bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Multilayer Transducer and Omnidirectional Reflection Model for Active Reflection Control
Authors: Park Beom Hoon , Choi Han Bin , Seo Hee-Seon , Je Yub , Yi Hak , Park Kwan Kyu
Published: January 2023

Underwater detection is accomplished using an underwater ultrasonic sensor, sound navigation and ranging (SONAR). Stealth to avoid detection by SONAR plays a major role in modern underwater warfare. In this study, we propose a smart skin that avoids detection by SONAR via controlling the signal reflected from an unmanned underwater vehicle (UUV). The smart skin is a multilayer transducer composed of an acoustic window, a double-layer receiver, and a single-layer transmitter. It separates the incident signal from the reflected signal from outside through the time-delay separation method and cancels the reflected wave from the phase-shifted transmission sound. The characteristics of the receiving and transmitting sensors were analyzed using a finite element analysis. Three types of devices were compared in the design of the sensors. Polyvinylidene fluoride (PVDF), which had little effect on the transmitted sound, was selected as the receiving sensor. A stacked piezoelectric transducer with high sensitivity compared to a cymbal transducer was used as the transmitter. The active reflection control system was modeled and verified using 2D 360° reflection experiments. The stealth effect that could be achieved by applying a smart skin to a UUV was presented through an active reflection–control omnidirectional reflection model.
Single-Pixel Background Modeling Algorithm for Strong Sky Scenes Based on Local Region Spatial Bases
Authors: Li Biao , Xu Zhiyong , Zhang Jianlin , Zhao Quanyou
Published: January 2023

In the dim-small target detection field, background suppression is a key technique for stably extracting the target. In order to effectively suppress the background to enhance the target, this paper presents a novel background modeling algorithm, which constructs base functions for each pixel based on the local region background and models the background of each pixel, named single pixel background modeling (SPB). In SPB, the low-rank blocks of the local backgrounds are first obtained to construct the background base functions of the center pixel. Then, the background of the center pixel is optimally estimated by the background bases. Experiments demonstrate that in the case of extremely low signal-to-noise ratio (SNR < 1.5 dB) and complex motion state of targets, SPB can stably and effectively separate the target from the strongly undulant sky background. The difference image obtained via SPB background modeling has the characters: the non-target residual could be white noise, and the target is significantly enhanced. Compared with the other typical five algorithms, SPB remarkably outperforms other algorithms to detect the target of a low signal-to-noise ratio.
Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas
Authors: Nishimura Yuki , Yamaguchi Tomoyuki
Published: January 2023

Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass cutting tasks are dangerous owing to the unstable positioning of workers. For robots to perform these grass cutting tasks, slipping and falling must be prevented on inclined surfaces. In this study, a robot based on stable propeller control and four-wheel steering was developed to provide stable locomotion during grass cutting tasks. The robot was evaluated in terms of locomotion for different steering methods, straight motion on steep slopes, climbing ability, and coverage area. The results revealed that the robot was capable of navigating uneven terrains with steep slope angles. Moreover, no slipping actions that could have affected the grass cutting operations were observed. We confirmed that the proposed robot is able to cover 99.95% and 98.45% of an area on a rubber and grass slope, respectively. Finally, the robot was tested on different slopes with different angles in hilly and mountainous areas. The developed robot was able to perform the grass cutting task as expected.
A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing
Authors: Babaghayou Messaoud , Chaib Noureddine , Lagraa Nasreddine , Ferrag Mohamed Amine , Maglaras Leandros
Published: January 2023

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use.
Use of a Thermodynamic Sensor in Monitoring Fermentation Processes in Gluten-Free Dough Proofing
Authors: Adamek Martin , Zvonkova Magdalena , Buresova Iva , Buran Martin , Sevcikova Veronika , Sebestikova Romana , Adamkova Anna , Skowronkova Nela , Mlcek Jiri
Published: January 2023

