bluuebunny/crossref_metadata_2025 huggingface dataset:
DP-ViT: A Dual-Path Vision Transformer for Real-Time Sonar Target Detection
Authors: Sun Yushan , Zheng Haotian , Zhang Guocheng , Ren Jingfei , Xu Hao , Xu Chao
Published: November 2022

Sonar image is the main way for underwater vehicles to obtain environmental information. The task of target detection in sonar images can distinguish multi-class targets in real time and accurately locate them, providing perception information for the decision-making system of underwater vehicles. However, there are many challenges in sonar image target detection, such as many kinds of sonar, complex and serious noise interference in images, and less datasets. This paper proposes a sonar image target detection method based on Dual Path Vision Transformer Network (DP-VIT) to accurately detect targets in forward-look sonar and side-scan sonar. DP-ViT increases receptive field by adding multi-scale to patch embedding enhances learning ability of model feature extraction by using Dual Path Transformer Block, then introduces Conv-Attention to reduce model training parameters, and finally uses Generalized Focal Loss to solve the problem of imbalance between positive and negative samples. The experimental results show that the performance of this sonar target detection method is superior to other mainstream methods on both forward-look sonar dataset and side-scan sonar dataset, and it can also maintain good performance in the case of adding noise.
High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images
Authors: Bousbaa Mostafa , Htitiou Abdelaziz , Boudhar Abdelghani , Eljabiri Youssra , Elyoussfi Haytam , Bouamri Hafsa , Ouatiki Hamza , Chehbouni Abdelghani
Published: November 2022

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.
A High-Utility Differentially Private Mechanism for Space Information Networks
Authors: Zhuo Ming , Huang Wen , Liu Leyuan , Zhou Shijie , Tian Zhiwen
Published: November 2022

Nowadays, Space Information Networks represented by the satellite internet are developing rapidly. For example, the Starlink of SpaceX plans to provide network access services worldwide and has drawn much attention. To operate and maintain Space Information Networks, e.g., performing collision avoidance maneuvers and forensic investigation, statistic information on networks such as the average of orbital inclination needs to be shared with analysts. However, for some particular reasons, such as safety or confidentiality, accurate information on networks cannot be shared with analysts. To solve this contradiction, we design a differentially private mechanism for the Space Information Network so that the entities of a network can keep accurate information privacy while sharing statistic information. In particular, we extend differentially private mechanisms based on personalized sampling to distributed communication systems such as Space Information Networks. In comparison with other mechanisms, the proposed mechanism has better data utility. Moreover, the proposed mechanism has a hierarchical privacy guarantee. In particular, there are differences between the privacy guarantees made between system entities and between the system and the user.
Optimal Design Parameters of Thermal Flowmeter for Fuel Flow Measurement
Authors: Korobiichuk Igor , Ilchenko Andrii
Published: November 2022

The article analyses the influence, relationship and value of the design parameters of the thermal flowmeter on its radial and axial heat fluxes in the tube. The purpose of the analyses is to check the change in the error of fuel flow measurement by the thermal flowmeter directly on the vehicle when using heating elements of different diameters. The influence of the radial heat flux of the flowmeter tube on the accuracy of fuel flow measurement is substantiated. Recommendations on the choice of design parameters of a thermal flowmeter at the stage of its design, development or use are developed under the condition of reducing the influence of the radial heat flow on the axial one, which will reduce the total error in the measurement of fuel flow rate.
A Simple Model for Assessing Millimeter-Wave Attenuation in Brownout Conditions
Authors: Zilberman Arkadi , Kopeika Natan
Published: November 2022

Flying helicopters in adverse environmental conditions, such as low heights in arid regions, can be dangerous, especially during landing and take-off, since during hovering, the rotors produce a dust cloud of particles. This phenomenon is known as the “brownout” condition. Unlike visible and infrared systems, the radar devices in the microwave or millimeter wave region offer the capability of sufficient transmission through atmospheric obscurants, such as fog, smoke, sand/dust storms, and brownout. In this work, we present a theoretical evaluation of mm-wave (85–100 GHz) attenuation/scattering and power transfer in brownout conditions. The model includes attenuation/scattering prediction and radiant flux, or power collected by the receiver. We are considering the case of sand grain clouds created by helicopter rotor airflow during landing in arid areas. The evaluated scenarios are brownout environments over ranges up to 50 m. The predicted values from the mathematical model are compared with findings in the field and the literature. A simple model for mm-wave power transfer estimation shows satisfactory agreement with the measured values.
Simultaneous Measurement of Refractive Index and Temperature Based on SMF–HCF–FCF–HCF–SMF Fiber Structure
Authors: Xu Ronghui , Ke Chengran , Xue Yipu , Xu Yifei , Xue Minmin , Ye Jingfu , Liu Houquan , Chen Ming , Qu Shiliang , Yuan Libo
Published: November 2022

