bluuebunny/crossref_metadata_2025 huggingface dataset:
Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps
Authors: Fergus Paul , Chalmers Carl , Longmore Steven , Wich Serge , Warmenhove Carmen , Swart Jonathan , Ngongwane Thuto , Burger André , Ledgard Jonathan , Meijaard Erik
Published: May 2023

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason: negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged USD 1.7 billion per year; however this is a fraction of the global biodiversity budget available (between USD 124 billion and USD 143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on “Interspecies Money”, where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach, 27 camera traps were deployed over a 400 km2 area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with GBP 100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount—mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian. The trial demonstrated that it is possible to achieve high animal detection accuracy across the 12 species with a sensitivity of 96.38%, specificity of 99.62%, precision of 87.14%, F1 score of 90.33%, and an accuracy of 99.31%. The successful detections facilitated the transfer of GBP 185.20 between animals and their associated guardians.
Evaluating the Capability of Sentinel-1 Data in the Classification of Canola and Wheat at Different Growth Stages and in Different Years
Authors: Zhao Lingli , Wang Shuang , Xu Yubin , Sun Weidong , Shi Lei , Yang Jie , Dash Jadunandan
Published: May 2023

Canola and wheat are the main oilseed crop and grain crop, respectively, and they often have similar phenological stages. The understanding of the interactions between microwave signals with wheat and canola in different stages is important for their monitoring using synthetic aperture radar (SAR) imagery. This paper investigates the characteristics of canola and wheat through the use of backscattering profiles from multi-year Sentinel-1 images. Large fluctuations are observed for the temporal backscattering profiles of canola and wheat in different growth statuses induced by agrometeorological conditions in different years. The capability and stability of Sentinel-1 for wheat and canola mapping is further investigated using single- and multi-temporal SAR images hosted in Google Earth Engine (GEE) using the random forest classifier. Although different agrometeorological conditions and field managements make the temporal profiles of backscattering variations, the large difference in canopy structure allows SAR images to make the separability of canola and wheat stable on Sentinel-1 images in different phenology stages. The classification accuracies and the feature importance scores from multi-temporal classification in different years show that the backscattering features obtained at flowering to maturity stages make more contributions to the good-quality mapping of canola and wheat than those at other stages. The F1 scores of canola and wheat achieve 0.95 during the canola flowering and podding period, and the minimum F1 scores of 0.85 were also obtained at other stages. These findings show that SAR images have great potential in the good-quality mapping of canola and wheat in a wide phenology window.
Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
Authors: Wei Jianchong , Cao Yan , Yang Kunping , Chen Liang , Wu Yi
Published: May 2023

Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (DCP) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.
Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications
Authors: Lytkin Sergey , Badenko Vladimir , Fedotov Alexander , Vinogradov Konstantin , Chervak Anton , Milanov Yevgeny , Zotov Dmitry
Published: May 2023

At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmentation tasks, which has a classification scheme suitable for geospatial applications. Our dataset (Saint Petersburg 3D) contains both real-world (34 million points) and synthetic (34 million points) subsets that were acquired using real and virtual sensors with the same characteristics. An original classification scheme is proposed that contains a set of 10 universal object categories into which any scene represented by dense outdoor mobile LiDAR point clouds can be divided. The evaluation procedure for semantic segmentation of point clouds for geospatial applications is described. An experiment with the Kernel Point Fully Convolution Neural Network model trained on the proposed dataset was carried out. We obtained an overall 92.56% mIoU, which demonstrates the high efficiency of using deep learning models for point cloud semantic segmentation for geospatial applications in accordance with the proposed classification scheme.
Quantitative Assessment of Cropland Exposure to Agricultural Drought in the Greater Mekong Subregion
Authors: Ming Wenting , Luo Xian , Luo Xuan , Long Yunshu , Xiao Xin , Ji Xuan , Li Yungang
Published: May 2023

