bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Ayush-64 (a Polyherbal Formulation) and Its Ingredients in the Syrian Hamster Model for SARS-CoV-2 Infection Reveals the Preventative Potential of Alstonia scholaris
Authors: Rizvi Zaigham Abbas , Madan Upasna , Tripathy Manas Ranjan , Goswami Sandeep , Mani Shailendra , Awasthi Amit , Dikshit Madhu
Published: September 2023

In the current study, we evaluated the efficacy of Ayush-64 (A64), a polyherbal formulation containing Alstonia scholaris (L.) R. Br. (A. scholaris), Caesalpinia crista L. (C. crista), Picrorhiza kurroa Royle ex Benth (P. kurroa), and Swertia chirata (Roxb.) H. Karst. (S. chirata) against COVID-19 in a Syrian hamster infection model. Preventative use of A64 resulted in the late-phase recovery of body weight loss in severe acquired respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected hamsters, suppression of pro-inflammatory cytokines, and blunted pulmonary pathology. In addition, we also investigated the efficacy of individual ingredients of A64, viz., A. scholaris, C. crista, P. kurroa, and S. chirata, in the hamster model. The hamster challenge data showed robust anti-viral and immunomodulatory potential in A. scholaris, followed by P. kurroa. However, C. crista and S. chirata of A64 showed prominent immunomodulatory potential without limiting the lung viral load. In order to better understand the immunomodulatory potential of these herbal extracts, we used an in vitro assay of helper T cell differentiation and found that A. scholaris mediated a more profound suppression of Th1, Th2, and Th17 cell differentiation as compared to A64 and other ingredients. Taken together, our animal study data identifies the ameliorative potential of A64 in mitigating coronavirus disease-19 (COVID-19) pulmonary pathology. A. scholaris, a constituent extract of A64, showed relatively higher anti-viral and immunomodulatory potential against COVID-19. The present study warrants further investigations to identify the active pharmaceutical ingredients of A. scholaris for further studies.
Feature Reviews in Pharmaceutical Technology
Authors: Zustiak Silviya Petrova , Jain Era
Published: September 2023

We are excited to present the Special Issue, “Feature Reviews in Pharmaceutical Technology”, aiming to highlight exciting developments in pharmaceutical technologies [...]
The p38-MITOGEN-ACTIVATED PROTEIN KINASE Signaling Pathway Is Involved in Leonurus artemisia Extract-Induced Inhibition of the Proliferation of Human Bladder Cancer BFTC-905 Cells via G1/G0 Arrest and Causes Apoptosis In Vitro
Authors: Lin Jian-Hui , Hung Chein-Hui , Huang Yun-Ching , Chen Chih-Shou , Ho Dong-Ru
Published: September 2023

Bladder cancer is a urothelial malignancy. Bladder cancer starts in the urothelial cells lining the inside of the bladder. The 5-year recurrence rate for bladder cancer ranges from 31% to 78%, and the progression rate is approximately 45%. To treat bladder cancer, intravesical drug therapy is often used. Leonurus artemisia extract (LaE) was obtained from medicinal samples of Chinese motherwort Scientific Chinese Medicine; L. artemisia has various biological effects. This study investigated the impact of LaE on human bladder cancer cells (the BFTC-905 cell line) and the molecular mechanism underlying apoptosis resulting from the activation of cell signal transduction pathways in bladder cancer cells. A cell counting kit-8 (CCK-8) assay was used to determine the effect of LaE on cell growth. The effect of LaE on migration ability was observed using a wound healing assay. The effects of LaE on the cell cycle, reactive oxygen species production, and apoptosis were investigated. Western blot analysis detected apoptosis-related and mitogen-activated protein kinase signaling pathway-related protein concentrations. At non-toxic concentrations, LaE inhibited the proliferation of BFTC-905 cells in a concentration-dependent manner, and the half-maximal inhibitory concentration (IC50) was 24.08172 µg/µL. LaE impaired the migration ability of BFTC-905 cells. LaE arrested the cell cycle in the G1 and G0 phases, increased reactive oxygen species production, and induced apoptosis. LaE increased Bax and p-ERK concentrations and decreased Bcl-2, cleaved caspase-3, and p-p38 concentrations. No differences in PARP, C-PARP, vimentin, e-cadherin, p-JNK, or TNF-alpha concentrations were observed. These results suggest that LaE inhibits the proliferation of human bladder cancer cells. Moreover, the mitogen-activated protein kinase signaling pathway is involved in the inhibition of the proliferation of BFTC-905 cells.
Development of 3D-Printed Bicompartmental Devices by Dual-Nozzle Fused Deposition Modeling (FDM) for Colon-Specific Drug Delivery
Authors: Shojaie Fatemeh , Ferrero Carmen , Caraballo Isidoro
Published: September 2023

