bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Outcomes of 5-Fluorouracil-Related Early-Onset Toxicities: A Retrospective Cohort Study
Authors: Tentoni Nicolás , Combs Ryan , Hwang Miriam , Ward Suzanne , McCracken Andrea , Lowe Jennifer , Howard Scott C.
Published: December 2024

Objectives: We aimed to determine whether the occurrence of early-onset toxicities due to 5-fluorouracil (5-FU) in treatment-naive patients undergoing their first cycle of FOLFOX/FOLFIRINOX was associated with decreased overall survival, increased risk of treatment cessation, and hospitalization. Methods: This was a retrospective cohort study using patient information from community oncology practices. Patients who received their first dose of 5-FU from 1 January 2015 through 1 August 2023 were included. The occurrence of an early-onset 5-FU-related toxicity (during 5-FU infusion or up to 96 h after infusion completion) in the first cycle of FOLFOX/FOLFIRINOX was the explanatory variable. The primary endpoint was overall survival (OS); secondary endpoints included early treatment cessation and early hospital admission. Results: In total, 3988 patients were included; the median age was 62.9 years and 57.5% were male. Early-onset toxicities were observed in 19.1%, with vomiting, thrombocytopenia, and diarrhea being most common. Patients with early-onset toxicities had a median OS of 2.5 years [95% CI 2.2 to 2.9] compared with 5.3 years [95% CI 4.7 to 5.8] in patients without early-onset toxicities (p < 0.001). The occurrence of early-onset toxicities was associated with an adjusted hazard ratio of 1.61 [95% CI 1.44 to 1.80] and was also significantly associated with early treatment cessation (odds ratio [OR] 1.53, 95% CI 1.30 to 1.80) and early hospital admission (OR 8.69, 95% CI 3.45 to 24.18). Conclusions: Early-onset toxicities related to 5-FU during the first cycle of FOLFOX/FOLFIRINOX treatment were associated with poor outcomes. Early recognition and prompt intervention are pertinent to improve outcomes in patients receiving fluoropyrimidine chemotherapy.
Immune-Modified Glasgow Prognostic Score Predicts Therapeutic Effect of Pembrolizumab in Recurrent and Metastatic Head and Neck Cancer
Authors: Ueda Natsuko , Kuroki Masashi , Shibata Hirofumi , Matsubara Manato , Akita Saki , Yamada Tatsuhiko , Kato Rina , Iinuma Ryota , Kawaura Ryo , Okuda Hiroshi , Terazawa Kosuke , Mori Kenichi , Saijo Ken , Ohashi Toshimitsu , Ogawa Takenori
Published: December 2024

Background: Previously, we proposed that the immune-modified Glasgow Prognostic Score (imGPS), which adds the lymphocyte count to the mGPS, is helpful as a prognostic marker for patients with head and neck squamous cell carcinoma. In this study, we investigated the imGPS as a marker for the therapeutic effect of pembrolizumab in treating recurrent and metastatic head and neck cancer (RMHNC). Methods: This study included RMHNC patients who were treated with pembrolizumab from December 2019 to April 2024. ALB, CRP, lymphocyte counts, neutrophil-to-lymphocyte ratios (NLRs), mGPSs, and imGPSs were extracted as biomarkers, and the response rate and prognosis were analyzed for each. Results: A total of 54 patients were enrolled. Lymphocyte counts were correlated with the overall response rates (ORRs) (p = 0.0082). Although the mGPS did not show significant differences in ORRs, imGPSs revealed a significant difference (p = 0.013). CRP, ALB, and lymphocyte counts were correlated with overall survival (OS) and/or progression-free survival (PFS). NLRs, mGPSs, and imGPSs were also correlated with OS and/or PFS, with imGPSs showing the greatest area under the curve (OS; AUC = 0.795, PFS; AUC = 0.754). Conclusions: This study demonstrates that the imGPS is an excellent predictive marker for the therapeutic effect and prognosis of pembrolizumab for RMHNC. The imGPS can be employed with daily blood tests, highlighting the potential to forecast the impact of the ICI with high reliability.
Effect of Small-Molecule Natural Compounds on Pathologic Mast Cell/Basophil Activation in Allergic Diseases
Authors: Werner Robert , Carnazza Michelle , Li Xiu-Min , Yang Nan
Published: December 2024

