bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN
Authors: Pan Yuyun , Zhu Nengzhi , Ding Lu , Li Xiuhua , Goh Hui-Hwang , Han Chao , Zhang Muqing
Published: November 2022

Sugarcane seedling emergence is important for sugar production. Manual counting is time-consuming and hardly practicable for large-scale field planting. Unmanned aerial vehicles (UAVs) with fast acquisition speed and wide coverage are becoming increasingly popular in precision agriculture. We provide a method based on improved Faster RCNN for automatically detecting and counting sugarcane seedlings using aerial photography. The Sugarcane-Detector (SGN-D) uses ResNet 50 for feature extraction to produce high-resolution feature expressions and provides an attention method (SN-block) to focus the network on learning seedling feature channels. FPN aggregates multi-level features to tackle multi-scale problems, while optimizing anchor boxes for sugarcane size and quantity. To evaluate the efficacy and viability of the proposed technology, 238 images of sugarcane seedlings were taken from the air with an unmanned aerial vehicle. Outcoming with an average accuracy of 93.67%, our proposed method outperforms other commonly used detection models, including the original Faster R-CNN, SSD, and YOLO. In order to eliminate the error caused by repeated counting, we further propose a seedlings de-duplication algorithm. The highest counting accuracy reached 96.83%, whilst the mean absolute error (MAE) reached 4.6 when intersection of union (IoU) was 0.15. In addition, a software system was developed for the automatic identification and counting of cane seedlings. This work can provide accurate seedling data, thus can support farmers making proper cultivation management decision.
Acoustic Emission Source Characterisation during Fatigue Crack Growth in Al 2024-T3 Specimens
Authors: Yao Xinyue , Vien Benjamin Steven , Davies Chris , Chiu Wing Kong
Published: November 2022

While acoustic emission (AE) testing can be used as a valuable technique in structural health monitoring and non-destructive testing, little research has been conducted to establish its sources, particularly in 2024-T3 aluminium alloys. The major contribution of this work is that it provides a method to obtain a better linear relationship of count rate with crack growth rate based on waveform. This paper aims to characterise AE sources by synchronising the AE waveforms with load levels and then to propose possible dominant frequency ranges. The AE waveforms during fatigue crack growth in edge-notched 2024-T3 aluminium specimens, from an initial crack length of 10 mm to 70 mm, were collected at two different load ratios R = 0.125 and 0.5. At the same time, the crack growth rate was determined using thermal imaging and associated control software. The AE waveforms obtained were processed using the fast Fourier transform. It was shown that a significantly higher AE count rate was recorded at R = 0.125 compared to R = 0.5 when the maximum load was kept the same. This means that the R-ratio would affect the total amount of AE activities collected. It was also found that the dominant frequency range of the AE waveforms directly related to crack growth was 152–487 kHz, and the ranges due to crack closure were likely to be 310 kHz–316 kHz and 500–700 kHz. Based on the proposed frequency ranges, waveform selection was conducted and a better linear relationship between count rate and crack growth rate was observed. This study provides a better understanding of the AE sources and waveforms for future structural health monitoring applications.
Investigation of High-Resolution Distributed Fiber Sensing System Embedded in Flexible Silicone Carpet for 2D Pressure Mapping
Authors: Katrenova Zhanerke , Alisherov Shakhrizat , Abdol Turar , Yergibay Madina , Kappassov Zhanat , Tosi Daniele , Molardi Carlo
Published: November 2022

Fiber-optic sensors are a powerful tool to investigate physical properties like temperature, strain, and pressure. Such properties make these sensors interesting for many applications including biomedical applications. Fiber sensors are also a great platform for distributed sensing by using the principles of optical frequency domain reflectometry. Distributed sensing is becoming more and more used to achieve high-resolution measurements and to map physical properties of biomaterials at small scale, thus obtaining 2D and 3D mapping of a particular area of interest. This work aims at building and investigating a 2D sensing carpet based on a distributed fiber sensing technique, to map local pressure applied to the carpet. The two-dimensional mapping is obtained by embedding a single-mode optical fiber inside a soft silicone carpet. The fiber has been bent and arranged in a specific configuration characterized by several parallel lines. Different fiber fixation methods have been investigated by means of a comparative analysis to perform better characterization and to achieve a more precise response of the carpet. The best pressure sensitivity coefficient (0.373 pm/kPa or considering our setup 1.165 nm/kg) was detected when the fiber was fully embedded inside the silicone carpet. This paper demonstrates the possibility of mapping a 2D distributed pressure over a surface with a resolution of 2 mm by 2 mm. The surface of investigation is 2 cm by 6 cm, containing 310 sensing points. The sensing carpet has been validated selecting several preferential positions, by testing the consistency of the results over different portions of the carpet.
Fatigue Performance of Type I and Type II Fibre Bragg Gratings Fabricated by Femtosecond Laser Inscription through the Coating
Authors: Zhang Naizhong , Turk Suzana , Davis Claire , Chiu Wing K. , Boilard Tommy , Bernier Martin
Published: November 2022

