bluuebunny/crossref_metadata_2025 huggingface dataset:
Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems
Authors: Kiamanesh Bahareh , Behravan Ali , Obermaisser Roman
Published: October 2022

Heating, ventilation, and air-conditioning (HVAC) systems are large-scale distributed systems that can be subject to multiple faults affecting the electronics, sensors, and actuators, potentially causing high energy consumption, occupant discomfort, degraded indoor air quality and risk to critical infrastructure. Fault injection (FI) is an effective experimental method for the validation and dependability evaluation of such HVAC systems. Today’s FI frameworks for HVAC systems are still based on a single fault hypothesis and do not provide insights into dependability in the case of multiple faults. Therefore, this paper presents modeling patterns of numerous faults in HVAC systems based on data from field failure rates and maintenance records. The extended FI framework supports the injection of multiple faults with exact control of the timing, locality, and values in fault-injection vectors. A multi-dimensional fault model is defined, including the probability of the occurrence of different sensor and actuator faults. Comprehensive experimental results provide insights into the system’s behavior for concrete example scenarios using patterns of multiple faults. The experimental results serve as a quantitative evaluation of key performance indicators (KPI) such as energy efficiency, air quality, and thermal comfort. For example, combining a CO2 sensor fault with a heater actuator fault increased energy consumption by more than 70%.
Smart Home Control System Using VLC and Bluetooth Enabled AC Light Bulb for 3D Indoor Localization with Centimeter-Level Precision
Authors: Xu Bo , Hussain Babar , Wang Yiru , Cheng Hoi Chuen , Yue Chik Patrick
Published: October 2022

Smart home systems (SHSs) are a modern lifestyle trend, changing daily lives in the most intuitive way. To connect and operate various smart devices under one system, an accurate, convenient, and secure control method is of utmost significance. Nowadays, most smart home control systems are based on radio-frequency (RF) technologies such as Bluetooth, Wi-Fi, and Zigbee. They tend to suffer from poor location accuracy in high-density environments due to the interference and noise in RF signals as well as their penetration through walls, leading to security vulnerabilities and low-precision control. To address these issues, this paper presents a smart home control system based on visible light communication (VLC), with enhanced security and accurate localization for precise and convenient control. The system includes an AC lightbulb, a smartphone running the position and control applications, and a cloud server with location-based access and a database of smart home devices. The design of the AC lightbulb integrates VLC and Bluetooth connectivity in a standard form factor for easy installation and plug-n-play capability. A smartphone camera-based 3D indoor positioning and orientation algorithm that allows precise control by pointing the smartphone the device is also presented. We demonstrate the feasibility of this system through prototype implementation and experimental verification.
Communication System Based on Magnetic Coils for Underwater Vehicles
Authors: Canales-Gómez Giovanni , León-Gónzalez Gloria , Jorge-Muñoz Neguib , Arroyo-Núñez José Humberto , Antonio-Yañez Elba Dolores , Núñez-Cruz Rafael Stanley
Published: October 2022

In this work, a wireless communication system based on magnetic coils for underwater vehicles is presented. Firstly, the mathematical model of magnetic field induction using magnetic coils is discussed. Then, a description of the proposed communication system is presented, including the main components of the transmitter and receiver module. The experimental results show that due to the properties of the magnetic field, the proposed communication system can work properly in different environments such as air or water with the same efficiency. Underwater tests were carried out in different water circumstances: varying the temperature in a range from 10 °C to 35 °C, varying concentrations of clay in a range from 0% to 10%, and varying the salinity concentration in a range from 1000 ppm ( parts per million) to 35,000 ppm. It was observed that these conditions do not affect the information transfer. Finally, the advantages of using the proposed system compared to existing submarine communication systems are discussed.
Towards Transfer Learning Techniques—BERT, DistilBERT, BERTimbau, and DistilBERTimbau for Automatic Text Classification from Different Languages: A Case Study
Authors: Silva Barbon Rafael , Akabane Ademar Takeo
Published: October 2022

