bluuebunny/crossref_metadata_2025 huggingface dataset:
Death and Regeneration of an Amazonian Mangrove Forest by Anthropic and Natural Forces
Authors: Cardenas Sergio M. M. , Cohen Marcelo C. L. , Ruiz Diana P. C. , Souza Adriana V. , Gomez-Neita Juan. S. , Pessenda Luiz C. R. , Culligan Nicholas
Published: December 2022

The Amazon Macrotidal Mangrove Coast contains the most extensive and continuous mangrove belt globally, occupying an area of ~6500 km2 and accounting for 4.2% of global mangroves. The tallest and densest mangrove forests in the Amazon occur on the Bragança Peninsula. However, road construction that occurred in 1973 caused significant mangrove degradation in the area. A spatial-temporal analysis (1986–2019) based on optical, Synthetic Aperture Radar (SAR), drone images, and altimetric data obtained by photogrammetry and validated by a topographic survey were carried out to understand how the construction of a road led to the death of mangroves. The topographic data suggested that this road altered the hydrodynamical flow, damming tidal waters. This process killed at least 4.3 km2 of mangrove trees. Nevertheless, due to natural mangrove recolonization, the area exhibiting degraded mangrove health decreased to ~2.8 km2 in 2003 and ~0.73 km2 in 2019. Climatic extreme events such as “El Niño” and “La Niña” had ephemeral control over the mangrove degradation/regeneration. In contrast, the relative sea-level rise during the last several decades caused long-term mangrove recolonization, expanding mangrove areas from lower to higher tidal flats. Permanently flooded depressions in the study area, created by the altered hydrodynamical flow due to the road, are unlikely to be recolonized by mangroves unless connections are re-established between these depressions with drainage on the Caeté estuary through pipes or bridges to prevent water accumulation between the road and depressions. To minimize impacts on mangroves, this road should have initially been designed to cross mangrove areas on the highest tidal flats and to skirt the channel headwaters to avoid interruption of regular tidal flow.
Satellite and sUAS Multispectral Remote Sensing Analysis of Vegetation Response to Beaver Mimicry Restoration on Blacktail Creek, Southwest Montana
Authors: Askam Ethan , Nagisetty Raja M. , Crowley Jeremy , Bobst Andrew L. , Shaw Glenn , Fortune Josephine
Published: December 2022

Beaver dam analogs (BDAs) are being installed on streams where restoration goals include reconnecting the stream to its floodplain, increasing water storage in the stream corridor, and improving the extent and vigor of riparian vegetation. This study evaluated the effects on vegetation vigor of a BDA treatment on Blacktail Creek in southwest Montana, USA, using data from Sentinel-2 satellites and a small unmanned aerial system (sUAS; a.k.a. drone). The goal of this research was to determine if BDA installation increased the health of riparian vegetation. Sentinel-2 remote sensing data from 2016 to 2021 were used to compare the pre- and post-treatment periods, and to evaluate effects in the treated area relative to control areas. Enhanced Vegetation Index (EVI) values were calculated to quantify vegetation response from the addition of BDAs. These data suggest that installing BDAs at this site has not led to an apparent increase in late-summer vegetation vigor relative to the controls. One potential explanation for these results is that the vegetation was not water limited prior to treatment in this study reach. This is an important consideration for water resource managers prior to installation of BDAs if the main restoration goal is the improvement of riparian vegetation health. Two high spatial resolution sUAS multispectral datasets were collected to evaluate the bias introduced by using the relatively course resolution (10 m) satellite imagery to assess these changes. High-resolution sUAS data allow fine-scale differences in vegetation and inundated area to be distinguished; however, historical sUAS datasets are rarely available. Satellite-based remote sensing has much lower resolution; however, Sentinel-2 satellite data have been available for the entire earth since 2016. This study demonstrates that the combination of sUAS and satellite based remote sensing data provides a method to compare high-resolution datasets for spatial analysis while gaining insight into relatively low-resolution historical data for temporal analysis.
Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT
Authors: Liu Fengkai , Huang Darong , Guo Xinrong , Feng Cunqian
Published: December 2022

Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust.
The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)
Authors: Guessoum Sonia , Belda Santiago , Ferrandiz Jose M. , Modiri Sadegh , Raut Shrishail , Dhar Sujata , Heinkelmann Robert , Schuh Harald
Published: December 2022

Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.
A Low Complexity Persistent Reconnaissance Algorithm for FANET
Authors: Guo Yuan , Tang Hongying , Qin Ronghua
Published: December 2022

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs’ high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC.
Coherently Radiating Periodic Structures for Feeding Concentric Rings Array with Reduced Number of Phase Shifters
Authors: Sanchez Brian , Panduro Marco A. , Covarrubias David H. , Reyna Alberto , Juárez Elizvan
Published: December 2022

This paper presents the application of CORPS (coherently radiating periodic structures) for feeding CRA (concentric rings array) with a reduced number of phase shifters. The proposed design technique for the structure of concentric rings provides a better scanning capability with respect to other existing configurations. This design technique utilizes 2 × 3 or 4 × 7 CORPS networks depending on the configuration or the number of antenna elements in the phased array system. These CORPS networks are set strategically in the feeding network to provide several advantages with respect to others in the scanning capability and the reduction of the number of phase shifters of the array system. The contribution of this paper is the full antenna system design of phased CRA for analyzing scanning and the reduction of phase shifters. The proposed phased array reduces the number of phase shifter devices in CRA for a scanning range of ±25° in the elevation plane. Differential evolution (DE) was applied to optimize the amplitudes of the proposed system. Several design cases were analyzed using full-wave simulation results to verify the phased array model and to take mutual coupling into account. Full-wave simulation results provide radiation patterns with low SLL in all scanning directions. The proposed phased array was validated by experimental measurements of the full antenna system prototype.
An Efficient Authenticated Key Agreement Scheme Supporting Privacy-Preservation for Internet of Drones Communications
Authors: Li Chun-Ta , Weng Chi-Yao , Chen Chin-Ling , Lee Cheng-Chi , Deng Yong-Yuan , Imoize Agbotiname Lucky
Published: December 2022

In recent years, due to the rapid development of Internet of things (IoTs), various physical things (objects) in IoTs are smart enough to make their own decisions without the involvement of humans. The smart devices embedded in a drone can sense, collect, and transmit real-time data back to the controller from a designated environment via wireless communication technologies. The mobility, flexibility, reliability and energy efficiency of drones makes them more widely used in IoT environments such as commercial, military, entertainment applications, traffic surveillance and aerial photography. In a generalized IoD architecture, we have communications among the drones in a flying zone, among the drones and the control server, and also among the drones and authorized user. IoD still has many critical issues that need to be addressed, such as data access being carried out through a public channel and battery operated drones. To address these concerns in IoD communications, in this paper, an efficient authentication and secure communication scheme with privacy preservation is proposed and it only uses secure one-way hash function and bitwise XOR operations when control server, drone and user mutually authenticate each other. After the successful authentication, both IoD-based participants can agree on a common session key to secure the subsequent communication messages. The widely accepted ProVerif and BAN logic analysis have been used to assure that the proposed scheme is provably secure against existing well-known security attacks and ensures privacy. Finally, a comparative analysis is presented to demonstrate the proposed scheme preserves efficiency when compared to existing competitive schemes.
Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients
Authors: Budig Mario , Stoohs Riccardo , Keiner Michael
Published: December 2022

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11–0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
Image Classification Using Multiple Convolutional Neural Networks on the Fashion-MNIST Dataset
Authors: Nocentini Olivia , Kim Jaeseok , Bashir Muhammad Zain , Cavallo Filippo
Published: December 2022