Strain sensing technology using fibre Bragg grating (FBG) sensors is an attractive capability for aerospace structural health monitoring (SHM) and assessment because they offer resistance to harsh environments, low maintenance, and potential for high density and high strain sensing. The development of FBG inscription techniques through the fibre polymer coating using infrared (IR) lasers has overcome the mechanical weaknesses introduced by removal of the fibre coating, which is typically required for conventional UV laser inscription of FBGs. Type I and Type II femtosecond gratings are fabricated using through-coating inscription techniques, but the higher laser energy used for Type II gratings damages the glass fibre core, impacting mechanical performance. This paper investigates the fatigue performance of Type I and Type II through-coating FBG sensors with different fibre geometries and photosensitisation approaches to evaluate their overall reliability and durability, with a view to assess their performance for potential use in civil and defence SHM applications. The fatigue performance of FBG sensors was assessed under high-strain and high-frequency mechanical loading conditions by using a custom-designed electro-dynamically actuated loading assembly. In addition, pre- and post-fatigue microscopic analyses and high-resolution reflection spectrum characterisation were conducted to investigate the failure regions of the fibres and the effect of fatigue loading on reflection spectrum features. As expected, Type I gratings had a significantly higher fatigue life compared to Type II gratings. However, Type II gratings performed significantly better than conventional UV laser-inscribed FBGs and electrical foil strain gauges. Type II gratings withstand higher temperatures, and are therefore more suitable for application in harsh environments.
3D Visible Light-Based Indoor Positioning System Using Two-Stage Neural Network (TSNN) and Received Intensity Selective Enhancement (RISE) to Alleviate Light Non-Overlap Zones
Authors: Hsu Li-Sheng , Chow Chi-Wai , Liu Yang , Yeh Chien-Hung
Published: November 2022

The high precision three-dimensional (3D) visible light-based indoor positioning (VLIP) systems have gained much attention recently for people or robot navigation, access tracking, etc. In this work, we put forward and present the first demonstration, up to the authors’ knowledge, of a 3D VLIP system utilizing a two-stage neural network (TSNN) model. The positioning performance would degrade when the distance between the light emitting diode (LED) plane and the receiver (Rx) plane increases; however, because of the finite LED field-of-view (FOV), light non-overlap zones are created. These light non-overlap zones will produce high positioning error particularly for the 3D VLIP systems. Here, we also propose and demonstrate the Received-Intensity-Selective-Enhancement scheme, known as RISE, to alleviate the light non-overlap zones in the VLIP system. In a practical test-room with dimensions of 200 × 150 × 300 cm3, the experimental results show that the mean errors in the training and testing data sets are reduced by 54.1% and 27.9% when using the TSNN model with RISE in the z-direction, and they are reduced by 39.1% and 37.8% in the xy-direction, respectively, when comparing that with using a one stage NN model only. At the cumulative distribution function (CDF) P90, the TSNN model with RISE can reduce the errors by 36.78% when compared with that in the one stage NN model.
Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review
Authors: Sánchez-Reolid Roberto , López de la Rosa Francisco , Sánchez-Reolid Daniel , López María T. , Fernández-Caballero Antonio
Published: November 2022

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
Zoom-In Neural Network Deep-Learning Model for Alzheimer’s Disease Assessments
Authors: Wang Bohyun , Lim Joon S.
Published: November 2022

Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer’s disease (AD) mild cognitive impairment (MCI), compared with normal control (NC) persons, using the zoom-in neural network (ZNN) deep-learning algorithm. ZNN stacks a set of zoom-in learning units (ZLUs) in a feedforward hierarchy without backpropagation. The resting-state fMRI (rs-fMRI) dataset for AD assessments was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The Automated Anatomical Labeling (AAL-90) atlas, which provides 90 neuroanatomical functional regions, was used to assess and detect the implicated regions in the course of AD. The features of the ZNN are extracted from the 140-time series rs-fMRI voxel values in a region of the brain. ZNN yields the three classification accuracies of AD versus MCI and NC, NC versus AD and MCI, and MCI versus AD and NC of 97.7%, 84.8%, and 72.7%, respectively, with the seven discriminative regions of interest (ROIs) in the AAL-90.
Characterization of Fluorescence Tracers for Thermometry and Film Thickness Measurements in Liquid Coolants Relevant for Thermal Management of Electric and Electronic Components
Authors: Koegl Matthias , Delwig Moritz , Zigan Lars
Published: November 2022

This study investigated a novel two-color LIF (laser-induced fluorescence) technique for thermometry in coolants relevant for electric components. In principle, this diagnostic enables thermometry in liquid flows but also a simultaneous determination of film thickness and film temperature, which is relevant, e.g., for jet impingement cooled electric components. Temperature measurements are based on a temperature-sensitive intensity ratio of special tracers realized by suitable band pass filters within the respective emission spectra. For this purpose, the heat transfer fluids Fragoltherm F12, Marlotherm LH, and a water–glycol mixture WG20 (80 vol.% water, 20 vol.% glycol) and its individual components were doped with suitable tracers. The tracer Eosin-Y was utilized for polar coolants (water, WG20, and glycol) and Nile red was utilized for non-polar solvents (Fragoltherm F12 and Marlotherm LH). The spectral LIF intensities were recorded for a wide range of temperatures (253–393 K), which are relevant for cooling of electric motors, batteries, and power electronics. Furthermore, absorption spectra were analyzed as well. The temperature-dependent fluorescence measurements revealed different behavior for the polar and non-polar solvents. A temperature increase in the polar solvents (water, WG20, glycol) led to a spectral shift of the emission peaks of Eosin-Y towards longer wavelengths (red-shifted), while the peaks of Nile red in the non-polar solvents (Fragoltherm F12 and Marlotherm LH) showed an opposite behavior and were blue-shifted. The highest average temperature sensitivity was achieved for Marlotherm LH (4.22%/K), followed by glycol (1.99%/K), WG20 (1.80%/K), water (1.62%/K), and Fragoltherm F12 (1.12%/K). These sensitivities are similar to or even much higher than the literature data of other LIF tracers, which were, however, not determined in those coolants. Consequently, the two novel proposed dyes for the studied heat transfer liquids enable a reliable temperature determination.
Enhanced Robustness of a Bridge-Type Rf-Mems Switch for Enabling Applications in 5G and 6G Communications
Authors: Casals-Terré Jasmina , Pradell Lluís , Heredia Julio César , Giacomozzi Flavio , Iannacci Jacopo , Contreras Adrián , Ribó Miquel
Published: November 2022

In this paper, new suspended-membrane double-ohmic-contact RF-MEMS switch configurations are proposed. Double-diagonal (DDG) beam suspensions, with either two or three anchoring points, are designed and optimized to minimize membrane deformation due to residual fabrication stresses, thus exhibiting smaller mechanical deformation and a higher stiffness with more release force than previously designed single diagonal beam suspensions. The two-anchor DDGs are designed in two different orientations, in-line and 90°-rotated. The membrane may include a window to minimize the coupling to the lower electrode. The devices are integrated in a coplanar-waveguide transmission structure and fabricated using an eight-mask surface-micro-machining process on high-resistivity silicon, with dielectric-free actuation electrodes, and including glass protective caps. The RF-MEMS switch behavior is assessed from measurements of the device S parameters in ON and OFF states. The fabricated devices feature a measured pull-in voltage of 76.5 V/60 V for the windowed/not-windowed two-anchor DDG membranes, and 54 V/49.5 V for the windowed/not-windowed three-anchor DDG membranes, with a good agreement with mechanical 3D simulations. The measured ON-state insertion loss is better than 0.7 dB/0.8 dB and the isolation in the OFF state is better than 40 dB/31 dB up to 20 GHz for the in-line/90°-rotated devices, also in good agreement with 2.5D electromagnetic simulations.
UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low Vision
Authors: Yang Anbang , Beheshti Mahya , Hudson Todd E. , Vedanthan Rajesh , Riewpaiboon Wachara , Mongkolwat Pattanasak , Feng Chen , Rizzo John-Ross
Published: November 2022

Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases, from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible at scale. Herein, we propose a novel vision-based localization pipeline for a specific use case: navigation support for end users with blindness and low vision. Given a query image taken by an end user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space. The geolocations of these similar images are utilized in a downstream task that employs a weighted-average method to estimate the end user’s location. Another downstream task utilizes the perspective-n-point (PnP) algorithm to estimate the end user’s direction by exploiting the 2D–3D point correspondences between the query image and the 3D environment, as extracted from matched images in the database. Additionally, this system implements Dijkstra’s algorithm to calculate a shortest path based on a navigable map that includes the trip origin and destination. The topometric map used for localization and navigation is built using a customized graphical user interface that projects a 3D reconstructed sparse map, built from a sequence of images, to the corresponding a priori 2D floor plan. Sequential images used for map construction can be collected in a pre-mapping step or scavenged through public databases/citizen science. The end-to-end system can be installed on any internet-accessible device with a camera that hosts a custom mobile application. For evaluation purposes, mapping and localization were tested in a complex hospital environment. The evaluation results demonstrate that our system can achieve localization with an average error of less than 1 m without knowledge of the camera’s intrinsic parameters, such as focal length.
Tuning the Surface Functionality of Fe3O4 for Sensitive and Selective Detection of Heavy Metal Ions
Authors: K. S. Manjunatha Kumara , Nagaraju D. H. , Yhobu Zhoveta , H. N. Nayan Kumar , Budagumpi Srinivasa , Kumar Bose Shubhankar , P. Shivakumar , Palakollu Venkata Narayana
Published: November 2022

The functionalization of materials for ultrasensitive detection of heavy metal ions (HMIs) in the environment is crucial. Herewith, we have functionalized inexpensive and environmentally friendly Fe3O4 nanoparticles with D-valine (Fe3O4–D–Val) by a simple co-precipitation synthetic approach characterized by XRD, FE-SEM, and FTIR spectroscopy. The Fe3O4–D–Val sensor was used for the ultrasensitive detection of Cd+2, Pb+2, and Cu+2 in water samples. This sensor shows a very low detection limit of 11.29, 4.59, and 20.07 nM for Cd+2, Pb+2, and Cu+2, respectively. The detection limits are much lower than the values suggested by the world health Organization. The real water samples were also analyzed using the developed sensor.
Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability—What We Know and What Is Coming
Authors: Alugubelli Navya , Abuissa Hussam , Roka Attila
Published: November 2022

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.
Evaluation of an Accelerometer-Based Device for Testing the Softness of Bedding Materials Used for Livestock
Authors: Weimar Karina Regina , Pichlbauer Barbara , Guse Christian , Schramel Johannes Peter , Peham Christian , Drillich Marc , Iwersen Michael
Published: November 2022

Lying is a high priority behavior for dairy cows. As the quality of cubicles can influence their lying time, the interest in finding objective methods to assess the quality of floors has increased substantially over recent decades. This study aimed to evaluate a technical device for measuring elastic properties of floors for the application to bedding materials for cows. Ten different floor types were used: horse manure, recycled manure solids, bark mulch, sand, sawdust, and three different rubber mats. Horse manure and bark mulch were additionally tested with chopped straw as a top layer. Two devices of the same kind and two examiners were available for performing comparative measurements. Regression analyses and an ANOVA were conducted to compare the devices, examiners, and different surfaces. Most of the floors differed significantly from each other. Sawdust was the softest material, followed by sand and recycled manure solids. The agreement between the devices (Lin’s concordance correlation coefficient (CCC) > 0.99, Spearman’s rank correlation coefficient (rS) = 0.99) and examiners (CCC = 0.99, rS = 0.99) was almost perfect. These findings indicate that this device can be used as a new method for assessing the softness of bedding materials for dairy cows objectively.
Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis
Authors: Martinez-Vega Beatriz , Tkachenko Mariia , Matkabi Marianne , Ortega Samuel , Fabelo Himar , Balea-Fernandez Francisco , La Salvia Marco , Torti Emanuele , Leporati Francesco , Callico Gustavo M. , Chalopin Claire
Published: November 2022

Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.
Maximizing Average Throughput of Cooperative Cognitive Radio Networks Based on Energy Harvesting
Authors: Wang Yaqing , Chen Shiyong , Wu Yucheng , Zhao Chengxin
Published: November 2022

Energy harvesting (EH) and cooperative communication techniques have been widely used in cognitive radio networks. However, most studies on throughput in energy-harvesting cooperative cognitive radio networks (EH-CCRNs) are end-to-end, which ignores the overall working state of the network. For the above problems, under the premise of prioritizing the communication quality of short-range users, this paper focuses on the optimization of the EH-CCRN average throughput, with energy and transmission power as constraints. The formulated problem was an unsolved non-deterministic polynomial-time hardness (NP-hard) problem. To make it tractable to solve, a multi-user time-power resource allocation algorithm (M-TPRA) is proposed, which is based on sub-gradient descent and unary linear optimization methods. Simulation results show that the M-TPRA algorithm can improve the average throughput of the network. In addition, the energy consumed by executing the M-TPRA algorithm is analyzed.
In-Line Vis-NIR Spectral Analysis for the Column Chromatographic Processes of the Ginkgo biloba L. Leaves. Part II: Batch-to-Batch Consistency Evaluation of the Elution Process
Authors: Li Wenlong , Wang Xi , Chen Houliu , Yan Xu , Qu Haibin
Published: November 2022

An in-line monitoring method for the elution process of Ginkgo biloba L. leaves using visible and near-infrared spectroscopy in conjunction with multivariate statistical process control (MSPC) was established. Experiments, including normal operating batches and abnormal ones, were designed and carried out. The MSPC model for the elution process was developed and validated. The abnormalities were detected successfully by the control charts of principal component scores, Hotelling T2, or DModX (distance to the model). The results suggested that the established method can be used for the in-line monitoring and batch-to-batch consistency evaluation of the elution process.
Disembodiment and Delusion in the Time of COVID-19
Authors: Andreescu Florentina C.
Published: November 2022

This article proposes an analytical framework that highlights embodiment’s ontological complexities and the ways in which the securitization of the body, during the COVID-19 pandemic, brought our embodied existence under the scrutiny of the invasive gaze of multiple social authorities, framing public and private modes of being as existential security risks. It engages with the research developed by psychiatrist Iain McGilchrist and clinical psychologist Louis A. Sass on schizophrenia, tracing the extent to which COVID-19 reshaped reality displays a dynamic akin to this mental disorder, through its abnegation of embodied presence, retreat into virtual register, and abnormal interpretations of reality. To spotlight this dynamic’s consequences, the article explores three interconnected features of schizophrenia, namely hyper-reflexivity, diminished self-presence, and disturbed grip on the world. These help to contextualize the ways in which a large segment of the population in the United States responded to the COVID-19 pandemic. To that end, the article highlights the development of a virtual universe of conspiracy theories, shaping a citizenry which, akin to schizophrenics are simultaneously cynical and gullible, manifesting a vehement distrust of aspects of life that need to be implicit, while readily embracing conspiratorial worldviews.
Telegram Channels and Bots: A Ranking of Media Outlets Based in Spain
Authors: Herrero-Solana Victor , Castro-Castro Carlos
Published: November 2022

Telegram, an Industry 4.0 style communication service, is one of the world’s most widespread communication platforms. The availability of channels and bots has opened as a broadcast channel for any media outlet. We asked the following questions: Do media outlets from Spain use Telegram channels? Which media outlets? Are they verified? What is their volume of subscribers? Can this information be used to rank media outlets? We identified many media channels and data were collected from each one. We present the results in a ranking. Forty-two media based in Spain have Telegram channels, 26 of which are ranked in the directory. Less than half of these channels are verified by the platform, and only three are linked to their website. This lack of verification could lead to the proliferation of fake channels. The article ends with a series of recommendations for channel managers to make it easier for the end user to identify and verify each media outlet.
The Life Opportunities of Young Refugees: Understanding the Role, Function and Perceptions of Local Stakeholders
Authors: Aydar Zeynep
Published: November 2022