The Internet of Things is a paradigm that interconnects several smart devices through the internet to provide ubiquitous services to users. This paradigm and Web 2.0 platforms generate countless amounts of textual data. Thus, a significant challenge in this context is automatically performing text classification. State-of-the-art outcomes have recently been obtained by employing language models trained from scratch on corpora made up from news online to handle text classification better. A language model that we can highlight is BERT (Bidirectional Encoder Representations from Transformers) and also DistilBERT is a pre-trained smaller general-purpose language representation model. In this context, through a case study, we propose performing the text classification task with two previously mentioned models for two languages (English and Brazilian Portuguese) in different datasets. The results show that DistilBERT’s training time for English and Brazilian Portuguese was about 45% faster than its larger counterpart, it was also 40% smaller, and preserves about 96% of language comprehension skills for balanced datasets.
Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method
Authors: Limbu Sarita , Dakshanamurthy Sivanesan
Published: October 2022

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.
Secure Decentralized IoT Service Platform Using Consortium Blockchain
Authors: Zhang Ruipeng , Xu Chen , Xie Mengjun
Published: October 2022

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform’s transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.
Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis
Authors: Dzierżak Róża , Omiotek Zbigniew
Published: October 2022

The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.
Suppression of Modulation Instability Induced Phase Noise in the Long-Haul Phase-Sensitive Optical Time Domain Reflectometry
Authors: Zhang Yichi , Zhu Qi , Lu Yang , Meng Zhou , Hu Xiaoyang
Published: October 2022

Modulation instability (MI) is the main limitation factor of the maximum optical power in long-haul phase-sensitive optical time domain reflectometry (Φ-OTDR), and induces signal fading and serious phase noise. In this paper, a method of coherent seed injection is proposed to suppress the MI-induced phase noise in long-haul Φ-OTDR. The spontaneous MI is suppressed by stimulating induced MI in an optical fiber. The visibility of the signal in Φ-OTDR is enhanced and the phase noise is suppressed significantly. This paper offers an effective method to increase the maximum input power with the MI-induced phase noise suppressed in the long-haul Φ-OTDR system. As a result, the maximum input power and sensing distance can be potentially increased, which is greatly beneficial to the enhancement of the performance of long-haul Φ-OTDR.
Spectral Analysis and Information Entropy Approaches to Data of VLF Disturbances in the Waveguide Earth-Ionosphere
Authors: Rapoport Yuriy , Reshetnyk Volodymyr , Grytsai Asen , Grimalsky Volodymyr , Liashchuk Oleksandr , Fedorenko Alla , Hayakawa Masashi , Krankowski Andrzej , Błaszkiewicz Leszek , Flisek Paweł
Published: October 2022

Very low frequency (VLF) signals are considered as an important tool to study ionosphere disturbances. We have studied variations in signal amplitude of the Japanese JJI transmitter received by a network of eight Japan stations. The distinctions between characteristics of daytime and nighttime disturbances are considered. Signal processing based on spectral analysis is used to evaluate typical periodicities in the VLF signals in the time range from minutes to hours. In particular, we have retrieved quasi-wave oscillations of the received signal with periods of 4–10 and 20–25 min, which can be associated with atmospheric gravity waves excited by the solar terminator, earthquakes or other reasons. In addition, oscillations at periods of 3–4 h are observed, probably, caused by long-period gravity waves. We also calculate the information entropy to identify main details in daily VLF variations and influence of solar flares. It is shown that the information entropy increases near sunrise and sunset with seasonal variation, and that solar flares also lead to the growth in information entropy. A theoretical interpretation is given to the typical features of ultra-low frequency modulation of VLF electronagnetic wave spectra in Waveguide Earth-Ionosphere, found by processing the experimental data.
Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning
Authors: Mukhiddinov Mukhriddin , Muminov Azamjon , Cho Jinsoo
Published: October 2022

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.
An Improved Acoustic Pick-Up for Straight Line-Type Sagnac Fiber Optic Acoustic Sensing System
Authors: Chen Jianjun , Wang Jiang , Wang Ning , Ruan Juan , Zhang Jie , Zhu Yong
Published: October 2022

