bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Plant Diversity Using Multi-Seasonal Remotely Sensed and Geodiversity Data in a Mountainous Area
Authors: Rahmanian Soroor , Nasiri Vahid , Amindin Atiyeh , Karami Sahar , Maleki Sedigheh , Pouyan Soheila , Borz Stelian Alexandru
Published: January 2023

Plant diversity measurement and monitoring are required for reversing biodiversity loss and ensuring sustainable management. Traditional methods have been using in situ measurements to build multivariate models connecting environmental factors to species diversity. Developments in remotely sensed datasets, processing techniques, and machine learning models provide new opportunities for assessing relevant environmental parameters and estimating species diversity. In this study, geodiversity variables containing the topographic and soil variables and multi-seasonal remote-sensing-based features were used to estimate plant diversity in a rangeland from southwest Iran. Shannon’s and Simpson’s indices, species richness, and vegetation cover were used to measure plant diversity and attributes in 96 plots. A random forest model was implemented to predict and map diversity indices, richness, and vegetation cover using 32 remotely sensed and 21 geodiversity variables. Additionally, the linear regression and Spearman’s correlation coefficient were used to assess the relationship between the spectral diversity, expressed as the coefficient of variation in vegetation indices, and species diversity metrics. The results indicated that the synergistic use of geodiversity and multi-seasonal remotely sensed features provide the highest accuracy for Shannon, Simpson, species richness, and vegetation cover indices (R2 up to 0.57), as compared to a single model for each date (February, April, and July). Furthermore, the strongest relationship between species diversity and the coefficient of variation in vegetation indices was based on the remotely-sensed data of April. The approach of multi-model evaluations using the full geodiversity and remotely sensed variables could be a useful method for biodiversity monitoring.
Assessment of Runoff Components of River Flow in the Karakoram Mountains, Pakistan, during 1995–2010
Authors: Haq Mateeul , Iqbal Muhammad Jawed , Alam Khan , Huang Zhongwei , Blaschke Thomas , Qureshi Salman , Muhammad Sher
Published: January 2023

Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. Thus, this study is based on a long-term trend analysis of the computed runoff components using satellite data with continuous spatial and temporal coverage incorporated into a simple degree day Snowmelt Runoff Model (SRM). The trends of melt runoff components can help us understanding the future scenarios of the glaciers in the study area. The SRM was calibrated against the recorded river flows in the Hunza River Basin (HRB). Our simulations showed that runoff contribution from rain, snow, and glaciers are 14.4%, 34.2%, and 51.4%, respectively during 1995–2010. The melting during the summer has slightly increased, suggesting overall but modest glacier mass loss which consistent with a few recent studies. The annual stream flows showed a rising trend during the 1995–2010 period, while, rainfall and temperatures showed contrasting increasing/decreasing behavior in the July, August, and September months during the same period. The average decreasing temperatures (0.08 °C per annum) in July, August, and September makes it challenging and unclear to explain the reason for this rising trend of runoff but a rise in precipitation in the same months affirms the rise in basin flows. At times, the warmer rainwater over the snow and glacier surfaces also contributed to excessive melting. Moreover, the uncertainties in the recorded hydrological, meteorological, and remote sensing data due to low temporal and spatial resolution also portrayed contrasting results. Gradual climate change in the HRB can affect river flows in the near future, requiring effective water resource management to mitigate any adverse impacts. This study shows that assessment of long-term runoff components can be a good alternative to detect changes in melting glaciers with minimal field observations.
Techno-Economic Comparison of Stationary Storage and Battery-Electric Buses for Mitigating Solar Intermittency
Authors: Ahmed Arif , Massier Tobias
Published: January 2023