Dough fermentation in gluten-free bakery products is problematic due to the absence of gluten, which provides advantageous rheological properties. A thermodynamic sensor (TDS) system combined with an electronic nose was tested as an alternative to conventional methods monitoring dough development based on mechanical properties. In the first part, the configuration of the sensors in the thermodynamic system and their response to different heat-source positions, which significantly affect the output signal from the measurement system, were investigated. The practical contribution lies in the application of the measurements to the example of gluten-free doughs with and without edible insect enrichment. An optimized configuration of the thermodynamic system (one sensor on the inner wall of the container at the bottom and another in the middle of the container closer to the top of the dough) in combination with an experimental electronic nose was used for the aforementioned measurement. In some cases, up to 87% correlation between the signal from the TDS and the signals from a professional rheofermentometer Rheo F-4 (Chopin) was demonstrated. The differences between the results can be explained by the use of different techniques. Using a combination of sensor systems in one place, one time and one sample can lead to more comprehensive and robust results. Furthermore, it was shown that the fermentation activity increased in corn dough with the addition of insects compared to dough without the addition. In rice flour dough with the addition of edible insects, fermentation activity was similar to that of the flour without the addition.
Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems
Authors: Hossain Shifat , Kim Ki-Doo
Published: January 2023

The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland–Altman bias values were found to be −0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.
Long-Period Fiber Grating Sensors for Chemical and Biomedical Applications
Authors: Cai Jintao , Liu Yulei , Shu Xuewen
Published: January 2023

Optical fiber biosensors (OFBS) are being increasingly proposed due to their intrinsic advantages over conventional sensors, including their compactness, potential remote control and immunity to electromagnetic interference. This review systematically introduces the advances of OFBS based on long-period fiber gratings (LPFGs) for chemical and biomedical applications from the perspective of design and functionalization. The sensitivity of such a sensor can be enhanced by designing the device working at or near the dispersion turning point, or working around the mode transition, or their combination. In addition, several common functionalization methods are summarized in detail, such as the covalent immobilization of 3-aminopropyltriethoxysilane (APTES) silanization and graphene oxide (GO) functionalization, and the noncovalent immobilization of the layer-by-layer assembly method. Moreover, reflective LPFG-based sensors with different configurations have also been introduced. This work aims to provide a comprehensive understanding of LPFG-based biosensors and to suggest some future directions for exploration.
An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset
Authors: Rao Yamarthi Narasimha , Suresh Babu Kunda
Published: January 2023

In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset. We propose an Imbalanced Generative Adversarial Network (IGAN) to address the problem of class imbalance by increasing the detection rate of minority classes while maintaining efficiency. To limit the effect of the minimum or maximum value on the overall features, the original data was normalized and one-hot encoded using data preprocessing. To address the issue of the low detection rate of minority attacks caused by the imbalance in the training data, we enrich the minority samples with IGAN. The ensemble of Lenet 5 and Long Short Term Memory (LSTM) is used to classify occurrences that are considered abnormal into various attack categories. The investigational findings demonstrate that the proposed approach outperforms the other deep learning approaches, achieving the best accuracy, precision, recall, TPR, FPR, and F1-score. The findings indicate that IGAN oversampling can enhance the detection rate of minority samples, hence improving overall accuracy. According to the data, the recommended technique valued performance measures far more than alternative approaches. The proposed method is found to achieve above 98% accuracy and classifies various attacks significantly well as compared to other classifiers.
PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer
Authors: Peng Bo , Chen Qile
Published: January 2023

Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments.
Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model
Authors: Li Zhonghan , Zhang Yongbo , Shi Yutong , Yuan Shangwu , Zhu Shihao
Published: January 2023

In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error model is proposed, which comprehensively utilizes the mechanism error model and propagation error model. Based on this model, the INS and ultra-wideband (UWB) fusion positioning method is derived relying on the extended Kalman filter (EKF) method. To further improve accuracy, the data prefiltering algorithm of the wavelet shrinkage method based on Stein’s unbiased risk estimate–Shrink (SURE-Shrink) threshold is summarized for raw inertial measurement unit (IMU) data. The experimental results show that by employing the SURE-Shrink wavelet denoising method, positioning accuracy is improved by 76.6%; by applying the two-level error model, the accuracy is further improved by 84.3%. More importantly, at the point when the vehicle motion state changes, adopting the two-level error model can provide higher computational stability and less fluctuation in trajectory curves.
A Dynamic Lane-Changing Driving Strategy for CAV in Diverging Areas Based on MPC System
Authors: Liu Hongben , Song Xianghui , Liu Bo , Liu Jia , Gao Huan , Liang Yunyi
Published: January 2023

