bluuebunny/crossref_metadata_2025 huggingface dataset:
Antidepressant Effect of Heracleum moellendorffii Extract on Behavioral Changes in Astrocyte Ablation Mouse Model of Depression by Modulating Neuroinflammation through the Inhibition of Lipocalin-2
Authors: Hong Soonsang , Kim Yunna , Kwon YongJu , Cho Seung-Hun
Published: June 2024

Astrocyte dysfunction and inflammation play a pivotal role in depression. In this study, we evaluated the antidepressant properties of Heracleum moellendorffii root extract (HME), which is traditionally used for inflammation-related diseases, in a mouse model with astrocyte depletion that resembles the prefrontal cortex pathology of depressive patients. Mice were divided into four groups, with 10 mice per group. To induce astrocyte ablation in the mice’s prefrontal cortex (PFC), we used astrocytic toxin L-alpha-aminoadipic acid (L-AAA) and administered HME orally at 200 and 500 mg/kg for 22 days. We utilized the tail suspension test (TST) to assess depression-like behaviors and the open field test (OFT) to evaluate anxiety-like activities. Additionally, astrocytic and inflammatory markers in the PFC were evaluated using immunohistochemistry and ELISA. The results showed that infusion of L-AAA significantly decreased the expression of astrocytic glial fibrillary acidic protein (GFAP), which was accompanied by increased depression and anxiety-like behaviors. However, HME significantly reversed these effects by dose-dependently enhancing GFAP expression and modulating inflammatory markers, such as TNF-α, IL-6, and particularly lipocalin-2, a master proinflammatory mediator. These results imply that HME contributes to the alleviation of depression and anxiety-like behaviors by promoting astrocyte recovery and reducing neuroinflammation, especially through lipocalin-2 inhibition.
Proteomic Characterization of a Lunasin-Enriched Soybean Extract Potentially Useful in the Treatment of Helicobacter pylori Infection
Authors: Franca-Oliveira Giselle , Monreal Peinado Sara , Alves de Souza Stephanny Miranda , Kalume Dario Eluan , Ferraz de Souza Theo Luiz , Hernández-Ledesma Blanca , Martinez-Rodriguez Adolfo J.
Published: June 2024

Helicobacter pylori infection affects over 50% of the world’s population and leads to chronic inflammation and gastric disorders, being the main pathogen correlated to gastric cancer development. Increasing antibiotic resistance levels are a major global concern and alternative treatments are needed. Soybean peptides and other compounds might be an alternative in the treatment to avoid, eradicate and/or control symptoms of H. pylori infection. This study aimed to characterize a lunasin-enriched soybean extract (LSE) using proteomics tools and to evaluate its antioxidant, anti-inflammatory and antibacterial properties against H. pylori infection. By LC–MS/MS analysis, 124 proteins were identified, with 2S albumin (lunasin and large-chain subunits) being the fourth most abundant protein (8.9%). Lunasin consists of 44 amino acid residues and an intramolecular disulfide bond. LSE at a low dose (0.0625 mg/mL) reduced ROS production in both H. pylori-infected and non-infected AGS gastric cells. This led to a significant reduction of 6.71% in the levels of pro-inflammatory interleukin (IL)-8. LSE also showed antibacterial activity against H. pylori, which can be attributed to other soybean proteins and phenolic compounds. Our findings suggest that LSE might be a promising alternative in the management of H. pylori infection and its associated symptoms.
Asymptomatic Malaria Reservoirs in Honduras: A Challenge for Elimination
Authors: Banegas Sharon , Escobar Denis , Pinto Alejandra , Moncada Marcela , Matamoros Gabriela , Valdivia Hugo O. , Reyes Allan , Fontecha Gustavo
Published: June 2024

