bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Active Learning for Failure Mode Acquisition
Authors: Kulkarni Amol , Terpenny Janis , Prabhu Vittaldas
Published: March 2023

Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances in Natural Language Processing (NLP), efforts have been made to automate this process. However, it is not only time consuming, but extremely challenging to obtain maintenance records that list failure modes. Unsupervised learning methods such as topic modeling, clustering, and community detection are promising approaches for automatic processing of maintenance records to identify failure modes. However, the nascent state of NLP tools combined with incompleteness and inaccuracies of typical maintenance records pose significant technical challenges. As a step towards addressing these challenges, this paper proposes a framework in which online active learning is used to identify failure modes from maintenance records. Active learning provides a semi-supervised machine learning approach, allowing for a human in the training stage of the model. The hypothesis of this paper is that the use of a human to annotate part of the data and train a machine learning model to annotate the rest is more efficient than training unsupervised learning models. Results demonstrate that the model is trained with annotating less than ten percent of the total available data. The framework is able to achieve ninety percent (90%) accuracy in the identification of failure modes in test cases with an F-1 score of 0.89. This paper also demonstrates the effectiveness of the proposed framework with both qualitative and quantitative measures.
Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training
Authors: Liu Zhaohui , Zhao Shiji , Wang Xiao
Published: March 2023

The issues of the degradation of the visual sensor’s image quality in foggy weather and the loss of information after defogging have brought great challenges to obstacle detection during autonomous driving. Therefore, this paper proposes a method for detecting driving obstacles in foggy weather. The driving obstacle detection in foggy weather was realized by combining the GCANet defogging algorithm with the detection algorithm-based edge and convolution feature fusion training, with a full consideration of the reasonable matching between the defogging algorithm and the detection algorithm on the basis of the characteristics of obvious target edge features after GCANet defogging. Based on the YOLOv5 network, the obstacle detection model is trained using clear day images and corresponding edge feature images to realize the fusion of edge features and convolution features, and to detect driving obstacles in a foggy traffic environment. Compared with the conventional training method, the method improves the mAP by 12% and recall by 9%. In contrast to conventional detection methods, this method can better identify the image edge information after defogging, which significantly enhances detection accuracy while ensuring time efficiency. This is of great practical significance for improving the safe perception of driving obstacles under adverse weather conditions, ensuring the safety of autonomous driving.
Evapotranspiration in Semi-Arid Climate: Remote Sensing vs. Soil Water Simulation
Authors: Chakroun Hedia , Zemni Nessrine , Benhmid Ali , Dellaly Vetiya , Slama Fairouz , Bouksila Fethi , Berndtsson Ronny
Published: March 2023

Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops’ biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0 from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d−1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d−1).
Testing and Evaluation of Wi-Fi RTT Ranging Technology for Personal Mobility Applications
Authors: Orfanos Manos , Perakis Harris , Gikas Vassilis , Retscher Günther , Mpimis Thanassis , Spyropoulou Ioanna , Papathanasopoulou Vasileia
Published: March 2023

The rapid growth in the technological advancements of the smartphone industry has classified contemporary smartphones as a low-cost and high quality indoor positioning tools requiring no additional infrastructure or equipment. In recent years, the fine time measurement (FTM) protocol, achieved through the Wi-Fi round trip time (RTT) observable, available in the most recent models, has gained the interest of many research teams worldwide, especially those concerned with indoor localization problems. However, as the Wi-Fi RTT technology is still new, there is a limited number of studies addressing its potential and limitations relative to the positioning problem. This paper presents an investigation and performance evaluation of Wi-Fi RTT capability with a focus on range quality assessment. A set of experimental tests was carried out, considering 1D and 2D space, operating different smartphone devices at various operational settings and observation conditions. Furthermore, in order to address device-dependent and other type of biases in the raw ranges, alternative correction models were developed and tested. The obtained results indicate that Wi-Fi RTT is a promising technology capable of achieving a meter-level accuracy for ranges both in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, subject to suitable corrections identification and adaptation. From 1D ranging tests, an average mean absolute error (MAE) of 0.85 m and 1.24 m is achieved, for LOS and NLOS conditions, respectively, for 80% of the validation sample data. In 2D-space ranging tests, an average root mean square error (RMSE) of 1.1m is accomplished across the different devices. Furthermore, the analysis has shown that the selection of the bandwidth and the initiator–responder pair are crucial for the correction model selection, whilst knowledge of the type of operating environment (LOS and/or NLOS) can further contribute to Wi-Fi RTT range performance enhancement.
Study on the Hierarchical Predictive Control of Semiconductor Silicon Single Crystal Quality Based on the Soft Sensor Model
Authors: Wan Yin , Liu Ding , Ren Jun-Chao , Wu Shi-Hai
Published: March 2023