In this research, we proposed and experimentally verified a compact all-fiber sensor that can measure refractive index (RI) and temperature simultaneously. Two segments of hollow-core fiber (HCF) are connected to the two ends of the four-core fiber (FCF) as a beam splitter and a coupler, and then spliced with two sections of single-mode fibers (lead-in and lead-out SMF), respectively. The two hollow-core fibers can excite the higher-order modes of the four-core fiber and recouple the core modes and higher-order modes into the outgoing single-mode fiber, thereby forming inter-mode interference. The different response sensitivities of two interference dips to RI and temperature manifest that the proposed structure can achieve simultaneous measurement. From the experimental results, it can be seen that the maximum sensitivity of the sensor to RI and temperature is 275.30 nm/RIU and 94.4 pm/°C, respectively. When the wavelength resolution is 0.02 nm, the RI and temperature resolutions of the sensor are 7.74 × 10−5 RIU and 0.335 °C. The proposed dual-parameter optical sensor has the advantages of high sensitivities, good repeatability, simple fabrication, and structure. In addition, it has potential application value in multi-parameter simultaneous measurement.
A Single-Image Noise Estimation Algorithm Based on Pixel-Level Low-Rank Low-Texture Patch and Principal Component Analysis
Authors: Li Yong , Liu Chenguang , You Xiaoyu , Liu Jian
Published: November 2022

Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.
RF Line-Element Filters for Structural-Health-Monitoring Applications
Authors: Musa Idris , Hedley John
Published: November 2022

RF-based sensors are an attractive option for structural-health-monitoring applications, due to the ease of access of interrogating such sensors. However, in most work, only scalar quantities are measured, giving no indication of the direction of strain or displacements. In this paper, a novel approach to displacement sensing is presented, in which relative displacements are tracked in all three degrees of freedom. The sensor design is based on a pair of coupled line-element filters whose frequency-dependent forward-power transfer is sensitive to relative positions between the two filters. Multiple features in the S21 parameter are used to differentiate displacement direction. Gold-based devices were fabricated on quartz substrates, and characterised through vector-network-analyzer measurements. Results demonstrate uncoupled sensitivities of −1.41 MHz/mm, −1.74 MHz/mm and 12.23 MHz/mm for x, y and z displacements, respectively.
A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots
Authors: Lu Jiaoyang , Zou Ting , Jiang Xianta
Published: November 2022

Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6—a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature.
Problem Characteristics and Dynamic Search Balance-Based Artificial Bee Colony for the Optimization of Two-Tiered WSN Lifetime with Relay Nodes Deployment
Authors: Yu Wenjie , Li Xiangmei , Zeng Zhi , Luo Miao
Published: November 2022

Lifetime optimization is one of the key issues among the many challenges of wireless sensor networks. The introduction of a small number of high-performance relay nodes can effectively improve the quality of the network services. However, how to deploy these nodes reasonably to fully enhance the network lifetime becomes a very difficult problem. In this study, a modified and enhanced Artificial Bee Colony is proposed to maximize the lifetime of a two-tiered wireless sensor network by optimal deployment of relay nodes. First, the dimension of the problem is introduced into the candidate search equation and the local search is adjusted according to the fitness of the problem and number of iterations, which helps to balance the exploration and exploitation ability of the algorithm. Second, in order to prevent the algorithm from falling into local convergence, a dynamic search balance strategy is proposed instead of the scout bee phase in the original Artificial Bee Colony. Then, a feasible solution formation method is proposed to ensure that the relay nodes can form the upper-layer backbone of the network. Finally, we employ this algorithm on a test dataset obtained from the literature. The simulation results show that the proposed algorithm for two-tiered wireless sensor network lifetime optimization can obtain higher and stable average network lifetime and more reasonable relay node deployment compared to other classical and state-of-the-art algorithms, verifying the competitive performance of the proposed algorithm.
Two-Channel Detecting Sensor with Signal Cross-Correlation for FTIR Instruments
Authors: Achtenberg Krzysztof , Mikołajczyk Janusz , Bielecki Zbigniew
Published: November 2022