Accurate and reliable information on the spatiotemporal characteristics of agricultural drought is important in understanding complicated drought processes and their potential impacts. We proposed an integrated approach for detecting agricultural droughts and their cropland exposure using remote sensing data over the Greater Mekong Subregion (GMS) collected from 2001 to 2020. The soil moisture (SM) dataset (0.05°) was first reconstructed based on an ESACCI SM dataset using a random forest (RF) model. Subsequently, the standardized soil moisture index (SSMI) was used to identify the agricultural droughts by a three-dimensional (latitude-longitude-time) identification method. In addition, the cropland’s exposure to agricultural droughts was evaluated. Results showed that: (1) the reconstructed SM data achieved spatial continuity and improved spatial resolution. The verified consequences showed that the reconstructed SM data agreed well with the in situ SM data. Additionally, the SSMI based on reconstructed SM had good correlations with the standardized precipitation evapotranspiration index (SPEI) calculated from station observations. (2) Twenty agricultural drought events lasting at least 3 months were identified over the GMS region. The averaged durations, areas, and severity were 7 months, 9 × 105 km2, and 45.6 × 105 month·km2, respectively. The four worst drought events ranked by severity were the 2019–2020 event, the 2015–2016 event, the 2009–2010 event, and the 2004–2005 event. (3) Based on the 20 identified agricultural drought events, cropland exposure was high in Myanmar, Thailand, and Cambodia. On average, the cropland exposure over the GMS was 1.71 × 105 km2, which accounts for 34% of the total cropland. Notably, the four severest drought events swept over 80% of the total cropland area. This study enriched our understanding of the development process of agricultural droughts from a space-time perspective, which was pivotal for assessing drought impacts and managing agricultural water resources.
Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment
Authors: Ma Siyuan , Shao Xiaoyi , Xu Chong
Published: May 2023

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous area. From 5 to 10 May 2016, Sanming City in Fujian Province, China, experienced a heavy rainfall event that caused massive landslides, leading to significant loss of life and property. Using high-resolution satellite imagery, we created a detailed inventory of landslides triggered by this event, which totaled 2665 across an area of 3700 km2. The majority of landslides were small-scale, shallow and elongated, with a dominant distribution in Xiaqu town. We analyzed the correlations between the landslide abundance and topographic, geological and hydro-meteorological factors. Our results indicated that the landslide abundance index is related to the gradient of the hillslope, distance from a river and total rainfall. The landslide area density, i.e., LAD increases with the increase in these influencing factors and is described by an exponential or linear relationship. Among all lithological types, Sinian mica schist and quartz schist (Sn-s) were found to be the most prone to landslides, with over 35% of landslides occurring in just 10% of the area. Overall, the lithology and rainfall characteristics primarily control the abundance of landslides, followed by topography. To gain a better understanding of the triggering conditions for shallow landslides, we conducted a physically based spatio-temporal susceptibility assessment in the landslide abundance area. Our numerical simulations, using the MAT.TRIGRS tool, show that it can accurately reproduce the temporal evolution of the instability process of landslides triggered by this event. Although rainfall before 8 May may have contributed to decreased slope stability in the study area, the short duration of heavy rainfall on 8 May is believed to be the primary triggering factor for the occurrence of massive landslides.
Correction: Lowe, T.; Pinskier, J. Tree Reconstruction Using Topology Optimisation. Remote Sens. 2023, 15, 172
Authors: Lowe Thomas , Pinskier Joshua
Published: May 2023

Text Correction [...]
A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
Authors: Zhang Jianlong , Liu Yifan , Wang Bin , Chen Chen
Published: May 2023

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives.
Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations
Authors: Shao Huanhuan , Li Hui , Jin Shikuan , Fan Ruonan , Wang Weiyan , Liu Boming , Ma Yingying , Wei Ruyi , Gong Wei
Published: May 2023

