bluuebunny/crossref_metadata_2025 huggingface dataset:
Surfactant–Polymer Flooding: Chemical Formula Design and Evaluation for High-Temperature and High-Salinity Qinghai Gasi Reservoir
Authors: Sun Jinlong , Liu Yifeng , Zhu Xiuyu , Hu Futang , Wang Yuanyuan , Yi Xiaoling , Zhu Zhuoyan , Liu Weidong , Zhu Youyi , Hou Qingfeng
Published: May 2024

The Gasi reservoir in the Qinghai oilfield is a typical high-temperature and high-salinity reservoir, with an average temperature and average salinity of 70.0 °C and 152,144 mg/L, respectively. For over 30 years since 1990, water flooding has been the primary method for enhancing oil recovery. Recently, the Gasi reservoir has turned into a mature oilfield. It possesses a high water cut of 76% and a high total recovery rate of 47%. However, the main developing enhanced oil recovery (EOR) technology for the development of the Gasi reservoir in the next stage is yet to be determined. Surfactant–polymer (SP) flooding, which can reduce the oil–water interfacial tension and increase the viscosity of the water phase, has been widely applied to low-temperature and low-salinity reservoirs across China in the past few decades, but it has rarely been applied to high-temperature and high-salinity reservoirs such as the Gasi reservoir. In this study, the feasibility of SP flooding for high-temperature and high-salinity reservoirs was established. Thanks to the novel surfactant and polymer products, an SP flooding formula with surfactants ZC-2/B2 and polymer BRH-325 was proposed for Gasi. The formula showed a low interfacial tension of 10−2 mN/m and a high viscosity of 18 MPa·s in simulated reservoir conditions. The oil displacement experiment demonstrated that this formula can enhance the oil recovery rate by 26.95% upon water flooding at 64.64%. This study provides a feasible EOR candidate technology for high-temperature and high-salinity reservoirs, as exemplified by the Qinghai Gasi reservoir.
A Comprehensive Review on the Use of Wastewater in the Manufacturing of Concrete: Fostering Sustainability through Recycling
Authors: Maddikeari Manjunath , Das Bibhuti Bhusan , Tangadagi Ranjitha B. , Roy Suman , Nagaraj Priyanka Bangalore , Ramachandra Manjunatha Lokanahally
Published: May 2024

The primary aim of this review article is to find the influence of wastewater and its characteristics on recycling as an alternative to potable water for concrete preparation. On the other hand, scarcity, and the demand for freshwater for drinking are also increasing day by day around the globe. About a billion tons of freshwater is consumed daily for concrete preparation for various operations such as mixing and curing, to name a few. The rapid development of certain industries such as textile, casting, stone cutting, and concrete production has caused the water supply to be severely affected. Recycling wastewater in concrete offers various potential benefits like resource conservation, environmental protection, cost savings, and enhanced sustainability. This article reviews the effect of various types of wastewater on various physical and chemical properties of wastewater, rheological characteristics, strength, durability, and microstructure properties of concrete. It also explores the potential effects of decomposing agents on enhancing concrete properties. Currently, limited research is available on the use of various types of wastewater in concrete. Hence, there is a need to develop various methods and procedures to ensure that the utilization of wastewater and treated wastewater is carried out in the production of concrete in a sustainable manner. Although wastewater can reduce the workability of fresh concrete, it can also increase its strength and long-term performance of concrete. The use of various types of wastewater, such as reclaimed water and tertiary-treated wastewater, was found to be superior compared to those using industrial- or secondary-treated wastewater. Researchers around the globe agree that wastewater can cause various detrimental effects on the mechanical and physical properties of concrete, but the reductions were not significant. To overcome limited scientific contributions, this article reviews all the available methods of using various types of wastewater to make concrete economically and environmentally friendly. This research also addresses possible challenges with respect to the demand for freshwater and the water crisis.
Nature, Place, and Ritual: Landscape Aesthetics of Jingfu Mountain “Grotto-Heavens and Blissful Lands” in South China
Authors: Xu Yingjin , Zeng Canxu , Tang Xiaoxiang , Bai Ying , Wang Xin
Published: May 2024

