bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images
Authors: Barraza-Moraga Francisca , Alcayaga Hernán , Pizarro Alonso , Félez-Bernal Jorge , Urrutia Roberto
Published: November 2022

Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R2 greater than 0.87 and RMSE and MAE with errors less than 6 μg L−1. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations.
Semantic Segmentation of 3D Point Clouds Based on High Precision Range Search Network
Authors: Su Zhonghua , Zhou Guiyun , Luo Fulin , Li Shihua , Ma Kai-Kuang
Published: November 2022

Semantic segmentation for 3D point clouds plays a critical role in the construction of 3D models. Due to the sparse and disordered natures of the point clouds, semantic segmentation of such unstructured data yields technical challenges. A recently proposed deep neural network, PointNet, delivers attractive semantic segmentation performance, but it only exploits the global features of point clouds without incorporating any local features, limiting its ability to recognize fine-grained patterns. For that, this paper proposes a deeper hierarchical structure called the high precision range search (HPRS) network, which can learn local features with increasing contextual scales. We develop an adaptive ball query algorithm that designs a comprehensive set of grouping strategies. It can gather detailed local feature points in comparison to the common ball query algorithm, especially when there are not enough feature points within the ball range. Furthermore, compared to the sole use of either the max pooling or the mean pooling, our network combining the two can aggregate point features of the local regions from hierarchy structure while resolving the disorder of points and minimizing the information loss of features. The network achieves superior performance on the S3DIS dataset, with a mIoU declined by 0.26% compared to the state-of-the-art DPFA network.
A Multi-Frame GLMB Smoothing Based on the Image-Observation Sensor for Tracking Multiple Weak Targets Using Belief Propagation
Authors: Cao Chenghu , Zhao Yongbo
Published: November 2022

The previous multi-frame version of the generalized labeled multi-Bernoulli model (MF-GLMB) only accounts for standard measurement models. It is not suitable for application in the detection and tracking of multiple weak targets (low signal-to-noise ratio) due to the measurement information loss. In this paper, we introduce a MF-GLMB model that formally incorporates a track-before-detect scheme for point targets using an image sensor model. Furthermore, a belief propagation algorithm is adopted to approximately calculate the marginal association probabilities of the multi-target posterior density. In this formulation, an MF-GLMB model based on the track-before-detect measurement model (MF-GLMB-TBD smoothing) enables multi-target posterior recursion for multi-target state estimation. By taking the entire history of the state estimation into account, MF-GLMB-TBD smoothing achieves superior performance in estimation precision compared with the corresponding GLMB-TBD filter. The simulation results demonstrate that the performance of the proposed algorithm is comparable to or better than that of the Gibbs sampler-based version.
Investigating the Effects of Super Typhoon HAGIBIS in the Northwest Pacific Ocean Using Multiple Observational Data
Authors: Jeon Jonghyeok , Tomita Takashi
Published: November 2022

Various multi-source observational platforms have enabled the exploration of ocean dynamics in the Northwest Pacific Ocean (NPO). This study investigated daily oceanic variables in response to the combined effect of the 2019 super typhoon HAGIBIS and the Kuroshio current meander (KCM), which has caused economic, ecological, and climatic changes in the NPO since August 2017. During HAGIBIS, the six-hourly wind speed data estimated a wind stress power (Pw) which strengthened around the right and left semicircles of the typhoon, and an Ekman pumping velocity (EPV) which intensified at the center of the typhoon track. As a result, firstly, the sea temperature (ST) decreased along a boundary with a high EPV and a strong cyclonic eddy area, and the mixed layer depth (MLD) was shallow. Secondly, a low sea salinity (SS) concentration showed another area where heavy rain fell on the left side of the typhoon track. Phytoplankton bloom (PB) occurred with a large concentration of chlorophyll a (0.641 mg/m3) over a wide extent (56,615 km2; above 0.5 mg/m3) after one day of HAGIBIS. An analysis of a favorable environment of the PB’s growth determined the cause of the PB, and a shift of the subsurface chlorophyll maximum layer (SCML; above 0.7 mg/m3) was estimated by comprehensive impact analysis. This study may contribute to understanding different individually-estimated physical and biological mechanisms and predicting the recurrence of ocean anomalies.
Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features
Authors: Tsai Cheng-Yu , Huang Huei-Tyng , Cheng Hsueh-Chien , Wang Jieni , Duh Ping-Jung , Hsu Wen-Hua , Stettler Marc , Kuan Yi-Chun , Lin Yin-Tzu , Hsu Chia-Rung , Lee Kang-Yun , Kang Jiunn-Horng , Wu Dean , Lee Hsin-Chien , Wu Cheng-Jung , Majumdar Arnab , Liu Wen-Te
Published: November 2022

