bluuebunny/crossref_metadata_2025 huggingface dataset:
Suckling Behaviour of Beef Calves during the First Five Days Postpartum
Authors: Hogan Lindsay A. , McGowan Michael R. , Johnston Stephen D. , Lisle Allan T. , Schooley Kylie
Published: August 2022

Observations of 17 heifer-calf pairs were collected over the first 5 days postpartum (p.p.) to study nursing and maternal behaviour of beef cattle. Cattle were managed in a 6 ha paddock and heifer–calf interactions were determined from both regular visual observations and video recordings. Of 17 potential calves, one was stillborn and 3 died in the first 5 days p.p. associated with dystocia and congenital malformation. Four further potential neonatal calf mortality risks were identified, which included poor calving site selection (n = 1), calf misadventure (n = 1), heifer-calf separation (n = 3) and mismothering (n = 3), with each resulting in distress, physical exhaustion of the calf and impaired nursing. There was marked variation between heifers in the expression of optimal maternal behaviours, with only 39% classified as ‘good’ mothers. Dam terminated nursing bouts were 27% shorter in duration than calf terminated nursing bouts; 29% of heifers terminated nursing bouts at least 50% of the time. Only 68% of observed suckling interactions were considered effective. On average, the nursing behaviour lasted 7.0 min, with sucking making up 54% of the total nursing time, the sucking rate was 2.0 sucks/s, and the calves performed 7.5 teat-switches, 2.4 butts and 0.9 teat-strips per min of nursing. In 67% of nursing interactions, the calves sucked on all four teats. By three days p.p., all calves developed a clear, consistent suckling pattern. Prior to this, the calves had shorter nursing bouts, spent less time nursing and manipulating the udder, paused more, switched teats and butted less, and had a slower sucking rate. The behaviour of some calves (i.e., low teat fidelity and high levels of milk stimulation behaviours) suggested that their dam milk availability was low. This study has quantified early post-partum nursing behaviour of neonatal beef calves and highlighted dam and calf behaviours that may adversely affect milk intake and, therefore, impact calf survival.
Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator
Authors: Montenegro Guelis , Chacón Roberto , Fabregas Ernesto , Garcia Gonzalo , Schröder Karla , Marroquín Alberto , Dormido-Canto Sebastián , Farias Gonzalo
Published: August 2022

This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of experience. The model was tested under several scenarios and control goals (i.e., position control, path-following and formation control) with control strategies such as reinforcement learning, and Villela and IPC algorithms. The results of these approaches were compared using performance indexes to analyze the performance of the model under different scenarios. The model and examples with different control scenarios are available online.
Characteristic Analysis of a Chipless RFID Sensor Based on Multi-Parameter Sensing and an Intelligent Detection Method
Authors: Liu Luyi , Chen Lan
Published: August 2022

Chipless radio frequency identification (RFID) technology has been widely used in the field of structural health monitoring (SHM), but most of the current research mainly focuses on the detection of mechanical properties and there are few studies on the multi-physical parameters (for example, temperature and humidity) in the climatic environment around the structure. Thus, it is necessary to design a small and compact sensor for multi-parameter detection. This paper proposes a multi-parameter chipless RFID sensor based on microstrip coupling, which supports 4-bit ID code and integrates two detection functions of temperature and humidity. Through linear normalization fitting, the sensitivity of the sensor is about 2.18 MHz/RH in the ambient relative humidity test and the sensitivity of the sensor is about 898.63 KHz/°C in the experimental test of water bath heating from 24.6 °C to 75 °C. In addition, this paper proposes an engineering application detection method, designs a lightweight dynamic spectrum detection and wireless transmission platform based on a lightweight vector network analyzer (VNA) and realizes the real-time extraction and transmission of RFID spectrum sensing data. The means are more flexible and economical than traditional experimental scenarios.
A Method for Measuring the Quality of Graphic Transfer to Materials with Variable Dimensions (Wood)
Authors: Wagnerova Renata , Jurek Martin , Czebe Jiri , Gebauer Jan
Published: August 2022