Dual-nozzle fused deposition modeling (FDM) is a 3D printing technique that allows for the simultaneous printing of two polymeric filaments and the design of complex geometries. Hence, hybrid formulations and structurally different sections can be combined into the same dosage form to achieve customized drug release kinetics. The objective of this study was to develop a novel bicompartmental device by dual-nozzle FDM for colon-specific drug delivery. Hydroxypropylmethylcellulose acetate succinate (HPMCAS) and polyvinyl alcohol (PVA) were selected as matrix-forming polymers of the outer pH-dependent and the inner water-soluble compartments, respectively. 5-Aminosalicylic acid (5-ASA) was selected as the model drug. Drug-free HPMCAS and drug-loaded PVA filaments suitable for FDM were extruded, and their properties were assessed by thermal, X-ray diffraction, microscopy, and texture analysis techniques. 5-ASA (20% w/w) remained mostly crystalline in the PVA matrix. Filaments were successfully printed into bicompartmental devices combining an outer cylindrical compartment and an inner spiral-shaped compartment that communicates with the external media through an opening. Scanning electron microscopy and X-ray tomography analysis were performed to guarantee the quality of the 3D-printed devices. In vitro drug release tests demonstrated a pH-responsive biphasic release pattern: a slow and sustained release period (pH values of 1.2 and 6.8) controlled by drug diffusion followed by a faster drug release phase (pH 7.4) governed by polymer relaxation/erosion. Overall, this research demonstrates the feasibility of the dual-nozzle FDM technique to obtain an innovative 3D-printed bicompartmental device for targeting 5-ASA to the colon.
Microglial-Targeted nSMase2 Inhibitor Fails to Reduce Tau Propagation in PS19 Mice
Authors: Huang Meixiang , Tallon Carolyn , Zhu Xiaolei , Huizar Kaitlyn D. J. , Picciolini Silvia , Thomas Ajit G. , Tenora Lukas , Liyanage Wathsala , Rodà Francesca , Gualerzi Alice , Kannan Rangaramanujam M. , Bedoni Marzia , Rais Rana , Slusher Barbara S.
Published: September 2023

The progression of Alzheimer’s disease (AD) correlates with the propagation of hyperphosphorylated tau (pTau) from the entorhinal cortex to the hippocampus and neocortex. Neutral sphingomyelinase2 (nSMase2) is critical in the biosynthesis of extracellular vesicles (EVs), which play a role in pTau propagation. We recently conjugated DPTIP, a potent nSMase2 inhibitor, to hydroxyl-PAMAM-dendrimer nanoparticles that can improve brain delivery. We showed that dendrimer-conjugated DPTIP (D–DPTIP) robustly inhibited the spread of pTau in an AAV-pTau propagation model. To further evaluate its efficacy, we tested D-DPTIP in the PS19 transgenic mouse model. Unexpectantly, D-DPTIP showed no beneficial effect. To understand this discrepancy, we assessed D-DPTIP’s brain localization. Using immunofluorescence and fluorescence-activated cell-sorting, D-DPTIP was found to be primarily internalized by microglia, where it selectively inhibited microglial nSMase2 activity with no effect on other cell types. Furthermore, D-DPTIP inhibited microglia-derived EV release into plasma without affecting other brain-derived EVs. We hypothesize that microglial targeting allowed D-DPTIP to inhibit tau propagation in the AAV-hTau model, where microglial EVs play a central role in propagation. However, in PS19 mice, where tau propagation is independent of microglial EVs, it had a limited effect. Our findings confirm microglial targeting with hydroxyl-PAMAM dendrimers and highlight the importance of understanding cell-specific mechanisms when designing targeted AD therapies.
Nedaplatin/Peganum harmala Alkaloids Co-Loaded Electrospun, Implantable Nanofibers: A Chemopreventive Nano-Delivery System for Treating and Preventing Breast Cancer Recurrence after Tumorectomy
Authors: Sedky Nada K. , Arafa Kholoud K. , Abdelhady Manal M. M. , Issa Marwa Y. , Abdel-Kader Nour M. , Mahdy Noha Khalil , Mokhtar Fatma A. , Alfaifi Mohammad Y. , Fahmy Sherif Ashraf
Published: September 2023