Background: Efforts on a global scale for combating malaria have achieved substantial progress over the past twenty years. Two Central American nations have accomplished their goal of eliminating malaria: El Salvador and Belize. Honduras has decreased the incidence of malaria and now reports fewer than 4000 malaria cases annually, aspiring to reach elimination by 2030. To accomplish this goal, it is essential to assess the existing strategies employed for malaria control and to address the task of incorporating novel intervention strategies to identify asymptomatic reservoirs. Methods: A survey for detecting asymptomatic cases was carried out in the community of Kaukira, in Gracias a Dios, Honduras, focusing on malaria transmission during 2023. Asymptomatic community members were recruited as participants, malaria screening was performed through a rapid diagnostic test in situ, and a blood sample was collected on filter paper. Highly sensitive molecular assays based on photo-induced electron transfer PCR (PET-PCR) were performed to detect the two species of Plasmodium circulating in Honduras: Plasmodium vivax and Plasmodium falciparum. In addition, the identification of the parasite species was verified by amplifying three genetic markers (Pvmsp3α, Pvmsp3ß, and Pfmsp1). Results: A total of 138 participants were recruited, mostly adult women. All individuals tested negative on the rapid diagnostic test. Positive results for malaria were detected by PET-PCR in 17 samples (12.3%). Most samples (12 out of 17) were amplified with a Ct value between 37 and 42, indicating very low parasitemias. Out of the 17 samples, 16 of them also showed amplification in the species assays. There were nine cases of P. falciparum infections and seven cases of P. vivax infections that were further confirmed by nested PCR (nPCR) of Pvmsp3 and Pfmsp1. Parasitemias ranged from 100 p/μL to less than 0.25 p/μL. One sample showed mixed infection. Conclusions: The existence of asymptomatic malaria reservoirs in Honduras can contribute to disease transmission and pose a challenge that may hinder elimination efforts, requiring public health authorities to modify surveillance strategies to identify the disease and treat this population accordingly.
Synthesis and Psychotropic Properties of Novel Condensed Triazines for Drug Discovery
Authors: Paronikyan Ervand G. , Dashyan Shushanik Sh. , Mamyan Suren S. , Paronikyan Ruzanna G. , Nazaryan Ivetta M. , Balyan Kristine V. , Gasparyan Hrachik V. , Buloyan Sona A. , Hunanyan Lernik S. , Hobosyan Nina G.
Published: June 2024

The exploration of heterocyclic compounds and their fused analogs, featuring key pharmacophore fragments like pyridine, thiophene, pyrimidine, and triazine rings, is pivotal in medicinal chemistry. These compounds possess a wide array of biological activities, making them an intriguing area of study. The quest for new neurotropic drugs among derivatives of these heterocycles with pharmacophore groups remains a significant research challenge. The aim of this research work was to develop a synthesis method for new heterocyclic compounds, evaluate their neurotropic and neuroprotective activities, study histological changes, and perform docking analysis. Classical organic synthesis methods were used in the creation of novel heterocyclic systems containing pharmacophore rings. To evaluate the neurotropic activity of these synthesized compounds, a range of biological assays were employed. Docking analysis was conducted using various software packages and methodologies. The neuroprotective activity of compound 13 was tested in seizures with and without pentylenetetrazole (PTZ) administration. Histopathological examinations were performed in different experimental groups in the hippocampus and the entorhinal cortex. As a result of chemical reactions, 16 new, tetra- and pentacyclic heterocyclic compounds were obtained. The biologically studied compounds exhibited protection against PTZ seizures as well as some psychotropic effects. The biological assays evidenced that 13 of the 16 studied compounds showed a high anticonvulsant activity by antagonism with PTZ. The toxicity of the compounds was low. According to the results of the study of psychotropic activity, it was found that the selected compounds have a sedative effect, except compound 13, which exhibited activating behavior and antianxiety effects (especially compound 13). The studied compounds exhibited antidepressant effects, especially compound 13, which is similar to diazepam. Histopathological examination showed that compound 13 produced moderate changes in the brain and exhibited neuroprotective effects in the entorhinal cortex against PTZ-induced damage, reducing gliosis and neuronal loss. Docking studies revealed that out of 16 compounds, 3 compounds bound to the γ-aminobutyric acid type A (GABAA) receptor. Thus, the selected compounds demonstrated anticonvulsant, sedative, and activating behavior, and at the same time exhibited antianxiety and antidepressant effects. Compound 13 bound to the GABAA receptor and exhibited antianxiety, antidepressant, and neuroprotective effects in the entorhinal cortex against PTZ-induced changes.
Targeted Delivery Strategies for Multiple Myeloma and Their Adverse Drug Reactions
Authors: Li Shuting , Wang Hongjie , Xiong Shijun , Liu Jing , Sun Shuming
Published: June 2024