The need to reduce greenhouse gas emissions from power generation has led to more and more installation of renewable energies such as wind and solar power. However, the high intermittency of these generators poses a threat to electrical grid stability. The power output of solar photovoltaic (PV) installations, for instance, depends on the solar irradiance, and consequently on weather conditions. In order to mitigate the adverse effects of solar intermittency, storage such as batteries can be deployed. However, the cost of a stationary energy storage system (SESS) is high, particularly for large PV installations. Battery electric vehicles (BEVs) are an alternative to SESS. With increasing number of BEVs, more and more storage capacity becomes available while these vehicles are charging. In this paper, we compare stationary batteries to mobile batteries of battery electric buses (BEBs) in a public bus terminus for balancing fluctuations of solar PV installations. Public buses have been chosen due to their large batteries and because they are more easily manageable than private cars. An optimisation model has been developed considering both the bus operator’s and the PV operator’s objectives. Cycle ageing of batteries is included in the investigation. Our analysis reveals that utilising public BEBs with high battery capacity to balance solar PV fluctuations can present a positive financial case.
Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization
Authors: Deng Zhaopeng , Song Shengzhi , Han Shuangyang , Liu Zeqi , Wang Qiang , Jiang Liuyang
Published: January 2023

Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.
A Recommender System for Robust Smart Contract Template Classification
Authors: Gec Sandi , Stankovski Vlado , Lavbič Dejan , Kochovski Petar
Published: January 2023

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts’ correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system’s algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts’ development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.
Recent Implementations of Hydrogel-Based Microbial Electrochemical Technologies (METs) in Sensing Applications
Authors: Wang Zeena , Li Dunzhu , Shi Yunhong , Sun Yifan , Okeke Saviour I. , Yang Luming , Zhang Wen , Zhang Zihan , Shi Yanqi , Xiao Liwen
Published: January 2023

Hydrogel materials have been used extensively in microbial electrochemical technology (MET) and sensor development due to their high biocompatibility and low toxicity. With an increasing demand for sensors across different sectors, it is crucial to understand the current state within the sectors of hydrogel METs and sensors. Surprisingly, a systematic review examining the application of hydrogel-based METs to sensor technologies has not yet been conducted. This review aimed to identify the current research progress surrounding the incorporation of hydrogels within METs and sensors development, with a specific focus on microbial fuel cells (MFCs) and microbial electrolysis cells (MECs). The manufacturing process/cost, operational performance, analysis accuracy and stability of typical hydrogel materials in METs and sensors were summarised and analysed. The current challenges facing the technology as well as potential direction for future research were also discussed. This review will substantially promote the understanding of hydrogel materials used in METs and benefit the development of electrochemical biosensors using hydrogel-based METs.
Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs
Authors: Huang Xiaona , Liu Yang , Li Yuhan , Qi Keying , Gao Ang , Zheng Bowen , Liang Dong , Long Xiaojing
Published: January 2023

Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of fetal brain tissue is cumbersome and time-consuming, so using an automatic segmentation method can greatly simplify the process. In addition, the fetal brain undergoes a variety of changes throughout pregnancy, such as increased brain volume, neuronal migration, and synaptogenesis. In this case, the contrast between tissues, especially between gray matter and white matter, constantly changes throughout pregnancy, increasing the complexity and difficulty of our segmentation. To reduce the burden of manual refinement of segmentation, we proposed a new deep learning-based segmentation method. Our approach utilized a novel attentional structural block, the contextual transformer block (CoT-Block), which was applied in the backbone network model of the encoder–decoder to guide the learning of dynamic attentional matrices and enhance image feature extraction. Additionally, in the last layer of the decoder, we introduced a hybrid dilated convolution module, which can expand the receptive field and retain detailed spatial information, effectively extracting the global contextual information in fetal brain MRI. We quantitatively evaluated our method according to several performance measures: dice, precision, sensitivity, and specificity. In 80 fetal brain MRI scans with gestational ages ranging from 20 to 35 weeks, we obtained an average Dice similarity coefficient (DSC) of 83.79%, an average Volume Similarity (VS) of 84.84%, and an average Hausdorff95 Distance (HD95) of 35.66 mm. We also used several advanced deep learning segmentation models for comparison under equivalent conditions, and the results showed that our method was superior to other methods and exhibited an excellent segmentation performance.
A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders
Authors: Peimankar Abdolrahman , Winther Trine Straarup , Ebrahimi Ali , Wiil Uffe Kock
Published: January 2023

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer’s disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.
Evaluation of Lower-Limb Kinematics during Timed Up and Go (TUG) Test in Subjects with Locomotive Syndrome (LS) Using Wearable Gait Sensors (H-Gait System)
Authors: Kataoka Yoshiaki , Saito Yuki , Takeda Ryo , Ishida Tomoya , Tadano Shigeru , Suzuki Teppei , Nakamura Kentaro , Nakata Akimi , Osuka Satoshi , Yamada Satoshi , Samukawa Mina , Tohyama Harukazu
Published: January 2023

