bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring Uncertainty Analysis of the New Leveling Staff Calibration System
Authors: Baričević Sergej , Staroveški Tomislav , Barković Đuro , Zrinjski Mladen
Published: July 2023

Besides precise levels, precise leveling staffs are a crucial part of the measuring equipment when carrying out geodetic (geometric) leveling measurements. The leveling staffs define the scale of the height reference system, so it is important to calibrate them periodically and when necessary. This paper shortly describes the development of the new method of calibrating leveling staffs in the Laboratory for Measurements and Measuring Technique of the Faculty of Geodesy, University of Zagreb. The existing horizontal comparator was upgraded by installing a servo-motorized positioning drive with a mounted CCD camera and telecentric lens that is used to record graduations of the leveling staffs. The software was developed to support the management of the comparator system, as well as for the analysis and processing of images and measurement data and, most importantly, giving the result in the form of a calibration report. The main subject of this paper is a detailed assessment of the measurement uncertainty of determining the position of the edges of the graduation lines and determining the scale of precise centimeter and coded leveling staffs. The estimates were confirmed by experimental measurements.
Generative Deep Learning-Based Thermographic Inspection of Artwork
Authors: Liu Yi , Wang Fumin , Jiang Zhili , Sfarra Stefano , Liu Kaixin , Yao Yuan
Published: July 2023

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.
Use of Different Types of Magnetic Field Sensors in Diagnosing the State of Ferromagnetic Elements Based on Residual Magnetic Field Measurements
Authors: Roskosz Maciej , Mazurek Paweł , Kwaśniewski Jerzy , Wu Jianbo
Published: July 2023

The early identification of micro-defects in ferromagnetic elements such as steel wire ropes significantly impacts structures’ in-service reliability and safety. This work investigated the possibility of detecting mechanically introduced discontinuities using different magnetic sensors without magnetization of the tested object with a strong external field. This is called the passive magnetic testing method, and it is becoming increasingly popular. This research used differential sensors (measuring differences in field values at the nanotesla level) and absolute sensors (enabling the measurement of the magnetic field vector module or its components at the microtesla level). Each measurement result obtained from the sensors allowed for detecting discontinuities in the line. The problem to be solved is the quantitative identification of changes in the metallic cross-section of a rope.
Soft Sensor Modeling for 3D Transient Temperature Field of Large-Scale Aluminum Alloy Workpieces Based on Multi-Loss Consistency Optimization PINN
Authors: Shen Ling , Chen Zhipeng , Wang Xinyi , He Jianjun
Published: July 2023

Uniform temperature distribution during quenching thermal treatment is crucial for achieving exceptional mechanical and physical properties of alloy materials. Accurate and rapid prediction of the 3D transient temperature field model of large-scale aluminum alloy workpieces is key to realizing effective thermal treatment. This paper establishes a 3D transient temperature field model of large aluminum alloy workpieces and proposes a multi-loss consistency optimization-based physics-informed neural network (MCO-PINN) to realize soft sensing of the 3D temperature field model. The method is based on a MLP structure and adopts Gaussian activation functions. A surrogate model of the partial differential equation (PDE) is first constructed, and the residuals of the PDE, initial and boundary conditions, and observed data are encoded into the loss functions of the network. By establishing a Gaussian probability distribution model of each loss function and combining it with maximum likelihood estimation, the weight consistency optimization method of each loss function is then proposed to further improve the approximation ability of the model. To optimize the training speed of the network, an adaptive initial-value-eigenvector coding clustering (AIV-ECC) algorithm is finally proposed, which quickly determines the parameters of the Gaussian activation function, reduces the dependence on the initial value and improves the generalization performance of the network. Simulation and industrial experiments demonstrate that the proposed MCO-PINN can solve the 3D transient temperature field model with high precision and high time efficiency based on sparse measurements.
Multi-Object Tracking on SWIR Images for City Surveillance in an Edge-Computing Environment
Authors: Park Jihun , Hong Jinseok , Shim Wooil , Jung Dae-Jin
Published: July 2023

