bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis
Authors: Xu Qinghong , Jiang Hong , Zhang Xiangfeng , Li Jun , Chen Lan
Published: April 2023

Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel–space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.
Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems
Authors: Mancuso Dominga , Castagnolo Giulia , Porto Simona M. C.
Published: April 2023

Animal welfare is becoming an increasingly important requirement in the livestock sector to improve, and therefore raise, the quality and healthiness of food production. By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.
Addressing Multi-User Interference in Vehicular Visible Light Communications: A Brief Survey and an Evaluation of Optical CDMA MAC Utilization in a Multi-Lane Scenario
Authors: Plascencia Emmanuel , Guan Hongyu , Chassagne Luc , Căilean Alin-Mihai , Barrois Olivier , Shagdar Oyunchimeg
Published: April 2023

Visible Light Communications (VLC) are developing as an omnipresent solution for inter-vehicle communications. Based on intensive research efforts, the performance of vehicular VLC systems has significantly improved in terms of noise resilience, communication range, and latencies. Nevertheless, in order to be ready for deployment in real applications, solutions for Medium Access Control (MAC) are also required. In this context, this article provides an intensive evaluation of several optical CDMA MAC solutions and of their efficiency in mitigating the effect of Multiple User Interference (MUI). Intensive simulation results showed that an adequately designed MAC layer can significantly reduce the effects of MUI, ensuring an adequate Packet Delivery Ratio (PDR). The simulation results showed that based on the use of optical CDMA codes, the PDR can be improved from values as low as 20% up to values between 93.2% and 100%. Consequently, the results provided in this article show the high potential of optical CDMA MAC solutions in vehicular VLC applications, reconfirm the high potential of the VLC technology in inter-vehicle communications, and emphasize the need to further develop MAC solutions designed for such applications.
A Mixed-Reality-Based Unknown Space Navigation Method of a Flexible Manipulator
Authors: Chen Ronghui , Zhu Xiaojun , Chen Zhang , Tian Yu , Liang Lunfei , Wang Xueqian
Published: April 2023

A hyper-redundant flexible manipulator is characterized by high degree(s) of freedom (DoF), flexibility, and environmental adaptability. It has been used for missions in complex and unknown spaces, such as debris rescue and pipeline inspection, where the manipulator is not intelligent enough to face complex situations. Therefore, human intervention is required to assist in decision-making and control. In this paper, we designed an interactive navigation method based on mixed reality (MR) of a hyper-redundant flexible manipulator in an unknown space. A novel teleoperation system frame is put forward. An MR-based interface was developed to provide a virtual model of the remote workspace and virtual interactive interface, allowing the operator to observe the real-time situation from a third perspective and issue commands to the manipulator. As for environmental modeling, a simultaneous localization and mapping (SLAM) algorithm based on an RGB-D camera is applied. Additionally, a path-finding and obstacle avoidance method based on artificial potential field (APF) is introduced to ensure that the manipulator can move automatically under the artificial command in the remote space without collision. The results of the simulations and experiments validate that the system exhibits good real-time performance, accuracy, security, and user-friendliness.
Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features
Authors: Chen Guangyi , Krzyzak Adam
Published: April 2023

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.
A Tube Model Predictive Control Method for Autonomous Lateral Vehicle Control Based on Sliding Mode Control
Authors: Dai Yong , Wang Duo
Published: April 2023

This paper aims to enhance the lateral path tracking control of autonomous vehicles (AV) in the presence of external disturbances. While AV technology has made significant strides, real-world driving scenarios often pose challenges such as slippery or uneven roads, which can adversely affect the lateral path tracking control and reduce driving safety and efficiency. Conventional control algorithms struggle to address this issue due to their inability to account for unmodeled uncertainties and external disturbances. To tackle this problem, this paper proposes a novel algorithm that combines robust sliding mode control (SMC) and tube model predictive control (MPC). The proposed algorithm leverages the strengths of both MPC and SMC. Specifically, MPC is used to derive the control law for the nominal system to track the desired trajectory. The error system is then employed to minimize the difference between the actual state and the nominal state. Finally, the sliding surface and reaching law of SMC are utilized to derive an auxiliary tube SMC control law, which helps the actual system keep up with the nominal system and achieve robustness. Experimental results demonstrate that the proposed method outperforms conventional tube MPC, linear quadratic regulator (LQR) algorithms, and MPC in terms of robustness and tracking accuracy, especially in the presence of unmodeled uncertainties and external disturbances.
Deep Spread Multiplexing and Study of Training Methods for DNN-Based Encoder and Decoder
Authors: Kim Minhoe , Lee Woongsup
Published: April 2023

