bluuebunny/crossref_metadata_2025 huggingface dataset:
Air and O2-Assisted Catalytic VACNT Growth Optimization for Uniformity and Throughput
Authors: Strobl Karlheinz , Rajab Fahd
Published: May 2023

The development of an optimized air or O2-assisted multi-wall vertically aligned carbon nanotubes (VACNT) process that adjusts the vertical height profile of a standard H2O vapor-assisted VACNT process is reported. The effect of the air or O2 chemical vapor deposition (CVD) precursor flow rate, the catalytic Fe layer thickness, the process growth temperature, and the H2/C2H4 ratio on VACNT length was first investigated to find the optimum growth conditions. Spatial distribution height mapping of VACNT structures on six patterned 4′′ catalyst Si wafers prepared with a 70–90 min long O2-assisted growth step shows an average growth height of 1.8–2.2 mm, with a standard deviation of less than 10%. Characterization techniques included Raman spectroscopy, scanning electron microscopy (SEM), and spatial height mapping analysis for a range of Fluid channel Array Brick (FAB) components with a length of 30 mm, a width range of 2.5–15 mm, a fluid channel diameter range of d = 5–100 mm, and a fluid channel closest gap range of g = 5–50 mm. A significant finding is that the O2-assisted VACNT growth process optimization efforts enable 2 mm parts processing with square edges, flat top surfaces, uniform height tolerances, and maximum catalyst wafer utilization for application in engineering devices.
Data-Driven Operation of Flexible Distribution Networks with Charging Loads
Authors: Wang Guorui , Qian Zhenghao , Feng Xinyao , Ren Haowen , Zhou Wang , Wang Jinhe , Ji Haoran , Li Peng
Published: May 2023

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs.
Efficient Approaches for Layout Problems of Large Chemical Plants Based on MILP Model
Authors: Li Hao , Zhou Li , Ji Xu , Dai Yiyang , Dang Yagu
Published: May 2023

This paper presents two novel solution approaches for addressing large-scale multi-floor process plant layout problems. Based on the mixed integer linear programming (MILP) model, the first solution approach employs a multi-directional search strategy while the second improves solution efficiency by reducing model size through an iterative framework. Both approaches determine the spatial arrangement of the plant equipment considering equipment-floor allocation, non-overlapping constraints, tall equipment penetrating multiple floors, etc. The computational results indicate that the proposed approaches achieved potential cost savings for four illustrative examples when compared to the previous studies. Finally, engineering experience constraints were included to represent a more complex industrial situation, and their applicability was tested with the last example.
Institutionalizing the Relationship between Religious Teaching and Religious Freedom: The Case of the Republic of San Marino
Authors: Porcarelli Andrea
Published: May 2023

This paper examines the principles of denominational religious teaching in public schools at the background of a broader debate, concerning its compatibility with the standards of freedom of/from religion, by assessing the case of the Republic of San Marino. In doing so, revisions were made to some of the solutions proposed within the framework of international documents, such as the Toledo Guiding Principles on Teaching about Religions and Beliefs in Public Schools (OSCE/ODIHR). Among the various solutions, the Toledo document proposes the preparation of an alternative lesson of equal dignity, for those pupils who do not avail themselves of denominational education. The preparation of an alternative lesson is to be followed by a detailed analysis of the regulatory procedure that led to the achievement of this solution in the Republic of San Marino; the procedure entailed setting up a course on “Ethics, culture and society” in public schools for those who do not wish to avail themselves of Catholic religious education. These types of lessons were introduced, experimentally, in the academic year of 2019–2020 and the monitoring of the experiment is currently in progress. Shown, in conclusion, will be how this educational experiment could be a good practice for institutionalizing religious freedom in a small country, in which denominational religious teaching is mandatory.
Spectral-Swin Transformer with Spatial Feature Extraction Enhancement for Hyperspectral Image Classification
Authors: Peng Yinbin , Ren Jiansi , Wang Jiamei , Shi Meilin
Published: May 2023

Hyperspectral image classification (HSI) has rich applications in several fields. In the past few years, convolutional neural network (CNN)-based models have demonstrated great performance in HSI classification. However, CNNs are inadequate in capturing long-range dependencies, while it is possible to think of the spectral dimension of HSI as long sequence information. More and more researchers are focusing their attention on transformer which is good at processing sequential data. In this paper, a spectral shifted window self-attention based transformer (SSWT) backbone network is proposed. It is able to improve the extraction of local features compared to the classical transformer. In addition, spatial feature extraction module (SFE) and spatial position encoding (SPE) are designed to enhance the spatial feature extraction of the transformer. The spatial feature extraction module is proposed to address the deficiency of transformer in the capture of spatial features. The loss of spatial structure of HSI data after inputting transformer is supplemented by proposed spatial position encoding. On three public datasets, we ran extensive experiments and contrasted the proposed model with a number of powerful deep learning models. The outcomes demonstrate that our suggested approach is efficient and that the proposed model performs better than other advanced models.
On the Use of Circadian Cycles to Monitor Individual Young Plants
Authors: Cordier Mathis , Torres Cindy , Rasti Pejman , Rousseau David
Published: May 2023

Occlusion is a very common problem in computer vision. The presence of objects seen as overlapped under a camera negatively impacts object recognition, object counting or shape estimation. This problem is especially important in plant imaging because plants are very self-similar objects which produce a lot of self-occlusions. A possible way to disentangle apparent occlusions is to acquire the same scene from different points of view when the object is motionless. Such a realization is not necessary if the objects move themselves under the camera and thus offer different points of view for free. This is the case in plant imagery, since plants have their own natural movements, including the so-called circadian rhythms. We propose to use these movements to solve some self-occlusion problems with a set of simple yet innovative sampling algorithms to monitor the growth of individualized young plants. The proposed sampling methods make it possible to monitor the growth of the individual plants until their overlap is definitive. The gain is significant with an average maximum duration of observation increase from 3 days to more than 10 days by comparison with a sampling method that would stop when the first overlap occurs.
Recent Active Fires in Indonesia’s Southern Papua Province Caused by El Niño Conditions
Authors: Yulianti Nina , Hayasaka Hiroshi
Published: May 2023

This study was conducted to identify the fire weather conditions needed to assess future peatland fires under climate change. Recent peatland fires in Indonesia have resulted in globally significant environmental impacts. Nevertheless, diurnal fire weather in the peatlands has not been clarified. The objective of this study was to determine the fire weather conditions needed to assess future peatland fires under climate change. An analysis of fire weather using diurnal weather data during the most active fire period in 2015 showed a strong wind speed of 35.7 km h−1 at 3 p.m. that continued to blow for about two weeks, suggesting that peatland fires in 2015 became very active under these very strong wind conditions. The temperature increase rate (ΔT), the RH decrease rate (ΔRH), and the wind speed increase rate (ΔWS) during morning hours from 6:00 a.m. to 9:00 a.m. were 2.3 °C h−1, −10.3% h−1, and 5.2 (km h−1) h−1 respectively. These weather parameters will be used to predict occurrences of active fires. The results of this report may help to predict fire activity under high temperatures in the future based on predictions of global warming made by other researchers. The rapid air temperature increase rate from morning will be useful for fire forecast in Papua.
Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling
Authors: Fuse Takashi , Imose Kazuki
Published: May 2023

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.
Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality
Authors: Chen Fan , Fu Hong , Yu Hengyong , Chu Ying
Published: May 2023

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of the human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and the dorsal pathway of the HVS, a dual-pathway convolutional neural network is proposed for BIQA tasks. The proposed method consists of two pathways: the “what” pathway, which mimics the ventral pathway of the HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of the HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, gradient images weighted by contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. Experiments conducted on six databases show that the proposed method achieves state-of-the-art performance.
Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson’s Disease
Authors: Castiglia Stefano Filippo , Trabassi Dante , Conte Carmela , Ranavolo Alberto , Coppola Gianluca , Sebastianelli Gabriele , Abagnale Chiara , Barone Francesca , Bighiani Federico , De Icco Roberto , Tassorelli Cristina , Serrao Mariano
Published: May 2023

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson’s disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.
Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review
Authors: Ding Shaohu , Yang Chenchen , Zhang Sen
Published: May 2023

Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.
Investigation of Goubau Wave Propagation on Large Pipes for Sensing Applications
Authors: Greve David W. , Devkota Jagannath , Ohodnicki Paul R. , Wright Ruishu
Published: May 2023

We examine the application of guided waves on a single conductor (Goubau waves) for sensing. In particular, the use of such waves to remotely interrogate surface acoustic wave (SAW) sensors mounted on large-radius conductors (pipes) is considered. Experimental results using a small-radius (0.0032 m) conductor at 435 MHz are reported. The applicability of published theory to conductors of large radius is examined. Finite element simulations are then used to study the propagation and launching of Goubau waves on steel conductors up to 0.254 m in radius. Simulations show that waves can be launched and received, although energy loss into radiating waves is a problem with current launcher designs.
Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs
Authors: Achirei Stefan-Daniel , Mocanu Razvan , Popovici Alexandru-Tudor , Dosoftei Constantin-Catalin
Published: May 2023

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.
Determination and Measurement of Melanopic Equivalent Daylight (D65) Illuminance (mEDI) in the Context of Smart and Integrative Lighting
Authors: Trinh Vinh Quang , Bodrogi Peter , Khanh Tran Quoc
Published: May 2023

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER), and four other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model reached ± 3.3% after matrix transformation and illuminance processing combined with the successful mDER calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research.
Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs
Authors: Ganeva Dessislava , Roumenina Eugenia , Dimitrov Petar , Gikov Alexander , Jelev Georgi , Dyulgenova Boryana , Valcheva Darina , Bozhanova Violeta
Published: May 2023

This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.
Unlocking the Potential of N-Doped SnO2 for Sustainable Photocatalytic Degradation of Carcinogenic Dyes
Authors: Bhawna  , Sharma Ritika , Kumar Sanjeev , Kumar Ravinder , Sahu Prasanta , Kumari Vandana , Mishra Ajay , Kumar Vinod
Published: May 2023

Environmental adulteration is an emerging concern due to the discharge of wastewater effluents from several sources. Several carcinogenic dyes are the major contaminants in these water bodies. These could cause long-lasting and detrimental effects to humans as well as aquatic ecosystems. For efficient degradation of such dyes, the exploration of nanotechnology has demonstrated huge potential. Herein, the degradation of dyes (MB, CV, and MO) has been carried out photocatalytically using N-doped SnO2 nanoparticles (N:SnO2 NPs) as well as in presence of a sacrificial agent, EDTA. These NPs were synthesized at an ambient temperature. Different characterization techniques were used throughout the analysis of the synthesized NPs. The PXRD analysis reveals formation of single-phase rutile structure with tetragonal symmetry. Using the Scherrer formula, the size of the NPs was found to be less than 5 nm, exhibiting increases in size with N doping. Further, morphological analysis through field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) examined the existence of highly agglomerated, spherical NPs. The thermogravimetric analysis (TGA) results depict the thermal stability of the synthesized NPs up to a temperature of 800 °C. These synthesized N:SnO2 NPs exhibit potent efficiency for the photocatalytic degradation of MB, MO, and CV dyes with an efficiency of 93%, 83%, and 73% degradation, respectively, under UV light irradiation. Additionally, the effect of the sacrificial agent, EDTA, was observed on the degradation process and resulted in a degradation of ~90% MB dye, 88% CV dye, and 86% MO dye within 15 min of UV light irradiation.
Cellulolytic Bacillus Strain: Production Optimization Using Wheat Bran under Solid-State Fermentation and Investigation of Its Probiotic Potential
Authors: Bouzaiene Taroub , Ziadi Manel , Enneifer Malek , Sellami Abir , Aydi Abdelkarim , Cherif Ameur , Hamdi Moktar
Published: May 2023

Attention to the association of cellulolytic bacteria with probiotic potential as an additive in animal feeding has risen in the last decades. Such additive use in livestock feed is essential in improving animal health, growth, and production performances. This study was designed to identify probiotic characteristics and test the cellulolytic ability of Bacillus strains isolated from the dromedary gastrointestinal tract. Thus, thirty-two Bacillus strains were tested for their cellulolytic ability on cellulose Congo-red agar media. Among the isolates, only the strain D1B3 showed the largest degradation zone (2.4 cm) and was identified as Bacillus amyloliquefacians by 16S rRNA gene sequence analysis. Solid-state fermentation (SSF) retained this strain for cellulase and biomass production using wheat bran as a substrate. The fermentation was optimized through a central composite design, by exploring three factors: incubation temperature, moisture ratio, and pH. Biomass and cellulose enzyme activity were selected as responses and corresponding regression coefficients were calculated. The optimal parameters were: liquid-to-solid ratio (1.19%), pH buffer (6.2), and incubation temperature (36.99 °C) to obtain the highest level of biomass and cellulose enzyme activity reaching a value of 9.828 log CFU/g and 0.0144 g/L.min, respectively. The potentiality of Bacillus amyloliquefacians D1B3 as a probiotic was examined in vitro. It also showed antimicrobial activity against Pseudomonas aeruginosa 9027, Klebsiella pneumoniae, and Escherichia coli 10536. The isolate tolerates low pH and bile salt (0.3% Oxygall). The hydrophobicity and coaggregation abilities were 1.7% and 69.79%, respectively. The results indicated that Bacillus amyloliquefacians D1B3 could be a potential probiotic additive for improving in vitro fermentation of wheat bran and suggests the possibility of combining the probiotic attributes of this strain with its cellulolytic ability to enhance the rumen fermentation of animal feed.
A Single DC Source Five-Level Switched Capacitor Inverter for Grid-Integrated Solar Photovoltaic System: Modeling and Performance Investigation
Authors: Islam Md. Tariqul , Alam Md. Ahsanul , Lipu Molla Shahadat Hossain , Hasan Kamrul , Meraj Sheikh Tanzim , Masrur Hasan , Rahman Md. Fayzur
Published: May 2023

Boost converters and multilevel inverters (MLI) are frequently included in low-voltage solar photovoltaic (PV) systems for grid integration. However, the use of an inductor-based boost converter makes the system bulky and increases control complexity. Therefore, the switched-capacitor-based MLI emerges as an efficient DC/AC voltage convertor with boosting capability. To make classical topologies more efficient and cost-effective for sustainable power generation, newer topologies and control techniques are continually evolving. This paper proposes a reduced-component-count five-level inverter design for generating stable AC voltages for sustainable grid-integrated solar photovoltaic applications. The proposed topology uses seven switching devices of lower total standing voltage (TSV), three diodes, and two DC-link capacitors to generate five-level outputs. By charging and discharging cycles, the DC capacitor voltages are automatically balanced. Thus, no additional sensors or control circuitry is required. It has inherent voltage-boosting capability without any input boost converter. A low-frequency-based half-height (HH) modulation technique is employed in the standalone system for better voltage quality. Extensive simulations are performed in a MATLAB/Simulink environment to estimate the performance of the proposed topology, and 17.58% THDs are obtained in the phase voltages. Using a small inductor in series or an inductive load, the current THD reduces to 8.23%. Better dynamic performance is also observed with different loading conditions. A miniature five-level single-phase laboratory prototype is developed to verify the accuracy of the simulation results and the viability of the proposed topology.
Opportunities and Constraints for Creating Edible Cities and Accessing Wholesome Functional Foods in a Sustainable Way
Authors: Świąder Katarzyna , Čermak Dražena , Gajewska Danuta , Najman Katarzyna , Piotrowska Anna , Kostyra Eliza
Published: May 2023

Malnutrition, food security and food safety will remain major global issues as the world’s population grows and the consequences of climate change prevail, so we need to rethink how we grow and source food to create sustainable systems for future generations. Edible cities, as innovative solutions to use public spaces for urban food production, can bridge this evident gap between the present and the future. The aim of this review was to analyze the opportunities and constraints for creating edible cities and accessing wholesome functional foods in a sustainable way and explore existing solutions that can be strengthened. We can grow food in urban environments using ideas such as controlled-environment farms (CEAs), home food gardens on balconies, roofs and terraces, underground farming and foraging. As citizens become more aware of complex foods with nutritional benefits, we should take this opportunity to teach them about edible wholesome functional foods and how they can be grown instead of using plants. There are still many constraints such as pollution, a lack of government support and the economic aspects of urban farms that need to be resolved in order for edible cities and access to functional foods in them to become the standard worldwide. The goal is worthwhile as citizens would benefit from climate control, reduced resource consumption, a safer food supply, improved mental and physical health, reduced malnutrition and nutritional deficiencies and connected communities that share knowledge and resources to further innovation and welfare.
Simulation Study on the Characteristics of Gas Extraction from Coal Seams Based on the Superposition Effect and Hole Placement Method
Authors: Yan Jin , Zhang Minbo , Zhang Weizhong , Kang Qinrong
Published: May 2023

In order to obtain a reasonable extraction drilling method for coal seam working faces and to carry out targeted as well as cost-effective hole placement optimization, a gas–solid coupled model based on the coal rock deformation field and the matrix–fissure dual seepage–diffusion field was established and numerically solved via the use of COMSOL Multiphysics finite element software to optimize the gas transport parameters of the Dongpang coal mine based on the study of the coal seam gas transport law. This study shows the following: With an increase in the extraction time, the gas content of the coal seam was reduced to a minimum. It shows that, with an increase in the extraction time, the gas pressure and seepage velocity keep decreasing the stable value, the main stress around the borehole redistributes, and the coal permeability keeps decreasing with an increase in the decay coefficient. The extraction radius of the boreholes increases exponentially with the extraction time, and the reasonable extraction hole size is 94 mm; the use of multiple boreholes for pre-drawing gas via the use of the interval between the effective extraction radius and the influence radius results in a blind extraction zone caused by the superposition effect between the boreholes, and the residual gas value of the coal seam increases with an increase in the borehole spacing. The use of an equilateral triangular hole layout can avoid the extraction blind zone, and the pre-sumping effect is good after field application, which ensures the safe production of coal mines as well as the reasonable use of resources.
A Sustainable Supply Chain Framework for Dairy Farming Operations: A System Dynamics Approach
Authors: Shamsuddoha Mohammad , Nasir Tasnuba , Hossain Niamat Ullah Ibne
Published: May 2023

The dairy industry plays a significant role in the global food system, providing essential nutrients for human consumption and creating rural employment. A small-scale dairy can assist a family in maintaining their livelihoods in Bangladesh. However, it is also associated with various environmental and social impacts, making it crucial for achieving sustainability. The triple bottom line of sustainability intends to achieve sustainability through improving productivity, implementing sustainable practices, and incorporating waste management. The dairy industry can continue to provide nutritious diets, ensuring sustainability practices. This research is a follow-up paper of Nasir et al. to find better sustainable results. It considers the triple bottom line of sustainability theory to improve the farm environment by reducing waste, managing resources efficiently, and promoting environmentally friendly practices. This paper is a case study on a dairy farm of 400 cattle in Bangladesh. The system dynamics method and simulation modeling were employed to draw dairy supply chain networks and examine the existing dataset to find better utilization of the dairy waste produced on the farm. Consequently, the simulation model incorporates waste management and value addition concepts to find better resource exploitation for gaining sustainable outcomes. Finally, this paper summarizes the simulation outcomes and articulates possible extensions for achieving further economic, social, and environmental benefits for the industry and surrounding community.
Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines
Authors: Ding Xiaohua , Jamei Mehdi , Hasanipanah Mahdi , Abdullah Rini Asnida , Le Binh Nguyen
Published: May 2023

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.
The Impact of Coordinated Development Policy on the Spatiotemporal Changes of Industrial Structure of Beijing–Tianjin–Hebei Urban Agglomeration
Authors: Liu Ruowen , Wang Hao , Ning Xiaogang , Cai Xingfei , Zhang Xiaoyuan
Published: May 2023

The coordinated development of Beijing–Tianjin–Hebei (BTH) is a major regional strategy in China that aims to alleviate Beijing’s non-capital functions and address the “big city disease”. Understanding the spatial distribution and changing trends of industrial development in BTH is critical for achieving BTH’s coordinated development goals. In particular, it is important to assess the effectiveness of Beijing’s non-capital functions at the industrial level. This study utilized the 2013 and 2018 economic census data and statistical methods such as spatial Gini coefficient, trend analysis, specialization index, and industry similarity. We first characterized the industrial development pattern by analyzing the spatiotemporal changes of the operating income and the number of legal entities in BTH urban agglomeration. Then, we identified the changes in the leading industries and industrial structure of different cities in BTH urban agglomeration from 2013 to 2018. The results indicate that the coordinated development policy has influenced the industrial structure of the BTH urban agglomeration, with an 85.53% increase in the number of legal entities and a 14.61% increase in operating income. Beijing’s non-capital functions have achieved initial results, mainly involving technology-intensive and knowledge-intensive tertiary industries such as information technology, finance, and scientific research. The division of industry and the development positioning of the three regions are gradually becoming clear. Our results show how the economic census data and spatial analysis can support significant advances in evaluating industrial and economic development patterns, and they can be used worldwide in the future.
Ecological and Economic Effects of Applying the Future Agricultural Production Structure Model (FAPSMS): The Case Study of the Barička River Basin
Authors: Tričković Nikola , Rončević Vukašin , Živanović Nikola , Grujić Tara , Stefanović Luka , Jovanović Nikola , Zlatić Miodrag
Published: May 2023

It is necessary to harmonize the needs of society in terms of agricultural production and land protection from various forms of degradation throughout sustainable land management. Assessing the justification of investment in sustainable management of land resources is an important step in this process. Consequently, an analysis of soil erosion risk was carried out in the suburban area of the morphological unit of the Barička river watershed, using the Revised Universal Soil Loss Equation (RUSLE) method, with the existing and projected structure of agricultural production according to the Future Agricultural Production Structure Model from the Aspect of Preserving Land Resources for Mountain Catchment Areas of Serbia (FAPSMS). The value of the existing and projected production structure from an economic aspect was also examined using dynamic economic methods. In order to assess the risk and uncertainty of investments, a sensitive analysis of dynamic methods was carried out. The results show that soil erosion losses are already below tolerance values with the existing production structure and that they could be reduced even more by applying the designed structure. Economic indicators show that the investment is justified and that it is more sensitive to changes in income.
Impact of China Railway Express on Regional Resource Mismatch—Empirical Evidence from China
Authors: He Ling-Yun , Liu Sha
Published: May 2023

Based on the perspective of regional resource mismatch, we explore the impact of the China Railway Express (CRE) opening by applying the difference-in-difference method (DID). The study found that the CRE has aggravated the degree of regional resource misallocation, and this conclusion was still valid after robustness tests. The heterogeneity analysis shows that the role of CRE in the western region and large-scale urban resource mismatch is more obvious. In terms of impact mechanism, the large subsidies and disorderly competition of local governments have a negative impact on the market-oriented normal operation of CRE, which is an important mechanism to aggravate the degree of regional resource mismatch. Therefore, in order to alleviate the policy impact of CRE on regional resource misallocation and improve the quality of the development of the CRE, the state, local governments, enterprises, and other forces should work together to establish a benign interaction between the government and the market.
Optimal Parking Path Planning and Parking Space Selection Based on the Entropy Power Method and Bayesian Network: A Case Study in an Indoor Parking Lot
Authors: Xue Jingwei , Wang Jiaqing , Yi Jiyang , Wei Yang , Huang Kaijian , Ge Daming , Sun Ruiyu
Published: May 2023

