bluuebunny/crossref_metadata_2025 huggingface dataset:
Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes
Authors: Bernaś Marcin , Płaczek Bartłomiej , Lewandowski Marcin
Published: December 2022

Nowadays, sensor-equipped mobile devices allow us to detect basic daily activities accurately. However, the accuracy of the existing activity recognition methods decreases rapidly if the set of activities is extended and includes training routines, such as squats, jumps, or arm swings. Thus, this paper proposes a model of a personal area network with a smartphone (as a main node) and supporting sensor nodes that deliver additional data to increase activity-recognition accuracy. The introduced personal area sensor network takes advantage of the information from multiple sensor nodes attached to different parts of the human body. In this scheme, nodes process their sensor readings locally with the use of recurrent neural networks (RNNs) to categorize the activities. Then, the main node collects results from supporting sensor nodes and performs a final activity recognition run based on a weighted voting procedure. In order to save energy and extend the network’s lifetime, sensor nodes report their local results only for specific types of recognized activity. The presented method was evaluated during experiments with sensor nodes attached to the waist, chest, leg, and arm. The results obtained for a set of eight activities show that the proposed approach achieves higher recognition accuracy when compared with the existing methods. Based on the experimental results, the optimal configuration of the sensor nodes was determined to maximize the activity-recognition accuracy and reduce the number of transmissions from supporting sensor nodes.
Adaptive Inertial Sensor-Based Step Length Estimation Model
Authors: Vezočnik Melanija , Juric Matjaz B.
Published: December 2022

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.
Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches
Authors: Knudson Karin C. , Gupta Anoopum S.
Published: December 2022

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity.
Near Real-Time Volumetric Estimates Using Unmanned Aerial Platforms Equipped with Depth and Tracking Sensors
Authors: Amitrano Donato , Cicala Luca , Cuciniello Giovanni , De Mizio Marco , Poderico Mariana , Tufano Francesco
Published: December 2022

Volume estimation of specific objects via close-range remote sensing is a complex task requiring expensive hardware and/or significant computational burden, often discouraging users potentially interested in the technology. This paper presents an innovative system for cost-effective near real-time volume estimation based on a custom platform equipped with depth and tracking cameras. Its performance has been tested in different application-oriented scenarios and compared against measurements and state-of-the-art photogrammetry. The comparison showed that the developed architecture is able to provide estimates fully comparable with the benchmark, resulting in a quick, reliable and cost-effective solution to the problem of volumetric estimates within the functioning range of the exploited sensors.
Integration of IoT Sensors to Determine Life Expectancy of Face Masks
Authors: Ratnayake Mudiyanselage Vilanya , Lee Kevin , Hassani Alireza
Published: December 2022

Personal protective equipment (PPE) is widely used around the world to protect against environmental hazards. With the emergence of the COVID-19 virus, the use of PPE domestically has increased dramatically. People use preventive and protective mechanisms now more than ever, leading to the important question of how protective is the PPE that is being used. Face masks are highly recommended or mandatory during the time of the COVID-19 pandemic due to their protective features against aerosol droplets. However, an issue faced by many users of face masks is that they are entirely manual, with users having to decide for themselves whether their mask is still protective or if they should replace their mask. Due to the difficulty in determining this, people tend to overuse masks beyond their optimal usage. The research presented in this paper is an investigation of the viability of integrating IoT sensors into masks that are capable of collecting data to determine its usage. This paper demonstrates the usage of humidity and temperature sensors for the purpose of determining a mask’s usage status based on changes in these variables when a mask is put on and taken off. An evaluation was made on the usage of the two sensors, with the conclusion that a humidity sensor provides more accurate results. From this, we present a framework that takes into consideration the factors that affect a mask’s performance, such as time, humidity and temperature, to calculate the life expectancy of a mask.
Design of Precision-Aware Subthreshold-Based MOSFET Voltage Reference
Authors: Mu Shuzheng , Chan Pak Kwong
Published: December 2022