This paper’s purpose was to demonstrate a performance of a novel approach in a low-noise optical sensor for an FTIR spectrometer. Methods: Compared to the standard FTIR detection setup, our sensor ensures a higher signal-to-noise ratio (SNR) and lower signal standard deviation by reducing the uncorrelated noise components (e.g., thermal and 1/f noises of the detection module). Its construction is based on two-channel detection modules and a processing unit with implemented cross-correlation signal analyses. Each module was built of LWIR HgCdTe photodiodes and low-noise transimpedance amplifiers. Results: the experiments demonstrated a decrease in a signal standard deviation of about 1.7 times with a 10 dB-improvement in the SNR. Conclusion: this result indicates our sensor’s main benefit, especially in registered “weak” and noisy interferograms.
Estimation of Water Depth on Road Surfaces Using Accelerometric Signals
Authors: Riahi Ebrahim , Edjeou Wiyao , Buisson Sébastien , Gennesseaux Manuela , Do Minh-Tan
Published: November 2022

The paper presents an experimental study conducted to evaluate the feasibility of using accelerometers as an indirect means to estimate water depths on road surfaces. It makes use of the vibration of the vehicle’s wheel arch due to water droplets projected by a tire rolling on a wet road surface. A trailer equipped with a wheel and towed by a van was used. The test setups to spread water on the road surface and before the test wheel, measure the water depth and visualize the water spray are described. The test program, conducted on a test track closed to the traffic, includes three surfaces and two speeds. Visualization of water flows by means of high-speed cameras makes it possible to choose a suitable location for the accelerometers. It turns out that signals provided by the accelerometers are affected by the trailer’s movement; a filtering method has been successfully developed to remove noises. Results show a tight relationship between the mean amplitude of accelerometric signals and actual water depths. Discussions are made in terms of effects of the vehicle speed and the road surface texture. Perspectives for using the developed system to improve passenger safety under autonomous driving conditions are presented.
Antioxidant, Anti-Cancer Activity and Phytochemicals Profiling of Kigelia pinnata Fruits
Authors: Ramadan Khaled M. A. , El-Beltagi Hossam S. , Mohamed Heba I. , Shalaby Tarek A. , Galal Ahmed , Mansour Abdallah Tageldein , Aboul Fotouh Mohamed M. , Bendary Eslam S. A.
Published: November 2022

Over the past few years, research studies on the therapeutic benefits of medicinal plants with potent antioxidant activity and few side effects have grown significantly. This has sparked interest in determining whether naturally occurring antioxidants could take the place of synthetic antioxidants, which are currently being constricted because of their toxic and carcinogenic properties. The identification and quantification of phytochemicals in the methanolic extract of Kigelia pinnata fruits was measured using gas chromatography–mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography–mass spectrometry (UPLC-MS/MS) techniques. Additionally, the methanolic extract of fruits was used to determine antioxidant activity. Free radical-scavenging (DPPH) and ferric ion-reducing antioxidant power were measured using spectrophotometry, and total antioxidant capacity (TAC) was compared with two common antioxidants, vitamin C and α-tocopherol. Moreover, mature fruits have high DDPH, ferric ion-reducing antioxidant power and total antioxidant capacity. Furthermore, mature fruits have high levels of total phenolic, flavonoid, and tannin content; these compounds are thought to be the sources of the antioxidant activity. The major constituents of the methanolic extracts from the mature fruits of K. pinnata were found to be larixinic acid, 3,5-Dihydroxy-6-methyl-2,3-dihydro-4H-pyran-4-one (DMDP), and 5-Hydrxoymethylfurfural. We performed the elemental analysis of the whole fruit. Huh-7 (liver cancer), PANC-1 (pancreatic cancer), Colo-205 (colorectal cancer), HT-29 (colorectal cancer), SNU-16 (gastric carcinoma), SW620 (colorectal adenocarcinoma) and HCT116 (colon carcinoma) were tested in vitro for anticancer activity. Both methanolic and ethyl acetate extracts of mature fruits had a positive effect on all cancer cell lines as compared to the doxorubicin drug. In addition, the methanolic extracts of mature fruits showed more potent cytotoxic effects than the ethyl acetate extracts. Moreover, the most pronounced cytotoxic effects of the methanolic extract were detected in SW620 (colorectal adenocarcinoma), with an IC50 value of 6.79 μg/mL, SNU-16 (gastric carcinoma), with and IC50 value of 8.69 μg/ ml, and in PANC-1 (pancreatic cancer) with an IC50 value of 10.34 μg/mL. Moreover, the results show that the water, ethyl acetate and methanolic extracts of mature fruits have antioxidant capacity, ferric ion-reducing antioxidant power, DPPH scavenging activity and also anticancer activity. Therefore, the present study suggests that the phytochemical profiles of mature fruits of K. pinnata may be used as potential natural antioxidants and anti-cancer cell lines.
New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
Authors: Marakakis Konstantinos , Tairidis Georgios K. , Foutsitzi Georgia A. , Antoniadis Nikolaos A. , Stavroulakis Georgios E.
Published: November 2022