Pathologic mast cells and basophils, key effector cells in allergic reactions, play pivotal roles in initiating and perpetuating IgE-mediated allergic responses. Conventional therapies for allergies have limitations, prompting exploration into alternative approaches such as small-molecule natural compounds derived from botanical sources. This review synthesizes the existing literature on the effects of these compounds on pathologic mast cells and basophils, highlighting their potential in allergy management, and utilizes the PubMed database for literature acquisition, employing keyword-based searches to identify relevant peer-reviewed sources. Additionally, mechanistic insights were evaluated to contextualize how small-molecule natural compounds can inhibit mast cell/basophil activation, degranulation, and signaling pathways crucial for IgE-mediated allergic reactions. Small-molecule natural compounds exhibit promising anti-allergic effects, yet despite these findings, challenges persist in the development and translation of natural compound-based therapies, including bioavailability and standardization issues. Future research directions include optimizing dosing regimens, exploring synergistic effects with existing therapies, and employing systems pharmacology approaches for a holistic understanding of their mechanisms of action. By harnessing the therapeutic potential of small-molecule natural compounds, effective treatments for allergic diseases may be realized, offering hope for individuals with allergies.
A High-Throughput Neurosphere-Based Colony Formation Assay to Test Drug and Radiation Sensitivity of Different Patient-Derived Glioblastoma Lines
Authors: Kumar Manoj , Nassour-Caswell Lauren C. , Alrefai Hasan , Anderson Joshua C. , Schanel Taylor L. , Hicks Patricia H. , Cardan Rex , Willey Christopher D.
Published: December 2024

The gold standard assay for radiation response is the clonogenic assay, a normalized colony formation assay (CFA) that can capture a broad range of radiation-induced cell death mechanisms. Traditionally, this assay relies on two-dimensional (2D) cell culture conditions with colonies counted by fixing and staining protocols. While some groups have converted these to three-dimensional (3D) conditions, these models still utilize 2D-like media compositions containing serum that are incompatible with stem-like cell models such as brain tumor initiating cells (BTICs) that form self-aggregating spheroids in neural stem cell media. BTICs are the preferred patient-derived model system for studying glioblastoma (GBM) as they tend to better retain molecular and phenotypic characteristics of the original tumor tissue. As such, it is important that preclinical radiation studies should be adapted to BTIC conditions. In this study, we describe a series of experimental approaches for performing CFA experiments with BTIC cultures. Our results indicate that serum-free clonogenic assays are feasible for combination drug and radiation testing and may better facilitate translatability of preclinical findings.
Molecular Mechanisms of Fibrosis in Cholestatic Liver Diseases and Regenerative Medicine-Based Therapies
Authors: Wang Wei-Lu , Lian Haoran , Liang Yingyu , Ye Yongqin , Tam Paul Kwong Hang , Chen Yan
Published: December 2024

The aim of this review is to explore the potential of new regenerative medicine approaches in the treatment of cholestatic liver fibrosis. Cholestatic liver diseases, such as primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and biliary atresia (BA), due to the accumulation of bile, often progress to liver fibrosis, cirrhosis, and liver failure. When the disease becomes severe enough to require liver transplantation. Deeply understanding the disease’s progression and fibrosis formation is crucial for better diagnosis and treatment. Current liver fibrosis treatments mainly target the root causes and no direct treatment method in fibrosis itself. Recent advances in regenerative medicine offer a potential approach that may help find the ways to target fibrosis directly, offering hope for improved outcomes. We also summarize, analyze, and discuss the current state and benefits of regenerative medicine therapies such as mesenchymal stem cell (MSC) therapy, induced pluripotent stem cells (iPSCs), and organoid technology, which may help the treatment of cholestatic liver diseases. Focusing on the latest research may reveal new targets and enhance therapeutic efficacy, potentially leading to more effective management and even curative strategies for cholestatic liver diseases.
Development of Edible Coatings Based on Different Biopolymers to Enhance the Internal Shelf-Life Quality of Table Eggs
Authors: Cortés-Ramírez Georgina S. , Velasco J. I. , Plascencia Miguel Ángel , Absalón Ángel E. , Cortés-Espinosa Diana V.
Published: December 2024

Fresh hen eggs constitute a perishable food and are widely consumed worldwide because of their nutritional value. The eggshell is a natural barrier that protects the egg. However, it is very porous and fragile, which makes it susceptible to breakage, contamination, and deterioration, affecting its internal quality during storage, reducing the half-life of the egg for consumption, and causing economic losses to producers. This study aimed to evaluate different edible composites based on biopolymers and proteins for their application as coatings for preservation and shelf-life extension. First, 32 formulations were prepared and evaluated on eggs stored at 4 °C and 25 °C for 6 weeks. Subsequently, 11 coating solutions with the lowest weight loss were selected, and 216 eggs were evaluated; the response variables were HU, YI, and yolk pH and white pH during weeks 1, 3, and 6 of storage. Finally, four formulations, biobased in chitosan, pectin, and alginate derivative composites, presented the highest internal quality results for at least 3 weeks compared with uncoated eggs. These results expand the range of biopolymers available for use as egg coatings compared with the currently used chitosan, as their production process is less expensive than that of chitosan and more attractive to the poultry industry.
Amphibians and Reptiles of the Pacific Lowlands Biogeographic Province of Mexico: Diversity, Similarities, and Conservation
Authors: Lemos-Espinal Julio A. , Smith Geoffrey R.
Published: December 2024