An improved acoustic pick-up is presented to enhance the acoustic sensing sensitivity of the straight line-type Sagnac fiber optic acoustic sensing system. A hollow elastomer cylinder is introduced into the system, and the optical fiber is tightly wound around the cylinder to construct the pick-up. The theoretical analysis was finished, and it showed that the improved pick-up will bring in an extra phase change and let the phase difference increase almost an order. The extra phase change will enhance the sensing sensitivity correspondingly. The titanium alloy elastic cylinder was designed and manufactured. The experiment system was built, and a sinusoidal acoustic signal was used as the sound source. The tests were taken during 100–1500 Hz, and the experimental study showed that the sensitivity was more than 130 mV/Pa when the pick-up was used as the acoustic sensing element, and compared to the fiber only system, the sensitivity was enhanced more than 71.2%. The improved pick-up will be helpful in sound recognition and expand the application area of the Sagnac acoustic sensing system.
Refractive Index and Alcohol-Concentration Sensor Based on Fano Phenomenon
Authors: Wang Qiang , Yan Shubin , Liu Jilai , Zhang Xiaoyu , Shen Lifang , Liu Pengwei , Cui Yang , Li Tingsong , Ren Yifeng
Published: October 2022

A novel nano-refractive index sensor based on the Fano resonance phenomenon is proposed in this paper. The sensor consists of the metal-insulator-metal (MIM) waveguide and a V-ring cavity with a groove (VRCG). We analyzed the performance of the nanoscale sensor using the finite element method. The simulation results show that the asymmetry of the geometric structure itself is the main factor leading to Fano resonance splitting. In Fano splitting mode, the Fano bandwidth of the system can be significantly reduced when the sensor sensitivity is slightly reduced, so that the figure of merit (FOM) of the sensor can be substantially improved. Based on the above advantages, the sensor’s sensitivity in this paper is as high as 2765 nm/RIU, FOM = 50.28. In addition, we further applied the sensor to alcohol concentration detection. The effect is good, and the sensitivity achieves about 150. This type of sensor has a bright future in the precision measurement of solution concentrations.
Deep Learning Hybrid Techniques for Brain Tumor Segmentation
Authors: Munir Khushboo , Frezza Fabrizio , Rizzi Antonello
Published: October 2022

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by 7.5% in dice score, 23.91% insensitivity, and 7.09% in specificity. Depth-wise separable MI-Unet showed a performance increase by 10.83% in dice score, 2.97% in sensitivity, and 12.72% in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of 9.71% in dice score, 3.56% in sensitivity, and 12.6% in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by 15.45% in dice score, 20.56% in sensitivity, and 12.22% in specificity.
Design and Optimization for a New XYZ Micropositioner with Embedded Displacement Sensor for Biomaterial Sample Probing Application
Authors: Dang Minh Phung , Le Hieu Giang , Phan Thu Thi Dang , Chau Ngoc Le , Dao Thanh-Phong
Published: October 2022

An XYZ compliant micropositioner has been widely mentioned in precision engineering, but the displacements in the X, Y, and Z directions are often not the same. In this study, a design and optimization for a new XYZ micropositioner are developed to obtain three same displacements in three axes. The proposed micropositioner is a planar mechanism whose advantage is a generation of three motions with only two actuators. In the design strategy, the proposed micropositioner is designed by a combination of a symmetrical four-lever displacement amplifier, a symmetrical parallel guiding mechanism, and a symmetrical parallel redirection mechanism. The Z-shaped hinges are used to gain motion in the Z-axis displacement. Four flexure right-circular hinges are combined with two rigid joints and two flexure leaf hinges to permit two large X-and-Y displacements. The symmetrical four-lever displacement amplifier is designed to increase the micropositioner’s travel. The displacement sensor is built by embedding the strain gauges on the hinges of the micropositioner, which is developed to measure the travel of the micropositioner. The behaviors and performances of the micropositioner are modeled by using the Taguchi-based response surface methodology. Additionally, the geometrical factors of the XYZ micropositioner are optimized by teaching–learning-based optimization. The optimized design parameters are defined with an A of 0.9 mm, a B of 0.8 mm, a C of 0.57 mm, and a D of 0.7 mm. The safety factor gains 1.85, while the displacement achieves 515.7278 µm. The developed micropositioner is a potential option for biomedical sample testing in a nanoindentation system.
Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue
Authors: Zhang Jing-Wen , Pan Lei-Qing , Tu Kang
Published: October 2022