Currently, the main pillars in treating breast cancer involve tumorectomy pursued by hormonal, radio, or chemotherapies. Nonetheless, these approaches exhibit severe adverse effects and might suffer from tumor recurrence. Therefore, there is a considerable demand to fabricate an innovative controlled-release nano-delivery system to be implanted after tumor surgical removal to guard against cancer recurrence. In addition, combining platinum-based drugs with phytochemicals is a promising approach to improving the anticancer activity of the chemotherapeutics against tumor cells while minimizing their systemic effects. This study designed polycaprolactone (PCL)-based electrospun nanofiber mats encapsulating nedaplatin (N) and Peganum harmala alkaloid-rich fraction (L). In addition to physicochemical characterization, including average diameters, morphological features, degradation study, thermal stability, and release kinetics study, the formulated nanofibers were assessed in terms of cytotoxicity, where they demonstrated potentiated effects and higher selectivity towards breast cancer cells. The dual-loaded nanofiber mats (N + L@PCL) demonstrated the highest antiproliferative effects against MCF-7 cells with a recorded IC50 of 3.21 µg/mL, as well as the topmost achieved selectivity index (20.45) towards cancer cells amongst all the tested agents (N, L, N@PCL, and L@PCL). This indicates that the dual-loaded nanofiber excelled at conserving the normal breast epithelial cells (MCF-10A). The combined therapy, N + L@PCL treatment, resulted in a significantly higher percent cell population in the late apoptosis and necrosis quartiles as compared to all other treatment groups (p-value of ≤0.001). Moreover, this study of cell cycle kinetics revealed potentiated effects of the dual-loaded nanofiber (N + L@PCL) at trapping more than 90% of cells in the sub-G1 phase and reducing the number of cells undergoing DNA synthesis in the S-phase by 15-fold as compared to nontreated cells; hence, causing cessation of the cell cycle and confirming the apoptosis assay results. As such, our findings suggest the potential use of the designed nanofiber mats as perfect implants to prevent tumor recurrence after tumorectomy.
Directions for Anarchist Studies
Authors: Ferguson Kathy E.
Published: September 2023

Anarchism is a fertile site for nurturing the sorts of encounters that feminists have called intersectionality. Anarchism and intersectionality share the goal of critically examining familiar as well as emergent flows of power and meaning, and understanding their relations to one another. This paper focuses on three compelling directions for anarchist studies: Indigenous anarchism, anarchism developing with new materialism, and anarchism emergent in radical book arts. Each thread has established roots while also moving in new directions. Anarchist encounters with Indigeneity, new materialism, and book arts resonate with each other: they can foster “a commitment to the particular” through which we can immerse ourselves in rich and dense worlds where specific Indigenous theories and practices, detailed encounters with non-human things, and particular artistic + intellectual productions of materials can emerge.
Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
Authors: Liu Wenyao , Chen Xuehen , Liu Miao , Hong Yanqing
Published: September 2023