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid–liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified.
Characterizing Cold Days and Spells and Their Relationship with Cold-Related Mortality in Bangladesh
Authors: Alam Md. Mahbub , Mahtab A. S. M. , Ahmed M. Razu , Hassan Quazi K.
Published: March 2023

This research examined the characteristics of cold days and spells in Bangladesh using long-term averages (1971–2000) of maximum (Tmax) and minimum temperatures (Tmin) and their standard deviations (SD). Cold days and spells were calculated and their rate of change during the winter months (December–February) of 2000–2021 was quantified. In this research, a cold day was defined as when the daily maximum or minimum temperature is ≤−1.5 the standard deviations of the long-term daily average of maximum or minimum temperature and the daily average air temperature was equal to or below 17 °C. The results showed that the cold days were more in the west-northwestern regions and far less in the southern and southeastern regions. A gradual decrease in cold days and spells was found from the north and northwest towards the south and southeast. The highest number of cold spells (3.05 spells/year) was experienced in the northwest Rajshahi division and the lowest (1.70 spells/year) in the northeast Sylhet division. In general, the number of cold spells was found to be much higher in January than in the other two winter months. In the case of cold spell severity, Rangpur and Rajshahi divisions in the northwest experienced the highest number of extreme cold spells against the highest number of mild cold spells in the Barishal and Chattogram divisions in the south and southeast. While nine (out of twenty-nine) weather stations in the country showed significant trends in cold days in December, it was not significant on the seasonal scale. Adapting the proposed method would be useful in calculating cold days and spells to facilitate regional-focused mitigation and adaptation to minimize cold-related deaths.
Optical Methods of Methane Detection
Authors: Kwaśny Mirosław , Bombalska Aneta
Published: March 2023

Methane is the most frequently analyzed gas with different concentrations ranging from single ppm or ppb to 100%. There are a wide range of applications for gas sensors including urban uses, industrial uses, rural measurements, and environment monitoring. The most important applications include the measurement of anthropogenic greenhouse gases in the atmosphere and methane leak detection. In this review, we discuss common optical methods used for detecting methane such as non-dispersive infrared (NIR) technology, direct tunable diode spectroscopy (TDLS), cavity ring-down spectroscopy (CRDS), cavity-enhanced absorption spectroscopy (CEAS), lidar techniques, and laser photoacoustic spectroscopy. We also present our own designs of laser methane analyzers for various applications (DIAL, TDLS, NIR).
A Novel Method for Detecting Fe2+ at a Micromolar Concentration Based on Multiple Self-Mixing Interference Using a Fiber Laser
Authors: Sun Wu , Yang Zhuo , Feng Guo , Chen Zhou , Chang Qiaoyun , Hai Lan , Guo Zeqing
Published: March 2023

The concentration of an electrolyte is an optical characteristic of drinking water. We propose a method based on the multiple self-mixing interference with absorption for detecting the Fe2+ indicator as the electrolyte sample at a micromolar concentration. The theoretical expressions were derived based on the lasing amplitude condition in the presence of the reflected lights considering the concentration of the Fe2+ indicator via the absorption decay according to Beer’s law. The experimental setup was built to observe MSMI waveform using a green laser whose wavelength was located in the extent of the Fe2+ indicator’s absorption spectrum. The waveforms of the multiple self-mixing interference were simulated and observed at different concentrations. The simulated and experimental waveforms both contained the main and parasitic fringes whose amplitudes varied at different concentrations with different degrees, as the reflected lights participated in the lasing gain after absorption decay by the Fe2+ indicator. The experimental results and the simulated results showed a nonlinear logarithmic distribution of the amplitude ratio, the defined parameter estimating the waveform variations, versus the concentration of the Fe2+ indicator via numerical fitting.
Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks
Authors: Wang Zilong , Guan Ben , Tang Wenbo , Wu Suowei , Ma Xuejie , Niu Hao , Wan Xiangyuan , Zang Yong
Published: March 2023