We propose a deep spread multiplexing (DSM) scheme using a DNN-based encoder and decoder and we investigate training procedures for a DNN-based encoder and decoder system. Multiplexing for multiple orthogonal resources is designed with an autoencoder structure, which originates from the deep learning technique. Furthermore, we investigate training methods that can leverage the performance in terms of various aspects such as channel models, training signal-to-noise (SNR) level and noise types. The performance of these factors is evaluated by training the DNN-based encoder and decoder and verified with simulation results.
Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review
Authors: Fei Lunlin , Han Bing
Published: April 2023

Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.
Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes
Authors: Fang Shuqi , Zhang Bin , Hu Jingyu
Published: April 2023

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.
Review of Zinc Oxide Piezoelectric Nanogenerators: Piezoelectric Properties, Composite Structures and Power Output
Authors: Bhadwal Neelesh , Ben Mrad Ridha , Behdinan Kamran
Published: April 2023

Lead-containing piezoelectric materials typically show the highest energy conversion efficiencies, but due to their toxicity they will be limited in future applications. In their bulk form, the piezoelectric properties of lead-free piezoelectric materials are significantly lower than lead-containing materials. However, the piezoelectric properties of lead-free piezoelectric materials at the nano scale can be significantly larger than the bulk scale. This review looks at the suitability of ZnO nanostructures as candidate lead-free piezoelectric materials for use in piezoelectric nanogenerators (PENGs) based on their piezoelectric properties. Of the papers reviewed, Neodymium-doped ZnO nanorods (NRs) have a comparable piezoelectric strain constant to bulk lead-based piezoelectric materials and hence are good candidates for PENGs. Piezoelectric energy harvesters typically have low power outputs and an improvement in their power density is needed. This review systematically reviews the different composite structures of ZnO PENGs to determine the effect of composite structure on power output. State-of-the-art techniques to increase the power output of PENGs are presented. Of the PENGs reviewed, the highest power output belonged to a vertically aligned ZnO nanowire (NWs) PENG (1-3 nanowire composite) with a power output of 45.87 μW/cm2 under finger tapping. Future directions of research and challenges are discussed.
Spectral Measurements of Muzzle Flash with a Temporally and Spatially Modulated LWIR-Imaging Fourier Transform Spectrometer
Authors: Yang Zhixiong , Li Kun , Yu Chunchao , Yuan Mingyao , Wang Boyang , Feng Jie
Published: April 2023

It is important to obtain information on an instantaneous target. A high-speed camera can capture a picture of an immediate scene, but spectral information about the object cannot be retrieved. Spectrographic analysis is a key tool for identifying chemicals. Detecting dangerous gas quickly can help ensure personal safety. In this paper, a temporally and spatially modulated long-wave infrared (LWIR)-imaging Fourier transform spectrometer was used to realize hyperspectral imaging. The spectral range was 700~1450 cm−1 (7~14.5 μm). The frame rate of infrared imaging was 200 Hz. The muzzle-flash area of guns with calibers of 5.56 mm, 7.62 mm, and 14.5 mm were detected. LWIR images of muzzle flash were obtained. Spectral information on muzzle flash was obtained using instantaneous interferograms. The main peak of the spectrum of the muzzle flash appeared at 970 cm−1 (10.31 μm). Two secondary peaks near 930 cm−1 (10.75 μm) and 1030 cm−1 (9.71 μm) were observed. Radiance and brightness temperature were also measured. The spatiotemporal modulation of the LWIR-imaging Fourier transform spectrometer provides a new method for rapid spectral detection. The high-speed identification of hazardous gas leakage can ensure personal safety.
Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning
Authors: Faqih Mochammad , Omar Madiah Binti , Ibrahim Rosdiazli
Published: April 2023