The growth models of total bacterial count in freshly squeezed strawberry juice were established by gas and taste sensors in this paper. By selecting the optimal sensors and fusing the response values, the Modified Gompertz, Logistic, Huang and Baranyi models were used to predict and simulate the growth of bacteria. The results showed that the R2 values for fitting the growth model of total bacterial count of the sensor S7 (an electronic nose sensor), of sweetness and of the principal components scores were 0.890–0.944, 0.861–0.885 and 0.954–0.964, respectively. The correlation coefficients, or R-values, between models fitted by the response values and total bacterial count ranged from 0.815 to 0.999. A single system of electronic nose (E-nose) or electronic tongue (E-tongue) sensors could be used to predict the total bacterial count in freshly squeezed strawberry juice during cold storage, while the higher rate was gained by the combination of these two systems. The fusion of E-nose and E-tongue had the best fitting-precision in predicting the total bacterial count in freshly squeezed strawberry juice during cold storage. This study proved that it was feasible to predict the growth of bacteria in freshly squeezed strawberry juice using E-nose and E-tongue sensors.
A New Scheme of Applying CORPS and Crossovers to Reduce the Number of Phase Shifters in Antenna Arrays
Authors: Calvillo Gilberto , Panduro Marco A. , Sanchez Brian , Reyna Alberto
Published: October 2022

This paper presents a new scheme of applying CORPS (coherently radiating periodic structures) for reducing the number of phase shifters in linear antenna arrays. This scheme can be seen as a combination of the properties of two techniques: CORPS and butler. The proposed system applies an interleaving of several blocks of 2 × 3 CORPS networks. This interleaving of two stages of 2 × 3 CORPS networks is made in a convenient way to provide the required progressive phase for beam-scanning and the level of amplitude excitations necessary for achieving the radiation characteristics of low SLL. Interesting results are provided based on experimental measurements and full-wave simulations to analyze and evaluate the performance of the feeding network based on CORPS and the reduction capability of the number of phase shifters in the antenna system. The proposed design methodology achieves a reduction capability of 66% in the number of phase shifters used in linear antenna arrays. This reduction in the complexity of the antenna system is reached maintaining a peak SLL of −22 dB with scanning ranges of until ±25°. A good design option is provided to simplify the complexity of the feeding network in antenna array applications.
Supporting Electrolyte Manipulation for Simple Improvement of the Sensitivity of Trace Vanadium(V) Determination at a Lead-Coated Glassy Carbon Electrode
Authors: Tyszczuk-Rotko Katarzyna , Gorylewski Damian , Kozak Jędrzej
Published: October 2022

The paper presents a very simple way to extremely improve the sensitivity of trace V(V) determination. The application of a new supporting electrolyte composition (CH3COONH4, CH3COOH, and NH4Cl) instead of the commonly used acetate buffer (CH3COONa and CH3COOH) significantly enhanced the adsorptive stripping voltammetric signal of vanadium(V) at the lead-coated glassy carbon electrode (GCE/PbF). A higher enhancement was attained in the presence of cupferron as a complexing agent (approximately 10 times V(V) signal amplification) than in the case of chloranilic acid and bromate ions (approximately 0.5 times V(V) signal amplification). Therefore, the adsorptive stripping voltammetric system with the accumulation of V(V)–cupferron complexes at −1.1 V for 15 s in the buffer solution (CH3COONH4, CH3COOH, and NH4Cl) of pH = 5.6 ± 0.1 was selected for the development of a simple and extremely sensitive V(V) analysis procedure. Under optimized conditions, the sensitivity of the procedure was 6.30 µA/nmol L−1. The cathodic peak current of V(V) was directly proportional to its concentration in the ranges of 1.0 × 10−11 to 2.0 × 10−10 mol L−1 and 2.0 × 10−10 to 1.0 × 10−8 mol L−1. Among the electrochemical procedures, the lowest detection limit (2.8 × 10−12 mol L−1) of V(V) was obtained for the shortest accumulation time (15 s). The high accuracy of the procedure was confirmed on the basis of the analysis of certified reference material (estuarine water) and river water samples.
Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data
Authors: Misra Sajal , Kumar Satish , Sayyad Sameer , Bongale Arunkumar , Jadhav Priya , Kotecha Ketan , Abraham Ajith , Gabralla Lubna Abdelkareim
Published: October 2022