Freeway-diverging areas are prone to low traffic efficiency, congestion, and frequent accidents. Because of the fluctuation of the surrounding traffic flow distribution, the individual decision-making of vehicles in diverging areas is typically unable to plan a departure trajectory that balances safety and efficiency well. Consequently, it is critical that vehicles in freeway-diverging regions develop a lane-changing driving strategy that strives to improve both the safety and efficiency of divergence areas. For CAV leaving the diverging area, this study suggested a full-time horizon optimum solution. Since it is a dynamic strategy, an MPC system based on rolling time horizon optimization was constructed as the primary algorithm of the strategy. A simulation experiment was created to verify the viability of the proposed methodology based on a mixed-flow environment. The results show that, in comparison with the feasible strategies exiting to off-ramp, the proposed strategy can take over 60% reduction in lost time traveling through a diverging area under the premise of safety and comfort without playing a negative impact on the surrounding traffic flow. Thus, the MPC system designed for the subject vehicle is capable of performing an optimal driving strategy in diverging areas within the full-time and space horizon.
Crack Shape Coefficient: Comparison between Different DFOS Tools Embedded for Crack Monitoring in Concrete
Authors: Howiacki Tomasz , Sieńko Rafał , Bednarski Łukasz , Zuziak Katarzyna
Published: January 2023

The article presents research on the performance of different distributed fibre optic sensing (DFOS) tools, including both layered cables and monolithic composite sensors. The main need for the presented research was related to the growing applications of the DFOS techniques for the measurements of cracked concrete structures. There are no clear guidelines on the required parameters of the DFOS tools, which, despite their different designs, are offered for the same purpose (strain sensing). The state-of-the-art review and previous experiences show noticeable differences in the quality of the results depending on the applied DFOS tool. The technical construction of selected solutions was described with its theoretical consequences, and then laboratory tests on full-size reinforced concrete beams were discussed. Beams equipped with embedded tools were investigated in four-point bending tests, causing the formation of multiple cracks in the tension zone along the beams’ length. The results in the form of strain profiles registered by selected DFOS tools were analysed regarding the qualitative (crack detection) and quantitative (width estimation) crack assessment. The comparison between crack-induced strain profiles was based on a new parameter called crack shape coefficient CSC, which could be applied to assess the effectiveness of the particular DFOS tool in crack detection and analysis. It was one of the world’s first research allowing for such direct comparison between the layered and monolithic sensing tools. The summary indicates practical guidelines referring to the preferable design of the tools best suitable for crack measurements, as well as the field proofs based on data from two concrete bridges in Germany.
Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves
Authors: Wang Jinghua , Li Xiang , Wang Wancheng , Wang Fan , Liu Quancheng , Yan Lei
Published: January 2023

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.
Enhancing Mask Transformer with Auxiliary Convolution Layers for Semantic Segmentation
Authors: Xia Zhengyu , Kim Joohee
Published: January 2023

Transformer-based semantic segmentation methods have achieved excellent performance in recent years. Mask2Former is one of the well-known transformer-based methods which unifies common image segmentation into a universal model. However, it performs relatively poorly in obtaining local features and segmenting small objects due to relying heavily on transformers. To this end, we propose a simple yet effective architecture that introduces auxiliary branches to Mask2Former during training to capture dense local features on the encoder side. The obtained features help improve the performance of learning local information and segmenting small objects. Since the proposed auxiliary convolution layers are required only for training and can be removed during inference, the performance gain can be obtained without additional computation at inference. Experimental results show that our model can achieve state-of-the-art performance (57.6% mIoU) on the ADE20K and (84.8% mIoU) on the Cityscapes datasets.
Intimate Partner Rape: A Review of Six Core Myths Surrounding Women’s Conduct and the Consequences of Intimate Partner Rape
Authors: Lilley Caroline , Willmott Dominic , Mojtahedi Dara , Labhardt Danielle
Published: January 2023

The focus of this paper is to highlight and review the evidence surrounding common intimate partner rape (IPR) myths, their prevalence in society, and identify those who are most likely to endorse such beliefs. Six core IPR myths are discussed related to misconceptions surrounding (1) women’s decisions to remain in abusive relationships, (2) why women delay or never report IPR, (3) women’s perceived motivations when an IPR report is made, (4) a perceived lack of trauma that occurs as a consequence of this type of rape, (5) male sexual entitlement within intimate relationships, and (6) whether it is even possible to rape a marital partner. This article draws together a wealth of studies and research that evidence why such IPR myths are indeed factually inaccurate and examines how victims, justice professionals, police practitioners, and legal decision-makers endorsement of false beliefs pertaining to intimate partner rape serve to hinder various justice pathways. We discuss the consequences of rape mythology in so far as they create social barriers that prohibit the reporting of rape, impact the progression of an allegation through the criminal justice system and ultimately, obstruct rape victims’ access to justice. The review concludes by considering evidence regarding the possible benefits of education interventions in reducing the problematic influence of rape myths.
Transfer Effects of a Multiple-Joint Isokinetic Eccentric Resistance Training Intervention to Nontraining-Specific Traditional Muscle Strength Measures
Authors: Spencer Steven , Thompson Brennan J. , Bressel Eadric , Louder Talin , Harrell David C.
Published: January 2023