Dry-Low Emission (DLE) technology significantly reduces the emissions from the gas turbine process by implementing the principle of lean pre-mixed combustion. The pre-mix ensures low nitrogen oxides (NOx) and carbon monoxide (CO) production by operating at a particular range using a tight control strategy. However, sudden disturbances and improper load planning may lead to frequent tripping due to frequency deviation and combustion instability. Therefore, this paper proposed a semi-supervised technique to predict the suitable operating range as a tripping prevention strategy and a guide for efficient load planning. The prediction technique is developed by hybridizing Extreme Gradient Boosting and K-Means algorithm using actual plant data. Based on the result, the proposed model can predict the combustion temperature, nitrogen oxides, and carbon monoxide concentration with an accuracy represented by R squared value of 0.9999, 0.9309, and 0.7109, which outperforms other algorithms such as decision tree, linear regression, support vector machine, and multilayer perceptron. Further, the model can identify DLE gas turbine operation regions and determine the optimum range the turbine can safely operate while maintaining lower emission production. The typical DLE gas turbine’s operating range can operate safely is found at 744.68 °C –829.64 °C. The proposed technique can be used as a preventive maintenance strategy in many applications involving tight operating range control in mitigating tripping issues. Furthermore, the findings significantly contribute to power generation fields for better control strategies to ensure the reliable operation of DLE gas turbines.
MLP-mmWP: High-Precision Millimeter Wave Positioning Based on MLP-Mixer Neural Networks
Authors: Zheng Yadan , Huang Bin , Lu Zhiping
Published: April 2023

Millimeter wave (MMW) communication, noted for its merit of wide bandwidth and high-speed transmission, is also a competitive implementation of the Internet of Everything (IoE). In an always-connected world, mutual data transmission and localization are the primary issues, such as the application of MMW application in autonomous vehicles and intelligent robots. Recently, artificial intelligence technologies have been adopted for the issues in the MMW communication domain. In this paper, MLP-mmWP, a deep learning method, is proposed to localize the user with respect to MMW communication information. The proposed method employs seven sequences of beamformed fingerprints (BFFs) to estimate localization, which includes line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. As far as we know, MLP-mmWP is the first method to apply the MLP-Mixer neural network to the task of MMW positioning. Moreover, experimental results in a public dataset demonstrate that MLP-mmWP outperforms the existing state-of-the-art methods. Specifically, in a simulation area of 400 × 400 m2, the positioning mean absolute error is 1.78 m, and the 95th percentile prediction error is 3.96 m, representing improvements of 11.8% and 8.2%, respectively.
Mapping Protein Targets of Carnosol, a Molecule Identified in Rosmarinus officinalis: In Silico Docking Studies and Network Pharmacology
Authors: Taboada-Alquerque María , Pajaro-Valenzuela Danilo , Caballero-Gallardo Karina , Cifuentes Alejandro , Ibáñez Elena , Ahumedo-Monterrosa Maicol , Stashenko Elena E. , Olivero-Verbel Jesus
Published: April 2023

Carnosol is a natural diterpene present in Rosmarinus officinalis L. (rosemary) with anti-tumor and anti-inflammatory properties. Despite its importance, the pharmacological mechanisms underlying the interactions between carnosol and human targets are still unclear. The goal was to identify plausible human target for carnosol and the network pharmacology. Rosemary was analyzed using HPLC-QTOF-MS/MS. Potential carnosol targets were identified using docking and a public database (CTD). Carnosol was screened against 708 human proteins using AutoDock Vina, and affinity values were used as prioritization criteria. The targets set was uploaded to WebGestalt to obtain Gene Ontology (GO) and KEGG pathway enrichment analysis. HPLC-QTOF-MS/MS analyses allowed the tentative annotation of nine chemicals, with carnosol being the most ionized. There were 53 plausible targets for carnosol, with 20 identified using virtual screening, including Hsp90α (−10.9 kcal/mol), AKR1C3 (−10.4 kcal/mol), and Hsp90β (−10.4 kcal/mol), and 33 identified from CTD. The potential targets for carnosol identified with PPI and molecular docking were HSP90AA1, MAPK1, MAPK3, CAT, JUN, AHR, and CASP3. GO terms and KEGG pathways analysis found that carnosol is closely related to infection (Chagas, influenza A, toxoplasmosis, and pertussis) and inflammation (IL-17 and TNF signaling pathway and Th-17 cell differentiation). These results demonstrated that carnosol may induce an immuno-inflammatory response.
Low Energy Availability (LEA) and Hypertension in Black Division I Collegiate Athletes: A Novel Pilot Study
Authors: Purdom Troy , Cook Marc , Colleran Heather , Stewart Paul , San Diego Lauren
Published: April 2023

