Abstract: Purpose: The purpose of this study is to evaluate the use of fertility-preserving (FP) treatments and fertility counseling that was offered in a cohort of newly diagnosed children with classical Hodgkin lymphoma (cHL). Methods: In this observational study, boys and girls with cHL aged ≤ 18 years with scheduled treatment according to the EuroNet-PHL-C2 protocol were recruited from 18 sites (5 countries), between January 2017 and September 2021. In 2023, a subset of Dutch participants (aged ≥ 12 years at time of diagnosis) and parents/guardians were surveyed regarding fertility counseling. Results: A total of 101 boys and 104 girls were included. Most post-pubertal boys opted for semen cryopreservation pre-treatment (85% of expected). Invasive FP treatments were occasionally chosen for patients at a relatively low risk of fertility based on scheduled alkylating agent exposure (4/5 testicular biopsy, 4/4 oocyte, and 11/11 ovarian tissue cryopreservation). A total of 17 post-menarchal girls (20%) received GnRH-analogue co-treatment. Furthermore, 33/84 parents and 26/63 patients responded to the questionnaire. Most reported receiving fertility counseling (97%/89%). Statements regarding the timing and content of counseling were generally positive. Parents and patients considered fertility counseling important (94%/87% (strongly agreed) and most expressed concerns about (their child’s) fertility (at diagnosis 69%/46%, at present: 59%/42%). Conclusion: Systematic fertility counseling is crucial for all pediatric cHL patients and their families. FP treatment should be considered depending on the anticipated risk and patient factors. We encourage the development of a decision aid for FP in pediatric oncology. Full article

This review aimed to examine the effects of curcumin on chronic inflammatory metabolic disease by extensively evaluating meta-analyses of randomized controlled trials (RCTs). We performed a literature search of meta-analyses of RCTs published in English in PubMed^®/MEDLINE up to 31 July 2023. We identified 54 meta-analyses of curcumin RCTs for inflammation, antioxidant, glucose control, lipids, anthropometric parameters, blood pressure, endothelial function, depression, and cognitive function. A reduction in C-reactive protein (CRP) levels was observed in seven of ten meta-analyses of RCTs. In five of eight meta-analyses, curcumin intake significantly lowered interleukin 6 (IL-6) levels. In six of nine meta-analyses, curcumin intake significantly lowered tumor necrosis factor α (TNF-α) levels. In five of six meta-analyses, curcumin intake significantly lowered malondialdehyde (MDA) levels. In 14 of 15 meta-analyses, curcumin intake significantly reduced fasting blood glucose (FBG) levels. In 12 of 12 meta-analyses, curcumin intake significantly reduced homeostasis model assessment of insulin resistance (HOMA-IR). In seven of eight meta-analyses, curcumin intake significantly reduced glycated hemoglobin (HbA1c) levels. In eight of ten meta-analyses, curcumin intake significantly reduced insulin levels. In 14 of 19 meta-analyses, curcumin intake significantly reduced total cholesterol (TC) levels. Curcumin intake plays a protective effect on chronic inflammatory metabolic disease, possibly via improved levels of glucose homeostasis, MDA, TC, and inflammation (CRP, IL-6, TNF-α, and adiponectin). The safety and efficacy of curcumin as a natural product support the potential for the prevention and treatment of chronic inflammatory metabolic diseases. Full article

