of the areas that will be most affected by climate change in the future. In addition, the Spanish economy depends a lot on its tourism and, thus, on its coastal cities. Therefore, the study of the impact of climate change on Spanish ports and coastal communities is essential. This article presents a review of the studies carried out until now on the effects of climate change on Spanish ports, and it identifies research gaps and weaknesses and suggests new research lines.
The Lightly Robust Max-Ordering Solution Concept for Uncertain Multiobjective Optimization Problems: An Ambulance Location Problem with Unavailability
Authors: Boriwan Pornpimon , Phoka Thanathorn , Petrot Narin
Published: June 2022

This study introduces a robust concept for considering uncertain multiobjective optimization problems, called the lightly robust max-ordering solution. This introduced solution concept offers the best option for solving issues based on the maximum cost in the worst-case scenario. Introducing a tolerable relaxation parameter can be used to increase the robustness of the solution but, at the same time, the desirable property of such a solution with respect to the nominal scenario might be decreased. Subsequently, the two measurements, which are the ‘gain in robustness’ and the ‘price to be paid for robustness’, are considered. These measurements are needed to support a decision maker to find more desirable lightly robust max-ordering solutions with a beneficial trade-off between the robustness of solutions and the quality of solutions in an undisturbed situation. Moreover, an algorithm for finding the proposed solution is presented and discussed. An instance of the benefits of the suggested solution concept is used on an example of ambulance location planning if ambulances may be unavailable.
Environmental Economics and the SDGs: A Review of Their Relationships and Barriers
Authors: Setioningtyas Widhayani Puri , Illés Csaba Bálint , Dunay Anna , Hadi Abdul , Wibowo Tony Susilo
Published: June 2022

Recently, environmental issues have increased, whereas the Earth’s natural resources have deteriorated. These problems have forced people and companies to engage in environmental economics to achieve sustainability. However, several barriers have been identified in the implementation of environmental economics. This literature review provides insights into environmental economics and the sustainable development goals (SDGs), as well as the correlation between these two subjects in general. Thus, information about potential barriers to the implementation of environmental economics and possible solutions will be presented. A total of 75 documents were analyzed, including articles, books, official reports, or paperwork from governments and/or related institutions. This study is beneficial, especially for developing countries that are just preparing for, or in the process of conducting, the initial implementation of environmental economics, as well as achieving the sustainable development goals. Interdisciplinary topics that integrate human aspects with environmental economics are limited yet crucial for future research.
Quality 4.0 Maturity Assessment in Light of the Current Situation in the Czech Republic
Authors: Nenadál Jaroslav , Vykydal David , Halfarová Petra , Tylečková Eva
Published: June 2022

Production companies throughout the world are currently facing challenges with the extensive transformation of their processes with regard to digitalisation and other features of Industry 4.0. Simultaneously, traditional quality management processes must be significantly changed, and the umbrella term “Quality 4.0” is used to represent this transformation. Many companies, including Czech enterprises, are still struggling with the concept of Quality 4.0, as there are a lot of uncertainties, misunderstandings, and false approaches. Additionally, a lot of questions arise about how to provide an objective and comprehensive assessment of the company’s maturity or readiness for Quality 4.0. Therefore, the main objective of this article is to present our proposal of a framework and methodology on how to assess and measure the maturity level for the implementation of Quality 4.0. This proposal will be supplemented by a presentation of the main results obtained from a special field study, which was conducted at Czech production companies with the aim of obtaining a representative set of information showing their readiness to adopt Quality 4.0 and convert existing quality management systems into the new era. The authors also identify some research gaps, including the necessity to confirm the mutual relationship between the dimensions and items of Quality 4.0 and the sustainable development of companies or society.