The “Grotto-Heavens and Blissful Lands” (dongtian and fudi, 洞天福地) is a unique concept of sacred space in China and even in East Asia, combining beautiful natural scenery, rich historical heritage, and diverse cultural heritage. This paper tries to explain Mount Jingfu’s (jingfu shan, 靜福山) aesthetic representations. The results show that the landscape’s physical environment projects the spatio-temporal system and the concept of the universe in Daoist aesthetic ideals. With the spatial evolution of divine immortals’ abodes from imagination to reality, people’s yearning for divine cave palaces is transformed into their connection with and their expression of the palaces in exploring space interests and aesthetic trends that are then integrated into the secular life of thousands of households through living religious rituals. Preserved by local religious believers, the ritual activities incorporated geographic, familial, and divine interactions, and characterised essential social aesthetics. By exploring a typical case of Lingnan Region (lingnan, 嶺南, an old term for South China), this paper aims to elucidate the significance of the Grotto-Heavens and Blissful Lands as living heritage in contemporary society across multiple dimensions, and to provide a theoretical basis for the protection of its system.
Creation, Thomas Berry, and the Church in Africa
Authors: Otu Idara
Published: May 2024

The ecological crisis is a sign of the times, demanding adequate understanding, appropriate interpretation, and pastoral action in light of the Christian faith. Scholarly discourse on the ecological crisis continues to inspire partnership and collaborative actions in Africa. Sometimes, these efforts have yielded significant results, though amid uncharted territories. Scholars appeal for humans to embrace a mutually enhancing relationship with creation to build a common home for all creatures. A suitable and sustainable ecological vision that supports a communitarian approach to resolving the ecological crisis is Thomas Berry’s cosmology. Berry’s cosmological vision reclaims a creation theology that fosters a mutual relationship between humans and other creatures. Humans are beings in communion and beings in relationships created by God and thus from a common origin and moving toward a common destiny. This cosmological presupposition of Berry aligns with the ecological vision of Pope Francis in his encyclical Laudato Si’. The Pope calls humanity to a new cosmic relationship that would foster interdependency of the ecosystem and respect the intrinsic value of every creature. Given the unprecedented ecological challenges, humans have a unique role as intelligent beings toward other creatures, for future generations and the common good of the earth. This human responsibility toward the earth can be realized through an ecological ethics that fosters interdependence, interconnection, and mutuality in the human–earth relationship. Berry’s insights and Catholic ecological teachings merit consideration for African Catholicism.
Unravelling the Influence of Buddhist Liberal Arts Education on College Students’ Self-Reflection
Authors: Kwon Junghyun , Kim Jiyun , Jung Younghee , Son Jin
Published: May 2024

The purpose of this paper is to investigate the measurable impact of Buddhist liberal arts courses on college students’ ability to engage in self-reflection. This study uses the data gathered to establish, through statistical analysis, any correlations between two variables—Buddhist liberal arts as courses as the independent variable and self-reflection intelligence as the continuous dependent variable. First, the following results of the study show that the mean of the four self-reflection sub-categories for the students who found that the courses were helpful was higher than that of the students who found that they were not helpful: self-awareness (4.10 ± 0.49 > 3.90 ± 0.87), self-design (3.84 ± 0.66 > 3.40 ± 0.97), self-regulation (4.01 ± 0.04 > 3.48 ± 0.18), and self-examination (4.21 ± 0.03 > 3.94 ± 0.15), respectively. Second, the mean of the four self-reflection sub-categories for students who experienced a positive change of their perception in the courses was higher than that of students who experienced a negative change, as follows: self-awareness (4.08 ± 0.50 > 3.75 ± 0.82), self-design (3.84 ± 0.68 > 3.51 ± 0.93), self-regulation (4.00 ± 0.59 > 3.56 ± 0.67), and self-examination (4.17 ± 0.49 > 4.04 ± 0.73), respectively. This study illuminates the role of Buddhist-related liberal arts courses in higher education, specifically in fostering students’ self-reflection skills. It offers valuable insights into educational practices aimed at enhancing self-reflection levels.
Bridging the Chasm in the Holy Lands: The Antithesis of Islamic Law to Warfare in the Middle East
Authors: Fakhkhar Toosi Javad
Published: May 2024