Currently, multiple myeloma (MM) is a prevalent hematopoietic system malignancy, known for its insidious onset and unfavorable prognosis. Recently developed chemotherapy drugs for MM have exhibited promising therapeutic outcomes. Nevertheless, to overcome the shortcomings of traditional clinical drug treatment, such as off-target effects, multiple drug resistance, and systemic toxicity, targeted drug delivery systems are optimizing the conventional pharmaceuticals for precise delivery to designated sites at controlled rates, striving for maximal efficacy and safety, presenting a promising approach for MM treatment. This review will delve into the outstanding performance of antibody–drug conjugates, peptide–drug conjugates, aptamer–drug conjugates, and nanocarrier drug delivery systems in preclinical studies or clinical trials for MM and monitor their adverse reactions during treatment.
Diverse Approaches in Wet-Spun Alginate Filament Production from the Textile Industry Perspective: From Process Optimization to Composite Filament Production
Authors: Var Cansu , Palamutcu Sema
Published: June 2024

Alginate, categorized as a natural-based biodegradable polymer, stands out for its inherently exclusive properties. Although this unique polymer is widely processed using film, coating, and membrane technologies for different usage areas, textile applications are still limited. This study aims to compile promising approaches that will pave the way for the use of wet-spun alginate filaments in textile applications. In this regard, this study provides information about the molecular structure of alginate, the gel formation mechanism, and cross-linking using different techniques. Our literature review categorizes parameters affecting the mechanical properties of wet-spun alginate filaments, such as the effect of ion source and spinning dope concentration, needle diameter, temperature, and coagulants. Following this, a detailed and comprehensive literature review of the various approaches, such as use of additives, preparation of blended filaments, and grafted nanocrystal addition, developed by researchers to produce composite alginate filaments is presented. Additionally, studies concerning the use of different cations in the coagulation phase are reported. Moreover, studies about the functionalism of wet-spun alginate filaments have been offered.
The Challenge of Synodality
Authors: Inguanez Joe , Froehle Bryan
Published: June 2024

While the Synod on Synodality (2023–2024) called by Pope Francis is meant to develop deeper unity within the Catholic Church, it has nonetheless provided an occasion to deepen tensions between the Church. While synodality has deep theological roots, its practical implications involve more than theology. Though the self-understanding of the Catholic Church and the meaning of synodality are naturally theological, they are expressed in historically embedded, socially constructed ways. The tensions experienced in the preparation and reception of the Synod on Synodality stem from implied structural changes. Conflict is normal when social change is involved, particularly when questions of power and meaning-making are involved, and all the more so when there is no certainty as to the outcome. Practical theology offers a means of understanding the Synod and the ways in which synodality may or may not be expressed within the contemporary Catholic Church. Such an approach brings theology and social science into dialogue for deeper ecclesiological understanding.
Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds
Authors: Peng Lingbing , Lu Zhi , Lei Tao , Jiang Ping
Published: June 2024

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems.
Generation of High-Resolution Blending Data Using Gridded Visibility Data and GK2A Fog Product
Authors: Suh Myoung-Seok , Han Ji-Hye , Yu Ha-Yeong , Kang Tae-Ho
Published: June 2024

In this study, 10 min and 2 km high-resolution blended fog data (HRBFD) were generated using grid visibility data (GVD) and data from a GK2A (GEO-KOMPSAT-2A) fog product (GKFP) in Korea. As the blending method, the decision tree method (DTM) was used to consider the different characteristics of the two-input data (categorical data and continuity data). The blending of the two datasets was performed according to the presence or absence of the input data and considered the spatial representation of the GVD and the accuracy of the GKFP. The quality of the HRBFD was evaluated through visual comparison using GVD, GKFP, and visible images of the GK2A. The HRBFD seems to have partly solved the problem of fog detection in areas where visibility meters are rare or absent through the detection of fog occurring in the sea or mountain areas. In addition, the critical problem of the GKFP, which has limitations in detecting fog occurring under clouds, has been mostly overcome. Using the DTM, we generated 23 fog cases of 10 min and 2 km HRBFD. The results confirmed that detailed spatiotemporal characteristics of fog in Korea can be analyzed if such HRBFD is generated for a long time.
Reconstructing Snow-Free Sentinel-2 Satellite Imagery: A Generative Adversarial Network (GAN) Approach
Authors: Oluwadare Temitope Seun , Chen Dongmei , Oluwafemi Olawale , Babadi Masoud , Hossain Mohammad , Ibukun Oluwaseun
Published: June 2024