The transfer of graphics to a product’s surface is a widely known technology. Printing, engraving, and etching are used every day in production processes with countless types of materials. This paper deals with quality control for laser engraving on surfaces with variable dimensions via optical sensors. The engraving process, apart from colour changes, can induce volume and moisture changes, which lead to dimension changes in some materials. Natural materials and biomaterials are among the ones most affected. Combined with the porous and inhomogeneous structure of such a material, it can be difficult to measure the quality of graphic transfer, especially for shaded products. The quality control of laser-engraved photographs on thin layers of wood veneer was selected as a suitable problem to solve. A complex method for the quality measurement of the specified production was designed and tested. We used an affine transformation to determine the system behaviour and to determine the transfer function of material changes during the production process. Moreover, there is a possibility to compensate the image deformation of the engraved product.
Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley (Hordeum vulgare L.)
Authors: Christ Andreas , Schmittmann Oliver , Schulze Lammers Peter
Published: August 2022

In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green–red, and blue–yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure.
Detecting and Localizing Cyber-Physical Attacks in Water Distribution Systems without Records of Labeled Attacks
Authors: Housh Mashor , Kadosh Noy , Haddad Jack
Published: August 2022

Modern water distribution systems (WDSs) offer automated controls and operations to improve their efficiency and reliability. Nonetheless, such automation can be vulnerable to cyber-attacks. Therefore, various approaches have been suggested to detect cyber-attacks in WDSs. However, most of these approaches rely on labeled attack records which are rarely available in real-world applications. Thus, for a detection model to be practical, it should be able to detect and localize events without referring to a predetermined list of labeled attacks. This study proposes a semi-supervised approach that relies solely on attack-free datasets to address this challenge. The approach utilizes a reduction in dimensionality by using maximum canonical correlation analysis (MCCA) followed by support vector data description (SVDD). The developed algorithm was tested on two case studies and various datasets, demonstrating consistently high performance in detecting and localizing cyber-attacks.
Convolutional Neural Network Approach Based on Multimodal Biometric System with Fusion of Face and Finger Vein Features
Authors: Wang Yang , Shi Dekai , Zhou Weibin
Published: August 2022

In today’s information age, how to accurately identify a person’s identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which uses a convolutional neural network (CNN), and the fusion occurs in the feature layer. The self-attention mechanism is used to obtain the weights of the two biometrics, and combined with the RESNET residual structure, the self-attention weight feature is cascaded with the bimodal fusion feature channel Concat. To prove the high efficiency of bimodal feature layer fusion, AlexNet and VGG-19 network models were selected in the experimental part for extracting finger vein and face image features as inputs to the feature fusion module. The extensive experiments show that the recognition accuracy of both models exceeds 98.4%, demonstrating the high efficiency of the bimodal feature fusion.
Efficient Color Quantization Using Superpixels
Authors: Frackiewicz Mariusz , Palus Henryk
Published: August 2022

We propose three methods for the color quantization of superpixel images. Prior to the application of each method, the target image is first segmented into a finite number of superpixels by grouping the pixels that are similar in color. The color of a superpixel is given by the arithmetic mean of the colors of all constituent pixels. Following this, the superpixels are quantized using common splitting or clustering methods, such as median cut, k-means, and fuzzy c-means. In this manner, a color palette is generated while the original pixel image undergoes color mapping. The effectiveness of each proposed superpixel method is validated via experimentation using different color images. We compare the proposed methods with state-of-the-art color quantization methods. The results show significantly decreased computation time along with high quality of the quantized images. However, a multi-index evaluation process shows that the image quality is slightly worse than that obtained via pixel methods.
Two-Stage Stochastic Optimization for the Pre-Position and Reconfiguration of Microgrid Defense Resources against Natural Disasters
Authors: Jiang Bo , Lei Hongtao , Huang Shengjun , Li Wenhua , Jiao Peng
Published: August 2022