In this study, a new method for the optimal design of multimode shunt-damping circuits is presented. A modification of the “current-flowing” shunt circuit is employed to control multiple vibration modes of a piezoelectric laminate beam. In addition to the resistor damping components, the method considers the capacitances and the shunting branch inductors as new design variables. The H∞ norm of the damped system is minimized using the particle swarm optimization (PSO) method in the suggested optimization strategy. Two additional numerical models are addressed in order to compare the proposed method with other methods from the literature and to thoroughly examine the effect of the design variables on damping performance. To simulate the dynamic behavior of the piezoelectric composite beam, a finite-element model is created which provides more accurate modeling of thick beam structures. Results show that the suggested method may improve damping efficiency when compared to other models, since it generates a highest peak amplitude reduction of 39.61 dB for the second mode and 55.92 dB for the third mode. Finally, another benefit provided by the suggested optimal design is the reduction of the required shunt inductance values.
Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout
Authors: Dayaratna Kevin , Crosson Jesse , Hubbard Chandler
Published: November 2022

Understanding the factors that influence voter turnout is a fundamentally important question in public policy and political science research. Bayesian logistic regression models are useful for incorporating individual level heterogeneity to answer these and many other questions. When these questions involve incorporating individual level heterogeneity for large data sets that include many demographic and ethnic subgroups, however, standard Markov Chain Monte Carlo (MCMC) sampling methods to estimate such models can be quite slow and impractical to perform in a reasonable amount of time. We present an innovative closed form Empirical Bayesian approach that is significantly faster than MCMC methods, thus enabling the estimation of voter turnout models that had previously been considered computationally infeasible. Our results shed light on factors impacting voter turnout data in the 2000, 2004, and 2008 presidential elections. We conclude with a discussion of these factors and the associated policy implications. We emphasize, however, that although our application is to the social sciences, our approach is fully generalizable to the myriads of other fields involving statistical models with binary dependent variables and high-dimensional parameter spaces as well.
An Evaluation Tool for Physical Accessibility of Cultural Heritage Buildings
Authors: Marín-Nicolás José , Sáez-Pérez Mª Paz
Published: November 2022

Persons with disabilities (PWD) account for 10–15% of the world’s population. The lack of accessibility in the built environment imposes a constraint on its use by these individuals. In heritage buildings, this restriction includes access to other main qualities in addition to use. This problematic issue has been dealt with in different sectors, especially in the tourism sector. The objective of this study is to design and implement a physical accessibility assessment tool adapted to the particularities of heritage buildings. The methodology consists of two phases. In the first phase, the tool and the necessary instruments for its use are designed. Accessibility levels are also established. In the second phase, the tool is applied to a sample size of 45 buildings. The results show the tool and related instruments, as well as the data obtained from the analysis of the sample: identified barriers, the level of accessibility of the entire sample, and the level of accessibility by architectural typologies. The conclusions and discussion reflect on the utility of this tool, the feasibility of its extension to other domains, and the relevance of the information obtained from the sample to improve accessibility in architectural heritage.
Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data
Authors: Suryono Hady , Kuswanto Heri , Iriawan Nur
Published: November 2022

The United Nations Sustainable Development Goals (SDGs) have had a considerable impact on Indonesia’s national development policies for the period 2015 to 2030. The agricultural industry is one of the world’s most important industries, and it is critical to the achievement of the SDGs. The second major aspect of the SDGs, i.e., zero hunger, addresses food security (SDG 2). To measure the status of food security, accurate statistics on paddy production must be accessible. Paddy phenological classification is a way to determine a food plant’s growth phase. Imbalanced data are a common occurrence in agricultural data, and machine learning is frequently utilized as a technique for classification issues. The current trend in agriculture is to use remote sensing data to classify crops. This paper proposes a new approach—one that uses two phases in the bootstrap stage of the random forest method—called a two-phase stratified random forest (TPSRF). The simulation scenario shows that the proposed TPSRF outperforms CART, SVM, and RF. Furthermore, in its application to paddy growth phase data for 2019 in Lamongan Regency, East Java, Indonesia, the proposed TPSRF showed higher overall accuracy (OA) than the compared methods.
Influences of Power Structure Evolution on Coffee Commodity Markets: Insights from Price Discovery and Volatility Spillovers
Authors: Zhang Wei , Saghaian Sayed , Reed Michael
Published: November 2022