The purpose of this study was to investigate the relationship between low energy availability (LEA) and nutritional content with high blood pressure (HBP) in African American Division I athletes. Twenty-three D1 African American pre-season athletes were recruited to participate. HBP was defined as >120 systolic blood pressure (BP) and <80 diastolic BP. Athletes self-reported nutritional intake using a non-consecutive 3-day food recall which was then reviewed by a sports dietitian. LEA was evaluated as total energy intake—total daily energy expenditure (TDEE), which was predicted. Additionally, micronutrients were evaluated. A statistical analysis relied on Spearman correlation (R), standardized mean difference with 95% confidence interval, mean ± SD, and odds ratios (OR). Correlation values were categorized: 0.20–0.39 = low; 0.40–0.69 = moderate; 0.70–1.0 = strong. A moderate relationship was observed between HBP and LEA (R = 0.56) with 14/23 having HBP. Of the 14 athletes observed with HBP, 78.5% (11/14) were calorically deficient (−529 ± 695 kcal) with an OR of 7.2. Micronutrient intake deficiencies were ubiquitous among the 23 HBP athletes: poly-unsaturated fatty acid −29.6%; omega-3 −26.0%; iron −46.0%; calcium −25.1%; and sodium −14.2%, amongst others. LEA and micronutrient deficiencies may contribute to HBP in Black D1 athletes, which has been shown to be the most common modifiable risk factor to decrease the risk of sudden cardiac death.
Ecological Potential of Building Components in Multi-Storey Residential Construction: A Comparative Case Study between an Existing Concrete and a Timber Building in Austria
Authors: Fischer Henriette , Aichholzer Martin , Korjenic Azra
Published: April 2023

With the introduction of energy-efficient buildings, the importance of embodied energy in new buildings has become increasingly relevant to minimising the impact of climate change. This study compares two existing four-storey residential buildings: one building has a reinforced concrete (RC) structure and the other has a timber structure. The study’s aim is to find out which building components are responsible for the largest embodied impacts and whether there are differences between the two construction methods. The specificity of the wooden building is the combined use of solid and lightweight timber elements. The methodology consists of a general life cycle assessment (LCA) and a more detailed analysis of the product stage using the eco2soft software. The heating and cooling energy demand was calculated using the WUFI Plus software with recent regional climate data sets. The results show that for both types of construction in multi-storey buildings, it is not only the superstructure that needs to be considered, but also the floor structures, which have a major influence on the embodied impact. The timber building requires less energy to maintain the indoor climate within the set temperatures. As climate change has progressed rapidly in Austria in recent years, it is recommended that the standards for climate models be updated more quickly to allow realistic prediction of thermal comfort at the design stage.
The Role of Privatisation in Sustaining Auditor Independence: Evidence from the Developing Markets
Authors: Al Natour Abdul Rahman , Al-Qadi Naim Salameh , Meqbel Rasmi , Zaidan Hala , Al-Mawali Hamzah , Al-Okaily Manaf
Published: April 2023

This paper investigates the role of structural ownership reforms in sustaining auditor independence through split-share structure reform (SSSR). Studying a sample of 1826 Chinese listed firms over the SSSR period in China, the results showed that auditor independence sustainability was less pronounced in local state-owned enterprises (LSOEs) compared with non-state-owned Enterprises (NSOEs). Nevertheless, after the SSSR, there is a significant enhancement in sustaining auditor independence. In particular, auditor independence sustainability is pronounced by providing an unqualified audit opinion, including an ‘emphasis of matter paragraph’ instead of issuing a modified audit opinion. This study contributes to the literature in two ways. First, it gives an empirical investigation into auditor independence sustainability efforts by the Chinese government through the SSSR. Second, it helps regulators and policy-makers in China and other emerging markets in evaluating the SSSR efforts to improve auditor independence.
Spatial Network Analysis on the Coupling Coordination of Digital Finance and Technological Innovation
Authors: Li Jiayi , Ye Shujun , Wang Shujuan
Published: April 2023

