bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery
Authors: Sun Jialin , Yan Shuai , Alexandridis Thomas , Yao Xiaochuang , Zhou Han , Gao Bingbo , Huang Jianxi , Yang Jianyu , Li Ying
Published: April 2024

Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM.
Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism
Authors: Sun Yu , Zhi Xiyang , Jiang Shikai , Shi Tianjun , Song Jiachun , Yang Jiawei , Wang Shengao , Zhang Wei
Published: April 2024

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.
Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors
Authors: Ni Ziming , Chen Xianzhong , Hou Qingwen , Zhang Jie
Published: April 2024

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.
Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model
Authors: Yang Chao , Zhai Han , Fu Meijuan , Zheng Que , Fan Dasheng
Published: April 2024

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA.
A Simple Artificial Neural Model to Predict Dambovita River Temperature Affected by Urban Heat Islands in Bucharest City
Authors: Ionescu Cristina-Sorana , Opriș Ioana , Gogoașe Nistoran Daniela-Elena , Copilău Cristian
Published: April 2024

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures.
Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers
Authors: Cremons Daniel R. , Clarke Gregory B. , Sun Xiaoli
Published: April 2024

The low obliquity of the Moon leads to challenging solar illumination conditions at the poles, especially for passive reflectance measurements aimed at determining the presence and extent of surface volatiles. A nascent alternate method is to use active laser illumination sources in either a multispectral or hyperspectral design. With a laser spectral source, however, the achievable reflectance precision may be limited by speckle noise resulting from the interference effects of a coherent beam interacting with a rough surface. Here, we have experimentally tested the use of laser linewidth broadening to reduce speckle noise and, thus, increase reflectance precision. We performed a series of speckle imaging tests with near-infrared laser sources of varying coherence, compared them to both theory and speckle pattern simulations, and measured the reflectance precision using calibrated targets. By increasing the laser linewidth, we observed a reduction in speckle contrast and the corresponding increase in reflectance precision, which was 80% of the theoretical improvement. Finally, we discuss methods of laser linewidth broadening and spectral resolution requirements for planetary laser reflectance spectrometers.
Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria
Authors: Zikiou Nadia , Rushmeier Holly , Capel Manuel I. , Kandakji Tarek , Rios Nelson , Lahdir Mourad
Published: April 2024

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.
Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment
Authors: Kairuz-Cabrera David , Hernandez-Rodriguez Victor , Schalm Olivier , Martinez Alain , Laso Pedro Merino , Alejo-Sánchez Daniellys
Published: April 2024

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.
Frequency- and Temperature-Dependent Uncertainties in Hysteresis Measurements of a 3D-Printed FeSi wt6.5% Material
Authors: Kocsis Bence , Orosz Tamás
Published: April 2024

Additive manufacturing of soft magnetic materials is a promising technology for creating topologically optimized electrical machines. High-performance electrical machines can be made from high-silicon-content FeSi alloys. Fe-6.5wt%Si material has exceptional magnetic properties; however, manufacturing this steel with the classical cold rolling methodology is not possible due to the brittleness of this material. Laser powder bed fusion technology (L-PBF) offers a solution to this problem. Finding the optimal printing parameters is a challenging task. Nevertheless, it is crucial to resolve the brittleness of the created materials so they can be used in commercial applications. The temperature dependence of magnetic hysteresis properties of Fe-6.5wt%Si materials is presented in this paper. The magnetic hysteresis properties were examined from 20 °C to 120 °C. The hysteresis measurements were made by a precision current generator–based hysteresis measurement tool, which uses fast Fourier transformation–based filtering techniques to increase the accuracy of the measurements. The details of the applied scalar hysteresis sensor and the measurement uncertainties were discussed first in the paper; then, three characteristic points of the static hysteresis curve of the ten L-PBF-manufactured identical toroidal cores were investigated and compared at different temperatures. These measurements show that, despite the volumetric ratio of the porosities being below 0.5%, the mean crack length in the samples is not significant for the examined samples. These small defects can cause a significant 5% decrement in some characteristic values of the examined hysteresis curve.
End-to-End Ultrasonic Hand Gesture Recognition
Authors: Fertl Elfi , Nguyen Do Dinh Tan , Krueger Martin , Stettinger Georg , Padial-Allué Rubén , Castillo Encarnación , Cuéllar Manuel P.
Published: April 2024