Sentinel-2 satellites are one of the major instruments in remote sensing (RS) technology that has revolutionized Earth observation research, as its main goal is to offer high-resolution satellite data for dynamic monitoring of Earth’s surface and climate change detection amongst others. However, visual observation of Sentinel-2 satellite data has revealed that most images obtained during the winter season contain snow noise, posing a major challenge and impediment to satellite RS analysis of land surface. This singular effect hampers satellite signals from capturing important surface features within the geographical area of interest. Consequently, it leads to information loss, image processing problems due to contamination, and masking effects, all of which can reduce the accuracy of image analysis. In this study, we developed a snow-cover removal (SCR) model based on the Cycle-Consistent Adversarial Networks (CycleGANs) architecture. Data augmentation procedures were carried out to salvage the effect of the limited availability of Sentinel-2 image data. Sentinel-2 satellite images were used for model training and the development of a novel SCR model. The SCR model captures snow and other prominent features in the Sentinel-2 satellite image and then generates a new snow-free synthetic optical image that shares the same characteristics as the source satellite image. The snow-free synthetic images generated are evaluated to quantify their visual and semantic similarity with original snow-free Sentinel-2 satellite images by using different image qualitative metrics (IQMs) such as Structural Similarity Index Measure (SSIM), Universal image quality index (Q), and peak signal-to-noise ratio (PSNR). The estimated metric data shows that Q delivers more metric values, nearly 95%, than SSIM and PRSN. The methodology presented in this study could be beneficial for RS research in DL model development for environmental mapping and time series modeling. The results also confirm the DL technique’s applicability in RS studies.
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
Authors: Cheng Guangliang , Huang Yunmeng , Li Xiangtai , Lyu Shuchang , Xu Zhaoyang , Zhao Hongbo , Zhao Qi , Xiang Shiming
Published: June 2024

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.
Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices
Authors: Lee Youn Joo , Hwang Jun Young , Park Jiwon , Jung Ho Gi , Suhr Jae Kyu
Published: June 2024

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time.
Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery
Authors: Briceño de Urbaneja Idania C. , Pardo-Pascual Josep E. , Cabezas-Rabadán Carlos , Aguirre Catalina , Martínez Carolina , Pérez-Martínez Waldo , Palomar-Vázquez Jesús
Published: June 2024

Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism.
Multi-Objective Distributed Client Selection in Federated Learning-Assisted Internet of Vehicles
Authors: Cha Narisu , Chang Long
Published: June 2024

Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate in the next round. However, the cost of maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we propose a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbors. In the evaluator, four variables are considered, including the sample quantity, available throughput, computational capability, and the quality of the local dataset. We adopt fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show that our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead.
Federated Learning-Oriented Edge Computing Framework for the IIoT
Authors: Liu Xianhui , Dong Xianghu , Jia Ning , Zhao Weidong
Published: June 2024

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less.
Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis
Authors: Liang Guanlong , Song Xuewei , Liao Zhiqiang , Jia Baozhu
Published: June 2024

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University’s (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.
Adaptable Hybrid Beamforming with Subset Optimization Algorithm for Multi-User Massive MIMO Systems
Authors: Huang Ziyang , Yang Longcheng , Tan Weiqiang , Wang Han
Published: June 2024

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system’s sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS–users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.
Real-Time Tool Localization for Laparoscopic Surgery Using Convolutional Neural Network
Authors: Benavides Diego , Cisnal Ana , Fontúrbel Carlos , de la Fuente Eusebio , Fraile Juan Carlos
Published: June 2024

Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.
A Simple and Efficient Approach to Determine Lithium and Multi-Elements in Lithium-Bearing Clay Minerals through a Partial Extraction Using Ammonium Hydrogen Fluoride
Authors: Zhang Feige , Zhang Qinfeng , Chen Zhigui , Fang Xiaoqing , Yu Xiaoqi , Li Guangyi , Chen Di
Published: June 2024