The Pacific Lowlands (PL) of Mexico hosts a rich diversity of amphibians and reptiles, comprising 326 native species (76 amphibians and 250 reptiles) across 45 families. This region is significant from a biodiversity standpoint, accounting for 23.3% of Mexico’s total amphibian and reptile species, with 30 species being endemic to the PL. Notably, the PL ranks sixth in amphibian and reptile diversity among Mexico’s fourteen biogeographic provinces. The species composition indicates a complex biogeographic relationship for the province, which shares the highest percentages of species with the neighboring Transition Zone provinces, while also acting as a vital corridor for species adapted to varied environmental conditions. Despite its diversity, 24 species in the PL are of conservation concern, highlighting threats from habitat loss, urbanization, and the impacts of introduced species in the province. Our findings underscore the ecological significance of the PL and the urgent need for conservation efforts to protect its unique biodiversity.
Proteins Associated with Salinity Adaptation of the Dinoflagellates: Diversity and Potential Involvement in Species Evolution
Authors: Annenkova Nataliia V.
Published: December 2024

Protists inhabit marine, brackish and fresh waters. The salt barrier plays an important role in the origin of their diversity. Salinity tolerance differs among species and sometimes even among different strains of the same species, indicating local adaptation. Dinoflagellates from the Apocalathium genus are represented by at least four species, which originated via rapid and recent radiation. Water salinity was suggested as one of the key factors for this radiation. A previous study found RNA transcripts, which belong exclusively to saline strains of Apocalathium, and were absent in its freshwater strains. In the present paper, the diversity of these transcripts and their orthologs from marine and freshwater protists were analysed using bioinformatic approaches. First, it was found that these specific transcripts translated to the proteins, which are important for osmoregulation (e.g., transport of various compounds including glycine betaine, regulation of microtubule organisation, post transcriptional modifications). This supports the idea that speciation within Apocalathium resulted in the loss of osmoregulatory genes by freshwater species. Second, protein distribution was not highly species specific, because their orthologs were found in different dinoflagellates and were relatively common in other phototrophic protists, though the sequences were highly variable. Proteins from 13 orthogroups were absent or very rare in studied freshwater genomes and transcriptomes. They could play a specific role in protists salinity tolerance. Third, detailed phylogenetic analyses of betaine-like transporter and chloride transmembrane transporters, which probably are one of the key proteins associated with salinity tolerance, revealed high levels of multiple and variable copies that were not eliminated from the genome during the evolution. The expression of their genes could be important in the adaptation of dinoflagellates to salinity changes, as it was already shown for some other protists.
Reflectance Confocal Microscopy Can Help in Detecting Clinically Silent Mammary Paget’s Disease
Authors: Cantisani Carmen , Taliano Alberto , Longo Caterina , Astorino Stefano , Gomes Vito , Caruso Gianluca , Suppa Mariano , Guida Stefania , Pogorzelska-Antkowiak Anna , Pellacani Giovanni
Published: December 2024

Early detection and comprehensive diagnostic approaches for breast cancer are essential for improving prognosis. When it comes to changes in the skin of the breast or the nipple–areola complex (NAC), particularly if they are unilateral, it is essential to be vigilant, as these changes could be an early sign of underlying malignancy or other pathologies. Primary breast malignancies, such as mammary Paget’s disease (MPD), can manifest as erythema, scaling, or ulceration of the NAC, while secondary cutaneous metastases from other breast carcinomas may present as nodules, erythematous plaques, or inflammatory reactions. Non-malignant inflammatory conditions, including eczema or mastitis, can also mimic these changes; histologic evaluation is the gold-standard diagnostic tool. The usefulness of conventional diagnostic techniques breast lesions has been confirmed, but in recent years, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) have emerged as additional tools to diagnose cases characterized by cutaneous changes; they may, therefore, result in new perspectives on the non-invasive diagnosis of MPD. RCM is a non-invasive diagnostic technique that allows high-resolution images of the skin at microscopic level in real time, offering a promising approach to the non-invasive diagnosis of MPD, particularly when a lesion is not clinically evident and may mimic other benign or inflammatory conditions. We describe an atypical clinical presentation of mammary Paget’s disease diagnosed early by reflectance confocal microscopy evaluation and confirmed histologically.
Somatostatin Receptor Imaging in the Diagnosis and Management of Parathyroid Neuroendocrine Neoplasia
Authors: Tsoy Uliana , Pogosian Karina , Ryzhkova Daria , Yudina Olga , Yakovenko Ksenia , Ryazanov Pavel , Matsueva Irina , Sokolnikova Polina , Salov Maksim , Karonova Tatiana , Grineva Elena
Published: December 2024

