bluuebunny/crossref_metadata_2025 huggingface dataset:
Drivers and Barriers in the Production and Utilization of Second-Generation Bioethanol in India
Authors: Pattnaik Falguni , Patra Biswa R. , Nanda Sonil , Mohanty Mahendra K. , Dalai Ajay K. , Rawat Jaya
Published: February 2024

Second-generation biorefinery refers to the production of different types of biofuels, biomaterials, and biochemicals by using agri-based and other lignocellulosic biomasses as substrates, which do not compete with arable lands, water for irrigation, and food supply. From the perspective of transportation fuels, second-generation bioethanol plays a crucial role in minimizing the dependency on fossil-based fuels, especially gasoline. Significant efforts have been invested in the research and development of second-generation bioethanol for commercialization in both developing and developed countries. However, in different developing countries like India, commercialization of second-generation bioethanol has been obstructed despite the abundance and variety of agricultural feedstocks. This commercial obstruction was majorly attributed to the recalcitrance of the feedstock, by-product management, and marginal subsidies compared to other nations. This article reviews the major roadblocks to the viability and commercialization of second-generation biofuels, especially bioethanol in India and a few other leading developed and developing nations. This article also reviews the biomass availability, technological advancements, investments, policies, and scale-up potential for biorefineries. A thorough discussion is made on the prospects and barriers to research, development, and demonstration as well as strengths, weaknesses, opportunities, and threats for the commercialization of second-generation bioethanol.
Intercultural and Interreligious Dialogue Competences in Adolescents in Barcelona and Melilla (Spain)
Authors: Alemany-Arrebola Inmaculada , Gallardo-Vigil Miguel Ángel , Ortiz-Gómez María del Mar , Vilà-Baños Ruth
Published: February 2024

Contemporary societies are increasingly multireligious, multiethnic, and multicultural, but to what extent are they ready for coexistence? This paper evaluates the competencies for intercultural and interreligious dialogue in two very different contexts: Barcelona and Melilla, two cities with great ethnic and cultural diversity, in which it is easy to have contact with people of different cultures and religions. To this end, we worked with a total of 1353 adolescents and young people, and four scales were used to evaluate intercultural and interreligious sensitivity, conflict management skills, and prejudices towards unaccompanied migrant minors. The findings suggest that in neither of these two contexts do adolescents and young people form relationships with these minors, despite accepting religious diversity and being educated at school in topics relating to coexistence and spirituality. In fact, participants showed a high level of prejudice towards this population. Implementation of more effective intervention programs in both cities is therefore recommended.
Enhancing SAR Multipath Ghost Image Suppression for Complex Structures through Multi-Aspect Observation
Authors: Lin Yun , Tian Ziwei , Wang Yanping , Li Yang , Shen Wenjie , Bai Zechao
Published: February 2024

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image.
Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism
Authors: Li Yuanyuan , Zhou Zhengguo , Qi Guanqiu , Hu Gang , Zhu Zhiqin , Huang Xin
Published: February 2024

With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.
Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography
Authors: Xu Pan , Xu Shijie , Shi Kequan , Ou Mingyu , Zhu Hongna , Xu Guojun , Gao Dongbao , Li Guangming , Zhao Yun
Published: February 2024

Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.
Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation
Authors: Meyer Manuel de Figueiredo , Gonçalves José Alberto , Bio Ana Maria Ferreira
Published: February 2024

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass.
HyperVein: A Hyperspectral Image Dataset for Human Vein Detection
Authors: Ndu Henry , Sheikh-Akbari Akbar , Deng Jiamei , Mporas Iosif
Published: February 2024

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.
A Circular, Wireless Surface-Electromyography Array
Authors: Deprez Kenneth , De Baecke Eliah , Tijskens Mauranne , Schoeters Ruben , Velghe Maarten , Thielens Arno
Published: February 2024

Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients’ movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient’s arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology.
First Acyclovir Determination Procedure via Electrochemically Activated Screen-Printed Carbon Electrode Coupled with Well-Conductive Base Electrolyte
Authors: Tyszczuk-Rotko Katarzyna , Staniec Katarzyna , Gorylewski Damian , Keller Aleksy
Published: February 2024