A new precision-aware subthreshold-based MOSFET voltage reference is presented in this paper. The circuit was implemented TSMC−40 nm process technology. It consumed 9.6 μW at the supply voltage of 1.2 V. In this proposed work, by utilizing subthreshold-based MOSFET instead of bipolar junction transistor (BJT), relatively lower power consumption was obtained in the design while offering comparable precision to that offered by its BJT counterpart. Through the proposed second-order compensation, it achieved the temperature coefficient (T.C.) of 3.0 ppm/°C in the TT corner case and a 200-sample Monte-Carlo T.C. of 12.51 ppm/°C from −40 °C to 90 °C. This shows robust temperature insensitivity. The process sensitivity of Vref without and with trimming was 2.85% and 0.75%, respectively. The power supply rejection (PSR) was 71.65 dB at 100 Hz and 52.54 dB at 10 MHz. The Figure-of-Merit (FOM) for the total variation in output voltage was comparable with representative BJT circuits and better than subthreshold-based MOSFET circuits. Due to low T.C., low process sensitivity, and simplicity of the circuit architecture, the proposed work will be useful for sensor circuits with stringent requirements or other analog circuits that require high precision applications.
Three-Dimensional Acoustic Device for Testing the All-Directional Anisotropic Characteristics of Rock Samples
Authors: Zhang Kai , Li Shengqing , Su Yuanda , Tan Baohai , Wu Wenjie , Xin Shoutao
Published: December 2022

Many oil and gas fields, especially non-conventional shale and compacted sand reservoirs, have formation anisotropy. The acoustic anisotropy measurement of cores in these reservoirs can guide drilling, well logging, and exploitation. However, almost all core holders are designed for cylinder cores, which are not suitable for all-directional measurements. A three-dimensional measurement device was designed on the basis of the cross-hole sonic logging method. This device mainly consisted of two pairs of transducers, a signal generator, an oscillograph, an omnidirectional positioning system, and a computer control system. By adjusting the measurement latitude and longitude circle automatically, this device scanned spherical sample rocks and obtained full-wave waveforms in all directions. Experiments were performed taking granite from the Jiaodong Peninsula, China, as an example, and the arrival times and velocities of the longitudinal and shear waves were calculated based on the full-wave waveforms. Thereafter, anisotropic physical characterizations were carried out on the basis of these velocities. These data play an important role in guiding formation fracturing and analyzing the stability of borehole walls.
Preventing Attacks on Wireless Networks Using SDN Controlled OODA Loops and Cyber Kill Chains
Authors: Zanna Paul , Radcliffe Peter , Kumar Dinesh
Published: December 2022

Impersonation-based attacks on wireless networks are easy to perform and can significantly impact network security. Their detection is problematic due to the attacks utilizing legitimate functions. This paper proposes a novel algorithm based on Observe-Orientate-Decide-Act (OODA) loop and Cyber Kill Chain (CKC) strategies to detect and neutralize these attacks. To evaluate this approach, we conducted experiments using four attack methods on a wireless router equivalent device, five wireless client devices, and two attack devices. The system employs a Radio Frequency (RF) device identification system and attack state machine implemented using a Software Defined Networking (SDN) architecture and the P4 programming language. The technique remains compliant with the IEEE 802.11 standard and requires no client-side modifications. The results show that the RF section detected 97.5% (average) of impersonated frames, and the overall method neutralized all attacks in the four attack scenarios. This outcome demonstrates that this technique, built on the OODA loops and CKC methodology, using SDN architecture and P4, is suitable for real-time detection and prevention of wireless impersonation attacks.
A Cost-Effective Distributed Acoustic Sensor for Engineering Geology
Authors: Gorshkov Boris G. , Alekseev Alexey E. , Simikin Denis E. , Taranov Mikhail A. , Zhukov Konstantin M. , Potapov Vladimir T.
Published: December 2022