With the aggravation and evolution of global warming, natural disasters such as hurricanes occur more frequently, posing a great challenge to large-scale power systems. Therefore, the pre-position and reconfiguration of the microgrid defense resources by means of Mobile Energy Storage Vehicles (MEVs) and tie lines in damaged scenarios have attracted more and more attention. This paper proposes a novel two-stage optimization model with the consideration of MEVs and tie lines to minimize the shed loads and the outage duration of loads according to their proportional priorities. In the first stage, tie lines addition and MEVs pre-position are decided prior to a natural disaster; in the second stage, the switches of tie lines and original lines are operated and MEVs are allocated from staging locations to allocation nodes according to the specific damaged scenarios after the natural disaster strikes. The proposed load restoration method exploits the benefits of MEVs and ties lines by microgrid formation to pick up more critical loads. The progressive hedging algorithm is employed to solve the proposed scenario-based two-stage stochastic optimization problem. Finally, the effectiveness and superiority of the proposed model and applied algorithm are validated on an IEEE 33-bus test case.
Image Classification of Wheat Rust Based on Ensemble Learning
Authors: Pan Qian , Gao Maofang , Wu Pingbo , Yan Jingwen , AbdelRahman Mohamed A. E.
Published: August 2022

Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.
Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
Authors: Domino Małgorzata , Borowska Marta , Zdrojkowski Łukasz , Jasiński Tomasz , Sikorska Urszula , Skibniewski Michał , Maśko Małgorzata
Published: August 2022

As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.
Long-Standing Themes and Future Prospects for the Inspection and Maintenance of Façade Falling Objects from Tall Buildings
Authors: Chew Michael Y. L. , Gan Vincent J. L.
Published: August 2022

The increasing number of accidents arising from falling objects from the façade of tall buildings has attracted much attention globally. To regulators, a preventive approach based on a mandatory periodic façade inspection has been deemed as a necessary measure to maintain the functionality and integrity of the façade of tall buildings. Researchers worldwide have been working towards a predictive approach to allow for the assessment of the likely failure during some future period, by measuring the condition of the façade to detect latent defects and anomalies. The methods proposed include laser scanning, image-based sensing and infrared thermography to support the automatic façade visual inspection. This paper aims to review and analyse the state-of-the-art literature on the automated inspection of building façades, with emphasis on the detection and maintenance management of latent defects and anomalies for falling objects from tall buildings. A step-by-step holistic method is leveraged to retrieve the available literature from databases, followed by the analyses of relevant articles in different long-standing research themes. The types and characteristics of façade falling objects, legislations, practices and the effectiveness of various inspection techniques are discussed. Various diagnostic, inspection and analytical methods which support façade inspection and maintenance are analysed with discussion on the potential future research in this field.
A PNU-Based Methodology to Improve the Reliability of Biometric Systems
Authors: Capasso Paola , Cimmino Lucia , Abate Andrea F. , Bruno Andrea , Cattaneo Giuseppe
Published: August 2022

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.
Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones
Authors: Tian Shuangxi , Chen Honghui , Wu Guohua , Cheng Jiaqi
Published: August 2022

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.
A 10-Bit 400-KS/s Low Noise Asynchronous SAR ADC with Dual-Domain Comparator for Input-Referred Noise Reduction
Authors: Lee Sang-Hun , Lee Won-Young
Published: August 2022

This paper presents a low noise 0.6-V 400-kS/s asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) for input-referred noise reduction. A dual-domain comparator is proposed to optimize the power, noise, and sampling rate of the ADC in the 10-bit conversion. In order to optimize the figure of merits (FoM) of the ADC, the 10-bit conversion consists of a 7-bit coarse conversion with the double-tail dynamic comparator and a 3-bit fine conversion with the VCDL-based time-domain comparator. An asynchronous timing controller is also proposed to improve the ADC sampling rate and optimize the power consumption of the dual-domain comparator. The proposed SAR ADC is fabricated in 180-nm CMOS technology with an area of 0.836 mm2. At a 0.6-V supply voltage and a 400-kS/s sampling rate, the implemented SAR ADC achieves a signal-to-noise and distortion ratio (SNDR) of 56.59 dB and an effective number of bits (ENOB) of 9.16 bits. The peak values of DNL and INL are +0.47/−0.53 LSB and +0.92/−0.64 LSB, respectively. The FoM is 10.31 fJ/conversion step with a power consumption of 2.36 μW.
Estimation of Soluble Solids for Stone Fruit Varieties Based on Near-Infrared Spectra Using Machine Learning Techniques
Authors: Escárate Pedro , Farias Gonzalo , Naranjo Paulina , Zoffoli Juan Pablo
Published: August 2022