As the elderly population grows, there is a need for caregivers, which may become unsustainable for society. In this situation, the demand for automated help increases. One of the solutions is service robotics, in which robots have automation and show significant promise in working with people. In particular, household settings and aged people’s homes will need these robots to perform daily activities. Clothing manipulation is a daily activity and represents a challenging area for a robot. The detection and classification are key points for the manipulation of clothes. For this reason, in this paper, we proposed to study fashion image classification with four different neural network models to improve apparel image classification accuracy on the Fashion-MNIST dataset. The network models are tested with the highest accuracy with a Fashion-Product dataset and a customized dataset. The results show that one of our models, the Multiple Convolutional Neural Network including 15 convolutional layers (MCNN15), boosted the state of art accuracy, and it obtained a classification accuracy of 94.04% on the Fashion-MNIST dataset with respect to the literature. Moreover, MCNN15, with the Fashion-Product dataset and the household dataset, obtained 60% and 40% accuracy, respectively.
Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model
Authors: Zheng Chao , Hu Wei , Huang Jiyan , Wang Pengfei , Liu Yufei , Yang Chenyu
Published: December 2022

The localization of sensors in wireless sensor networks has recently gained considerable attention. The existing location methods are based on a one-spot measurement model. It is difficult to further improve the positioning accuracy of existing location methods based on single-spot measurements. This paper proposes two location methods based on multi-spot measurements to reduce location errors. Because the multi-spot measurements model has more measurement equations than the single-spot measurements model, the proposed methods provide better performance than the traditional location methods using one-spot measurement in terms of the root mean square error (RMSE) and Cramer–Rao lower bound (CRLB). Both closed-form and iterative algorithms are proposed in this paper. The former performs suboptimally with less computational burden, whereas the latter has the highest positioning accuracy in attaining the CRLB. Moreover, a novel CRLB for the proposed multi-spot measurements model is also derived in this paper. A theoretical proof shows that the traditional CRLB in the case of single-spot measurements performs worse than the proposed CRLB in the case of multi-spot measurements. The simulation results show that the proposed methods have a lower RMSE than the traditional location methods.
Three-Dimensional Encoding Approach for Wearable Tactile Communication Devices
Authors: Lin Yan-Ni , Li Yan-Cheng , Ge Song , Xu Jing-Jing , Li Lian-Lin , Xu Sheng-Yong
Published: December 2022

In this work, we presented a novel encoding method for tactile communication. This approach was based on several tactile sensory characteristics of human skin at different body parts, such as the head and neck, where location coordinates in the three-dimensional (3D) space were clearly mapped in the brain cortex, and gentle stimulations of vibrational touching with varied strengths were received instantly and precisely. For certain applications, such as playing cards or navigating walk paths for blinded people, we demonstrated specifically designed code lists with different patterns of tactile points in varied temporal sequences. By optimizing these codes, we achieved excellent efficiency and accuracy in our test experiments. As this method matched well with the natural habits of tactile sensory, it was easy to learn in a short training period. The results of the present work have offered a silent, efficient and accurate communication solution for visually impaired people or other users.
Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning
Authors: Harimi Ali , Majd Yahya , Gharahbagh Abdorreza Alavi , Hajihashemi Vahid , Esmaileyan Zeynab , Machado José J. M. , Tavares João Manuel R. S.
Published: December 2022

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning—among others—mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.
Biogenic Amine Sensing Based on Rosamine a N-Methylpyridinium Derivative Supported on Silica Materials from Rice Husk
Authors: Queirós Carla , Ribeiro Susana O. , Silva Ana M. G. , Leite Andreia
Published: December 2022