As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.
Microwave Dielectric Response of Bovine Milk as Pregnancy Detection Tool in Dairy Cows
Authors: Galindo Cindy , Levy Guy , Feldman Yuri , Roth Zvi , Shalev Jonathan , Raz Chen , Mor Edo , Argov-Argaman Nurit
Published: April 2024

The most reliable methods for pregnancy diagnosis in dairy herds include rectal palpation, ultrasound examination, and evaluation of plasma progesterone concentrations. However, these methods are expensive, labor-intensive, and invasive. Thus, there is a need to develop a practical, non-invasive, cost-effective method that can be implemented on the farm to detect pregnancy. This study suggests employing microwave dielectric spectroscopy (MDS, 0.5–40 GHz) as a method to evaluate reproduction events in dairy cows. The approach involves the integration of MDS data with information on milk solids to detect pregnancy and identify early embryonic loss in dairy cows. To test the ability to predict pregnancy according to these measurements, milk samples were collected from (i) pregnant and non-pregnant randomly selected cows, (ii) weekly from selected cows (n = 12) before insemination until a positive pregnancy test, and (iii) daily from selected cows (n = 10) prior to insemination until a positive pregnancy test. The results indicated that the dielectric strength of Δε and the relaxation time, τ, exhibited reduced variability in the case of a positive pregnancy diagnosis. Using principal component analysis (PCA), a clear distinction between pregnancy and nonpregnancy status was observed, with improved differentiation upon a higher sampling frequency. Additionally, a neural network machine learning technique was employed to develop a prediction algorithm with an accuracy of 73%. These findings demonstrate that MDS can be used to detect changes in milk upon pregnancy. The developed machine learning provides a broad classification that could be further enhanced with additional data.
Enhancing Disaster Resilience for Sustainable Urban Development: Public–Private Partnerships in Japan
Authors: Ishiwatari Mikio , Kawakami Haruki , Sasaki Daisuke , Sakamoto Akiko , Nakayama Mikiyasu
Published: April 2024

A resilient building environment is crucial for securing sustainable development in urban areas, as the 2030 Agenda for Sustainable Development Goal 11 stresses. In developing countries in particular, the risk of disasters is increasing due to the poorly built environment caused by urbanization. However, building disaster resilience in vulnerable urban environments characterized by aging houses, limited public spaces, and complex land rights and tenancy issues poses a major challenge. This study aims to identify critical factors influencing effective disaster-resilient urban development by examining Japan’s experience, with a focus on approaches facilitating public–private partnerships. Driven by disasters like the 1995 Kobe Earthquake, Japan has promoted innovative strategies to improve urban resilience and mitigate disaster impacts. The Disaster Mitigation Zone Implementation Program represents a novel program designed to revitalize densely populated areas with aging wooden structures highly vulnerable to disasters. Through semi-structured interviews, a literature review, and an in-depth case study in Tokyo, this research analyzes the development and effectiveness of this targeted redevelopment approach. Findings underscore the pivotal role of policies promoting public–private collaboration, consensus-building mechanisms among stakeholders, flexibility in project formulation, and financial incentives via government subsidies. Engaging the private sector ensures project feasibility through urban development expertise, while simpler, smaller-scale projects attract greater private investment. Japan’s experience offers valuable insights into collaborative, context-sensitive strategies for enhancing urban disaster resilience through targeted redevelopment of high-risk areas.
Study of the Effect of Seepage–Cyclic Load Coupling Disturbance on the Physical Field in Old Urban Underground Spaces
Authors: Yang Jinghu , Cheng Ye , Cui Dawei , Zhang Zewei , Zhang Bo , Gan Yixiong
Published: April 2024