The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.
SWCNT Network-FET Device for Human Serum Albumin Detection
Authors: Yahya Iskandar , Hassan Muhamad Azuddin , Maidin Nur Nasyifa Mohd , Mohamed Mohd Ambri
Published: October 2022

A thin film of single-walled carbon nanotube (SWCNT) network field-effect transistor (FET) was fabricated by a simple, fast, and reliable deposition method for electronic applications. This study aims to develop a method for fabricating a thin film of random SWCNTs to be used as a transducer to detect human serum albumin (HSA) in biosensor applications. The random SWCNT network was deposited using the airbrush technique. The morphology of the CNT network was examined by utilising atomic force microscopy (AFM) and field-emission scanning electron microscopy (FESEM), while electrical characteristics were analysed using three-terminal IV measurements. The thin film (SWCNT network) was applied as a transducer to detect human serum albumin (HSA) based on its covalent interaction with antibodies. HSA plays a significant part in the physiological functions of the human body. The surface alteration of the SWCNTs was verified using Fourier transform infrared (FTIR) spectroscopy. Electrical current–voltage measurements validated the surface binding and HSA detection. The biosensor linearly recorded a 0.47 fg/mL limit of detection (LOD) and a high sensitivity of 3.44 μA (g/mL)−1 between 1 fg/mL and 10 pg/mL. This device can also be used to identify a genuine HSA despite interference from other biomolecules (i.e., bovine serum albumin (BSA)), thus demonstrating the random SWCNT-FET immunosensor ability to quantify HSA in a complex biological environment.
Fabrication of Hydroxypropyl Methylcellulose Orodispersible Film Loaded Mirtazapine Using a Syringe Extrusion 3D Printer
Authors: Chaiwarit Tanpong , Aodsab Niphattha , Promyos Pimonnart , Panraksa Pattaraporn , Udomsom Suruk , Jantrawut Pensak
Published: October 2022

Depression is a mental illness causing a continuous negative feeling and loss of interest and affects physical and mental health. Mirtazapine (MTZ) is an effective medicine for treating depression, but patients lack compliance. However, transforming a pharmaceutical dosage form to an orodispersible film (ODF) could resolve this issue. This study aims to fabricate ODF-loading mirtazapine, using a syringe extrusion 3D printer, and compare its properties with the solvent-casting method. The ODFs were prepared by dissolving the mirtazapine in a hydroxypropyl methylcellulose E15 solution, and then fabricated by a 3D printer or casting. The 3D printing was accurate and precise in fabricating the ODFs. The SEM micrographs showed that the mirtazapine-printed ODF (3D-MTZ) was porous, with crystals of mirtazapine on the film’s surface. The 3D-MTZ exhibited better mechanical properties than the mirtazapine-casted ODF (C-MTZ), due to the 3D-printing process. The disintegration time of the 3D-MTZ in a simulated salivary fluid, pH 6.8 at 37 °C, was 24.38 s, which is faster than the C-MTZ (46.75 s). The in vitro dissolution study, in 0.1 N HCl at 37 °C, found the 3D-MTZ quickly released the drug by more than 80% in 5 min. This study manifested that 3D-printing technology can potentially be applied for the fabrication of ODF-containing mirtazapine.
Untargeted Metabolomics by Using UHPLC–ESI–MS/MS of an Extract Obtained with Ethyl Lactate Green Solvent from Salvia rosmarinus
Authors: Castañeta Grover , Cifuentes Nicolas , Sepulveda Beatriz , Bárcenas-Pérez Daniela , Cheel José , Areche Carlos
Published: October 2022