The focus on the local level in migration research became common when analyzing arrival contexts. Despite the relative autonomy of the local level and its crucial position in the multi-level migration governance, there is limited research on the role, function and perspectives of local stakeholders in Germany. This paper investigates the dynamics of local actors and aims at understanding their contribution to the life opportunities of young refugees. A post-industrial city, namely Dortmund, has been used as a case study for this explorative task. Building on 20 expert interviews conducted between November 2020 and September 2021, the results show that while the migration history of the city has positive influence on stakeholder perspectives, there are concrete horizontal discrepancies between governmental and non-governmental actors. Albeit being engaged with inclusive migration measures for decades, the governmental actors are found to be limiting youth’s chances, as they are bound to the legal framework of the national and federal levels. Contrarily, the non-governmental actors are of great importance as they challenge the system of burdens and actively create further possibilities for these youths. However, the article found that it is beyond the power of non-governmental actors to eliminate structural and legal barriers. The vertical and horizontal conflicts in multi-governance system are the major barriers for this. Nonetheless, local level actors appear critical in creating further opportunities and advocating for youth; therefore, their potential operational strength should not be undervalued.
Seeking the Optimal Dimension of Local Administrative Units: A Reflection on Urban Concentration and Changes in Municipal Size
Authors: Bartolacci Francesca , Salvia Rosanna , Quaranta Giovanni , Salvati Luca
Published: November 2022

In the search for a better administrative functioning as a key dimension of economic performances, changes in municipal boundaries and the creation (or suppression) of local administrative units reflect a progressive adjustment to a spatially varying population size and density. With intense population growth, municipal size reflects the overall amount (and spatial concentration) of services and infrastructures, being functionally related with agglomeration economies, land availability for building, and specific sociodemographic attributes of local communities. Based on these premises, the intrinsic relationship between settlement expansion, population growth, and municipal size in a metropolitan region of Southern Europe (Attica, hosting the Greater Athens’ area in Central Greece) was investigated in this study over nearly one century as a contribution to a refined investigation of the (changing) organization of local administrative units under a complete metropolitan cycle from urbanization to reurbanization. Based on descriptive statistics, mapping, (parametric and nonparametric) correlation coefficients, and econometric techniques, a quantitative analysis of the relationship between population size and density and municipal area provides pivotal knowledge to policy and planning adjustments toward a more balanced spatial distribution of population and administered land among local government units. Together with a slight decrease in the average municipal size over time, the average population density per municipal unit increased systematically, with a considerable reduction in spatial heterogeneity of settlements. The observed goodness-of-fit of the linear model explaining municipal area with population density, increased significantly over time. Empirical results of our study indicate that municipal size has slowly adjusted to population density across metropolitan areas, determining an imbalanced spatial distribution of resident population and a supposedly less efficient government partition. The recent administrative reform of local authorities in Greece (the so-called ‘Kallikratis’ law) seems to consolidate–rather than rebalance this organizational structure over space, reflecting spatially polarized settlements. Such conditions represent a base for informed analysis of the spatial structure of local administrative units as a pivotal element of economic sustainability and may contribute to the debate on the optimal size of municipalities at both urban and metropolitan scales of governance.
A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)
Authors: Bhuyan Bikram Pratim , Tomar Ravi , Cherif Amar Ramdane
Published: November 2022

Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge representation and reasoning” is devoted to the representation of information about the environment in a way where a computer system can utilise it to accomplish difficult problems. This research is an extensive survey of the knowledge representation techniques used in smart agriculture, and specifically in the urban agricultural domain. Relevant articles on the knowledge base are extracted from the retrieved set to study the fulfillment of the criteria of the system. Various interesting findings were observed after the review. Spatial–temporal characteristics were rarely approached. A generalised representation technique to include all domains in agriculture is another issue. Finally, proper validation technique is found to be missing in such an ontology.
Productive and Sustainable H2 Production from Waste Aluminum Using Copper Oxides-Based Graphene Nanocatalysts: A Techno-Economic Analysis
Authors: Amrani Mokhtar Ali , Haddad Yara , Obeidat Firas , Ghaleb Atef M. , Mejjaouli Sobhi , Rahoma Ibrahim , Galil Mansour S. A. , Shameeri Mutahar , Alsofi Ahmed A. , Saif Amin
Published: November 2022