Particle matter (PM) mass concentrations have an important influence on human and environmental health. Lidar plays an important role in the monitoring of PM concentrations. However, the accuracy of PM concentrations retrieved via lidar depends on the performance of the conversion model from the aerosol extinction coefficient (EC) to PM concentration. Therefore, surface PM concentrations, aerosol EC and five meteorological factors are used to build the conversion model that can also be applicable to lidar for retrieving PM concentrations. In this study, the traditional linear model (LM), random forest (RF) and artificial neural network (ANN) algorithms are used to estimate the mass concentrations of PM with aerodynamic diameters < 1 µm (PM1), 2.5 µm (PM2.5) and 10 µm (PM10). The influence of meteorological factors on the conversion model is analyzed. The results show that the meteorological parameters play a non-ignorable role in the model of PM retrieval based on EC, especially when retrieving PM10. Moreover, the performance of three models is investigated by comparing with the surface measurements. The results indicate that the RF and ANN models are more suitable to estimate PM than the LM model. The diurnal variations in mean relative error (MRE) from the three models are then analyzed. There is a diurnal pattern in MRE values, meaning that the maximum values occur in the afternoon and the minimum values occur at night. In addition, there are subtle differences in performance between two machine learning (ML) models. After analysis, it is found that for PM10, the RF method is superior to the ANN when the EC value is small, while the ANN method is superior to the RF when the EC value is relatively high, and the EC threshold is set to 0.6 km−1. For PM1 and PM2.5 estimation, the ANN is the most appropriate model. Finally, accurate diurnal variations in PM1 and PM2.5 based on the ANN model and PM10 based on the combined model of RF and ANN (named RA) are investigated. The results exhibit that the daily maximum values of PM1, PM2.5 and PM10 in the Wuhan area all occur at approximately 08:00–10:00 local time (LT), which is mainly due to the impact of commuter vehicle emissions and the impact of secondary photochemistry response aggravated by sufficient illumination and temperature rises after sunrise. These research results provide an important basis for particulate matter monitoring.
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Authors: Duong Huu-Thanh , Le Viet-Tuan , Hoang Vinh Truong
Published: May 2023

Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.
Real-Time Continuous Monitoring of Oral Soft Tissue Pressure with a Wireless Mouthguard Device for Assessing Tongue Thrusting Habits
Authors: Matsumoto Hidekazu , Tomoto Keisuke , Kawase Gentaro , Iitani Kenta , Toma Koji , Arakawa Takahiro , Mitsubayashi Kohji , Moriyama Keiji
Published: May 2023

In orthodontics, understanding the pressure of oral soft tissues on teeth is important to elucidate the cause and establish treatment methods. We developed a small wireless mouthguard (MG)-type device that continuously and unrestrainedly measures pressure, which had previously been unachieved, and evaluated its feasibility in human subjects. First, the optimal device components were considered. Next, the devices were compared with wired-type systems. Subsequently, the devices were fabricated for human testing to measure tongue pressure during swallowing. The highest sensitivity (51–510 g/cm2) with minimum error (CV < 5%) was obtained using an MG device with polyethylene terephthalate glycol and ethylene vinyl acetate for the lower and upper layers, respectively, and with a 4 mm PMMA plate. A high correlation coefficient (0.969) was observed between the wired and wireless devices. In the measurements of tongue pressure on teeth during swallowing, 132.14 ± 21.37 g/cm2 for normal and 201.17 ± 38.12 g/cm2 for simulated tongue thrust were found to be significantly different using a t-test (n = 50, p = 6.2 × 10−19), which is consistent with the results of a previous study. This device can contribute to assessing tongue thrusting habits. In the future, this device is expected to measure changes in the pressure exerted on teeth during daily life.
Terahertz Meets AI: The State of the Art
Authors: Farhad Arshad , Pyun Jae-Young
Published: May 2023

Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.
Surgical Instrument Detection Algorithm Based on Improved YOLOv7x
Authors: Ran Boping , Huang Bo , Liang Shunpan , Hou Yulei
Published: May 2023

The counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only improve efficiency, but also reduce medical disputes and promote the development of medical informatization. However, during the counting process, surgical instruments may be densely arranged or obstruct each other, and they may be affected by different lighting environments, all of which can affect the accuracy of instrument recognition. In addition, similar instruments may have only minor differences in appearance and shape, which increases the difficulty of identification. To address these issues, this paper improves the YOLOv7x object detection algorithm and applies it to the surgical instrument detection task. First, the RepLK Block module is introduced into the YOLOv7x backbone network, which can increase the effective receptive field and guide the network to learn more shape features. Second, the ODConv structure is introduced into the neck module of the network, which can significantly enhance the feature extraction ability of the basic convolution operation of the CNN and capture more rich contextual information. At the same time, we created the OSI26 data set, which contains 452 images and 26 surgical instruments, for model training and evaluation. The experimental results show that our improved algorithm exhibits higher accuracy and robustness in surgical instrument detection tasks, with F1, AP, AP50, and AP75 reaching 94.7%, 91.5%, 99.1%, and 98.2%, respectively, which are 4.6%, 3.1%, 3.6%, and 3.9% higher than the baseline. Compared to other mainstream object detection algorithms, our method has significant advantages. These results demonstrate that our method can more accurately identify surgical instruments, thereby improving surgical safety and patient health.
Proximal Active Optical Sensing Operational Improvement for Research Using the CropCircle ACS-470, Implications for Measurement of Normalized Difference Vegetation Index (NDVI)
Authors: Conley Matthew M. , Thompson Alison L. , Hejl Reagan
Published: May 2023

Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.
GWO-Based Multi-Stage Algorithm for PMDC Motor Parameter Estimation
Authors: Pawlowski Adam , Ciezkowski Maciej , Romaniuk Slawomir , Kulesza Zbigniew
Published: May 2023

During the design of a wheeled mobile robot, the problem of the proper selection of the parameters of its motor controllers was encountered. Knowing the parameters of the robot’s Permanent Magnet Direct Current (PMDC) motors, precise tuning of the controllers can be performed, which then results in improved robot dynamics. Among many methods of parametric model identification, optimization-based techniques, particularly genetic algorithms, have gained more and more interest recently. The articles on this topic present the results of parameter identification, but they do not refer to the search ranges for individual parameters. With too wide a range, genetic algorithms do not find solutions or are time-inefficient. This article introduces a method for determining the parameters of a PMDC motor. The proposed method performs an initial estimation of the range of searched parameters to shorten the estimation time of the bioinspired optimization algorithm.
Detection and Prediction of the Early Thermal Runaway and Control of the Li-Ion Battery by the Embedded Temperature Sensor Array
Authors: Zhang Hengyi , Zhang Xiaoshan , Wang Wenwu , Yu Ping
Published: May 2023

Sorts of Li-ion batteries (LIB) have been becoming important energy supply and storage devices. As a long-standing obstacle, safety issues are limiting the large-scale adoption of high-energy–density batteries. Strategies covering materials, cell, and package processing have been paid much attention to. Here, we report a flexible sensor array with fast and reversible temperature switching that can be incorporated inside batteries to prevent thermal runaway. This flexible sensor array consists of PTCR ceramic sensors combined with printed PI sheets for electrodes and circuits. Compared to room temperature, the resistance of the sensors soars nonlinearly by more than three orders of magnitude at around 67 °C with a 1 °C/s rate. This temperature aligns with the decomposition temperature of SEI. Subsequently, the resistance returns to normal at room temperature, demonstrating a negative thermal hysteresis effect. This characteristic proves advantageous for the battery, as it enables a lower-temperature restart after an initial warming phase. The batteries with an embedded sensor array could resume their normal function without performance compromise or detrimental thermal runaway.
Will Your Next Therapist Be a Robot?—A Review of the Advancements in Robotic Upper Extremity Rehabilitation
Authors: Fareh Raouf , Elsabe Ammar , Baziyad Mohammed , Kawser Tunajjina , Brahmi Brahim , Rahman Mohammad H.
Published: May 2023

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.
Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
Authors: Li Xiaofei , Guo Hainan , Xu Langxing , Xing Zezheng
Published: May 2023

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.
Evaluation of Chinese Prickly Ash and Cinnamon to Mitigate Heterocyclic Aromatic Amines in Superheated Steam-Light Wave Roasted Lamb Meat Patties Using QuEChERS Method Coupled with UPLC-MS/MS
Authors: Suleman Raheel , Ijaz Muawuz , Liu Huan , Alarcon-Rojo Alma D. , Wang Zhenyu , Zhang Dequan
Published: May 2023