Accurate real-time classification of fluorescently labelled maize kernels is important for the industrial application of its advanced breeding techniques. Therefore, it is necessary to develop a real-time classification device and recognition algorithm for fluorescently labelled maize kernels. In this study, a machine vision (MV) system capable of identifying fluorescent maize kernels in real time was designed using a fluorescent protein excitation light source and a filter to achieve optimal detection. A high-precision method for identifying fluorescent maize kernels based on a YOLOv5s convolutional neural network (CNN) was developed. The kernel sorting effects of the improved YOLOv5s model, as well as other YOLO models, were analysed and compared. The results show that using a yellow LED light as an excitation light source combined with an industrial camera filter with a central wavelength of 645 nm achieves the best recognition effect for fluorescent maize kernels. Using the improved YOLOv5s algorithm can increase the recognition accuracy of fluorescent maize kernels to 96%. This study provides a feasible technical solution for the high-precision, real-time classification of fluorescent maize kernels and has universal technical value for the efficient identification and classification of various fluorescently labelled plant seeds.
Preparation of Discontinuous Cu/SiO2 Multilayers—AC Conduction and Determining the Measurement Uncertainty
Authors: Wilczyńska Aleksandra , Kociubiński Andrzej , Kołtunowicz Tomasz N.
Published: March 2023

This paper presents a test stand for testing alternating current electrical parameters of Cu–SiO2 multilayer nanocomposite structures obtained by the dual-source non-reactive magnetron sputtering method (resistance, capacitance, phase shift angle, and dielectric loss angle tangent δ). In order to confirm the dielectric nature of the test structure, measurements in the temperature range from room temperature to 373 K were carried out. The alternating current frequencies in which the measurements were made ranged from 4 Hz to 7.92 MHz. To improve the implementation of measurement processes, a program was written to control the impedance meter in the MATLAB environment. Structural studies by SEM were conducted to determine the effect of annealing on multilayer nanocomposite structures. Based on the static analysis of the 4-point method of measurements, the standard uncertainty of type A was determined, and taking into account the manufacturer’s recommendations regarding the technical specification, the measurement uncertainty of type B.
Dynamic Response of the Pitot Tube with Pressure Sensor
Authors: Pieniążek Jacek , Cieciński Piotr , Ficek Daniel , Szumski Marek
Published: March 2023

This paper presents an attempt to determine the dynamic properties of a measuring system based on total pressure measurement with the use of a Pitot tube and a semiconductor pressure transducer. The presented research uses computed fluid dynamics (CFD) simulation and real data from the pressure measurement system for determination of the dynamical model of the Pitot tube with the transducer. An identification algorithm is applied to the data from the simulation, and the model in the form of a transfer function is an identification result. The oscillatory behavior is detected, and this result is confirmed by frequency analysis of the recorded pressure measurements. One of the resonant frequencies is the same in both experiments, but the second is slightly different. The identified dynamical models permit the possibility to predict deviations caused by dynamics and to select the appropriate tube for a particular experiment.
An Adaptive Pedaling Assistive Device for Asymmetric Torque Assistant in Cycling
Authors: Lozinski Jesse , Heidary Seyed Hamidreza , Brandon Scott C. E. , Komeili Amin
Published: March 2023

Dynamic loads have short and long-term effects in the rehabilitation of lower limb joints. However, an effective exercise program for lower limb rehabilitation has been debated for a long time. Cycling ergometers were instrumented and used as a tool to mechanically load the lower limbs and track the joint mechano-physiological response in rehabilitation programs. Current cycling ergometers apply symmetrical loading to the limbs, which may not reflect the actual load-bearing capacity of each limb, as in Parkinson’s and Multiple Sclerosis diseases. Therefore, the present study aimed to develop a new cycling ergometer capable of applying asymmetric loads to the limbs and validate its function using human tests. The instrumented force sensor and crank position sensing system recorded the kinetics and kinematics of pedaling. This information was used to apply an asymmetric assistive torque only to the target leg using an electric motor. The performance of the proposed cycling ergometer was studied during a cycling task at three different intensities. It was shown that the proposed device reduced the pedaling force of the target leg by 19% to 40%, depending on the exercise intensity. This reduction in pedal force caused a significant reduction in the muscle activity of the target leg (p < 0.001), without affecting the muscle activity of the non-target leg. These results demonstrated that the proposed cycling ergometer device is capable of applying asymmetric loading to lower limbs, and thus has the potential to improve the outcome of exercise interventions in patients with asymmetric function in lower limbs.
Editorial–Special Issue on “Sensor Technology for Enhancing Training and Performance in Sport”
Authors: Kong Pui Wah
Published: March 2023