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.
A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators
Authors: Grillo Luis O. S. , Wengerkievicz Carlos A. C. , Batistela Nelson J. , Kuo-Peng Patrick , Freitas Luciano M. de
Published: November 2022

Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected.
A Novel Sparse Framework for Angle and Frequency Estimation
Authors: Zhao Guilian , Huang Dongmei , Cai Changxin , Wu Peng
Published: November 2022

The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space–time intervals must be smaller than given thresholds, which results in complicated hardware costs and a huge computational burden. This paper aims to reduce the complexity for JAFE, and a novel sparsity-aware framework is proposed. Unlike the current uniform sampling architectures, the incoming narrow-band singles are sampled by a series of space–time coprime samplers. An improved rotational invariance estimator is introduced, which offers closed-form solutions for both angle and frequency estimation. The mathematical treatments indicate that our methodology is inherent in larger spatial/temporal aperture than the uniform sampling architectures; hence, it provides more accurate JAFE compared to alternative approaches relying on uniform sampling. Additionally, it attains nearly the same complexity as the current rotational invariance approach. Numerical results agree with the theoretical advantages of our methodology.
Sensor Fault Diagnosis Method Based on Rotor Slip Applied to Induction Motor Drive
Authors: Tran Cuong Dinh , Kuchar Martin , Sobek Martin , Sotola Vojtech , Dinh Bach Hoang
Published: November 2022

A novel diagnosis method based on the rotor slip is proposed in the paper to correctly detect current and speed sensor failures during the induction motor drive (IMD) operation. In order to enhance reliability and avoid confusion in the diagnosis algorithm due to the influence of measured signal quality, each fault type is determined in a priority order defined by the diagnosis method. Based on the features of the IMD applying the field-oriented control (FOC) technique, an innovative model uses the measured currents and reference speed as the input signals to estimate the rotor slip for the current sensor fault detection. After verifying the quality of the feedback of the current signals, a speed sensor fault function is continued, and performs according to relations among the reference speed, estimated speed based on the sliding mode method, and measured rotor speeds. Finally, the estimated quantities are selected to replace the wrong measured current or speed signals. The feasibility of the proposed approach is verified by simulations using Matlab-Simulink software as well as by practical experiments using an IMD prototype with a rated power of 2.2 kW and a DSC-TMS320F28335-based control system. The obtained simulation and experimental results demonstrated the feasibility, effectiveness, and reliability of the proposed diagnosis technique in detecting sensor failures and maintaining the stable operation of the IMD.
A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach
Authors: Arizala Asier , Zubizarreta Asier , Pérez Joshué
Published: November 2022

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities.
Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors
Authors: Kurniawan Ade , Ohsita Yuichi , Murata Masayuki
Published: November 2022

Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cause the machine learning system to generate incorrect outputs. Considering the architecture using multiple sensor devices, hacking even a few sensors can create a significant risk; an attacker can attack the machine learning model through the hacked sensors. Some studies demonstrated the possibility of adversarial examples on the deep neural network (DNN) model based on IoT sensors, but it was assumed that an attacker must access all features. The impact of hacking only a few sensors has not been discussed thus far. Therefore, in this study, we discuss the possibility of attacks on DNN models by hacking only a small number of sensors. In this scenario, the attacker first hacks few sensors in the system, obtains the values of the hacked sensors, and changes them to manipulate the system, but the attacker cannot obtain and change the values of the other sensors. We perform experiments using the human activity recognition model with three sensor devices attached to the chest, wrist, and ankle of a user, and demonstrate that attacks are possible by hacking a small number of sensors.
Quantitative Investigation of Acoustic Emission Waveform Parameters from Crack Opening in a Rail Section Using Clustering Algorithms and Advanced Signal Processing
Authors: Mahajan Harsh , Banerjee Sauvik
Published: November 2022