Background: Parathyroid tumors are classified as parathyroid neuroendocrine neoplasia (NEN) by the IARC-WHO classification. These tumors can occur with NENs from other sites, which often require total-body [68Ga]-DOTA-peptides PET/CT. This study aimed to assess the utility of [68Ga]-DOTA-peptide PET/CT in imaging parathyroid NENs and to evaluate the underlying mechanisms. Methods: Fifty patients with primary hyperparathyroidism (PHPT) and parathyroid NENs histologically confirmed as parathyroid adenomas (PAs) were included. PET/CT with [68Ga]-DOTA-peptide was performed in 16 patients with localized PAs, including 10 with MEN1 syndrome. Somatostatin receptor types 2 and 5 (SST2 and SST5) staining was performed on PAs from 48 patients. Somatostatin analogs (SSA) were prescribed in four patients with MEN 1 syndrome and 1 with persistent acromegaly, all with PAs and PHPT. The therapy effects on calcium and parathyroid hormone (iPTH) were evaluated. Results: [68Ga]-DOTA-peptide PET/CT detected 20 PAs with high radiopharmaceutical uptake. SST2 expression was negative on PA cell membranes in all cases and positive on endothelium in 39 (81%) PAs. Membrane SST5 expression was positive in 25 (52%) PAs and endothelial was positive in 40 (83%). Serum calcium levels decreased in patients on SSA therapy, while iPTH did not. Conclusions: PET/CT with [68Ga]-DOTA-peptides can detect parathyroid NENs. The incidental detection of high [68Ga]-DOTA-peptide uptake in the parathyroid region during whole-body PET/CT may prompt biochemical evaluation for PHPT. We suggest that endothelial SST expression mediates high radiopharmaceutical uptake by PAs and that SSA treatment can reduce hypercalcemia in PHPT patients.
Near-Wall Slow Flow Contributes to Wall Enhancement of Middle Cerebral Artery Bifurcation Aneurysms on Vessel Wall MRI
Authors: Liu Linfeng , Li Zhuhao , Xiao Weiping , Yang Yibing , Yan Yan , Bai Lu , Quan Lingzhi , Qi Tiewei , Liang Feng
Published: December 2024

Background: The mechanism of aneurysm wall enhancement (AWE) in middle cerebral artery (MCA) bifurcation aneurysms on vessel wall magnetic resonance imaging (VW-MRI) remains unclear. We aimed to explore the morphologically related hemodynamic mechanism for the AWE of MCA bifurcation aneurysms. Methods: Patients with unruptured MCA bifurcation aneurysms undergoing VW-MRI were enrolled. Logistic regression analyses were performed to determine the risk factors for AWE. Based on the results of retrospective analyses, bifurcation aneurysm silicone models with a specific aspect ratio (AR) were designed and underwent VW-MRI with different inlet velocities. Computational fluid dynamics (CFD) analyses were conducted on both silicone models and patients’ aneurysms. Results: A total of 104 aneurysms in 95 patients (mean age 60; 34 males) were included for baseline analysis and morphological analysis. Logistic regression analysis indicated AR (OR, 5.92; 95% CI, 2.00–17.55; p = 0.001) was associated with AWE. In the high-AR group of 45 aneurysms with AWE, the aneurysm sac exhibited lower blood flow velocity, lower wall shear stress, a larger proportion of low-flow regions and higher wall enhancement values. In total, 15 silicone models were analyzed, divided into three subgroups based on neck width (4 mm, 6 mm, and 8 mm). Each subgroup contained aneurysms with five different ARs: 1.0, 1.25, 1.5, 1.75, and 2.0. In silicone models, contrast enhancement (CE) was mainly located beneath the dome of the aneurysm wall. With the same inlet velocity, CE gradually increased as the AR increased. Similarly, at the same AR, CE increased as the inlet velocity decreased. CFD demonstrated a moderate positive correlation between the near-wall enhancement index and the ratio of the low-velocity area (r = 0.6672, p < 0.001). Conclusions: The AR is associated with the AWE of MCA bifurcation aneurysms. A high AR may promote wall enhancement by causing near-wall slow flow.
Molecular Biomarkers in Cutaneous Photodynamic Therapy: A Comprehensive Review
Authors: Naharro-Rodriguez Jorge , Bacci Stefano , Fernandez-Guarino Montserrat
Published: December 2024