Sensor technology opens up exciting opportunities for sports [...]
Epoxy-Coated Side-Polished Fiber-Optic Temperature Sensor for Cryogenic Conditions
Authors: Sampath Umesh , Song Minho
Published: March 2023

We propose coating side-polished optical fiber (SPF) with epoxy polymer to form a fiber-optic sensor for cryogenic temperature measuring applications. The thermo-optic effect of the epoxy polymer coating layer enhances the interaction between the SPF evanescent field and surrounding medium, considerably improving the temperature sensitivity and robustness of the sensor head in a very low-temperature environment. In tests, due to the evanescent field–polymer coating interlinkage, transmitted optical intensity variation of 5 dB and an average sensitivity of −0.024 dB/K were obtained in the 90–298 K range.
Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing
Authors: Wang Yeniu Mickey , Ostendorf Bertram , Pagay Vinay
Published: March 2023

Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for the non-destructive and rapid detection of plant diseases. The present study used proximal hyperspectral sensing to detect virus infection in Pinot Noir (red-berried winegrape cultivar) and Chardonnay (white-berried winegrape cultivar) grapevines. Spectral data were collected throughout the grape growing season at six timepoints per cultivar. Partial least squares-discriminant analysis (PLS-DA) was used to build a predictive model of the presence or absence of GLD. The temporal change of canopy spectral reflectance showed that the harvest timepoint had the best prediction result. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. Our results provide valuable information on the optimal time for GLD detection. This hyperspectral method can also be deployed on mobile platforms including ground-based vehicles and unmanned aerial vehicles (UAV) for large-scale disease surveillance in vineyards.
A Non-Equal Time Interval Incremental Motion Prediction Method for Maritime Autonomous Surface Ships
Authors: Zhou Zhijie , Xu Haixiang , Feng Hui , Li Wenjuan
Published: March 2023

Recent technological advancements facilitate the autonomous navigation of maritime surface ships. The accurate data given by a range of various sensors serve as the primary assurance of a voyage’s safety. Nevertheless, as sensors have various sample rates, they cannot obtain information at the same time. Fusion decreases the accuracy and reliability of perceptual data if different sensor sample rates are not taken into account. Hence, it is helpful to increase the quality of the fusion information to precisely anticipate the motion status of ships at the sampling time of each sensor. This paper proposes a non-equal time interval incremental prediction method. In this method, the high dimensionality of the estimated state and nonlinearity of the kinematic equation are taken into consideration. First, the cubature Kalman filter is employed to estimate a ship’s motion at equal intervals based on the ship’s kinematic equation. Next, a ship motion state predictor based on a long short-term memory network structure is created, using the increment and time interval of the historical estimation sequence as the network input and the increment of the motion state at the projected time as the network output. The suggested technique can lessen the effect of the speed difference between the test set and the training set on the prediction accuracy compared with the traditional long short-term memory prediction method. Finally, comparison experiments are carried out to validate the precision and effectiveness of the proposed approach. The experimental results show that the root-mean-square error coefficient of the prediction error is decreased on average by roughly 78% for various modes and speeds when compared with the conventional non-incremental long short-term memory prediction approach. Additionally, the proposed prediction technology and the traditional approach have virtually the same algorithm times, which may fulfill the real engineering requirements.
Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG
Authors: Silva Rafael , Fred Ana , Plácido da Silva Hugo
Published: March 2023

Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.
Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management
Authors: Ijemaru Gerald K. , Ang Li-Minn , Seng Kah Phooi
Published: March 2023

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.
Political Disaffection and Digital Political Participation in Latin America: A Comparative Analysis of the Period 2008–2020
Authors: Cazorla-Martín Ángel , Montabes-Pereira Juan , Hernández-Tristán Mateo Javier
Published: March 2023

