bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres
Authors: Rahman Sherin Khadeeja , Al-Ameri Riyadh
Published: August 2022

The environmental concerns regarding the production of the most widely consumed cement construction material have led to the need for developing sustainable alternatives. Using recycled industry waste products such as fly ash and slag via geopolymerisation has led to the development of geopolymer cement—an efficient replacement for ordinary Portland cement (OPC). Adopting geopolymer cement and concrete as a construction material reduces greenhouse gas and promotes the recycling of waste products. This study explores the suitability of a unique geopolymer concrete mix made of recycled cementitious materials including industry waste products such as fly ash, micro fly ash and slag for use in aggressive environments. Sorptivity tests are conducted to assess the durability of concrete and indicate the cementitious material’s ability to transmit water through the capillary forces. This study thus reports on the sorptivity characteristics of a newly developed self-compacting geopolymer concrete and two other fibre geopolymer concrete mixes containing 1% (by weight) of 12 mm- or 30 mm-long basalt fibres. The addition of basalt fibres indicated less water absorption and moisture ingress than the mix without fibres. The study used 18 specimens from three geopolymer concrete mixes, and the results showed that adding fibres improved the durability performance in terms of resistance to moisture ingress. Finally, an artificial neural network model is developed to predict the absorption rates of geopolymer concrete specimens using MATLAB. The prediction models reported excellent agreement between experimental and simulated datasets.
U.S. Federal Investment in Religiousness/Spirituality and Health Research: A Systematic Review
Authors: Park Crystal L. , George Jamilah R. , Awao Saya , Carney Lauren M. , Batt Steven , Salsman John M.
Published: August 2022

Objectives: Although robust associations between aspects of religiousness/spirituality (R/S) and physical health have been established, little systematic information is available about federal funding support for this area of research. To address this question, we conducted a comprehensive systematic review and analysis. Study Design: Systematic review. Methods: We used the information provided by the Federal RePORTER and searched from earliest date through the end of 2018. Abstracts were included if they were an empirical study and included both a religion/spirituality variable and a health variable. Results: Our search yielded 194 grants reflecting over USD 214 million in research expenditures, with the vast majority (85%) funded by the NIH. Most common were community-based observational studies with healthy populations (70%). Nearly three-quarters (73%) of studies specifying age focused on adults, but children and adolescents were also well represented in these projects. The proportion of studies focused on racial/ethnic minorities (47%) was disproportionate to their representation in the U.S. population, which could reflect either heightened efforts to address health disparities or a view that R/S is primarily or mostly relevant to minority groups. Less than half of funded studies (41%) considered religion a central focus and publications for R/S-focused studies were less common than for non-R/S-focused studies (M = 7.0 to M = 13.3, respectively, p = 0.06). Overall funding levels appear to be declining in more recent years, although this trend was not statistically significant (p = 0.52). Many abstracts did not provide adequate details for coding. Conclusions: Overall, the present review suggests that U.S. federal funding for research on R/S and health is substantial, but most of this has only peripherally considered R/S and has yielded modest return on investment. Promising future directions include a continued focus on racial and ethnic minority populations as well as in emerging areas such as religious gratitude and compassion along with well-designed intervention trials.
Interfaith Marriage in Islam: Classical Islamic Resources and Contemporary Debates on Muslim Women’s Interfaith Marriages
Authors: Elmali-Karakaya Ayse
Published: August 2022

In Islamic tradition, both Muslim men and women are prohibited from marrying mushriks and are required to marry only other believers. However, Muslim scholars’ definitions of ‘believers’ and ‘mushriks’ differ for men and women. Whereas kitabī (Jewish and Christian) women are accepted as believers, not mushriks; kitabī men, who believe in the same religion as kitabī women, are not accepted as believers. Thus, there is a prohibition of Muslim women marrying men of different faiths in Islam. This prohibition is mainly based on the consensus of scholars, which is mostly derived from the cultural and social understanding of marriage and gender roles in the family rather than strictly from religious sources of reference. The aim of this article is to discuss how classical and contemporary Muslim scholars have approached the question of Muslim women’s interfaith marriages in Islam. Classical Muslim scholars did not consider the changing circumstances in their society and reconsider the religious rule regarding Muslim women’s interfaith marriages, as they did for Muslim men’s interfaith marriages. On the other hand, some contemporary Muslim scholars argue that the absence of any explicit prohibition in the Qurʾān indicates that Islam leaves the decision regarding whom to marry up to the Muslim woman and that she should consider her conditions and her prospective husband’s attitude toward her religious faith before making the decision for herself.
Optimal Liquidation, Acquisition and Market Making Problems in HFT under Hawkes Models for LOB
Authors: Roldan Contreras Ana , Swishchuk Anatoliy
Published: August 2022