For sustainable economic development, it is extremely important to understand how digital finance and technological innovation are coupled and how the spatial coupling network is connected. Based on panel data collected from 31 Chinese provinces between 2011 and 2020, this paper calculates the technological innovation index using the entropy method, and adopts the coupling coordination model to measure the coupling coordination degree of digital finance and technological innovation. Furthermore, this paper utilizes the improved gravity model to determine the spatial correlation matrix and uses the social network analysis (SNA) method to investigate the spatial spillover characteristics of the coupling network. The results demonstrate the following: (1) While the index of digital finance and technological innovation rose and digital finance developed rapidly, the level of technological innovation remained low. (2) There was an improvement in the degree of coupling coordination between digital finance and technological innovation, which was higher in the eastern region and lower in the west. (3) The overall network density and the number of associations increased; meanwhile, the network hierarchy and network efficiency declined, indicating that the spatial structure was strengthened. (4) The centrality of some developed eastern regions, such as Beijing, Shanghai, and Zhejiang, was greater than that of some underdeveloped areas in the midwest and northeast regions. (5) The coupling coordination network can be classified into four types: the “main inflow plate” mainly includes underdeveloped regions in the midwest areas; the “main outflow plate” and “bidirectional spillover plate” primarily include the developed eastern areas; and the “agent plate” mainly includes the central provinces. This research provides a foundation for enhancing the cross-regional coupling coordinated development of digital finance and technological innovation.
Mechanism and Control of Asymmetric Floor Heave in Deep Roadway Disturbed by Roof Fracture
Authors: Wei Wensheng , Zhang Guojun , Li Chunyuan , Zhang Wenshuai , Shen Yupeng
Published: April 2023

In view of the serious problem of bottom-drum damage in deep mining along empty roadways, the asymmetric bottom-drum damage characteristics and control mechanisms of deep mining along an empty roadway were studied using the trackway of the 11060 working face in Zhao Gu II mine as the research background. Based on the slip-line theory, support-pressure distribution law, and Griffith’s damage-criterion theory, the mechanism of asymmetric bottom drums and the maximum fracture-development depth of the bottom plate in a deep roadway under top-plate fracture perturbation were analyzed. The 3DEC discrete-element software was used to simulate and analyze the characteristics and evolution of the asymmetric bottom bulge of the roadway under dynamic-load disturbance, and the asymmetric control scheme of “slurry anchor reinforcement + top cutting and pressure relief” was proposed. The results show that, under the influence of static load of deep high-abutment pressure and the dynamic-load impact of the instability of the masonry-beam structure under periodic pressure of the adjacent working face, the deep-mining goaf roadway was prone to producing asymmetric floor heave. The floor-heave degree and maximum fracture-development range of the roadway in the affected area under the influence of dynamic load > those in goaf roadway > those in the roadway in the stable area affected by tunneling. The distribution of stress, displacement, and maximum floor heave was skewed to the side of the coal pillar in the goaf, showing an inverted right oblique V shape. The asymmetric floor heave of a roadway can be effectively controlled by grouting anchor-cable reinforcement (increasing the anti-damage limit) and roof-cutting pressure relief (cutting off the dynamic-load source). The research results can provide an important reference for the control of roadway floors under similar geological conditions.
A Study on the Visual Communication and Development of Green Design: A Cross-Database Prediction Analysis from 1972 to 2022
Authors: Du Yixuan , Ma Hailan
Published: April 2023

This paper resulted from 1775 pieces of literature from the WoS database and 1923 from the CNKI database. The research framework, development process, internal relations, and key hot topics of green design were explored through the tool of CiteSpace metrology. Four main research results were presented: (1) Green design began to grow explosively around 2015. The design revolved around energy saving, material selection, and other aspects, with a high rate of cooperation, high reference, a large number of publications, and other upsurge phenomena in each branch of the formation. (2) There is the highest volume and popularity of research in the CNKI database. At the same time, in WoS, the United States still has the most disciplinary influence and academic exchange freedom. (3) WoS focuses on solving practical problems of branch disciplines, mainly chemical engineering, experiments, and case analyses. The hot topics in CNKI tend to be design subjects, mainly design technology, management, and theory. (4) There are few basic types of research on the WoS database; CNKI pays more attention to design theoretical research. The two databases form complementary solutions to ensure the future development of green design. The results indicate that green design should be envisioned as an eco-friendly approach, emphasizing optimizing human and management practices, innovative design principles, sustainable processes, and consideration of sociocultural impacts.
Operationalizing Community Placemaking: A Critical Relationship-Based Typology
Authors: Fingerhut Zohar , Alfasi Nurit
Published: April 2023