One of the issues facing the field of political behaviour analysis in recent years has been the transformation of political participation among citizens, in a context of increasing change, profoundly marked by the spread of a new digital paradigm. Network society has brought with it new forms of political participation, where different types of participatory citizenship coexist in a process of increasing interaction which, in turn, creates new morphologies, and where online and offline modes are reciprocal, generating new patterns of behaviour. Of these different types of participatory citizenship, that of the disaffected is perhaps among the most important in recent years and, in particular, since the start of the so-called “Great Recession” around 2008, and the subsequent global COVID-19 crisis. This recent context can be characterised by a significant increase in political disaffection, resulting from a loss of trust in institutions and from the constant distancing of a certain section of the citizenry from politics as a coded punishment of those governments and institutions they see as ineffective. This paper provides an analysis of citizenship types in Latin America, particularly that of the disaffected, describing their relationship to the following of political information through digital media and social networks, and identifying patterns of evolution and development in some of the trends. Results show that a clear distinction exists between the different types of citizenship and associated forms of participation, both online and offline, while also describing differences in both political perceptions and attitudes, and between areas or regions in Latin America. Likewise, important differences are found according to citizen type in relation to the following of different social networks, especially among citizens categorised as critical or disaffected.
Happy but Deviant: How Does Positive Affect Disrupt Social Sustainability?
Authors: Kim Moon Joung , Choi Jin Nam
Published: March 2023

Despite extensive research on the role of negative affect (NA) in causing interpersonal deviance, the role of positive affect (PA) remains unclear. Responding to the call for more nuanced research on the interpersonal effects of PA, this study explores the neglected facet of PA, the agentic rather than communal aspect, in predicting interpersonal deviance. Drawing upon regulatory focus theory and social labeling theory, we explore how PA predicts interpersonal deviance. We further propose that this relationship will be mediated by social risk-taking and moderated by task interdependence. Multi-source field data provide support for the hypothesis that PA is positively related to interpersonal deviance, which is mediated by social risk-taking. In addition, the relationship is significant only in the condition of high task interdependence that activates the agentic aspect of PA. This study concludes that the beneficial effects of PA are not universal, and that PA at work may have unintended negative consequences, such as interpersonal deviance. Implications for theory and research are discussed.
Influence of Peat Soil Environment on Mechanical Properties of Cement-Soil and Its Mechanism
Authors: Song Yunfei , Cao Jing , Ding Wenyun , Song Zhigang , Liu Hong , Huang Siyang , Zhu Weiming
Published: March 2023

The influence of peat soil environment (PSE) on the mechanical properties of cement-soil in the area around Dianchi Lake and Erhai Lake in Yunnan Province has attracted much attention. This study explores the change law of cement-soil UCS in the PSE, and provides guidance for the development and sustained usage of peat soil foundation. The paper discusses the preparation of cement-soil samples by adding humic acid (HA) and cement to cohesive soil with low organic matter content (blending method) and soaking it in fulvic acid (FA) solution and deionized water (steeping method) to simulate the actual working environment of cement-soil. Unconfined compressive strength (UCS), acid consumption, ion leaching, scanning electron microscope (SEM), and X-ray diffraction (XRD) tests are carried out on cement-soil samples soaked for 90 days. The results show that HA can significantly reduce the UCS of cement-soil. FA can reduce the UCS of cement-soil when the content of HA is less than 18%. However, when the amount of HA is more than 18%, the UCS of cement-soil increases slightly. FA makes the deformation and failure type of cement-soil gradually change from brittle shear failure to plastic shear failure. FA reacts with the cement hydration products in the sample so that the cumulative acid consumption of the cement-soil sample continues to increase, and the dissolution of Ca2+, Mg2+, Al3+, and Fe3+ in the sample increases the ion concentration of the soaking solution. In addition, SEM and XRD show that HA can increase the macropores and connectivity of cement-soil, while FA fills part of the pores of the wetting layer. In the PSE, FA can strengthen the inner structure of HA particles and fill and cement the layers of cohesive particles, enhancing the construction of cement-soil with HA content greater than 18%, so that its UCS is relatively improved. However, when the amount of HA is less than 18%, there are more small pores in the cement-soil. The interaction between FA and HA in the cement-soil is weak. The influence of FA on cement-soil is mainly a weakening effect, and its UCS is relatively reduced.
Experimental Investigation of Usability of Treatment Sud Ash with Road Filling Materials in Highways
Authors: Akyıldız Mehmet Hayrullah , Yön Seda
Published: March 2023