In this work new rosamine–silica composites were prepared and their sensing ability towards different amines was assessed. Rice husk wastes were used as a biogenic silica source. Silica was extracted by thermal treatment, before rice husk ash and after acid leaching with citric acid–treated rice husk ash. Mesoporous material (SBA-15) was also prepared using the extracted silica. The prepared materials were characterized by several techniques such as FTIR, XRD, SEM and N2 adsorption. The materials were then used as adsorbents of the chromophore N-methylpyridinium rosamine (Ros4PyMe). The obtained loaded composites were tested in solution for amines sensing (n-butylamine, aniline, putrescine and cadaverine). The detection studies were analyzed by fluorescence and revealed 40% and 48% quenching in fluorescence intensity for the composite Ros4PyMe@SBA in the presence of the biogenic amines cadaverine and putrescine, respectively. The composite was also sensitive in the powder form, changing the color from violet to pale pink in the presence of putrescine vapors with a fast response (around 2 min), the process being reversible by exposure to air.
Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
Authors: Dang Xiaochao , Wei Kefeng , Hao Zhanjun , Ma Zhongyu
Published: December 2022

This paper uses millimeter-wave radar to recognize gestures in four different scene domains. The four scene domains are the experimental environment, the experimental location, the experimental direction, and the experimental personnel. The experiments are carried out in four scene domains, using part of the data of a scene domain as the training set for training. The remaining data is used as a validation set to validate the training results. Furthermore, the gesture recognition results of known scenes can be extended to unknown stages after obtaining the original gesture data in different scene domains. Then, three kinds of hand gesture features independent of the scene domain are extracted: range-time spectrum, range-doppler spectrum, and range-angle spectrum. Then, they are fused to represent a complete and comprehensive gesture action. Then, the gesture is trained and recognized using the three-dimensional convolutional neural network (CNN) model. Experimental results show that the three-dimensional CNN can fuse different gesture feature sets. The average recognition rate of the fused gesture features in the same scene domain is 87%, and the average recognition rate in the unknown scene domain is 83.1%, which verifies the feasibility of gesture recognition across scene domains.
Towards Inclusive Higher Education: A Multivariate Analysis of Social and Gender Inequalities
Authors: Gómez Marcos Mayte , Ruiz Toledo Marcelo , Ruff Escobar Claudio
Published: December 2022

Universities are a key element in preventing any form of discrimination. Therefore, the United Nations 2030 Agenda recognizes their role through goal 4 regarding the quality of education. The 2030 agenda also includes goals 5 and 10, regarding gender equity and reducing inequalities as cross-cutting elements to boost social inclusion. The purpose of this research is to carry out a multivariate and dynamic analysis of the most outstanding universities in the global list of the THE Impact Rankings, which is the only tool that classifies these institutions in terms of the sustainable development goals (SDGs) to study how they are positioned in the indicators related to inequality. We also examine its evolution in the last three years and the relationship between goals 4, 5, and 10. The results show that less than half of the leading universities in sustainability carry out an active social inclusion policy. Additionally, most of them underwent significant changes in their trajectories to approach the indicator of gender equity. The research suggests there is still a long way to go to achieve social justice.
Involvement of Nitric Oxide in Methyl Jasmonate-Mediated Regulation of Water Metabolism in Wheat Plants under Drought Stress
Authors: Lubyanova Alsu R. , Bezrukova Marina V. , Shakirova Farida M.
Published: December 2022

Drought is a serious challenge that causes significant crop loss worldwide. The developmental processes of plants are regulated by phytohormones and signaling molecules that crosstalk together in signaling cascades. We suppose that nitric oxide (NO) is a secondary messenger of the JAs signaling pathway, as 10−7 M methyl jasmonate (MeJA) pretreatment regulates NO accumulation in wheat plants under drought stress, modulated by 12% polyethylene glycol (PEG), and in control plants. This study aimed to compare 2 × 10−4 M nitric oxide donor sodium nitroprusside (SNP) and MeJA pretreatments in regulating growth and water balance parameters at the vulnerable initial first-leaf stage of wheat growth. The application of 12% PEG decreased transpiration intensity twofold, relative water content (RWC) by 7–9%, and osmotic potential of cell sap by 33–40% compared with those of control plants. Under drought, MeJA- and SNP-pretreated plants decreased transpiration intensity by 20–25%, RWC by 3–4%, and osmotic potential of cell sap by 16–21% compared with those of control plants, and enhanced the proline content by 25–55% compared with MeJA- and SNP-untreated plants. Our results suggest that pretreatment with MeJA as well as SNP could mitigate drought stress in wheat plants. Similarities in MeJA- and SNP-induced shifts in plant water balance suggested that NO is a mediator of MeJA-induced regulation of wheat water content during water deficit.
The Coupling Coordination between Digital Economy and Industrial Green High-Quality Development: Spatio-Temporal Characteristics, Differences and Convergence
Authors: Liu Li , Gu Tingting , Wang Hao
Published: December 2022