Few studies have dealt with lower-limb kinematics during the timed up and go (TUG) test in subjects with locomotive syndrome (LS). This study aimed to evaluate the characteristics of lower-limb kinematics during the TUG test in subjects with LS using the wearable sensor-based H-Gait system. A total of 140 participants were divided into the non-LS (n = 28), the LS-stage 1 (n = 78), and LS-stage 2 (n = 34) groups based on the LS risk test. Compared with the non-LS group, the LS-stage 1 and LS-stage 2 groups showed significantly smaller angular velocity of hip and knee extension during the sit-to-stand phase. The LS-stage 2 group showed significantly smaller peak angles of hip extension and flexion during the walking-out phase compared to the non-LS group. These findings indicate that the evaluation of the lower-limb kinematics during the TUG test using the H-Gait system is highly sensitive to detect LS, compared with the evaluation of the lower-limb kinematics when simply walking.
Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization
Authors: Melchiorre Jonathan , Manuello Bertetto Amedeo , Rosso Marco Martino , Marano Giuseppe Carlo
Published: January 2023

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.
Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved Sparrow Search Algorithm
Authors: Zheng Yuanzhou , Li Lei , Qian Long , Cheng Bosheng , Hou Wenbo , Zhuang Yuan
Published: January 2023

Objective: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. Method: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. Result: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. Conclusions: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory.
A Different Processing of Time-Domain Induced Polarisation: Application for Investigating the Marine Intrusion in a Coastal Aquifer in the SE Iberian Peninsula
Authors: Díaz-Curiel Jesús , Biosca Bárbara , Arévalo-Lomas Lucía , Miguel María Jesús
Published: January 2023

This study presents the developments regarding the time-domain induced polarisation method as a supporting tool for resistivity soundings during investigations of coastal detrital aquifers that are salinized by marine intrusion. The interpretation of resistivity measurements in such aquifers, which have variable hydrochemistry and lithology, involves uncertainties owing to the presence of low-resistivity lithologies, such as clays. To reduce these uncertainties, the use of other geophysical parameters is necessary; hence, this study focuses on induced polarisation since it can be measured simultaneously with resistivity. In detail, we propose the determination of induced polarisation using 1D techniques while developing a different algorithm for processing the induced polarisation data. The aim is to extend the results of this phenomenon, using, instead of chargeability, the concepts of polarisability and decay time, which are extracted from the decay curve, given that they represent more intrinsic properties of the various analyzed subsurface media. Results were obtained by applying this methodology to a Quaternary aquifer of the Costa del Sol in the SE Iberian Peninsula (in the province of Almería) during two different campaigns, one in mid-autumn and one late winter (i.e., in October and February, respectively) are presented. The results reveal the position of the saline front during each campaign while reflecting the seasonal movement of the marine intrusion.
Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System
Authors: Miao Jiansong , Chen Haoqiang , Li Hairui , Bai Shanling
Published: January 2023

A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.
Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review
Authors: Baglat Preety , Hayat Ahatsham , Mendonça Fábio , Gupta Ankit , Mostafa Sheikh Shanawaz , Morgado-Dias Fernando
Published: January 2023

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.
Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System
Authors: Marchionna Luca , Pugliese Giulio , Martini Mauro , Angarano Simone , Salvetti Francesco , Chiaberge Marcello
Published: January 2023

The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block’s pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row.
Prevalence and Management of Phytopathogenic Seed-Borne Fungi of Maize
Authors: Erasto Rehema , Kilasi Newton , Madege Richard Raphael
Published: January 2023