Salvia rosmarinus (Lamiaceae), previously known as Rosmarinus officinalis, is a plant cultivated worldwide, native to the Mediterranean region. Its leaves are traditionally used for cooking. This species possesses numerous biological activities, including antioxidant, antimicrobial, anticancer, anti-inflammatory, and hepatoprotective properties. These biological properties are due to the presence of phenolic compounds, including rosmarinic acid and phenolic diterpenoids, such as carnosic acid and carnosol. In this study, we investigated the chemical composition of a green extract obtained by maceration with ethyl lactate for the first time. Seventy-five compounds were tentatively identified by UHPLC–ESI–MS/MS, including six organic acids, six cinnamic acid derivatives, five fatty acids, eighteen flavonoids, and thirty-eight terpenoids. Thus, abietane-type diterpenoids from the ethyl lactate extract were the predominant diterpenoids in the Chilean S. rosmarinus species, in contrast to the Chinese species, in which labdane and isopimarane-type diterpenoids were found for the first time. Finally, our study confirms that the extraction of S. rosmarinus with green ethyl lactate as a solvent is efficient and sustainable for the identification of flavonoids, phenols, and terpenoids from leaves.
Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
Authors: Piper Jonathan , Yuen Peter W. T. , James David
Published: October 2022

In recent years, a wide range of hyperspectral imaging systems using coded apertures have been proposed. Many implement compressive sensing to achieve faster acquisition of a hyperspectral data cube, but it is also potentially beneficial to use coded aperture imaging in sensors that capture full-rank (non-compressive) measurements. In this paper we analyse the signal-to-noise ratio for such a sensor, which uses a Hadamard code pattern of slits instead of the single slit of a typical pushbroom imaging spectrometer. We show that the coded slit sensor may have performance advantages in situations where the dominant noise sources do not depend on the signal level; but that where Shot noise dominates a conventional single-slit sensor would be more effective. These results may also have implications for the utility of compressive sensing systems.
Refugee Education under International NGOs: A Major Shift from National Institutions to Patron–Client Relations
Authors: Akhtar Sheraz , Rata Elizabeth
Published: October 2022

What happens when a group of structurally powerless refugees exist within a nation-state’s territory but outside its regulatory institutions? An empirical study of the education of Pakistani Christian refugees in Bangkok, Thailand, identifies an entrenched gap between the education provided by INGOs and Pakistani Christian refugee expectations of the academic education of their children. We generalise from the specific problem of the entrenched educational discrepancy to a deeper structural inequality by using a ‘realist conceptual methodology’ characterised by the type of co-dependency found in the historical form of patron–client relations. The patron–client relationship is the outcome of being placed outside a nation-state’s institutions and the co-dependence that the relationship itself creates between the INGO providers and the refugees. We suggest that patron–client theory is a useful conceptual tool with which to explain the sociopolitical position of groups today who find themselves placed outside a modern nation-state’s institutions.
MEC-Enabled Fine-Grained Task Offloading for UAV Networks in Urban Environments
Authors: Yu Sicong , Zheng Huiji , Ma Caihong
Published: October 2022

In recent years, with the continuous development of information technology, the amount of data generated and hosted by cloud service platforms in urban environments is unprecedented. Mobile edge computing (MEC) is combined with UAV networks to better realize the ability to provide nearby services to a large number of terminal devices in cities. Unmanned aerial vehicles (UAVs) are highly maneuverable and inexpensive and are good carriers for carrying MEC platforms. In UAV edge networks, we usually face the problem of fine-grained task offloading based on relevant features of urban environments. We need to address high energy consumption and task processing delays to help achieve urban sustainability goals. Therefore, we combine the software definition network (SDN) technology and, on this basis, we propose two task offloading strategies based on an improved EFO intelligent algorithm for different user scales. At the same time, we run the proposed offloading system in the UAV sensor. The experiment shows that, compared with the traditional strategy, the unloading efficiency of the proposed method can be improved by about 10%.
Ethical Construction and Development of Mining Engineering Based on the Safe, Efficient, Green, and Low-Carbon Concept
Authors: Wang Fangtian , Qu Hongfei , Tian Wei , Zhai Shilei , Ma Liqiang
Published: October 2022