The safety and sustainability of urban underground spaces have become crucial considerations in development projects. Seepage and cyclic loads are the principal reasons for the instability and failure of old underground space structures. This study investigates the variations in physical fields of underground spaces in cities under the coupling disturbance of seepage and cyclic loads, focusing on underground civil air defense engineering in Beijing as a case study. Different seepage conditions and the effects of seepage–cyclic load coupling were simulated using the numerical calculation software Plaxis 3D V20. The results show that change in groundwater can affect the deformation of underground space, and the severity is related to the quantity and intersection state of tunnels, the location of rivers above, and the strength of materials. The coupling effect of seepage–cyclic load on urban underground space structures is more serious than that of a single percolation. Decrease in material strength and high traffic loads are the principal reasons for the failure of underground structures. A 30% decrease in material strength causes the displacement to increase almost 1.5 times, and maximum displacement under different traffic loads can vary by 3 times. This study holds significant implications for the design, maintenance, and engineering management of underground spaces, emphasizing the importance of sustainable practices in urban development and infrastructure.
Water Resources Management under Climate Change: A Review
Authors: Ciampittiello Marzia , Marchetto Aldo , Boggero Angela
Published: April 2024

Climate change affects water resources through the decrease in rainfall and the increase in temperatures and evapotranspiration. An indirect impact of climate change is also the increase in water uses by human activities. In this review, 320 papers were retrieved, of which 134, spanning five continents and dealing with impacts and solutions, were selected to be used to better understand the effects of climate change on water resources, ecosystems, human health, security, and socio-economic aspects. Here, suggestions and proposals towards solutions by scientists from around the world, tips and ideas to deal with climate change, and the best solutions for future water management are presented. The main solutions highlighted concern integrated water resource management, political direction, policies, an increase in knowledge, and new technologies. Furthermore, most of the analyzed papers underline that water resource management needs to incorporate the protection and restoration of ecosystems and their services. Nature-based solutions need to be the starting point of new scientific and innovative ways to deal with climate change and look towards future climate adaptation. In this complex evolution of the water resource, the political position of Italy is also shown, illustrating what actions could be implemented for water resource management.
Niches Seeking Legitimacy: Notes about Social Innovation and Forms of Social Enterprise in the Italian Renewable Energy Communities
Authors: De Vidovich Lorenzo
Published: April 2024

Renewable energy communities (RECs) are increasingly seen as key innovations for the development of decentralized energy systems that leverage on renewable energy co-production, sharing, and consumption. However, they still represent a niche in search of development and consolidation, and the social innovations they might bring to the energy systems need to be further scrutinized. Innovations related to the capacity to support forms of social entrepreneurship in the energy system are central to this analysis, even if they have been little studied in Italy in relation to the emerging field of renewable energy communities. Through a theoretical discussion aimed at fulfilling descriptive objectives, this paper examines, on the one hand, the social innovations related to community energy projects and, with a focus on Italy, the legal forms required to create an REC in the light of the constantly changing regulatory framework, where new actors, such as the Third Sector, can play a leading role as intermediary actors to develop the social implementation of REC initiatives. The paper navigates through the literature on community energy in the light of the social innovation they can bring to the energy system, discusses the niche condition of RECs, and addresses the Italian case with particular reference to the role of the Third Sector in disseminating REC innovations.
Trade and Water Pollution: Evidence from China
Authors: Yang Wenhao , Huang Yuanzhe , Ye Jinsong , Zhong Changbiao
Published: April 2024

China’s economy has achieved significant success by integrating itself into the globalized production system over an extended period. However, it is crucial to address the environmental consequences that accompany rapid economic progress. The correlation between trade and environmental pollution is still controversial in the existing literature, with a lack of research specifically investigating this relationship using detailed data at the firm level. Based on the quasi-natural experiment of China’s accession to the WTO, this study uses the DID method to evaluate the causal relationship between trade and the environment experimentally. It is found that trade liberalization significantly increases firms’ industrial wastewater emissions, and the empirical results remain robust after parallel trend tests, placebo tests, and replacement variables. The mechanism of action suggests that trade expansion enhances corporate pollution emissions through two channels: attracting foreign investment into the country and intensifying energy consumption. A heterogeneity analysis reveals that the pollution-enhancing effect of trade expansion on enterprises is mainly concentrated in export-oriented enterprises, labor-intensive industries, and coastal regions. Additionally, further analysis shows that trade liberalization not only has local impacts but also spatial spillover effects on enterprise pollution. It is found that enhancing environmental governance and reducing corruption can effectively mitigate the adverse environmental consequences caused by trade liberalization.
Using Deep Learning Approaches to Quantify Landscape Preference of the Chinese Grand Canal: An Empirical Case Study of the Yangzhou Ancient Canal
Authors: Li Yiwen , Qiu Bing
Published: April 2024