This study examines the influences of the power structure evolution along the global coffee value chain on coffee spot-futures commodity markets. Specifically, this study aims to analyze the mechanisms and extent to which the coffee spot-futures commodity markets are affected by shifts in the power structure in each phase following the collapse of the ICA from the perspective of price discovery and volatility spillovers by employing the PT–IS and bivariate EGARCH models. This research covers two actively traded coffee types, Arabica and Robusta, and utilizes daily time-series price data over 1990:01–2020:04 for Arabica and over 2008:01–2020:04 for Robusta. The empirical results indicate that coffee spot markets play a dominant role in price discovery for both Arabica and Robusta over all periods, and volatility spillovers occur from the coffee spot market to the futures markets. This study demonstrates to coffee market players that power structure evolutions across coffee’s global value chain have not significantly changed the underlying socio-spatial distribution of the coffee value chain over the post-ICA period. The results further imply that a buyer-driven governance is emerging in the coffee industry and large coffee roasters are beginning to dominate the global coffee value chain. Moreover, large coffee roasters are incentivized to diversify their marketing strategies by considering more market factors as part of their market differentiation strategies. Government interventions are necessary to establish price risk-management mechanisms to protect small-scale coffee growers.
Multidimensional Model of High-Growth Companies: Do COVID-19 and the Ukraine–Russia Crisis Lead to Differences?
Authors: Frešer Blaž
Published: November 2022

Economies and the companies operating within them are currently facing numerous challenges and threats that are caused by the COVID-19 outbreak and recovery and are prolonged by the Ukraine–Russia crisis. Both have drastically changed the way companies operate. High-growth companies (HGCs) can be an important group because of their characteristics and can provide guidelines for efficiently addressing these challenges and creating new opportunities. Our research contributes to this field, as the objective of the article is to find out whether the global challenges (i.e., the COVID-19 and Ukraine–Russia crisis) lead to a difference in the strength of the influence between the analyzed determinants—the growth factors of HGCs. For this purpose, a structural model equation (SEM) was applied and a multigroup analysis between the two data sets (before and during the global challenges) was performed on the pooled sample of n = 242 HGCs from the Republic of Slovenia. The results showed some statistically significant differences that can be explained by the time perspective and possibly by the influence of global challenges. Thus, this paper makes an important contribution to science, as a reassessment of the theories and implicit assumptions in current research is needed due to global challenges. It is also useful for policy makers who want to consider the impact of recent global challenges in their policy recommendations and for HGCs seeking sustainable high growth.
A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry
Authors: Shi Zhiming , Li Yisong , Bohács Gábor , Zhou Qiang
Published: November 2022

With the development of industrial production technologies and market economy, Postponement is increasingly being adopted by continuous production enterprise as a method that enables multi-product, mass customization production. In order to make use of the Postponement in manufacturing production enterprises need to achieve customized production and reduce enterprise costs. A modeling is conducted in the production process of Postponement in iron and steel enterprises in this paper. With the goal of minimizing total costs on a specific customer service level, mixed integer nonlinear programming and particle swarm algorithms are adopted for modeling and solving, to determine the optimal location and optimal semi-finished product inventory of PDP and CODP. Finally, taking an iron and steel enterprise as an example, the feasibility and effectiveness of the model and algorithm are verified. The study shows that the change of the optimal locations of PDP and CODP is affected by the change of customer service level and delay penalty coefficient, but the speed of change of the optimal location of PDP shows longer delays. In addition, the size of the capacity of semi-finished product inventory corresponding to CODP has a direct influence on whether the semi-finished product inventory corresponding to PDP participates in production, which in turn affects the optimal semi-finished product inventory on all levels. Through the analysis, it is found that the model constructed in this paper can better describe the overall situation and the influence relationship of the Postponement, and the study supplements the deficiency of the research on Postponement in the continuous manufacturing enterprises and enriches the content of the quantitative research on the Postponement.
Digital Transition Methodology of a Warehouse in the Concept of Sustainable Development with an Industrial Case Study
Authors: Kihel Yousra El
Published: November 2022