(1) Background: Neurologic and musculoskeletal diseases represent a considerable portion of the underlying etiologies responsible for the widely prevalent symptoms of pain, weakness, numbness, and paresthesia. Because of the subjective and often nonspecific nature of these symptoms, different diagnostic modalities have been explored and utilized. (2) Methods: Literature review. (3) Results: Nerve and muscle biopsy remains the gold standard for diagnosing many of the responsible neurological and musculoskeletal conditions. However, the need for invasive tissue sampling is diminishing as more investigations explore alternative diagnostic modalities. Because of this, it is important to explore the current role of neurosurgical intervention for nerve and muscle biopsies and its current relevance in the diagnostic landscape of neurological and musculoskeletal disorders. With consideration of the role of nerve and muscle biopsy, it is also important to explore innovations and emerging techniques for conducting these procedures. This review explores the indications and emerging techniques for neurological intervention for nerve and muscle biopsies. (4) Conclusions: The role of neurosurgical intervention for nerve and muscle biopsy remains relevant in diagnosing many neurological and musculoskeletal disorders. Biopsy is especially relevant as a supportive point of evidence for diagnosis in atypical cases. Additionally, emerging techniques have been explored to guide diagnostics and biopsy, conduct less invasive biopsies, and reduce risks of worsening neurologic function and other symptoms secondary to biopsy. Full article

This paper investigates the complex relationship between technology, religion, and sacrifice, positing this last term as a pivotal concept for understanding the evolution and impact of technological advancements. Through a detailed examination of various cultural and religious frameworks, it explores how artificial intelligence and other modern technologies both challenge and redefine traditional notions of the sacred and the profane. By analyzing historical and contemporary practices, the study highlights the paradoxical role of sacrifice in the digital age, serving as both a metaphor for the loss inherent in technological progress and a foundational principle that shapes the ethical landscapes of innovation. Full article

Abstract: Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node’s immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network’s broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible–infected–recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model’s progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics. Full article

Food allergy (FA) has shown an increasing prevalence in the last decades, becoming a major public health problem. However, data on the prevalence of FA across the world are heterogeneous because they are influenced by several factors. Among IgE-mediated FA, an important role is played by FA related to plant-derived food which can result from the sensitization to a single protein (specific FA) or to homologous proteins present in different foods (cross-reactive FA) including non-specific lipid transfer proteins (nsLTPs), profilins, and pathogenesis-related class 10 (PR-10). In addition, the clinical presentation of FA is widely heterogeneous ranging from mild symptoms to severe reactions up to anaphylaxis, most frequently associated with nsLTP-related FA (LTP syndrome). Considering the potential life-threatening nature of nsLTP-related FA, the patient’s geographical setting should always be taken into account; thereby, it is highly recommended to build a personalized approach for managing FA across the world in the precision medicine era. For this reason, in this review, we aim to provide an overview of the prevalence of nsLTP-mediated allergies in the Mediterranean area and to point out the potential reasons for the different geographical significance of LTP-driven allergies with a particular focus on the allergenic properties of food allergens and their cross reactivity. Full article

The aim of this study was to investigate the impact of extended bridge expertise on rapid perceptual processing and brain functional plasticity in early adulthood, utilizing functional magnetic resonance imaging (fMRI). In this investigation, we compared 6 high-level college bridge players with 25 college students lacking bridge experience, assessing their intelligence and working memory. Additionally, we scrutinized behavioral performance and whole-brain activation patterns during an image perceptual judgment task. Findings indicated significant group and interaction effects at the behavioral level. Bridge players exhibited prolonged reaction times and enhanced accuracy on card tasks. At the neural level, the activation level of bridge players in the occipital lobe exceeded that of ordinary college students, with more pronounced group effects in the motor area and inferior parietal lobule during card tasks. This implies that bridge expertise in early adulthood induces functional plasticity changes in regions associated with visual processing and automated mathematical computation. Full article