The present paper is focused on the solution of optimal control problems such as optimal acquisition, optimal liquidation, and market making in relation to the high-frequency trading market. We have modeled optimal control problems with the price approximated by the diffusion process for the general compound Hawkes process (GCHP), using results from the work of Swishchuk and Huffman. These problems have been solvedusing a price process incorporating the unique characteristics of the GCHP. The GCHP was designed to reflect important characteristics of the behaviour of real-world price processes such as the dependence on the previous process and jumping features. In these models, the agent maximizes their own utility or value function by solving the Hamilton–Jacobi–Bellman (HJB) equation and designing a strategy for asset trading. The optimal solutions are expressed in terms of parameters describing the arrival rates and the midprice process from the price process, modeled as a GCHP, allowing such characteristics to influence the optimization process, aiming towards the attainment of a more general solution. Implementations of the obtained results were carried out using real LOBster data.
Factors of Risk Analysis for IoT Systems
Authors: Andrade Roberto , Ortiz-Garcés Iván , Tintin Xavier , Llumiquinga Gabriel
Published: August 2022

The increasing rate at which IoT technologies are being developed has enabled smarter and innovative solutions in the sectors of health, energy, transportation, etc. Unfortunately, some inherent characteristics of these technologies are compromised to attack. Naturally, risk analysis emerges, as it is one of many steps to provide a reliable security strategy. However, the methodologies of any risk analysis must first adapt to the dynamics of the IoT system. This article seeks to shed light on whatever factors are part of an IoT system and thus contribute to security risks, IoT device vulnerabilities, susceptibility due to the application domain, attack surfaces, and interdependence as a product of the interconnection between IoT devices. Consequently, the importance of these factors in any risk evaluation is highlighted, especially the interdependence generated by IoT systems, which can cause the generation of an uncontrollable cascade of effects that can occur under certain conditions of any systematic risk event.
The Substantial Increase of Forest Cover in Central Poland Following Extensive Land Abandonment: Szydłowiec County Case Study
Authors: Shahbandeh Mahsa , Kaim Dominik , Kozak Jacek
Published: August 2022

Nowadays, Poland is one of the European countries most affected by agricultural land abandonment (ALA). Though considered to be a negative phenomenon, ALA opens up several options for planning future land uses critical for biodiversity conservation or future carbon sequestration. So far, many studies of ALA have been done in the mountainous areas in Poland, but less is known about the magnitude of ALA in other regions. In this paper we use the declassified CORONA satellite imagery (1969) to backdate the information on land cover and land abandonment from topographic maps from 1970s for the region located in central Poland and currently affected by widespread ALA. The information from archival materials is compared with current High-Resolution Layers and airborne laser scanning products, indicating that a forest cover increase of 23% was observed. The output of vegetation height analysis confirmed significant land use transformation from non-forest and ALA into forest area. Additionally, analysis of forest pattern change revealed that although forest core areas have increased since 1970, its share in total forest cover decreased due to newly established small forest patches. Our research shows the importance of archival remote sensing materials and indicates their role in understanding ALA-related forest cover change in Poland over the last 50 years.
A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies
Authors: Hu Haochen , Li Boyang , Yang Wenyu , Wen Chih-Yung
Published: August 2022

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.
Machine Learning Approaches to Automatically Detect Glacier Snow Lines on Multi-Spectral Satellite Images
Authors: Prieur Colin , Rabatel Antoine , Thomas Jean-Baptiste , Farup Ivar , Chanussot Jocelyn
Published: August 2022

Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a proof of concept of machine learning approaches applied to multi-spectral images to detect the snow line and quantify its average altitude. The tested approaches include the combination of different image processing and classification methods, and takes into account cast shadows. The efficiency of these approaches is evaluated on mountain glaciers in the European Alps by comparing the results with manually annotated data. Solutions provided by the different approaches are robust when compared to the ground truth’s snow lines, with a Pearson’s correlation ranging from 79% to 96% depending on the method. However, the tested approaches may fail when snow lines are not continuous or exhibit a strong change of elevation. The major advantage over the state of the art is that the proposed approach does not require one calibration per glacier.
An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight
Authors: Wang Huaxia , Cheng Yongmei , Liu Nan , Zhao Yongqiang , Cheung-Wai Chan Jonathan , Li Zhenwei
Published: August 2022

Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.
Identifying Oceanic Responses with Validated Satellite Observations after the Passage of Typhoons in the Northern South China Sea
Authors: Jin Weifang , Liang Chujin , Tian Xinliang , Hu Junyang , Ding Tao , Zhou Beifeng , Chen Xiaoyan , Wang Yuntao
Published: August 2022

Tropical cyclone-induced upwelling has an important influence on ocean temperature and chlorophyll-a (Chl-a) concentrations, which are modified by the existence of mesoscale eddies. This paper investigates the regional dynamics and associated variability in temperature and Chl-a during the passage of three typhoons (Kammuri, Nuri, and Hagupit) with similar tracks in the northern South China Sea (SCS) during 2008 using remote sensing and in situ observations. The measurements of wind and sea surface temperature obtained by a buoy and satellite were found to be similar, and both showed that typhoons have prominent impacts on the ocean’s upper layer. Sea surface cooling is first identified during the passage of each typhoon, particularly on the right side of typhoon tracks. Increased Chl-a concentrations were observed in the surrounding areas after the passage of typhoons Nuri and Hagupit, with large offshore blooms (Chl-a increases of 0.27–0.33 mg m−3) identified along the tracks of Nuri and Hagupit 3 to 4 days after their passage. Such blooms are highly dependent on the cooling associated with typhoon-induced mixing and upwelling. The pre-existing eddies modified the surface variations, and a perfect match was identified between the polarities of the eddies and the ocean surface changes. An anomalously high offshore Chl-a enhancement entrained by a cyclonic eddy occurred along the track of Nuri 4 days after its passage; in this instance, typhoon-induced upwelling was reinforced by the cyclonic eddy. In comparison, typhoon-induced upwelling is inhibited by anticyclonic eddies, resulting in much less prominent changes. The combination of typhoon winds and eddies can modify the productivity of marine phytoplankton, and a comprehensive understanding of typhoon-induced dynamics will aid in understanding ecosystem responses to typhoons.
Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System
Authors: Li Shuqun , Hu Hao , Fang Chenggege , Wang Sichen , Xun Shangpei , He Binfang , Wu Wenyu , Huo Yanfeng
Published: August 2022

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles.
Detection and Characterisation of Conductive Objects Using Electromagnetic Induction and a Fluxgate Magnetometer
Authors: Elson Lucy , Meraki Adil , Rushton Lucas M. , Pyragius Tadas , Jensen Kasper
Published: August 2022

Eddy currents induced in electrically conductive objects can be used to locate metallic objects as well as to assess the properties of materials non-destructively without physical contact. This technique is useful for material identification, such as measuring conductivity and for discriminating whether a sample is magnetic or non-magnetic. In this study, we carried out experiments and numerical simulations for the evaluation of conductive objects. We investigated the frequency dependence of the secondary magnetic field generated by induced eddy currents when a conductive object is placed in a primary oscillating magnetic field. According to electromagnetic theory, conductive objects have different responses at different frequencies. Using a table-top setup consisting of a fluxgate magnetometer and a primary coil generating a magnetic field with frequency up to 1 kHz, we were able to detect aluminium and steel cylinders using the principle of electromagnetic induction. The experimental results were compared to numerical simulations, with good overall agreement. This technique enables the identification and characterisation of objects using their electrical conductivity and magnetic permeability.
Implementation and Optimization of Zero-Knowledge Proof Circuit Based on Hash Function SM3
Authors: Yang Yang , Han Shangbin , Xie Ping , Zhu Yan , Ding Zhenyang , Hou Shengjie , Xu Shicheng , Zheng Haibin
Published: August 2022