Modern mining engineering has become a huge system project with the increased intensification and complexity of mining engineering, which interwinds, involving many factors. Ethical issues in the main body of mining engineering have become more and more prominent. What must complement ongoing discussions is a more professional and systematic analysis that engages with mining engineering on the socio-technical systems. In this paper, first, the connotation and basic principles of mining engineering ethics are put forward. Then, the ethical responsibilities that mining engineers may face are analyzed. It is suggested that the code of mining engineering ethics can, in practice, provide engineers with the necessary guidelines to avoid mine accidents caused by wrong decisions. In addition, a case base is introduced to train students to analyze engineering ethics in practical cases, and four typical case studies are discussed in detail. Then, the implementation paths of mining engineering ethics are studied, which are centered on the concept of safe, efficient, green, and low-carbon development. Finally, we suggest that improving the ethical norms of mining engineering, personnel training system, and moral supervision of mining projects will enable engineers to deal with the ethical issues of mining engineering more effectively, thereby improving the sustainability of mining engineering.
The Communication Mechanism in the Workshop and Its Implications for the Sustainable Development of Traditional Crafts: A Case Study of Lacquer Culture in Taomi Eco-Village
Authors: Sun Yikang , Liao Chia-Chan , Chang Sai-Ching , Lin Rungtai
Published: October 2022

Many countries have been digging deeply into local culture and characteristics, and constructing appropriate models for culture, industry, and consumers is a critical problem. Employing a qualitative research method, this study focused on the communication mechanism in the workshop and its implications for the sustainable development of lacquer in Taiwan. A creation-exhibit-popularize model was constructed. Data were collected using semi-structured interviews with one lacquer artist. The results showed that cultural sustainability needs to be addressed from different aspects in order to benefit all participants. This can allow those who have no artistic background to participate, feel the charm of art, and realize the connotation and value of culture, and the artworks can convey the value of cultural features to tourists. A follow-up study should focus on how participants and visitors can better appreciate the value of activities such as workshops, and explore ways to invite them to give feedback. Due to the limitation of the number of people in these activities, and because many people may not have time to participate, it is advisable to use technology to achieve simultaneous offline and online implementation, which is of great benefit to cultural sustainability.
Waste-to-Energy Generation: Complex Efficiency Analysis of Modern Technologies
Authors: Vukovic Natalia , Makogon Evgenia
Published: October 2022

Recycling of Municipal Solid Waste (MSW) is a significant challenge all over the world. Waste-to-Energy generation solves the problem of MSW recycling and produces power for urban territories. In this study, the researchers implemented complex economic and ecological efficiency analyses of modern Waste-to-Energy technologies. The fundamental challenge of modern Waste-to-Energy generations is finding the balance between economics, ecology, and productivity. Thus, to assess the effectiveness of various thermal technologies, statistics from enterprises were used. The Balanced Scorecard (BSC) method was implemented to calculate an integral effectiveness of a particular Waste-to-Energy technological approach. Environmental and economic analysess of thermal MSW disposal technologies was carried out by selecting the data from at least 146 functioning plants in Canada, China, Finland, France, Germany, Italy, Japan, the Netherlands, Sweden, and Thailand. The research results confirm that gasification technology was the most promising and the most environmentally and cost effective. Incineration Moving Grate technology was the least effective and attractive Waste-to-Energy technology according to the results of the environmental and economic efficiency assessments. The research results can be used for urban planning in waste recycling projects and the new energy national and municipal agenda. The research results can also be useful for municipal strategic energy and sustainable plans and programs.
The Impact of High-Speed Rail Systems on Tourist Attractiveness in Italy: Regression Models and Numerical Results
Authors: Gallo Mariano , La Rocca Rosa Anna
Published: October 2022

This paper evaluates the impact of high-speed rail systems on tourist attractiveness in Italy. The analysis is carried out with reference to provincial capitals, only some of which are served by high-speed railway lines. To achieve this objective, two multiple linear regression models were specified and calibrated, which relate arrivals and presences in accommodation facilities to several factors that could influence the tourist destination: cultural, historical, and monumental heritage, commercial activities, recreational activities, accessibility, etc. Both models showed that the availability of high-speed railway services is an important factor in the choice of tourist destination, being, moreover, the only accessibility variable found to be significant; furthermore, the elasticity of tourist demand to this factor was significant too.
Spatial Disproportions of the Green Economy and the Financial Situation of Polish Voivodeships in 2010–2020
Authors: Dziekański Paweł , Prus Piotr , Sołtyk Piotr , Wrońska Magdalena , Imbrea Florin , Smuleac Laura , Pascalau Raul , Błaszczyk Karolina
Published: October 2022