Challenges in enhancing the multiclass segmentation of remotely sensed data include expensive and scarce labeled samples, complex geo-surface scenes, and resulting biases. The intricate nature of geographical surfaces, comprising varying elements and features, introduces significant complexity to the task of segmentation. The limited label data used to train segmentation models may exhibit biases due to imbalances or the inadequate representation of certain surface types or features. For applications like land use/cover monitoring, the assumption of evenly distributed simple random sampling may be not satisfied due to spatial stratified heterogeneity, introducing biases that can adversely impact the model’s ability to generalize effectively across diverse geographical areas. We introduced two statistical indicators to encode the complexity of geo-features under multiclass scenes and designed a corresponding optimal sampling scheme to select representative samples to reduce sampling bias during machine learning model training, especially that of deep learning models. The results of the complexity scores showed that the entropy-based and gray-based indicators effectively detected the complexity from geo-surface scenes: the entropy-based indicator was sensitive to the boundaries of different classes and the contours of geographical objects, while the Moran’s I indicator had a better performance in identifying the spatial structure information of geographical objects in remote sensing images. According to the complexity scores, the optimal sampling methods appropriately adapted the distribution of the training samples to the geo-context and enhanced their representativeness relative to the population. The single-score optimal sampling method achieved the highest improvement in DeepLab-V3 (increasing pixel accuracy by 0.3% and MIoU by 5.5%), and the multi-score optimal sampling method achieved the highest improvement in SegFormer (increasing ACC by 0.2% and MIoU by 2.4%). These findings carry significant implications for quantifying the complexity of geo-surface scenes and hence can enhance the semantic segmentation of high-resolution remote sensing images with less sampling bias. Full article

Remote sensing images are usually obtained from high-altitude observation. The spatial resolution of the images varies greatly and there are scale differences both between and within object classes, resulting in a diversified distribution of object scales. In order to solve these problems, we propose a novel object detection algorithm that maintains adaptability to multi-scale object detection based on feature interaction and Gaussian distribution in remote sensing images. The proposed multi-scale feature interaction model constructs feature interaction modules in the feature layer and spatial domain and combines them to fully utilize the spatial and semantic information of multi-level features. The proposed regression loss algorithm based on Gaussian distribution takes the normalized generalized Jensen–Shannon divergence with Gaussian angle loss as the regression loss function to ensure the scale invariance of the model. The experimental results demonstrate that our method achieves 77.29% mAP on the DOTA-v1.0 dataset and 97.95% mAP on the HRSC2016 dataset, which are, respectively, 1.12% and 1.41% higher than that of the baseline. These experimental results indicate the effectiveness of our method for object detection in remote sensing images. Full article

The aim of this research is to explore the potential effects of two microalgae-based additives included in finishing feeds on the quality and shelf-life of seabream fillets. In a 41-day feeding trial, seabream specimens were fed with experimental aquafeeds containing 10% of the bioactive supplements. These additives consisted of a blend of Nannochloropsis gaditana and Arthrospira platensis biomass, which was utilized as either raw (LB-CB) or enzymatically hydrolyzed (LB-CBplus). A control group received a microalgae-free diet. The results showed that the functional aquafeeds improved the nutritional profile of seabream fillets, increasing protein and PUFA-n3 contents while reducing the atherogenic index, especially for the LB-CBplus treatment. LB-CBplus also enhanced the texture parameters (hardness and chewiness) of fillets during the initial 5 days under cold storage. Regarding skin pigmentation, fillets showed increased greenish and yellowish coloration compared to control fish, mostly attributed to the inclusion of crude algal biomass (LB-CB). Moreover, diets enriched with microalgae additives effectively delayed muscle lipid oxidation processes under refrigeration for up to 12 days, with LB-CBplus exhibiting higher antioxidant effects. These findings highlight the potential of microalgae-based additives to enhance both the nutritional and organoleptic quality of seabream fillets. Full article