The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS–NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS–NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of 98.9% was obtained for the CNN classification model and a correlation coefficient of Rc>0.7109 for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.
A Self-Powered Strain Sensor Applied to Real-Time Monitoring for Movable Structures
Authors: Wu Yan-Kuei , Shen Sheng-Chih , Lee Chun-Yen , Chen Yen-Ju
Published: August 2022

This study uses near-field electrospinning (NFES) technology to make a novel self-powered strain sensor and applies it to the real-time monitoring of a bending structure, so that the measurement equipment can be reduced in volume. A self-powered strain sensor consists of polyvinylidene difluoride (PVDF) fibers, a PDMS fixed substrate, and an aluminum electrode. PVDF fibers are spun with DMSO and acetone using NFES technology, with a diameter of about 8 μm, Young’s modulus of 1.1 GPa, and piezoelectric effect of up to 230 mV. The fixed substrate is a film made of PDMS by thermal curing, then adhered to the PDMS film surface of the sheet Al metal as an Al electrode, and then combined with PVDF fiber film, to become a self-powered strain sensor. As a result, the XRD β value of the self-powered strain sensor reaches 2112 and the sensitivity is increased by 20% over a traditional strain sensor. The cumulative angle algorithm can be applied to measure the angular change of the object over a unit of time or the cumulative displacement of the object over the entire period of motion. The experimental results demonstrate that the self-powered strain sensor combined with the angle accumulation algorithm may be applied to monitor the bending structure, thereby achieving continuous measurements of bending structure changes, and improving on traditional piezoelectric sensors, which can only be sensed once. In the future, self-powered strain sensors will have the ability to continuously measure in real-time, enabling the use of piezoelectric sensors for long-term monitoring of structural techniques.
Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods
Authors: Wang Meng , Liu Changan , Han Dongrui , Wang Fei , Hou Xuehui , Liang Shouzhen , Sui Xueyan
Published: August 2022

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman–Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.
WER-Net: A New Lightweight Wide-Spectrum Encoding and Reconstruction Neural Network Applied to Computational Spectrum
Authors: Ding Xinran , Yang Lin , Yi Mingyang , Zhang Zhiteng , Liu Zhen , Liu Huaiyuan
Published: August 2022

The computational spectrometer has significant potential for portable in situ applications. Encoding and reconstruction are the most critical technical procedures. In encoding, the random mass production and selection method lacks quantitative designs which leads to low encoding efficiency. In reconstruction, traditional spectrum reconstruction algorithms such as matching tracking and gradient descent demonstrate disadvantages like limited accuracy and efficiency. In this paper, we propose a new lightweight convolutional neural network called the wide-spectrum encoding and reconstruction neural network (WER-Net), which includes optical filters, quantitative spectral transmittance encoding, and fast spectral reconstruction of the encoded spectral information. The spectral transmittance curve obtained by WER-net can be fabricated through the inverse design network. The spectrometer developed based on WER-net experimentally demonstrates that it can achieve a 2-nm high resolution. In addition, the spectral transmittance encoding curve trained by WER-Net has also achieved good performance in other spectral reconstruction algorithms.
A Compact Ultra-Thin 4 × 4 Multiple-Input Multiple-Output Antenna
Authors: Zhang Chuanba , Huang Jianlin , Shi Xiaojing , Dong Guiting , Cai Jing , Liu Gui
Published: August 2022

This article reported a compact ultra-thin tightly arranged 4 × 4 multiple-input multiple-output (MIMO) antenna pair (AP) functioning in the fifth-generation (5G) n78 band (3.4–3.6 GHz) for the ultra-thin 5G mobile handset. Two APs were printed on the center of two sideboards. A T-shaped open-ended slot was utilized in the grounding plane to improve the port impedance matching and attenuate the reciprocal magnetic coupling. A minimized total volume of 145 × 75 × 5 mm3 was obtained, and the area of each radiating unit was only 8.5 × 4.2 mm2 (0.1λ0 × 0.05λ0, λ0 is the free-space wavelength at the frequency of 3.5 GHz). By placing two radiating elements in an exceeding closed (1 mm or 0.01167λ0) distance, the designed AP precisely resonated at 3.5 GHz, and an acceptable measured isolation performance superior to 17 dB was attained. A prototype of this presented APs system was printed and tested, and remarkable consistency was observed between the simulated and measured curves. Numerous indicators were computed to assess its MIMO performance, such as Envelope Correlation Efficiency (ECC), Diversity Gain (DG), Total Active Reflection Coefficient (TARC), and Multiplexing Efficiency (ME).
Multi-Model Running Latency Optimization in an Edge Computing Paradigm
Authors: Li Peisong , Wang Xinheng , Huang Kaizhu , Huang Yi , Li Shancang , Iqbal Muddesar
Published: August 2022