Lithium (Li) is vital to the world economy as an irreplaceable element in new-generation sustainable energy applications. Quantifying Li levels in clay minerals, a key new resource, supports extensive mineralization surveys. The major limitation in these analyses is complete decomposition, which often requires the use of strong acids and is labor-intensive, particularly in batch processing. This paper presents a partial extraction strategy for quantifying Li and multiple elements, employing ammonium hydrogen fluoride to extract from clay minerals while avoiding the use of various strong acids. Following extraction, the efficiency for Li reaches 98.61%, with values ranging from 62.68% to 91.43% for multiple elements including three major elements (Ca, Fe, Mg) and eleven trace elements (Be, Co, Cr, Cu, Ga, Mn, Ni, P, Rb, Sr, V), demonstrating favorable precision (RSD below 7.34%). This approach offers a promising tool for a simple and efficient batch analysis in the survey of mineralization in Li-bearing clay deposits.
Creating Mentally Healthy Universities: Lessons from Staff Experiences of Transition through the COVID-19 Pandemic
Authors: Jayman Michelle , Lynam Siobhan
Published: June 2024

The COVID-19 pandemic generated unprecedented challenges for educators world-wide. University teaching staff were forced to rapidly adapt to virtual classrooms during lockdown and the return to campus has seen continuing flux. Poor student mental wellbeing is a major concern and although nascent digital mental health interventions can increase reach and augment in-person services, research on the effectiveness of digital interventions is still in its infancy. The implementation of hybrid solutions is challenging due to the complexity and diversity of institutions; however, important lessons can be learned from the switch to online teaching and the integration of digital technologies during the transition process. This paper explores staff experiences of transition through the pandemic using interpretive phenomenological analysis (IPA). Semi-structured interviews were conducted with three teaching academics from English universities. Analysis yielded four group experiential themes: Transition was a traumatic process; Relationships as a source of support and strain; Opportunities for learning and growth; and Surviving and inspiring the mental wellbeing environment. Key findings revealed the switch to online working presented unique stressors, while relationships were pivotal for navigating transition and healthy personal development. Findings can inform transition guidance including the integration of hybrid pathways to support mental wellbeing for the whole learning community.
Liquid Nanoclay: Synthesis and Applications to Transform an Arid Desert into Fertile Land
Authors: Abd-Elsalam Kamel A. , Mehmood Mirza Abid , Ashfaq Muhammad , Abdelkhalek Toka E. , Hassan Rawan K. , Ravichandran Mythili
Published: June 2024

Nanoclay, a processed clay, is utilized in numerous high-performance cement nanocomposites. This clay consists of minerals such as kaolinite, illite, chlorite, and smectite, which are the primary components of raw clay materials formed in the presence of water. In addition to silica, alumina, and water, it also contains various concentrations of inorganic ions like Mg2+, Na+, and Ca2+. These are categorized as hydrous phyllosilicates and can be located either in interlayer spaces or on the planetary surface. Clay minerals are distinguished by their two-dimensional sheets and tetrahedral (SiO4) and octahedral (Al2O3) crystal structures. Different clay minerals are classified based on the presence of tetrahedral and octahedral layers in their structure. These include kaolinite, which has a 1:1 ratio of tetrahedral to octahedral layers, the smectite group of clay minerals and chlorite with a 2:1 ratio. Clay minerals are unique due to their small size, distinct crystal structure, and properties such as high cation exchange capacity, adsorption capacity, specific surface area, and swelling behavior. These characteristics are discussed in this review. The use of nanoclays as nanocarriers for fertilizers boasts a diverse array of materials available in both anionic and cationic variations. Layered double hydroxides (LDH) possess a distinctive capacity for exchanging anions, making them suitable for facilitating the transport of borate, phosphate, and nitrate ions. Liquid nanoclays are used extensively in agriculture, specifically as fertilizers, insecticides, herbicides, and nutrients. These novel nanomaterials have numerous benefits, including improved nutrient use, controlled nutrient release, targeted nutrient delivery, and increased agricultural productivity. Arid regions face distinct challenges like limited water availability, poor soil quality, and reduced productivity. The addition of liquid nanoclay to sandy soil offers a range of benefits that contribute to improved soil quality and environmental sustainability. Liquid nanoclay is being proposed for water management in arid regions, which will necessitate a detailed examination of soil, water availability, and hydrological conditions. Small-scale trial initiatives, engagement with local governments, and regular monitoring are required to fully comprehend its benefits and drawbacks. These developments would increase the practicality and effectiveness of using liquid nanoclay in desert agriculture.
Towards Water-Efficient Irrigation of Cup Plant (Silphium perfoliatum L.) for Energy Production: Water Requirements and Rainfall Deficit
Authors: Figas Anna , Rolbiecki Roman , Rolbiecki Stanisław , Jagosz Barbara , Łangowski Ariel , Sadan-Ozdemir Hicran A. , Pal-Fam Ferenc , Atilgan Atilgan
Published: June 2024