Relatively few investigations have examined the transfer effects of multiple-joint isokinetic eccentric only (MJIE) resistance training on non-specific measures of muscle strength. This study investigated the transfer effects of a short-term MJIE leg press (Eccentron) resistance training program on several non-specific measures of lower-body strength. Fifteen participants performed Eccentron training three times/week for four weeks and were evaluated on training-specific Eccentron peak force (EccPF), nontraining-specific leg press DCER one-repetition maximum (LP 1 RM), and peak torques of the knee extensors during isokinetic eccentric (Ecc30), isokinetic concentric (Con150) and isometric (IsomPT) tasks before and after the training period. The training elicited a large improvement in EccPF (37.9%; Cohen’s d effect size [ES] = 0.86). A moderate transfer effect was observed on LP 1 RM gains (19.0%; ES = 0.48) with the magnitude of the strength improvement being about one-half that of EccPF. A small effect was observed on IsomPT and Ecc30 (ES = 0.29 and 0.20, respectively), however, pre-post changes of these measures were not significant. Con150 testing showed no effect (ES = 0.04). These results suggest a short term MJIE training program elicits a large strength improvement in training-specific measures, a moderate strength gain transfer effect to DCER concentric-based strength of a similar movement (i.e., LP 1 RM), and poor transfer to single-joint knee extension measures.
The Repercussions of Economic Growth, Industrialization, Foreign Direct Investment, and Technology on Municipal Solid Waste: Evidence from OECD Economies
Authors: Shah Wasi Ul Hassan , Yasmeen Rizwana , Sarfraz Muddassar , Ivascu Larisa
Published: January 2023

The paper’s main objective is to evaluate the repercussions of economic growth, industrialization, and foreign direct investment (FDI) on OECD (Organization for Economic Cooperation and Development) municipal solid waste (MSW) from 2000–2020. Further study includes the role of technology in managing waste activities’ repercussions. We also explore the mediation impact of technology and industrialization with economic growth on the waste of OECD economies. The empirical assessment is carried out in two ways. First, we use graphs to assess the evolution over the years and their association with the core factors. Second, we apply a proper econometrics series to examine the empirical nexuses between the relevant factors. The study finds that economic growth and industrialization evolve over time, increasing the waste of OECD economies. FDI inflow is unfavorable and increases waste production. However, the magnitude impact of FDI is lower than that of economic growth and industrialization. Technological advancement (research and development) is a significant factor in reducing waste generation. The later phase of economic growth is still not advantageous to reduce waste generation in the OECD. The OECD needs to manage industrialization and economic activities through a proper mechanism and tax on such activities that can increase unwanted waste. Further, through technology, the management of waste can be improved.
Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output
Authors: Shi Lei , Gao Shan , Xu Airong , Zheng Kexin , Ji Yuanpeng , Dong Xianlei , Xing Lizhi
Published: January 2023

In the context of economic globalization, innovation has become a major drive for the sustainable development of enterprises, which emphasizes the importance of studying the influencing factors of enterprise innovation output. The purpose of this study is to clarify the influence mechanism of different indicators on enterprise innovation output, and then provide relevant suggestions for improving enterprise innovation ability. This paper takes 562 enterprises in Chaoyang Sub-park and Fengtai Sub-park of Zhongguancun in Beijing within the time span between 2015 and 2016 as the research objects, and constructs a comprehensive indicator system of influencing factors for enterprise innovation output from the perspective of enterprise co-opetition relationship, factor input and environmental factors. A quantitative model of innovation output and influencing factors was built and then solved, via spike-and-slab sparse function and stepwise regression, aiming at analyzing the influence of different indicators on enterprise innovation output. In addition, this paper also classifies enterprises according to their innovation level and explores the influence of indicators on different types of enterprises. The innovation of this study lies in the modeling of competition and cooperation between enterprises and the establishment of a relatively comprehensive indicator system of influencing factors for enterprise innovation output. The results show that the degree of technological collaboration between enterprises, the level of financing and the degree of capital and labor input in innovation activities have significant positive effects on enterprise innovation output. On the contrary, product and service competition, as well as capital competition, tends to have a negative impact, which urges enterprises to pay heed to the intensity of competition faced by products and services as well as the diversity of financing sources and investment targets to reduce the negative impacts. In addition, enterprises with different levels of innovation should take customized measures in terms of factor input and co-opetition relationships, in that some indicators such as network structure indicators negatively influence the innovation output of enterprises with lower levels of innovation, but has positive impacts on those with higher levels of innovation.
Toward Regenerative Sustainability: A Passive Design Comfort Assessment Method of Indoor Environment
Authors: Kujundzic Kosara , Stamatovic Vuckovic Slavica , Radivojević Ana
Published: January 2023