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications.
Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms
Authors: Reichert Christoph , Klemm Lisa , Mushunuri Raghava Vinaykanth , Kalyani Avinash , Schreiber Stefanie , Kuehn Esther , Azañón Elena
Published: August 2022

Decoding natural hand movements is of interest for human–computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.
Two New HPLC Methods, Assessed by GAPI, for Simultaneous Determination of Four Antipsychotics in Pharmaceutical Formulations: A Comparative Study
Authors: Abdel Hameed Eman A. , Abd El-Naby Zaitona A. , El Gindy Alaa , Zaitone Sawsan A. , Alshaman Reem , Saraya Roshdy E. , Khairy Gasser M.
Published: August 2022

Antipsychotics are widely used to treat various mental disorders. Combination therapies were approved by the FDA to treat manic states. Quetiapine fumarate, aripiprazole, asenapine maleate, and chlorpromazine HCl are frequently used for treatment of these disorders. Green analytical chemistry is primarily concerned with reducing waste generated during sample preparation or analysis. Green solvents, such as ethanol, are being used in HPLC as an alternative to acetonitrile. To this purpose, two new chromatographic methods were developed to determine these four drugs simultaneously in their bulk and pharmaceutical formulations. The greenness of both methods was assessed by the green analytical procedure index (GAPI)—one of them was found to be green ecofriendly, and the other had some environmental hazards (conventional)—and this helps laboratories to choose a method that suits their capabilities. The chromatographic separation for both methods was carried out on a Thermo® C18 column. The total separation times were about 11 min and 9 min for the green and the conventional methods, respectively. Using the Student’s t-test and the F-ratio, there was no significant difference between the results of the two methods. These methods have been validated and successfully applied to the analysis of commercial pharmaceutical formulations. Our study could successfully be used in central quality control laboratories, which need a single analytical method to separate more than one compound with similar pharmacological action.
The Application of Blockchain Technology to Smart City Infrastructure
Authors: Alnahari Mohammed S. , Ariaratnam Samuel T.
Published: August 2022

A smart city can be defined as an integration of systems comprising a plethora of task-oriented technologies that aim to evolve and advance with city and infrastructure needs while providing services to citizens and resolving urban challenges through intersystem and data-driven analytical means, with minimal human intervention. Applications of technology include management, operations, and finance. One such technology is Blockchain. A main advantage of Blockchain is the simplification of processes that are costly and time-consuming. This is accomplished by simplifying operations to minimize costs resulting from the decentralization of assets. Blockchain has been proven to facilitate transparency, security, and the elimination of data fragmentation. However, as a relatively new technology, it poses regulatory obstacles. This issue can be attributed to the fact that many infrastructural governing organizations have incomplete knowledge of their infrastructure, which can lead to confusion when attempting to comprehend the different elements of the infrastructure, resulting in a lack of direction when trying to solve a problem. This paper explores the different applications of Blockchain technology in the sectors of energy, transportation, water, construction, and government, and provides a mechanism for implementing this technology in smart cities. As a present component of infrastructure management systems, Blockchain may potentially serve as the initial step toward upgrading infrastructure technology.
Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches
Authors: Azrak Rajae , Mélard Guy
Published: August 2022

Autoregressive-moving average (ARMA) models with time-dependent (td) coefficients and marginally heteroscedastic innovations provide a natural alternative to stationary ARMA models. Several theories have been developed in the last 25 years for parametric estimations in that context. In this paper, we focus on time-dependent autoregressive (tdAR) models and consider one of the estimation theories in that case. We also provide an alternative theory for tdAR processes that relies on a ρ-mixing property. We compare these theories to the Dahlhaus theory for locally stationary processes and the Bibi and Francq theory, made essentially for cyclically time-dependent models, with our own theory. Regarding existing theories, there are differences in the basic assumptions (e.g., on derivability with respect to time or with respect to parameters) that are better seen in specific cases such as the tdAR(1) process. There are also differences in terms of asymptotics, as shown by an example. Our opinion is that the field of application can play a role in choosing one of the theories. This paper is completed by simulation results that show that the asymptotic theory can be used even for short series (less than 50 observations).
Research of the Smart City Concept in Romanian Cities
Authors: Bălășescu Simona , Neacșu Nicoleta Andreea , Madar Anca , Zamfirache Alexandra , Bălășescu Marius
Published: August 2022