Seed-borne fungi are solemn and deleterious pathogens capable of causing significant losses of quantity and quality losses in maize seeds and seedlings. They infect the crop at all points of the production chain from farms to stores. A yield loss of up to 50% can be encountered. Currently, chemical control of the disease is being implemented, though it is accompanied by several negative effects. This study aimed at identifying seed-borne fungi of maize and effective management options. A deep-freezing blotter method and morphological identification of the fungal species were implemented. The seed-borne fungi detected were Fusarium verticillioides, Aspergillus flavus, Aspergillus niger, Penicillium spp., Rhizopus spp., and Curvularia spp. However, in farmer-saved seeds, fungal incidences were significantly higher (p < 0.01) than in certified seeds. To identify more effective management options, the efficacy of water and ethanol-extracted bio-fungicides from three plant species, namely, neem (Azadirachta indica), ginger (Zingiber officinale), and coffee (Coffea arabica) were evaluated. From in vitro assays, ethanol-extracted bio-fungicides have a 100% inhibitory effect on fungal growth, whilst the inhibitory effects of water-extracted bio-fungicides are 55.88% (Azadirachta indica) and 46.31% (Zingiber officinale), followed by 5.15% (Coffea arabica). For the case of an in vivo assay, maize seeds treated with water-extracted bio-fungicides have higher seed germination and seedling vigor percentages. For germination, seeds treated with water-extracted bio-fungicides have higher percentages (neem and ginger (90%) followed by coffee (72.5%)) than ethanol-extracted bio-fungicides (neem (0%), ginger (2.5%), and coffee (0%)). A similar observation is made for seedling weight. Therefore, the tested water-extracted bio-fungicides can be used in treating seeds before sowing them. Further studies on effective methods of extracting bioactive compounds, and improving their shelf life, are recommended.
Compaction Uniformity Evaluation of Subgrade in Highway Based on Principal Components Analysis and Back Propagation Neural Networks
Authors: Xu Changchun , Li Ting , Li Xujia , Yang Guangqing
Published: January 2023

This paper proposes a comprehensive method for the compaction uniformity evaluation of subgrade in highways based on the principle components analysis and BP neural network. A field test on resilient and Young’s moduli of subgrade during compaction is performed on Zun-Qin highway. The moduli representing the compaction uniformity are the key factors in the principal component analysis, and the components are used as input in Back Propagation (BP) neural networks. The degree of variation and synthesis score of moduli in three subgrade sections are discussed, and the results show that the comprehensive method has a good performance in evaluating the compaction uniformity of the subgrade. The insight from this study provides a novel evaluation method and incites a better understanding of the compaction uniformity of subgrade in highways.
A Sustainable Academic Journey in the Middle East: An Exploratory Study of Female College Students’ Self-Efficacy and Perceived Social Support
Authors: Mulhem Huda Al , Alaoui Khadija El , Pilotti Maura A. E.
Published: January 2023

In a society that is set to move from a patriarchal, gender-segregated workforce to one characterized by gender equity, female college students are the focal agents of change. Thus, they were selected to examine the contribution that perceived social support of women toward other women plays in their self-efficacy (i.e., confidence in one’s abilities) and academic performance (as measured by class grades). Self-reports were used to collect information about perceived social support, its nature and sources, and general self-efficacy from a sample of 131 female college students (age range: 18–24). Regression analyses were used to examine the participants’ responses. Surprisingly, the stronger the perceived positive social support reported by the respondents, the lower their academic performance. The contribution of the sources of negative social support (i.e., criticism) to either performance or self-efficacy was contingent on its being either received or given. Namely, explaining the criticism that women receive from other women by attributing it to their social environment was linked to higher performance, whereas explaining the criticism that women generate toward other women by attributing it to their social environment was linked to lower self-efficacy. However, the more likely women were to respond to criticism of women in person or via social media, the higher their self-efficacy. It was concluded that, for the selected sample, the contribution of social support, either positive or negative, to self-confidence and performance might depend on the extent to which female students perceive themselves as independent agents capable of self-determination in an environment making steps toward gender equity.
Opportunity or Challenge? Research on the Influence of Digital Finance on Digital Transformation of Agribusiness
Authors: Liu Xinmin , Wang Xinjiang , Yu Wencheng
Published: January 2023