Placemaking is a relatively new planning technique formulated as an alternative to formal, comprehensive, top-down land-use planning. Instead of the statutory process and product, placemaking offers an open-ended, unstructured framework for planning and implementing focused interventions. This study applies a critical look at how this relatively loose framework operates in practice. Based on an investigation of community placemaking projects in southern Israeli cities, we present four models of placemaking, organized around two main axes: the goal axis, which ranges from a broad community goal to a narrow, predetermined aim, and the motivation axis, which ranges from internal to external motivation. The four types of placemaking emerging from the combination of these considerations are (1) traditional, (2) governmental, (3) artistic-economic, and (4) segregative, based on the varied socio-spatial relations between the stakeholders. This typology serves as a warning sign for possible ways that processes with loose boundaries can be exploited, and the setbacks to which they can lead. It offers a helpful framework for further advancements in placemaking, making it an effective tool for socially and environmentally sustainable urbanism.
The Effect of Degradation on Cold Climate Building Energy Performance: A Comparison with Hot Climate Buildings
Authors: Taki Ahmad , Zakharanka Anastasiya
Published: April 2023

The issues of reducing energy consumption in buildings and their decarbonisation are currently among the most pressing. However, such an important aspect of the problem under discussion as the impact of unavoidable degradation processes on energy demand in buildings remains poorly understood. In addition, there are only a limited number of practical guidelines that can be used to take this factor into account at the design stage and during the further operation of buildings. The aim of this work was to assess the potential impact of component degradation and ageing on heating energy consumption in buildings, including insulated glass units, thermal insulation, airtightness, heat recovery of mechanical ventilation systems, and photovoltaic modules. The detached and apartment buildings were considered to be in a cold climate in the context of the Republic of Belarus. The study was based on simulation research using EnergyPlus. As a result, it was found that a possible increase in heating energy consumption might reach 17.6–61.2% over 25 years in detached houses and up to 23.6–89.8% in apartment buildings. These indicators turned out to be higher than the previously identified values for cooling energy consumption in a hot–humid climate. Based on the findings, recommendations for considering the degradation factor in cold climates in practice were developed, which were compared and integrated into the author’s existing guidelines.
Socioeconomic Urban Environment in Latin America: Towards a Typology of Cities
Authors: Santos Gervásio F. dos , Vives Vergara Alejandra , Fuentes-Alburquenque Mauricio , de Sousa Filho José Firmino , Paiva Aureliano Sancho , Useche Andres Felipe , Yamada Goro , Alfaro Tania , Friche Amélia A. Lima , Andrade Roberto F. S. , Barreto Maurício L. , Caiaffa Waleska Teixeira , Diez-Roux Ana V.
Published: April 2023

This paper aims to identify typologies of Latin American cities based on socioeconomic urban environment patterns. We used census data from 371 urban agglomerations in 11 countries included in the SALURBAL project to identify socioeconomic typologies of cities in Latin America. Exploratory factor analysis was used to select a set of variables, and finite mixture modelling (FMM) was applied to identify clusters to define the typology of cities. Despite the heterogeneities among the Latin American cities, we also found similarities. By exploring intersections and contrasts among these clusters, it was possible to define five socioeconomic regional typology patterns. The main features of each one are low-education cities in Northeast Brazil; low-unemployment cities in Peru and Panama; high-education cities in Argentina, Chile, Colombia, Costa Rica, Nicaragua and Mexico; high female labor participation, with high primary education in Argentina and low primary education in Brazil; and low female labor participation and low education in Brazil, Colombia, El Salvador, Guatemala, and Mexico. Identifying clusters of cities with similar features underscores understanding of the urban social and economic development dynamics and assists in studying how urban features affect health, the environment, and sustainability.
Corporate Governance Research in Asian Countries: A Bibliometric and Content Analysis (2001–2021)
Authors: Wahyuningrum Indah Fajarini Sri , Chegenizadeh Amin , Humaira Natasya Ghinna , Budihardjo Mochamad Arief , Nikraz Hamid
Published: April 2023