With the increasing demand for privacy protection in the blockchain, the universal zero-knowledge proof protocol has been developed and widely used. Because hash function is an important cryptographic primitive in a blockchain, the zero-knowledge proof of hash preimage has a wide range of application scenarios. However, it is hard to implement it due to the transformation of efficiency and execution complexity. Currently, there are only zero-knowledge proof circuits of some widely used hash functions that have been implemented, such as SHA256. SM3 is a Chinese hash function standard published by the Chinese Commercial Cryptography Administration Office for the use of electronic authentication service systems, and hence might be used in several cryptographic applications in China. As the national cryptographic hash function standard, the zero-knowledge proof circuit of SM3 (Chinese Commercial Cryptography) has not been implemented. Therefore, this paper analyzed the SM3 algorithm process, designed a new layered circuit structure, and implemented the SM3 hash preimage zero-knowledge proof circuit with a circuit size reduced by half compared to the automatic generator. Moreover, we proposed several extended practical protocols based on the SM3 zero-knowledge proof circuit, which is widely used in blockchain.
Radar Detection-Inspired Signal Retrieval from the Short-Time Fourier Transform
Authors: Abratkiewicz Karol
Published: August 2022

This paper presents a novel adaptive algorithm for multicomponent signal decomposition from the time–frequency (TF) plane using the short-time Fourier transform (STFT). The approach is inspired by a common technique used within radar detection called constant false alarm rate (CFAR). The areas with the strongest magnitude are detected and clustered, allowing for TF mask creation and filtering only those signal modes that contribute the most. As a result, one can extract a particular component void of noise and interference regardless of the signal character. The superiority understood as an improved reconstructed waveform quality of the proposed method is shown using both simulated and real-life radar signals.
High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor
Authors: Nogoy Kim Margarette Corpuz , Chon Sun-il , Park Ji-hwan , Sivamani Saraswathi , Lee Dong-Hoon , Choi Seong Ho
Published: August 2022

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.
A Universal Decoupled Training Framework for Human Parsing
Authors: Li Yang , Zuo Huahong , Han Ping
Published: August 2022

Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure.
Demod-CNN: A Robust Deep Learning Approach for Intelligent Reflecting Surface-Assisted Multiuser MIMO Communication
Authors: Sejan Mohammad Abrar Shakil , Rahman Md Habibur , Song Hyoung-Kyu
Published: August 2022

The intelligent reflecting surface (IRS) is a novel and innovative communication technology that aims at the control of the wireless environment. The IRS is considered as a promising technology for sixth-generation wireless communication. In the last few years, machine learning has emerged as a powerful tool for solving complex problems in diverse application areas. In this paper, we propose a convolutional neural network (CNN)-based demodulation technique called Demod-CNN in IRS-based wireless communication for multiple users. A multiple-input multiple-output based orthogonal multiple frequency division multiplexing system is considered for channel modeling. The received signal data are used for training and testing the model. The simulation results show that the proposed model performs better than the conventional demodulation technique.
New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm
Authors: Wang Zhenzhu , Ding Hongbo , Wang Bangxin , Liu Dong
Published: August 2022

Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds (50, 100, and 1000 pulses) of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal.
Towards High Accuracy Pedestrian Detection on Edge GPUs
Authors: Zhou Huaping , Wu Tao , Sun Kelei , Zhang Chunjiong
Published: August 2022

Despite the rapid development of pedestrian detection algorithms, the balance between detection accuracy and efficiency is still far from being achieved due to edge GPUs (low computing power) limiting the parameters of the model. To address this issue, we propose the YOLOv4-TP-Tiny based on the YOLOv4 model, which mainly includes two modules, two-dimensional attention (TA) and pedestrian-based feature extraction (PFM). First, we integrate the TA mechanism into the backbone network, which increases the attention of the network to the visible area of pedestrians and improves the accuracy of pedestrian detection. Then, the PFM is used to replace the original spatial pyramid pooling (SPP) structure in the YOLOv4 to obtain the YOLOv4-TP algorithm, which can adapt to different sizes of people to obtain higher detection accuracy. To maintain detection speed, we replaced the normal convolution with a ghost network with a TA mechanism, resulting in more feature maps with fewer parameters. We constructed a one-way multi-scale feature fusion structure to replace the down-sampling process, thereby reducing network parameters to obtain the YOLOv4-TP-Tiny model. The experimental results show that the YOLOv4-TP-tiny has 58.3% AP and 31 FPS in the winder person pedestrian dataset. With the same hardware conditions and dataset, the AP of the YOLOv4-tiny is 55.9%, and the FPS is 29.
Aqueous Extracts of Fish Roe as a Source of Several Bioactive Compounds
Authors: Guedes Marta , Gonçalves Virgínia M. F. , Tiritan Maria Elizabeth , Reis Rui L. , Ferreira Helena , Neves Nuno M.
Published: August 2022