A simple and cost-effective architecture of a distributed acoustic sensor (DAS) or a phase-OTDR for engineering geology is proposed. The architecture is based on the dual-pulse acquisition principle, where the dual probing pulse is formed via an unbalanced Michelson interferometer (MI). The necessary phase shifts between the sub-pulses of the dual-pulse are introduced using a 3 × 3 coupler built into the MI. Laser pulses are generated by direct modulation of the injection current, which obtains optical pulses with a duration of 7 ns. The use of an unbalanced MI for the formation of a dual-pulse reduces the requirements for the coherence of the laser source, as the introduced delay between sub-pulses is compensated in the fiber under test (FUT). Therefore, a laser with a relatively broad spectral linewidth of about 1 GHz can be used. To overcome the fading problem, as well as to ensure the linearity of the DAS response, the averaging of over 16 optical frequencies is used. The performance of the DAS was tested by recording a strong vibration impact on a horizontally buried cable and by the recording of seismic waves in a borehole in the seabed.
A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation
Authors: Shen Yu , Liu Yuhang , Tian Yonglin , Liu Zhongmin , Wang Feiyue
Published: December 2022

Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.
An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
Authors: Boulouard Zakaria , Ouaissa Mariyam , Ouaissa Mariya , Siddiqui Farhan , Almutiq Mutiq , Krichen Moez
Published: December 2022

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.
A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
Authors: Nguyen Hoa Xuan , Zhu Shaoshu , Liu Mingming
Published: December 2022

Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice architecture from prototype design to large-scale service deployment. To appreciate the big picture of this emerging trend, we provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications. To begin, we identify the key areas in which GNNs are applied, and then we review in detail how GNNs can be designed to address the challenges in specific areas found in the literature. Finally, we outline potential research directions where GNN-based solutions can be further applied. Our research shows the popularity of leveraging convolutional graph neural networks (ConGNNs) for microservice-based applications in the current design of cloud systems and the emerging area of adopting spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) for more advanced studies.
GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery
Authors: Cao Zhicheng , Schmid Natalia A. , Cao Shufen , Pang Liaojun
Published: December 2022

Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper proposes a hybrid solution that takes advantage of both traditional feature engineering and modern deep learning techniques to overcome the issue of limited imagery as encountered in the SWIR band. Firstly, the paper revisits the theory of measurement levels. Then, two new operators are introduced which act at the nominal and interval levels of measurement and are named the Nominal Measurement Descriptor (NMD) and the Interval Measurement Descriptor (IMD), respectively. A composite operator Gabor Multiple-Level Measurement (GMLM) is further proposed which fuses multiple levels of measurement. Finally, the fused features of GMLM are passed through a succinct and efficient neural network based on PCA. The network selects informative features and also performs the recognition task. The overall framework is named GMLM-CNN. It is compared to both traditional hand-crafted operators as well as recent deep learning-based models that are state-of-the-art, in terms of cross-spectral verification performance. Experiments are conducted on a dataset which comprises frontal VIS and SWIR faces acquired at varying standoffs. Experimental results demonstrate that, in the presence of limited data, the proposed hybrid method GMLM-CNN outperforms all the other methods.
An Evaluation Process for IoT Platforms in Time-Sensitive Domains
Authors: García-Valls Marisol , Palomar-Cosín Eva
Published: December 2022

Determining the temporal behavior of an IoT platform is of utmost importance as IoT systems are time-sensitive. IoT platforms play a central role in the operation of an IoT system, impacting the overall performance. As a result, initiating an IoT project without the exhaustive knowledge of such a core software piece may lead to a failed project if the finished systems do not meet the needed temporal response and scalability levels. Despite this fact, existing works on IoT software systems focus on the design and implementation of a particular system, providing a final evaluation as the validation. This is a risky approach as an incorrect decision on the core IoT platform may involve great monetary loss if the final evaluation proves that the system does not meet the expected validation criteria. To overcome this, we provide an evaluation process to determine the temporal behavior of IoT platforms to support early design decisions with respect to the appropriateness of the particular platform in its application as an IoT project. The process defines the steps towards the early evaluation of IoT platforms, ranging from the identification of the potential software items and the determination of the validation criteria to running the experiments and obtaining results. The process is exemplified on an exhaustive evaluation of a particular mainstream IoT platform for the case of a medical system for patient monitoring. In this time-sensitive scenario, results report the temporal behavior of the platform regarding the validation parameters expressed at the initial steps.
Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems
Authors: Buriboev Abror , Muminov Azamjon
Published: December 2022