The rapid development of digital finance in China has subtly influenced many aspects of social and economic development. However, agricultural enterprises can hardly enjoy the same digital dividend as traditional enterprises because of special attributes such as internal and external resource constraints, environmental constraints and cyclicality. To clarify the relationship between digital finance and digital transformation of agribusiness, and also to explore the factors that influence the effectiveness of digital finance, we use the “Peking University Digital Finance Index” to evaluate the micro impact of digital finance on the digital transformation of agribusiness. The results show that there is a “transition period” in which digital finance contributes to the digital transformation of agribusinesses, i.e., the two show a U-shaped non-linear relationship. This effect still exists after considering endogeneity and a series of robustness tests. In addition, further research determined that financing constraints and financial risk are the key paths through which digital finance affects the digital transformation of agribusinesses. Effective financial regulation weakens the U-shaped relationship between digital finance and agribusiness digital transformation and is an important means of attenuating the negative impact of digital finance. Overall, we provide a micro explanation for the accelerated popularization of digital finance in emerging markets, which is urgently needed for most agribusinesses seeking high-quality development.
A Practical Green Infrastructure Intervention to Mitigate Air Pollution in a UK School Playground
Authors: Redondo Bermúdez María del Carmen , Chakraborty Rohit , Cameron Ross W. , Inkson Beverley J. , Val Martin Maria
Published: January 2023

Air pollution severely compromises children’s health and development, causing physical and mental implications. We have explored the use of site-specific green infrastructure (green barriers) in a school playground in Sheffield, UK, as an air-pollution-mitigation measure to improve children’s environment. The study assessed air quality pre-post intervention and compared it with two control sites. Nitrogen dioxide (NO2) and particulate matter <2.5 µm in size (PM2.5) concentration change was assessed via three methods: (1) continuous monitoring with fixed devices (de-seasonalised); (2) monthly monitoring with diffusion tubes (spatial analysis); (3) intermittent monitoring with a mobile device at children’s height (spatial analysis). De-seasonalised results indicate a reduction of 13% for NO2 and of 2% for PM2.5 in the school playground after two years of plant establishment. Further reductions in NO2 levels (25%) were observed during an exceptionally low mobility period (first COVID-19 lockdown); this is contrary to PM2.5 levels, which increased. Additionally, particles captured by a green barrier plant, Hedera helix ‘Woerner’, were observed and analysed using SEM/EDX techniques. Particle elemental analysis suggested natural and potential anthropogenic origins, potentially signalling vehicle traffic. Overall, green barriers are a valid complementary tool to improve school air quality, with quantifiable and significant air pollution changes even in our space-constrained site.
A Study on the Policy Effects of the Establishment of Guangdong–Hong Kong–Macao Greater Bay Area on Logistics Efficiency
Authors: Hong Shuquan , Jiang Huiyuan , Cheng Shuang , Huang Yuejun , Feng Chao
Published: January 2023

Logistics efficiency is an important indicator when measuring the level of development of the logistics industry, and policy factors are the most difficult to measure among the factors affecting logistics efficiency. This study aimed to construct a new empirical model by combining a three-stage data envelopment analysis (DEA) model and the econometric method propensity score matching and difference-in-differences (PSM–DID) to measure and analyze the net change in logistics efficiency in the Guangdong–Hong Kong–Macao Greater Bay Area under the influence of this policy factor. The empirical evidence shows that different amounts of change occurred in the two time periods after the establishment of the Greater Bay Area and a significant increase in logistics efficiency occurred in the second period, further demonstrating that the economic policy of the Greater Bay Area is effective in improving logistics efficiency and providing a case reference for other countries or regions with similar conditions.
A Comparative Study of Factors Influencing Hydration Stoppage of Hardened Cement Paste
Authors: Mezhov Alexander , Kulisch Daniele , Goncharov Antonina , Zhutovsky Semion
Published: January 2023

There is no consensus on which hydration stoppage method is optimal to preserve the microstructure and mineral composition of samples, especially considering the specific aspects of different testing methods, such as TGA, MIP, or XRD. This paper presents a quantitative comparison between the most popular hydration stoppage strategies and parameters such as the sample piece size, the soaking time in a solvent, and the type, as examined on cement paste hydrated for 7 days. It was found that the carbonation appears either for samples smaller than 2.36 mm and bigger than 4.75 mm or samples soaked in a solvent for longer than 1 h. Fast solvent replacement leads to ettringite diminution and total pore volume increase. Among others, solvent replacement with subsequent gentle heating under a vacuum was found to be the most efficient, whereas it was experimentally demonstrated that isopropyl alcohol stops hydration faster than ethanol and acetone.
Wearables-Assisted Smart Health Monitoring for Sleep Quality Prediction Using Optimal Deep Learning
Authors: Hamza Manar Ahmed , Abdalla Hashim Aisha Hassan , Alsolai Hadeel , Gaddah Abdulbaset , Othman Mahmoud , Yaseen Ishfaq , Rizwanullah Mohammed , Zamani Abu Sarwar
Published: January 2023