Regular consumption of seafood and, in particular, fish has been associated with important health benefits. A fish product that has been increasingly included in the human nutrition is roe. Despite its nutritional value has been established (fatty acid profile and protein content), the knowledge of the composition of its aqueous extracts is still limited. This work describes the bioactive compounds profile in the roe-derived aqueous extracts of three different marine species (sardine, horse mackerel and sea bass) using a method based on liquid chromatography coupled to high-resolution mass spectrometry with an electrospray ionisation source (LC-ESI/HRMS). The presence of substances with well-known nutritional and functional properties (e.g., antioxidant and anti-inflammatory properties) was demonstrated, namely essential amino acids (e.g., taurine), peptides (e.g., anserine and carnosine), B-group vitamins (e.g., nicotinamide) and gadusol. Therefore, roe-derived aqueous extracts are excellent sources of bioactive compounds and may be used as a font of functional components for several medical and veterinary applications.
A Literature Review on the Usage of Agent-Based Modelling to Study Policies for Managing International Migration
Authors: De Luca Gabriele , Lampoltshammer Thomas J. , Parven Shahanaz , Scholz Johannes
Published: August 2022

This literature review is dedicated to the subject of agent-based modelling for the system of international migration, and of the modelling of policies that are known to aid in its management. The reason for the selection of agent-based modelling as a framework for studying international migration is that the system of international migration presents the characteristics of a complex system: notably, its property of emergence, which therefore imposes the usage of a methodology for its modelling that is capable of reflecting its emergent traits. The policies that we study are those that intervene in the country of origin of emigrants and that are aimed at decreasing the aggregate volume of emigrants from that country. The reason for this choice is that policies in the countries of origin have become particularly attractive today, especially in European countries, under the assumption that it should be possible to prevent the migrants from reaching the point of destination of their journey if some kind of action is undertaken before the migrants arrive. We start by discussing the theoretical constraints that suggest how this approach may only partially be valid. Then, to assist the development of future agent-based models that study migration, we identify via topic mining the ten topics that are most commonly discussed in the literature on the application to the international migration of agent-based models; this lets us highlight the characteristics of an agent-based model that should be included when the research task relates to the usage of ABM to study international migration and its associated policies. Finally, we indicate why the existing literature on the modelling of international migration is missing a key aspect that is required to correctly model policies: the integration between agent-based approaches and systems dynamics.
Narrating Resistant Citizenships through Two Pandemics
Authors: Squire Corinne , de Lemos Jamilson Bernardo
Published: August 2022

Covid has intensified inequalities in the UK, particularly for those already living with structural disadvantage, and despite community and popular resistance to those losses. Covid has also disproportionately affected people with HIV, especially those already living with multi-dimensional inequalities. However, many people with HIV have, as they have done before, made strong and often successful efforts to resist and to restore or reconstruct their citizenships, in opposition to dominant, dispossessing discourses during Covid times. A narrative approach offers a means of mapping these citizenly technologies. This article draws on a 2020 study conducted with 16 people living with HIV in the UK. The study explored, through telephone semi-structured interviews, the health, economic, and psychosocial resources with which these participants lived with HIV and how Covid has impacted those resources. Narrative analysis showed losses of HIV and other health resources, constituting reductions in health citizenship, resisted largely by reconstitutions of alternatives within the HIV sector; losses of economic citizenship, despite oppositional, anti-political attempts to retain it, and of psychosocial citizenship, in spite of family and friendship networks; resistant, ‘alter’ development of renewed HIV citizenships; and across fields, resistance by complaint. This study indicates that ‘de-citizening’ citizenship losses are likely to also affect other groups with long-term conditions, illnesses, and disabilities. Resistant ‘re-citizening’ technologies, while important, had limited effects. The study suggests potential future resistant effects of repeated ‘complaint’ about Covid-era citizenship losses, and the more general significance of histories of dissent for future effective resistance.
Sustainability-Focused Excellence: A Novel Model Integrating the Water–Energy–Food Nexus for Agro-Industrial Companies
Authors: Caixeta Fernando , Carvalho André M. , Saraiva Pedro , Freire Fausto
Published: August 2022