Hydrogen has universally been considered a reliable source of future clean energy. Its energy conversion, processing, transportation, and storage are techno-economically promising for sustainable energy. This study attempts to maximize the production of H2 energy using nanocatalysts from waste aluminum chips, an abundant metal that is considered a potential storage tank of H2 energy with high energy density. The present study indicates that the use of waste aluminum chips in the production of H2 gas will be free of cost since the reaction by-product, Al2O3, is denser and can be sold at a higher price than the raw materials, which makes the production cost more efficient and feasible. The current framework investigates seven different copper oxide-based graphene nanocomposites that are synthesized by utilizing green methods and that are well-characterized in terms of their structural, morphological, and surface properties. Reduced graphene oxide (rGO) and multi-layer graphene (MLG) are used as graphene substrates for CuO and Cu2O NPs, respectively. These graphene materials exhibited extraordinary catalytic activity, while their copper oxide composites exhibited a complete reaction with feasible techno-economic production. The results revealed that the H2 production yield and rates increased twofold with the use of these nanocatalysts. The present study recommends the optimum reactor design considerations and reaction parameters that minimize water vaporization in the reaction and suggests practical solutions to quantify and separate it. Furthermore, the present study affords an economic feasibility approach to producing H2 gas that is competitive and efficient. The cost of producing 1 kg of H2 gas from waste aluminum chips is USD 6.70, which is both economically feasible and technically applicable. The unit cost of H2 gas can be steeply reduced by building large-scale plants offering mass production. Finally, the predicted approach is applicable in large, medium, and small cities that can collect industrial waste aluminum in bulk to generate large-scale energy units.
Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions
Authors: Sansine Vateanui , Ortega Pascal , Hissel Daniel , Hopuare Marania
Published: November 2022

Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO) algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT). The implemented daily SI forecasts relied on an hourly time-step. The input data were composed of outputs from the numerical forecasting model AROME (Météo France) combined with historical meteorological data. Our three hybrid models were compared with other stand-alone models, namely, artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM, GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals (PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting compared with the other benchmark models, through overall deterministic and probabilistic metrics.
Research on the Algal Density Change Features of Water Bodies in Urban Parks and the Environmental Driving Factors
Authors: Zhang Yichuan , Qin Wenke , Qiao Lifang
Published: November 2022

Water bodies in urban parks are important for scenic and recreational areas, yet algal bloom problems seriously affect public use; therefore, it is important to study the features of algal density (AD) changes and environmental driving factors (EDFs) for water body management. In this study, five scenic water bodies in urban parks of Xinxiang City are taken as the objects for studying the AD and nine environmental indicators from March to October 2021, in accordance with time-series monitoring. The features of AD change in different layers of the water bodies are analyzed, and the main environmental impact factors of AD changes are screened by Pearson correlation analysis and principal components analysis (PCA), with main EDFs further extracted according to multiple linear regression analysis (MLRA), and multiple regression equation established. According to the data, ADs at different depth layers increase at first and then decrease with time, reaching the peak in August. According to the PCA, three principal components (PCs) are extracted in the 0.5 m and 1.0 m water layer, the variance contribution of which is 87.8% and 87.3%, respectively. The variance contribution of four PCs extracted in the 1.5 m water layer is 81.7%. After MLRA, it is found that the main EDFs of algal density in the 0.5 m water layer are electrical conductivity (EC), dissolved oxygen (DO), and water temperature (WT), in the 1.0 m water layer are WT and DO, and in the 1.5 m water layer are WT, DO, total nitrogen (TN), and EC. Generally speaking, WT and DO are decisive factors affecting AD. The EDFs’ leads to the AD changes in different water layers are analyzed, and it is proved that stratification occurs in scenic water bodies in urban parks. This study is expected to provide basic data and a theoretical basis for ecosystem system protection and water quality management of scenic water bodies in urban parks.
Zero Waste Systems: Barriers and Measures to Recycling of Construction and Demolition Waste
Authors: Abarca-Guerrero Lilliana , Lobo-Ugalde Susi , Méndez-Carpio Nicole , Rodríguez-Leandro Rosibel , Rudin-Vega Victoria
Published: November 2022