The present article scrutinizes the intricacies surrounding defensive warfare aimed at reclaiming territories perceived by Muslims as under non-Muslim occupation through the lens of the four Sunni schools and Twelver Shia perspectives. In Shia jurisprudence, in the absence of the twelfth Imam, the concept of defensive war does not extend to reclaiming Islamic territories per se; rather, Muslims are not sanctioned to engage in warfare solely for territorial defense. Instead, the imperative for Muslim involvement in armed conflict arises only when the fundamental tenets of Islam face jeopardy. According to Shia’s legal interpretation, jeopardizing the core principles of Islam implies a scenario where the erasure of Prophet Muhammad’s legacy and the utter annihilation of Sharia law are imminent. Such a predicament is not contingent upon the occupation of Islamic lands. However, Sunni jurisprudence posits a prerequisite for Muslims to engage in warfare, even in defense, necessitating a certainty of prevailing in the conflict. Consequently, given the contemporary milieu, wherein the requisite conditions for assured victory remain unattainable and are likely unattainable, defensive warfare for territorial reclamation is deemed impermissible. The findings of this inquiry propose an Islamic legal framework that advocates abstention from resorting to warfare concerning the mentioned territories in current circumstances, absolving Muslims of any obligation in this regard. Closing the avenue of armed conflict thereby paves the way for alternative avenues such as negotiation, reconciliation, and peace-building. It underscores Islam’s reverence for human life, prioritizing it over territorial concerns.
Optimized Transfer Learning for Chlorophyll Content Estimations across Datasets of Different Species Using Sun-Induced Chlorophyll Fluorescence and Reflectance
Authors: Zhou Yu-an , Huang Zichen , Zhou Weijun , Cen Haiyan
Published: May 2024

Remote sensing-based techniques have been widely used for chlorophyll content (Cab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for Cab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models’ transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting Cab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting Cab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively.
Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods
Authors: Oliveira Santos Victor , Guimarães Bruna Monallize Duarte Moura , Neto Iran Eduardo Lima , de Souza Filho Francisco de Assis , Costa Rocha Paulo Alexandre , Thé Jesse Van Griensven , Gharabaghi Bahram
Published: May 2024

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.
Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction
Authors: Dahhani Sara , Raji Mohamed , Bouslihim Yassine
Published: May 2024

Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values.
Assessing Slip Rates on the Xianshuihe Fault Using InSAR with Emphasis on Phase Unwrapping Error and Atmospheric Delay Corrections
Authors: Xi Peiyan , Li Xing , Song Chuang , Wang Bin , Yin Zhi , Wang Shuai
Published: May 2024

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake.
A Robust Mismatch Removal Method for Image Matching Based on the Fusion of the Local Features and the Depth
Authors: Ling Xinpeng , Liu Jiahang , Duan Zexian , Luan Ji
Published: May 2024

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise.
Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences
Authors: Hu Yuan , Tian Aodong , Yan Qingyun , Liu Wei , Wickert Jens , Yuan Xintai
Published: May 2024

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.
Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling
Authors: Fragkos Konstantinos , Fountoulakis Ilias , Charalampous Georgia , Papachristopoulou Kyriakoula , Nisantzi Argyro , Hadjimitsis Diofantos , Kazadzis Stelios
Published: May 2024

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.
Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023
Authors: Li Yundan , Wang Wencai
Published: May 2024

Dust aerosols serve as a crucial nutrient source to the oceans and profoundly influence marine ecosystems. This study used satellite and ground observations to explore a strong dust event on 9–13 April 2023, emanating from the Gobi Desert, shared by Mongolia and China’s Inner Mongolia region. We investigated the deposition of dust particles and their effects on marine phytoplankton communities. Our findings revealed that the dust event was intense, enduring, and expansive, illustrated by hourly PM10 concentrations peaking at 5055 µg/m3 near the source and consistently exceeding 1000 µg/m3, even at considerable distances. The dust traveled along two different trajectories and was deposited in the same area of the Northwest Pacific. Total dust deposition in the study area (37°N–42°N, 145°E–165°E) was 79.88 mg/m2 from 13 to 18 April, much higher than the 2019–2022 average deposition of 33.03 mg/m2 for the same period. With dust deposition, the observed mean chlorophyll-a concentrations in the area increased to 2.78 mg/m3 on 14 April, an extraordinary 692% increase above the long-term average. These results highlight the profound impact of dust on the productivity of marine phytoplankton communities by inputting more nutrients into the ocean through different pathways.
An Optical Detection Model for Stratospheric Airships
Authors: Xu Huiqiang , Cui Shengcheng , Qiao Zhi , Liu Xiaodan , Yang Shizhi , Wei Heli
Published: May 2024