Acoustic emission (AE) is an emerging technology for real-time non-destructive testing of structures. While research on a simulated AE source in rail and testing on rail material using small beam samples have been conducted, a study is required in lab environment to investigate AE waveform characteristics generated by crack in rail. In this paper, a three-point bending test is conducted on an actual rail section of 1500 mm with transverse damage of 38% head area to simulate AE source due to crack opening in the rail. AE signals are recorded for three different loads. For data analysis, unsupervised machine learning algorithms such as k-means, fuzzy-C mean and gaussian mixture model are used to cluster and filter out usable signals from the whole dataset corrupted by noisy signals from various sources. k-mean with principal component was observed to be best technique based on silhouette score. The frequency and amplitude of waveform have been discussed in relation to load and crack opening displacement. This study establishes a baseline for linking load, crack opening, and AE wave characteristics. This work can ultimately aid in the development of robust denoising, and damage analysis algorithms based on the frequency content and dispersion of the AE waveform.
Edge–Fog–Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems
Authors: Daraghmi Yousef-Awwad , Daraghmi Eman Yaser , Daraghma Raed , Fouchal Hacène , Ayaida Marwane
Published: November 2022

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.
MEMSbased Double-Stacked Tower Biosensor Array with Integrated Readout Circuitry for Detection of Salivary pH as a Diagnostic Biomarker Applied for Chronic Periodontal Disease
Authors: Lin Wei-Cheng
Published: November 2022

MEMS based 3D double stacked tower pixel biosensor 10 × 10 array with integration of readout circuit for detection of saliva pH ion is demonstrated. The pixel biosensor comprised a driving electrode, sensing electrode and double stack tower pixel structure. The sensitivity of double stacked tower biosensor can be auxiliary enhanced by proposed lower-jitter low dropout regulator circuit and dual offset cancellation comparator. The double stacked tower sensor is fabricated by MEMS backend-of-line CMOS process, it is compatible with CMOS frontend readout circuits and integrated as a system-on-chip (SoC). The double stacked tower pixel by MEMS process is to obtain a larger volume ratio of charge groups in a pixel of biosensor to enhance the sensitivity and linearity for ion detection. With the double stacked tower structure in biosensor, the sensitivity is improved by 31% than that of single tower structure proved by simulation. A wide-range linearity from pH 2.0 to pH 8.3, high sensitivity of −21 ADC counts/pH (or 212 mV/pH), response time of 5 s, repetition of 98.9%, and drift over time of 0.5 mV are achieved. Furthermore, the proposed biosensor was performed to confirm the artificial saliva from healthy gingiva, chronic gingivitis and chronic periodontitis, the measured ADC counts from proposed biosensor SoC was in consistent of that measured cyclic voltametric (CV) method very well. The proposed 3D double stack tower biosensor and readout circuit can be further integrated with internet-of-thing (IoT) device and NFC for data transmission for continuous pH sensing to facilitate the chronic gingiva disease health care at home.
Data-Aided SNR Estimation for Bandlimited Optical Intensity Channels
Authors: Gappmair Wilfried
Published: November 2022

Not only for radio frequency but also for optical communication systems, knowledge of the signal-to-noise ratio (SNR) is essential, e.g., for an adaptive network, where modulation schemes and/or error correction methods should be selected according to the varying channel states. In the current paper, this topic is discussed for a bandlimited optical intensity link under the assumption that the data symbols are known to the receiver unit in form of pilot sequences. This requires a unipolar signal design regarding the symbol constellation, but also a non-negative pulse shape satisfying the Nyquist criterion is necessary. Focusing on this kind of scenario, the modified Cramer–Rao lower bound is derived, representing the theoretical limit of the error performance of the data-aided SNR estimator developed in this context. Furthermore, we derive and analyze a maximum likelihood algorithm for SNR estimation, which turns out to be particularly simple for specific values of the excess bandwidth, among them the most attractive case of minimum bandwidth occupation. Numerical results confirming the analytical work conclude the paper.
Gated Recurrent Unit Network for Psychological Stress Classification Using Electrocardiograms from Wearable Devices
Authors: Zhong Jun , Liu Yongfeng , Cheng Xiankai , Cai Liming , Cui Weidong , Hai Dong
Published: November 2022