The fact that people spend a major part of their lifetime indoors, together with the lethal COVID-19 pandemic which caused people to spend even more time inside buildings, has drawn attention to the significance of achieving Agenda 2030 SD goal number three: good health and well-being, in reference to the indoor environment. The research subject is the health and well-being of building users explored through the sustainable (passive) design principles having an impact on the comfort and quality of the indoor environment. It is set within a regenerative sustainability framework encompassing the physiological, biophilic, psychological and social aspects of comfort. The Comfort Assessment Model’s categories, to some extent, rely on the first author’s doctoral thesis, with further modifications regarding the passive design criteria and indicators. A comparative analysis of the model with international sustainability certification (rating) systems has been performed, proving the significance of introducing more passive design comfort (health) related criteria into sustainability assessment models. In addition, a focus group of expert architects contributed to the research conclusions by responding to a questionnaire addressing the issues of sustainability, comfort and passive design, in terms of the health and well-being of building users, which confirmed the relevance of applied passive design measures for providing comfort indoors and fulfilling sustainable development goals.
The Potential of Wastewater Reuse and the Role of Economic Valuation in the Pursuit of Sustainability: The Case of the Canal de Isabel II
Authors: del Villar Alberto , García-López Marcos
Published: January 2023

Wastewater reuse is an activity that reduces pollution from discharges while increasing the available water resources. However, the high financial costs of this activity affect the viability of projects, either because of low water productivity or because of the presence of a cheaper alternative, such as natural water sources. The existence of environmental or social benefits makes reuse a positive option for society for its environmental and social benefits. This leaves the public sector to decide whether the benefit obtained justifies its participation in the development of reuse programs, for which it is necessary to use a tool such as cost-benefit analysis, which combines diverse costs and benefits. This article studies the potential for water reuse in Spain and the importance of informed decision-making, based on information regarding water stress, wastewater reuse, and the case study of the Canal de Isabel II (Madrid). The results confirm the potential of wastewater reuse; agriculture has a water productivity below EUR 1 per cubic meter and industry and services need their own sources of water, but financial constraints prevent the greater use of reclaimed water in all economic sectors and public intervention is necessary to obtain the optimum scenario for society. The case study from Madrid that we have analyzed in this paper shows the importance of considering all factors, since the results of prioritizing the financial criterion would have been detrimental to society, in the form of EUR 200 million in environmental damage, while EUR 740 million of non-financial benefits would make wastewater reuse an advantageous alternative for society, thus justifying the public investment.
Mass and Volumetric Abrasive Wear Measurements of the Mining Conical Picks
Authors: Krauze Krzysztof , Mucha Kamil , Wydro Tomasz , Pawlik Jan , Wróblewska-Pawlik Aleksandra
Published: January 2023