This study proposes a numerical model to simulate the thermal characteristics of stratospheric airships (SA). This model introduces a simple yet accurate method for the selection of the optical bands for target detection based on multispectral features. The key to this model is the selection of the spectral bands in which a significant radiation difference exists between the target and its surrounding background. Using the signal-to-noise ratio (SNR), detection probability, and false alarm rate as model evaluation metrics, and based on the analysis of the radiation differences between the target and atmospheric background, it is found that there are two narrow bands possible for SA detection: one with a central wavelength of 8.83 µm and a bandwidth of 0.35 µm and the other with a central wavelength of 11.51 µm and a bandwidth of 0.34 µm. Further numerical simulations and theoretical analyses under varying space environments demonstrate that considerable detection potential can be achieved; thus, the presented optical detection model is useful for the night detection of SA targets.
The Diverse Mountainous Landslide Dataset (DMLD): A High-Resolution Remote Sensing Landslide Dataset in Diverse Mountainous Regions
Authors: Chen Jie , Zeng Xu , Zhu Jingru , Guo Ya , Hong Liang , Deng Min , Chen Kaiqi
Published: May 2024

The frequent occurrence of landslides poses a serious threat to people’s lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. However, the existing datasets do not cover diverse types of mountainous landslides. To address this issue, we propose a high-resolution (1 m) diverse mountainous landslide remote sensing dataset (DMLD), including 990 landslide instances across different terrain in southwestern China. To evaluate the performance of the DMLD, seven state-of-the-art deep learning models with different loss functions were implemented on it. The experiment results demonstrate not only that all of these deep learning methods with different characteristics can adapt well to the DMLD, but also that the DMLD has potential adaptability to other geographical regions.
Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions
Authors: Mousavi Vahid , Rashidi Maria , Mohammadi Masoud , Samali Bijan
Published: May 2024

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.
CSAN-UNet: Channel Spatial Attention Nested UNet for Infrared Small Target Detection
Authors: Zhong Yuhan , Shi Zhiguang , Zhang Yan , Zhang Yong , Li Hanyu
Published: May 2024

Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the nuances of small infrared target segmentation due to its reliance on downsampling operations, such as pooling, which often results in the loss of critical target information. This paper introduces the Channel Spatial Attention Nested UNet (CSAN-UNet), an innovative architecture designed specifically to enhance the detection and segmentation of small infrared targets. Central to CSAN-UNet’s design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priority and Spatial Attention Cascade Module (CPSAM) represents another pivotal advancement within CSAN-UNet, prioritizing channel-level adjustments alongside spatial attention mechanisms to efficiently extract deep semantic information pertinent to small infrared targets. Empirical validation conducted on two public datasets confirms that CSAN-UNet surpasses existing state-of-the-art algorithms in segmentation performance, while simultaneously reducing computational overhead.
Activated Sparsely Sub-Pixel Transformer for Remote Sensing Image Super-Resolution
Authors: Guo Yongde , Gong Chengying , Yan Jun
Published: May 2024

Transformers have recently achieved significant breakthroughs in various visual tasks. However, these methods often overlook the optimization of interactions between convolution and transformer blocks. Although the basic attention module strengthens the feature selection ability, it is still weak in generating superior quality output. In order to address this challenge, we propose the integration of sub-pixel space and the application of sparse coding theory in the calculation of self-attention. This approach aims to enhance the network’s generation capability, leading to the development of a sparse-activated sub-pixel transformer network (SSTNet). The experimental results show that compared with several state-of-the-art methods, our proposed network can obtain better generation results, improving the sharpness of object edges and the richness of detail texture information in super-resolution generated images.
Enhancing Linearity of Light Response in Avalanche Photodiodes by Suppressing Electrode Size Effect
Authors: Gan Hongyi , Yu Junwen , Wang Xiangfu
Published: May 2024