In recent years, research on human psychological stress using wearable devices has gradually attracted attention. However, the physical and psychological differences among individuals and the high cost of data collection are the main challenges for further research on this problem. In this work, our aim is to build a model to detect subjects’ psychological stress in different states through electrocardiogram (ECG) signals. Therefore, we design a VR high-altitude experiment to induce psychological stress for the subject to obtain the ECG signal dataset. In the experiment, participants wear smart ECG T-shirts with embedded sensors to complete different tasks so as to record their ECG signals synchronously. Considering the temporal continuity of individual psychological stress, a deep, gated recurrent unit (GRU) neural network is developed to capture the mapping relationship between subjects’ ECG signals and stress in different states through heart rate variability features at different moments, so as to build a neural network model from the ECG signal to psychological stress detection. The experimental results show that compared with all comparison methods, our method has the best classification performance on the four stress states of resting, VR scene adaptation, VR task and recovery, and it can be a remote stress monitoring solution for some special industries.
An Adaptive and Spectrally Efficient Multi-Channel Medium Access Control Protocol for Dynamic Ad Hoc Networks
Authors: Beg Abdurrahman , Mostafa Saud Mohammad , AbdulGhaffar AbdulAziz , Sheltami Tarek R. , Mahmoud Ashraf
Published: November 2022

Medium access control (MAC) protocols in ad hoc networks have evolved from single-channel independent transmission mechanisms to multi-channel concurrent mechanisms to efficiently manage the demands placed on modern networks. The primary aim of this study is to compare the performance of popular multi-channel MAC (MMAC) protocols under saturated network traffic conditions and propose improvements to the protocols under these conditions. A novel, dynamically adaptive MMAC protocol was devised to take advantage of the performance capabilities of the evaluated protocols in changing wireless ad hoc network conditions. A simulation of the familiar MAC protocols was developed based on a validated simulation of the IEEE 802.11 standard. Further, the behaviors and performances of these protocols are compared against the proposed MMAC protocols with a varying number of ad hoc stations and concurrent wireless channels in terms of throughput, Jain’s fairness index, and channel access delay. The results show that the proposed MMAC protocol, labeled E-SA-MMAC, outperforms the existing protocols in throughput by up to 11.9% under a constrained number of channels and in channel access delays by up to 18.3%. It can be asserted from these observations that the proposed approach provides performance benefits against its peers under saturated traffic conditions and other factors, such as the number of available wireless channels, and is suitable for dynamic ad hoc network deployments.
Wireless Communication Platform Based on an Embroidered Antenna-Sensor for Real-Time Breathing Detection
Authors: El Gharbi Mariam , Fernández-García Raúl , Gil Ignacio
Published: November 2022

Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2, with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from −80 dBm to −72 dBm, −88 dBm to −79 dBm and −85 dBm to −80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.
GC-MS Analysis, Phytochemical Screening, and Antibacterial Activity of Cerana indica Propolis from Kashmir Region
Authors: Mohiuddin Ishfaq , Kumar T. Ramesh , Zargar Mohammed Iqbal , Wani Shahid Ud Din , Mahdi Wael A. , Alshehri Sultan , Alam Prawez , Shakeel Faiyaz
Published: November 2022