Several crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their workloads is the best way to assess the performance of the computer’s state. Especially, analyzing the particular or whole influence of components on another component gives more reliable information about the state of computer systems. There are many evaluation techniques proposed by researchers. The bulk of them have complicated metrics and parameters such as utilization, time, throughput, latency, delay, speed, frequency, and the percentage which are difficult to understand and use in the assessing process. According to these, we proposed a simplified evaluation method using components’ utilization in percentage scale and its linguistic values. The use of the adaptive neuro-fuzzy inference system (ANFIS) model and fuzzy set theory offers fantastic prospects to realize use impact analyses. The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. The suggested method is founded on keeping an eye on how computer parts behave. The developed method can be applied for all kinds of computing system, such as personal computers, mainframes, and supercomputers by considering that the inference engine of the proposed ANFIS model requires only its own behavior data of computers’ components and the number of inputs can be enriched according to the type of computer, for instance, in cloud computers’ case the added number of clients and network quality can be used as the input parameters. The models present linguistic and quantity results which are convenient to understand performance issues regarding specific bottlenecks and determining the relationship of components.
Testing Variational Bias Correction of Satellite Radiance Data in the ACCESS-C: Australian Convective-Scale NWP System
Authors: Samrat Nahidul Hoque , Smith Fiona , Lee Jin , Smith Andrew
Published: December 2022

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, “the Bureau”), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau’s regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.
Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP
Authors: Fernández-Rodríguez Álvaro , Darves-Bornoz Aube , Velasco-Álvarez Francisco , Ron-Angevin Ricardo
Published: December 2022

Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain–computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.
Perovskite Piezoelectric-Based Flexible Energy Harvesters for Self-Powered Implantable and Wearable IoT Devices
Authors: Pattipaka Srinivas , Bae Young Min , Jeong Chang Kyu , Park Kwi-Il , Hwang Geon-Tae
Published: December 2022

In the ongoing fourth industrial revolution, the internet of things (IoT) will play a crucial role in collecting and analyzing information related to human healthcare, public safety, environmental monitoring and home/industrial automation. Even though conventional batteries are widely used to operate IoT devices as a power source, these batteries have a drawback of limited capacity, which impedes broad commercialization of the IoT. In this regard, piezoelectric energy harvesting technology has attracted a great deal of attention because piezoelectric materials can convert electricity from mechanical and vibrational movements in the ambient environment. In particular, piezoelectric-based flexible energy harvesters can precisely harvest tiny mechanical movements of muscles and internal organs from the human body to produce electricity. These inherent properties of flexible piezoelectric harvesters make it possible to eliminate conventional batteries for lifetime extension of implantable and wearable IoTs. This paper describes the progress of piezoelectric perovskite material-based flexible energy harvesters for self-powered IoT devices for biomedical/wearable electronics over the last decade.
The Assistive Technology Passport: A Resource for Enhancing Capabilities as a Result of Better Access to Assistive Technology
Authors: Maalim Mohamed I. , MacLachlan Malcolm
Published: December 2022