Urbanization, population growth, increased consumption, infrastructure, and housing needs are some of the factors that result in increased waste. Recycling has been a crucial way to reduce the amount of materials that end up in disposal sites and is how citizens, more aware of the impacts on the environment, participate in some of the schemes to reduce waste. Zero waste is an approach developed to preserve the finite resources available, but major barriers are hindering its efficient and effective implementation. This study intended to unveil those barriers in the Costa Rican construction sector and to propose measures to increase the recycling rates. In order to achieve the objective, construction companies, cement producers, waste managers, personnel of refuse material facilities, waste transformers, construction material distributors, and a director of the Ministry of Health were interviewed to determine the challenges in the valorization of concrete, wood, metal, and packaging waste materials. This article reports the findings, which include, among others, the fact that most construction companies dispose the waste without any separation, except for metals. The cement producer companies do not participate in any form of collection system in spite of the available technology and equipment and the absence of innovative technologies for the transformation of materials.
Encirclement of Natural Resources, Green Investment, and Economic Complexity for Mitigation of Ecological Footprints in BRI Countries
Authors: Qian Chen , Madni Ghulam Rasool
Published: November 2022

Environmental deterioration has been increasing constantly for many years and has become the major reason for climatic changes. Consequently, policy makers and researchers are enquiring into the factors affecting environmental quality. The earlier literature describes little about the impact of economic complexity, natural resources, and green investment on ecological footprint for countries participating in the Belt and Road Initiative (BRI), so this research is an effort to close this gap for the 45 BRI countries covering the time period 1995–2020. We applied a number of methods to address the issue of cross-sectional dependence, then cointegration is determined through the Lagrange multiplier bootstrap method. The Driscoll–Kraay standard error method is employed to find the long-run estimates while the robustness of the estimated findings is checked through panel-corrected standard errors (PCSE) and feasible generalized least squares (FGLS). The estimated outcomes suggest a significant negative effect of green investment on ecological footprint while natural resources, economic complexity, economic growth, and globalization have significant and positive effects on ecological footprint. These conclusions provide profound insight into natural resources, green investments, and economic complexity for sustainable development of the environment in BRI participating economies and provide a meaningful reference for ecological safety for other economies in the world. This study also highlights some necessary insights for policy makers and practitioners engaged in obtaining the target of sustainable development policies.
The UAE Employees’ Perceptions towards Factors for Sustaining Big Data Implementation and Continuous Impact on Their Organization’s Performance
Authors: Mustapha S. M. F. D. Syed
Published: November 2022

The UAE has officially launched the Big Data initiative in the year 2022; however, the interest in and adoption of Big Data technologies and strategies had started much earlier in the private and public sectors. This research aims to explore the perceptions of the UAE employees on factors needed to implement sustainable Big Data and the continuous impact on their organizational performance. A total of 257 employees were randomly selected for an online survey, and data were collected using a Likert-style five-point scale that was tested for validity and reliability. The findings indicate that employees believe that Big Data Sustainable Implementation leads to Business Performance. Additionally, employees consider factors such as Big Data Architecture Quality, Human Cognitive Factors, and Organizational Readiness to significantly impact on Sustainable Implementation. Further, a moderating impact of Human Cognitive Factors was found on the relationship between Big Data Architecture Quality and Sustainable Implementation. The study provides managerial insights and recommendations for policymaking.
To Be or Not to Be? Strategic Analysis of Carbon Tax Guiding Manufacturers to Choose Low-Carbon Technology
Authors: Mu Yanfen , Niu Feng
Published: November 2022

This paper analyzes the environmental tax’s effect on manufacturers’ choice of low-carbon technology in competitive supply chains. The existing studies only consider a single oligopoly enterprise and ignore the competition between supply chains. Few papers study the manufacturer’s technology choice under the carbon tax policy in the competitive supply chains, especially investigating the factors influencing the technology choice, including the market volume, and technology carbon emission reduction efficiency because different industry sectors have their distinctive carbon emissions reduction efficiencies and facing the different market volume. The study adopts a game theoretical approach, including the three-level supply chain consisting of the regulator, the manufacturers, and the retailers. A high carbon tax does not always help firms choose low-carbon technology. However, the monotonous effect of the carbon tax on manufacturer technology selection is no longer valid if the market volume and the carbon-reducing efficiency are considered. When the market volume is large, the regulator can set a high carbon tax to induce the manufacturers to choose low-carbon technology. We identify cases where the manufacturers are caught in a prisoner’s dilemma. When the market volume is small, and the carbon-reducing efficiency is high, the competitive manufacturers adopt the common technology. However, if the regulator increases the carbon tax, the manufacturers acquire the differential technology strategic choice, which is the Pareto optimal. We also extend the base model to the imperfect substitutable Cournot model and the Bertrand model to check the robustness and find our main results still hold in these extensions.