Landscape preference emerges from the dynamic interaction between individuals and their environment and plays a pivotal role in the preservation and enhancement of the Chinese Grand Canal’s scenery. As a vast linear heritage, employing conventional methods for analyzing landscape preferences can be resource-intensive in terms of both time and labor. Amid the rapid advancement of Big Data and Artificial Intelligence (AI), a cognitive framework for understanding the Chinese Grand Canal’s landscape preferences has been developed, encompassing two primary aspects: the characteristic features of landscape preference and its spatial organization. Geotagged photographs from tourism media platforms focused on the Yangzhou Ancient Canal were utilized, and the EasyDL deep learning platform was employed to devise a model. This model assesses current landscape preferences through an analysis of photographic content, element composition patterns, and geospatial distribution, integrating social network and point density analyses. Our findings reveal that the fusion of Yangzhou Ancient Canal and classical gardens creates a sought-after ‘Canal and Watercraft Remains’ landscape. Tourists’ preferences for different landscape types are reflected in the way the elements are combined in the photographs. Overall, landscape preferences are dense in the north and sparse in the south. Differences in tourists’ perceptions of the value of and preferences for heritage sites lead to significant variations in tourist arrivals at different sites. This approach demonstrates efficiency and scalability in evaluating the Chinese Grand Canal landscape, offering valuable insights for its strategic planning and conservation efforts.
Emerging Contaminants in the Effluent of Wastewater Should Be Regulated: Which and to What Extent?
Authors: Yang Weiwei , Bu Qingwei , Shi Qianhui , Zhao Ruiqing , Huang Haitao , Yang Lei , Tang Jianfeng , Ma Yuning
Published: April 2024

Effluent discharged from urban wastewater treatment plants (WWTPs) is a major source of emerging contaminants (ECs) requiring effective regulation. To this end, we collected discharge datasets of pharmaceuticals (PHACs) and endocrine-disrupting chemicals (EDCs), representing two primary categories of ECs, from Chinese WWTP effluent from 2012 to 2022 to establish an exposure database. Moreover, high-risk ECs’ long-term water quality criteria (LWQC) were derived using the species sensitivity distribution (SSD) method. A total of 140 ECs (124 PHACs and 16 EDCs) were identified, with concentrations ranging from N.D. (not detected) to 706 μg/L. Most data were concentrated in coastal regions and Gansu, with high ecological risk observed in Gansu, Hebei, Shandong, Guangdong, and Hong Kong. Using the assessment factor (AF) method, 18 high-risk ECs requiring regulation were identified. However, only three of them, namely carbamazepine, ibuprofen, and bisphenol-A, met the derivation requirements of the SSD method. The LWQC for these three ECs were determined as 96.4, 1010, and 288 ng/L, respectively. Exposure data for carbamazepine and bisphenol-A surpassed their derived LWQC, indicating a need for heightened attention to these contaminants. This study elucidates the occurrence and risks of ECs in Chinese WWTPs and provides theoretical and data foundations for EC management in urban sewage facilities.
Exploring the Toxicity of Oxytetracycline in Earthworms (Eisenia fetida) Based on the Integrated Biomarker Response Method
Authors: Zhou Haoran , Jiao Xiaoguang , Li Yunfei
Published: April 2024