The value of Assistive Technology in enabling active and equal participation in political, social, economic, and cultural life of people with disabilities, people ageing, and people with chronic conditions is broadly accepted. However, most of the global population who need assistive technology (AT) lack access to it, hence the call for international efforts to improve access to AT. Drawing from the capability approach (CA) developed by Amartya Sen and Martha C. Nussbaum, we explore factors that may hinder or facilitate access to AT. We examine the idea of an AT Passport as an innovative user-centered approach for improving access to AT. We used Interpretative Phenomenological Analysis (IPA) to explore service users’ lived experiences of access to AT and their understanding of the AT Passport concept. We identified the core values of human diversity, equity, access to opportunity, and individual freedom to choose a life of significance to them. Access to AT is central to expanding these capabilities subject to conducive personal and social-contextual issues. The AT Passport could be developed as a capability-enhancing resource by facilitating access to AT, harnessing the diversity of people’s personal, social, and environmental factors to enrich peoples’ capability sets. Further research is required to identify core AT Passport functionalities, usability, and acceptability features.
Are Civility Norms Morality Norms’ Little Sister? The Truth Value That Lay Thinking Associates with Civility and Morality Social Norms
Authors: Rodríguez-Pérez Armando , Rodríguez-Torres Ramón , Betancor Verónica , Chen-Xia Xing Jie , Rodríguez-Gómez Laura
Published: December 2022

Previous research shows that civility norms, such as morality norms, are necessary navigational charts to orient an individual in social life. However, there are no studies that establish the extent to which people consider civility norms as objective facts in a similar way to how many moral norms are considered. This research examines the perceived objectivity of civility norms in contrast to morality norms. The results show, firstly, that immoral norms are perceived to be significantly more objective than moral norms, but the opposite occurs with civility norms. Second, there is a high correlation between what participants consider objective and what they believe the rest of their community considers objective. However, this correlation was greater for immoral behaviors than for moral behaviors, and the opposite occurred for civility behaviors. Finally, participants estimated that the percentage of people in their group who engage in uncivil behaviors is higher than those who engage in immoral behaviors.
Emancipatory Urban Citizenship Regimes in Postpandemic Catalonia, Scotland, and Wales
Authors: Calzada Igor
Published: December 2022

Wide tensions regarding the organization of nation-state power have been triggered over the last years in the UK and Spain. By contrast, in the UK, (i) the plebiscite on Scottish Independence has been characterized since 2014 so far by a regular hegemony of the SNP in Scotland, and (ii) more recently, distinct resilient responses to tackle COVID-19 have dramatically shifted perceptions about the potential constitutional arrangements in Wales partially opposing a state-centric vision of the UK. By contrast, the role played by the constitutionally illegal but socially constitutive referendum in Catalonia on 1 October 2017, remarkably provoked the re-emergence of the Spanish far-right narrative through the surge of the new political party called Vox. In both cases, the urban in Glasgow, Cardiff, and Barcelona has been shaping various oppositions to state-centric agendas, and such oppositions have shaped elections in the UK and Spain. This article sheds light on the distinct, emerging, and emancipatory urban citizenship regimes in Catalonia, Scotland, and Wales, particularly illustrating the roles that Barcelona, Glasgow, and Cardiff, respectively, are playing in articulating a counter-reaction by rescaling a state-centric vision. This article employs past elections’ evidence to illustrate such regimes amid postpandemic times in datafied states.
Where You Live Does Matter: Impact of Residents’ Place Image on Their Subjective Well-Being
Authors: Zhang Hui , Li Wenhui
Published: December 2022

Residents’ subjective well-being is closely related to their perception of specific attributes of the city they live in, such as physical appearance, entertainment facilities, and job opportunities. This study aims to examine the effect of place image on residents’ subjective well-being as well as the mediation effect of place attachment on this relationship. Residents living in Guangzhou for more than a year were targeted to collect data, and 420 valid samples were gathered. Structure equation modeling was used to test the hypothesized relationships. The results show that place image has a significant positive impact on life satisfaction and positive affect, while it has a significant negative impact on negative affect. Place identity and place dependence mediate the positive impacts of place image on life satisfaction and positive affect. Finally, management implications and limitations were discussed.
Bibliometric Analysis of Environmental, Social, and Governance Management Research from 2002 to 2021
Authors: Siao Hung-Jung , Gau Sue-Huai , Kuo Jen-Hwa , Li Ming-Guo , Sun Chang-Jung
Published: December 2022