The Smart City concept has emerged in the last decade as a fusion of ideas about how information and communication technologies could improve the functioning of cities. A new concept, that of a sustainable Smart City, is already under discussion. This article aims at analyzing the Smart City concept in Romania. The resulting advantages, but also the difficulties and obstacles that need to be confronted, are considered when becoming a Smart City. When a city wants to become smart, it must consider both the advantages and the difficulties it will face in this endeavor. This paper has been able to take into account and group the four key features of a sustainable Smart City. The authors conducted research in two parts. The first consisted of conducting a comparative analysis of the most important results of Smart City projects implemented in the four reference cities in Romania compared to London. The second, a quantitative analysis, aimed to analyze the opinions and attitudes of Romanians regarding the Smart City concept in relation to sustainability. An important finding of the study shows that over half of the respondents are familiar with the Smart City concept and 41.9% consider health as a priority for investment in technology. The authors of the article propose clearer highlighting and division of cities from the point of view of creating a Smart City.
Educational Trials to Quantify Agronomic Information in Interdisciplinary Fieldwork in Pursat Province, Cambodia
Authors: Ye Rongling , Kodo Taisuke , Hirooka Yoshihiro , Sanara Hor , Soben Kim , Kobayashi Satoru , Homma Koki
Published: August 2022

Improving agricultural research and education is highly recommended to control agricultural development and environmental sustainability in Cambodia. Agricultural research mostly focuses on interviews with farmers as a first measure in developing countries, but a lack of quantitative accuracy remains one of the major constraints. In this situation, we conducted educational activities for master’s degree students of the Royal University of Agriculture (RUA) to append agronomic information with popular equipment in interdisciplinary fieldwork in Pursat Province, Cambodia. For the popular equipment, an RGB camera, a reflectometer as well as pH and EC meters were selected. The agronomic information collected by the students supported the results obtained during the interviews. For example, the difference in fertilizer application between the irrigated and nonirrigated areas was confirmed by the soil ammonium concentration evaluated with a reflectometer; the difference in rice growth among water conditions was confirmed by the leaf area percentage evaluated with an RGB camera. Since the majority of the students lacked agricultural and statistical knowledge, the agronomic information quantified by popular equipment provided proper educational materials. The interdisciplinary fieldwork also indicated serious problems in the study area, such as low beneficial crop production and environmental sustainability. To overcome these problems, improving agricultural education is required to foster skillful agricultural professionals, and the quantification of agronomic information is an essential issue.
An AI-Based Automatic Risks Detection Solution for Plant Owner’s Technical Requirements in Equipment Purchase Order
Authors: Kim Chae-Yeon , Jeong Jong-Gwan , Choi So-Won , Lee Eul-Bum
Published: August 2022

Maintenance activities to replace, repair, and revamp equipment in the industrial plant sector are gradually needed for sustainability during the plant’s life cycle. In order to carry out these revamping activities, the plant owners exchange many purchase orders (POs) with equipment suppliers, including technical and specification documents and commercial procurement content. As POs are written in various formats with large volumes and complexities, it is often time-consuming for the owner’s engineer to review them and it may lead to errors and omissions. This study proposed the purchase order recognition and analysis system (PORAS), which automatically detects and compares risk clauses between plant owners’ and suppliers’ POs by utilizing artificial intelligence (AI). The PORAS is a comprehensive framework consisting of two independent modules and four model components that accurately reflect on the added value of the PORAS. The table recognition and comparison (TRC) module is utilized for risk clauses in POs written in tables with its two components, the table comparison (TRC-C) and table recognition (TRC-R) models. The critical terms in general conditions (CTGC) module analyzes the patterns of risk clauses in general texts, then extracts them with a rule-based algorithm and compares them through entity matching. In the TRC-C model using machine learning (Ditto model), a few errors occurred due to insufficient training data, resulting in an accuracy of 87.8%, whereas in the TRC-R model, a rule-based algorithm, errors occurred in only some exceptional cases; thus, its F1 score was evaluated to be 96.9%. The CTGC module’s F2 score for automatic extraction performance was evaluated as 79.1% due to some data’s bias. Overall, the validation study shows that while a human review of the risk clauses in a PO manually took hours, it took only an average of 10 min with the PORAS. Therefore, this time saving can significantly reduce the owner engineer’s PO workload. In essence, this study contributes to achieving sustainable engineering processes through the intelligence and automation of document and risk management in the plant industry.
National Policies, Recent Research Hotspots, and Application of Sustainable Energy: Case of China, USA, and European Countries
Authors: Xu Min , Qu Jinjun , Li Mai
Published: August 2022