Background/Objectives: Photodynamic therapy (PDT) is widely utilized in dermatology for the treatment of various skin conditions. Despite its effectiveness, the exact biomolecular changes underlying therapeutic outcomes remain only partially understood. This review, through a transversal approach, aims to provide an in-depth exploration of molecular biomarkers involved in PDT, evaluate its underlying mechanisms, and examine how these insights can contribute to enhanced treatment protocols and personalized therapy approaches. Methods: A narrative review of the literature was conducted, targeting peer-reviewed articles and clinical trials that focus on PDT and its molecular biomarker effects on dermatological conditions. The databases searched included PubMed, Scopus, and Web of Science, and the inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses in English. Results: PDT effectively reduces the expression of critical biomarkers such as p53, Cyclin D1, and Ki-67 in AK and other cancerous lesions, leading to reduced cell proliferation and increased apoptosis. Additionally, PDT promotes extracellular matrix remodeling and stimulates collagen production, which has a rejuvenating effect on the skin and a promising role in the treatment of chronic wounds. Conclusions: PDT represents a powerful and versatile treatment option for various dermatological conditions due to its ability to target cellular pathways involved in proliferation and apoptosis. Further research into optimizing treatment parameters and combining PDT with other targeted therapies may enhance patient outcomes, reduce resistance, and pave the way for more individualized therapeutic approaches in dermatology.
Obesity: Friend or Foe in Sjögren’s Syndrome Patients?
Authors: Mezei Kincső , Nagy Laura , Orosz Viktória , Aradi Zsófia , Bói Bernadett , Szántó Antónia
Published: December 2024

Background/Objectives: In Sjögren’s syndrome, exocrine glands are destructed in an autoimmune-mediated process. Obesity is known to influence a wide range of diseases. This study aimed to examine whether obesity has an impact on the disease course of our patients with Sjögren’s syndrome. Methods: Out of the regularly followed-up patients, 125 were grouped based on their body mass index (BMI). Below a BMI of 25, they were listed as “non-obese” (n = 45), whereas above a BMI of 25, they were categorized as “obese” (n = 80). Demographic, laboratory, and immunological parameters; Sjögren’s syndrome disease activity index; certain extraglandular manifestations; and treatment modalities were compared using biostatistical methods. Results: Among the examined cardiovascular and cerebrovascular co-morbidities, type 2 diabetes and hypertension were significantly more frequent in the obese group. Considering the associated further autoimmune disorders and extraglandular manifestations, in our patients, there were no significant differences between the two groups. Among laboratory parameters, gamma glutamil transferase, alanine transaminase, hemoglobin, hematocrit, lymphocyte rate, triglyceride, and c3 and c4 complement levels were significantly higher in the obese group, while the proportion of rheumatoid factor positivity and the neutrophil granulocyte rate were significantly lower. Immunoglobulin G, A, and M levels did not differ significantly between the two subsets. Obese patients needed steroid therapy significantly less frequently; however, statin therapy was remarkably more frequent in that group. Furthermore, the European League Against Rheumatism (EULAR) Sjögren’s syndrome disease activity index (ESSDAI) was significantly lower in the group of overweight patients. Conclusions: Our results suggest that several immunological parameters of obese patients are more favorable compared to those with normal body weight. Behind that, we might suspect either the beneficial effect of statin therapy and/or the obesity paradox.
Surface Characterization of Bone-Level and Tissue-Level PEEK and Titanium Dental Implant Scan Bodies After Repeated Autoclave Sterilization Cycles
Authors: Qasim Syed Saad Bin , Akbar Aqdar A. , Sadeqi Haneen A. , Baig Mirza Rustum
Published: December 2024

Background: Sterilization is required for any biomedical device intended to be used in contact with the human body. Several studies have reported alterations in the bulk and surface properties of such devices after repeated sterilization cycles. These surface modifications may influence other clinical parameters. Therefore, the aim of this study was to investigate the surface and chemical properties of implant scan bodies (SBs) after consecutive autoclave sterilization procedures. Methods: The objective was to analyze the scan bodies using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) for chemical analysis and an optical profilometer to analyze the surface roughness. Results: FTIR spectra depicted the appearance of peak at 1741 and 1100 cm−1 due to the diphenyl ether band disappearance. The XPS spectra showed alterations in the elemental composition after autoclaving and roughness were significantly reduced in PEEK BL and TL SBs. Conclusions: These results indicated that some surface modifications were induced by repeated sterilization cycles.
Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss
Authors: Yang Zhengpeng , Yan Suyu , Ming Chao , Wang Xiaoming
Published: December 2024

Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.
Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems
Authors: Altinses Diyar , Torres David Orlando Salazar , Gobachew Asrat Mekonnen , Lier Stefan , Schwung Andreas
Published: December 2024

Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset’s effectiveness and operational viability.
Optimized Autonomous Drone Navigation Using Double Deep Q-Learning for Enhanced Real-Time 3D Image Capture
Authors: Sánchez-Soriano Javier , Rojo-Gala Miguel Ángel , Pérez-Pérez Guillermo , Bemposta Rosende Sergio , Gordo-Herrera Natalia
Published: December 2024

The proposed system assists in the automatic creation of three-dimensional (3D) meshes for all types of objects, buildings, or scenarios, using drones with monocular RGB cameras. All these targets are large and located outdoors, which makes the use of drones for their capture possible. There are photogrammetry tools on the market for the creation of 2D and 3D models using drones, but this process is not fully automated, in contrast to the system proposed in this work, and it is performed manually with a previously defined flight plan and after manual processing of the captured images. The proposed system works as follows: after the region to be modeled is indicated, it starts the image capture process. This process takes place automatically, with the device always deciding the optimal route and the framing to be followed to capture all the angles and details. To achieve this, it is trained using the artificial intelligence technique of Double Deep Q-Learning Networks (reinforcement learning) to obtain a complete 3D mesh of the target.
Applying Large Language Model to a Control System for Multi-Robot Task Assignment
Authors: Zhao Wen , Li Liqiao , Zhan Hanwen , Wang Yingqi , Fu Yiqi
Published: December 2024

The emergence of large language models (LLMs), such as GPT (Generative Pre-trained Transformer), has had a profound impact and brought about significant changes across various sectors of human society. Integrating GPT-3.5 into a multi-robot control system, termed MultiBotGPT (Multi-Robot Control System with GPT), represents a notable application. This system utilizes layered architecture and modular design to translate natural language commands into executable tasks for UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), enhancing capabilities in tasks such as target search and navigation. Comparative experiments with BERT (Bidirectional Encoder Representations from Transformers) in the natural language-processing component show that MultiBotGPT with GPT-3.5 achieves superior task success rates (94.4% and 55.0%) across 50 experiments, outperforming BERT significantly. In order to test the auxiliary role of the MultiBotGPT-controlled robot on a human operator, we invited 30 volunteers to participate in our comparative experiments. Three separate experiments were performed, Participant Control (Manual Control only), Mix Control (Mix Manual Contr and MultiBotGPT Control), and MultiBotGPT Control (MultiBotGPT Control only). The performance of MultiBotGPT is recognized by the human operators and it can reduce the mental and physical consumption of the human operators through the scoring of the participants’ questionnaires.
UAV Swarm Rounding Strategy Based on Deep Reinforcement Learning Goal Consistency with Multi-Head Soft Attention Algorithm
Authors: Wei Zhaotian , Wei Ruixuan
Published: December 2024

Aiming at the problem of target rounding by UAV swarms in complex environments, this paper proposes a goal consistency reinforcement learning approach based on multi-head soft attention (GCMSA). Firstly, in order to make the model closer to reality, the reward function when the target is at different positions and the target escape strategy are set, respectively. Then, the Multi-head soft attention module is used to promote the information cognition of the target among the UAVs, so that the UAVs can complete the target roundup more smoothly. Finally, in the training phase, this paper introduces cognitive dissonance loss to improve the sample utilization. Simulation experiments show that GCMSA is able to obtain a higher task success rate and is significantly better than MADDPG in terms of algorithm performance.
Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities
Authors: Pillay Shannyn Jade , Bangira Tsitsi , Sibanda Mbulisi , Kebede Gurmessa Seifu , Clulow Alistair , Mabhaudhi Tafadzwanashe
Published: December 2024

Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies.
Metastable Oscillatory Modes as a Signature of Entropy Management in the Brain
Authors: Xavier Marta , Figueiredo Patrícia , Deco Gustavo , Luppi Andrea I. , Cabral Joana
Published: December 2024