Propolis is a resinous compound produced by honey bees. It contains bioactive molecules that possess a wide range of biological functions. The chemical composition of propolis is affected by various variables, including the vegetation, the season, and the area from which the sample was collected. The aim of this study was to analyze the chemical composition and assess Cerana indica propoli’s antibacterial efficacy from the Kashmir region. Gas chromatography-mass spectrometry (GC-MS) analysis was used to determine the chemical composition of Kashmiri propolis. A range of bacterial strains was tested for antimicrobial activity using different extracts of propolis by agar well diffusion technique. Propolis was found to be rich in alkaloids, saponins, tannins, and resins. The chemical characterization revealed the presence of 68 distinct phytocompounds using GC-MS, and the most predominant compounds were alpha-D-mannopyranoside, methyl, cyclic 2,3:4,6-bis-ethyl boronate (21.17%), followed by hexadecanoic acid, methyl ester (9.91%), and bacteriochlorophyll-c-stearyl (4.41%). The different extracts of propolis showed specific antibacterial efficacy against multidrug-resistant (MDR) strains viz., Pseudomonas aeruginosa (MTCC1688), Escherichiacoli (MTCC443), Klebsiella pneumonia (MTCC19), Cutibacterium acnes (MTCC843), and Staphylococcus aureus (MTCC96). The EEKP showed the highest zone of inhibition against S. aureus (17.33) at 400 µg mL−1. According to the findings of this study, bee propolis contains a variety of secondary metabolites with various pharmacological activities. Furthermore, because of its broad spectrum of positive pharmacological actions and the fact that it is a promising antibacterial agent, more research on propolis is warranted.
Numerical Modeling and Analysis of Harvesting Atmospheric Water Using Porous Materials
Authors: Alkinani Sadeem S. , El-Amin Mohamed F. , Brahimi Tayeb
Published: November 2022

Nowadays, harvesting water from the atmosphere is becoming a new alternative for generating fresh water. To the author’s best knowledge, no mathematical model has been established to describe the process of harvesting water from the atmosphere using porous materials. This research seeks to develop a new mathematical model for water moisture absorption in porous materials to simulate and assess harvesting atmospheric water. The mathematical model consists of a set of governing partial differential equations, including mass conservation equation, momentum equation, associated parameterizations, and initial/boundary conditions. Moreover, the model represents a two-phase fluid flow that contains phase-change gas–liquid physics. A dataset has been collected from the literature containing five porous materials that have been experimentally used in water generation from the air. The five porous materials include copper chloride, copper sulfate, magnesium sulfate, manganese oxides, and crystallites of lithium bromide. A group of empirical models to relate the relative humidity and water content have been suggested and combined with the governing to close the mathematical system. The mathematical model has been solved numerically for different times, thicknesses, and other critical parameters. A comparison with experimental findings was made to demonstrate the validity of the simulation model. The results show that the proposed mathematical model precisely predicts the water content during the absorption process. In addition, the simulation results show that; during the absorption process, when the depth is smaller, the water content reaches a higher saturation point quickly and at a lower time, i.e., quick process. Finally, the highest average error of the harvesting atmospheric water model is around 1.9% compared to experimental data observed in manganese oxides.
Importance of Urban Green Areas in the Context of Current and Future Global Changes: Lessons Learned from a Case Study in Bratislava (Slovakia)
Authors: Belčáková Ingrid , Slámová Martina , Demovičová Zuzana
Published: November 2022

As one of the largest cities in Slovakia, Bratislava needs to proceed with the greenery concept with regard to mechanisms for adapting to climate change. The potential of developing new areas can be seen in public, semi-public, and private spaces. In this sense, the aim of our research was to map the current situation of urban greenery in the city, identify the capacity for enhancement of small urban green spaces, and present the option of participating in the development of green areas in the selected places to the city’s inhabitants. The condition of urban greenery was analysed and described, with a special emphasis on the untapped potential of green areas on housing estates. Using examples from abroad, cases of successful revitalisation of urban greenery areas on housing estates are given, and the main problems faced by the selected region were assessed. Apart from the above-mentioned main aim, there was the intention to discover new trends for using greenery in the urban environment, to compare past and present conditions, or to present new options and possibilities for designing greenery. A proposal to set up a new system of private and semi-public green areas is viewed as a possible promising result. As another required step, we analysed the effect that different types of information media have on the strengthening of relationship between the city and its inhabitants, i.e., to improve the communication dialogue by establishing an online platform on greenery issues and to increase attention as well as inhabitants’ participation in public life.
Characteristics and Hazards Analysis of Vortex Shedding at the Inverted Siphon Outlet
Authors: Xu Xinyong , Chen Suiqi , Meng Xiangyang , Jiang Li
Published: November 2022