The provincial government, a separate association of regional communities in the state structure, carries out public tasks that affect the quality of life of the local community and the activities of businesses. Geographical conditions and the effect of socioeconomic factors lead to different levels of development in different regions. The endogenous potential of the local government is the result of a combination of local conditions, determining its opportunities and directions of development. The aim of this article was to assess and identify the spatial differentiation in the relationship between the green economy and the financial situation of voivodeships in Poland using synthetic measures. The choice of variables for the period of 2010–2020 was determined by the availability of the data collected from the regional system in the CSO Local Data Bank. The Technique for Order of Preference by Similarity to the Ideal Solution was used to construct the synthetic measures. Financial resources are the basis for the operation of local government units, conditioning the implementation of their current and developmental tasks. Knowledge of the current state of finances allows authorities to make comparisons with other units and is helpful in making financial decisions. The green economy is one of the paths to sustainable development. The green economy increases the quality of life and social equality while reducing environmental risks and natural resource consumption. As a result of the research procedure, the spatial differentiation of provinces in Poland in 2010–2020 is presented in terms of the level of the synthetic measure of the green economy and financial situation. In 2010, the synthetic measure of the green economy ranged from 0.31 to 0.42, and in 2020, it ranged from 0.40 to 0.53. The synthetic measure of financial risk in 2010 ranged from 0.37 to 0.61, and in 2020, it ranged from 0.40 to 0.77. The measures are shaped by economic activity and working conditions, health, education, leisure and social relations, economic and physical security, and environmental quality. The results of this survey enable local governments to make comparisons. The lessons learned can allow them to identify potential directions for optimizing development policies.
Vermicomposting as a Sustainable Option for Managing Biomass of the Invasive Tree Acacia dealbata Link
Authors: Quintela-Sabarís Celestino , Mendes Luís A. , Domínguez Jorge
Published: October 2022

The tree Acacia dealbata is native to Australia but has become invasive in many parts of the world thanks to its N-fixing capacity and to the allelopathic compounds present in its biomass. We conducted a pilot-scale study to assess the potential conversion of A. dealbata biomass by vermicomposting via the earthworm Eisenia andrei. The flowering aerial A. dealbata biomass was shredded and placed in a vermireactor under greenhouse conditions for 56 days. The vermicomposted material was sampled every two weeks to analyse its biological and chemical parameters. The phytotoxicity of the fresh A. dealbata material and vermicompost was assessed via an ecotoxicological test with Lepidium sativum seeds. The activity of the earthworms caused strong modifications of the properties of the processed material: the electric conductivity, basal respiration, and organic matter content were reduced, whereas the concentrations of other elements such as N, P, or Zn increased. The earthworm biomass increased steadily until day 42 and then decreased, probably due to the depletion of labile organic matter during the initial stages of vermicomposting. The fresh A. dealbata material reduced the germination and radicle elongation of L. sativum, whereas vermicompost showed the same values as control. The produced vermicompost was an organic fertiliser rich in N and was not phytotoxic. Vermicomposting provides an opportunity to create a new value chain for the control of the invasive tree A. dealbata.
War Impact on Air Quality in Ukraine
Authors: Zalakeviciute Rasa , Mejia Danilo , Alvarez Hermel , Bermeo Xavier , Bonilla-Bedoya Santiago , Rybarczyk Yves , Lamb Brian
Published: October 2022

In the light of the 21st century, after two devastating world wars, humanity still has not learned to solve their conflicts through peaceful negotiations and dialogue. Armed conflicts, both international and within a single state, still cause devastation, displacement, and death all over the world. Not to mention the consequences that war has on the environment. Due to a lack of published research about war impact on modern air quality, this work studies air pollution evolution during the first months of the Russian-Ukrainian conflict. Satellite images of NO2, CO, O3, SO2, and PM2.5 over Ukrainian territory and PM2.5 land monitoring data for Kyiv were analyzed. The results showed that NO2 and PM2.5 correlated the most with war activities. CO and O3 levels increased, while SO2 concentrations reduced four-fold as war intensified. Drastic increases in pollution (especially PM2.5) from bombing and structural fires, raise additional health concerns, which might have serious implications for the exposed local and regional populations. This study is an invaluable proof of the impact any armed conflict has on air quality, the population, and environment.