In this work, a new voltammetric procedure for acyclovir (ACY) trace-level determination has been described. For this purpose, an electrochemically activated screen-printed carbon electrode (aSPCE) coupled with well-conductive electrolyte (CH3COONH4, CH3COOH and NH4Cl) was used for the first time. A commercially available SPCE sensor was electrochemically activated by conducting cyclic voltammetry (CV) scans in 0.1 mol L−1 NaOH solution and rinsed with deionized water before a series of measurements were taken. This treatment reduced the charge transfer resistance, increased the electrode active surface area and improved the kinetics of the electron transfer. The activation step and high conductivity of supporting electrolyte significantly improved the sensitivity of the procedure. The newly developed differential-pulse adsorptive stripping voltammetry (DPAdSV) procedure is characterized by having the lowest limit of detection among all voltammetric procedures currently described in the literature (0.12 nmol L−1), a wide linear range of the calibration curve (0.5–50.0 and 50.0–1000.0 nmol L−1) as well as extremely high sensitivity (90.24 nA nmol L−1) and was successfully applied in the determination of acyclovir in commercially available pharmaceuticals.
Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving
Authors: Zhou Yingtong , Han Tiansi , Nie Qiong , Zhu Yuxuan , Li Minghu , Bian Ning , Li Zhiheng
Published: February 2024

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR–camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.
Negative Charge-Carrying Glycans Attached to Exosomes as Novel Liquid Biopsy Marker
Authors: Kosutova Natalia , Lorencova Lenka , Hires Michal , Jane Eduard , Orovcik Lubomir , Kollar Jozef , Kozics Katarina , Gabelova Alena , Ukraintsev Egor , Rezek Bohuslav , Kasak Peter , Cernocka Hana , Ostatna Veronika , Blahutova Jana , Vikartovska Alica , Bertok Tomas , Tkac Jan
Published: February 2024

Prostate cancer (PCa) is the second most common cancer. In this paper, the isolation and properties of exosomes as potential novel liquid biopsy markers for early PCa liquid biopsy diagnosis are investigated using two prostate human cell lines, i.e., benign (control) cell line RWPE1 and carcinoma cell line 22Rv1. Exosomes produced by both cell lines are characterised by various methods including nanoparticle-tracking analysis, dynamic light scattering, scanning electron microscopy and atomic force microscopy. In addition, surface plasmon resonance (SPR) is used to study three different receptors on the exosomal surface (CD63, CD81 and prostate-specific membrane antigen-PMSA), implementing monoclonal antibodies and identifying the type of glycans present on the surface of exosomes using lectins (glycan-recognising proteins). Electrochemical analysis is used to understand the interfacial properties of exosomes. The results indicate that cancerous exosomes are smaller, are produced at higher concentrations, and exhibit more nega tive zeta potential than the control exosomes. The SPR experiments confirm that negatively charged α-2,3- and α-2,6-sialic acid-containing glycans are found in greater abundance on carcinoma exosomes, whereas bisecting and branched glycans are more abundant in the control exosomes. The SPR results also show that a sandwich antibody/exosomes/lectins configuration could be constructed for effective glycoprofiling of exosomes as a novel liquid biopsy marker.
A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction
Authors: He Jigang , Yuan Luyao , Lei Haotian , Wang Kaixuan , Weng Yang , Gao Hongli
Published: February 2024

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple—feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson’s Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.
Duplex-Hierarchy Representation Learning for Remote Sensing Image Classification
Authors: Yuan Xiaobin , Zhu Jingping , Lei Hao , Peng Shengjun , Wang Weidong , Li Xiaobin
Published: February 2024

Remote sensing image classification (RSIC) is designed to assign specific semantic labels to aerial images, which is significant and fundamental in many applications. In recent years, substantial work has been conducted on RSIC with the help of deep learning models. Even though these models have greatly enhanced the performance of RSIC, the issues of diversity in the same class and similarity between different classes in remote sensing images remain huge challenges for RSIC. To solve these problems, a duplex-hierarchy representation learning (DHRL) method is proposed. The proposed DHRL method aims to explore duplex-hierarchy spaces, including a common space and a label space, to learn discriminative representations for RSIC. The proposed DHRL method consists of three main steps: First, paired images are fed to a pretrained ResNet network for extracting the corresponding features. Second, the extracted features are further explored and mapped into a common space for reducing the intra-class scatter and enlarging the inter-class separation. Third, the obtained representations are used to predict the categories of the input images, and the discrimination loss in the label space is minimized to further promote the learning of discriminative representations. Meanwhile, a confusion score is computed and added to the classification loss for guiding the discriminative representation learning via backpropagation. The comprehensive experimental results show that the proposed method is superior to the existing state-of-the-art methods on two challenging remote sensing image scene datasets, demonstrating that the proposed method is significantly effective.
CSINet: A Cross-Scale Interaction Network for Lightweight Image Super-Resolution
Authors: Ke Gang , Lo Sio-Long , Zou Hua , Liu Yi-Feng , Chen Zhen-Qiang , Wang Jing-Kai
Published: February 2024