The water–energy–food (WEF) nexus approach is gaining attention due to the challenge of better managing natural elements. Agro-industrial companies, given their environmental impacts, need to take sustainability into proper account. However, this sector lacks the novel tools needed to integrate current methodologies with additional quality frameworks, such as business excellence models (BEMs). Therefore, the present research aims to propose a sustainability-focused excellence model by integrating the principles and objectives of the WEF nexus with existing BEM and proposing its application to agro-industrial companies. For that purpose, a new conceptual model to integrate sustainability and excellence was built. The proposed novel model can become a decision-support tool in helping agro-industrial companies transition toward improved sustainability while managing existing tradeoffs and synergies.
Archaeology as Sustainability Science: Perspectives from Ancient Island Societies
Authors: LeFebvre Michelle J. , Erlandson Jon M. , Fitzpatrick Scott M.
Published: August 2022

Humans and the diverse ecosystems we inhabit face numerous sustainability challenges due to climate change, rising seas, population growth, overfishing, natural habitat destruction, accelerating extinctions, and more. As an interdisciplinary paradigm that leverages both natural and social sciences to better understand linkages between humans and the environment, sustainability science focuses on how these connections shape understandings of and approaches to sustainability challenges. Here, we argue that archaeology and historical ecology are essential components of sustainability science. We view sustainability as a long-term process where historical sciences are critical to effectively measuring where we stand today and modeling future trajectories based on the baselines from the past that archaeology and historical ecology provide. We demonstrate that islands around the world are central to this endeavor because they serve as model systems that can capture the timing of human arrival, subsequent effects of cultural behaviors on pristine environments, and how humans adapted, survived, and often thrived for centuries or millennia. These cases provide important lessons about human responses in the past to similar challenges that we now currently face. In the uncertain futures of the Anthropocene, such historical baselines will contribute significantly to scientific approaches for building more resilient and sustainable societies.
Spatiotemporal Patterns and Influencing Factors of Industrial Ecological Efficiency in Northeast China
Authors: Li Wai , Chen Xiaohong , Wang Ying
Published: August 2022

Scientific measurement of regional industrial ecological efficiency and discussion of the development and changes of its spatiotemporal pattern are of great significance to accelerate the high-quality development of regional economy and coordinate the development of industrial economy and ecological environment. Taking the old industrial bases in Northeast China as the research case and 2004–2019 as the research period, a super-slack-based model was used to measure the industrial ecological efficiency of 34 prefecture-level cities in the region. Meanwhile, the spatial autocorrelation model and the geographically and temporally weighted regression (GTWR) model were used to analyze the spatiotemporal pattern characteristics and the spatiotemporal heterogeneity of influencing factors. The results showed that: (1) From a time change perspective, the overall industrial ecological efficiency of Northeast China declined, the mean of the 34 cities decreased from 0.675 to 0.612, the number of cities with a high level of industrial ecological efficiency decreased significantly, the number of cities with a low level of industrial ecological efficiency increased significantly, and the development gap between cities within the region widened. (2) In terms of spatial pattern, the difference in the spatial pattern in the east–west direction decreased, and the spatial pattern in the south–north direction was enhanced. The industrial ecological efficiency of the central part of Northeast China gradually became the highest in the whole region. (3) From 2017, the industrial ecological efficiency had stable spatial autocorrelation characteristics. The local spatial autocorrelation was dominated by L-H-type cluster areas in the mountainous regions and L-L-type cluster areas in central and southern Liaoning province. H-H and H-L types had small numbers. In addition, the trend of H-H cities transforming into H-L cities was obvious, and the high level of urban space spillover effect showed good development. (4) The science and technology input, industrial agglomeration intensity, and environmental regulation of the government generally had a promoting effect on the improvement in industrial ecological efficiency, while the economic extroverted degree had a negative impact. The high-value area of science and technology investment and industrial agglomeration intensity concentrated significantly in the central part. The government focused on ecological protection areas and ecologically sensitive areas, and the economic extroverted degree had a significant positive impact on the two major urban agglomerations in central Northeast China. Therefore, differentiating measures should be taken according to the actual situation of each city to improve the industrial ecological efficiency level in Northeast China.
Heat Transfer Modeling on High-Temperature Charging and Discharging of Deep Borehole Heat Exchanger with Transient Strong Heat Flux
Authors: Zhao Yazhou , Qin Xiangxi , Shi Xiangyu
Published: August 2022