Logistics is one of the sectors that is evolving in parallel with Industry 4.0, which refers to the integration of new technologies, information, and agents, with the common goal of improving the efficiency and responsiveness of a logistics management system. The warehouse is an essential link in logistics management, a factor of competitiveness, and a link between the partners of the entire logistics chain. It has become essential to manage warehouses effectively and to allocate their resources efficiently. The digitalization of warehouses is currently one of the research topics of Logistics 4.0. This work presents a methodology of the digital transition of warehouse management, which consists of four main steps: the diagnosis of a warehouse to identify the different processes, the degree of involvement of the employees, a calculation of the degree of maturity to identify the new technology and means of data transfer, and the associated software for the collection of information and the methods of data processing. This digital transition methodology was applied to an industrial company. The results obtained allowed for the improvement of all the indicators measuring the performance of the warehouse on economic, social, and environmental levels.
Sustainable and Health-Protecting Food Ingredients from Bioprocessed Food by-Products and Wastes
Authors: Minervini Fabio , Comitini Francesca , De Boni Annalisa , Fiorino Giuseppina Maria , Rodrigues Francisca , Tlais Ali Zein Alabiden , Carafa Ilaria , De Angelis Maria
Published: November 2022

Dietary inadequacy and nutrition-related non-communicable diseases (N-NCDs) represent two main issues for the whole society, urgently requesting solutions from researchers, policy-makers, and other stakeholders involved in the health and food system. Food by-products and wastes (FBPW) represent a global problem of increasing severity, widely recognized as an important unsustainability hotspot, with high socio-economic and environmental costs. Yet, recycling and up-cycling of FBPW to produce functional foods could represent a solution to dietary inadequacy and risk of N-NCDs onset. Bioprocessing of FBPW with selected microorganisms appears to be a relatively cheap strategy to yield molecules (or rather molecules mixtures) that may be used to fortify/enrich food, as well as to formulate dietary supplements. This review, conjugating human health and sustainability in relation to food, describes the state-of-the-art of the use of yeasts, molds, and lactic acid bacteria for producing value-added compounds from FBPW. Challenges related to FBPW bioprocessing prior to their use in food regard will be also discussed: (i) loss of product functionality upon scale-up of recovery process; (ii) finding logistic solutions to the intrinsic perishability of the majority of FBPW; (iii) inserting up-cycling of FBPW in an appropriate legislative framework; (iv) increasing consumer acceptability of food and dietary supplements derived from FBPW.
How the Education Industries React to Synchronous and Asynchronous Learning in COVID-19: Multigroup Analysis Insights for Future Online Education
Authors: Persada Satria Fadil , Prasetyo Yogi Tri , Suryananda Xabitha Vanessa , Apriyansyah Bahalwan , Ong Ardvin K. S. , Nadlifatin Reny , Setiyati Etsa Astridya , Putra Raden Aditya Kristamtomo , Purnomo Agung , Triangga Bigraf , Siregar Nazaria Jotur , Carolina Donna , Maulana Fairuz Iqbal , Ardiansyahmiraja Bobby
Published: November 2022

The phenomenon of the COVID-19 pandemic requires prevention actions, such as social and physical distancing. In education, there is no choice but to adapt to online learning from traditional face-to-face learning. Online learning is divided into two approaches, namely synchronous and asynchronous learning. In practice, both synchronous and asynchronous learning have their respective advantages and disadvantages that can affect the efficiency and the effectiveness of online learning that are felt by students. The present study has analyzed students’ acceptance of synchronous learning and asynchronous learning by comparing the results of the two approaches that were used in educational institutions. Data from 162 respondents in the synchronous and 147 asynchronous online learning surveys were obtained through distributing online questionnaires. The present research utilized multigroup partial least square-structural equation modeling (PLS-SEM) for data analysis. The results indicate that students’ acceptance of synchronous online learning is influenced by the characteristics of the teachers, technological innovations, and the quality of the learning system. At the same time, the teacher characteristics, the organizational and technical support, technological innovations, and the trust affect the acceptance of asynchronous online learning. The R2 that was generated from the two groups was 58 percent and 62 percent for synchronous and asynchronous learning, respectively. The managerial implications that can be implemented are also described here in order to assist institutions in increasing the acceptance of synchronous and asynchronous online learning for future online education.
Digital Economy, Technological Innovation, and Environmental Quality Improvement
Authors: Yang Shiming , Jia Jianlin
Published: November 2022