Although Short-Wave Infrared (SWIR) sensors have advantages in terms of robustness in bad weather and low-light conditions, the SWIR images have not been well studied for automated object detection and tracking systems. The majority of previous multi-object tracking studies have focused on pedestrian tracking in visible-spectrum images, but tracking different types of vehicles is also important in city-surveillance scenarios. In addition, the previous studies were based on high-computing-power environments such as GPU workstations or servers, but edge computing should be considered to reduce network bandwidth usage and privacy concerns in city-surveillance scenarios. In this paper, we propose a fast and effective multi-object tracking method, called Multi-Class Distance-based Tracking (MCDTrack), on SWIR images of city-surveillance scenarios in a low-power and low-computation edge-computing environment. Eight-bit integer quantized object detection models are used, and simple distance and IoU-based similarity scores are employed to realize effective multi-object tracking in an edge-computing environment. Our MCDTrack is not only superior to previous multi-object tracking methods but also shows high tracking accuracy of 77.5% MOTA and 80.2% IDF1 although the object detection and tracking are performed on the edge-computing device. Our study results indicate that a robust city-surveillance solution can be developed based on the edge-computing environment and low-frame-rate SWIR images.
Creation of Artificial Luciferase 60s from Sequential Insights and Their Applications to Bioassays
Authors: Kim Sung-Bae , Furuta Tadaomi , Kitada Nobuo , Maki Shojiro A.
Published: July 2023

In this study, a series of new artificial luciferases (ALucs) was created using sequential insights on missing peptide blocks, which were revealed using the alignment of existing ALuc sequences. Through compensating for the missing peptide blocks in the alignment, 10 sibling sequences were artificially fabricated and named from ALuc55 to ALuc68. The phylogenetic analysis showed that the new ALucs formed an independent branch that was genetically isolated from other natural marine luciferases. The new ALucs successfully survived and luminesced with native coelenterazine (nCTZ) and its analogs in living mammalian cells. The results showed that the bioluminescence (BL) intensities of the ALucs were interestingly proportional to the length of the appended peptide blocks. The computational modeling revealed that the appended peptide blocks created a flexible region near the active site, potentially modulating the enzymatic activities. The new ALucs generated various colors with maximally approximately 90 nm redshifted BL spectra in orange upon reaction with the authors’ previously reported 1- and 2-series coelenterazine analogs. The utilities of the new ALucs in bioassays were demonstrated through the construction of single-chain molecular strain probes and protein fragment complementation assay (PCA) probes. The success of this study can guide new insights into how we can engineer and functionalize marine luciferases to expand the toolbox of optical readouts for bioassays and molecular imaging.
MFAFNet: A Lightweight and Efficient Network with Multi-Level Feature Adaptive Fusion for Real-Time Semantic Segmentation
Authors: Lu Kai , Cheng Jieren , Li Hua , Ouyang Tianyu
Published: July 2023

Currently, real-time semantic segmentation networks are intensely demanded in resource-constrained practical applications, such as mobile devices, drones and autonomous driving systems. However, most of the current popular approaches have difficulty in obtaining sufficiently large receptive fields, and they sacrifice low-level details to improve inference speed, leading to decreased segmentation accuracy. In this paper, a lightweight and efficient multi-level feature adaptive fusion network (MFAFNet) is proposed to address this problem. Specifically, we design a separable asymmetric reinforcement non-bottleneck module, which designs a parallel structure to extract short- and long-range contextual information and use optimized convolution to increase the inference speed. In addition, we propose a feature adaptive fusion module that effectively balances feature maps with multiple resolutions to reduce the loss of spatial detail information. We evaluate our model with state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Without any pre-training and post-processing, our MFAFNet has only 1.27 M parameters, while achieving accuracies of 75.9% and 69.9% mean IoU with speeds of 60.1 and 82.6 FPS on the Cityscapes and Camvid test sets, respectively. The experimental results demonstrate that the proposed method achieves an excellent trade-off between inference speed, segmentation accuracy and model size.
Preliminary Design and On-Site Testing Methodology of Roof-Cutting for Entry Retaining in Underground Coal Mine
Authors: Chen Ying , Zhang Zikai , Bao Shiji , Yang Hongtao , Shi Mingzhe , Cao Chen
Published: July 2023