Corporate failure suggests that weak corporate governance leads to frail institutions and exposes them to severe crises. Asian countries have faced financial crises in three different periods, most recently due to the COVID-19 pandemic. A crisis will trigger structural changes in corporate governance to enable firms to either respond to, or prevent, the reoccurrence of potentially similar events. The characteristic of corporate governance practice in Asian countries are also unique due to some institutional and informal factors. These will alter direction and future trend of research in corporate governance in Asian region. The objective of this study is to utilize a bibliometric analysis which focuses on research trends and themes, and citations (with additional inclusive visualization) and perform in-depth content analysis to trace the evolution and identify knowledge of corporate governance in Asian countries from 2001 to 2021. Following bibliometric analysis, a sample of 656 articles on corporate governance in Asian countries has been extracted and analyzed from the Scopus database. The results indicate that there is a growing of interest in corporate governance in Asian countries from 2001 to 2021. Eight major themes have been recognized: corporate governance, corporate social responsibility and financial performance, corporate strategy and performance, agency theory, corporate sustainability, audit and agency problems, firm size, and business ethics. Major findings, shortcomings, and directions for future research are also discussed in this study. In general, most cited articles related to corporate governance theme explain the importance of corporate governance in companies with the focus on preventing financial fraud, impact on earnings management, and cost of equity capital in the market and reporting methods.
Social and Personal Norms in Shaping Customers’ Environmentally Sustainable Behavior in Restaurants’ Social Media Communities
Authors: Jang Yoon-Jung , Kim Eojina
Published: April 2023

This study aimed to investigate how personal and social norms affect customer engagement with social media that promote environmentally sustainable behaviors. A self-administered survey of potential participants was conducted. Hypothesized relationships were tested using structural equation modeling and multigroup analysis. The findings confirmed the strong positive effect of social norms on customer engagement; this engagement had a significant influence on brand commitment and sustainable behavior. The moderating roles of social rewards were indicated by significantly greater effects of social norms on customer engagement in the high social reward group.
Eco-Innovation as a Positive and Happy Industry Externality: Evidence from Mexico
Authors: Galván-Vela Esthela , Ruíz-Corrales Missael , Ahumada-Tello Eduardo , Ravina-Ripoll Rafael
Published: April 2023

This research has carried out structural equation modelling to empirically examine whether Mexican industrial firms have a green policy to reduce their environmental impact. It will allow them to enjoy sustainable development based on eco-innovation and happiness management principles. This type of innovation can be initiated by the companies’ own will or by external factors, such as market requirements, the demands of customers, consumers, suppliers, and civil society through corporate social responsibility. The findings of this article highlight three main aspects. The first is that the statistically analysed eco-innovation actions undertaken by the companies are essentially motivated by market pressures through customers, the financial sector, and civil society. Secondly, economic incentives in Mexico are crucial for manufacturing companies to implement environmentally friendly production patterns within their organisations since this generates the possibility of developing new businesses. Furthermore, eco-innovation generates competitive advantages when the production of the examined companies minimises environmental pollution. It allows access to new markets. In this way, eco-innovation becomes a cornerstone in the economic and international growth of the corporations in this study.
Research on Digital Transformation and Green Technology Innovation—Evidence from China’s Listed Manufacturing Enterprises
Authors: Zhang Ge , Gao Yuxiang , Li Gaoyong
Published: April 2023

Green development and the digital economy are receiving increasing attention among scholars, practitioners, and policy makers, as the link between the two remains unclear, and exploring the study of the mechanisms at play between the two to achieve quality economic development is an urgent issue to be addressed. This study addresses this gap and aims to provide clarity by analyzing examples of business practices in developing countries. Using a total of 20,283 datasets from 2049 listed manufacturing firms from China from 2007 to 2020 as the study sample, the mechanism of digital transformation’s impact on firms’ green technological innovation capability is empirically examined and the mediating role of firms’ green dynamic capabilities is verified. This study finds that: (1) Digital transformation significantly enhances the level of green technology innovation of enterprises. (2) There is a partial mediating effect of green dynamic capabilities in the process of digital transformation positively affecting enterprises’ green technology innovation. (3) Digital transformation by state-owned, central and western regions and by medium-sized enterprises is more significantly effective in promoting green technology innovation than non-state-owned, eastern regions and small and large enterprises. (4) The analysis of economic consequences shows that digital transformation can mitigate the incremental costs incurred in the process of digital transformation by empowering enterprises to achieve green development and cost reduction through green technology innovation.
The Process Capability Index of Pareto Model under Progressive Type-II Censoring: Various Bayesian and Bootstrap Algorithms for Asymmetric Data
Authors: EL-Sagheer Rashad M. , El-Morshedy Mahmoud , Al-Essa Laila A. , Alqahtani Khaled M. , Eliwa Mohamed S.
Published: April 2023