The nonlinear characteristics of avalanche photodiodes (APDs) inhibit their performance in high-speed communication systems, thereby limiting their widespread application as optical detectors. Existing theoretical models have not fully elucidated complex phenomena encountered in actual device structures. In this study, actual APD structures exhibiting lower linearity than their ideal counterparts were revealed. Simulation analysis and physical inference based on GaN APDs reveal that electrode size is a noteworthy factor influencing response linearity. This discovery expands the nonlinear theory of APDs, suggesting that APD linearity can be enhanced by suppressing the electrode size effect. A physical model was developed to explain this phenomenon, which is attributed to charge accumulation at the edge of the contact layer. Therefore, we proposed an improved APD design that incorporates an additional gap layer and a buffer layer to stabilize the internal gain under high-current-density conditions, thereby enhancing linearity. Our improved APD design increases the linear threshold for optical input power by 4.46 times. This study not only refines the theoretical model for APD linearity but also opens new pathways for improving the linearity of high-speed optoelectronic detectors.
Research on Convex Fiber Grating Tactile Sliding Sensor Based on Mechanical Fingers
Authors: Lu Guan , Fu Shiwen , Xu Yiming
Published: May 2024

In order to solve the problem of flexible sliding tactile composite sensing in the actual grasp of intelligent robot fingers, this paper proposes a research on a convex fiber grating tactile sliding sensor based on mechanical fingers. Based on the sensing principle of fiber Bragg grating, 3D printing technology was used to encapsulate the FBG sensor array with elastic 50 A resin, a double-layer “hemispherical cuboid” distributed sensing unit was designed, and the FBG slippery tactile sensor was actually pasted on the surface of the mechanical finger for static and dynamic experiments. The experimental results show that the slippery tactile sensor designed in this paper has a good linear relationship with temperature and strain. The temperature sensitivities of the polymer-packaged FBGs are KT1 = 13.04 pm/°C and KT2 = 12.91 pm/°C, and they have a pressure sensitivity of 40.4 pm/N and 31.2 pm/N, respectively. The FBG sliding tactile sensor not only realizes the identification of the sliding signal generation point and the end point but also completes the classification and identification of sandpaper, cardboard, and polypropylene plastic, and it has a high degree of fit with the robot finger, which has certain application value for the intelligent robot sliding tactile signal perception.
The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial User Network Analysis
Authors: Kanilmaz Umut Nefta , Resch Bernd , Holzinger Roland , Wasner Christian , Steinmaurer Thomas
Published: May 2024

The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements were formed not only in the virtual world but also through local protest gatherings. In contrast to previous research that studied movements in the virtual world through digital network analysis, this study recognizes the importance of the spatial dimension of social movements through local interaction. We therefore introduce a large-scale spatial–social network analysis of a georeferenced Twitter user network to understand the regional connections and transnational influences of the local movement through the virtual network. Our findings indicate that the virtual social network is distinctly structured along geographic and linguistic boundaries. Furthermore, our analysis of transnational influences reveals that the connections within Austria itself hold greater significance compared to their impact on external regions.
How to Promote the Development of Cultural and Creative Industries from an Evolutionary Game Perspective: Policy Mechanisms for Certification + Incentives
Authors: Li Yangyang , Sun Jianing , Chen Juan , Li Jinlei , Sun Li , Cao Kewang
Published: May 2024

Existing studies need to pay more attention to the policy mechanisms of how to promote the development of cultural and creative industries through coordinating multi-stakeholder participation. Based on evolutionary game theory, this study constructs a three-party evolutionary game model to analyze the behavioral patterns of the government, cultural and creative enterprises, and consumers under different support policies. In addition, it takes the Chinese intangible cultural heritage of “Huaihe willow weaving” as a typical case for simulation. The results show that the probability of the system converging to the expected equilibrium point is low after implementing the certification policy, but the probability of the system converging to the expected equilibrium is significantly increased after implementing the incentive policy. Producer subsidies have significant advantages over consumer subsidies. At this stage, the government should consider jointly implementing the “certification + incentives” policy in phases to promote the development of cultural and creative industries.
Research on Inventory Sustainable Development Strategy for Maximizing Cost-Effectiveness in Supply Chain
Authors: Tao Sunyu , Liu Shenglin , Zhou Huijie , Mao Xiaoyan
Published: May 2024

This study addresses the challenges of the expanding global online retail market by developing an innovative inventory management strategy that promotes sustainable development. We propose an integrated planning model that utilizes a newly enhanced Parallel Chicken Swarm Optimization (PCSO) algorithm alongside the established Economic Order Quantity (EOQ) model and the (s, S) inventory policy. Our model demonstrates its effectiveness through simulation experiments, showcasing its ability to dynamically adjust inventory thresholds based on daily forecasted demand, which significantly optimizes replenishment strategies. Notably, our model achieves a total cost reduction while maintaining a high service level of 0.761467 and reduces computational time to 82.32 s, highlighting its efficiency and adaptability. The innovation of this research lies in the application of the PCSO algorithm to dynamically manage inventory in real-time, aligning with sustainable inventory management practices. This study significantly contributes to the field by enhancing the economic and environmental sustainability of supply chain operations.
Climate Trends and Wheat Yield in Punjab, Pakistan: Assessing the Change and Impact
Authors: Shah Syed Ali Asghar , Wu Huixin , Farid Muhammad Fahad , Tareen Waqar-Ul-Hassan , Badar Iftikhar Hussain
Published: May 2024

Climate change has made weather patterns less predictable, making situations more challenging for farmers throughout the production process. This study investigates the impact of climatic variables (maximum and minimum temperature, rainfall, humidity at 8 AM and 5 PM) and fertilizer application on wheat production in Bahawalnagar district, a major wheat producing region of Punjab, Pakistan. The study utilizes the Mann–Kendall and multiple linear regression analysis to check climatic trends and identify the factors influencing wheat yield from 1991 to 2022. The study utilized a regression model to compare actual and predicted wheat yields. The results showed a decreasing trend in rainfall and an increasing trend in both maximum and minimum temperatures during the wheat growing season. Sen’s slope values for maximum temperature (0.037), minimum temperature (0.007), humidity at 8 AM (0.275), and humidity at 5 PM (0.167) indicate the direction and magnitude of trends. The regression model explained about 92% of the variance in the wheat yield. The regression analysis of humidity at both 8 AM (p = 0.001) and 5 PM (p = 0.001) shows a significant positive correlation with wheat yield. Fertilizer use exhibited a significant positive association with wheat yield (β = 9.58). Fertilizer application for wheat crops increased from 112.4 kg/ha in 1991 to 284.3 kg/ha in 2021. The regression model identifies that the average wheat yield loss from 1991 to 2022 is approximately 0.1208 t/ha per year because of the influence of climatic factors. The study findings underscore the importance of the utilization of adaptive agricultural practices that can ensure food security and improve agricultural sustainability in the region.
Laboratory Safety Evaluation and Weed Control Potential of Pre- and Post-Emergence Herbicides for Quinoa
Authors: Xiong Haojun , Li Cheng , Fida Mujeeba , Yu Mengyuan , Tao Xiangyu , Bi Yaling
Published: May 2024

In this study, we aimed to identify suitable herbicides for quinoa fields in Anhui Province and explore the value of their potential application in order to achieve the sustainable weed management of the crop and tackle the global issue of unregistered herbicides in quinoa fields. Employing a pre-emergence seed soaking method, we evaluated the effects of different herbicides, such as anilofos, prometryn, pendimethalin, and atrazine on the germination inhibition rate of quinoa seeds, as well as their impacts on the growth indicators of quinoa seedlings. Our findings show that, while these herbicides initially suppressed quinoa seed germination, this effect decreased over time, allowing for the successful germination of all seeds, suggesting the existence of a recovery mechanism in quinoa. An increase in herbicide concentration was correlated with significant decreases in the germination vigor and index of quinoa seeds, along with a decrease in plant height, root length, and fresh weight. Notably, anilofos, prometryn, pendimethalin, and atrazine demonstrated significant inhibitory effects on quinoa seedlings, thus providing critical insights into the sensitivity of quinoa to these chemicals. Greenhouse pot experiments showed that pre-emergence herbicides, such as napropamide, pretilachlor, s-metolachlor, and anilofos, and post-emergence herbicides, including fluroxypyr, penoxsulam, clethodim, quizalofop-P-ethyl, oxaziclomefone, metamifop, benzobicyclon, nicosulfuron, and pinoxaden, are safe for quinoa and suitable for further field trials, broadening the options for integrated weed management strategies. The results of the mixture experiments indicated that penoxsulam and metamifop are safe for quinoa at a ratio of 1:4.6, and their combined activities against dominant weeds in quinoa fields in Anhui Province, such as Digitaria sanguinalis, Cyperus iria, and Amaranthus viridis, were higher than those of single-agent doses, with fresh weight inhibition rates ranging from 66.98% to 92.16% and selectivity indexes ranging from 176.88 to 3282.17. Therefore, this mixture offers a promising approach to enhanced weed control in a sustainable manner.
Exploring Determinants of Second-Hand Apparel Purchase Intention and Word of Mouth: A Stimulus–Organism–Response Approach
Authors: Tymoshchuk Olga , Lou Xingqiu , Chi Ting
Published: May 2024