In atmosphere free-space optical communication (FSO) systems, the scintillation effect produced by turbulence effects increases the bit error rate (BER) of the communication system and reduces the system’s performance. However, a high correlation of turbulent noise occurs in the two transmission channels when a signal transmitted in the bidirectional atmospheric channel with channel reciprocity. The performance of the FSO system can be increased by extracting channel state information (CSI) in forward transmission and using adaptive power technology to reduce turbulence in inverse transmission. In this research, we propose a bidirectional atmospheric channel reciprocity-based adaptive power transmission (CR-APT) technique that lowers the bit error rate of the transmitted signal by using the CSI of the relevant channel. To verify the effectiveness of the technique, a bidirectional atmospheric channel with various turbulence intensities is built in the simulation program, along with various background sounds to vary the channel reciprocity, and the impact of reciprocity on signal transmission is examined. The simulation findings demonstrate that adaptive power transmission with high reciprocity is excellent under the weak turbulence condition, and its future development is promising.
Sustainable Nanomagnetism: Investigating the Influence of Green Synthesis and pH on Iron Oxide Nanoparticles for Enhanced Biomedical Applications
Authors: Abdullah Johar Amin Ahmed , Díaz-García Álvaro , Law Jia Yan , Romero Alberto , Franco Victorino , Guerrero Antonio
Published: September 2023

This study comprehensively analyzed green nanomagnetic iron oxide particles (GNMIOPs) synthesized using a green method, investigating their size, shape, crystallinity, aggregation, phase portions, stability, and magnetism. The influence of pH and washing solvents on the magnetic properties of the nanoparticles and their incorporation into PCL membranes was examined for biomedical applications. Polyphenols were utilized at different pH values (1.2, 7.5, and 12.5), with washing being performed using either ethanol or water. Characterization techniques, including XRD, SEM, TEM, FTIR, and VSM, were employed, along with evaluations of stability, magnetic properties, and antioxidant activity. The findings indicate that both pH levels and the washing process exert a substantial influence on several properties of NMIOPs. The particle sizes ranged from 6.6 to 23.5 nm, with the smallest size being observed for GNMIOPs prepared at pH 12.5. Higher pH values led to increased crystallinity, cubic Fe3O4 fractions, and reduced crystalline anisotropy. SEM and TEM analyses showed pH-dependent morphological variations, with increased aggregation being observed at lower pH values. GNMIOPs displayed exceptional magnetic behavior, with the highest saturation magnetization being observed in GNMIOPs prepared at pH 7.5 and 12.5 and subsequently washed with ethanol. The zeta potential measurements indicated a stability range for GNMIOPs spanning from −31.8 to −41.6 mV, while GNMIOPs synthesized under high-pH conditions demonstrated noteworthy antioxidant activity. Furthermore, it was explored how pH and washing solvent affected the morphology, roughness, and magnetic properties of GNMIOP-infused nanofiber membranes. SEM showed irregularities and roughness due to GNMIOPs, varying with pH and washing solvent. TEM confirmed better dispersion with ethanol washing. The magnetic response was stronger with ethanol-washed GNMIOPs, highlighting the influence of pH and washing solvent on membrane characteristics.
An Overview of Scaffolds and Biomaterials for Skin Expansion and Soft Tissue Regeneration: Insights on Zinc and Magnesium as New Potential Key Elements
Authors: Hassan Nourhan , Krieg Thomas , Zinser Max , Schröder Kai , Kröger Nadja
Published: September 2023

The utilization of materials in medical implants, serving as substitutes for non-functional biological structures, supporting damaged tissues, or reinforcing active organs, holds significant importance in modern healthcare, positively impacting the quality of life for millions of individuals worldwide. However, certain implants may only be required temporarily to aid in the healing process of diseased or injured tissues and tissue expansion. Biodegradable metals, including zinc (Zn), magnesium (Mg), iron, and others, present a new paradigm in the realm of implant materials. Ongoing research focuses on developing optimized materials that meet medical standards, encompassing controllable corrosion rates, sustained mechanical stability, and favorable biocompatibility. Achieving these objectives involves refining alloy compositions and tailoring processing techniques to carefully control microstructures and mechanical properties. Among the materials under investigation, Mg- and Zn-based biodegradable materials and their alloys demonstrate the ability to provide necessary support during tissue regeneration while gradually degrading over time. Furthermore, as essential elements in the human body, Mg and Zn offer additional benefits, including promoting wound healing, facilitating cell growth, and participating in gene generation while interacting with various vital biological functions. This review provides an overview of the physiological function and significance for human health of Mg and Zn and their usage as implants in tissue regeneration using tissue scaffolds. The scaffold qualities, such as biodegradation, mechanical characteristics, and biocompatibility, are also discussed.
Selection and Optimization Design of PDC Bits Based on FEM Analysis for Drilling Long Horizontal Sections of Shale Formations
Authors: Kong Lulin , Wang Zhaowei , Wang Haige , Cui Mingyue , Liang Chong , Kong Xiangwen , Wang Ping
Published: September 2023