Increasing environmental pollution due to the rapid increase in the world population is one of the most important problems of humanity, perhaps even the first. Environmental elements that are vital for living things air, soil and water are becoming more polluted and unusable day by day. For this reason, various improvement studies are carried out in these areas. One of them is wastewater treatment plants built to control water pollution. While the water treated in the sedimentation ponds in these facilities is recycled into nature, it leaves waste treatment sludge behind. These waste sludges collected in a certain place are destroyed by various methods such as composting, drying or incineration. In previous studies, the usability of sewage sludge ash in soil improvement was investigated. In this study, the usability of sewage sludge ash as road filling material was investigated (Ash was added to the clay soil in determined percentages. These percentages are respectively; 0%, 2.5%, 5%, 7.5%, 10%, 12.5% and 15%). Treatment sludge ash was obtained from Bursa Fluidized Bed Sludge Incineration and Energy Recovery Facility. Then, the samples prepared by adding treatment sludge ash to the clay soil at certain rates were subjected to the CBR test with the Standard Proctor test. The values obtained as a result of the experiments were compared with the values given in the Highways Technical Specifications and it was determined whether they were suitable as a road filling material. The engineering properties of the soil used indicate that the soil is weak (clay soil). The treatment sludge ash added as an additive has improved all the properties of the soil to a certain extent.
Effects of Replacement of Fish Oil with Microbial Oil (Schizochytrium sp. T18) on Membrane Lipid Composition of Atlantic Salmon Parr Muscle and Liver Tissues
Authors: Guerra Nigel , Parrish Christopher C. , Wei Minmin , Perry Judy , Armenta Roberto E. , Colombo Stefanie M.
Published: March 2023

A 16-week feeding trial was conducted to investigate effects of replacing dietary fish oil (FO) with docosahexaenoic acid (DHA)-rich microbial oil (MO) from Schizochytrium sp. (T18) on membrane lipid composition of Atlantic salmon parr liver and muscle tissues. Four nutritionally balanced diets were formulated with varying levels of FO, MO, and canola oil (CO), including a control diet with 20% FO, a secondary control diet with 10% FO and 10% CO, and two experimental diets that completely replaced FO with a low (5%) and high (10%) proportion of MO. No significant differences were observed in growth parameters (81–98 g; weight gain), total lipid class composition, and total sterol content among the dietary treatments. However, there were significant differences in the proportions of individual ꞷ3 and ꞷ6 fatty acids in both liver and muscle tissues, reflecting the different dietary treatments. Notably, the presence of low eicosapentaenoic acid (EPA) in the MO diets did not affect the growth performance of the fish, suggesting a lower requirement for EPA in the diet and a greater necessity for DHA. The results also showed that DHA was present in very high proportions in the cellular membrane, particularly in muscle tissue, with low levels of linoleic acid and alpha-linolenic acid. Overall, the findings suggest that MO derived from Schizochytrium sp. (T18) could be a potential substitute for FO in the diet of farmed Atlantic salmon.
Transdisciplinary Research along the Logic of Empowerment: Perspectives from Four Urban and Regional Transformation Projects
Authors: Hölsgens Rick , Wascher Eva , Bauer Carolin , Boll Judith , Bund Stephanie , Dankwart-Kammoun Saskia , Heese Irina , Schrot Katharina , Schultze Jürgen , Tenambergen Robert
Published: March 2023

Transformative research requires transdisciplinary collaboration, forcing researchers out of their disciplinary comfort zones. In transdisciplinary research projects, the role of (social) scientists changes, and non-scientific actors become part of research projects. Transdisciplinary research is particularly suited to not only generate scientific knowledge, but also invent real-world solutions and to innovative. This, however, does not come without challenges. Implementing a transdisciplinary project is time-consuming and requires the alignment of both the research and impact ambitions of all project partners. In this paper we build upon experiences gained in four transdisciplinary research projects and ask: (1) What is the transdisciplinary approach followed by the project? (2) Which opportunities and challenges can be identified for successful transdisciplinary collaborations? (3) What is the rationale for engaging in transdisciplinary research from the perspective of social scientists? Building upon the logics of interdisciplinary, a fourth logic, called the logic of empowerment, is identified as a driver for transdisciplinary research. Transdisciplinary collaboration empowers researchers to not only ‘discover’ innovations, i.e., to invent, but also to implement, i.e., to innovate.
Barriers to, and Enablers for, Stakeholder Collaboration in Risk-Sensitive Urban Planning: A Systematised Literature Review
Authors: Ganeshu Pavithra , Fernando Terrence , Keraminiyage Kaushal
Published: March 2023