In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named “CSINet”. This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).
Analysis of the Scenarios of Use of an Innovative Technology for the Fast and Nondestructive Characterization of Viscoelastic Materials in the Tires Field
Authors: Farroni Flavio , Timpone Francesco , Genovese Andrea
Published: February 2024

The properties of tires related to their viscoelastic behavior have a significant impact in the field of vehicle dynamics. They affect the performance and safety of a vehicle based on how they change when the tire performs in variable thermal conditions, interacts with various kinds of road surfaces, and accumulates mileage over time. To analyze and understand such properties of viscoelastic materials, destructive tests like dynamic mechanical analysis (DMA) are used, which make the tire unusable after the test; these are usually carried out on specimens cut from the zone of interest. The development of an innovative testing methodology connected to a hardware device called VESevo allows the characterization of the viscoelastic properties of tire compounds belonging to tread or other parts in a fast and nondestructive way. This new device provides valuable information about the evolution of the tire’s viscoelastic properties, allowing it to monitor them throughout the whole lifecycle. In the paper, an overview of the possible sensitivities that can be investigated thanks to the VESevo is provided: The tread viscoelasticity was characterized and monitored for several tire tread compounds, over tire mileage, over tread thermal curing cycles, and as an index of the tread quality and uniformity in production. Preliminary results were collected and are presented. In the final paragraph, further recent applications developed from the tire field, which are not directly related, are reported.
A Security Information Transmission Method Based on DHR for Seafloor Observation Network
Authors: Ying Fei , Zhao Shengjie , Wang Jia
Published: February 2024

A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity.
Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments
Authors: Li Guannan , Wang Chongchong , Liu Lamei , Fang Xi , Kuang Wei , Xiong Chenglong
Published: February 2024

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.
Ad Hoc Mesh Network Localization Using Ultra-Wideband for Mobile Robotics
Authors: Juston Marius F. R. , Norris William R.
Published: February 2024

This article explores the implementation of high-accuracy GPS-denied ad hoc localization. Little research exists on ad hoc ultra-wideband-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots, and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node’s field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration ad hoc localization system (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF then fuses the robot’s odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts its estimated position to its neighbors to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile ad hoc localization network.
Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations
Authors: Li Guannan , Wang Yong , Zhang Chunzhi , Xu Chengliang , Zhan Lei
Published: February 2024

Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the “Energy Detective” dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model’s predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty.
A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model
Authors: Gao Qingji , Deng Haozhi , Zhang Gaowei
Published: February 2024

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network’s learning rate to derive the optimal group’s weight information and ultimately improve the model’s detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.
Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement
Authors: Hashimoto Atsushi , Suehara Ken-ichiro , Kameoka Takaharu
Published: February 2024

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein–nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.
Effects of Digital Transformation on Total Factor Productivity of Cultural Enterprises—Empirical Evidence from 251 Listed Cultural Enterprises in China
Authors: Feng Yaoyao , Zhao Meng , Yang Xiuyun
Published: February 2024

By analyzing the sample data of 251 listed cultural enterprises’ annual reports from 2011 to 2019 in China, this study empirically analyzed the effects of digital transformation on the total factor productivity (TFP), paths of effects, and heterogeneity characteristics of cultural enterprises in China. The findings indicate that digital transformation can significantly increase the TFP of Chinese cultural enterprises. The results obtained through robustness tests and endogeneity questions verify the credibility of the findings. The paths of effects mainly include improving the efficiency of content creation, financing ability, and research and development (R&D) investment of cultural enterprises, and the boosting effect of digital transformation on TFP is more significant among enterprises that are smaller in scale, located in midwest China, and positioned within the upstream chain of a segmented industry.
Research on the Effect of Digital Economy Development on the Carbon Emission Intensity of Agriculture
Authors: Wang Wei , Mei Tingting
Published: February 2024

The integration of the digital economy with the real economy has facilitated the application of a variety of digital methods in agriculture, resulting in significant reductions in its carbon intensity. This study employs a spatial Durbin model to analyze the spatiotemporal effects of digital economic growth on agricultural carbon emissions, using Chinese inter-provincial panel data spanning from 2011 to 2020. The empirical results indicate a substantial decline in agricultural carbon emissions associated with the evolution of the digital economy. Additionally, a discernible spatial spillover effect is identified. The suppressive impact of the digital economy on agricultural carbon emission intensity exhibits clear heterogeneity, with more pronounced effects observed in the main grain-producing regions and western regions. Based on these findings, recommendations are proposed to accelerate the development of digital economy infrastructure tailored to local conditions, promote the integration of digital technology and agriculture, stimulate innovation in agricultural science and technology, and advocate for the adoption of energy-efficient and environmentally sustainable agricultural practices.
Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications
Authors: Yu Xiaohong , Liu Bin , Lai Yongzeng
Published: February 2024

The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.
Leather Industry Waste Management for Architectural Design
Authors: Paucar Samaniego Mayra Alejandra , Santamaría Aguirre Jorge Luis , Amancha Pablo , Pilamunga Poveda Marcelo
Published: February 2024

The leather and footwear cluster in Tungurahua state has a main role in the country’s production chain, supplying 76% of the country’s tanned hides for the textile, footwear, and furniture industries, among others. The processes involved in leather tanning generate liquid, gaseous, and solid waste, the latter including the shavings from the leather trimming process, which, due to their composition and volume, are compressed and disposed of in sanitary landfills. Through Strategic Design and circular processes, as axes of change in production, new processes and strategies are established for the creation of products derived from the reuse of tannery waste; as a result, a decorative block is obtained for the design of architectural spaces with dimensions of 150 × 75 × 355 mm, 300 g in weight, and a compressive strength of 15.72 MPa. This is subjected to physicochemical tests for its validation.
The Impacts and Spatial Characteristics of High-Standard Farmland Construction on Agricultural Carbon Productivity
Authors: Xiaokaiti Xiayire , Zhang Hongli , Jia Nan
Published: February 2024

Agricultural carbon productivity combines the dual attributes of reducing carbon emissions and stabilizing economic growth, and is a core aspect of the new era of low-carbon agricultural development. The construction of high-standard farmland is an important initiative to promote high-yield and high-efficiency agriculture, as well as environmentally sustainable development through land improvement in China. However, the impact of high-standard farmland construction on agricultural carbon productivity and its mechanisms is still in the process of being determined. In order to fill this gap, this study aims to construct a theoretical, analytical framework for the impact of high-standard farmland construction on agricultural carbon productivity. Based on the current situation of high-standard farmland construction and the characteristics of agricultural carbon productivity in China, this study used the panel data of 31 provinces, from 2003 to 2017 in China, to empirically examine the effects, mediating paths, and spatial characteristics of the construction of high-standard farmland on agricultural carbon productivity using a double fixed-effects regression model, a mediating-effects model, and a spatial econometric model. The results show that: (1) High-standard farmland construction has a positive effect on agricultural carbon productivity, with a direct effect coefficient of 0.139 after adding a series of control variables. (2) Furthermore, heterogeneity analysis shows that the impact of high-standard farmland construction on agricultural carbon productivity will vary greatly depending on the topographic characteristics of the studied area, the level of economic development, and whether it is a main grain-producing area. (3) Mechanism analysis shows that agricultural scale operation, agricultural planting structure, and agricultural technology progress all have partial mediating roles in the impact of high-standard farmland construction on agricultural carbon productivity, with mediating effect coefficients of 0.025, 0.024, and 0.013, respectively. (4) Agricultural carbon productivity has a spatial correlation, and for every 1% increase in the level of high-standard farmland construction, agricultural carbon productivity increases by 0.117%, with a direct effect of 0.074% and a spatial spillover effect of 0.043%. Our study explains the impact effects, mechanisms, and spatial spillover effects of high-standard farmland construction on agricultural carbon productivity from theoretical and empirical perspectives, thus deepening the literature on the relationship between high-standard farmland construction and agricultural carbon productivity, and providing a theoretical basis and practical references for improving agricultural carbon productivity from the perspective of high-standard farmland construction policy.
A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China
Authors: Wu Shaolei , Wu Jianing , Lu Di , Azadi Hossein , Liu Jie
Published: February 2024

The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.