This paper studies Karman vortex shedding and water-level fluctuation in the inverted siphon structure of the Middle Route of the South-to-North Water Diversion Project (MR-SNWDP) in China. Field investigations and numerical simulations for the inverted siphon outlet were performed to explore the characteristics and hazards of the vortex. Numerical results were compared with measured data to verify the effectiveness and reliability of the model. Based on the model, it is found that the periodic water-level fluctuations caused by the Karman vortex street will not only excite surges to beat the gate but will also induce periodical force on the gate pier. Those will damage the building structure and affect the delivery capacity in the long-term operation. Based on this, countermeasures of altering different pier tail shapes are proposed to control vortex shedding, and the effect is noticeable. The study presents a hydraulic process for the inverted siphon outlet and provides a theoretical reference for water delivery safety of inverted siphons and similar structures in MR-SNWDP.
A Comprehensive Review of Modern Cold Chain Shipping Solutions
Authors: Ren Tianran , Ren Jun , Matellini Dante Ben , Ouyang Wu
Published: November 2022

Packaging is one of the most important factors affecting the overall cold chain efficiency. This paper aims to examine the current trends of cold chain packaging by conducting a comprehensive review on the current commercially available cold chain shipping solutions. A conceptual model of the cold chain shipping solutions including the structure, categories and components is proposed to provide analytical comparisons with the existing literature. It is found that the shipping solutions can improve the overall cold chain performance in many aspects which include improving temperature control performance, flexibility, safety, sustainability and knowledge sharing of the cold chains. Despite all the advantages, there are still limitations and challenges posed which are also discussed. This is the first paper presenting an overview of commercially available cold chain shipping solutions. It not only contributes to the literature by presenting new knowledge but is also beneficial for all the stakeholders in cold chain packaging by providing practical information and guidelines.
Measuring Environmental Resilience Using Q-Methods: A Malaysian Perspective
Authors: Tariq Hisham , Pathirage Chaminda , Fernando Terrence , Sulaiman Noralfishah , Nazir Umber , Abdul Latib Siti Kursiah Kamalia , Masram Haidaliza
Published: November 2022

Communities increasingly need tools that can help them assess the environmental risks they face to understand better their capacities in mitigation, preparedness, response, and recovery. Environmental resilience (ER) is a crucial feature of community resilience that is not adequately covered in the literature. This paper proposes an inclusive, participatory approach to achieve stakeholder engagement on the definitions, objectives, and indicators for measuring ER at the community level. This study uses a 5-step approach utilising Q-methods to contextualise a resilience index for Environmental Resilience (ER). An initial set of 57 indicators from 13 frameworks from the literature was reduced to 25 by combining the indicators of similar type, format and terminology. A total of 10 participants from two groups (academics and practitioners) took part in the interviews and Q-sort workshops in Malaysia in this study. Both stakeholder groups identified Ecosystem monitoring as one of the most critical indicators to understand ER, closely followed by rapid damage assessments and an effective communication system. The exercise also revealed marked differences between them regarding the importance of fair access to basic needs and services for citizens, a priority for academics, and the value of building green infrastructure, a priority for practitioners, with the most significant difference between the two groups on the importance of measuring the natural defences of a community. The Environmental Resilience Capacity Assessment Tool (ER-CAT), proposed in this paper, can be used by local governments and communities for engagement, discussion and consensus building to select the resilience indicators that are most relevant to them in their contexts.
Sustainable Agriculture and Rural Poverty Eradication in Pakistan: The Role of Foreign Aid and Government Policies
Authors: Sikandar Furqan , Erokhin Vasilii , Xin Li , Sidorova Marina , Ivolga Anna , Bobryshev Alexey
Published: November 2022