The lack of stakeholder collaboration has been recognised as a prominent hindrance in implementing risk-sensitive urban planning for creating resilient environments for communities against climate-induced risks. This study presents the outcome of a systematised literature review that was conducted to identify the barriers to stakeholder collaboration, as well as the enablers that assist stakeholder collaboration in risk-sensitive urban planning. The study identified thirty-three barriers which were classified under thirteen themes and four major categories (external barriers, inter-organisational barriers, intra-organisational barriers, and personal barriers). The review also identified forty-five enablers which can be used in surmounting the identified barriers. The dependency analysis of the barriers, conducted by adopting the interpretive structural modelling approach, shows that the barriers with a high driving power are external barriers, which are political barriers, policies and legislation-related barriers, and governance-related barriers that are beyond the organisational boundary and control. The research found that the enablers to overcome those high driving barriers are gaining political support for implementing inter-organisational collaboration or adapting the apolitical approach for the collaboration process, strengthening the laws and forming adaptive policies to support collaboration, and establishing transparent and accountable collaborative governance.
Design and Insights Gained in a Real-World Laboratory for the Implementation of New Coastal Protection Strategies
Authors: Kempa Daniela , Karrasch Leena , Schlurmann Torsten , Prominski Martin , Lojek Oliver , Schulte-Güstenberg Evke , Visscher Jan , Zielinski Oliver , Goseberg Nils
Published: March 2023

Novel strategies in coastal protection are needed to cope with climate change-induced sea level rise. They aim at the sustainable development of coastal areas in light of intensification and land use changes. A promising approach is the design of nature-based solutions (NbS), complementing the safety levels of technical infrastructure. However, NbS lack a widespread and large-scale implementation. To address this deficit, co-design concepts are needed that combine experiences from science and practice. This work presents and discusses the approach of a coast-specific real-world laboratory (RwL) addressing the inclusive design of ecosystem-based coastal protection. Strategies of RwLs are applied for the first time in a coastal context along the North Sea coastline in Germany. We found the concept of RwLs suitable for coastal transdisciplinary research, although adaptions in the spatial reference level or flexibility in location and time of experimentation are necessary. A profound actor analysis is indispensable to specify participatory processes and interaction levels. A criteria-based cooperative selection of RwL sites helps to reveal and solve conflicting interests to achieve trust between science and practice. Addressing site-specific characteristics and practitioners’ needs, our coastal RwL provides a mutual learning space to develop and test NbS to complement technical coastal protection.
Recovery Assessment of Permanent Housing after the 2004 Tsunami in Thailand toward Sustainable Planning
Authors: Kamthonkiat Daroonwan , Leelawatthanaphong Thanawan , Unapumnuk Kessinee , Vu Tuong Thuy
Published: March 2023

In this study, a recovery assessment of the permanent housing and living conditions in the aftermath of the 2004 Tsunami in Phang-nga Province, Thailand, was conducted using geoinformatics technologies, field observations, and living-related parameters from basic minimum need (BMN) data retrieved from the Ministry of Interior. In the results, 29 permanent housing projects were mapped, classified into five sizes (very small, small, medium, large, and very large), and overlaid with the tsunami-inundated zone visually interpreted from satellite images. Thirteen out of twenty-nine projects were reconstructed in the inundation zone (in situ), while the rest were relocated to higher ground. Permanent houses were rebuilt in 18 communities in three patterns: single-story or one-story houses (511 houses), single-story and raised-basement houses (58 houses), and two-story houses (712 houses). The selected BMN’s living-related parameters, such as sufficient water for household consumption (dimension: dwelling), employment of people between 15 and 60 years old (dimension: economy), and participation in communities’ activities (dimension: participation), which covered 2002–2015 at the community-based level, were compared annually to its criterion and indicated as passing or not passing the standard. The reconstructed communities recovered (passing the requirements) within four years of transferring to the reconstructed houses.
Investment Estimation in the Energy and Power Sector towards Carbon Neutrality Target: A Case Study of China
Authors: Wang Han , Dong Wenjuan , Li Hongji , Du Ershun
Published: March 2023