The coupling coordination between the digital economy and industrial green high-quality development has become an inevitable choice to promote economic high-quality development in China. This paper conducts an empirical analysis of the coupling coordination degree between the digital economy and industrial green high-quality development via the entropy evaluation method, coupling coordination model, Dagum Gini coefficient and its decomposition, and β spatial convergence model, based on the spatial data of 31 provinces in China for a time period ranging from 2008 to 2020. The major research findings of this study are as follows: (1) the coupling coordination between the digital economy and industrial green high-quality development represents a whole steady-upward trend in China, with large regional differences. (2) There is a gradual decrease in the overall spatial difference of the coupling coordination, with the largest intra-regional difference in the eastern region, and significant inter-regional differences in the east-west, east-northeast, and east-central regions. Moreover, the hypervariable density serves as the major source of the regional differences. (3) There exists β Convergence for coupling coordination degrees of the whole country and the four regions. However, the spatial effects are different in different regions due to different influencing factors. Therefore, sufficient attention should be paid to the dynamic trend, the difference, and the imbalance of the coupling coordination degree between the digital economy and industrial green high-quality development. The research is of great significance for accurately implementing policies according to different levels of local resource endowments and economic development, and narrowing the regional differences of the coupling coordination between the digital economy and industrial green high-quality development.
Improving the Thermal Performance of Building Envelopes: An Approach to Enhancing the Building Energy Efficiency Code
Authors: Haj Hussein Muhannad , Monna Sameh , Abdallah Ramez , Juaidi Adel , Albatayneh Aiman
Published: December 2022

Cities on the east Mediterranean coast, especially in Palestine, are struggling to move towards sustainability as they are vulnerable to climate change and lack natural resources, especially energy resources, and this situation is further aggravated by high energy prices. The problem is the building sector, which is the most challenging sector when it comes to cities’ sustainability and, specifically, energy sustainability. In Palestine, this sector is the main consumer of energy but it lacks energy efficiency measures, such as up-to-date building energy codes. This study analyzed building thermal performance under different scenarios with a focus on building envelopes. We aimed to evaluate the benefits of introducing an updated building energy code—mainly addressing U-values for building envelopes—on future reductions in energy demand. We used a simulation tool (DesignBuilder) to evaluate typical existing building-envelope thermal and energy performances. Then, we undertook a comparison between the existing conditions and the proposed application of different scenarios, including the existing Palestinian building energy code and green building guidelines, the ASHRAE code for building envelopes, and the Jordanian building energy code, in order to introduce an updated building envelope energy code. The results showed that the current situation—building without applying any energy code or applying the existing Palestinian building energy code—is far from the high-energy performance that could be achieved by applying international or local green building codes. The use of thermal insulation could reduce the energy demand for heating by 83 to 43%, depending on the building type, climatic zone, and U-value. We recommend utilizing different U-values for building envelopes in different climatic zones to achieve high thermal performance. The results from this study have implications for construction industry professionals, local governments, and researchers seeking to establish high-energy-performance building envelopes.
Flood Susceptibility Mapping Using Watershed Geomorphic Data in the Onkaparinga Basin, South Australia
Authors: Ahmed Alaa , Alrajhi Abdullah , Alquwaizany Abdulaziz , Al Maliki Ali , Hewa Guna
Published: December 2022