This study tracks the variety of nations dealing with the issue of energy transition. Through process tracing and a cross-national case study, a comparison of energy policies, research hotspots, and technical aspects of three sustainable energy systems (solar cells, recharge batteries, and hydrogen production) was conducted. We provide an overview of the climate-change political process and identify three broad patterns in energy-related politics in the United States, China, and Europe (energy neo-liberalism, authoritarian environmentalism, and integrated-multinational negotiation). The core processes and optimization strategies to improve the efficiency of sustainable energy usage are analyzed. This study provides both empirical and theoretical contributions to research on energy transitions.
Research on Recycling Strategies for New Energy Vehicle Waste Power Batteries Based on Consumer Responsibility Awareness
Authors: Fan Jiajing , Teng Hao , Wang Yibo
Published: August 2022

Due to the limited service life of new energy vehicle power batteries, a large number of waste power batteries are facing “retirement”, so it will soon be important to effectively improve the recycling and reprocessing of waste power batteries. Consumer environmental protection responsibility awareness affects the recycling of waste power batteries directly. Therefore, under the two recycling modes of new energy vehicle manufacturers and third-party recycling enterprises, this study analyzes the impact of consumer environmental protection responsibility awareness on the recycling price of waste power batteries and profit in the supply chain. The influence of factors such as recycling income, recycling input cost, and black-market recycling prices on consumer awareness of responsibility is also analyzed. Through theoretical research, it was found that: Under the model that third-party recycling enterprises are responsible for recycling, it can obtain better overall supply chain benefits; consumer environmental protection responsibility awareness and recycling benefits are positively correlated with supply chain benefits overall; and recycling benefits have a certain role in promoting consumer awareness of responsibility, while the increase in informal recycling prices inhibits consumer awareness of responsibility.
The Contribution of Environmental and Cultural Aspects of Pastoralism in the Provision of Ecosystem Services: The Case of the Silesian Beskid Mts (Southern Poland)
Authors: Salachna Anna , Marcol Katarzyna , Broda Jan , Chmura Damian
Published: August 2022

Grazing has proven to be a successful management tool in terms of the maintenance and restoration of biodiversity. Sheep and pastoralism can provide a various set of ecosystem services, i.e., benefits for humans from the environment. The Silesian Beskids, the westernmost part of the Carpathian Mountains, is an area with a long tradition of pastoralism. Since the 13th century, sheep have grazed clearings located in forests and in large areas of grasslands in mountains. In this study, we tried to identify all possible aspects of provisional, regulating and cultural services provided by sheep and the pasturage tradition bearing in mind the specificity of this region. The biodiversity maintenance and habitat creation due to grazing and the cultural values of pastoralism was illustrated using a literature review of data concerning cultural heritage, tourism and education and using our own phytosociological field studies performed in nine vegetation types within grazing sites. In order to examine the impact of grazing, an observation of sheep’s daily grazing activity during the vegetation season using tractive GPS locators was conducted. The results showed that grazing has a positive impact on the maintenance of vegetation including EU-protected habitats. In addition, the pastoral activity introduced to the Beskids by the Vlachs contributed to the development of a specific highlander culture that continues to this day. Not all potential ecosystem services associated with the use of sheep are realized in the study area. Amongst the provisional services, sheep bones are not used while dairy is the most popular followed by meat. In turn, the cultural heritage of pastoralism is one of the most important drivers for the touristic attraction of Silesian Beskid.