The wearing of cutting tools is a bottleneck of many branches in the industry. However, the tools used in the mining sector are extraordinarily prone to rapid deterioration since many rocks exhibit aggressive abrasive properties. A typical example of a fast wearing cutting tool is a conical pick. It is used in the mining industry and other businesses requiring rock cutting, such as in roadworks and tunnelling. Both manufacturers and users attempt to find a way to enhance the lifespan of the working surface of conical picks via different approaches, namely heat treatment, chemical treatment, work hardening, and hardfacing, etc. To correctly estimate the resistance to abrasive wear for a particular conical pick, one must select appropriate procedures and methods. By this time, the most common estimation method is to measure the mass loss before and after cutting, preserving the specified and constant conditions. This method was developed for users (mines) and manufacturers of cutting tools, especially conical picks. Alternative methods of assessing the picks’ wear are also sought. In this paper, the authors perform additional volumetric loss measurements via a photogrammetric approach, which results in a 3D scan of brand-new and exploited conical pick. Three different sets of four picks were measured both in the domain of mass loss and volume loss, and the results were compared. Slight differences in parameters C2 and C3 were found. Additionally, the authors enclose recommendations regarding the proper use of the methods mentioned above, mainly focusing on the ability to perform linear and angular measurements of the tool performed on the 3D scan.
Inven!RA Architecture for Sustainable Deployment of Immersive Learning Environments
Authors: Morgado Leonel , Coelho António , Beck Dennis , Gütl Christian , Cassola Fernando , Baptista Ricardo , van Zeller Maria , Pedrosa Daniela , Cruzeiro Tiago , Cota Duarte , Grilo Ricardo , Schlemmer Eliane
Published: January 2023

The objective of this work was to support the sustainable deployment of immersive learning environments, which face varied obstacles, including the lack of support infrastructures for active learning pedagogies. Sustainability from the perspective of the integration of these environments in educational practice entails situational awareness, workload, and the informed assessment ability of participants, which must be supported for such activities to be employed in a widespread manner. We have approached this wicked problem using the Design Science Research paradigm and produced the Inven!RA software architecture. This novel result constitutes a solution for developing software platforms to enable the sustainable deployment of immersive learning environments. The Inven!RA architecture is presented alongside four demonstration scenarios employed in its evaluation, providing a means for the situational awareness of immersive learning activities in support of pedagogic decision making.
A Study on the Psychological Field Model of Drivers in Traffic Conflict Environments
Authors: Gong Shunmei , Feng Songbao , Yu Lemei , Wang Shiquan , Chen Yuanyuan , Zhang Qigui , Xu Qiang , Zhou Tianfeng
Published: January 2023

Most current traffic safety evaluations using traffic conflict techniques are based on the principles of vehicle dynamics and kinematics. In these evaluations, a lack of consideration of the coupling mechanism among drivers, road users and the road environment leads to a large limitation in the practical application of such evaluations. In this paper, we firstly considered the driver characteristics in traffic conflicts and introduced the electric field model to establish the psychological field model of a driver in a conflict environment and to obtain the psychological field strength of the driver. Secondly, based on the classification criteria of traffic conflict severity, the psychological field strengths of drivers in different traffic conflict scenarios were categorized, and the range values of psychological field strengths of different severity levels were obtained. Finally, the analysis of the experimental results showed that the mean value and standard deviation of the drivers’ mental field strengths were the largest in the opposite-direction conflict, indicating the largest dispersion of the field strengths; in the construction conflict, the mean value and standard deviation of the drivers’ mental field strengths were the smallest, indicating the smallest dispersion of the field strengths.
A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia
Authors: Selamat Siti Norsakinah , Abd Majid Nuriah , Mohd Taib Aizat
Published: January 2023

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.
Modeling and Complex Characteristics of Urban Subway Co-Opetition Network: A Case Study of Wuhan
Authors: Pan Yilei , Chang Mengying , Feng Shumin , Hao Dongsheng
Published: January 2023

In this study, to explore the co-opetition (competition–cooperation) relationship between urban subway lines, we constructed three directed weighted subway models of a competition network, cooperation network, and co-opetition network based on the Space R model of a complex network. Taking Wuhan, China, as the research area, we established a Wuhan subway co-opetition network and analyzed the network’s complex characteristics. Through the analysis, we found that the competition network, cooperation network, and co-opetition network are all scale-free networks that present the characteristics of a high-clustering and short-distance small-world network. The co-opetition relationship between subway lines was mostly of a conventional type (55.56%) and unrelated type (41.67%), with only cooperative-dominant types found among the conventional types. The co-opetition effect between lines in the long-term network increased from 7.616 to 15.17, and the relationship strength of the competition and cooperation between lines increased significantly. The competition effect deflection angle of all subway lines in the network was found to be smaller than the cooperation effect declination angle. Additionally, all lines had a significant role in cooperation within the network, and cooperation played a dominant role in the co-opetition relationship.
Optimization of Ecological and Economic Aspects of the Construction Schedule with the Use of Metaheuristic Algorithms and Artificial Intelligence
Authors: Kulejewski Janusz , Rosłon Jerzy
Published: January 2023