Entropy management, central to the Free Energy Principle, requires a process that temporarily shifts brain activity toward states of lower or higher entropy. Metastable synchronization is a process by which a system achieves entropy fluctuations by intermittently transitioning between states of collective order and disorder. Previous work has shown that collective oscillations, similar to those recorded from the brain, emerge spontaneously from weakly stable synchronization in critically coupled oscillator systems. However, direct evidence linking the formation of collective oscillations to entropy fluctuations is lacking. In this short communication, we demonstrate how the emergence of Metastable Oscillatory Modes (MOMs) is directly associated with a temporary reduction in entropy in the ongoing dynamics. We apply Shannon entropy to the distribution of eigenvalues of phase covariance over sliding time windows, capturing the temporal evolution of entropy at the level of the entire dynamical system. By demonstrating how the formation of MOMs impacts a system’s entropy levels, we bridge theoretical works on the physics of coupled oscillators with the FEP framework, supporting the hypothesis that brain rhythms recorded experimentally are a signature of entropy management.
On the Capacity of the Peak-Limited and Band-Limited Channel
Authors: Peleg Michael , Shamai Shlomo
Published: December 2024

We investigate the peak-power limited (PPL) Additive White Gaussian Noise (AWGN) channels in which the signal is band-limited, and its instantaneous power cannot exceed the power P. This model is relevant to many communication systems; however, its capacity is still unknown. We use a new geometry-based approach which evaluates the maximal entropy of the transmitted signal by assessing the volume of the body, in the space of Nyquist-rate samples, comprising all the points the transmitted signal can reach. This leads to lower bounds on capacity which are tight at high Signal to Noise Ratios (SNRs). We find lower bounds on capacity, expressed as power efficiency, that were higher than the known ones by a factor of 3.3 and 8.6 in the low-pass and the band-pass cases, respectively. The gap to the upper bounds is reduced to a power ratio of 1.5. The new bounds are numerically evaluated for FDMA-style signals with limited duration and also are derived in the general case as a conjecture. The penalty in power efficiency due to the peak power constraint is roughly 6 dB at high SNRs. Further research is needed to develop effective modulation and coding for this channel.
Classification of Design Methodologies of Dual Active Bridge-Based Resonant Converters for Battery Energy Storage Systems
Authors: García Pérez Rafael Amadeo , Muñoz-Cruzado-Alba Jesús , Galván Díez Eduardo , Carrasco Solís Juan Manuel
Published: December 2024

Optimized guidelines for the design of power converters are crucial to achieve the expected goals in terms of performance, efficiency, power density, etc. Therefore, they are the basis for industrial success or failure. Resonant converters based on Dual Active Bridges (DABs) are particularly sensitive to the design process due to their inherently nonlinear behaviour; thus, they are in the spotlight for research and development at present. Plenty of design methodologies can be found in the literature but each of them is specific to the perspective of the authors, the performed analysis, the assumptions made, and the design objectives. It is critical to understand the Key Performance Indicators (KPIs) and design methodologies of a resonant DAB converter. There is a significant lack of articles that concisely and clearly summarize this. Different design methodologies are analyzed and compared with respect to the most important KPIs, and the most relevant demos and experiences are pointed out so that designers can select the best choice for their assignment. These results will help designers understand the design methodologies and carefully choose one based on the application, analysis, and design objectives.
Advanced Predictive Modeling of Tight Gas Production Leveraging Transfer Learning Techniques
Authors: Ma Xianlin , Chang Shilong , Zhan Jie , Zhang Long
Published: December 2024

Accurate production forecasting of tight gas reservoirs plays a critical role in effective gas field development and management. Recurrent-based deep learning models typically require extensive historical production data to achieve robust forecasting performance. This paper presents a novel approach that integrates transfer learning with the neural basis expansion analysis time series (N-BEATS) model to forecast gas well production, thereby addressing the limitations of traditional models and reducing the reliance on large historical datasets. The N-BEATS model was pre-trained on the M4 competition dataset, which consists of 100,000 time series spanning multiple domains. Subsequently, the pre-trained model was transferred to forecast the daily production rates of two gas wells over short-term, medium-term, and long-term horizons in the S block of the Sulige gas field, China’s largest tight gas field. Comparative analysis demonstrates that the N-BEATS transfer model consistently outperforms the attention-based LSTM (A-LSTM) model, exhibiting greater accuracy across all forecast periods, with root mean square error improvements of 19.5%, 19.8%, and 26.8% of Well A1 for short-, medium-, and long-term horizons, respectively. The results indicate that the pre-trained N-BEATS model effectively mitigates the data scarcity challenges that hinder the predictive performance of LSTM-based models. This study highlights the potential of the N-BEATS transfer learning framework in the petroleum industry, particularly for production forecasting in tight gas reservoirs with limited historical data.
Design and Performance Analysis of a SPECK-Based Lightweight Hash Function
Authors: Sevin Abdullah , Çavuşoğlu Ünal
Published: December 2024