Well structures with ultra-long sections have become one of the most applied technologies in the field of shale gas development. While there have been many technical challenges, enhancing the breaking efficiency and stability of polycrystalline diamond compact (PDC) bits has become an essential issue of focus. Since 2013, the well structure in the Duvernay area has been optimized multiple times, and the rate of penetration (ROP) of the entire wellbore has nearly doubled. However, there are significant differences in terms of the performances of different PDC bits, and there is still room for improvement to optimize these drill bits. For this reason, a confined compressive strength test was conducted to obtain the rock mechanical parameters from shale cores extracted from the long horizontal section. Using these data, a finite element model (FEM) was developed with a corresponding scale. A calibration of the elastic-plastic damage constitutive models was then performed using the FEM. The breaking mechanism of three different PDC bits was examined using a “PDC bit-bottom hole” interaction FEM model, facilitating guidance for bit selection and design optimization: (1) The type B PDC bit, which has four blades and 20 cutters, exhibited the highest mechanical specific energy (MSE) and the lowest vibration across three directional mechanical characteristics. This design is recommended for engineering applications. (2) Lower axial vibrations were produced when the CDE was used as the rear element when compared to those when using the BHE. However, an increase within an acceptable range was observed in the TOB and circumferential vibrations. Thus, for redesigning work on the type B bit, the assembly of the CDE is suggested. (3) A decrease in the MSE and vibration in three directional mechanical characteristics was observed when the depth of cut (DOC) was varied between 1.5 and 2.0 mm. A broadening in the range of lateral forces was noted when a DOC of 2.0 mm was used. Therefore, for the redesign of the type B bit, the assembly of CDEs as rear elements at a DOC of 1.5 mm is recommended. In conclusion, a new practical method for the selection and optimization of PDC bit design, based on rock mechanics and the FEM theory, is proposed.
Experimental Study on Methane Diffusion Characteristics of Different Metamorphic Deformed Coals Based on the Counter Diffusion Method
Authors: Ren Jiangang , Gao Liang , Wen Zhihui , Weng Hongbo , Liu Jianbao , Lv Runsheng , Qu Yanwei , Song Zhimin , Zhang Yongwang , Li Bing
Published: September 2023

The diffusion coefficient (D) is a key parameter that characterizes the gas transport occurring in coal seams. Typically, D is calculated using the desorption curve of particle coal. However, this method cannot accurately reflect the diffusion characteristics under the stress constraint conditions of in situ coal seams. In this study, different metamorphic deformed coals of medium and high coal rank were considered based on Fick’s law of counter diffusion. The change laws of D under different confining pressures, gas pressures, and temperature conditions were tested and analyzed, and the influencing mechanisms on D are discussed. The results showed that D of different metamorphic deformed coals exponentially decreased with an increase in confining pressures, and exponentially increased with increases in gas pressures and temperature. There is a limit diffusion coefficient. The influence of the confining pressure on D can essentially be determined by changes in the effective stress, and D negatively affects the effective stress, similar to permeability. The effect of gas pressure on D involves two mechanisms: mechanical and adsorption effects, which are jointly restricted by the effective stress and the shrinkage and expansion deformation of coal particles. Temperature mainly affects D by changing the root-mean-square speed and average free path of the gas molecules. Under the same temperature and pressure conditions, D first increased and then decreased with an increase in the degree of deformation. D of the fragmented coal was the largest. Under similar deformation conditions, D of the high-rank anthracite was larger than that of the medium-rank fat coal. Porosity is a key factor affecting the change in D in different metamorphic deformed coals.
The Use of Graph Theory for Modeling and Analyzing the Structure of a Complex System, with the Example of an Industrial Grain Drying Line
Authors: Myhan Ryszard , Jachimczyk Ewelina , Markowski Marek
Published: September 2023

This article describes a method for analyzing and modeling a complex agrotechnological system using the example of an industrial grain drying line. Elements of graph theory were used to develop an effective tool for modeling such a system and to formally validate its structure. The proposed method can be applied to transform a general structural model into a set of relational models, to formally evaluate the resulting models’ functionality, and to comprehensively analyze different variants of the process. The method can be deployed at the stage of designing and operating an industrial grain drying line, and it can also be adapted for use in other areas, such as processing lines in the agri-food industry.
Master Nian念法師 as the “Westerner”: Re-Establishing Orthodoxy in Chinese Abhidharma
Authors: Huang Lu
Published: September 2023

Sarvāstivāda Abhidharma treatises started to be carefully studied and commented on by monks in early medieval China with the translation of hṛdaya treatises, the *Aṣṭagrantha, and the *Vibhāṣas. However, with the “new” translations undertaken by Xuanzang and his clique, these were regarded as “old” translations and the thoughts of these scholars were gradually marginalized. Examining a doctrinal debate in Puguang’s Jushe lun ji, this article delves into the construction of authority by Xuanzang and his disciples in the field of Abhidharma. In this debate, Puguang criticizes Zhinian’s viewpoint solely based on its similarity with the view of “westerners” (xifang shi). The evidence Puguang presents is a quote from the newly translated *Mahavibhāṣa, which he uses to extol the value of Xuanzang’s “new” translation. It shows how Xuanzang and his team use the orthodoxy constructed by Kashmiri Vaibhāsikas to justify their own authority. Their success comes at a sacrifice of diversity of Abhidharma studies in China.
Scriptural Re-Interpretation and Social Identity Negotiation in the Corinthian Letters
Authors: Seal Darlene M.
Published: September 2023

This article describes the socially formative function of the Corinthian letters and the role that Paul’s reinterpretation of scripture plays in shaping the Corinthians’ social identity. Paul’s sustained engagement with scriptural texts in 1 Cor 10:1–22 and 2 Cor 3:1–4:6 provide the focus for analysis as two different interactions with exodus and wilderness narratives in two different social situations within the same correspondence. Like others in Second Temple Judaism, Paul uses the exodus and wilderness narratives of Israel’s paradigmatic rebellion to interpret a social situation, define group identity, and increase intergroup differentiation from outsiders and intragroup cohesiveness. Social Identity Theory (SIT) provides the conceptual framework for a robust interpretive model that identifies specific textual features that realize each aspect of social identity. This approach shows that in 1 Cor 10:1–22, Paul addresses the idol food issue by establishing shared experiences with the wilderness generation to interpret the Corinthians’ situation as parallel with the deviant idolatrous behavior of their forebears. In 2 Cor 3:1–4:6, Paul addresses tensions with the Corinthians using the veiling language of Exod 34 to differentiate the ingroup from outgroups according to their sight or blindness, respectively, which correlate to response to his ministry.
Characterizing the Effect of Ocean Surface Currents on Advanced Scatterometer (ASCAT) Winds Using Open Ocean Moored Buoy Data
Authors: Cheng Tianyi , Chen Zhaohui , Li Jingkai , Xu Qing , Yang Haiyuan
Published: September 2023

The ocean surface current influences the roughness of the sea surface, subsequently affecting the scatterometer’s measurement of wind speed. In this study, the effect of surface currents on ASCAT-retrieved winds is investigated based on in-situ observations of both surface winds and currents from 40 open ocean moored buoys in the tropical and mid-latitude oceans. A total of 28,803 data triplets, consisting of buoy-observed wind vectors, current vectors, and ASCAT Level 2 wind vectors, were collected from the dataset spanning over 10 years. It is found that the bias between scatterometer-retrieved wind speed and buoy-observed wind speed is negatively correlated with the ocean surface current speed. The wind speed bias is approximately 0.96 times the magnitude of the downwind surface current. The root-mean-square error between the ASCAT wind speeds and buoy observations is reduced by about 15% if rectification with ocean surface currents is involved. Therefore, it is essential to incorporate surface current information into wind speed calibration, particularly in regions with strong surface currents.
Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors
Authors: Zhao Mingxian , Dong Yingying , Huang Wenjiang , Ruan Chao , Guo Jing
Published: September 2023