The U.S. second-hand clothing industry is experiencing rapid growth, driven by increasing environmental awareness among consumers. However, there is a gap in understanding the driving forces behind this trend. This study aims to investigate the impact of external factors, including product quality, information quality, and service quality, on consumers’ internal emotions and examines how these emotional states, encompassing hedonic value, utilitarian value, environmental value, functional risk, aesthetic risk, and sanitary risk, influence their purchase intentions and word-of-mouth recommendations. Data were collected from 448 consumers who have shopped for second-hand clothing through an online survey conducted on Qualtrics. Multiple regression was applied to test the hypotheses. The findings indicate that product quality, information quality, and service quality enhance consumers’ perceived hedonic, utilitarian, and environmental values. Furthermore, service quality significantly reduces consumers’ perceived risks in terms of functionality, aesthetics, and sanitation. Additionally, consumers’ purchase intentions and word of mouth regarding second-hand clothing are positively influenced by their perceived hedonic, utilitarian, and environmental values. This research enriches the understanding of consumer behavior in the second-hand marketplace and offers insightful implications for retailers and marketers in the second-hand clothing industry.
Obtaining a Multi-Factor Optimum Blend Using Scrap within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company
Authors: Baş Aydoğan , Birgören Burak , Sakalli Ümit Sami
Published: May 2024

This study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing.
Review: Goat and Sheep Meat Production in Indonesia
Authors: Sujarwanta Rio Olympias , Afidah Ulil , Suryanto Edi , Rusman  , Triyannanto Endy , Hoffman Louwrens Christiaan
Published: May 2024

This review describes the production systems and products pertaining to goats and sheep (GS) in Indonesia. These small ruminant species are found across 38 provinces in Indonesia. Almost 99% of the GS are farmed by smallholders, with the animals either being housed in an enclosure (small shed near the farmer’s house) or in a mix of both grazing and confinement. The livestock are typically fed natural and/or artificial feed. Two sheep types, thin-tailed and fat-tailed are the most popular with smallholder producers, while Bali, Etawah, Boerawa, Jawa Randu Kosta, and Gembrong are the dominant goat breeds. As one of the biggest Muslim countries in the world, Indonesian production and consumption of GS is influenced by culture and religious systems. The Halal slaughter method applied is described, as are the religious/cultural festivals in Indonesia, which have a significant relationship on the GS demand and consumption, such as Eid al-Adha and Aqiqah. The preparation, processing, and consumption of GS meat in Indonesia are strongly influenced by the use of spices, being part of the Moluccas archipelago, also known as the “Spice islands” with over 30,000 species of spices grown. Some traditional meats and meat processing technologies in Indonesia are presented to provide insight into how GS meat is processed. The main challenges in the processing of GS meat in Indonesia and some current research on the development of GS production are presented in this paper. Implications: Goat and sheep belong to the most widely consumed meats in the world after pork, poultry, and beef. There are some reasons why GS meat has become one of the more popular protein choices for many families, of which its low-fat levels are one. GS meat is also considered one of the only meats with minimal religious taboos in the world. For these reasons, GS meat is also favored by Indonesian people in the most populous Muslim-majority country. However, information regarding goat and sheep in Indonesia is still lacking. This review aims to provide information on the distribution of GS in Indonesia over 38 provinces, the breed types that grow there, their production, and Indonesian consumption behavior, highlighting the religious festivals that cause the highest demand for these meats. This review provides information to the stakeholders of goat and sheep production chains.