According to the vehicle dynamics model and the requirements of reliable safety and minimal time, the path planning problem of parking in different types of parking spaces is solved by obstacle avoidance analysis and motion analysis in the case of the optimal solution, and the parking trajectory from the initial position to the designated parking space is obtained. In the static situation, different parking spaces in the parking space are occupied; analyze the parking space type, parking space left and right occupancy situation, and the distance between the vacant parking space and the starting point location of unoccupied cars; and establish the attribute information matrix R0 of the vacant parking space and calculate the KMO value of the matrix R0. This is completed to determine the weak correlation between the attributes of the vacant parking space and use the matrix R0 as the original evaluation matrix of the entropy weight method, using the entropy weight method to calculate the three attributes of parking space type, parking space left and right occupancy situation, and distance between starting point and parking space. These results are weighted in the optimal parking space selection process, the difficulty score of the vacant parking space is determined, and the optimal parking space is determined through the ranking of the scores. In the dynamic case, the number of parking spaces and parking space usage will change over time, with the help of the Bayesian network, the existing parking spaces and number of spaces in the parking lot at the previous moment are learned according to the computer clock, which can be used to reason about the number of parking spaces and parking space availability in the parking lot at the next moment. The weights of the three attributes of parking space type, parking space left and right situation, and distance between the starting point and parking space are updated in the case of a dynamic change of parking space, and then the parking difficulty score of a new vacant parking space using the entropy weight method is used to select the optimal parking space in the dynamic situation. The optimized parking path planning and parking space selection method could contribute to enhancing parking efficiency for the sustainable management of indoor parking lots.
Impact of Elements of Finance and Business Support on the SME Business Ecosystem in South Africa: An Econometric Analysis
Authors: Ogujiuba Kanayo K. , Eggink Maria , Olamide Ebenezer
Published: May 2023

Studies conducted in South Africa have revealed that despite SMEs’ contributions to economic growth, financing is still a problem, and business supports such as management and business development training and the creation of business plans are additional obstacles to their expansion and survival. This article specifically investigates the combined effects of financing and business support on the business environment of South African SMEs. We calculated a necessary sample size of 1937 using a sample size calculator, a margin of error of 2%, a confidence level of 95%, and a response rate of 20%. As a result, 2200 questionnaires were distributed, the target population was split into 3 strata, and then samples were chosen for the survey from each stratum. The SPSS version 25.0 was employed to analyze the data in three parts, namely, descriptive, correlation, and the outcome of the regression (coefficients/model summary/ANOVA). The level of significance was determined at 5%. The test–re-test reliability method (trustworthiness assessment of the questionnaire) and Cronbach Alpha test (internal constancy) resulted in values of 0.70 and 0.875, respectively, which were regarded as satisfactory. The SME business ecosystem in South Africa was positively and significantly impacted by each of these financial and business support components. The findings also give policymakers vital information about the need for more research into how money and business assistance interact throughout the business ecosystem. It is advised that the South African government intensify its current policies and programs on SMEs’ access to financing, particularly in the areas of grants, equity financing, and loan financing. Additionally, it is necessary to build an adequate information system that ensures unrestricted information flow to rural areas.
Identification of Gendered Trait Preferences among Rice Producers Using the G+ Breeding Tools: Implications for Rice Improvement in Ghana
Authors: Frimpong Benedicta Nsiah , Asante Bright Owusu , Asante Maxwell Darko , Ayeh Stephen John , Sakyiamah Bernard , Nchanji Eileen , Mujawamariya Gaudiose , Zenna Negussie , Tufan Hale
Published: May 2023

Rice is the main staple for more than half of the world’s population. In Ghana, rice is the fastest growing food commodity, and it is consumed by almost every household. However, yields continue to be low, as the pace of adoption of new varieties is low. The low rate of adoption has been attributed to failure of modern breeding to incorporate preferred traits for end users. This study thus employed an innovative set of breeding tools, the G+ tools, in identifying gendered trait preferences to develop a robust product profile through a mixed-method approach. The assertion that “men focus more on production and marketing related traits as women focus on production and cooking qualities” was also ascertained. Descriptive, inferential and content analyses were conducted, and the results indicate ecological differences in varietal choices. Production and marketing traits are jointly preferred by the gender groups. However, women and young women paid attention to post-harvest and cooking quality traits. The gender impact scores generated indicated there are tradeoffs in the traits preferred. These findings highlight the significance of recognizing geographical differences and gender heterogeneity in relation to varietal and trait preferences. In conclusion, the outcomes emphasize the necessity of gender-sensitive breeding work that considers the various needs and trait priorities of targeted men and women rice farmers in breeding decisions for a robust rice product profile.