Based on China’s provincial panel data from 2013 to 2020, this paper makes an empirical study on the influence of digital economy development on regional environmental quality by using the fixed effect model and mediating effect model. The results show that the development of a digital economy can effectively reduce environmental pollution and improve environmental quality. Secondly, a mechanism analysis shows that a digital economy can promote technological innovation and improve environmental quality; that is, there is a mediating effect when the total number of patents and utility model patents is a proxy variable of technological innovation, and there is no mediating effect when invention patents and design patents are proxy variables. Finally, through further analysis of regional differences, it is found that the development of the digital economy has obvious regional heterogeneity characteristics to the improvement of environmental quality, and the central and western regions are higher than the eastern regions. These conclusions provide new policy directions and inspiration for improving the regional environment, improving people’s quality of life, and promoting high-quality economic development.
Study on the Characteristics and Evolution Trends of Global Uranium Resource Trade from the Perspective of a Complex Network
Authors: Wang Zirui , Xing Wanli
Published: November 2022

With consensus on the climate issue, most countries have successfully put forward their carbon emission targets. Due to low carbon and mature technology, nuclear energy has become the focus of scholars. The development of nuclear energy is inseparable from the support of uranium resources. Due to the geographically uneven distribution of uranium resources, the flow of resources across the globe satisfies both supply and demand. Therefore, research on the characteristics of the global uranium resource trade and its evolution trends can provide a reference for decision-makers to formulate relevant uranium resource trade policies to ensure the security of the national uranium resource trade. Based on the perspective of the industry chain, this paper constructed an upstream natural uranium trade complex network (upstream) and an enriched uranium trade complex network (downstream) to analyze the characteristics and evolution trends of GURTNs at the global, community, and national levels. The results show that: (1) The trade of enriched uranium is mainly concentrated between developed countries such as European and North American countries. Natural uranium is a raw material, and its trade characteristics are greatly affected by uranium price fluctuations. (2) The evolution of the global natural uranium trade community is dominated by the significant uranium-resource-demanding countries. The global natural uranium trade pattern will be difficult to change in the short term. (3) With the expiration of the USA–Russia enriched uranium trade agreement, this will become an uncertain factor affecting the evolution of the global enriched uranium trade pattern. (4) Since the United States and France are deeply involved in the global uranium resources trade, both have a higher ability of anti-control and control in GURTNs, which is inseparable from the uranium resource trade strategies of the two countries. The paper concludes by suggesting corresponding policy recommendations that can inform policymakers in formulating relevant trade policies.
Performance Assessment of Direct Vapor Generation Solar Organic Rankine Cycle System Coupled with Heat Storage
Authors: Alvi Jahan Zeb , Jinghu Yu , Feng Yongqiang , Asim Muhammad , Qian Wang , Pei Gang
Published: November 2022

Phase change materials employed as thermal energy storage can aid in maximizing the use of stored solar energy. The current research examined the impact of three kinds of phase change materials (PCMs) on the dynamic performance of a solar organic Rankine cycle (ORC) system based on a direct vapor production. A number of evacuated flat plate collectors, a condenser, an expander, and an organic fluid pump make up this system. The thermodynamic cycle model of the direct vapor generation (DVG) solar ORC system was combined with the finite difference model of a phase change material heat storage tank created in MATLAB. The effect of PCMs (Organic, Inorganic and Eutectic PCMs) on the collector, ORC, and system efficiency, net power output, PCM temperature, and heat stored was studied weekly, monthly, and annually. Among the selected PCMs, Mg(NO3)2.6H2O had the highest system efficiency at 9.34%; KNO3-NaNO2 had the highest net power output at 33.80 kW; and MgCl2.6H2O stored the maximum energy of 20.18 MJ annually. Under the given operational and boundary conditions, the spring and fall were preferable to the summer and winter months for storing heat from phase change materials.
An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting
Authors: Sun Na , Zhang Nan , Zhang Shuai , Peng Tian , Jiang Wei , Ji Jie , Hao Xiangmiao
Published: November 2022