Extreme weather events caused by climate change have increased people’s focus on sustainability. Environmental, social, and governance management (ESGM) has become crucial for corporate operations and development; ESGM has attracted the attention of the academic communities, and the number of related studies has continued to increase. However, this topic is multidisciplinary and diverse; therefore, this study used the Web of Science Core Collection Database to conduct a bibliometric analysis of ESGM-related articles published from 2002 to 2021. Bibliometrix (R language), VOSviewer, and CiteSpace were used to identify and analyze research trends related to the number of studies, research fields, authors, national institutions, and keywords. The importance of management and governance was identified through keyword analysis; important keywords identified were financial performance, adaptive governance, property rights, sustainable development goals, and corporate governance.
Decision-Making and Coordination of Remanufacturing Closed-Loop Supply Chain with PIR under the Different Government Subsidy Strategies
Authors: Yao Jun , Chen Dongyan , Yu Hui
Published: December 2022

Government subsidies and process innovation for remanufacturing (PIR) have become effective measures to improve the recycling and remanufacturing efficiency of waste products and promote sustainable economic development. Under different government subsidy strategies, the PIR decision and coordination of the closed-loop supply chain (CLSC) of authorized competition remanufacturing are studied. This paper constructs five centralized and decentralized decision models of CLSC and analyzes the influence of government subsidy strategy and PIR input entity differences on the competition and cooperation relationship between manufacturers and remanufacturers and the performance of CLSC. A fixed license fee contract is designed to realize CLSC coordination and to improve the performance and operational efficiency of the CLSC. It is found that patent licensing fees can always play a role in sharing government subsidies between the manufacturer and remanufacturer, and making changes in government subsidy strategies only affects formulation of patent licensing fees. Manufacturer PIR input mode is more conducive to improving the market demand for new products, while remanufacturer PIR input mode is more conducive to improving the PIR input level, remanufactured product market demand, CLSC members and overall performance. Moreover, government subsidies can produce better efficiency and effects under remanufacturer PIR input mode. A fixed license fee contract can coordinate a CLSC effectively. The increase in government subsidies and PIR input effect can effectively expand the negotiation space between the manufacturer and remanufacturer, which is conducive to achievement of coordination contracts. Finally, the main conclusions are verified by numerical analysis.
Analyzing Pakistan’s Renewable Energy Potential: A Review of the Country’s Energy Policy, Its Challenges, and Recommendations
Authors: Xin Yongrong , Bin Dost Muhammad Khyzer , Akram Hamza , Watto Waqas Ahmad
Published: December 2022

Pakistan’s dependence on imported fuels has led to a massive electrical shortfall, stifling the country’s socioeconomic growth. Pakistan’s energy gap is between 5000 and 8000 megawatts (MW), with a 6–8% yearly growth predicted, therefore, it needs more sustainable and renewable energy sources. Pakistan uses solar, wind, hydropower, and biomass for renewable energy. Pakistan needs cheap energy to support its economic growth. Most of the world’s energy is currently conventionally produced. The objective of this research is to analyze the opportunities that are afforded by renewable energy sources as well as the role that the government plays in the process of policy formulation. An examination of qualitative data from research works, journals, and government annual performance reports is presented. This study investigates renewable energy sources and government policy. According to the study, renewable energy resources are abundant. The renewable energy plan has no effect on real implementation. This research paper examines electricity restrictions, the transition to renewable energy sources, and Pakistan’s renewable energy policy difficulties. After analyzing the nation’s renewable energy potential, practical implications were made to promote it, for example 1710-megawatt wind installations. There are currently 217-megawatt biogas power stations being developed. Accelerating the energy transition to the nation’s future energy perspectives is a priority for sustainable development.
Applying Latent Allocation Topic Model to Explore the Policy Changes of Teachers’ Morality Development for Teacher Educational Sustainability in China
Authors: Li Jian , Xue Eryong
Published: December 2022