The cup plant shows promise for biomass production and has significant potential for increasing biodiversity. This species primarily grows in habitats with high soil humidity. Precipitation deficits are common throughout Poland, especially in the central regions, necessitating crop irrigation. To design and manage resource-efficient irrigation for the cup plant, estimating water requirements and rainfall deficits is essential. This research aims to calculate water requirements, rainfall deficits, and irrigation demand and to assess their temporal and spatial variations for cup plants energy plantations from 1981 to 2020. The study, conducted using the Blaney–Criddle method, focused on the growing season (1 April–30 September) across four provinces in central Poland: Kuyavian–Pomeranian (18°01′ E, 53°08′ N), Greater Poland (16°50′ E, 52°25′ N), Masovian (20°59′ E, 52°09′ N), and Lodz (19°24′ E, 51°44′ N). The research revealed varying values of water requirements depending on the province, ranging from 445.9 mm in Lodz province to 465.2 mm in Kuyavian–Pomeranian province. The magnitude of water requirements also significantly depended on the month of the growing season, with the highest value occurring in July (from 103.6 mm in Lodz province to 108.9 mm in Kuyavian–Pomeranian province). Over the forty-year period, a notable upward trend in water requirements was observed across all provinces, ranging from 6.7 mm per decade to 12.5 mm per decade. On average, rainfall deficits during the growing season amounted to 125 mm in normal years, 237 mm in medium dry years, and 316 mm in very dry years. These findings are crucial for efficient irrigation management in central Poland, which, in line with sustainable agricultural development, will enable the maximization of yields of this plant while simultaneously conserving water resources.
Renewable Revolution: A Review of Strategic Flexibility in Future Power Systems
Authors: Shahzad Sulman , Jasińska Elżbieta
Published: June 2024

The global shift toward integrating renewable energy sources, such as solar and wind power, is revolutionizing the energy landscape, presenting both opportunities and challenges for power system operations. This paper delves into the critical concept of power system flexibility, emphasizing the importance of maintaining reliable service amidst unpredictable fluctuations in supply and demand. Given the intermittent nature of renewable energy, this paper explores key aspects of flexibility, including generation, demand-side, and grid flexibility, and their roles in sustaining grid stability. Various strategies to enhance flexibility in future power networks are examined, such as advanced energy storage technologies, demand response programs, grid expansion and interconnection, sophisticated forecasting methods, and adaptive power generation. Additionally, this paper underscores the pivotal role of market and regulatory reforms in facilitating the integration of renewable energy sources and enhancing grid adaptability. By analyzing the benefits, limitations, and future prospects of each strategy, this work provides a comprehensive overview of the evolving energy sector. It highlights the necessity of aligning regulatory frameworks with sustainability and climate goals to navigate the complexities of this transition effectively.
Lessons Learned from the Past: Tracing Sustainable Strategies in the Architecture of Al-Ula Heritage Village
Authors: Sirror Hala
Published: June 2024

The architecture of Al-Ula Heritage Village offers valuable lessons for sustainable strategies adaptable to contemporary architecture. By studying the green strategies employed in Al-Ula Heritage Village, insights into heritage villages’ future preservation and ecological development are gained. This study investigates Al-Ula’s historical buildings’ environmental, cultural, and social sustainable strategies. Ecological design principles include preserving natural conditions, urban design, site planning, and human comfort. Cultural considerations incorporate embracing change, integrating sustainable design strategies, protecting cultural heritage, engaging the community, and leveraging digital preservation tools. Social sustainability involves preserving cultural heritage, community engagement, and promoting social cohesion. A comprehensive conceptual framework for environmental, cultural, and social sustainability is developed and employed. Primary data from the Saudi Commission for Tourism and Antiquities and the existing literature on traditional Saudi vernacular architecture are utilized. The findings highlight the effectiveness of ancient design strategies such as topography preservation, non-disturbance of natural hydraulic processes, and the integration of open spaces. Urban design principles like mixed-use development, pollution reduction, and human comfort considerations are prevalent. The study outcomes emphasize the importance of cultural and social considerations. The study concludes that green architecture can be achieved through the thoughtful adaptation of traditional techniques, demonstrating how sustainable design rooted in historical practices can serve as a model for future developments.
The Power of Influencers: How Does Influencer Marketing Shape Consumers’ Purchase Intentions?
Authors: Chen Yiming , Qin Zhaoyue , Yan Yue , Huang Yi
Published: June 2024