Antibiotic contamination has become a global environmental issue of widespread concern, among which oxytetracycline contamination is very severe. In this study, earthworm (Eisenia fetida) was exposed to oxytetracycline to study its impact on the soil environment. The total protein (TP), catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), malondialdehyde (MDA), glutathione S-transferase (GST), and glutathione peroxidase (GPX) oxidative stress indicators in earthworms were measured, and the integrated biomarker response (IBR) approach was used to evaluate the toxic effect of oxytetracycline on earthworms. A Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and a path analysis model were used to explore the physiological and metabolic processes of earthworms after stress occurs. The results showed that SOD, GPX, and GST play important roles in resisting oxytetracycline stress. In addition, stress injury showed a good dose–effect relationship, and long-term stress from pollutants resulted in the most serious damage to the head tissue of earthworms. These results provide a theoretical basis for understanding the toxic effect of oxytetracycline on soil animals, monitoring the pollution status of oxytetracycline in soil, and conducting ecological security risk assessment.
Canine Mesenchymal Stromal Cell Exosomes: State-of-the-Art Characterization, Functional Analysis and Applications in Various Diseases
Authors: Saba Evelyn , Sandhu Mansur Abdullah , Pelagalli Alessandra
Published: April 2024

Canine mesenchymal stromal cells (MSCs) possess the capacity to differentiate into a variety of cell types and secrete a wide range of bioactive molecules in the form of soluble and membrane-bound exosomes. Extracellular vesicles/exosomes are nano-sized vesicles that carry proteins, lipids, and nucleic acids and can modulate recipient cell response in various ways. The process of exosome formation is a physiological interaction between cells. With a significant increase in basic research over the last two decades, there has been a tremendous expansion in research in MSC exosomes and their potential applications in canine disease models. The characterization of exosomes has demonstrated considerable variations in terms of source, culture conditions of MSCs, and the inclusion of fetal bovine serum or platelet lysate in the cell cultures. Furthermore, the amalgamation of exosomes with various nano-materials has become a novel approach to the fabrication of nano-exosomes. The fabrication of exosomes necessitates the elimination of extrinsic proteins, thus enhancing their potential therapeutic uses in a variety of disease models, including spinal cord injury, osteoarthritis, and inflammatory bowel disease. This review summarizes current knowledge on the characteristics, biological functions, and clinical relevance of canine MSC exosomes and their potential use in human and canine research. As discussed, exosomes have the ability to control lethal vertebrate diseases by administration directly at the injury site or through specific drug delivery mechanisms.
Biological Sunglasses in a Deep-Sea Squid: Pigment Migration in the Retina of Gonatus onyx
Authors: Howard Ryan B. , Kniller Jessica , Bolstad Kathrin S. R. , Acosta Monica L.
Published: April 2024

The outward migration of ommin pigment granules from the bases to the tips of the photoreceptors in response to light has been reported in the retina of several (mostly coastal) squid species. Following exposure to light and then dark conditions, we collected and processed retinal tissue from juvenile specimens of a deep-sea oegopsid squid, Gonatus onyx. We aimed to determine whether the ommin pigment returns to baseline, and to investigate the presence of glutamate neurotransmitter signaling under both dark and light conditions. We confirmed the presence of ommin granules but observed variability in the return of pigment to the basal layer in dark conditions, as well as changes in glutamate distribution. These findings provide support for the migration of retinal ommin pigment granules as a mechanism for regulating incoming light.
Safety of Tap Water in Terms of Changes in Physical, Chemical, and Biological Stability
Authors: Domoń Andżelika , Kowalska Beata , Papciak Dorota , Wojtaś Edyta , Kamińska Iwona
Published: April 2024