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.
Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data
Authors: Neupane Adarsha , Sawada Yohei
Published: September 2023

With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications for national policy, project’s annual budgets, and donor funding. Here, we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006–2011) and Landsat 8 OLI (2013–2022) images were used to develop a machine learning model, which classifies irrigated and non-irrigated areas in the study areas. The random forest classification achieved an overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine learning model outperforms the existing global cropland map, highlighting the need for such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated areas with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.
UniRender: Reconstructing 3D Surfaces from Aerial Images with a Unified Rendering Scheme
Authors: Yan Yiming , Zhou Weikun , Su Nan , Zhang Chi
Published: September 2023

While recent advances in the field of neural rendering have shown impressive 3D reconstruction performance, it is still a challenge to accurately capture the appearance and geometry of a scene by using neural rendering, especially for remote sensing scenes. This is because both rendering methods, i.e., surface rendering and volume rendering, have their own limitations. Furthermore, when neural rendering is applied to remote sensing scenes, the view sparsity and content complexity that characterize these scenes will severely hinder its performance. In this work, we aim to address these challenges and to make neural rendering techniques available for 3D reconstruction in remote sensing environments. To achieve this, we propose a novel 3D surface reconstruction method called UniRender. UniRender offers three improvements in locating an accurate 3D surface by using neural rendering: (1) unifying surface and volume rendering by employing their strengths while discarding their weaknesses, which enables accurate 3D surface position localization in a coarse-to-fine manner; (2) incorporating photometric consistency constraints during rendering, and utilizing the points reconstructed by structure from motion (SFM) or multi-view stereo (MVS), to constrain reconstructed surfaces, which significantly improves the accuracy of 3D reconstruction; (3) improving the sampling strategy by locating sampling points in the foreground regions where the surface needs to be reconstructed, thus obtaining better detail in the reconstruction results. Extensive experiments demonstrate that UniRender can reconstruct high-quality 3D surfaces in various remote sensing scenes.
A Fusion Encoder with Multi-Task Guidance for Cross-Modal Text–Image Retrieval in Remote Sensing
Authors: Zhang Xiong , Li Weipeng , Wang Xu , Wang Luyao , Zheng Fuzhong , Wang Long , Zhang Haisu
Published: September 2023

In recent years, there has been a growing interest in remote sensing image–text cross-modal retrieval due to the rapid development of space information technology and the significant increase in the volume of remote sensing image data. Remote sensing images have unique characteristics that make the cross-modal retrieval task challenging. Firstly, the semantics of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Different combinations of basic units of semantic expression can generate diverse text descriptions. Additionally, these images exhibit variations in resolution, color, and perspective. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method, the progressiveness of which has been proved in the cross-modal retrieval of natural images. By jointly training the model with three tasks: image–text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC), we enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in joint representation expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method, which achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness.
Spatiotemporal Analysis of Ecosystem Status in China’s National Key Ecological Function Zones
Authors: Zhang Xiongyi , Shao Quanqin , Wang Bing , Niu Xiang , Ning Jia , Chen Meiqi , Zhang Tingjing , Liu Guobo , Liu Shuchao , Niu Linan , Huang Haibo
Published: September 2023