The policy changes of teachers’ morality development in Chinese colleges and universities have depicted the evolution of teachers’ morality policy. However, there are few previous studies on applying the content analysis to examine the educational policy changes of teacher morality development from the insight of China’s higher education system. Thus, this study applies the LDA topic model as one content analysis approach to provide high-frequency word analysis, analysis of centrality, and cluster analysis to explore the overall policy trends of teachers’ morality development in China. It is found that the policy objectives and contents of teachers’ ethics construction in colleges and universities are constantly clear and definite. The policy content of teachers’ morality construction is continuously clear. The policy direction and policy value of the construction of teachers’ ethics in colleges and universities have been continuously broadened and deepened. The policy evaluation and supervision of teachers’ ethics construction in colleges and universities have been continuously optimized and improved. In addition, the conclusions and suggestions have been offered in the final section.
Soundscape Perception Preference in an Urban Forest Park: Evidence from Moon Island Forest Park in Lu’an City
Authors: Guo Yanlong , Wang Ke , Zhang Han , Jiang Zuoqing
Published: December 2022

Urban forest parks improve the environment by reducing noise, which can promote the development of physical and mental health. This study aimed to investigate the soundscape preferences of visitors in different spaces. It also provides practical suggestions for the study of urban green-space soundscapes. This study took the example of Moon Island Forest Park in Lu’an City, based on the questionnaire field survey that acquired public soundscape perception data. SPSS 26.0 was used to analyze five different spatial soundscape perception preferences in Moon Island Forest Park, starting from the subjective evaluation of users’ soundscape perception, based on user preference for different spatial sound source types. A one-way analysis of variance (ANOVA) was used and a separate analysis of soundscape preferences in each space was undertaken; the mean (SD) was also used to reveal the respondents’ preference for each sound-source perceptual soundscape. The study found that the five dimensions of different spaces were significantly correlated with sound perception preferences. First, the same sound source had different perceptual characteristics and differences in different functional areas. Second, different spatial features were influenced differently by typical sound sources. Third, in each functional area, water sound was the main sound source of positive impact and mechanical sound was the main source of negative impact. Mechanical sound had the greatest negative impact on the overall area. Overall, natural sound provided the most popular significant contribution to the soundscape preference; second was the human voice, and mechanical sound produced a negative effect. The results of these studies were analyzed from the perspective of soundscape characteristics in different spaces, providing a more quantitative basis for urban forest park soundscape design.
A GIS-Based Analysis of the Carbon-Oxygen Balance of Urban Forests in the Southern Mountainous Area of Jinan, China
Authors: Li Donghe , Mu Huigang , Gao Yelin , Lu Min , Liu Chunlu
Published: December 2022

The urban forest is a vital carbon sink base in a city. The carbon-oxygen balance capacity of urban forests affects the urban carbon cycle and urban sustainable development. The forests maintain the carbon-oxygen balance through carbon sequestration and oxygen release (CSOR) processes. The carbon-oxygen balance of urban forests is formed by offsetting the carbon release and oxygen consumption (CROC) process of urban social activities through the CSOR process of forestland. Based on GIS technology, this research used the carbon-oxygen balance model to analyze the CROC and CSOR and study the carbon-oxygen balance of urban forests in the southern mountainous area of Jinan, China. The results of the increase in the carbon-oxygen balance coefficients showed that the carbon-oxygen balance capacity of urban forests showed a decreasing trend, with the decrease in forest area and the increase in fossil energy consumption from 2000 to 2019 in the southern mountainous area of Jinan. To increase the urban carbon-oxygen balance capacity, the city should expand its woodland area to improve the urban forest’s CSOR capacity and adjust the urban energy consumption structure to reduce the CROC of urban social activities.
Sustainability Committee Research: A Bibliometric Study
Authors: Alcaide-Ruiz María Dolores , Bravo-Urquiza Francisco , Moreno-Ureba Elena
Published: December 2022