Monitoring the quality of tap water in the distribution system and the ability to estimate the risk of losing its sanitary safety is an important aspect of managing the collective water supply system. During monitoring, the physical, chemical, and biological stability of water was assessed, which is the main determinant ensuring the appropriate quality of water for consumers. The physicochemical and microbiological quality of water was analyzed for two distribution systems (DSs), including the analysis of heavy metals (Zn, Fe, Mn, Cr, Ni, Cu, Cd, Pb). The tests carried out showed that in both distribution systems, the water supplied to consumers met the guidelines for water intended for human consumption. It can be considered that the risk of uncontrolled changes in water quality in DSs with an average water production of <10,000 m3/d and the length of water pipelines < 150 km is very low. The water introduced into the system differed in the place of water intake and water purification technology, which influenced the final water quality. In DS(II), higher values were recorded for hardness, conductivity, calcium, alkalinity, nitrates, and DOC. It was found that the content of heavy metals during water transport to the consumer increased in the case of DS(I) for Zn, Ni, Cu, Cd, and Pb, and in the case of DS(II) for Fe, Mn, Ni, Cu, Cd, and Pb. The observed differences resulted from the different quality of the intake water as well as from different materials used to build internal installations and their age and technical condition. The analyzed tap water was characterized by physical and chemical stability. However, the water did not meet the guidelines for water biostability due to the increased content of biogenic substances.
A Distributed Catchment—Scale Evaluation of the Potential of Soil and Water Conservation Interventions to Reduce Storm Flow and Soil Loss
Authors: Alemie Tilashwork C. , Buytaert Wouter , Tilahun Seifu A. , Steenhuis Tammo S.
Published: April 2024

Finding effective ecosystem services (ESS) management practices to counteract land degradation and poverty is becoming increasingly urgent in the Ethiopian highlands, where livelihood security is strongly dependent on local ESS, particularly those provided by water and soil. In this paper, we test the effects of widely implemented soil and water conservation (SWC) interventions on storm flow and sediment concentration in the Debre Mawi watershed (representative of watersheds in the upper Blue Nile basin and Ethiopian highlands). The SWC interventions were tested with a Parameter Efficient Distributed (PED) model. The PED model simulates saturation excess runoff from degraded and saturated valley bottoms, and base and interflow from hillsides. The model was calibrated with observed runoff and sediment data in a 95-ha subcatchment. We found that the PED model simulated the discharge and soil loss well by decreasing the proportion of degraded lands due to installing SWC practices. The results show that four years after the implementation of SWC practices, the infiltration of rainwater was improved in 53% of the degraded lands. Thus, installing SWC practices on hillsides where infiltration is limited is most beneficial and will result in greater water availability during the dry phase, especially in locations where volcanic dikes block the lateral flow.
In Defense of Hitchens’s Razor
Authors: Loftus John W.
Published: April 2024

In this chapter, I’m going to defend what has come to be known as Hitchens’s Razor, which he stated as follows: “What can be asserted without evidence can also be dismissed without evidence.” The point Christopher Hitchens was making is that miracle claims asserted without any evidence can be dismissed. Period!
William of Ockham (1285‒1349) had previously articulated what is known as Ockham’s Razor, whereby “entities should not be multiplied without necessity” when it came to supernaturally caused miracles. According to Ockham, the best explanations are those that make the fewest assumptions, so simpler explanations are to be preferred over complex ones. This paved a pathway for modern scientific inquiry since the addition of supernatural entities adds unnecessary complexity into our scientific explanations.
Whole-Russian Scientific and Practical Conference «Well-Being Measuring and Analyzing»
Authors: Burova N. V. , Dekina M. P. , Neradovskaya Yu. V.
Published: April 2024

The article contains an overview of reports presented at the Whole-Russian scientific and practical conference «Well-being Measuring and Analyzing», held on 25–27 January 2024 at St. Petersburg State Economic University (UNECON). The program of the conference included 142 reports in face-to-face and online formats. Approximately a quarter of all the conference papers were presented by young researchers – students, undergraduates, and postgraduate students – who co-authored work with academic supervisors. Scientists from educational and research institutions from 19 constituent entities of the Russian Federation and seven foreign countries delivered their presentations.The conference participants discussed the accumulated domestic and international experience in measuring well-being, highlighting specific features and regional characteristics of this socio-economic phenomenon. Aspects of the application of statistical and econometric methods of analysis and forecasting in studying issues of welfare and social inequality were considered, including measuring the level of poverty, the impact of pension system reform, changes in consumer behavior of the population in the housing market, ways to solve social problems in certain regions, including, Arctic zone. The participants identified directions for improving the methodology for measuring well-being and measures to address social inequality at the federal and regional levels.
Methodological Approaches to Constructing Human Capital Index: Regional Dimension
Authors: Medyanik V. V. , Kurbatskii A. N. , Nagorny P. I. , Goryashin D. V.
Published: April 2024