Entry retaining via roof cutting is a new longwall mining method that has emerged in recent years, and is characterized by high resource utilization and environmental friendliness. Due to the complexity of this method, a field study is commonly employed for process optimization. Roof blasting is a key operation for retaining the entry, and the current practice involves dynamically adjusting blasting parameters through on-site testing and postblasting monitoring. However, the existing literature lacks detailed descriptions of blasting operations, making it difficult for field engineers to replicate the results. In this study, based on a roof cutting project for entry retaining, a preliminary design of blasting parameters is made based on theories and on-site geological conditions. The on-site test methods and equipment for roof-cutting blasting are described in detail, and the fractural patterns under different blasting parameters are analyzed. After the retreat of the working face, the state of roof caving in the goaf is analyzed based on monitoring data, and the effectiveness of top cutting is evaluated through reverse analysis, leading to dynamic adjustments of the blasting parameters. This research provides a reproducible construction method for roof-cutting operations and establishes the relationship between blasting parameters and post-mining monitoring data. It contributes to the development of fundamental theories and systematic technical systems for entry retaining via roof cutting, offering high-quality case studies for similar geological engineering projects.
An Interactive Image Segmentation Method Based on Multi-Level Semantic Fusion
Authors: Zou Ruirui , Wang Qinghui , Wen Falin , Chen Yang , Liu Jiale , Du Shaoyi , Yuan Chengzhi
Published: July 2023

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential applications in various fields, including medical imaging and robotics. Its compatibility with other machine learning methods for visual semantic analysis allows for integration into existing workflows. These aspects emphasize the significance of our contributions in advancing interactive image-segmentation techniques and their practical utility in real-world applications.
Evaluation of the Vibration Signal during Milling Vertical Thin-Walled Structures from Aerospace Materials
Authors: Kurpiel Szymon , Zagórski Krzysztof , Cieślik Jacek , Skrzypkowski Krzysztof , Brostow Witold
Published: July 2023

The main functions of thin-walled structures—widely used in several industries—are to reduce the weight of the finished product and to increase the rigidity of the structure. A popular method for machining such components, often with complex shapes, is using milling. However, milling involves undesirable phenomena. One of them is the occurrence of vibrations caused by the operation of moving parts. Vibrations strongly affect surface quality and also have a significant impact on tool wear. Cutting parameters, machining strategies and tools used in milling constitute some of the factors that influence the occurrence of vibrations. An additional difficulty in milling thin-walled structures is the reduced rigidity of the workpiece—which also affects vibration during machining. We have compared the vibration signal for different approaches to machining thin-walled components with vertical walls made of Ti6Al4V titanium alloy and Inconel 625 nickel alloy. A general-purpose cutting tool for machining any type of material was used along with tools for high-performance machining and high-speed machining adapted for titanium and nickel alloys. A comparison of results was made for a constant material removal rate. The Short-Time Fourier Transform (STFT) method provided the acceleration vibration spectrograms for individual samples.
Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements
Authors: Wang Shen , Li Yinya , Qi Guoqing , Sheng Andong
Published: July 2023

This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n (n≥2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor–target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach.
Training Specificity in Trail Running: A Single-Arm Trial on the Influence of Weighted Vest on Power and Kinematics in Trained Trail Runners
Authors: Cartón-Llorente Antonio , Rubio-Peirotén Alberto , Cardiel-Sánchez Silvia , Roche-Seruendo Luis E. , Jaén-Carrillo Diego
Published: July 2023

Participants in trail running races must carry their equipment throughout the race. This additional load modifies running biomechanics. Novel running powermeters allow further analyses of key running metrics. This study aims to determine the acute effects of running with extra weights on running power generation and running kinematics at submaximal speed. Fifteen male amateur trail runners completed three treadmill running sessions with a weighted vest of 0-, 5-, or 10% of their body mass (BM), at 8, 10, 12, and 14 km·h−1. Mean power output (MPO), leg spring stiffness (LSS), ground contact time (GCT), flight time (FT), step frequency (SF), step length (SL), vertical oscillation (VO), and duty factor (DF) were estimated with the Stryd wearable system. The one-way ANOVA revealed higher GCT and MPO and lower DF, VO, and FT for the +10% BM compared to the two other conditions (p < 0.001) for the running speeds evaluated (ES: 0.2–7.0). After post-hoc testing, LSS resulted to be higher for +5% BM than for the +10% and +0% BM conditions (ES: 0.2 and 0.4). Running with lighter loads (i.e., +5% BM) takes the principle of specificity in trail running one step further, enhancing running power generation and LSS.
Ectopic Expression of AtYUC8 Driven by GL2 and TT12 Promoters Affects the Vegetative Growth of Arabidopsis
Authors: Tan Chao , Song Jia , Zhang Tan , Liang Mengxiao , Li Suxin , Liu Huabin , Men Shuzhen
Published: July 2023