Chinese prickly ash and cinnamon contain many antioxidants, which scavenge free radicals and can reduce many harmful compounds, such as heterocyclic aromatic amines (HAAs). Modern technologies used for cooking, such as the use of superheated steam roasting, are beneficial in decreasing the development of HAAs. The current study was based on the use of these two spices in roasted lamb patties to mitigate the formation of HAAs in superheated steam roasted patties. Results exhibited significant differences (p < 0.05) in the content of both polar and non-polar HAAs as compared to control patties. In cinnamon roasted patties, polar HAAs were reduced from 23.76 to 10.56 ng g−1, and non-polar HAAs were reduced from 21.34 to 15.47 ng g−1. In Chinese prickly ash patties, polar and non-polar HAAs were 43.60 ng g−1 and 35.74 ng g−1, respectively. Similarly, cinnamon-treated patties showed a significantly higher (p < 0.05) reduction in polar HAAs (23.52 to 12.41 ng g−1) than non-polar (16.08 to 9.51 ng g−1) at concentrations of 0.5–1.5%, respectively, as compared to the control, with 45.81 ng g−1 polar and 35.09 ng g−1 non-polar HAAs. The polar HAAs tested were PhIP, DMIP, IQx, and 8-MeIQx, while the non-polar were harman and norharman. Both spices and superheated steam controlled HAAs to a significant level in lamb meat patties.
Toward Efficient Continuous Production of Biodiesel from Brown Grease
Authors: Atrash Melad , Molina Karen , Sharoni El-Or , Azwat Gilbert , Nisnevitch Marina , Albo Yael , Nakonechny Faina
Published: May 2023

An increase in energy consumption and the extended use of nonrenewable fossil fuels raises the need to develop alternative fuels as an energy supply that can protect the environment from unwanted emissions of pollutants. One alternative renewable fuel is biodiesel. Currently, most biodiesel feed sources are edible oils, but using them leads to the dilution of global food sources. The present study aims to find an effective method of biodiesel production using food industry fatty wastes called brown grease (BG). BG contains fats, mainly linoleic and oleic free fatty acids (FFAs), that can serve as raw materials for biodiesel production using esterification reactions. The esterification and transesterification reactions for biodiesel production were studied using commercial FFAs, commercial glyceryl trilinoleate (trilinolein), soybean oil, and BG. The reactions were carried out under ultrasonic activation using BF3 and AlCl3 Lewis acids as catalysts in both free and immobilized forms when immobilization was performed in silica matrices using the sol-gel synthesis route. Biodiesel production was examined in batch and continuous flow reactors. The BF3 catalyst was more efficient at the initial stages of the continuous operation, reaching a maximum conversion of 90%, with a gradual decrease in efficiency after 15 h of the process. The AlCl3 catalyst showed better stability, reaching maximum yields of 97% and maintaining efficiency until the end of the experiment. The proposed method offers an efficient and easy way to produce biodiesel from a variety of lipids sources, including fatty wastes (BG).
A Review of the Promotion of Sustainable Mobility of Workers by Industries
Authors: Santos Julio César dos , Ribeiro Paulo , Bento Ricardo Jorge Silva
Published: May 2023

The evolution and growth of cities present considerable challenges to the promotion of sustainable mobility, namely in commuting trips. In the present and recent past, many industries and companies of the economy’s productive sector have had to move as far as possible from urban areas, to minimize the impact of their activities on people’s health and quality of life. In more dispersed and low-density territories, working and residential areas are very far from each other, and there is typically poor public transport service and a lack of cycling networks for commuting purposes. This scenario encourages the use of private automobiles, not only as a necessity but also often as an obligation, making the mobility system nearly unsustainable. Therefore, it has become clear that companies can play an important role in promoting more sustainable mobility by reducing car use on commuting trips and by offering employees clean and more efficient transport alternatives to promote workers’ well-being and quality of life. Through an extensive literature review, a selection of scientific articles in the last 13 years was analyzed and discussed. The results highlighted that the location of industrial areas, the supply of public transport, the usage of active modes, and shared mobility systems are key factors to reduce car usage in workers’ commuting trips. Therefore, any sustainable mobility strategies that companies adopt will minimize the respective negative externalities, helping promote more environment-friendly ways of transportation, accessibility, social equity, and inclusion in workers’ communities. This justifies the need and urgency for the development of specific sustainable mobility plans oriented for companies/industries, instead of addressing this as just another element of a conventional urban mobility plan. Since this mobility represents a high volume of trips, repeated according to very regular patterns, it must be sustainability-oriented, allowing the improvement of system, trip, and vehicle efficiency.
Sustainable Low-Carbon Production: From Strategy to Reality
Authors: Szabo Denisa , Dragomir Mihai , Țîțu Mihail , Dragomir Diana , Popescu Sorin , Tofană Silvia
Published: May 2023