Context: Short-chain fatty acids (SCFAs) have been reported to be associated with the pathogenesis of irritable bowel syndrome (IBS), but the results are conflicting. Objective: Here, a systematic review of case–control studies detecting fecal SCFAs in IBS patients compared with healthy controls (HCs) and self-controlled studies or randomized controlled trials (RCTs) investigating fecal SCFA alterations after interventions were identified from several databases. Data sources: A systematic search of databases (PubMed, Web of Science, and Embase) identified 21 studies published before 24 February 2023. Data extractions: Three independent reviewers completed the relevant data extraction. Data analysis: It was found that the fecal propionate concentration in IBS patients was significantly higher than that in HCs, while the acetate proportion was significantly lower. Low-FODMAP diets significantly reduced the fecal propionate concentration in the IBS patients while fecal microbiota transplantation and probiotic administration did not significantly change the fecal propionate concentration or acetate proportion. Conclusions: The results suggested that the fecal propionate concentration and acetate proportion could be used as biomarkers for IBS diagnosis. A low-FODMAP diet intervention could potentially serve as a treatment for IBS while FMT and probiotic administration need more robust trials. Full article

This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66, p = 0.005) and inflammatory dermatoses (2.6 ± 0.67, p = 0). Similarly, in the teaching tool usefulness category, BCC-based chatbot differential diagnosis received higher scores (2.9 ± 0.4) compared to SCC (2.6 ± 0.67, p = 0.001) and inflammatory dermatoses (2.4 ± 0.81, p = 0). The abovementioned results underscore dermatologists’ familiarity with BCC dermoscopic images while highlighting the challenges associated with interpreting rigorous dermoscopic images. Moreover, by incorporating patient characteristics such as age, phototype, or immune state, the differential diagnosis list in each case was customized to include lesion types appropriate for each category, illustrating the AI’s flexibility in evaluating diagnoses and highlighting its value as a resource for dermatologists. Full article

Ubiquitin-specific protease 14 (USP14), one of the three major proteasome-associated deubiquitinating enzymes (DUBs), is known to be activated by the AKT-mediated phosphorylation at Ser432. Thereby, AKT can regulate global protein degradation by controlling the ubiquitin-proteasome system (UPS). However, the exact molecular mechanism of USP14 activation by AKT phosphorylation at the atomic level remains unknown. By performing the molecular dynamics (MD) simulation of the USP14 catalytic domain at three different states (inactive, active, and USP14-ubiquitin complex), we characterized the change in structural dynamics by phosphorylation. We observed that the Ser432 phosphorylation induced substantial conformational changes of USP14 in the blocking loop (BL) region to fold it from an open loop into a β-sheet, which is critical for USP14 activation. Furthermore, phosphorylation also increased the frequency of critical hydrogen bonding and salt bridge interactions between USP14 and ubiquitin, which is essential for DUB activity. Structural dynamics insights from this study pinpoint the important local conformational landscape of USP14 by the phosphorylation event, which would be critical for understanding USP14-mediated proteasome regulation and designing future therapeutics. Full article

Functionalization by noble metal catalysts and the construction of heterojunctions are two effective methods to enhance the gas sensing performance of metal oxide-based sensors. In this work, we adopt the porous ZIF-8 as a catalyst substrate to encapsulate the ultra-small Pt nanoparticles. The Pt/ZnO-In₂O₃ hollow nanofibers derived from Pt/ZIF-8 were prepared by a facile electrospinning method. The 25PtZI HNFs sensor possessed a response value of 48.3 to 100 ppm HCHO, 2.7 times higher than the pristine In₂O₃, along with rapid response/recovery time (5/22 s), and lower theoretical detection limit (74.6 ppb). The improved sensing properties can be attributed to the synergistic effects of electron sensitization effects and catalytic effects of Pt nanoparticles, and the high surface O⁻ absorbing capability of heterojunctions. The present study paves a new way to design high performance formaldehyde gas sensors in practical application. Full article

Theories of category learning have typically focused on how the underlying category structure affects the category representations acquired by learners. However, there is limited research as to how other factors affect what representations are learned and utilized and how representations might change across the time course of learning. We used a novel “5/5” categorization task developed from the well-studied 5/4 task with the addition of one more stimulus to clarify an ambiguity in the 5/4 prototypes. We used multiple methods including computational modeling to identify whether participants categorized on the basis of exemplar or prototype representations. We found that, overall, for the stimuli we used (schematic robot-like stimuli), learning was best characterized by the use of prototypes. Most importantly, we found that relative use of prototype and exemplar strategies changed across learning, with use of exemplar representations decreasing and prototype representations increasing across blocks. Full article