High-temperature charging and discharging by deep borehole heat exchanger is typical of a large heat exchange temperature difference and transient strong heat flux. Simulation of this problem is not only computationally expensive, but it is also challenging in terms of robustness and stability for numerical methods. This paper formulates a generic and efficient heat transfer model with two distinctive novelties: Firstly, it highlights unsteady- and quasi-steady-state modeling strategies for heat transfer outside and inside a borehole. Secondly, this model provides analytical solutions for the heat front propagation and heat flux density distribution for unsteady-state heat transfer in the rock zone. These analytical formulations prove to be generic and critical to relieve computational effort in the face of strong heat flux. This model is validated by a typical high-temperature heat storage case from the literature, as well as the pilot demonstration project in China. It was discovered that a large prediction error of the heat transfer model only exists in very short operation days during the initial unsteady stages of charging and discharging. Both relative errors under charging and discharging phases are within 5% during the steady-state period. A comparison of the simulation cost with OpenGeoSys software demonstrates its high efficiency. It proves that this heat transfer model achieves an acceleration ratio of 30 times relative to the fully numerical method. In general, the heat transfer model has four advantages: generic applicability, good accuracy, easy implementation, and high efficiency, but it is limited to the heat transfer of a single deep borehole heat exchanger under pure heat conduction.
Environmental Challenges and Green Innovation Strategy: A Vigorous Development of Greener Dynamics
Authors: Alnaim Abdullah F. , Abdelwahed Nadia Abdelhamid Abdelmegeed , Soomro Bahadur Ali
Published: August 2022

This study investigated the strengths of internal and external environmental challenges to the Green Innovation Strategy (GIS). This study tried, also, to observe GBIS’ predictive power of on Green Organizational Identity (GOI), Green Innovation (GI) and Green Creativity (GC). The researchers gathered the cross-sectional data from the top managers of Saudi Arabia’s manufacturing industries. We employed a Structural Equation Model (SEM) to obtain the outcomes to form the data. This study’s findings demonstrate that external pressures, such as Policy Pressure (PP) and Market Pressure (MP) and internal driving forces such as Innovation Resources (IR) and Innovation Capability (IC), have a positive and significant effect on GIS. This study’s findings demonstrate, also, that GIS has a positive and significant effect on GOI, GI and GC. Finally, this study’s findings show that GOI is a considerable predictor of GI and GC. This study’s findings provide the path for policymakers to formulate Environmental Regulations (ER) and laws regarding pressure and incentive regulations. Finally, and more particularly in the context of Gulf countries, this study’s findings contribute to the literature on green management, innovation and Environmental Management (EM).
Effects of the State of Emergency during the COVID-19 Pandemic on Tokyo Vegetable Markets
Authors: Aruga Kentaka , Islam Md. Monirul , Jannat Arifa
Published: August 2022

The state of emergency (SOE) period in Tokyo under the COVID-19 pandemic restricted people to staying in their homes and changed human mobility, which has impacted the major agricultural markets in Tokyo. In this research, we analyzed how the changes in people’s staying-at-home behaviors during the four SOE periods (7 April 2020–28 October 2021) in Tokyo affected the daily market prices of cabbage, tomato, Japanese radish, carrot, and potato. Using the autoregressive distributed lag (ARDL) model, the study reveals that all the investigated vegetables except potatoes have a long-term relationship with the staying-at-home index. The long-term influence of staying-at-home behaviors on cabbage, tomato, radish, and carrot markets during the early SOE periods had a negative impact on these vegetable prices, indicating that an increase in the hours of staying-at-home as related to SOE measures might have decreased the demand for these vegetables. The negative impact of the stay-at-home index on vegetable prices lessened in the fourth SOE period, likely because more people did not remain in their homes. Moreover, the study findings reveal that, compared to less perishable vegetables, the price of perishable vegetables is more likely to have been affected by human mobility constraints during the pandemic. Therefore, agricultural policymakers should consider providing subsidies to producers based on the negative influence on market prices of perishable and less perishable vegetables in pandemic situations, such as COVID-19.
EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture
Authors: Rosado Luís , Faria Pedro , Gonçalves João , Silva Eduardo , Vasconcelos Ana , Braga Cristiana , Oliveira João , Gomes Rafael , Barbosa Telmo , Ribeiro David , Nogueira Telmo , Ferreira Ana , Carlos Cristina
Published: August 2022

Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps.