bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Study of a Supersonic Swirl Separator
Authors: Liu Yi , Ding Chang
Published: July 2023

At present, as a new separation technology, supersonic separators have great potential in the separation of natural gases. However, their system performance is still low. In this paper, a supersonic swirl separator design is proposed with an integration approach of the discrete phase model (DPM), bi-coupling, and the random walk model, and it is used to predict the flow process of liquid droplets within the device. Such a numerical method is further employed to study the influence of key parameters on system performance. The results show that with an increase in the inlet port number and the ratio of the gas-liquid area, the separation performance decreases. As a result, the expansion, condensation effect, and economy of the separation system are greatly improved. When the deflection angle exceeds 20°, the separation temperature increases greatly. Consequently, this may ruin the condensing environment. The working pressure ranges are: (1) the boost ratio (the dry outlet pressure/total inlet pressure) is less than 0.76; (2) the wet pressure ratio (the wet outlet pressure/total inlet pressure)is less than 0.46. The increase in droplet diameter can improve the separation performance, and the droplets are completely separated as the diameter reaches 1.75 μm.
Container Terminal Digital Twin Yard System Construction
Authors: Du Xueqiang , Liang Chengji , Zhao Ning , Xuan Beng
Published: July 2023

New requirements for terminal production and operation have emerged as a result of the increase in container terminal throughput. Traditional terminals’ manufacturing capabilities fall short of the expanding service needs. By constructing a digital twin yard for container terminals, the production capacity of terminals can be effectively improved, and the production operation process can be optimized. This paper firstly constructs a digital twin yard system for container terminals, proposing that it is mainly composed of physical space, virtual space, data, services, and intelligent agents. This paper elaborates on the core technologies of digital twin yards and finally takes the container delivery and loading process as an example to solve the production bottlenecks of the yard in the container delivery business by reorganizing the operation process and targeting it, which can improve the terminal production efficiency to a certain extent.
Earnings Management and Sustainability Reporting Disclosure: Some Insights from Indonesia
Authors: Ningsih Sri , Prasetyo Khusnul , Puspitasari Novi , Cahyono Suham , Kamarudin Khairul Anuar
Published: July 2023

Earnings manipulation is often associated with deceiving public information that is displayed in sustainability reports. Therefore, the current study aims to explore the nexus between earnings management and sustainability reporting practices in the context of Indonesia. This study employs 408 firm-year observations from listed companies in Indonesia during the 2010–2021 period to test the hypothesis using fixed effect regression analyses with standard error estimates. By examining their sustainability reports and financial statements over a specific period, the authors assess the extent to which earnings management influences sustainability reporting practices. This implies that companies engaging in earnings management practices are more likely to exhibit higher-quality sustainability reporting practices. The results contribute valuable and significant empirical insights into the interplay between earnings management and sustainability reporting specifically within the Indonesian context. Furthermore, this study goes beyond examining the relationship itself and delves into potential factors that may influence this relationship.
Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform
Authors: Špicas Renatas , Neifaltas Airidas , Kanapickienė Rasa , Keliuotytė-Staniulėnienė Greta , Vasiliauskaitė Deimantė
Published: July 2023

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes.
Uncertainty Evaluation on Temperature Detection of Middle Atmosphere by Rayleigh Lidar
Authors: Li Xinqi , Zhong Kai , Zhang Xianzhong , Wu Tong , Zhang Yijian , Wang Yu , Li Shijie , Yan Zhaoai , Xu Degang , Yao Jianquan
Published: July 2023

Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency.
Spatio-Temporal Distribution Characteristics of Glacial Lakes in the Altai Mountains with Climate Change from 2000 to 2020
Authors: Wang Nan , Zhong Tao , Zheng Jianghua , Meng Chengfeng , Liu Zexuan
Published: July 2023

The evolution of a glacial lake is a true reflection of glacial and climatic change. Currently, the study of glacial lakes in the Altai Mountains is mainly concerned with the application of high-resolution remote sensing images to monitor and evaluate the potential hazards of glacial lakes. At present, there is no rapid and large-scale method to monitor the dynamical variation in glacial lakes in the Altai Mountains, and there is little research on predicting its future tendency. Based on the supervised classification results obtained by Google Earth Engine (GEE), combined with an analysis of meteorological data, we analyzed the spatial and temporal variations in glacial lakes in the Altai Mountains between 2000 and 2020, and used the MCE-CA-Markov model to predict their changes in the future. According to the results, as of 2020, there are 3824 glacial lakes in the Altai Mountains, with an area of 682.38 km2. Over the entire period, the glacial lake quantity growth rates and area were 47.82% and 17.07%, respectively. The distribution of glacial lakes in this region showed a larger concentration in the north than in the south. Most glacial lakes had areas smaller than 0.1 km2, and there was minimal change observed in glacial lakes larger than 0.2 km2. Analyzing the regional elevation in 100 m intervals, the study found that glacial lakes were predominantly distributed at elevations from 2000 m to 3000 m. Interannual rainfall and temperature fluctuations in the Altai Mountains have slowed since 2014, and the trends for the area and number of glacial lakes have stabilized. The growth of glacial lakes in both number and surface area is expected to continue through 2025 and 2030, although the pace of change will slow. In the context of small increases in precipitation and large increases in temperature, in the future, glacial lakes with faster surface area growth rates will be located primarily in the southern Altai Mountains.
An Improved VMD-LSTM Model for Time-Varying GNSS Time Series Prediction with Temporally Correlated Noise
Authors: Chen Hongkang , Lu Tieding , Huang Jiahui , He Xiaoxing , Yu Kegen , Sun Xiwen , Ma Xiaping , Huang Zhengkai
Published: July 2023

GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied in the field of high-precision time series prediction and is often combined with Variational Mode Decomposition (VMD) to form the VMD-LSTM hybrid model. To further improve the prediction accuracy of the VMD-LSTM model, this paper proposes a dual variational modal decomposition long short-term memory (DVMD-LSTM) model to effectively handle noise in GNSS time series prediction. This model extracts fluctuation features from the residual terms obtained after VMD decomposition to reduce the prediction errors associated with residual terms in the VMD-LSTM model. Daily E, N, and U coordinate data recorded at multiple GNSS stations between 2000 and 2022 were used to validate the performance of the proposed DVMD-LSTM model. The experimental results demonstrate that, compared to the VMD-LSTM model, the DVMD-LSTM model achieves significant improvements in prediction performance across all measurement stations. The average RMSE is reduced by 9.86% and the average MAE is reduced by 9.44%; moreover, the average R2 increased by 17.97%. Furthermore, the average accuracy of the optimal noise model for the predicted results is improved by 36.50%, and the average velocity accuracy of the predicted results is enhanced by 33.02%. These findings collectively attest to the superior predictive capabilities of the DVMD-LSTM model, thereby demonstrating the reliability of the predicted results.
Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism
Authors: Pongrac Blaž , Gleich Dušan
Published: July 2023

The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.
Integrated Node Infrastructure for Future Smart City Sensing and Response
Authors: Chen Dong , Zhang Xiang , Zhang Wei , Yin Xing
Published: July 2023

Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities.
Spatially Variant Error Elimination for High-Resolution UAV SAR with Extremely Small Incident Angle
Authors: Zhang Xintian , Tang Shiyang , Ren Yi , Han Jiahao , Jiang Chenghao , Zhang Juan , Li Yinan , Jiang Tong , Dong Qi
Published: July 2023

Airborne synthetic aperture radar (SAR) is susceptible to atmospheric disturbance and other factors that cause the position offset error of the antenna phase center and motion error. In close-range detection scenarios, the large elevation angle may make it impossible to directly observe areas near the underlying plane, resulting in observation blind spots. In cases where the illumination elevation angle is extremely large, the influence of range variant envelope error and phase modulations becomes more serious, and traditional two-step motion compensation (MOCO) methods may fail to provide accurate imaging. In addition, conventional phase gradient autofocus (PGA) algorithms suffer from reduced performance in scenes with few strong scattering points. To address these practical challenges, we propose an improved phase-weighted estimation PGA algorithm that analyzes the motion error of UAV SAR under a large elevation angle, providing a solution for high-order range variant motion error. Based on this algorithm, we introduce a combined focusing method that applies a threshold value for selection and optimization. Unlike traditional MOCO methods, our proposed method can more accurately compensate for spatially variant motion error in the case of scenes with few strong scattering points, indicating its wider applicability. The effectiveness of our proposed approach is verified by simulation and real data experimental results.
Integrated Fiber Ring Laser Temperature Sensor Based on Vernier Effect with Lyot–Sagnac Interferometer
Authors: Liu Yuhui , Lin Weihao , Hu Jie , Zhao Fang , Yu Feihong , Liu Shuaiqi , Chen Jinna , Liu Huanhuan , Shum Perry Ping , Zhang Xuming
Published: July 2023

The Vernier effect created using an incorporated Lyot–Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor’s performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.
Detection of Sensor Faults with or without Disturbance Using Analytical Redundancy Methods: An Application to Orifice Flowmeter
Authors: Sravani Vemulapalli , Venkata Santhosh Krishnan
Published: July 2023

Sensors and transducers play a vital role in the productivity of any industry. A sensor that is frequently used in industries to monitor flow is an orifice flowmeter. In certain instances, faults can occur in the flowmeter, hindering the operation of other dependent systems. Hence, the present study determines the occurrence of faults in the flowmeter with a model-based approach. To do this, the model of the system is developed from the transient data obtained from computational fluid dynamics. This second-order transfer function is further used for the development of linear-parameter-varying observers, which generates the residue for fault detection. With or without disturbance, the suggested method is capable of effectively isolating drift, open-circuit, and short-circuit defects in the orifice flowmeter. The outcomes of the LPV observer are compared with those of a neural network. The open- and short-circuit faults are traced within 1 s, whereas the minimum time duration for the detection of a drift fault is 5.2 s and the maximum time is 20 s for different combinations of threshold and slope.
Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network
Authors: Li Dongyang , Yang Jianyi , Pan Zaisheng , Li Nanyang
Published: July 2023

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.
Enantioselective Separation of Synthetic Cathinones by Capillary Electrophoresis with Ionic Liquid and Cyclodextrin Buffer Co-Additives
Authors: Roberts Jennifer Buchanan , Colyer Christa L.
Published: July 2023

The enantioselective separation of synthetic cathinones via capillary electrophoresis with ultraviolet detection (CE-UV) was successfully achieved using an acidic formate buffer with the ionic liquid tetrabutylammonium chloride (TBAC) and beta-cyclodextrin (β-CD) as co-additives. Synthetic cathinones (also known as “bath salts”) belong to a class of unregulated drugs labeled new psychoactive substances (NPS). These drugs are readily available and can cause paranoia, confusion, violence, and suicidal thoughts. The stereochemistry of synthetic cathinones, as with other drugs, can influence their potency, toxicity, metabolism, and interaction with other molecules. Thus, it is important to be able to effectively separate different types of synthetic cathinone as well as to resolve enantiomers of each. A study of buffer additives, pH, and counter ions was conducted to identify a system yielding complete enantioselective separation of synthetic cathinones by capillary electrophoresis. Buffer additives TBAC and β-CD, when used separately, did not afford the desired separation; however, when employed as co-additives, enantiomers of each of six different bath salt standards (pentylone, 4-MEC, methylone, MDPBP, MDPV, and naphyrone) were resolved. Achieving this separation of a complex mixture of closely related illicit drugs by CE using an ionic liquid and cyclodextrin together, as buffer co-additives, may provide a new starting point from which to approach the enantiomeric analysis of other drug samples as syntheses of NPS continue to rapidly evolve to evade regulation and law enforcement.
A Regional Road Network Capacity Estimation Model for Mountainous Cities Based on Auxiliary Map
Authors: Xing Ruru , Wang Fei , Cai Xiaoyu , Chen Ning , Yang Tao , Peng Bo
Published: July 2023

The focus of sustainable urban transportation development lies in realizing the untapped capacity potential of the existing road network and enhancing its operational efficiency without expanding its physical footprint. To quantify the supply capacity of road networks in mountainous cities, this paper converts the problem of solving the capacity of road networks into the problem of solving the minimum cut set in road networks from the perspective of road network capacity, using the idea of the auxiliary diagram method in graph theory. By improving the limitation that the auxiliary map method is only applicable to the single starting point and terminal point network, a regional road network capacity estimation model of a mountain city based on the auxiliary map is constructed. Combined with the actual regional road network, the model results presented in this paper show that the road network capacity calculated by the auxiliary graph method is 30,137 pcu/h. Using the improved traffic distribution simulation method, the network capacity is 38,776 pcu/h. Compared with the traffic distribution simulation method, the regional road network capacity model based on an auxiliary map proposed in this paper is more realistic.
Can the Digital Economy Accelerate “Carbon Neutrality”?—An Empirical Analysis Based on Provincial Data in China
Authors: Xue Dong , Liu Tongyang , Li Xiaomin , Zhao Xiaolei
Published: July 2023

The prosperous development of the digital economy can trigger a comprehensive green transformation from factors of production to productivity and production relationships, providing a new path for China to achieve its goals of “peak carbon emissions” and “carbon neutrality.” This paper measures the development level of the digital economy in each region using panel data of 30 Chinese provinces (autonomous regions, municipality directly under the central government) from 2007–2019, and explores the effect of the digital economy on CO2 emissions, its transmission mechanism, and its impact characteristics through theoretical and empirical analyses. The results indicate that: (1) the development of the digital economy can effectively reduce CO2 emissions; (2) in addition to direct effects, the digital economy can indirectly suppress CO2 emissions by lowering energy intensity, promoting economic agglomeration, and increasing the use of robots; (3) the suppression effect of the digital economy on CO2 emissions has significant regional heterogeneity; the digital construction in east, north, central, northeast, and southwest China has shown a better CO2 emissions reduction effect, while the development of the digital economy in south and southwest China has not yet exerted the suppression effect on CO2 emissions. In the next development process, it should improve the efficiency of energy use, give full effect to the positive externalities of economic agglomeration, lower the threshold of robot use and expand the application scenarios, and make full use of the green development advantages of the digital economy.
The Importance of Investing in the Energy Refurbishment of Hospitals: Results of a Case Study in a Mediterranean Climate
Authors: De Masi Rosa Francesca , Del Regno Nicoletta , Gigante Antonio , Ruggiero Silvia , Russo Alessandro , Tariello Francesco , Vanoli Giuseppe Peter
Published: July 2023

Because of the social importance of hospitals, characterized by energy-intensive users, large-scale refurbishment projects for these types of buildings are required. With the aim of helping researchers and designers, this paper proposes a multistage methodological approach for the optimization of retrofit designs based on energy, environmental, and economic indicators. Some guidelines are also highlighted thanks to the results obtained from a case study of a private hospital in Naples (Southern Italy, Mediterranean climate) located in a constrained landscape area. The first step consists of the calibration of a simulation energy model defined via in situ investigations, direct surveys and monitoring of energy loads and indoor quality. Then, the model is used to verify the effectiveness of several efficiency measures regarding the building envelope, the active energy systems, and the energy conversion from renewables in order to minimize the energy demand with acceptable economic profitability. This case study demonstrates that electricity demand can be reduced by up to 48% with an investment of around EUR 720,030.00; the payback time without national incentives is 10 years, but it can be halved with appropriate financial support.
Has the Decline in Trade Policy Uncertainty Promoted China’s Agricultural Exports?
Authors: Ma Jie , Cai Rong , Zhang Weifu
Published: July 2023

This article describes China’s entry into the World Trade Organization (WTO) as a quasi-natural experiment based on samples from the World Bank database, China Customs database, and China Industrial Enterprise Database from 2000 to 2007 and uses the difference-in-difference (DID) method to investigate the effect of trade policy uncertainty (TPU) on China’s agricultural exports. The study found that, first, a decline in TPU significantly increases the export volume of Chinese agricultural firms. Second, the decline in TPU significantly boosts companies engaged in general trade. Regarding export destination countries, the decline in TPU significantly promotes the agricultural firms whose export destination countries are developing countries. Regarding firms’ ownership, the promotion of agricultural exports by non-state-owned enterprises (non-SOEs) and Hong Kong-, Macao- and Taiwan-funded enterprises is even more pronounced. Third, the decline in TPU promotes the export of Chinese agricultural firms by alleviating their financing constraints. The study provides new explanations for changes in China’s agricultural exports and enriches research on the evaluation of TPU effects.
Energy Consumption and Carbon Emissions of Nearly Zero-Energy Buildings in Hot Summer and Cold Winter Zones of China
Authors: Ke Zikang , Liu Xiaoxin , Zhang Hui , Jia Xueying , Zeng Wei , Yan Junle , Hu Hao , Hien Wong Nyuk
Published: July 2023

Issues of energy efficiency and sustainability in buildings are gaining increasing attention in the context of the “3060” dual-carbon initiative. In recent years, nearly zero-energy buildings (nZEBs) have emerged as a potentially viable solution to the challenges of the energy crisis in the building sector, and it is important to study the factors influencing their energy consumption and carbon emissions. However, existing research lacks analyses of multifactor interactions, and the problem of high energy consumption has not been sufficiently addressed. Taking a typical residential building in the Yangtze River basin as the study subject, this study, jointly funded by the University of Nottingham and Hubei University of Technology, proposes a hybrid approach that combines building energy simulation and orthogonal experiments to investigate factors pertaining to buildings, people, and the environment to identify key influencing factors and explore the energy consumption and carbon emission characteristics of residential buildings in hot summer and cold winter (HSCW) zones. Our findings reveal the following: (1) The use of renewable energy sources, such as solar photovoltaic power generation and solar hot water, and renewable energy systems such as ground-source heat pumps, in the operation phase of a baseline building can result in a 61.76% energy-saving and a 71% renewable energy utilization rate. (2) To more easily meet the requirements of nZEB standards, it is recommended to keep KE within the range of 0.20–0.30 W/(m2·K), KR within the range of 0.15–0.20 W/(m2·K), and VT within the range of 0.6–0.7 h−1. This study will help to identify the critical factors affecting energy consumption and provide a valuable reference for building energy efficiency in HSCW zones.
The Design and Performance Prediction Model of an Integrated Scheme of a Membrane Bioreactor and Anaerobic Digester for the Treatment of Domestic Wastewater and Biowaste
Authors: Solomou Nicos , Malamis Dimitris , Barampouti Elli Maria , Mai Sofia , Loizidou Maria
Published: July 2023

An innovative and integrated scheme that encompasses two well-established waste treatment technologies, the aerobic biological degradation of organic matter bioprocess via membranes and anaerobic digestion, was demonstrated as a zero-waste approach that may effectively treat wastewater and biowaste in an integrated and symbiotic manner. Aiming to create a tool for the design, monitoring, and control of the scheme, prediction models were developed, validated, and implemented for the process simulation of the integrated scheme. The minimization of selected objective functions led to the estimation of the models’ parameters. The activated sludge model no. 1 (ASM1) was adopted for the simulation of the aerobic membrane bioreactor. The kinetic parameters were calibrated using volatile suspended solids and total nitrogen as the objective functions permitting the model to simulate the bioprocess satisfactorily (Nash–Sutcliffe efficiency > 0.86) and to calculate the concentration of the active biomass. The predominance of heterotrophic bacteria (4300 to 9770 mg COD/L) over autotrophic biomass (508 to 1422 mg COD/L) was showcased. For the anaerobic process unit, a simplified anaerobic digestion model 1 ADM1-R4 was used, and the first-order hydrolysis constants (kch 0.41 d−1, kpr 0.25 d−1, kli 0.09 d−1) and microbial decay rate (kdec 0.02 d−1) were evaluated, enabling an accurate prediction of biogas production rates. A full-scale implementation of the integrated scheme was conducted for a decentralized waste treatment plant in a small community. Preliminary design calculations were performed in order to estimate the values related to certain process and technical parameters. The performance of this full-scale plant was simulated by the developed model, presenting clear benefits for practical applications in waste treatment plants.
Positive or Negative Viewpoint Determines the Overall Scenic Beauty of a Scene: A Landscape Perception Evaluation Based on a Panoramic View
Authors: Chen Yue , Zhong Qikang , Li Bo
Published: July 2023

In the contemporary world, the swift advancement of urbanization, the pressing need for environmental conservation, and humanity’s unyielding quest for a better quality of life have jointly underscored the escalating importance of research on landscape aesthetics and perceptual experiences. Researchers have often evaluated the overall scene’s beauty based on photos taken from a single viewpoint. However, it has been observed that different viewpoints of the same scene can lead to varying degrees of beauty perception. Some positive viewpoints highlight landscape features that contribute to beauty preferences, while negative viewpoints emphasize aspects that may evoke discomfort and decrease perceived beauty. Therefore, a crucial question arises: which viewpoint, positive or negative, holds more influence over the overall beauty of the scene? This paper aimed to address this question by utilizing panoramic map technology to establish a landscape perception evaluation model. The model was based on empirical evidence from various spatial scenes along the Yaozijian Ancient Road in Anhua County, encompassing towns and villages. The study analyzed the functional relationship between landscape factors, positive and negative viewpoints, and the degree of scenic beauty. It was found that (1) it is difficult to reflect the overall scenic beauty of a scene (OSBS) of a single viewpoint photo, and both positive and negative viewpoints of scenic beauty have significant effects on the OSBS. In the empirical case study, it was found that the overall effect of a positive viewpoint of scenic beauty (PVSB) on OSBS was greater; (2) PVSB had a major effect on OSBS with a high visual hierarchy and cloud ratio and a low type of vegetation and proportion of man-made objects; (3) a negative viewpoint of scenic beauty (NVSB) had a major effect on OSBS with a low visual hierarchy of the landscape. The results of the study reveal the relationship between landscape factors of different viewpoints and the OSBS, which can be applied to landscape beauty evaluation and landscape planning and design processes.
Development of a Bamboo Toothbrush Handle Machine with a Human–Machine Interactive Interface for Optimizing Process Conditions
Authors: Wang Bo-Jyun , Lin Chia-Hong , Lee Wen-Chih , Hsiao Chun-Ching
Published: July 2023

Non-renewable materials like plastics are widely applied on toothbrush handles and bristles. Polypropylene (PP) or polyethylene (PE) is often used to fabricate the toothbrush handle, while nylon (PA) is used to form the bristle. These plastics are sourced from non-renewable fossil fuels. The primary greenhouse gases—nitrous oxide (N2O) and carbon dioxide (CO2)—in the Earth’s atmosphere are released during the production of these plastics. Bamboo can generate 30% more oxygen than most plants and trees, which absorbs twice as much carbon dioxide as trees. A comparison of the cradle-to-grave material requirements between bamboo and plastic toothbrushes reveals that bamboo toothbrushes entail hidden environmental costs. Nevertheless, bamboo toothbrushes can be completely decomposed in the environment, which makes them eco-friendly and sustainable green products. This research aims to develop a bamboo toothbrush handle machine with a human–machine interactive interface and production management for optimizing process conditions. The machine is designed as a double-group to stably mass-produce high-quality bamboo toothbrush handles under optimal process conditions. Although bamboo is a sustainable green material, the shaping process is difficult due to an extremely anisotropic property in the bamboo structures. An improper process condition will induce a rough or scorched surface, which may further cause a tearing crack. The bamboo toothbrush handle milling machine is usually designed by a profiler, which uses various molds to change the shapes and sizes of bamboo toothbrush handles. This machine cannot probe the accurate cutting force for optimizing the cutting operations, paths, and parameters. The proposed equipment with a double-group design includes two storage racks of raw materials, two feeding devices, two exchange clamping devices, and a dual-spindle milling system required to form the shaping process of bamboo toothbrush handles. The whole system is propelled by a computer numerical control (CNC) SYNTEC controller, which can fabricate the bamboo toothbrush handle with various shapes and dimensions. This controller is integrated with a LabVIEW human–machine interactive interface via a Modbus RTU communication protocol. The optimal milling paths, manufacturing methods, and feeding rates are verified by a surveillance system to detect the instant currents of both spindles via the trial-and-error method and mass production. The maximum output of the equipment can reach four bamboo toothbrush handles per minute and 1600 bamboo toothbrush handles per day.
Effects of FDI, External Trade, and Human Capital of the ICT Industry on Sustainable Development in Taiwan
Authors: Lin Yu Cheng , Park Sang Do
Published: July 2023

Understanding how international trade, FDI and human capital (FDI-HC and ET-HC) in the ICT industry affect Taiwan’s stable economic growth between 2001 and 2020 is the main objective of this study. The empirical analysis method used in this study is mainly divided into two steps: First, it uses variables with reliability and authenticity as keywords for primary, data mining, and semantic network analysis (SNA). Second, it investigates the long- and short-term interactions between the variables using the vector error correction model (VECM). The results of data mining and SNA using FDI and ET as keywords reveal that terms connected to HC have high levels of centrality, clustering, and frequency. This finding implies that the variables FDI-HC and ET-HC are reliable and can be utilized as interaction variables. Moreover, FDI–HC and ET–HC exert positive short- and long-term influences on GDP, and ET–HC exerts strong mid- to long-term impacts on GDP, FDI–HC, and ET.
Assessing Sustainable Ecotourism Opportunities in Western Rajasthan, India, through Advanced Geospatial Technologies
Authors: Chandel Rajeev Singh , Kanga Shruti , Singh Suraj Kumar , Ðurin Bojan , Oršulić Olga Bjelotomić , Dogančić Dragana , Hunt Julian David
Published: July 2023

The present study focuses on finding potential sites for ecotourism development using GIS and remote-sensing-based weightage sum overlay techniques in Western Rajasthan, India. Ecotourism is one of the fastest growing and revenue-making sectors incorporating a sustainable future. Western Rajasthan has a broad scope to develop tourism-based activity in various ways, mainly through cultural heritage, historical and archaeological wonders, and rare wildlife. Weightage sum overlay analysis is a useful and simple tool to compare each thematic layer. These values are based on various factors and understanding taken during the study. For this purpose, different data types have been taken from the USGS website. Arc GIS 10.8 and ERDAS Imagine software 2015 have been utilized to process the data. This research incorporates seven thematic layers, i.e., elevation, proximity to streams, land use/cover, population density, road connectivity, proximity to protected areas, and heritage hotspots. Based on the physical and cultural characteristics of Western Rajasthan, the weightage of each thematic layer has been decided, which is finally overlaid using Arc GIS software. After processing all the thematic layers, we finally get an outcome in the form of a suitability map. The final suitability map represents five suitability classes that divide the total area into the following categories, very high (37.31%), high (26.85%), moderate (7.89%), low (0.83%), and very low (27.12%), which represents the potential of ecotourism in Western Rajasthan.
Impact of Digital Finance on Manufacturing Technology Innovation: Fixed-Effects and Panel-Threshold Approaches
Authors: Sheng Xin , Chen Wenya , Tang Decai , Obuobi Bright
Published: July 2023

Digital finance (DF) has provided important financial support for the transformation and upgrading of China’s manufacturing industry. Innovation is the engine of industrial upgrading. To solve the dilemma of developing the manufacturing industry, it is necessary to enhance independent innovation capabilities. On this basis, this article studies the impact of DF on manufacturing technology innovation (MTI). It uses the data of listed manufacturing firms in the Shenzhen and Shanghai A-share markets from 2011 to 2020 to establish a fixed-effects model and a panel-threshold model for empirical analysis. The results revealed that, first, DF significantly accelerates technological innovation in manufacturing enterprises and has a significant positive impact on technological innovation. Secondly, DF drives manufacturing enterprises’ technological innovation by alleviating financial constraints (FCs). Thirdly, there is a dual-threshold effect based on market competition between DF and MTI based on market competition, and the promotion effect of DF on technology innovation decreases with the increasing degree of market competition. Finally, DF better enhances the technological innovation of non-state-owned manufacturing firms in the respective regions compared to state-owned firms. In terms of factor-intensive types, DF is more able to advance the innovative technologies of labor-intensive and capital-intensive enterprises, while it has no significant positive effect on technology-intensive enterprises. Policy implications are suggested to boost manufacturing technology innovation and aid future studies.
Outage Performance of Asymmetrical Cognitive Simultaneous Wireless Information and Power Transfer Networks Based on Non-Orthogonal Multiple Access with an Incremental Cooperation Relay and Hardware Impairments
Authors: Liu Shuai , Cui Man , Zhao Anxin , Zhang Chen , Zhang Yuanlong
Published: July 2023

This work aims to investigate an asymmetrical NOMA transmission network based on cognitive radio, where both the source and relay nodes of the secondary user are energy limited, and the energy could be harvested from the transmission signal of the power beacon. Considering some particular hardware impairments and asymmetry in the NOMA transmission, the closed-form expressions of outage probability for relay and destination nodes were derived using incremental relay methods. Furthermore, built on the expressions of the outage probability, the throughput performance was analyzed. Finally, the correctness of our theoretical analysis was verified using simulations.
The Landweber Iterative Regularization Method for Identifying the Unknown Source of Caputo-Fabrizio Time Fractional Diffusion Equation on Spherically Symmetric Domain
Authors: Chen Yong-Gang , Yang Fan , Tian Feng
Published: July 2023

In this article, the inverse problem for identifying the space-dependent source of time fractional diffusion equation on a spherically symmetric domain with Caputo–Fabrizio fractional derivative is discussed. This problem is a typical ill-posed problem and the Landweber iterative regularization method is used to obtain the approximation solution. The convergent error estimates under a priori regularization parameter choice rule and a posteriori regularization parameter choice rule are given, respectively. The numerical examples are given to show the effectiveness of the Landweber iterative regularization method.
Best Proximity Point Results for n-Cyclic and Regular-n-Noncyclic Fisher Quasi-Contractions in Metric Spaces
Authors: Fallahi Kamal , Ayobian Morteza , Soleimani Rad Ghasem
Published: July 2023

In this work, we introduce some new concepts such as n-cyclic Fisher quasi-contraction mappings, full-n-noncyclic and regular-n-noncyclic Fisher quasi-contraction mappings in metric spaces. We then generalize the results by Safari-Hafshejani, Amini-Harandi and Fakhar. Meanwhile, we answer the question “under what conditions does a full-n-noncyclic Fisher quasi-contraction mapping have n(n−1)/2 unique optimal pairs of fixed points?”. Further, to support the main results, we highlight all of the new concepts via non-trivial examples.
Einstein, Barcelona, Symmetry & Cosmology: The Birth of an Equation for the Universe
Authors: Elizalde Emilio
Published: July 2023

Albert Einstein visited Spain only once, precisely one hundred years ago. The circumstances, of a very different kind, of this visit will be explained here. In special, some important events happened to Einstein during that period, which, eventually, were key for converting modern cosmology into a genuine physical theory. Among them is the famous Einstein-Friedmann controversy, first, on the mathematical validity of Friedmann’s equations and, later, their possible usefulness as a reliable tool to describe the real world. A summary of the deepest ideas underlying Einstein’s contributions to the theory of relativity, which he had already completed before his visit, will precede the discussion, also supplemented with a description, in very simple terms, of the three main relativistic theories, namely Galileo’s one, and Einstein’s special and general theory. They pave the way towards a definitive theory of total relativity, so far unattainable. It will be recalled that the most general relativity principle, faithfully reflecting Ernst Mach’s far-reaching ideas, might have much to do with the symmetry-breaking paradigm, a most crucial tool in quantum field theory and high energy physics.
Symmetry in Quantum and Computational Chemistry: Volume 2
Authors: Novikov Alexander S.
Published: July 2023

The problem of symmetry in quantum and computational chemistry is a paradigm of development in this field of knowledge [...]