Forest road planning incorporates crucial strategies essential for sustainable timber extraction, minimizing environmental impacts and ensuring safe access to forest regions. This paper presents a literature review conducted to examine publications related to forest roads to ultimately filter and evaluate the information on the incorporation of classification systems in the planning strategies for forest roads. Using the Scopus database to gather publications, various data points were mapped, such as temporal distribution of publications, citation metrics, keyword inputs and other bibliometric markers. Through the bibliometric visualization software VOSviewer 1.6.19, this study determined that over the years, the forest road research subject has gained increasing attention with different shifts in focus. However, upon investigating the application of systems of classification implemented on forest road networks, it became evident that this approach is not a recent innovation and there is scarce documentation and development regarding this planning strategy. The information collected also reveals that this type of classification can be found more in technical documents, like design manuals. This outcome suggests that the subject under study is not relevantly covered in forest-related journals, but rather by institutions aiming to identify region-specific needs and develop corresponding systems accordingly. Full article

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID’s epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition. Full article

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future₁₆ (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios. Full article

In the context of ever-increasing water demand and pressures on natural resources, efficient water management is becoming a major priority for contemporary society. Since nitrogen and phosphorus, as essential nutrients, play a crucial role in the dynamics of aquatic ecosystems, but excessive concentrations can cause eutrophication of receptors, they need to be eliminated as completely as possible while respecting the principles of a sustainable economy, efficiency, and quality. In this study, the efficiency of optimizing the technological process of wastewater treatment by dosing FeCl₃ 40% solution to reduce nitrogen and phosphorus concentrations in treated water was investigated. The results obtained revealed that the use of this type of flocculant resulted in an increase in the efficiency of the removal process of total N by an average of 35.57 mg/L and total P by an average of 3.89 mg/L. Also, the results, which are interpreted by mathematical modeling, show that the optimal use of FeCl₃ 40% solution leads to a significant reduction in pollutants, well below the maximum permitted values (according to Romanian regulations, the maximum value for total phosphorus is 2 mg/L and total nitrogen is 15 mg/L for localities with a population between 10,000 and 100,000 inhabitants). This technical approach not only improves the quality of treated water but also contributes to minimizing the impact on aquatic ecosystems and promotes the principles of circular economy in water resource management. By optimizing the dosage of FeCl₃ 40% solution in the treatment process, the efficiency of the coagulation and flocculation processes is maximized, thus providing a viable and sustainable solution for reducing the environmental impact of nitrogen and phosphorus and promoting responsible and sustainable water resource management. Full article

With ageing, the risk of frailty increases, becoming a common condition that exposes older people to an increased risk of multiple adverse health outcomes. In Valencia (Spain), the ValueCare project develops and applies a value-based care approach that addresses the multidimensional nature of frailty by implementing integrated and personalized care to tackle psychosocial frailty. A pre–post controlled design with a baseline measurement at inclusion, at the end of implementation and a follow-up measurement after 6 months of intervention. In Valencia (Spain), 120 participants over 65 years of age are recruited from primary care centres to receive the ValueCare comprehensive and personalised care plan according to the results and are compared with 120 participants receiving “usual care”. An assessment questionnaire is designed using validated instruments, and a personalised care plan is developed specifically for each participant based on the results obtained. The study protocol has been registered under the ISRCTN registration number ISRCTN25089186. Addressing frailty as a multidimensional and multifactorial risk condition requires the development and implementation of comprehensive assessments and care. In this context, this study will provide new insights into the feasibility and effectiveness of a value-based methodology for integrated care supported by ICT for older people experiencing frailty. Full article

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River. Full article

The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements. Full article