In the near future, natural disasters and associated risks are expected to increase, mainly because of the impact of climate change. Australia is considered one of the most vulnerable areas for natural disasters, including flooding. Therefore, an evaluation of the morphometric characteristics of the Onkaparinga basin in South Australia was undertaken using the integration of remote sensing and geospatial techniques to identify its impact on flash floods. The Shuttle Radar Topography Mission (SRTM) and Landsat images with other available geologic, topographic, and secondary data were analysed in geographic information system (GIS) to outline the drainage basins, estimate the morphometric parameters, and rank the parameters to demarcate the flash flood susceptibility zones of the basin. The main goal was to develop a flash flood susceptibility map showing the different hazard zones within the study areas. The results showed that 10.87%, 24.27%, and 64.85% are classified as low, moderate, and highly susceptible for flooding, respectively. These findings were then verified against secondary data relating to the historic flood events of the area. About 30.77% of the historical floods are found located within the high to extremely susceptible zones. Moreover, a significant correlation has been found between the high precipitation concentration index (PCI) and the irregular rainfall and high potential for flooding. Finally, the social and economic vulnerability was applied to determine the impact of the flood hazards. The result indicates a widespread threat to the economy, environment, and community in the study area. This study can be utilized to support and assist decision makers with planning and the devotion of alleviation measures to reducing and avoiding catastrophic flooding events, especially in highly susceptible areas in the world, such as South Australian basins.
Fabrication of Nano-Ag Encapsulated on ZnO/Fe2V4O13 Hybrid-Heterojunction for Photodecomposition of Methyl Orange
Authors: Kaliyamoorthy Gowthami , Chinnasamy Surya , Pongiya Uma Devi , Rajadurai Vijayalakshmi , Ganesamoorthy Thirunarayanan , Thambusamy Stalin , Inbasekaran Muthuvel
Published: December 2022

Novel silver encapsulated nanocomposite zinc oxide/iron tetra-poly-vanadate (Ag-ZnO/Fe2V4O13) was synthesized with various wt% of silver (1.0–2.5 wt% of Ag) by cost-effective photo-deposition method under the irradiation of ultraviolet-A (UV-A) light. The nanostructure of the Ag-ZnO/Fe2V4O13 was explored by various characterization techniques. The surface functionalities were confirmed by Fourier transform infrared spectra and the crystalline nature of the material was revealed by X-ray diffraction patterns. Furthermore, the surface morphology and the optical properties of the composites were analyzed by scanning electron microscopy, energy dispersive X-ray–elemental color mapping (ECM), high-resolution transmission electron microscopy (HRTEM), ultraviolet–visible diffuse reflectance spectroscopy and photoluminescence. The crystallite size of Ag-ZnO/Fe2V4O13 was 28.5 nm which was consistent with HRTEM analysis. The photocatalytic activity was tested against aqueous methyl orange degradation under UV-A light irradiation. In all five runs, the stability of the catalyst was confirmed by reusability measurements and almost 98% of degradation was achieved. A suitable degradation pathway was proposed based on intermediates obtained during the degradation analyzed by gas chromatography–mass spectrometry. Trapping experiments confirmed that the superoxide radical anion (O2•−) was considered as the most active species for this degradation process. Complete mineralization was confirmed by the measurements of chemical oxygen demand.
An Investigation on the Potential of Cellulose for Soil Stabilization
Authors: Sujatha Evangelin Ramani , Kannan Govindarajan
Published: December 2022