For decades, agriculture has been central to economic growth and development in Pakistan. However, endemic rural poverty hinders the performance of agricultural production, and thus deteriorates the sustainable development perspectives of the agricultural sector and the entire country. The need for agricultural reform emphasizes raising farmers’ incomes as a cornerstone of the sustainable development of rural areas, and this study attempts to reveal the effects of foreign aid and government policies on the level of rural poverty in Pakistan. In total, 384 farmers from major agricultural areas of Pakistan completed the survey-based questionnaire. The partial least square structural equation modelling technique tested the results. They, using sustainable agriculture practices, improved agricultural production positively and significantly. Agriculture production positively and significantly reduces or mitigates poverty. This study aimed to reveal the role of foreign aid in sustaining agricultural production and eradicating rural poverty. Foreign aid positively moderated the association between sustainable agriculture practices and agricultural production. In addition, government policies negatively, yet insignificantly, moderate the association between agriculture production and poverty reduction. Furthermore, the findings indicate that agriculture production positively and significantly mediates the association between sustainable agriculture practices and poverty reduction. The study contributes to the literature by improving the understanding of linkages within the poverty-policies-aid-sustainability framework.
Internet of Things based Decision Support System for Green Logistics
Authors: Mejjaouli Sobhi
Published: November 2022

This work proposes an IoT-based Real-Time Decision Support System for Perishable Products. The proposed system collects data during the transportation process and will interfere in the case of failure. Three different simulation models corresponding to different configurations and mitigation plans are built. The simulation models consider decisions such as stopping transportation and rerouting shipments to minimize losses in case of failure. The three different supply chain simulation models are implemented through a case study that considers transporting a perishable fruit in the intercontinental United States. A financial and environmental analysis is conducted to show the benefits of the proposed system.
Distribution-Free Approaches for an Integrated Cargo Routing and Empty Container Repositioning Problem with Repacking Operations in Liner Shipping Networks
Authors: Liu Ming , Liu Zhongzheng , Liu Rongfan , Sun Lihua
Published: November 2022

Cargo (laden container) routing and empty container repositioning are crucial components in liner shipping, which are closely relevant. However, most existing works focus on cargo routing and empty container repositioning separately, which cannot optimize the global shipping cost. In addition, (1) appropriate repacking operations for laden containers can curtail the total handling costs, and (2) the perfect information for uncertain laden and empty container demands may be difficult to obtain due to insufficient historical data. This work investigates an integrated cargo routing and empty container repositioning problem with repacking operations in which only partial information about uncertain laden and empty container demands is known. The objective is to minimize the total costs including repacking operation cost and laden and empty container transportation costs. For the problem, a new chance-constrained programming model based on moment-based ambiguous sets is formulated. Then, four distribution-free solution approaches are adopted to solve the investigated problem. Numerical experiments are conducted to compare the proposed methods.
Environmental Assessment of Giant Freshwater Prawn, Macrobrachium rosenbergii Farming through Life Cycle Assessment
Authors: Haslawati Baharuddin , Saadiah Ibrahim , Siti-Dina Razman Pahri , Othman Murnira , Latif Mohd Talib
Published: November 2022

The giant freshwater prawn (GFP), Macrobrachium rosenbergii has emerged as a significant crustacean in global aquaculture. A cradle-to-farm Life Cycle Assessment (LCA) was used to assess the potential environmental impacts of GFP in Malaysia. The four main iterative farming phases involved were pond preparation, stocking, farming, and harvesting. The impact categories chosen were global warming, terrestrial ecotoxicity, terrestrial acidification, freshwater eutrophication, human non-carcinogenic toxicity, human carcinogenic activity, and water consumption. The software SimaPro 9.3.0.3 was used for impact analysis, with background data from the database Ecoinvent 3.0. and ReCiPe 2016 Midpoint (H) V1.06/World (2010). Among other environmental impact categories, stocking and harvesting phases contributed to human carcinogenic toxicity impact values of 33.33%, followed by farming (33.31%). Another impact category, freshwater ecotoxicity also produces the same pattern with the stocking and harvesting process, still generating the highest impact value of 33.34%, followed by farming (33.30%). Apart from the identified capital items that require consideration for future waste management in aquaculture, this LCA study found that M. rosenbergii farming generates a low impact to the environment, however, could inspire further research on other perspectives of sustainability.
An Experimental Study on Seismic Performance Evaluation of Multi-Ply Bellows Type Expansion Joint for Piping Systems
Authors: Jeon Bub-Gyu , Kim Sung-Wan , Yun Da-Woon , Ju Bu-Seog , Son Ho-Young
Published: November 2022