This study conducts a bibliometric analysis of research on sustainability committees. Specifically, our paper analyses the development of this field of research by identifying the most influential articles, authors, and relevant research themes, and highlighting potential future lines of research. Our sample is composed of the publications from the main collection of the Clarivate Analytics Web of Science database (WOS) for the period 1900–2021. Our findings stress the interdisciplinary nature of research about sustainability committees. In addition, our evidence emphasizes the need for more research to understand how firms respond to regulatory and societal pressures on sustainability matters. In addition, the network analysis highlights the main research themes and provides a basis for recognizing future research opportunities. Our paper is the first to perform a comprehensive bibliometric analysis for sustainability committees. Our evidence presents relevant implications for academics in the definition of their research projects.
Optimizing Maize Belt Width Enhances Productivity in Wheat/Maize Intercropping Systems
Authors: Chen Guodong , Zhai Yunlong , Zhou Jianguo , Li Yanfang , Lin Jiao , Wan Sumei , Wu Quanzhong
Published: December 2022

Wheat/maize intercropping has been widely practiced in northwestern China. It is crucial to optimize the canopy structure and geometric configurations to enhance the performance of the system. This research determined the responses of intercrops to the different canopy structures created by the different wheat/maize intercropping systems. Field experiments were carried out in 2012, 2013, and 2014 at Wuwei, Gansu. Three intercropping patterns—six rows of wheat alternated with two rows of maize (6W2M), six rows of wheat alternated with three rows of maize (6W3M), and six rows of wheat alternated with four rows of maize (6W4M)—were compared with sole wheat and sole maize. The results showed that maize plant heights differed between the inner rows and the border rows, and the difference was greater for the 6W3M system than for the 6W4M system. The three intercropping systems had an average land-use equivalent ratio (LER, calculated based on grain yield) of 1.25, indicating an increase in land-use efficiency by 25% compared to the corresponding sole crops. The shape of maize strips in 6W3M optimized the canopy structure and increased the productivity of wheat and maize. The wheat in 6W3M had significantly more grain yield compared with the sole wheat and the 6W2M due to the maize belt shape, which resulted in the soil evaporation negatively affecting the intercropped wheat grain yield of the 6W3M. Similarly, the maize belt shape facilitated the light penetration and enhanced the reproductive growth by increasing the two cobs per plant rate (TCR) of the maize. The highest TCR of the 6W3M produced a higher maize grain yield than the 6W2M and sole maize in the three growing seasons. The maize belt width in the strip intercropping system had a significant effect on the grain yield of both wheat and maize, which reduced water evaporation, harmonized light distribution, and increased productivity.
The Consumption Analysis of Economic Media at the Regional Level in a Developing Country
Authors: Nistor Andreea , Zadobrischi Eduard
Published: December 2022

This research aims to analyze how the media can favor economic development at the regional level, observing how mass media reports on the economy are relevant to Romania’s population, and if the content provided stimulates economic development action. Mass media is one of the main channels of information for many companies, and the acquisition of correct and qualitative information generates informed decisions. Additionally, the mass media has the role of producing changes in the behavior of the consuming public, and in its culture, and can improve its educational level. Although the variety of information sources, which has been improved over time by technology, does not guarantee their use, the mass media, through the information it provides, can change attitudes and values, and lead to a higher standard of living. The present study includes both qualitative and quantitative methods, which look at the media and its effects on companies. Through two surveys, data were collected on how the population relates to the information obtained from the mass media, as well as how the mass media is used by the companies in the Northeast Region of Romania. The benefits and challenges that companies encounter while using social networks have thus been determined. Additionally, the qualitative method was used, in order to be able to observe whether there are significant differences in the use of social networks to develop an emerging business, in terms of the male-female ratio.