In recent years, hash algorithms have been used frequently in many areas, such as digital signature, blockchain, and IoT applications. Standard cryptographic hash functions, including traditional algorithms such as SHA-1 and MD5, are generally computationally intensive. A principal approach to improving the security and efficiency of hash algorithms is the integration of lightweight algorithms, which are designed to minimize computational overhead, into their architectural framework. This article proposes a new hash algorithm based on lightweight encryption. A new design for the lightweight hash function is proposed to improve its efficiency and meet security requirements. In particular, efficiency reduces computational load, energy consumption, and processing time for resource-constrained environments such as IoT devices. Security requirements focus on ensuring properties such as collision resistance, pre-image resistance, and distribution of modified bit numbers to ensure reliable performance while preserving the robustness of the algorithm. The proposed design incorporates the SPECK lightweight encryption algorithm to improve the structure of the algorithm, ensuring robust mixing and security through confusion and diffusion, while improving processing speed. Performance and efficiency tests were conducted to evaluate the proposed algorithm, and the results were compared with commonly used hash algorithms in the literature. The test results show that the new lightweight hash algorithm has successfully passed security tests, including collision resistance, pre-image resistance, sensitivity, and distribution of hash values, while outperforming other commonly used algorithms regarding execution time.
Gas Leakage Detection Using Tiny Machine Learning
Authors: El Barkani Majda , Benamar Nabil , Talei Hanae , Bagaa Miloud
Published: December 2024

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards and preventing dangerous incidents. Traditional detection systems often rely on centralized data processing, which can lead to delays and scalability issues. To overcome these limitations, in this study, we present a solution based on tiny machine learning (TinyML) to process data directly on devices. TinyML has the potential to execute machine learning algorithms locally, in real time, and using tiny devices, such as microcontrollers, ensuring faster and more efficient responses to potential dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 and EfficientNet-B0, deployed on the Arduino Nano 33 BLE Sense. The results show that our system not only provides real-time analytics but does so with high accuracy—88.92% for MobileNetV1 and 91.73% for EfficientNet-B0—while achieving inference times of 1414 milliseconds and using just 124.8 KB of memory. Compared to existing solutions, our edge-based system overcomes common challenges related to latency and scalability, making it a reliable, fast, and efficient option. This work demonstrates the potential for low-cost, scalable gas detection systems that can be deployed widely to enhance safety in various environments. By integrating cutting-edge machine learning models with affordable IoT devices, we aim to make safety more accessible, regardless of financial limitations, and pave the way for further innovation in environmental monitoring solutions.
Vision-Based Prediction of Flashover Using Transformers and Convolutional Long Short-Term Memory Model
Authors: Mozaffari M. Hamed , Li Yuchuan , Hooshyaripour Niloofar , Ko Yoon
Published: December 2024

The prediction of fire growth is crucial for effective firefighting and rescue operations. Recent advancements in vision-based techniques using RGB vision and infrared (IR) thermal imaging data, coupled with artificial intelligence and deep learning techniques, have shown promising solutions to be applied in the detection of fire and the prediction of its behavior. This study introduces the use of Convolutional Long Short-term Memory (ConvLSTM) network models for predicting room fire growth by analyzing spatiotemporal IR thermal imaging data acquired from full-scale room fire tests. Our findings revealed that SwinLSTM, an enhanced version of ConvLSTM combined with transformers (a deep learning architecture based on a new mechanism called multi-head attention) for computer vision purposes, can be used for the prediction of room fire flashover occurrence. Notably, transformer-based ConvLSTM deep learning models, such as SwinLSTM, demonstrate superior prediction capability, which suggests a new vision-based smart solution for future fire growth prediction tasks. The main focus of this work is to perform a feasibility study on the use of a pure vision-based deep learning model for analysis of future video data to anticipate behavior of fire growth in room fire incidents.
Calculation of Crude Oil Processes Using Simplified Model Mixture
Authors: Krzywda Roman , Gierycz Paweł , Makowski Łukasz , Poświata Artur
Published: December 2024

This paper presents the modeling of the existing crude oil separation process in a system consisting of two rectification columns with side drafts operating at higher pressure. The composition of the crude oil was approximated by a model mixture of hydrocarbons. The installation calculations have been performed for two different model compositions containing 32 and 10 different hydrocarbons. The whole technological process was based on the assumption that the feed stream, containing a model crude oil, was introduced to the first column, and then the other expected products (different petroleum fractions), characterized by their respective boiling points, were collected (side drafts) from the appropriate trays of the distillation columns. The obtained calculation results for both of the model crude oils were compared with the results obtained in the existing petroleum process and discussed from the point of view of their practical applications. The detailed data concerning size, composition, and process parameters for all the streams of the investigated installations, as well as the necessary energy expenditure for each of the columns, have been determined. Moreover, some recommendations are presented for the modeling and optimization of industrial distillation processes of very complex, multi-component systems using simpler model compositions.