Auxin plays an essential role in regulating Arabidopsis growth and development. YUCCA (YUC) family genes encode flavin monooxygenases, which are rate-limiting enzymes in the auxin biosynthetic pathway. Previous studies showed that YUC8 overexpression (YUC8 OE), as well as ectopic expression of YUC8 driven by GL2 (GLABRA 2) and TT12 (TRANSPARENT TESTA 12) promoters, which are specifically expressed in the epidermis and inner seed integument, respectively, produced larger seeds compared to the Col. However, the impact of these transgenic lines on the vegetative growth of Arabidopsis remains unclear. Here, we show that the GL2pro:YUC8-GFP and TT12pro:YUC8-GFP transgenic plants produce a moderate excessive auxin accumulation phenotype compared to the YUC8 OE. These two transgenic lines produced smaller rosette and leaf, higher plant height, fewer branches, and longer siliques. These data will provide a basis for the study of the relationship between the ectopic expression of auxin synthesis genes and crop yield.
Design Strategy for Performance Enhancement of Vertical Plate Microdistillators
Authors: Muranaka Yosuke , Maki Taisuke , Nishigaya Kosuke , Mae Kazuhiro
Published: July 2023

Microdevices have been actively implemented in chemical processes, such as in mixing and reactions. However, microseparation devices, excluding extraction devices, are still under development. In distillation, the use of microdevices has been expected to improve separation performance, as their large specific surface area enables a rapid vapor–liquid equilibrium and for large temperature gradients to be easily realized. In this study, improvements in throughput and product purities in microdistillation devices were achieved for ethyl acetate–toluene distillation. At low feedstock flow rates, ethyl acetate was successfully purified to 99.5 wt%. Although the performance decreased with increasing feedstock flow rate, by increasing the channel length, this performance decrease was suppressed even at high flow rates. The thickness of the channel was also important, and the highest performance was observed at the lowest thickness of 0.5 mm. A performance evaluation using the HETP showed that the efficiency was seven times higher than that of conventional packed column distillators.
Fluorinated Poly(ionic liquid)s Coated Superhydrophobic Functional Materials with Efficient Oil/Water Separation Performance
Authors: Shen Fumin , Cheng Xuna , Yao Shunyang , Pei Yuanchao
Published: July 2023

There is an urgent need to develop new and improved oil-water separation materials with high stability and reusability for the cleanup of oily environmental pollutants. Here, fluorinated poly(ionic liquid)s were synthesized and their structure and property were characterized by nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy and thermogravimetric analysis. These fluorinated poly(ionic liquid)s were proposed as superhydrophobic coating on different metallic substrates through the combination of tethering fluorine groups in the PIL’s cation and anion exchange, and the superhydrophobic coating showed compactly stacked morphology under scanning electron microscope. The results of surface wettability experiments indicated that nearly all the fabricated materials showed a water contact angle larger than 150°, which is devoted to superhydrophobic nature. Moreover, for longer alkyl chain ILs and materials with smaller pore sizes, the water contact angle can be increased. At the same time, the fabricated superhydrophobic material exhibits a relatively high oil phase permeate flux, benefiting from the loose fibrous structure. Take the PIL@SSM300 for instance, the permeate fluxes were reached as high as 374,370 L·m−2·h−1, 337,200 L·m−2·h−1 and 302,013 L·m−2·h−1 for petroleum ether, hexane and cyclohexane, respectively. Instead, water is effectively repelled from the superhydrophobic surface. These virtues make the fabricated superhydrophobic material an effective membrane for oil/water separation under gravity. The separation efficiency and water contact angle are nearly unaffected after at least 20 cycles, confirming the excellent robustness of the coatings. These efficient poly(ionic liquid)s-based superhydrophobic materials possessed the potential to be used for oil/water separation.
Smart City Assessment in Developing Economies: A Scoping Review
Authors: Lacson Julius Jay , Lidasan Hussein Sinsuat , Spay Putri Ayuningtyas Vidya , Feliscuzo Larmie , Malongo Johann Heinrich , Lactuan Nove Joshua , Bokingkito Paul , Velasco Lemuel Clark
Published: July 2023