It is agreed by industry experts that manufacturing processes are evaluated using quantitative indicators of units produced from this process. For example, the Cpy process capability index is usually unknown and therefore estimated based on a sample drawn from the requested process. In this paper, Cpy process capability index estimates were generated using two iterative methods and a Bayesian method of estimation based on stepwise controlled type II data from the Pareto model. In iterative methods, besides the traditional probability-based estimation, there are other competitive methods, known as bootstrap, which are alternative methods to the common probability method, especially in small samples. In the Bayesian method, we have applied the Gibbs sampling procedure with the help of the significant sampling technique. Moreover, the approximate and highest confidence intervals for the posterior intensity of Cpy were also obtained. Massive simulation studies have been performed to evaluate the behavior of Cpy. Ultimately, application to real-life data is seen to demonstrate the proposed methodology and its applicability.
A Dynamic Fusion of Local and Non-Local Features-Based Feedback Network on Super-Resolution
Authors: Liu Yuhao , Chu Zhenzhong
Published: April 2023

Many Symmetry blocks were proposed in the Single Image Super-Resolution (SISR) task. The Attention-based block is powerful but costly on non-local features, while the Convolutional-based block is good at efficiently handling the local features. However, assembling two different Symmetry blocks will generate an Asymmetry block, making the classic Symmetry-block-based Super-Resolution (SR) architecture fail to deal with these Asymmetry blocks. In this paper, we proposed a new Dynamic fusion of Local and Non-local features-based Feedback Network (DLNFN) for SR, which focus on optimizing the traditional Symmetry-block-based SR architecture to hold two Symmetry blocks in parallel, making two Symmetry-blocks working on what they do best. (1) We introduce the Convolutional-based block for the local features and Attention-based network block for non-local features and propose the Delivery–Adjust–Fusion framework to hold these blocks. (2) we propose a Dynamic Weight block (DW block) which can generate different weight values to fuse the outputs on different feedback iterations. (3) We introduce the MAConv layer to optimize the In block, which is critical for our two blocks-based feedback algorithm. Experiments show our proposed DLNFN can take full advantage of two different blocks and outperform other state-of-the-art algorithms.
Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery
Authors: Gabhane Lalit Rajaramji , Kanidarapu NagamalleswaraRao
Published: April 2023

Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl–tert–butyl–ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.
Cadmium Monitoring at the Workplace: Effectiveness of a Combination of Air- and Biomonitoring
Authors: Lombaert Noömi , Gilles Mik , Verougstraete Violaine
Published: April 2023

Inhalation exposure to cadmium at the workplace has been associated with an increased risk of lung cancer and non-cancer respiratory effects. To ensure levels of cadmium remain below effect levels, air quality is monitored and regulations specifying an air limit value are implemented. The EU Carcinogens and Mutagens Directive of 2019 recommended values for the inhalable fraction and the respirable fraction but the latter only for a transitional period. Cadmium exposure has also been associated with systemic effects, following its storage in the kidneys and due to its long half-life. The accumulation of cadmium occurs via different exposure routes and from different sources, including workplace dust and fumes, food, and smoking. Biomonitoring (in blood, urine) has been identified as the most appropriate method to follow up cumulative exposure and total cadmium body burden, as it conveniently reflects intakes by all routes. However, it is not systematically implemented. This paper has a double objective: first, proposing a possible limit value for the respirable fraction, using an approach integrating epidemiological data. Secondly, demonstrating that the implementation of both air and biological limit values is key to protecting workers’ health in occupational settings. The paper summarizes the current knowledge on cadmium health effects and how biomarkers reflect those. It presents an approach to derive a respirable value, using recent human data, and describes how the combination of air monitoring and biomonitoring is applied by the EU industry to protect the workforce. While a respirable fraction value helps protect workers against local respiratory adverse health effects, air monitoring alone is not sufficient to protect workers against systemic effects of cadmium. Therefore, complementary biomonitoring and the implementation of a biological limit value is recommended.