The construction industry remains a significant contributor to global carbon emissions. Several sustainable alternatives have emerged to overcome this issue in geotechnical engineering. In this study, cellulose, an abundant biopolymer, is investigated for its potential to modify geotechnical properties favourably. Sodium carboxymethyl cellulose (NaCMC) is an anionic ether derivative of natural cellulose with good binding and moisture-retaining capacity. Experimental investigations were conducted on organic silt stabilized with 0.25% to 1.00% NaCMC, and the results indicate that unconfined compression strength (UCS) increased by 76.7% with 0.5% NaCMC treated soil after 28 days. Hydraulic conductivity (HC) of the 0.5% NaCMC treated soil decreased by 91.7% after 28 days, and the additives suppressed the compression index of the soil by 50%. The California bearing ratio (CBR) test indicated that the additive improved the subgrade strength by 33.2%, improving it from very poor to a fair sub-grade material. Microstructural analysis using a scanning electron microscope (SEM) and chemical investigation using x-ray diffraction (XRD) indicates that NaCMC’s interaction with soil did not form any new chemical compounds. However, the viscous nature of the material formed fibrous threads that bind the soil to enhance the geotechnical properties, establishing itself as a prominent stabilizer for ground improvement applications.
Runners Experience Lower Heart Rate, Increased Speed, and Joy/Calm on Routes with Trees, by the Sea and through Parks: Implications for Climate Change Design
Authors: Paraskevopoulou Angeliki T. , Chletsou Marina , Malesios Chrysovalantis
Published: December 2022

The beneficial effects of green and blue areas on human wellbeing are significant. Physical activity is a crucial factor for the promotion of good human health and wellbeing, and running constitutes one of the most popular forms of outdoor physical activity. This study examines the effect of six urban “route landscapes” with different landscape characters used for outdoor running on the heart rates and speeds of 30 people wearing a smartwatch while running. These routes included: (1) a running track in a stadium; (2) a road without street trees; (3) a road with street trees; (4) an urban park without sea view; (5) an urban park with sea view; and (6) a road by the sea. Additionally, a questionnaire survey was conducted to determine participants’ perceptions and preferred “route landscape” for running. In the study, higher heart rates were recorded on routes without trees, including the stadium. The lowest heart rate was recorded in the park by the sea, followed by the road by the sea, which were at the lowest altitudes amongst the routes studied. Additionally, the highest mean speed of participants was recorded during the run on the road by the sea. The results also showed that irrespectively of the routes, increases in altitude and gradient caused an increase in the heart rates. In the survey, the majority of respondents stated that they would have felt calm and joy during a run either in a park or in a park by the sea. With rising sea levels dictating not to build, carbon capture being necessary in parks, and trees by roads reducing the heat island effect, this study reveals the best “route landscapes” for runners’ health and global warming.
Global Advances on Insect Pest Management Research in Oil Palm
Authors: Egonyu James P. , Baguma John , Martínez Luis C. , Priwiratama Hari , Subramanian Sevgan , Tanga Chrysantus M. , Anankware Jacob P. , Roos Nanna , Niassy Saliou
Published: December 2022

Here, we review the advances in research on management of key oil palm insect pests globally, including defoliators, leaf/fruit scrapers, borers and sap feeders. The common oil palm pest management methods include synthetic insecticides, biopesticides, semiochemical lures, cultural practices, and integrated approaches. However, effectiveness, affordability, availability and impact of these methods on human and environmental health vary considerably based on the target insect and geographical location. The use of quarantine regulations to prevent the spread of invasive pests has also been applied with remarkable results. There are notable research advances in semiochemicals, bioacoustic detectors, nanotechnology, insect growth regulators, and entomophagy for better management of oil palm pests. We suggest the following research areas for improving effectiveness of oil palm pest management interventions: (i) exploration of semiochemical attractants for the majority of pests with no previous semiochemical work, and their integration in attract-and-kill devices laced with pathogenic microbes; (ii) expanding the application of digital sensing, predictive modeling and nano-technology in pest control strategies; (iii) developing effective technologies for mass trapping of edible insect pests for food or feed, especially among communities with a tradition of entomophagy; and (iv) strengthening regulatory frameworks for the management of quarantine oil palm insect pests.
Supply Chain Scheduling Optimization in an Agricultural Socialized Service Platform Based on the Coordination Degree
Authors: Xu Lingjingyuan , Yao Jianming
Published: December 2022

In order to create a sustainable agricultural production system and meet the multi-stage and differentiated production needs of farmers, this study proposes to build an agricultural service platform to dispatch agricultural service providers. Reasonably handling the collaborative relationship between farmers and service providers is a key issue in platform scheduling. Based on the analysis of the operation characteristics of the agricultural service platform, this study redefines the core issue of handling the collaborative relationship—the coordination degree—from the special characteristics of agricultural services, then analyzes it in depth and proposes a portrayal method. On this basis, a multi-stage and multi-objective scheduling optimization model, which integrally reflects the service utility and service combination operation efficiency, is constructed, and an improved genetic algorithm is proposed for solving it. Then, this study designs a numerical experiment which describes the multi-stage decision making of farmers, and the simulation results show that the optimization model can provide a balanced multi-objective supplier scheduling solution for them. Further, the validity and feasibility of the model and algorithm are verified through comparative tests of optimization effects and sensitive analysis. This study contributes to research on sustainable agriculture by modeling collaboration between smallholder farmers and agricultural service providers, and provides an effective decision-making tool for agricultural service platforms.
Modeling Sulphur Dioxide (SO2) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors
Authors: Alamoudi Mohammed , Taylan Osman , Keshtegar Behrooz , Abusurrah Mona , Balubaid Mohammed
Published: December 2022

Modeling air quality in city centers is essential due to environmental and health-related issues. In this study, machine learning (ML) approaches were used to approximate the impact of air pollutants and metrological parameters on SO2 quality levels. The parameters, NO, NO2, O3, PM10, RH, HyC, T, and P are significant factors affecting air pollution in Jeddah city. These factors were considered as the input parameters of the ANNs, MARS, SVR, and Hybrid model to determine the effect of those factors on the SO2 quality level. Hence, ANN was employed to approximate the nonlinear relation between SO2 and input parameters. The MARS approach has successful applications in air pollution predictions as an ML tool, employed in this study. The SVR approach was used as a nonlinear modeling tool to predict the SO2 quality level. Furthermore, the MARS and SVR approaches were integrated to develop a novel hybrid modeling scheme for providing a nonlinear approximation of SO2 concentration. The main innovation of this hybrid approach applied for predicting the SO2 quality levels is to develop an efficient approach and reduce the time-consuming calibration processes. Four comparative statistical considerations, MAE, RMSE, NSE, and d, were applied to measure the accuracy and tendency. The hybrid SVR model outperforms the other models with the lowest RMSE and MAE, and the highest d and NSE in testing and training processes.
Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm
Authors: Suanpang Pannee , Jamjuntr Pitchaya , Jermsittiparsert Kittisak , Kaewyong Phuripoj
Published: December 2022

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.
Consistency of Central and Regional Planning in the Agricultural Sectors and the Factors Affecting It in Indonesia
Authors: Wahyudi  , Fahmid Imam Mujahidin , Salman Darmawan , Suhab Sultan
Published: December 2022

A strategic issue in national development is the integration between national and regional planning. This research had the aims to: (1) analyse the consistency and integration between national and regional plans, (2) analyse the consistency of national and regional planning with the actual implementation, and (3) analyse the factors that influenced the inconsistencies of national and regional planning in the agricultural sectors. The research was carried out by the Ministry of Agriculture (MoA), Indramayu Regency, in West Java Province and South Lampung Regency in Lampung Province. The data were analysed using both content and consistency analyses and a comparative case study. The research results showed that: (1) the level of consistency between the planning documents from the MoA and those from Indramayu Regency reached 87.68% and 95.81%, when compared with those from South Lampung Regency; (2) in terms of the consistency between the documents and the actual implementation, South Lampung Regency had programs/activities which were more integrated with the MoA programs compared with Indramayu Regency; and (3) there were five main factors which caused inconsistencies: policies from the district heads and the parties involved, force majeure, the development priorities, the communication distortion, and support from the political parties. The central government (the MoA) and the Regional Agriculture Office must collaborate by making an electronic-based priority scale of development planning and by reinforcing the capacities of regional planners.