Piping systems are a representative social infrastructure to provide oil, gas, and water. Damage to piping systems may cause serious consequences, such as fire, water outage, and environmental pollution. Therefore, piping systems need to be protected from natural disasters, such as earthquakes. Earthquakes may cause deformation that exceeds piping design criteria. For example, large relative displacements and liquefaction of the ground resulting in loss of strength and ground subsidence, and the side-sway of primary structures subjected to a strong ground motion may cause critical damage to piping systems. Therefore, expansion joints to maintain flexibility can be applied to locations where excessive deformation is expected to improve the seismic performance of piping systems. Metal bellows, a type of expansion joints, are flexible, so they are highly durable against deformation and fatigue loads. This indicates that metal bellows can be used as seismic separation joints for piping. In this study, experimental research was conducted to analyze the seismic performance of multi-ply bellows type expansion joints, a type of metal bellows. Monotonic loading tests and cyclic loading tests were conducted on 2-ply bellows and 3-ply bellows, and the results were compared. In the cyclic loading tests, multi-step increasing amplitude cyclic loading, which used the displacement history amplified in stages, and constant amplitude cycling loading with various magnitudes were considered. The test results showed no significant difference in bending performance for monotonic loading between the two types of multi-ply bellows. The 3-ply bellows, however, showed higher performance for low-cycle fatigue than 2-ply bellows.
A Leap of Faith: Regenerative Agriculture as a Contested Worldview Rather Than as a Practice Change Issue
Authors: Page Camille , Witt Bradd
Published: November 2022

Regenerative agriculture (RA) is emerging as one alternative model for agricultural production. However, RA has not matured sufficiently for a clear definition to have emerged, or for claimed benefits to be rigorously tested. Thus, numerous competing discourses have emerged around what RA is, and its potential role in the future of farming, leading to a level of contention about is value. This research aimed to better understand RA as a social phenomenon by using Q methodology to explore and evaluate how common discourses around agricultural sustainability, the goals of farming, and RA resonated with different Australian beef farmers. Three perspectives emerged from the data, labelled as the regenerative, environmentally conscious, and productive perspectives and represented diverse views around the goals of agriculture, the role of RA in the future of Australian agriculture, and comfort level producers have with the term and approach to farming it represents. As RA continues to grow as a movement, worldview, and farming approach, understanding the differences in thinking and worldview of proponents will be important for better understanding the goals of RA, how to reach them and measure their impact, and improve communication around RA. This is important to avoid and resolve unnecessary or destructive tensions between the range of agricultural sustainability discourses.
On-Chip Design of a Broadband 850 nm TM-Pass/TE-Stop Polarizer with Tilted Subwavelength Gratings
Authors: Dong Yue , Liu Yu , Xu Yin , Zhang Bo
Published: November 2022

An integrated TM-pass/TE-stop polarizer centered at 850 nm is designed in this paper. The proposed polarizer is designed on a Si3N4 on insulator platform with tilted subwavelength gratings (SWG). Since the tilted SWGs have much more of an impact on the effective index of the TE polarization state than that of the TM polarization state, they help to achieve high TM and low TE transmission simultaneously. After geometries optimization, the polarizer’s working bandwidth, which is defined as the wavelength region with an extinction ratio higher than 20 dB, is determined to be 185 nm under a SWG tilting angle of 30 degrees. At the same time, the insertion loss is always less than 0.45 dB over the entire working wavelength band. Finally, the results of fabrication tolerance analysis show that the SWG ridge width jitter only degrades the polarizer’s working bandwidth by 16 nm.