Construction projects play a vital role in shaping the built environment and have a significant impact on the natural environment and economies around the world. The decisions made during the planning and execution stages of a project can have long-lasting implications for its environmental and economic performance. It is, therefore, essential to consider these factors carefully and make informed decisions that align with sustainable development goals. One way to achieve this is by using metaheuristic algorithms and artificial intelligence tools to optimize and reconcile sustainable development and economic parameters in construction project scheduling. By doing so, one can improve the overall efficiency and effectiveness of the construction process, while also contributing to the well-being of the communities in which these projects are located. In this article, authors propose a new ecological indicator that can be used to evaluate the sustainability of construction projects and provide a case study to illustrate its application. The authors’ findings and conclusions highlight the importance of using advanced analytical techniques to optimize the sustainability and economic performance of construction projects and suggest potential avenues for future research.
How Efficient China’s Tiered Pricing Is for Household Electricity: Evidence from Survey Data
Authors: Zhang Zihan , Li Enping , Zhang Guowei
Published: January 2023

Due to the wide coverage of first-tier electricity consumption and the small price difference between different tiers, the current tiered pricing for household electricity (TPHE) cannot give full play to the advantages of the increasing block electricity tariffs (IBTs). Based on the microscopic survey data provided by the Chinese General Social Survey (CGSS) in 2015, this paper innovatively uses the predicted average electricity price as the instrumental variable of electricity price to explore the influencing factors of household electricity consumption in order to solve the possible endogenous problems. Simultaneously, the samples are further grouped by income and electricity consumption, and the electricity consumption characteristics of different groups are discussed separately. The results show that, for low-income groups, the price elasticity of electricity consumption is relatively low because the electricity consumption of low-income households is concentrated on meeting the energy demand necessary for basic life, while the price elasticity of high-income groups is relatively high because the electricity consumption of the high-income households is mostly the energy demand generated by improving the quality of life.
A Moderated–Mediated Model for Eco-Conscious Consumer Behavior
Authors: Chen Lei , Matloob Sheema , Sunlei Yang , Qalati Sikandar Ali , Raza Ali , Limón Mónica Lorena Sánchez
Published: January 2023

Using the Attitude–Behavior–Context theory, this research aims to investigate the impact of green marketing (GM) and green customer value (GCV) on eco-conscious consumer behavior (ECB) toward the consumption of green products. This study involved a survey technique that comprised 700 consumers through a self-administered questionnaire disseminated through enumerators in two metropolitan cities of Pakistan (namely, Lahore and Karachi), of which 349 were usable for the data analysis process. The hypothesized relationships were validated using partial least squares structural equation modeling through SmartPLS 4.0. The empirical findings showed a positive impact of GM and GCV on brand awareness (BA), environmental concern (EC), and ECB. The findings also revealed the partial mediating effect of BA and EC on the relationship between GM, GCV, and ECB. In addition, this study observed the moderating impact of felt obligation (FO) on the relationship between BA and ECB. The findings show that ECB is essential for a sustainable environment. This study’s results may guide managers and marketers in developing suitable GM strategies.
Institutional Trust and Cognitive Motivation toward Water Conservation in the Face of an Environmental Disaster
Authors: Arjomandi A. Peyman , Yazdanpanah Masoud , Shirzad Akbar , Komendantova Nadejda , Kameli Erfan , Hosseinzadeh Mahdi , Razavi Erfan
Published: January 2023

The agricultural sector in general, and in Iran in particular, is a major consumer of water and now finds itself under significant pressure due to water deficiency. This study used the Protection Motivation Theory to detect reasons for the imprudent consumption of water in Iran and to further its conservation. The Theory was extended for particular application to a seriously affected water basin, the Urmia Lake Basin in Northwest Iran. The factors governing water-saving intention among farmers in the Basin were investigated. Three hundred farmers were selected through a multi-stage, clustered, random sampling method. The results of structural equation modeling illustrated that while the original model variables accounted for 58% of the variance in water-saving intention, this rate increased to 63% in the extended model when institutional trust was used as a variable. Whereas response efficacy showed itself to be the strongest determinant of water-saving intention, all factors except perceived severity were significant in both models. Furthermore, the results of a multi-group analysis revealed that the intention to adopt water conservation measures is commensurate with the distance from the water resource and proximity to the (drying) lake. The findings of the study are expected to provide important information for policymakers looking to tailor policies to work in extreme water deficiency cases like the Urmia Lake Basin.