The present paper approaches the timely topic of sustainable production with low carbon emissions, investigating the link between existing strategies and policies and the reality that manufacturers must deal with, with the appraisal going from high-level national and international plans to specific firm needs. This is in line with the preoccupations of manufacturers in Europe to retain and regain their market shares under strict environmental excellence, one of the defining features of the continent’s economy. The existing strategies, specialized plans and mechanisms for the reduction of emissions were analyzed to discern their structural relationships and the clarity and palpability of their content when passing through the successive levels of interest. The research methodology employs the MEAL Plan for determining the state of the art, and based on the findings, two specific tools were used for policy analysis and informing a brainstorming and discussion session aimed at future improvements. The instruments used are SWOT-Radar Screen methodology and latent semantic analysis as implemented by the Tropes Zoom software. Structural connections were revealed, together with an improved understanding of the interventions proposed in 5 European-level strategies, 14 national-level strategies (with a focus on Romania) and a case analysis for a generic manufacturing company. Among the main findings, the authors propose improved awareness development for all the stakeholders, strengthened and correlated monitoring of sustainability results and a better implementation of an institutional ecosystem for providing support to companies. The results obtained are intended for the use of policy makers to improve their future planning cycles in a way that supports the companies in achieving these societal goals.
Potential Applications of Food-Waste-Based Anaerobic Digestate for Sustainable Crop Production Practice
Authors: Ries Jonathan , Chen Zhihao , Park Yujin
Published: May 2023

The global food system is and will be facing many challenges in the coming decades, which will necessitate innovative solutions to address the issues of a diminishing fertilizer supply, an increasing food demand from growing populations, and frequent extreme climates due to greenhouse gas emissions. An advancement proposed is the synthesizing of fertilizer from food waste, here referred to as food waste anaerobic digestate (FWAD). This occurs through the process of anaerobic digestion, where organic matter such as food waste is contained in an anaerobic environment and allowed to be broken down by microorganisms. One of the resulting products is anaerobic digestate, which possesses the necessary nutrients for effective fertilization for crop production. In addition to reducing greenhouse gases and waste in landfills, the replacement of synthetic fertilizers with ones made from food waste would help to alleviate the impacts of the current fertilizer shortage being experienced worldwide, which will be exacerbated by a reducing supply of materials needed for synthetic fertilizer production. In this paper, we discuss the nutrient characteristics of FWAD, and recent studies utilizing FWAD in horticulture and crop production, to advance our understanding of the effectiveness and challenges of using FWAD as a fertilizer. By employing appropriate application methods, such as nitrification, dilution, and amendment, FWAD demonstrates considerable potential as an effective fertilizer for a wide range of leafy greens and some fruiting crops.
Intensive-Use-Oriented Performance Evaluation and Optimization of Rural Industrial Land: A Case Study of Wujiang District, China
Authors: Ye Xiaojun , Fan Lingyun , Lei Cheng
Published: May 2023

Rural industrialization is one of the core drivers of urban and rural spatial evolution and economic transformation in China. Given the background of stock and reduction planning, the development of rural industrial land, which has long relied on land inputs to increase production and inefficient expansion, is facing severe constraints and challenges. How to improve the spatial performance of rural industrial land and promote industrial upgrading and intensive land use have become vital issues for the healthy development of rural areas. This paper draws upon smart shrinkage theory to provide an analytical framework for the intensive-use-oriented performance evaluation of rural industrial land, unlike the evaluation method of efficiency orientation for industrial land, which emphasizes the core goal of the input and output of production factors per unit area. Based on the analysis framework, this study explored the parcel-microscale performance evaluation methods for rural industrial land, and the evaluation index system construction covers the four dimensions of economic performance, social performance, ecological performance, and land use structure performance. Wujiang District of Suzhou City was used as a case study to carry out a comprehensive performance evaluation and analyze the differences in RILP in space and industry. Based on the evaluation results, the key problems of rural industrial land were identified, and corresponding optimization strategies for rural industrial land are proposed from the aspects of land use control, spatial agglomeration, and industrial upgrading. This study intended to address the current major national strategic needs and solve the real dilemmas faced in the process of rural industrial land development. It is hoped that the study will provide a theoretical reference for the transformation of rural industrial land and policy-making for rural revitalization.
Residents’ Perceptions of Challenges Related to Implementation of Smart City Solutions by Local Government
Authors: Mańka-Szulik Małgorzata , Krawczyk Dariusz , Wodarski Krzysztof
Published: May 2023