The precise forecast of solar radiation is exceptionally imperative for the steady operation and logical administration of a photovoltaic control plant. This study proposes a hybrid framework (CBP) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced Gaussian process regression with a newly designed physical-based combined kernel function (PGPR), and the backtracking search optimization algorithm (BSA) for solar radiation forecasting. In the CEEMDAN-BSA-PGPR (CBP) model, (1) the CEEMDAN is executed to divide the raw solar radiation into a few sub-modes; (2) PACF (partial autocorrelation coefficient function) is carried out to pick the appropriate input variables; (3) PGPR is constructed to predict each subcomponent, respectively, with hyperparameters optimized by BSA; (4) the final forecasting result is produced by combining the forecasted sub-modes. Four hourly solar radiation datasets of Australia are introduced for comprehensive analysis and several models available in the literature are established for multi-step ahead prediction to demonstrate the superiority of the CBP model. Comprehensive comparisons with the other nine models reveal the efficacy of the CBP model and the superb impact of CEEMDAN blended with the BSA, respectively. The CBP model can produce more precise results compared with the involved models for all cases using different datasets and prediction horizons. Moreover, the CBP model is less complicated to set up and affords extra decision-making information regarding forecasting uncertainty.
The Establishment of Rapid Propagation System of ‘RED SUN’ Phalaenopsis aphrodite
Authors: Zhang Haibo , He Di , Li Xiaoling , Dun Bicheng , Wu Di , Huang Guiyun
Published: November 2022

Phalaenopsis aphrodite Rchb. F. is a plant of great ornamental and economic value, and its growth has strict requirements in terms of environment. In order to establish the rapid propagation of Phalaenopsis aphrodite Rchb. F., this study used the popular large-flowered variety ‘RED SUN’, which is found on the market, as the material, and studied the effects of pedicel taking time, site, plant growth regulators, and organic substances on the establishment of a regeneration system. The results indicated that the best combination of initiation culture was collecting peduncles in January + upper position of peduncles + 0.1% HgCl2 sterilization for 8 min. The best recipe for inducing adventitious buds was MS + TDZ 0.4 mg/L + NAA 0.5 mg/L. The optimal combination for adventitious bud propagation was MS + TDZ 0.2 mg/L + NAA 0.4 mg/L. The best recipe for strong seedling from aseptic seedlings was MS + NAA 0.4 mg/L + banana powder 20 g/L + tryptone 2 g. The best recipe for rooting for aseptic seedlings was 1/2 MS + IBA 0.5 mg/L + charcoal 2 g/L. The best combination of acclimation and transplanting for Phalaenopsis was using sphagnum in the training seedling room, which produced a seedling survival rate of 97%. Our findings, therefore, demonstrate the methods of rapid breeding of the Phalaenopsis orchid, which provide a scientific basis for the conservation of this species.
Strengthening the Teaching and Research Nexus (TRN) in Higher Education (HE): Systematic Review of Reviews
Authors: Uaciquete Adriano Simao , Valcke Martin
Published: November 2022

Background: The linkage between teaching and research—also labelled the Teaching Research Nexus (TRN)—is the object of a recurrent debate in higher education. The debate centres on the nature of the interrelation, TRN benefits and challenges, concrete TRN strategies, and its impact on students and academics. Methods: Based on a systematic search of papers published between 2012 and 2022, a systematic review of review studies was conducted, building on articles from the Web of Science and Scopus. Results: From an initial 151 records, 14 fit the review inclusion/exclusion criteria. Goal and review questions: To provide researchers, teachers, and policy decision-makers with an overview of TRN in higher education based on available peer-reviewed review studies, this systematic review was driven by the following guiding questions: What are the conceptual developments in TRN definitions? What are the outcomes of experimental TRN interventions? What are the implementation challenges of TRN in higher education? What TRN implementation strategies have been adopted? Finally, what do the reviews stress as future directions for TRN? Brief conclusion: The review results helped identify patterns in TRN studies, practices, and directions for future TRN research in higher education.
The Role of the Access Environment in Metro Commute Travel Satisfaction
Authors: Zacharias John , Liu Xinyi
Published: November 2022

The contributions of the access and egress portions of public transport trips to overall travel satisfaction merit more attention. This study collected responses from intercepted regular urban rail travelers at three metro stations with distinct built-form and land-use characteristics. Local conditions of access on foot, by bicycle, or on a bus were evaluated retrospectively on a five-point satisfaction scale and compared with an independent survey of the same access routes. Three-factor theory and dummy variable regression methods were used to identify the factor structure of environmental attributes under different access means. In the results, access and egress satisfaction were more important than metro trip satisfaction in overall trip satisfaction for walking and cycling modes. Access distance was not significant for walking and marginally negative for cycling satisfaction. For pedestrians, street connectivity, pathway directness, shade, greenery, and crossing safety were all significant (p < 0.05), explaining 51% of the variance in expressed satisfaction. For bicyclists, directness, distance, service, and parking facilities were significant in satisfaction, accounting for 62% of variance. In the bus access model, we found that bus stop location is very important, with passengers also very concerned about the walk experience to the bus stop. Satisfaction with access and egress environments is important in overall satisfaction with travel by public transport.