The paper analyses available foreign and domestic expertise in the treatment and methods for measuring human capital. The authors identify weaknesses of a common approach to measuring human capital based on international statistical standards not adapted to national features of information and statistical support for regional analysis in the face of prominent interregional differences in socio-economic development.A methodological approach to assessing human capital is substantiated using an integral indicator that combines subindices that reflect the accumulation and reproduction of human capital. Guided by this approach and statistics on the demographic situation, upgrading knowledge, skills and strengthening the public health, including that of the working population, and based on actual data for the regions of Russia (for the year 2020), a unified summary indicator of human capital for the constituent entities of the Russian Federation was calculated and the regions were ranked by a level of human capital development.According to the authors, the developed methodological approaches to measuring the human capital index shall make it possible to take more specific action on implementing the Programme for Socio-economic Development as applied to individual Russian regions
Forming the herd : A re-examination of Predynastic bovine figurines from settlement contexts
Authors: Brice Elizabeth
Published: April 2024

Since the early 20 th century, bovine figurines have been excavated from Upper Egyptian Predynastic settlements and cemeteries dating between Naqada IA–IIC/D (ca. 3800–3325 BCE). Though still rare finds for this period, they are currently the most commonly identi-fied zoomorphic figurine subject, far outnum-bering other wild and domestic animals. As a result of their greater quantity, bovine figu-rines have been the subject of numerous icon-ographic and contextual studies which have proposed that these objects were produced for religious rituals, ex-votos, and proxies of the real animal. Using bovine figurines from three settlements with similar features and archaeological contexts, namely el-Mahâsna, Zawaydah, and Armant, this paper provides another interpretation which places the evi-dence within an economic context, where the figurine acts as a record of the negotiations and exchanges of cattle. The analysis draws upon the visual and material attributes of the figurines within their broader archaeological context to compare with a hypothesis that was proposed by Wengrow (1998). The mul-tifaceted value of cattle as producers of pri-mary and secondary products, and indexes of wealth made them a significant animal to own ; thus, individuals may have documented the negotiation for live cattle and carcasses using bovine figurines.
Technical choices and processes of stone vessel manufacture under the reign of king Den : evidence from the Cemetery M at Abu Rawash
Authors: Takenouchi Keita
Published: April 2024

Stone vessels were produced on a large scale during the Early Dynastic Period. The pro-duction of stone vessels in this period is a typ-ical example of the development of attached specialists that occurred parallel to the emer-gence of the central government and admin-istration. However, we are not thoroughly familiar with the specifics of the manufac-turing techniques and system that supported this large-scale production in Early Dynastic society. This paper reconstructs the detailed manufacturing techniques and processes of the cylindrical jars and the open forms through the observation and analysis of the unpublished materials from the Cemetery M (excavated by Montet) at Abu Rawash. This research has found that travertine bowls were probably shaped by irregular and less precise polishing than mudstone. Addition-ally, the deeper bowls were found to have a intensive use of “figure-of-eight” shaped reamers. Craftsmen also selected techniques, processes, and tools according to vessel shape and size in addition to the hardness and nature of the stone. The detailed manufacturing process of the cylindrical jars is also reconstructed. These cylindrical jars can be classified into two categories : high-quality and low-quality jars. The low-quality and small-sized jars were manufactured by a more intensive use of “figure-of-eight” shaped reamer. A comparison with quantitative data from other cemeteries at Abu Rawash and other Early Dynastic sites indicates that the low-quality and the small jars were probably supplied to Abu Rawash as “commoditized products” during the 2 nd Dynasty. Finally, the article discusses the increased use of rota-tional reaming techniques with a “figure-of-eight” shaped reamer in the manufacture of both cylindrical jars and bowls during the 2 nd

Dynasty.