The aim of a survey conducted in October 2022 was to establish residents’ perceptions of the smart city concept and determine the degree of identification of the implemented solutions as elements of a smart city system. An important aspect of the survey was the communication dimension in terms of information policy and popularisation activities undertaken by the local government. The analysis covered those with declarations of civic participation in the implementation of modern solutions in social and economic life. The study was carried out on a sample of 500 residents of Zabrze through interviews with people in the street or at home. The results were juxtaposed with those of studies carried out in the same research field concerning the level of approval for the remote provision of public services and the formula for maintaining relationships between residents and local administration.
Factors Affecting Food Security among Households in Nigeria: The Role of Crop Diversity
Authors: Amao Ifeoluwapo Oluwaseun , Ogunniyi Adebayo Isaiah , Mavrotas George , Omotayo Abiodun Olusola
Published: May 2023

Agriculture is central in order to achieve nutrition goals through the provision of food, energy and essential micronutrients for the physical and mental development of humans. Dietary diversity is a good indicator of human food security status. Using a dataset obtained from the World Bank’s Living Standards Measurement Study—Integrated Surveys on Agriculture (LSMS-ISA), this paper examines the linkages between crop diversity and food security (measured as dietary diversity) among farming households in Nigeria using ordinary least squares, Poisson regression, and instrumental variables (IV) Poisson regression to estimate the relationship. In addition, we investigate the determinants that lead to the consumption of each food groups considered for generating dietary diversity using a logit regression model. The results show that food expenditure, asset ownership and location of households are the key factors driving the types of food consumed by the households. Furthermore, increased food expenditure and access to credit were found to positively influence food security. The result established a positive and significant relationship between crop diversity and dietary diversity. Our findings call for more attention to diet diversity, as well as the need to harmonize the roles of rural income improvement, especially through non-farm livelihood diversification in tackling multiple nutritional deficiencies in Nigeria.
Bibliometric Analysis on Wildfires and Protected Areas
Authors: Folharini Saulo , Vieira António , Bento-Gonçalves António , Silva Sara , Marques Tiago , Novais Jorge
Published: May 2023

Wildfire are increasingly frequent events on a planet undergoing climate change. With more favourable climate conditions for their occurrence, like prolonged periods of drought, the frequency and intensity of these catastrophes continue to increase. Protected areas are important territories for conserving ecosystems and biodiversity around the world and serve as a key management tool in national environmental policies. Many studies have been developed around the world to assess the conditions, frequency, and intensity of fires, and, on the other hand, protected areas are intensively studied to understand whether conservation actions are effective in keeping the ecosystem balanced. However, there are few studies that contemplate these two themes together. Consequently, this study offers a bibliometric analysis of the wildfires and its occurrence in protected areas, aiming to understand the spatial and temporal relation between them, characteristics of the research studies, and trending issues to be worked on. We used the Bibliometrix package in R language, which provides the option of different methods for analysing bibliometric data to understand the temporal evolution of the published documents about wildfires and protected areas. In total, 1116 publications were analysed, ranging from 1970 to 2022. The findings show that the terms “fires” (185 occurrences), “fire” (117), “wildfire” (101), and “forest fire” (92) are more frequently used and associated with studies on topics including biodiversity (304), conservation (221), environmental protection (188), deforestation (148), ecosystems (138), forestry (133), and climate change (126) as well as protected areas (399). The highest growth of research has been registered since 2002. The majority of the publications were published by authors from universities in the USA (77), South Africa (72), the United Kingdom (22), and Brazil (19). The USA leads all other countries with 7 of the top 10 international collaborations, followed by Brazil, Australia, and South Africa. However, only the United Kingdom continues to maintain an increased number of external (inter-country) collaborations, whereas the other nine continue to maintain an increased number of internal (intra-country). Considering the top 10 authors in terms of production, only 1 was active from 1999 to 2019, and the other 9 only have been active since 2009. The topics that stand out were subjected to a coupling analysis, which revealed that environmental protection is a motor theme, endangered species, Australia, and savannah are niche themes, and protected area, biodiversity, and fire management are basic themes.