There are limited research articles that focus on smart city assessment (SCA) applications as it is a relatively new field of research and practice. However, numerous studies have been conducted and published to date, particularly in developing countries, with the broad objective of building theoretical frameworks that are centered on smart city assessments. This study aimed to systematically examine the available literature on SCA, particularly in the context of developing economies, and provide valuable insights for the various stakeholders involved in smart city projects. The specific objectives of the study were to synthesize the existing literature on smart city assessment in developing economies, analyze the frameworks employed for smart city assessment, and identify critical gaps in these frameworks while providing recommendations for future research. The methodology employed involved a scoping review procedure, and the data that were collected and analyzed were specific to developing economies. The findings revealed that SCA often incorporates other research methods, such as mixed and quantitative analyses, and embraces a multidisciplinary approach that encompasses various subject areas. While social science emerged as a prominent subject area, sustainability, renewable energy, and industrial development also play crucial roles in smart city assessments. This study highlighted that ISO 37122:2019 is the most widely adopted framework due to its structured methodology, ability to measure progress over time, and potential for benchmarking against other cities. However, it is important to consider that each framework has its own strengths and weaknesses, and cities may opt to utilize multiple frameworks or tailor them to their specific needs. Our paper concludes by emphasizing the significance of this research in providing comprehensive insights into smart city assessment in developing economies and the need for further studies to address the identified gaps and enhance future assessments.
Machine Learning Methods for Woody Volume Prediction in Eucalyptus
Authors: Santana Dthenifer Cordeiro , Santos Regimar Garcia dos , da Silva Pedro Henrique Neves , Pistori Hemerson , Teodoro Larissa Pereira Ribeiro , Poersch Nerison Luis , de Azevedo Gileno Brito , de Oliveira Sousa Azevedo Glauce Taís , da Silva Junior Carlos Antonio , Teodoro Paulo Eduardo
Published: July 2023

Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species.
Power-Efficient Design of Large-Aperture Magnets for High-Energy Physics
Authors: Centanni Daniele , Davino Daniele , de Magistris Massimiliano , Fresa Raffaele , Loschiavo Vincenzo Paolo , Quercia Antonio , Scalera Valentino
Published: July 2023

A novel and sustainability-oriented approach to the design of large-aperture iron-dominated magnets is proposed, focusing on its application to charged particle momentum detection in high-energy experimental physics. As compared to classical design techniques, a broader number of goals and constraints is taken into account, considering jointly the detection performance, the minimization of both the electrical power and magnet size, and the electromagnetic efficiency. A case study is considered for the detector magnet of a specific experiment, where the optimal design is pursued with semi-analytical tools, duly introducing the main quantities’ scaling laws in analytical form and successively validating the results with 3D numerical tools. A solution at higher energy efficiency is obtained, as compared to a more traditional design point of view. The proposed methodology can be fruitfully employed also in the design of magnets with a reduced ecological footprint in a number of other industrial and medical applications.
Climate Change and Water Scarcity at the Focus of Environmental Impacts Associated with the COVID-19 Crisis in Spain
Authors: Cambeses-Franco Cristina , Urdaneta Humberto José , Feijoo Gumersindo , Moreira María Teresa , González-García Sara
Published: July 2023

The conditions of social distancing and home confinement imposed to contain the outbreak of the severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic have changed the dietary patterns and lifestyle of society. These changes have had significant consequences not only for our personal well-being but also for the health of the planet. In this context, the aim of this study was to describe the effect of household containment during the COVID-19 pandemic on greenhouse gas (GHG) emissions related to food and water consumption. In addition, the dietary pattern of the Spanish population during home confinement was compared with the usual dietary pattern of consumption for the immediately preceding reference year (2019) to elucidate in depth the environmental consequences derived from food consumption due to COVID-19 restrictions. Life Cycle Assessment (LCA) and Water Footprint Assessment (WFA) methodologies were used to assess carbon footprint and water footprint. The functional unit of reference was the average daily food intake per capita. The results showed that COVID-19 home confinement had a negative impact on the carbon footprint (CF) and water footprint (WF) of the Spanish dietary pattern mainly due to the increase in the amount of food consumed during 2020. The monthly analysis revealed two peaks in April and October for both environmental indicators in 2020 compared to the same period in 2019, which was mainly due to the increase in food consumption in Spanish households during the weeks of confinement as well as to the new COVID-19 wave in October. On the other hand, an upward trend in GHG emissions and water consumption could be observed in the last months of both years, which is attributed to increased consumption during the Christmas holidays. These findings indicate that people should be more aware than ever of the importance of maintaining a healthy lifestyle and dietary pattern, in line with health guidelines and the Sustainable Development Goals.
Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation
Authors: Muhammed Eslam , El-Shazly Adel , Morsy Salem
Published: July 2023

Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m2 and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO2 by an annual average value of 62%.
Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model
Authors: Yang Jinrong , Wu Yichun , Liu Xingyang
Published: July 2023

Research on the power prediction of proton exchange membrane fuel cells (PEMFCs) has garnered considerable attention. Because mainstream computational-fluid-dynamics-based methods are time-consuming, this study aimed to design a data-driven method based on Ridge regression (Ridge) and convolutional neural network (CNN) algorithms that can efficiently predict PEMFC power under uncertain conditions in real-world scenarios and reduce the time consumption. The measured data from a PEMFC test bench (3 kW) were collected as the data source for the model. First, we adopted Ridge to eliminate abnormal samples. Second, we analyzed and selected the variables that have a significant effect on PEMFC power. Moreover, we optimized the model using batch normalization, dropout, Nadam, Swish, and Huber techniques. Finally, the performance of the model was evaluated by combining real datasets and real polarization curves. The experimental results demonstrate that the polarization curves predicted by the CNN-based model agree with the real curves, with a prediction accuracy of approximately 0.96, a prediction time of 1 μs, and an iteration period of less than 1 s per cycle. A comparative analysis shows that the CNN-based model prediction precision was superior to that of other mainstream machine learning algorithms. In real scenarios, the CNN-based model accurately predicts the power of PEMFC.
Assessing Water Sustainability in Northwest China: Analysis of Water Quantity, Water Quality, Socio-Economic Development and Policy Impacts
Authors: Dai Dan , Alamanos Angelos , Cai Wenqian , Sun Qingqing , Ren Liangsuo
Published: July 2023

Northwest China (NWC) is one of the driest areas of the world. Over the past decades, NWC has experienced rapid socio-economic development, further stressing its freshwater quantity and quality. However, there is little knowledge on the long-term status of NWC’s water resources and the anthropogenic impacts—positive (environmental policies) or negative (uncontrolled development). We present a holistic spatiotemporal assessment of NWC’s water quantity, water scarcity, and water quality based on water use intensity (WUI), water scarcity index (WSI), and statistical analyses and tests, combining multiple datasets spanning the past two decades. Moreover, we analyze the impacts of socio-economic development on water resources and mention the relevant governmental efforts and policies to preserve NWC’s water resources. NWC’s water use was found to be unsustainable, having significantly increased by 10% over the past two decades, but without being able to adequately cover the needs of most sectors. Our results also reveal water scarcity inequalities among NWC’s provinces; perennial water scarcity exists in Xinjiang and Ningxia Provinces, and there is no water stress in Qinghai. A remarkable wastewater treatment rate (from 27.3% in 2003 to 97.1% in 2020) and river water quality improvement have been achieved under continuous efforts, huge restoration and water pollution control investments. However, water shortages are a persistent issue. Balancing the water availability and demand will be crucial to achieve a truly sustainable development.
A Unique Strategy for Improving Facility Layout: An Introduction of The Origin Algorithm
Authors: Nordin Nurul Nadia , Ab Razak Ruzanna , Marthandan Govindan
Published: July 2023

The Facility Layout Problem (FLP) is a challenging task with significant sustainability implications. Due to its multifaceted nature, it is classified as a non-deterministic polynomial-time (NP)-hard problem, further compounded by increasing computational demands. The FLP aims to optimise the allocation of factory facilities to maximise production efficiency while minimising material handling costs. To tackle these challenges, researchers have developed a two-phase heuristic algorithm called The Origin (TO), which draws inspiration from the experiential process of the bin-packing problem. The TO algorithm considers various factors, such as operating and capacity limitations, as well as material handling costs, to optimize facility layout designs. Extensive evaluations of the TO algorithm using benchmark sizes ranging from 14 to 125 have shown that it outperforms alternative metaheuristic approaches. The TO algorithm yields more sustainable outcomes by minimising material handling costs while improving production efficiency. These results highlight the importance of addressing various constraints, especially industrial constraints, in facility layout designs and demonstrates the potential of heuristic algorithms to support these efforts.
Sustainable Development and Corporate Sustainability of Chilean State Universities: Evidence from Their Strategic Elements
Authors: Abello-Romero Juan , Mancilla Claudio , Sáez Walter , Ganga-Contreras Francisco , Durán-Seguel Ivette
Published: July 2023