The transition towards low-carbon energy and power has been extensively studied by research institutions and scholars. However, the investment demand during the transition process has received insufficient attention. To address this gap, an energy investment estimation method is proposed in this paper, which takes the unit construction costs and potential development of major technology in the energy and power sector as input. The proposed estimation method can comprehensively assess the investment demand for various energy sources in different years, including coal, oil, natural gas, biomass, power, and hydrogen energy. Specifically, we applied this method to estimate the investment demand of China’s energy and power sector from 2020 to 2060 at five year intervals. The results indicate that China’s cumulative energy investment demand over this period is approximately 127 trillion CNY, with the power sector accounting for the largest proportion at 92.35%, or approximately 117 trillion CNY. The calculated cumulative investment demand is consistent with the findings of several influential research institutions, providing validation for the proposed method.
Understanding Customer Preferences of Delivery Services for Online Grocery Retailing in South Korea
Authors: Park Yoon-Joo
Published: March 2023

With the rapid growth of online grocery shopping in South Korea, e-grocery retailers have faced intense competition. Consequently, they attempt to differentiate themselves by offering diversified delivery services, such as providing early morning delivery services or eco-friendly packaging. The purpose of this paper is to analyze consumer preferences with regards to delivery services with different delivery times and packaging types targeting South Korea’s online grocery markets. Specifically, six delivery types consisting of combinations of two delivery time options (dawn, daytime) and three packaging types (paper box, market cooler bag, personal icebox) are examined. A survey was conducted in July 2020 with 218 consumers who regularly buy fresh food online. The collected data were analyzed by means of a conjoint analysis and ANOVA. The present study finds that customers most strongly prefer dawn delivery using a personal icebox, followed by dawn delivery using a market cooler bag, with daytime delivery using a paper box being the least preferable. Furthermore, consumers value the packaging type more than the delivery time when selecting a delivery service. Lastly, the preferences of consumers regarding delivery service types differ according to their characteristics. This study is expected to contribute to the establishment of a delivery strategy for online grocery companies.
Parking Charges: Ingeniously Effective and Publicly Accepted in Riyadh?
Authors: Elattar Samia , Albalawi Hind , Saleh Wafaa
Published: March 2023

The background of this study is relevant to parking facilities at shopping malls in the Kingdom of Saudi Arabia (KSA), where all parking services are mostly free, with very few exceptions. Demand on parking places at shopping malls and recreational areas in Saudi cities are generally very high. This is partly because most of the entertaining, shopping, and recreational areas are indoors and are typically located at shopping malls with huge parking provision. With the vast increase in car ownership and use in the country over the past few decades, a consequent increase in demand on roads and parking facilities has been observed, which has no doubt resulted in further congestion. This study aims to investigate the willingness to pay to save time searching for a parking place at shopping malls in Riyadh city in the KSA. The methodology includes interviewing shoppers in shopping mall car parks and asking about their search time for a parking space and their willingness to pay to save search time, as well as to report on their socio-economic characteristics including age, gender, employment, education, car ownership, and income. The outcome of this study shows that on average, shoppers spend about 9 min searching for a parking place for their shopping and recreational trips. The results also show that, on average, shoppers reported that they go shopping, eating out, etc., about three times per week, and the average willingness to pay to save time searching for parking space is about 10 Saudi Riyal (about 2.7 USD) per visit. The modelling results show that there is a higher willingness to pay from the middle income and car-ownership categories of the population than from the highest income and car ownership groups. This might reflect the fact that the middle-income groups are much more dependent on themselves to drive their cars and search for a parking space every time, while those of higher incomes could possibly rely on their private drivers most of the times to drive them and park their cars. This paper contributes to the literature by providing a first understanding on how demand for parking facilities is affected by various socio-economic factors and the willingness to pay to save search time at these parking locations. The implications of the outcome of this research will be of use for decision makers and city authorities in planning travel demand management policies and programs that aim at reducing demand on private cars and achieving sustainability.