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models.
In Search of Innovation Barriers to Tourist Destinations—Indications for Organizations Managing Destinations
Authors: Panfiluk Eugenia
Published: January 2023

The objective of this work was to evaluate the innovation processes in tourist destinations using the Quadruple Helix model and to develop guidelines for building innovation management strategies in the tourism sector for destination management organizations (DMO). The article identifies the drivers and barriers to innovation processes reported by entrepreneurs in the tourism industry in Poland. The analysis was carried out in relation to 218 enterprises of the tourism industry operating in destinations in large cities as well as in destinations in small towns and rural areas. The research was carried out using a diagnostic survey with elements of a telephone interview. The research confirmed the usefulness of the Quadruple Helix model for the assessment of innovation processes in tourist destinations. A relationship was observed between the level of development of innovation processes, the size of the tourist destination and the level of competitiveness of the tourist market in the destination. The study showed a significant variation in the spatial and geographical system, as well as between individual factors responsible for the innovation processes in a tourist destination. The influence of the market, including consumers, is a strong point of these processes. The barriers include poorly developed structures of cooperation between enterprises, DMO, scientific and research institutions, and civil society, as well as their participation in the innovation processes of tourist destinations. Final conclusions: it should be stated that the innovative processes in Polish tourist destinations are underdeveloped. They do not affect the development of tourism markets and the competitiveness of destinations.
A Cu/Polypyrrole-Coated Stainless Steel Mesh Membrane Cathode for Highly Efficient Electrocoagulation-Coupling Anti-Fouling Membrane Filtration
Authors: Li Yuna , Hao Zixin , Han Jinglong , Sun Yueyang , He Mengyao , Yao Yuang , Yang Fuhao , Liu Meijun , Zhang Haifeng
Published: January 2023

Membrane filtration fouling has become a significant issue that restricts its wide application. The electrocoagulation (EC) technique combines a variety of synergistic pollutant removal technologies (including flocculation, redox, and air flotation), which can be an ideal pretreatment process for membrane filtration. In this work, a novel Cu2+-doped and polypyrrole-coated stainless steel mesh membrane (Cu/PPy–SSM) was prepared by direct current electrodeposition, and it was introduced in an electrocoagulation-membrane reactor (ECMR) to construct an EC–membrane filtration coupling system. The Cu/PPy–SSM was applied as the cathode, while an aluminum plate was used as the anode in the ECMR. The ECMR enabled an excellent humic acid (HA) removal performance and could effectively mitigate the fouling of the Cu/PPy–SSM. Its performance can be attributed to the following: (1) the Cu/PPy–SSM can repel the negatively charged pollutants under the applied electric field; (2) the cathodic hydrogen gas produced on the Cu/PPy–SSM restrains the compacting of the cake layer and delays degradation of membrane flux; and (3) the resultant porous loose structure can perform as a dynamic membrane, which can effectively promote the separation performance of the Cu/PPy–SSM. The resultant ECMR enabled an improved HA removal rate of 92.77%, and the membrane-specific flux could be stabilized at more than 86%. Response surface methodology (RSM) was used to optimize the operation parameters of the ECMR, and the predicted HA removal rate reached 93.01%. Both the experimental results and modelled predictions show that using the Cu/PPy–SSM as a cathode can lead to excellent performance of the ECMR.
Improvement of Factory Transport Efficiency with Use of WiFi-Based Technique for Monitoring Industrial Vehicles
Authors: Kochańska Joanna , Burduk Anna , Markowski Marcin , Kłusek Alicja , Wojciechowska Monika
Published: January 2023

The in-factory transport influences production rate and quality, so it is characterized by the shortest displacement time and maximum utilization of available means of transport, accompanied by their lowest operating wear. In the paper, an improvement of factory transport management is suggested, employing the traffic analysis methods of industrial vehicles. The efficiency of internal logistics was analyzed with the use of the WiFi-based technique for monitoring industrial vehicles. In the research, the in-building location system Optimatik BI was used and implemented in a company manufacturing PVC profiles for the market of building materials and interior equipment. Stopping times of forklift trucks, their routes and covered distances were estimated. On this ground, factors of their effective use were calculated. Further, it was possible to analyze the traffic of the forklift trucks and to generate heat maps that, in turn, made it possible to identify problems and develop a suitable improvement plan. A mathematical model that allows for efficiency optimization by determining the best number of employees handling the transport was developed for one of the analyzed logistics tasks.
Identification of Salinity Tolerant Stable Sugarcane Cultivars Using AMMI, GGE and Some Other Stability Parameters under Multi Environments of Salinity Stress
Authors: Kumar Ravinder , Dhansu Pooja , Kulshreshtha Neeraj , Meena Mintu Ram , Kumaraswamy Mahadevaswamy Huskur , Appunu Chinnaswamy , Chhabra Manohar Lal , Pandey Sstish Kumar
Published: January 2023

Additive main effects and multiplicative interaction (AMMI), as well as various AMMI-derived statistics, Genotype, and Genotype × Environment Interaction (GGE) models were employed on 24 sugarcane genotypes planted during two seasons (2017–18, 2018–19) under different induced salinity stress environments using saline water irrigation (iw) viz., E1 (Normal iw during crop season 2017–18), E2 (Normal iw during crop season 2018–19), E3 (4 dsm−1 ECiw during crop season 2017–18), E4 (4 dsm−1 ECiw during crop season 2018–19), E5 (8 dsm−1 ECiw during crop season 2017–18), E6 (8 dsm−1 ECiw during crop season 2018–19), E7 (12 dsm−1 ECiw during crop season 2017–18) and E8 (12 dsm−1 ECiw during crop season 2018–19) to assess the genotype by the environment interaction for the cane yield, commercial cane sugar (CCS) yield, number of millable cane (NMC), single cane weight (SCW), and pol % in juice. Individual and interactive effects of the genotype and environment for all the traits were significant. In the expression of total variability, the environmental contribution was higher for the cane yield (66.98%), CCS yield (67.60%), NMC (65.78%), and SCW (43.27%), whereas genotypic contribution was higher in the expression of pol% (82.48%). As per AMMI Stability Value (ASV), G14 (Co 13033), G23 (Co 15026), G7 (Co 05009), G17 (Co 13036), and G2 (Co 15025) were the most stable genotypes for the cane yield. Whereas as per GSI (genotype selection index), genotypes G24 (Co 15027), G21 (Co 15023), G23 (Co 15026), and G17 (Co 13036) were found most stable. The Sustainability Index (SI) of the cane yield (CY) and its contributing and CY-based computed traits were low for most of the genotypes, which indicates the negative impact of increased levels of irrigation-induced salinity in the expression of these traits. In the mean vs stability biplot analysis, G21 (Co 15023), G24 (Co 15027), G16 (Co 13036), G6 (Co 0238), and G20 (Co 14036) were found to be highly productive and stable genotypes for the cane yield. The superior and stable performance of early maturing notified varieties G21 (Co 15023) and G6 (Co 0238) for CY and CCS yield indicates that they will help the farmers to obtain sustainable income in saline soil conditions.
Productivity Improvement from the Mixed-Ownership Reform: A Financial Frictions Perspective
Authors: Xie Fusheng
Published: January 2023

How to raise productivity level has become the core issue of ensuring China’s sustained Economic Growth in the Future. The mixed-ownership has both the financing advantage of the SOEs and the competitive ability of the Private firms, which can improve the governance of the firms. This paper builds a model based on the financial frictions literature, and studies the process of the mixed-ownership reform. The main results include: 1. On average, the mixed-ownership reform enhances the performance of the firms; 2. The relationship between the share of state ownership—full privatization, state-ownership, or mixed-ownership—and the performance depends on both the productivity and the restriction of financing; 3. When production efficiency is low, privatization works best; when production efficiency is medium, partial privatization works best; when production efficiency is high, nationalization works best; 4. Our model explain the puzzle of state-owned equity ratio and performance.