Corporate sustainability evolved in line with the advancement of the concept of sustainable development; thus, it is constituted as a strategy to respond to social and environmental problems. In this context, universities are understood as complex organizations, positioned as a key mechanism for delivering the sustainable development of society. This research aimed to analyze whether the strategic elements of Chilean state universities integrate components of sustainable development. For this purpose, qualitative research was undertaken through a documentary analysis of the strategic plans of the 18 Chilean state universities, focusing analysis on their strategic elements: their mission, vision and strategic institutional objectives. The results revealed that all universities mention at least one concept associated with one of the 17 Sustainable Development Goals (SDGs) in their strategic guidelines. They mainly focused on ‘Quality Education’ (SDG 4) and ‘Build resilient infrastructure, promoting inclusive and sustainable industrialization and foster innovation’ (SDG 9). Also, the results allow us to visualize how universities address corporate sustainability issues through their strategic plans.
Physical-Mechanical and Electrical Resistivity Properties of Cementitious Mortars Containing Fe3O4-MWCNTs Nanocomposite
Authors: Selen Veyis , Guler Omer , Nodehi Mehrab , Sarı Ahmet , Yaras Ali , Gencel Osman , Gholampour Aliakbar , Ozbakkaloglu Togay
Published: July 2023

Recent growth in materials science and engineering technologies has pushed the construction industry to engage in new applications, such as the manufacturing of smart and electrically conductive products. Such novel uses of conductive construction materials would potentially allow their use in conjunction with various fields, such as those referred to as “Industry 4.0.” The following study uses iron oxide (Fe3O4)-multi-walled carbon nanotubes (MWCNTs) nanocomposites synthesized by chemical vapor deposition (CVD) and incorporated into the cementitious mortars as a substitute for sand at 1, 2, and 3% ratios to enhance the electrical conductivity. Results reveal that the electrical resistivity of cementitious composites decreases (due to the increase in electrical conductivity) from 208.3 to 61.6 Ω·m with both the Fe3O4-MWCNTs nanocomposites ratio and the increasing voltage. The lowest compressive strengths at 7 and 28 days are 12.6 and 17.4 MPa for specimens with 3% Fe3O4-MWCNTs and meet the standards that comply with most applications. On the other hand, the highest porosity was reached at 26.8% with a Fe3O4-MWCNTs rate of 3%. This increase in porosity caused a decrease in both the dry unit weight and ultrasonic pulse velocity (from 5156 to 4361 m/s). Further, it is found that the incorporation of Fe3O4-MWCNT nanocomposites can have a negative effect on the hardening process of mortars, leading to localized air cavities and an inhomogeneous development of cementing products. Nonetheless, the improvement of the electrical conductivity of the samples without significantly compromising their physico-mechanical properties will allow their use in various fields, such as deicing applications with low-voltage electric current.
Suitability of Various Parameters for the Determination of the Condition of Soil Structure with Dependence to the Quantity and Quality of Soil Organic Matter
Authors: Tobiašová Erika , Lemanowicz Joanna , Dębska Bożena , Kunkelová Martina , Sakáč Juraj
Published: July 2023