In the current digital wave, social media is not only a hub for information exchange but also a shaper of new business marketing models, as is especially evident in the trend towards light and healthy eating. The influence of the influencer economy on consumer purchasing decisions is increasingly pronounced. This paper systematically investigates the impact of influencer marketing on consumer purchase intentions in social media utilizing the Consumer Attitude Theory. Through a sample survey of 654 consumers and empirical analysis using the fuzzy comprehensive evaluation model, the results show that the influencers’ credibility and professionalism and consumers’ satisfaction with live-streaming sales by influencers have a significant positive impact on enhancing consumers’ purchase intentions. To enhance consumers’ purchase intentions, this study suggests that influencers should transparently disclose their collaborations with brands, showcase the positive experiences of other users, and use relevant research and data to support their product recommendations in order to enhance their credibility. Simultaneously, influencers need to strengthen product knowledge, improve professional image and reputation, and meet consumer needs through personalized recommendations and carefully designed live-streaming content to promote brand-value enhancement.
The Impact of Desert Regions on Solar Energy Production with the Evaluation of Groundwater for Maintenance: A Case Study in Morocco
Authors: Ait Ali Ali , Ouhassan Youssef , Abouyaakoub Mohcine , Chahboun Mbarek , Hihi Hicham
Published: June 2024

The aim of this research work is to investigate the influence of temperature and wind-blown dust on solar energy production in a desert region of Morocco. Moreover, it aims to assess the quality of water, in particular the groundwater used for the maintenance of photovoltaic panels (quality analysis). This region is characterized by very high temperatures and wind-blown dust in the summer, which has a major impact on the production of the photovoltaic panels. Before installing this maintenance system (cooling and cleaning using water), we decided to assess the quality of this water, whose temperature generally varies between 10 and 16 °C at a depth of 4 m, whatever the season. This is an important, stable, and sustainable source of water that can be entirely used to protect the photovoltaic modules from wind-blown dust and temperature in order to improve their efficiency. However, this water can also have a major impact on the quality of the energy. It can be contaminated with limestone and salts, which can cause the photovoltaic panels to block. All the research and studies carried out in the context of maintenance using water do not take into account the nature of this water (whether it is good or bad). After simulating our model on the Matlab-Simulink environment, we can see that the temperature has a significant influence on solar energy production (a reduction of power by 20% at 45 °C) in this region. Moreover, after the assessment of the water quality in our school laboratory, we found that the water, and especially the groundwater in this desert region of Morocco, are suitable for the maintenance of photovoltaic panels.
Australian Geotourism Discovery Platform: A Sustainable and User-Friendly Platform for Accessible Exploration of Geosites, Geotrails, Cultural, and Mining Heritage Sites
Authors: Williams Mark A. , Wang Xinyuan , McHenry Melinda T. , Robinson Angus M.
Published: June 2024

Geotourism focuses on an areas’ geodiversity and cultural landscape to provide visitor engagement, learning, and enjoyment. Geotourism is pivotal in achieving the Sustainable Development Goals (SDGs), as recognised by the United Nations. This study examines the development of the Australian Geotourism Discovery Portal (AGDP) and its role in promoting sustainable geotourism, aligned with Australia’s National Geotourism Strategy strategic goals, and in providing a framework for the development of digital platforms for geotourism. The AGDP’s development was guided by a deductive development approach to examine the link between Geographical Information Technologies (GITs) and SDGs and subsequently applying findings to a stakeholder-led design process aligned with the needs of identified putative user groups. With a focus on two key user groups, the ‘Grey Nomads’ and ‘Students & Educators’, we used our deductive approach to iteratively test and refine the platform’s development based on the key attributes and preferences of these user groups for different accessibility, educational, and experiential needs. The AGDP employed ESRI ArcGIS Hub Web-GIS technology to promote geosites, geotrails, mining sites, indigenous cultural heritage sites, and GeoRegions in Australia. The implementation of the AGDP highlighted the potential to enhance public understanding of Australia’s natural and cultural heritage and the significant opportunity to leverage emerging GITs in maintaining the sustainable development initiatives of the geotourism sector. The framework established provides a replicable model that can be adapted and applied to other regions around the world, offering a tool and process development that can be used in a range of stakeholder- and community-led sustainable development initiatives.