The National Key Ecological Function Zones (NKEFZ) serve as crucial ecological security barriers in China, playing a vital role in enhancing ecosystem services. This study employed the theoretical framework of ecological benefits assessment in major ecological engineering projects. The primary focus was on the ecosystem macrostructure, ecosystem quality, and key ecosystem services, enabling quantitative analysis of the spatiotemporal changes in the ecosystem status of the NKEFZ from 2000 to 2019. To achieve this, remote sensing data, meteorological data, and model simulations were employed to investigate five indicators, including land use types, vegetation coverage, net primary productivity of vegetation, soil conservation services, water conservation services, and windbreak and sand fixation services. The analysis incorporated the Theil–Sen Median method to construct an evaluation system for assessing the restoration status of ecosystems, effectively integrating ecosystem quality and ecosystem services indicators. The research findings indicated that land use changes in NKEFZ were primarily characterized by the expansion of unused land and the in of grassland. The overall ecosystem quality of these zones improved, showing a stable and increasing trend. However, there were disparities in the changes related to ecosystem services. Water conservation services exhibited a decreasing trend, while soil conservation and windbreak and sand fixation services showed a steady improvement. The ecosystem of the NKEFZ, in general, displayed a stable and recovering trend. However, significant spatial heterogeneity existed, particularly in the southern region of the Qinghai–Tibet Plateau and at the border areas between western Sichuan and northern Yunnan, where some areas still experienced deteriorating ecosystem conditions. Compared to other functional zones, the trend in the ecosystem of the NKEFZ might not have been the most favorable. Nonetheless, this could be attributed to the fact that most of these areas were situated in environmentally fragile regions, and conservation measures may not have been as effective as in other functional zones. These findings highlighted the considerable challenges ahead in the construction and preservation of the NKEFZ. In future development, the NKEFZ should leverage their unique natural resources to explore distinctive ecological advantages and promote the development of eco-friendly economic industries, such as ecological industry, ecological agriculture, and eco-tourism, transitioning from being reliant on external support to self-sustainability.
Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau
Authors: He Rui , Qin Yan , Zhao Qiudong , Chang Yaping , Jin Zizhen
Published: September 2023

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.
Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach
Authors: Long Hui , Luo Jueling , Zhang Yalu , Li Shijie , Xie Si , Ma Haodong , Zhang Haonan
Published: September 2023

Indoor air pollution is an urgent issue, posing a significant threat to the health of indoor workers and residents. Individuals engaged in indoor occupations typically spend an average of around 21 h per day in enclosed spaces, while residents spend approximately 13 h indoors on average. Accurately predicting indoor air quality is crucial for the well-being of indoor workers and frequent home dwellers. Despite the development of numerous methods for indoor air quality prediction, the task remains challenging, especially under constraints of limited air quality data collection points. To address this issue, we propose a neural network capable of capturing time dependencies and correlations among data indicators, which integrates the informer model with a data-correlation feature extractor based on MLP. In the experiments of this study, we employ the Informer model to predict indoor air quality in an industrial park in Changsha, Hunan Province, China. The model utilizes indoor and outdoor temperature, humidity, and outdoor particulate matter (PM) values to forecast future indoor particle levels. Experimental results demonstrate the superiority of the Informer model over other methods for both long-term and short-term indoor air quality predictions. The model we propose holds significant implications for safeguarding personal health and well-being, as well as advancing indoor air quality management practices.
Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing
Authors: Mangalampalli Sudheer , Karri Ganesh Reddy , Gupta Amit , Chakrabarti Tulika , Nallamala Sri Hari , Chakrabarti Prasun , Unhelkar Bhuvan , Margala Martin
Published: September 2023

Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.
A New Dual-Input Deep Anomaly Detection Method for Early Faults Warning of Rolling Bearings
Authors: Kang Yuxiang , Chen Guo , Wang Hao , Pan Wenping , Wei Xunkai
Published: September 2023

To address the problem of low fault diagnosis accuracy caused by insufficient fault samples of rolling bearings, a dual-input deep anomaly detection method with zero fault samples is proposed for early fault warning of rolling bearings. First, the main framework of dual-input feature extraction based on a convolutional neural network (CNN) is established, and the two outputs of the main frame are subjected to the autoencoder structure. Then, the secondary feature extraction is performed. At the same time, the experience pool structure is introduced to improve the feature learning ability of the network. A new objective loss function is also proposed to learn the network parameters. Then, the vibration acceleration signal is preprocessed by wavelet to obtain multiple signals in different frequency bands, and the two signals in the high-frequency band are two-dimensionally encoded and used as the network input. Finally, the unsupervised learning of the model is completed on five sets of actual full-life rolling bearing fault data sets relying only on some samples in a normal state. The verification results show that the proposed method can realize earlier than the RMS, Kurtosis, and other features. The early fault warning and the accuracy rate of more than 98% show that the method is highly capable of early fault warning and anomaly detection.
Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
Authors: Sulaiman Adel , Nagu Bharathiraja , Kaur Gaganpreet , Karuppaiah Pradeepa , Alshahrani Hani , Reshan Mana Saleh Al , AlYami Sultan , Shaikh Asadullah
Published: September 2023

Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.