Soil structure (SS) plays an important role in relation to climatic change, with the most important task the decreasing of CO2 in the atmosphere by carbon sequestration in the soil and the prevention of floods by better water infiltration into the soil. However, the evaluation of its condition is very different because of the various parameters and their inappropriate uses. The aim of this study was to determine the responses of the parameters of SS on the soil type and tillage system as the most important factors that influence it through changes in the soil organic matter and soil texture. The soil factor, which was represented by seven soil types (EF, Eutric Fluvisol; MF, Mollic Fluvisol; HC, Haplic Chernozem; HL, Haplic Luvisol; ER, Eutric Regosol; EG, Eutric Gleysol; DS, Distric Stagnosol), should be included in all evaluations of SS because of the specifics of each soil type. The tillage factor (shallow non-inversion-reduced, RT; deeper with inversion-conventional, CT) was chosen because of a high sensitivity of SS to soil disruption by cultivation, which represents high potential for the mitigation of climate change. The study included 126 sampling places in different parts of Slovakia on real farms (7 soil types × 3 localities × 3 crop rotations × 2 tillage systems × 2 soil depths). The soils were analysed for the aggregate fraction composition, particle size distribution, and parameters of organic carbon. The data of different parameters of SS were calculated and evaluated. The most sensitive parameter of the tested ones was the coefficient of structure (Kst), which manifested up to the level of the fractions of humus substances and indicated a better condition of SS in more productive soils than less productive soils. The coefficient of soil structure vulnerability (Kv) and mean weight diameter in water-resistant macroaggregates (MWDw) showed a worse condition of SS in the soils, which developed on Neogene sediments. A better condition of SS in RT was predicted particularly by the primary parameters (index of crusting, Ic; critical content of soil organic matter, St), and in CT, they were mainly the secondary parameters (Kst; water-resistant of soil aggregates, Kw). Overall, the suitability of the parameters of SS should be evaluated in relation to a specific soil type with its characteristics and should not be used universally.
Combining Markowitz Portfolio Model and Simplex Algorithm to Achieve Sustainable Land Management Objectives: Case Study of Rivadavia Banda Norte, Salta (Argentina)
Authors: Gualotuña Parra José Alex , Valverde-Arias Omar , Tarquis Ana M. , Grau Olivé Juan B. , Speroni Federico Colombo , Saa-Requejo Antonio
Published: July 2023

Land use planning involves making an appropriate decision and selecting a use over other alternatives. A step-by-step methodology was developed to evaluate the optimal combination of regional land use technologies and the spatial allocation. For a realistic approach, a case study (specifically Rivadavia department, Salta, Argentina) is considered, which has deforestation problems and the advance of intensive and extractive agriculture. Five management techniques are considered for the area: precision agriculture (T1), advance livestock farming (T2), payment for ecosystem service (T3), traditional agriculture–livestock farming—Criollo (T4), and traditional forest management—Wichi (T5). A land evaluation on a GIS model is carried out to obtain the land suitability for each technique. Analyzing local experts’ opinions using the Markowitz portfolio methodology allows us to obtain an optimal combination of techniques. Finally, a Simplex method analysis linked with the GIS is performed to allocate the five techniques over the territory maximizing land suitability and in compliance with percent surface assignments. The result assigns each GIS polygon to a specific technique, reaching optimal land suitability in 92% of the territory. Natural capital and social attributes had a significant and complex impact on technology choice, but objective and optimized approaches in their allocation were possible and provides valuable information to guide public policies.
A New Method for Solving the Flow Shop Scheduling Problem on Symmetric Networks Using a Hybrid Nature-Inspired Algorithm
Authors: Baroud Muftah Mohamed , Eghtesad Amirali , Ahmed Mahdi Muhammed Ahmed , Bahojb Nouri Masoud Bahojb , Worya Khordehbinan Mohammad Worya , Lee Sangkeum
Published: July 2023

Recently, symmetric networks have received much attention in various applications. They are a single route for incoming and outgoing network traffic. In symmetric networks, one of the fundamental categories of wide-ranging scheduling problems with several practical applications is the FSSP. Strictly speaking, a scheduling issue is found when assigning resources to the activities to maximize goals. The difficulty of finding solutions in polynomial time makes the flow shop scheduling problem (FSSP) NP-hard. Hence, the utilization of a hybrid optimization technique, a new approach to the flow shop scheduling issue, on symmetric networks is given in the current research. In order to address this issue, each party’s strengths are maximized and their weaknesses reduced, and this study integrates the Ant Colony Algorithm with Particle Swarm Optimization (ACO-PSO). Even though these methods have been employed before, their hybrid approach improves their resilience in a variety of sectors. The ACO-PSO is put to the test by contrasting it with innovative algorithms in the literature. The search space is first filled with a variety of solutions by the algorithm. Using pheromones in the mutual region, the ACO algorithm locally controls mobility. Moreover, the PSO-based random interaction among the solutions yields the global maximum. The PSO’s random interaction among the solutions typically results in the global maximum. The computational research demonstrates that the recommended ACO-PSO method outperforms the existing ones by a large margin. The Friedman test also shows that the average algorithm ranks for ACO and PSO are 1.79 and 2.08, respectively. The proposed method has an average rank of 2.13 as well. It indicates that the suggested algorithm’s effectiveness increased.